Evaluating Market Attractiveness
- A New Venture Perspective -
DISSERTATION
der Universität St. Gallen,
Hochschule für Wirtschafts-,
Rechts- und Sozialwissenschaften (HSG)
zur Erlangung der Würde eines
Doktors der Wirtschaftswissenschaften
vorgelegt von
Daniel Spohn
aus
Deutschland
Genehmigt auf Antrag der Herren
Prof. Dr. Hans Jobst Pleitner
und
Prof. Dr. Beat Schmid
Dissertation Nr. 2917
Difo-Druck GmbH, Bamberg 2004
Die
Universität
St.
Gallen,
Hochschule
für
Wirtschafts-,
Rechts-
und
Sozialwissenschaften (HSG) gestattet hiermit die Drucklegung der vorliegenden
Dissertation, ohne damit zu den darin augesprochenen Anschauungen Stellung zu
nehmen.
St. Gallen, den 4. Mai 2004
Der Rektor:
Prof. Dr. Peter Gomez
I
Preface
For me, writing this dissertation has been in many ways like the experience of starting
a venture. For several years, many nights have been spent developing the project. As
with the start of a venture, there have been periods of restless enthusiasm and other
periods where it seemed impossible to get the project on a successful track. Over time
it has been satisfying to see the project progressing gradually. As the success of a
venture is generally based on the effort of more than one person, this dissertation
would have never been possible without the support and contribution from many sides.
First, I would like to thank my supervisors Prof. Dr. Hans Jobst Pleitner and Prof. Dr.
Beat Schmid for their support and for having given me the opportunity to investigate a
topic in which I personally was very interested. Furthermore, I would like to thank
Deutsche Telekom for its financial support, and Creditreform, ZEW and Deutsche
Ausgleichsbank for the provision of data sets, which have been essential for
conducting this study. The case studies were only possible thanks to the cooperation of
the entrepreneurs Dr. Christian Serarols, Claudia Eleuterio and Stefan Martinstetter.
Moreover I would like to thank Dr. Klaus Edel for his comments on the statistical
methodology, as well as Robert Hartl and my teachers and colleagues from the
European Doctoral Programme of entrepreneurship for their feedback on earlier drafts
of this study.
I owe special thanks to my brother Felix, who has carried a large part of my
responsibilities in our joint firm over the last year in order to give me the time needed
to finish the dissertation.
Most of all, I thank my wife Fabiola for her excellent comments on the dissertation,
for her continuous support and patience. Thanks to my son Diego for his
understanding when I’ve had to tell him, too often recently, that I was busy.
Writing a dissertation provides one reward that starting a venture does not provide. At
one point, at the end, I can say: I am happy that it is finished.
Barcelona, July 2004
Daniel Spohn
II
Outline
Preface.............................................................................................................................I
Outline........................................................................................................................... II
Table of contents .......................................................................................................... V
List of figures............................................................................................................. XII
List of tables..............................................................................................................XIV
List of abbreviations ................................................................................................XIX
1
Introduction .......................................................................................................... 1
1.1 Problem statement .......................................................................................... 1
1.2 General purpose of the study and objectives.................................................. 2
1.3 Definition of basic terms ................................................................................ 4
1.4 Delimitation of the study................................................................................ 7
1.5 Underlying assumptions and choice of general research strategy ................. 9
1.6 General structure of the study ...................................................................... 12
2
Literature review ................................................................................................ 15
2.1 Entrepreneurship research ............................................................................ 15
2.2 Strategy / Industrial organisation research................................................... 37
3
Development of a theoretical model of market attractiveness ....................... 53
3.1 Approach ...................................................................................................... 53
3.2 Additional applicable research programs for the theoretical framework..... 54
3.3 Alternative conceptual approaches towards structuring and classifying
the market environment................................................................................ 66
3.4 Development of an integrated model for analysing market attractiveness
from a new venture perspective ................................................................... 84
4
Methodology...................................................................................................... 121
4.1 Measurement of venture success on the industry level.............................. 123
III
4.2 Quantitative empirical study: Impact of industry factors on the
success of new ventures in Germany ......................................................... 128
4.3 Qualitative empirical study: Case studies of ventures in
telecommunication and e-commerce industries ......................................... 140
4.4 General considerations of validity and reliability ...................................... 146
5
Quantitative empirical study: Impact of industry factors on new venture
performance in Germany................................................................................. 149
5.1 Applied data sources .................................................................................. 149
5.2 Hypotheses on impact of industry variables on venture success ............... 159
5.3 Operationalisation of variables .................................................................. 169
5.4 Characteristics and performance of new venture activity based on the
DtA sample................................................................................................. 190
5.5 Analysis of the impact of industry factors on new venture performance .. 218
5.6 Synopsis of results...................................................................................... 265
6
Qualitative empirical study: Case studies of selected ventures from the
telecommunication and e-commerce industries............................................. 279
6.1 Case study – Imente ................................................................................... 279
6.2 Case study – Open House .......................................................................... 302
6.3 Case study – Tele-Ruf Kommunikations GmbH ....................................... 324
7
Conclusions, significance of results and perspectives for future research.. 343
7.1 Summarising conclusions........................................................................... 343
7.2 Significance of results for practice............................................................. 347
7.3 Scientific significance of results and perspectives for future research ...... 351
Bibliography .............................................................................................................. 354
Appendices................................................................................................................. 370
Appendix A: Calculations for operationalization of MES variable................... 370
Appendix B: Industry ranking: Startups per industry ........................................ 372
IV
Appendix C: Ranking of industries by subjective success evaluation............... 376
Appendix D: Ranking of industries by venture sales growth ............................ 381
Appendix E: Ranking of industries by venture profit level ............................... 386
Appendix F: Ranking of industries by years ventures needed to break even .... 391
Appendix G: Questionnaire guideline for case study interviews....................... 396
Appendix H: List of interviewees ...................................................................... 405
Appendix H: List of interviewees ...................................................................... 405
V
Table of contents
Preface.............................................................................................................................I
Outline........................................................................................................................... II
Table of contents .......................................................................................................... V
List of figures............................................................................................................. XII
List of tables..............................................................................................................XIV
List of abbreviations ................................................................................................XIX
1
Introduction .......................................................................................................... 1
1.1 Problem statement .......................................................................................... 1
1.2 General purpose of the study and objectives.................................................. 2
1.3 Definition of basic terms ................................................................................ 4
1.3.1
Market and industry........................................................................ 4
1.3.2
Market attractiveness...................................................................... 6
1.4 Delimitation of the study................................................................................ 7
1.5 Underlying assumptions and choice of general research strategy ................. 9
1.5.1
Assumptions about the nature of social science: Objectivist
approach ......................................................................................... 9
1.5.2
Type of study: Explanatory research ............................................ 11
1.5.3
General research approach: Triangulation ................................. 12
1.6 General structure of the study ...................................................................... 12
2
Literature review ................................................................................................ 15
2.1 Entrepreneurship research ............................................................................ 15
2.1.1
Research on market success factors ............................................. 15
2.1.2
Research on VC deal evaluation criteria...................................... 25
2.1.3
Conclusions and implications for the study.................................. 32
2.2 Strategy / Industrial organisation research................................................... 37
VI
3
2.2.1
Research on measuring industry effects ....................................... 37
2.2.2
Research on market success factors in strategy ........................... 42
2.2.3
Conclusions and implications for the study.................................. 49
Development of a theoretical model of market attractiveness ....................... 53
3.1 Approach ...................................................................................................... 53
3.2 Additional applicable research programs for the theoretical framework..... 54
3.2.1
Selection of research programs .................................................... 54
3.2.2
Industry economics ....................................................................... 54
3.2.3
Organisation ecology.................................................................... 57
3.2.4
Transaction costs economics ........................................................ 59
3.2.5
Contingency theory ....................................................................... 62
3.2.6
Game theory.................................................................................. 63
3.2.7
Summarising consideration of contributions in different fields ... 65
3.3 Alternative conceptual approaches towards structuring and classifying
the market environment................................................................................ 66
3.3.1
Model of Porter: Five forces model.............................................. 67
3.3.2
Baaken’s market framework ......................................................... 71
3.3.3
Dean and Meyer’s model.............................................................. 76
3.3.4
Hinterhuber’s framework ............................................................. 78
3.3.5
Summarising consideration of critical aspects for developing
structured models of market environments................................... 82
3.4 Development of an integrated model for analysing market attractiveness
from a new venture perspective ................................................................... 84
3.4.1
Objective of model ........................................................................ 84
3.4.2
Basic structure of the model: Five environmental levels of .............
analysis ........................................................................................ 85
3.4.3
Macro level: Opportunities in global & national environment.... 91
VII
3.4.4
Inter-country level: National competitiveness.............................. 95
3.4.5
Inter-industry level: Opportunities & threats from related
industries....................................................................................... 99
4
3.4.6
Intra-industry level: Market, dependencies & competitors........ 101
3.4.7
Venture / firm level: Relative positioning to competition........... 112
3.4.8
Summary ..................................................................................... 115
Methodology...................................................................................................... 121
4.1 Measurement of venture success on the industry level.............................. 123
4.2 Quantitative empirical study: Impact of industry factors on the success
of new ventures in Germany ...................................................................... 128
4.2.1
Variables under investigation..................................................... 131
4.2.2
Selection of data sources and sampling criteria......................... 134
4.2.3
Method of data analysis and interpretation: Analysis of
correlation and multivariate regression..................................... 137
4.3 Qualitative empirical study: Case studies of ventures in telecommunication and e-commerce industries............................................... 140
4.3.1
Industrial focus on cases from the telecommunications and ecommerce sectors........................................................................ 140
4.3.2
Variables under investigation..................................................... 141
4.3.3
Selection of case studies ............................................................. 141
4.3.4
Method of data collection ........................................................... 143
4.3.5
Method of data analysis and interpretation: Pattern-matching
and explanation-building............................................................ 145
4.4 General considerations of validity and reliability ...................................... 146
VIII
5
Quantitative empirical study: Impact of industry factors on new venture
performance in Germany................................................................................. 149
5.1 Applied data sources .................................................................................. 149
5.2 Hypotheses on impact of industry variables on venture success ............... 159
5.3 Operationalisation of variables .................................................................. 169
5.3.1
Operationalisation of venture success........................................ 169
5.3.2
Operationalisation of industry variables on the intra-industry
level and barriers to entry ......................................................... 174
5.3.2.1 Operationalisation of variables of market structure ......... 175
5.3.2.2 Operationalisation of variables of market dynamics......... 179
5.3.2.3 Operationalisation of variables of competitor structure ... 182
5.3.2.4 Operationalisation of variables of barriers to entry ......... 187
5.3.2.5 Operationalisation of additional control variables ........... 187
5.4 Characteristics and performance of new venture activity based on the
DtA sample................................................................................................. 190
5.4.1
Sample description on venture level ........................................... 190
5.4.1.1 Structure............................................................................. 190
5.4.1.2 Success measures ............................................................... 193
5.4.1.3 Inter-temporal development of success measures for .............
sub-samples........................................................................ 200
5.4.2
Sample description on aggregated industry level....................... 207
5.4.2.1 Structure............................................................................. 208
5.4.2.2 Success measures ............................................................... 210
5.4.3
Summary of characteristics and performance of new ventures
in sample .................................................................................... 217
5.5 Analysis of the impact of industry factors on new venture performance .. 218
5.5.1
Dependencies among variables .................................................. 218
IX
5.5.1.1 Dimensions of venture success .......................................... 219
5.5.1.2 Dimensions of independent industry variables.................. 220
5.5.2
Analysis of overall sample .......................................................... 227
5.5.2.1 Sales growth....................................................................... 227
5.5.2.2 Profit level.......................................................................... 231
5.5.2.3 Time to break even ............................................................. 234
5.5.2.4 Subjective absolute venture performance .......................... 238
5.5.3
Impact of contingency variables................................................. 242
5.5.3.1 Industry sector ................................................................... 242
5.5.3.2 Market growth rate ............................................................ 250
5.5.3.3 Venture growth rate ........................................................... 254
5.5.3.4 Intra-industry heterogeneity .............................................. 259
5.6 Synopsis of results...................................................................................... 265
6
Qualitative empirical study: Case studies of selected ventures from the
telecommunication and e-commerce industries............................................. 279
6.1 Case study – Imente ................................................................................... 279
6.1.1
Venture profile ............................................................................ 279
6.1.2
Competition and general market setting..................................... 281
6.1.3
Explanation of market impacts on venture success .................... 281
6.1.3.1 Macro level ........................................................................ 281
6.1.3.2 Inter-country level.............................................................. 283
6.1.3.3 Inter-industry level............................................................. 285
6.1.3.4 Intra-industry level............................................................. 287
6.1.3.5 Barriers to entry................................................................. 293
6.1.3.6 Venture / firm level............................................................. 294
X
6.1.4
Excursus: Derived strategic recommendations for Imente ........ 295
6.1.5
Summarising evaluation of the impact of critical market
characteristics on venture performance throughout different
evolution phases.......................................................................... 300
6.2 Case study – Open House .......................................................................... 302
6.2.1
Venture profile ............................................................................ 302
6.2.2
General market setting................................................................ 303
6.2.3
Explanation of market impacts on venture success .................... 304
6.2.3.1 Macro level ........................................................................ 304
6.2.3.2 Inter-country level.............................................................. 306
6.2.3.3 Inter-industry level............................................................. 308
6.2.3.4 Intra-industry level............................................................. 310
6.2.3.5 Barriers of entry................................................................. 316
6.2.3.6 Venture / firm level............................................................. 317
6.2.4
Excursus: Derived strategic recommendations for Open
House .......................................................................................... 320
6.2.5
Summarising evaluation of the impact of critical market
characteristics on venture performance throughout different
evolutionary phases .................................................................... 322
6.3 Case study – Tele-Ruf Kommunikations GmbH ....................................... 324
6.3.1
Venture profile ............................................................................ 324
6.3.2
General market setting................................................................ 325
6.3.3
Explanation of market impacts on venture success .................... 326
6.3.3.1 Macro level ........................................................................ 326
6.3.3.2 Inter-country level.............................................................. 328
6.3.3.3 Inter-industry level............................................................. 328
6.3.3.4 Intra-industry level............................................................. 330
XI
6.3.3.5 Barriers to entry................................................................. 337
6.3.3.6 Venture / firm level............................................................. 338
6.3.4
Excursus: Derived strategic recommendations for Tele-Ruf ..... 339
6.3.5
Summarising evaluation of the impact of critical market
characteristics on venture performance throughout different
evolutionary phases .................................................................... 341
7
Conclusions, significance of results and perspectives for future research.. 343
7.1 Summarising conclusions........................................................................... 343
7.2 Significance of results for practice............................................................. 347
7.3 Scientific significance of results and perspectives for future research ...... 351
Bibliography .............................................................................................................. 354
Appendices................................................................................................................. 370
Appendix A: Calculations for operationalization of MES variable................... 370
Appendix B: Industry ranking: Startups per industry ........................................ 372
Appendix C: Ranking of industries by subjective success evaluation............... 376
Appendix D: Ranking of industries by venture sales growth ............................ 381
Appendix E: Ranking of industries by venture profit level ............................... 386
Appendix F: Ranking of industries by years ventures needed to break even .... 391
Appendix G: Questionnaire guideline for case study interviews....................... 396
Appendix H: List of interviewees ...................................................................... 405
Appendix H: List of interviewees ...................................................................... 405
XII
List of figures
Figure 1: Delimitation of the study................................................................................. 9
Figure 2: Assumptions about the nature of social science: Objectivist approach ........ 11
Figure 3: General structure of chapters of study........................................................... 14
Figure 4: Aggregation of research on market factors in entrepreneurship ................... 33
Figure 5: Summary of research on market factors in strategy...................................... 50
Figure 6: Structure - conduct - performance paradigm (source Scherer
and Ross 1990:5).......................................................................................... 55
Figure 7: Key market factors in research programs...................................................... 66
Figure 8: Porter’s model of five competitive forces ..................................................... 68
Figure 9: Portfolio matrix of Baaken............................................................................ 74
Figure 10: Model of determinants of new venture formations by Dean and
Meyer (1996) ............................................................................................... 77
Figure 11: The three aggregation levels of environment.............................................. 86
Figure 12: The five environmental levels of the model................................................ 88
Figure 13: The 5 x 5 model of market attractiveness ................................................... 90
Figure 14: Macro level - Opportunities in global & national environment.................. 91
Figure 15: Inter-country level - National competitiveness ........................................... 95
Figure 16: Inter-industry level – Opportunities & threats from related industries....... 99
Figure 17: Intra-industry level – Market, dependencies & competitors..................... 101
Figure 18: Intra-industry level including barriers to entry ......................................... 110
Figure 19: Venture / firm level - Relative positioning to competition ....................... 112
Figure 20: The 5 x 5 model of market attractiveness including variables.................. 120
Figure 21: Orientation of research design................................................................... 122
Figure 22: Alternative measures of new venture success ........................................... 124
Figure 23: Investigated independent variables on the intra-industry level................. 175
XIII
Figure 24: Years of foundation of ventures in sample ............................................... 191
Figure 25: Average subjective performance measure by years of venture
foundation.................................................................................................. 194
Figure 26: Average venture growth in sales measure, by years of venture
foundation.................................................................................................. 196
Figure 27: Average venture profit (in 1,000 Euro) by years of venture foundation... 197
Figure 28: Average years needed to break even by years of venture foundation....... 199
Figure 29: Average share of ventures in % without breaking even until 2001 by years
of venture foundation ................................................................................ 199
Figure 30: Development of venture sales 1998-2001................................................. 201
Figure 31: Sales growth of ventures in percent for years of operation....................... 201
Figure 32: Annual growth of number of employees by year of venture foundation .. 202
Figure 33: Annual growth in taxable sales for Germany from the value-added tax
statistics ..................................................................................................... 202
Figure 34: Development of the number of employees 1998-2001 ............................. 203
Figure 35: Annual employee growth rates1998-2001 in percent ............................... 204
Figure 36: Annual growth of number of employees by year of venture foundation .. 204
Figure 37: Development of the venture profits 1998-2001 ........................................ 205
Figure 38: Mean of venture profits compared with sales ........................................... 206
Figure 39: Development of average venture profits by year of venture foundation .. 206
Figure 40: Annual venture profits in 1000 Euro 1998-2001 ...................................... 207
Figure 41: Distribution of industries among four major industry sectors .................. 208
XIV
List of tables
Table 1:
Studies of entrepreneurship research investigating sets of market
success factors ............................................................................................. 17
Table 2:
Summary of market success factors ............................................................ 20
Table 3:
Studies of entrepreneurship research investigating market-strategy
interactions .................................................................................................. 25
Table 4:
Studies of entrepreneurship research investigating VC deal evaluation
criteria.......................................................................................................... 26
Table 5:
Summary of VC deal evaluation criteria related to market......................... 29
Table 6:
Empirical studies on industry effects .......................................................... 40
Table 7:
Indicators for former underestimation of industry effects .......................... 41
Table 8:
Profiles of previous studies in industrial organisation/strategy research
dealing with the impact of individual market factors.................................. 43
Table 9:
Summary of market success factors in industrial organisation/ strategy
research........................................................................................................ 46
Table 10: Porter’s five competitive forces framework including second order
determinants ................................................................................................ 69
Table 11: Evaluation of Porter's "five forces model" .................................................. 70
Table 12: Baaken’s framework of the market environment........................................ 73
Table 13: Evaluation of the market framework of Baaken ......................................... 76
Table 14: Evaluation of the model of Dean and Meyer .............................................. 78
Table 15: Structure for environmental analysis and forecast by Hinterhuber............. 80
Table 16: Evaluation of the framework of Hinterhuber .............................................. 82
Table 17: Critical aspects for developing frameworks of market environments ........ 84
Table 18: Minimum aggregation level and research questions for five levels of
analysis ........................................................................................................ 89
Table 19: Model of market attractiveness: Macro level .............................................. 94
XV
Table 20: Model of market attractiveness: Inter-country level ................................... 98
Table 21: Model of market attractiveness: Inter-industry level ................................ 100
Table 22: Model of market attractiveness: Intra-industry level ................................ 109
Table 23: Model of market attractiveness: Barriers to entry..................................... 111
Table 24: Model of market attractiveness: Venture / firm level ............................... 115
Table 25: Summary of the unique characteristics of the developed model of
market attractiveness ................................................................................. 116
Table 26: Evaluation of alternative measures of venture success ............................. 127
Table 27: Profile of firms from case study ................................................................ 143
Table 28: Measures to minimise area of concern regarding validity and reliability . 148
Table 29: Data sources - Data collection and applied data sets ................................ 158
Table 30: Operationalisation of dependent variables of venture success.................. 174
Table 31: Operationalisation of market structure ...................................................... 179
Table 32: Operationalisation of market dynamics..................................................... 182
Table 33: Operationalisation of competitor structure................................................ 186
Table 34: Operationalisation of barriers to entry....................................................... 187
Table 35: Operationalisation of control variables ..................................................... 189
Table 36: Venture size in employees at year of start up............................................ 191
Table 37: Distribution of average venture size in average ventures sales in
1,000 Euro ................................................................................................. 192
Table 38: Distribution of ventures among four major industry sectors...................... 193
Table 39: Summarising descriptive analysis of success measures ............................. 194
Table 40: Frequency table of subjective performance measure ................................. 194
Table 41: Frequency table of venture sales growth .................................................... 195
Table 42: Frequency table of average venture profits in 1,000 Euro ......................... 197
Table 43: Frequency table of years to break even success measure........................... 198
XVI
Table 44: Top 25 industries with most business startups in Germany according
to the DtA sample...................................................................................... 209
Table 45: Top 25 industries by venture sales growth................................................ 212
Table 46: Top 25 industries by achieved level of venture profits ............................. 213
Table 47: Top 25 industries by years needed to break even...................................... 215
Table 48: Top 25 industries by subjective venture success evaluation ..................... 216
Table 49: Correlation table of dependent variables of venture performance ............ 219
Table 50: Correlation table of independent industry variables (part 1)..................... 225
Table 51: Correlation table of independent industry variables (part 2)..................... 226
Table 52: Correlation of industry factors with subjective venture performance....... 229
Table 53: Variables considered for inclusion in regression analysis (venture
growth – overall sample)........................................................................... 229
Table 54: Regression table of overall sample for venture growth............................. 230
Table 55: Correlation of industry factors with venture profits.................................. 232
Table 56: Variables considered for inclusion in regression analysis (venture
profit – overall sample) ............................................................................. 233
Table 57: Regression table of overall sample for venture profits ............................. 234
Table 58: Correlation of industry factors with years to break even .......................... 236
Table 59: Variables considered for inclusion in regression analysis (years to
break-even – overall sample) .................................................................... 237
Table 60: Regression table overall sample for years to break even .......................... 237
Table 61: Correlation of industry factors with subjective venture performance....... 240
Table 62: Variables considered for inclusion in regression analysis (subjective
venture performance – overall sample)..................................................... 240
Table 63: Regression table of overall sample for subjective performance ............... 241
Table 64: Correlation of industry factors with venture performance measures by
industry sector ........................................................................................... 243
XVII
Table 65: Correlation of industry factors with venture performance measures by
industry sector ........................................................................................... 245
Table 66: Summarising regression results of 16 regressions by industry sector....... 249
Table 67: Correlation of industry factors with venture performance measures by
market growth categories .......................................................................... 252
Table 68: Summarising regression results of 12 regressions by market growth
category ..................................................................................................... 254
Table 69: Correlation of industry factors with venture performance measures by
venture growth categories ......................................................................... 256
Table 70: Summarising regression results of 12 regressions by venture growth
category ..................................................................................................... 259
Table 71: Correlation of industry factors with venture performance measures by
intra-industry profit heterogeneity categories ........................................... 261
Table 72: Number of significant correlations for different categories of industry
heterogeneity by success measure............................................................. 262
Table 73: Summarising regression results of 12 regressions by industry
heterogeneity category .............................................................................. 264
Table 74: Summary table of significant correlations in different contexts with
results of hypotheses ................................................................................. 267
Table 75: Importance and generalisability of industry variables from hypotheses... 275
Table 76: Importance and generalisability of industry variables not included in
hypotheses ................................................................................................. 276
Table 77: Macro level analysis Imente...................................................................... 282
Table 78: Inter-country level analysis Imente ........................................................... 284
Table 79: Inter-industry analysis Imente ................................................................... 286
Table 80: Intra-industry level analysis Imente .......................................................... 290
Table 81: Barriers to entry analysis Imente............................................................... 293
Table 82: Venture level analysis Imente ................................................................... 295
XVIII
Table 83: Macro level analysis Open House ............................................................. 305
Table 84: Inter-country level analysis Open House .................................................. 307
Table 85: Inter-industry analysis Open House .......................................................... 308
Table 86: Intra-industry level analysis Open House ................................................. 313
Table 87: Barriers to entry analysis Open House...................................................... 317
Table 88: Venture level analysis Open House........................................................... 318
Table 89: Macro level analysis Tele-Ruf .................................................................. 327
Table 90: Inter-country level analysis Tele-Ruf........................................................ 328
Table 91: Inter-industry analysis Tele-Ruf................................................................ 329
Table 92: Intra-industry level analysis Tele-Ruf....................................................... 333
Table 93: Barriers to entry analysis Tele-Ruf ........................................................... 337
Table 94: Venture level analysis Tele-Ruf ................................................................ 338
XIX
List of abbreviations
CE
capital employed
Dep
dependent variable
diss.
dissertation
DtA
Deutsche Ausgleichsbank
e.g.
for example
EBIT
earnings before interest and taxes
ed.
editor
Eds
editors
et al.
et alii (and others)
EG
employee growth
EP
economic profit
EU
European Union
FS
firm survival
FP
firm profit
FTC
Federal Trade Commission
GBV
gross book value
GEM
Global Entrepreneurship Monitor
GNP
gross national product
Hyp
hypothesis
INP
industry profit
IP
internet protocol
IPO
initial public offering
JP
Japan
LOB
line of business
MS
market share
N
number of firms
XX
n.a.
not applicable
NACE
nomenclature of economic activities in the European Union
p.
page
P&E
plant and equipment
PCM
price cost margin
Pp
pages
PIMS
profit impact of market strategy
Prof.
professor
R&D
research and development
Rev.
revision
ROA
return on assets
ROE
return on equity
ROI
return on investment
ROS
return on sales
SBU
standard business unit
SEC
Securities & Exchange Commission
SG
sales growth
SIC
standard industrial classification
sig.
significance
SME
small and medium sized enterprise
Std. Er.
standard error
TMV
total market value
Toler.
tolerance
transl.
Translation
US
United States
VC
venture capitalist
V
variable
XXI
vs.
versus
ZEW
Zentrum für Europäische Wirtschaftsforschung
XXII
CHAPTER 1 - INTRODUCTION
1
1.1
1
Introduction
Problem statement
Since the beginnings of entrepreneurship and business research, the primary
motivation of most research has been to understand why some firms are more
successful than others. Understanding the secrets - the factors and constellations - that
have made some companies perform better in the past may not only serve to satisfy
scientific curiosity, but it may also help to build more successful companies in the
future under the premise that entrepreneurs are able to influence the factors and
constellations that are identified as being related to higher venture success.
The different dimensions of these factors related to higher success have been
investigated in earlier studies. For many years, research in entrepreneurship has
concentrated on the impact of the characteristics of the entrepreneur on venture
success, and a considerable body of evidence has shown that the entrepreneur’s
characteristics explain a relatively high share of venture success.
In the mid 1980s more comprehensive models were developed in entrepreneurship
research, which included, apart from variables related to the entrepreneur, the variables
of the industry in which the venture operated and those of the strategies chosen. This
extension of the dimensions taken into consideration resulted from the intention of
building models with a better capacity to account for entrepreneurial success. It has
been shown that environmental factors also explain, to a large extent, the venture
success phenomenon. Recognition of the importance of environmental factors was
fuelled by the increasing popularity of other scientific fields such as organisational
ecology, industrial economics and industrial organisation. These fields shared a
common belief in the dominant importance of the industry structure for organisational
success. Nowadays, environmental factors are commonly accepted as constituting one
of the most important dimensions when studying venture success.
Research on the impact of the strategy dimension on venture success has led to the
basic conclusion that there is not one strategy that leads to venture success, but that
there might well be one strategy that leads to venture success in specific environmental
settings. Consequently, understanding the environmental setting in which an
organisation operates is a prerequisite for defining successful strategies.
2
CHAPTER 1 - INTRODUCTION
There are clear differences regarding the extent to which an entrepreneur can actively
influence the three main dimensions for creating more successful companies, namely:
the characteristics of the entrepreneur, the environment and strategy. With respect to
the dimension of successful entrepreneurs, it can be observed that many of the
identified characteristics are innate or are personality traits which, for an adult, can
only be changed to a limited degree. Without any doubt, these factors do have a great
impact on venture success. However, from the view of the individual entrepreneur who
seeks ways to create a more successful company, the majority of the identified success
factors regarding the characteristics of the entrepreneur have to be considered as given
and are rarely changeable1. On the other hand, on business start-up, the entrepreneurs
have substantially more possibilities to influence the industry dimension and the
strategies employed than their own personality traits.
A better understanding of market and industry environments and their impact on
venture success might enable entrepreneurs to recognise risks, choose attractive
environments and, as a consequence, start more successful ventures. Knowledge of the
industry and the market environment is essential to choosing successful strategies.
Despite the acknowledged importance of the industry and market environments in
general, research results regarding the specific impacts of the industry and market
environments are considered weak and highly contradictory2.
1.2
General purpose of the study and objectives
The purpose of this research is to gain a better understanding of the influence of
market environments on venture performance with the aim of contributing answers to
the primary research questions that have arisen from the stated problem.
Specifically, the following main research questions will be addressed:
1. Which particular characteristics of market environments increase the
probability of new venture success and what is their respective strength of
impact?
1
As stated by Ian MacMillan in a speech at Växjö University on May 5th, 1999: “However this research [on the
characteristics of the entrepreneur] turned out to be of not much value”.
2
Deficits in the current knowledge about the impact of the environment on venture success have been pointed
out by Levenhagen and Thomas 1993, Schmude 1994, Brüderl, Preisendörfer, and Ziegler 1996, MüllerBöling and Klandt 1996, Low, and Abrahamson 1997.
CHAPTER 1 - INTRODUCTION
3
2. To what extent does the impact of market environment characteristics vary
among different contexts such as economic sectors and market lifecycle
stages?
3. What
are
the
underlying
mechanisms
through
which
market
characteristics impact venture performance?
This study takes into consideration some of the above-mentioned shortcomings of
previous research by focusing solely on the market environment dimension and by
including a more comprehensive factor set.
The general objective of this study is to identify specific indicators or constellations of
indicators for higher probabilities of venture success. This will help both investors and
entrepreneurs to evaluate venture environments and to identify the related risks. A
better understanding could lead to an increase in venture success.
The specific objectives of this study, derived from the above mentioned research
questions, are:
1. To analyse the empirical results from relevant studies in the areas of
entrepreneurship and strategy research with respect to market–venture
success relations.
2. To study, from different theoretical perspectives, the market factors that
affect organisational success.
3. To propose a new proprietary theoretical model of market attractiveness
that concentrates on the specific perspective of new ventures and provides
a structuring framework for the analysis of the impact of market variables
on venture success.
4
CHAPTER 1 - INTRODUCTION
4. To empirically identify markets in which new ventures have been the most
successful.
5. To empirically study the relationship between industry variables and the
success of new ventures.
6. To empirically study the impact of contingent variables on the relationship
between industry variables and the success of new ventures.
7. To empirically identify the mechanisms through which market factors
influence venture success.
The individual objectives of the study are interrelated. The results of previous
empirical research and theory (objectives 1 and 2) are the basis for the development of
a proprietary theoretical model of market attractiveness (objective 3). This proprietary
model in turn will serve as a reference for the subsequent empirical investigation. The
empirical study is not aimed towards the empirical verification of the theoretical
model, instead it is the objective of the empirical study to investigate specific aspects
of the relationship between industry variables and venture success (objectives 4 to 7)
referring to the model as a structuring framework.
1.3
Definition of basic terms
1.3.1 Market and industry
Market: A market is defined as an "exchange mechanism which brings together the
sellers and buyers of a product. Markets, in practice embrace a number of product,
CHAPTER 1 - INTRODUCTION
5
spatial and physical dimensions"3. Throughout this study the focus will be on markets
from a product perspective. According to such a perspective, a narrow market
definition defines markets as "a group of goods or services, which are viewed as
substitute products by buyers"4.
A further distinction has to be made according to the inter-temporal nature of markets.
The traditional school, based on an industrial economic perspective, views market
structure as exogenous and stable. In contrast, the Schumpeterian and Chicago schools
view market structure as dynamic and constantly evolving. The Schumpeterian school
focuses on revolutionary innovations that make rivals’ positions obsolete and change
industry structure. Similarly, the Chicago school believes in the long-term
convergence of competitive patterns as and when less successful firms imitate the
strategies of the more successful ones5. As opposed to the industrial economic
perspective, the dynamic nature of markets is acknowledged in this study and therefore
general environmental changes and elements of market dynamics are taken into
consideration.
Industry: An industry is defined as "a branch of commercial enterprise concerned with
the output of related goods"6. Contrary to the notion of markets, industry aggregations
are generally defined on the basis of similarities from a supply-side, rather than a
demand-side perspective. Moreover, the more broadly defined term ‘industry’
comprises companies that operate in several markets. Particularly large enterprises
operate frequently in a large number of markets at the same time.
In practice, both of the terms industry and market describe very similar phenomena
when defining markets and industries based on a product dimension and both terms
comprise demand and supply elements. In a more general market definition, the supply
side of markets is explicitly included when markets are defined in a broader sense as a
“group of demanders including their needs, of goods as utility creating bundles of
attributes and of suppliers with the instruments of the utility creation“ 7. Commonly,
3
Collins Dictionary of Business 1995: 388.
Collins Dictionary of Business 1995: 389.
5
Mauri and Michaels 1998.
6
Collins Dictionary of Business 1995: 313.
7
Vahlens Großes Wirtschaftslexikon 1993: 1394 transl. from German “Menge von Nachfragern samt ihren
Bedürfnissen, von Gütern als nutzenstiftenden Eigenschaftsbündeln und von Anbietern mit den Instrumenten
der Nutzenstiftung“.
4
6
CHAPTER 1 - INTRODUCTION
the term industry is applied to describe rather comprehensive aggregates on a narrow
classification depth, while the term market is frequently applied for smaller, more
specific aggregates at a deeper classification depth.
The theoretical part of the study mainly refers to markets rather than industries, since
the impacts of specific settings and conditions are investigated and the term industry
could lead to misunderstandings with respect to the desired narrow aggregation level.
The quantitative study, however, refers mainly to industry. Because no statistical data
is normally available on market categories, the quantitative study is based on industry
data collected from standard industry classifications. This seems to be legitimate since,
in the context of SMEs, market and industry might capture very similar phenomena.
SMEs frequently have a low degree of diversification and often focus on solely one
market.
1.3.2 Market attractiveness
The term market attractiveness stems from the business portfolio planning technique
literature, which suggests that firms should invest in markets with certain attractive
characteristics. The one most popular business portfolio planning technique is the
Boston Consulting Group matrix8, which classifies businesses according to the two
dimensions of market growth and profitability. In this simple matrix model, business
profitability and market growth are considered independent of each other, and market
growth is applied as the only external determinant for market attractiveness. However,
the attractiveness of markets is influenced by many more factors than market growth.
As is shown in the literature review, this limitation of the term market or industry
attractiveness to one external market factor or a small number of factors has been very
common in earlier research, neglecting the complexity and diversity of market
environments.
The term of environmental munificence has frequently been used in strategy literature
as an aggregate for attractive market environments. Environmental munificence has
been defined as "scarcity or abundance of critical resources needed by firms operating
within an environment"9. However, the adoption of this definition for market
8
Hammond and Allan 1975, see also Pleitner 1983 and Rupp 1988 for in-depth discussion of portfolio matrices
and product/market strategies.
9
Castrogiovanni 1991.
CHAPTER 1 - INTRODUCTION
7
attractiveness has to be rejected, since it excludes relevant dimensions of
attractiveness, including several aspects of industry structure and competitive setting.
Within the context of this study, market attractiveness must, in a broader sense,
comprise all characteristics that affect the organisational success of a collective group
of organisations within one market and neither contribute on an individual level to the
human characteristics within these organisations or characteristics of individual
organisations, nor to characteristics that arise from a specific geographic context.
1.4
Delimitation of the study
In order to prevent a basic misunderstanding of the purpose of the study, it is important
to stress that this study does not claim that new venture performance is solely a
function of market characteristics. Previous research has provided ample evidence
indicating that the performance of new ventures is not only dependant on
environmental factors, but also on the individual profile of the entrepreneurs, the
specific product or service concept of the venture, as well as the strategic postures
adopted. Rather, the purpose of this study is to undertake a focused investigation
regarding the impact of the market dimension on the success of new ventures.
Focus on investigated variables: Market variables
According to previous studies, market factors accounted for an average of only 20% of
overall performance variance. It might therefore be assumed that this study explains a
larger share of overall performance variance by taking into account some of the gaps
in earlier studies by applying a highly comprehensive list of variables, by taking into
consideration the contextual setting with regard to industry and market lifecycle
stages, as well as by focusing on the service sector. Still, the impact of factors other
than market factors might distort the findings. Therefore, the quantitative part of the
study will apply averages of success measures among a large number of firms within
an industry in order to level the unquestionably significant impact of management,
venture concept and strategy.
Sectoral focus: Mainly service industries
The study will mainly focus on industries in the service sector. McGahan and Porter
(1997) discovered that market characteristics account for far higher shares of
performance variance in industries from the service sector than from the
8
CHAPTER 1 - INTRODUCTION
manufacturing sector. A focus on service industries will therefore decrease distortions
by contingent non-market factors. In addition, previous research has largely neglected
the service sector, despite the fact that its volume exceeds the volume of the
manufacturing sector in most developed countries and that it has a continuously
growing share of the GNP. While the general theoretical model of market
attractiveness will be independent of the industry sector, the quantitative part of the
study will rely mainly on data from companies in the service sector. This reflects the
dominant share of start-ups in the service sector among the overall number of startups.
However, data from the manufacturing sector will not be excluded but incorporated
into the analysis in order to allow cross-sector comparisons.
Organisational focus: New venture perspective
The study will focus on the evaluation of market constellations from the perspective of
new ventures as organisational objects of analysis. New ventures have been defined as
firms that have started operations no more than eight years ago. The empirical
investigations can therefore only provide results for the success of market entry and
retention within the first years of operation. The study will be limited with regard to a
short- and medium-term development of success measures. Specifically, it will not be
able to identify market structures that might favour a high sustainable long-term
performance. The findings might also be limited to small firms, as ventures generally
start at a limited organisational size. Overall, it has to be concluded that the findings
claim no validity for the decisions of market diversification in large firms with a longterm performance expectation.
Geographic focus: One national context (quantitative part), several national
contexts (qualitative part)
The quantitative data set will only include organisations within Germany. Otherwise,
differences on the national macro level may distort the market effects. The findings
might therefore be restricted to the investigated geographic context. However, while
the importance of particular critical market factors can vary among different national
contexts, the underlying more abstract relationships between market characteristics
and venture success might be to a large extent transferable to other geographic
contexts, at least among western European countries. The qualitative study will
CHAPTER 1 - INTRODUCTION
9
consider cases from Spain and Germany in order to permit also cross-country
comparisons.
performance
market
VARIABLES
service
SECTOR
ORGANISATION
GEOGRAPHIC
new ventures
Germany /
Western Europe
entrepreneur
strategy
manufacturing
established companies
rest of world
Figure 1: Delimitation of the study
1.5
Underlying assumptions and choice of general research
strategy
1.5.1 Assumptions about the nature of social science: Objectivist approach
This study will be primarily grounded on assumptions about the nature of social
science, as proposed by the objectivist approach. These assumptions will underpin the
objectives and the design of the study.
First, a realistic rather than a nominalist view on ontology will guide the assessment of
market characteristics in specific market contexts. Even though it will be
acknowledged that the environment is perceived by individuals differently, it is
assumed in this study, that a real, objective market exists. The dimensions of the
market are assumed to be measurable. The nominalist interpretation of markets as an
individual mental construct is negated in this study. This would render the
measurement of market characteristics rather difficult, if not impossible.
Second, a positivist rather than an anti-positivist view on epistemology will guide the
objectives of the study and the applied methods of data collection and analysis. In
accordance with the positivist approach, the author strongly believes in the ability of
10
CHAPTER 1 - INTRODUCTION
science to transmit knowledge among individuals. The experience of specific market
settings might be transmittable to other market settings. The radical anti-positivist
stance reduces the ability of knowledge acquisition to personal experience. Such a
standpoint would basically negate the feasibility of explanatory research.
Third, a nomothetic, rather than an ideographic view on methodology will guide the
overall research design. The study aims at the identification of causal relationships
between markets and venture success that could be generalisable in certain contexts.
While maintaining a nomothetic view and the orientation towards generalisable causal
relationships, the level of generalisability of the causal relationships will be critically
discussed. In accordance with the ideographic critics of universal generalisations,
causal relationships will be investigated on low industry aggregations levels within the
quantitative study as well as in specific contexts as life-cycle stages. Within the
qualitative part of the study, the proposal of ideographic research for further
disaggregation on an individual level will be acknowledged. However, it is still
assumed that different individuals might perceive the environments of specific markets
similarly.
Fourth, a deterministic rather than voluntaristic view on human nature will guide the
purpose of the study and the method of data collection. It should be noted that this
assumption should be solely considered as a simplifying model projecture. Especially
in dynamic industries, individual market players can certainly impact the market
environment in which they operate with radical innovations that change the "rules of
the game" of the market or by the intentional instrumentalisation of their market
power. Nevertheless, a deterministic view seems to be justified for several reasons.
First, new ventures entering a market do not generally have the option of changing the
environment by market power, only by radical innovation. Even in markets with low
capital intensity, it can be observed that from the total number of new ventures that
enter a market, only a very few are able to introduce radical innovations that change
the "rules of the game". Moreover, most of these new ventures that are able to change
the "rules of the game“ will do this only for a market niche and not for an overall
market. Second, it is obvious, that market environments can certainly not be created at
will by any individual organisation. Existing market settings impose clear limitations
on the feasibility of venture concepts.
CHAPTER 1 - INTRODUCTION
11
OBJECTIVIST APPROACH
ONTOLOGY
Realistic
view
Market is not a mental
construct but real
Nominalistic
view
Knowledge is
transferable, not limited
to personal experience
Positivist
Anti
Positivist
METHODOLOGY
Nomothetic
Ideographic
Market-success
relations generalisable
HUMAN NATURE
Deterministic
Voluntaristic
Existing market
settings not modifiable
at will
EPISTOMOLOGY
Figure 2: Assumptions about the nature of social science: Objectivist approach
1.5.2 Type of study: Explanatory research
The study aims to reach beyond the identification of relevant market characteristics
and their descriptions. In the empirical part of the study, causal relationships between
market characteristics and success measures will be investigated on the basis of several
theoretical perspectives in the field of business and economic theory. Thus the study
will be explanatory in nature.
The combination of quantitative and qualitative methodologies within the study allows
the consideration of both inductive and deductive modes of argument. Deductive
elements will be applied in the initial stage of the study when aggregating market
variables proposed by a broad scope of theories and empirical investigations within the
fields of business and economics. These theories will guide the selection of relevant
market variables to be measured in the later empirical stages of the study. In the
second stage of the study, hypotheses are deduced for relationships between market
factors and venture performance. In the third stage, the phenomena investigated within
the case studies will be interpreted on the basis of explanations from scientific theories
and the findings of the second stage.
12
CHAPTER 1 - INTRODUCTION
1.5.3 General research approach: Triangulation
In social science research, triangulation means using different methods10 in order to
examine the combination of methodologies in the study of the same phenomenon. The
rationale for the application of triangulation follows the argument of Wilson (1986):
"qualitative and quantitative approaches are complementary rather than competitive
methods. Each supplies a kind of information that is not only different from the other,
but also essential for interpreting the other. Quantitative studies reveal patterns of
regularities in situated actions, while qualitative investigations shed light on the
concrete social processes by which particular patterns of situated actions are
produced". Quantitative techniques will be applied in the first part of the empirical
study in order to identify critical market characteristics with regard to venture
performance. Qualitative techniques will then be applied in the second part of the
empirical study. The case studies will produce insights into the processes and
mechanisms by which market factors influence venture performance. The qualitative
approach could especially supplement the explanation for barely measurable
mechanisms of impact of market factors on new venture success. Both the use of
multiple stages and triangulation are recommended by Hofer and Bygrave (1992) for
research on entrepreneurship.
1.6
General structure of the study
Following the proposition of Hofer and Bygrave, several stages of research design will
be applied: "The holistic nature of the entrepreneurial process combined with the
number of antecedent variables involved, strongly argues for a multiple stage design,
in which the initial stages are used to explicitly identify the factors to be studied in
later stages, as well as the ways that will be used to study them"11.
The study is structured along consecutive stages:
STAGE 1: THEORETICAL PART
The theoretical part leads to the development of a conceptual model of market
attractiveness. A general introduction in chapter one is followed by a literature review
of market-related research in the fields of entrepreneurship research and strategy in
10
11
In particular different types of data collection techniques or different types of measures.
Hofer and Bygrave 1992: 93.
CHAPTER 1 - INTRODUCTION
13
chapter two. In chapter three, a broad range of scientific research programs in business
and economics are evaluated on their applicability to explain the relationship between
markets and venture success. The different conceptual explanatory approaches are
presented and the most important market factors of previous empirical research are
identified. Finally, a new conceptual model for structuring market factors is developed.
This model integrates former findings in different areas of research and guides the
empirical part of the study that follows.
STAGE 2: EMPIRICAL PART
The empirical part of the study is introduced with an explanation of the applied
methodology in chapter 4. It includes clarifications regarding the general research
approach, success measures, and for each following substage, details on investigated
variables, method of data collection, analysis and interpretation.
Substage: Quantitative study
In chapter five, those market factors for which applicable secondary data is available,
are investigated within a quantitative study. The quantitative study is based on
comprehensive cross-industry data sets, including data on new venture performance.
As a result, critical market factors are identified and indications for variations among
different contexts are obtained.
Substage: Qualitative study
The qualitative study is based on the findings of the former stages. In chapter six, the
case study approach allows the inclusion of a broader range of relevant factors than the
quantitative study, which is inherently limited by the availability of data. Moreover,
the qualitative study should supplement the previous findings through insights into the
underlying mechanisms of the market-performance relationship among new ventures.
This part of the empirical study is focused on selected segments of the
telecommunication and e-commerce industries.
Finally, in chapter seven, conclusions and comments about the significance of the
results as well as perspectives for future research are presented.
14
CHAPTER 1 - INTRODUCTION
STAGE 1: THEORETICAL PART
1. INTRODUCTION
2. LITERATURE REVIEW
- Entrepreneurship
- Strategy
3. MARKET MODEL
DEVELOPMENT
- Additional research programs
- Concepts for structuring markets
NEW MARKET MODEL
STAGE 2: EMPIRICAL PART
4. METHODOLOGY
5. QUANTITATIVE STUDY:
Analysis of impact of market
factors on startups in Germany
6. QUALITATIVE STUDY:
Case studies of the telecommunication
and e-commerce industries from a
start-up perspective
7. CONCLUSIONS, SIGNIFICANCE OF RESULTS AND PERSPECTIVE
Figure 3: General structure of chapters of study
CHAPTER 2 - LITERATURE REVIEW
2
15
Literature review
Within the literature review, studies from the two major research fields of
entrepreneurship and strategy have been revised with the intention of achieving
objective number one of this study (compare chapter 1.2) by aggregating relevant
empirical findings on the market–firm performance relationship. These findings are
applied in chapter three for the development of a theoretical model. Within the
entrepreneurship literature, the two areas of success factor research and VC decisionmaking research are reviewed. Within the strategy literature, research on success
factors and research on measuring industry effects are revised.
2.1
Entrepreneurship research
2.1.1 Research on market success factors
An extensive body of literature can be found that deals with the identification of
critical factors for venture success. In accordance with the general orientation of the
whole field of entrepreneurship, most of the studies from the 1960s until the beginning
of the 1980s are centred on factors relating to the entrepreneur. In the 1980s more
comprehensive models were developed that included the market dimension, as well as
strategy, organisation and industry-strategy interactions. This chapter discusses only
those studies that examine the impact of market factors on venture success in terms of
profitability or sales measures12: First, studies empirically investigating sets of market
success factors, and second, studies investigating interactions between market success
factors and strategy.
12
The whole area of research on firm survival will not be included. The purpose of this study is to investigate
market attractiveness. Therefore the underlying expectation of venture success goes beyond venture survival
only. For studies investigating the impact of market factors on venture success in terms of alternative
measures compare especially Romanelli 1987 for impact of market growth and competitive concentration on
venture survival, Dubini 1989 for the impact of a broad range of market variables on the motivation of
entrepreneurs, and Dean and Meyer 1996 for a systematic investigation of the impact market variables on
venture formations.
16
CHAPTER 2 - LITERATURE REVIEW
Studies of entrepreneurship research investigating sets of market success factors
Author
Sandberg
and Hofer
Year
1987
Market factors
Significance
investigated
(if investigated)
SECTOR OF THE ECONOMY
INDUSTRY STAGE
p=0,05 (for first stages)
INDUSTRY STRUCTURE
- concentration
Stuart and
Abetti
1987
- product heterogeneity
p=0,016 (for
heterogeneous products)
INDUSTRY
DISEQUILIBRIUM
p=0,025 (for
disequilibrium)
BARRIERS TO SUBSEQUENT
ENTRY
p=0,025 (barriers exist)
MARKET ATTRACTIVENESS
p=0.01 (reverse impact
on quantified success!!)
- large market volume
- predictable and quantifiable
- early stage lifecycle
- rapidly growing
- favourable socio-political
environment
# of
Findings
units
investigated
Unit of
Dependent
analysis
variable
Disregarding interactive effects,
industry structure had a greater
impact on new venture
performance, than either strategy or
the characteristics of the
entrepreneur
17
new ventures
seeking venture
capital
- ROE
Market factors, in terms of market
attractiveness and market
dynamism were surprisingly found
to have negative impact on venture
performance. It appeared that one
can achieve initial success more
easily and rapidly in the more
stable less dynamic markets.
24
new technical
ventures
- quantifiable
initial success
Industry effects have significant
impact on venture success, beside
founder's characteristics and
strategy chosen. However
influences vary geographically and
over time.
127
VC backed
ventures in
computer
industry in the
US, Europe
and Japan
- ROI for venture
capitalists
Ventures could achieve market
share gains more easily in early life
cycle stages, less fragmented
markets with low competitor
dependence.
Market growth had no impact on
market share and a negative impact
on ROI, which might be related to
high investments in growing
markets.
161
Corporate
startups from
PIMS startup
database
- market share
(MS)
- ROI
Venture growth in number of
employees was high for low
competition on prices in market,
high competition on innovation in
market, stabile customer bases, and
low seasonality. Venture survival
was high in industries with low
rivalry, high competition on quality
and innovation, high concentration
of competitors, high market
dynamics and high concentrations
of orders. No factor was found to
have significant effect on growth in
sales.
1216
ventures in 46
industries in
Northern
Bavaria /
Germany
- g growth in sales
(SG),
- growth in
number of
employees (EG),
- firm survival
(FS)
- subjective initial
success
- clear customer identity
- clear customer needs
- few competitors
MARKET DYNAMISM
p=0.10 (reverse impact
on subjective success!)
- rapid, chaotic evolution
- numerous innovations
- high number of competitors
- low entry barriers
- unclear product requirements
- need for specialized products
Keeley,
Roure et al.
1987
industry category “low-tech“
p=0,001(- for Japan)
competitor strength
P=0,001(- for Japan)
buyer concentration
P=0,01(+ for US)
market stage
P=0,05 (+ for US)
Growth
P=0,001 (+ for US)
future barriers
Segmentation
Tsai,
MacMillan
and Low
Brüderl,
Preisendörf
er et al.
1991
1996
MUNIFICENCE
- life cycle
- buyer concentration
- growth
HOSTILITY
- competitors Dependence
- top three share
rivalry
competition on prices
competition on quality
competition on innovation
concentration of competitors
market dynamics
change of customer base
concentration of orders
seasonal changes
p=0,01 (on MS)
p=0,01 (on MS)
p=0,01 (on ROI)
p=0,01 (on MS)
p=0,05 (- on FS)
p=0,05 (- on EG)
p=0,05 (+ on FS)
p=0,05 (+ on FS & EG)
p=0,05 (+ on FS)
p=0,05 (+ on FS)
p=0,05 (- on EG)
p=0,05 (+ on FS)
p=0,05 (- on EG)
CHAPTER 2 - LITERATURE REVIEW
Wilson
Robinson
1998
1998
17
relative market share
investment intensity
industry growth rate
revenue/employee
p=0,001
stage of market life cycle
p=0,05
industry concentration
entry barriers
product differentiation
p=0,01
Relative market share and high
revenue / employee were found to
be positively related to
profitability.
4006
firms of 8
service
industries in
US (Census of
service
industries data)
- pre-tax
profitability
Life cycle has significant impact on
profitability measures but not on
change of sales. Entry into
industries with high concentration
and low product differentiation
might be more difficult.
199
ventures
in 71 SIC
industries from
Compustat
- change in sales,
- sales level. Net
profit,
- EBIT, ROS,
ROI, ROE
Table 1: Studies of entrepreneurship research investigating sets of market success factors
Among the studies that apply regression analysis to investigate sets of market factors,
the study of Sandberg and Hofer13 is particularly noteworthy, since it was, at the time
of publication in 1987, one of the first studies in entrepreneurship that provided a
comprehensive and empirically tested model of success factor dimensions, including
market factors. This study defines new venture performance as a function of the
following dimensions: characteristics of the entrepreneur, industry structure and
venture strategy. Based on data from 17 ventures seeking venture capital, the study
found that by disregarding interactive effects, the industry structure had a greater
impact on new venture performance than either strategy or the characteristics of the
entrepreneur. In addition, strong interactions between these characteristics were
identified. As a result, significant relations to new venture performance could be
identified for heterogeneous industry structures, existing barriers of entry, high
industry disequilibrium and an early stage of industry evolution. This basic model of
new venture performance is widely referred to in later studies in the field.
In the same year, Stuart and Abetti tested a comprehensive model of success predictors
on 24 new ventures. The results were rather astonishing. Market attractiveness as a
congregate of market growth, size and other market variables exhibited a significantly
negative relationship with quantified initial success. In contrast with previous
explanation models, startups in smaller or slower growing markets exhibited a higher
quantifiable venture success than those in larger, faster growing markets. With regard
to subjective initial venture success, a negative correlation was shown for market
dynamism. The authors provided the following explanation for this unexpected
phenomenon: “It appears that one can achieve initial success more easily and rapidly
13
Sandberg and Hofer 1987.
18
CHAPTER 2 - LITERATURE REVIEW
in the more stable, less dynamic markets, by carving and keeping an appropriate
market niche”14.
A study from Keeley et al.15 introduced an investigation of success factors in different
geographical contexts. VC-backed ventures of the computer industry in the US,
Europe and Japan have been analysed according to managerial, industry and strategy
variables. The influence of these variables varied strongly by geographical contexts.
For US firms, a high buyer concentration and a fast-growing overall market were
significantly correlated with ROI. By contrast, in Japanese firms no correlation was
found between those variables, but negative correlations with ROI were identified for
high competition and non-technical industry sectors. Finally, for European firms, no
significant correlations with industry variables could be identified. Even though the
composition of the samples was quite different for each country, the study showed that
the importance of individual industry effects may vary by geographic contexts.
Tsai, MacMillan and Low16 investigated the impact of strategy and market variables
on venture success on the basis of a sample of corporate ventures. The market
environment was categorized into the dimensions of market munificence and hostility.
They found out that ventures could achieve market share gains more easily in early life
cycle stages and achieve less fragmented markets with low competitor dependence.
Market growth had no impact on market share and had a negative impact on ROI,
which could be related to high investments in growing markets.
In Germany, Brüderl17 investigated the effect of a broad range of variables on the
different measures of venture success, on the basis of a very large sample of more than
1000 ventures. With regard to the market dimension, the significant empirical
evidence for growth in terms of number of employees suggested that new ventures can
achieve growth more easily in industries with a stabile customer base, low seasonality
and markets characterised by competition on innovation. However, this evidence could
only be found for growth in employee numbers. No significant relationship at all was
found for growth in terms of sales. On the basis of the evidence on firm survival, the
study comes to the conclusion that new ventures will more frequently survive in
14
Stuart and Abetti 1987: 226.
Keeley et al 1987.
16
Tsai, MacMillan and Low 1991.
17
Brüderl et al 1996.
15
CHAPTER 2 - LITERATURE REVIEW
19
industries that face rapid changes in market conditions. Dynamic markets frequently
generate market gaps and niches that established companies cannot rapidly adapt to.
Wilson18 investigated some of the most important findings of the PIMS study in the
context of service industries. His investigation introduces the importance of a high
revenue per employee ratio on profitability. Additionally, relative market share was
found to be positively related to profitability. Even though industry growth was not
statistically significant, a negative relationship between industry growth and
profitability was identified, contrary to the initially expected positive relation.
Robinson19 conducted a study focused on the market-venture performance
relationship. He examined the influence of the stage of the market life cycle, industry
concentration, entry barriers and product differentiation on eight different performance
indicators, including both performance and sales measures. This research indicated
that the stage of the market life cycle at the time of venture entry is the most important
determinant of profitability. However, no statistically significant relationship could be
shown for changes in sales, neither could any significant relationship be shown for the
other industry factors.
Tsai,
Sandberg
Stuart and
Keeley,
MacMillan and
Brüderl et
and Hofer
Abetti
Roure et al.
Low
al.
1987
1987
1987
1991
1996
Wilson
Robinson
1998
1998
MARKET
high market dynamism /
disequilibrium*
early industry life cycle stage*
+ ROE
- (subjective)
+ ROE
high market growth*
- (ROI/SG
etc)
high barriers to entry*
+ ROE
high product heterogeneity*
+ ROE
+ FS
+ ROI US
+ MS
+ ROI US
+ ROI
+ ROI & o SG
o ROI
o ROI
o ROI
(investment
intensity)
o ROI & SG
low technology level of industry
- ROI JP
high buyer concentration*
+ ROI US
low segmentation
o ROI
+ MS
high change of customer base
- EG
high concentration of orders
+ FS
high seasonal changes
- EG
high revenue per employee
18
19
Wilson 1998.
Robinson 1998.
o ROI & SG
+ ROI
20
CHAPTER 2 - LITERATURE REVIEW
COMPETITION
high competition*
high industry / competitor
concentration*
low competitor dependence
- ROI JP
o ROE
- FS
o MS & ROI
+ FS
+ MS
competition on prices
- EG
competition on quality
+ FS
competition on innovation
+ FS & EG
* repeatedly investigated factors
o ROI & SG
+ positively related
– negatively related
o not related
MS=market share FS=firm survival EG=employee growth SG=sales growth ROI=return on investment ROE=return on equity
Table 2: Summary of market success factors
By aggregating the variables investigated in former studies on success factors in
entrepreneurship, variables can be structured by the two major constructs of market
and competition. Instead of an inclusion of competition and competitive settings as
part of the market, a clear distinction can be found in most studies between these two
concepts.
Among the most frequently investigated factors related to the market structure are the
variables market dynamism, life cycle stage, market growth, barriers to entry, product
heterogeneity and buyer concentration. Of these factors, only the life cycle stage was
consistently considered significant in relation to new venture success. All of the four
studies that investigated industry life cycle demonstrated that entering an industry at an
early life cycle stage was significantly related to venture success measures as diverse
as ROE, ROI, market share and sales growth. More than any other individual factor,
the life cycle stage has also been the subject of in-depth research in entrepreneurship20.
Of particular note is the elaborated theoretical model of the effect of market life cycle
stages on new venture success by Low and Abrahamson21. The authors concluded that
“fundamentally different processes may be at work at different stages of industry
evolution”22. They recommend that entrepreneurship research should pay more
attention to the context of the life cycle stage. Empirical findings suggest that early life
cycle stages provide better opportunities for new ventures than later stages. They
might also benefit in the future from a superior competitive position in the market.
20
Compare e.g. Robinson 1998, Robinson and McDougall 2001 confirming the importance of the life cycle
stage in their empirical research.
21
Low and Abrahamson 1997 analyse entrepreneurial networks, entrepreneurial behaviour, stakeholders and
strategy/structure in the context of different market life cycle stages.
22
Low and Abrahamson 1997: 437.
CHAPTER 2 - LITERATURE REVIEW
21
The most contradictory finding may be related to market growth. Both early market
stage and market growth may capture similar phenomena. However, the important
distinction between the two is that “life cycle also measures how long the market can
be expected to last”23. While Keeley, Roure et al., and Tsai, MacMillan and Low did
find a significantly higher ROI for ventures in markets with high growth rates in
accordance with conventional expectations, Stuart and Abetti came up with a negative
correlation for market growth with a composite venture success measure of ROI and
sales growth. Stuart and Abetti’s findings have received much recognition among
entrepreneurship scholars. The diverging findings may result from varying impacts of
market growth on ROI and sales growth24. On the other hand, the study by Stuart and
Abetti was based on a relatively small sample and future research may show if similar
results can be found for larger sample sizes. It could be expected that growing markets
would also provide better growth opportunities for the individual venture, however,
there might be other mechanisms associated with market growth as well, which, in
turn, could moderate these opportunities. High growth markets may also attract
numerous competitors to enter the market.
Contradictory results are also found with regard to the influence of market dynamism
and disequilibrium. While Sandberg and Hofer identified a positive correlation
between high market dynamism and profitability, and Brüderl et al. found a positive
correlation with firm survival, Stuart and Abetti found a significantly negative
correlation with a subjective firm success measure. A highly dynamic market was
specified by Stuart and Abetti as a market “in a stage of rapid, perhaps chaotic
evolution, with numerous product and technological innovations, a high number of
competitors and low entry barriers, unclear product requirements and the need for
specialized as opposed to standardized products”25. This concept of market dynamism
is in many aspects related to a market in an early life cycle stage. Therefore, one might
assume a positive correlation with venture performance in accordance with Sandberg
and Hofer. Rapid evolution may often be associated to a high market growth.
Consequently, Stuart and Abetti’s finding is consistent with their identification of a
negative correlation with market growth. However, their finding has to be moderated
23
Tsai, MacMillan, and Low 1991: 15.
Compare McDougall, Covin et al. 1994.
25
Stuart and Abetti 1987: 219.
24
22
CHAPTER 2 - LITERATURE REVIEW
by the fact that the correlation with market dynamism was weaker than correlation
with market growth and that the correlation with market dynamism could only be
identified for the subjective success measure and not for the objective success
measure. Overall, one can deduce from the literature that market dynamism is a very
crucial factor, although earlier studies do not provide clear insights due to very broad
definitions of market dynamism and the application of different success measures.
Following the importance that is attributed to barriers to entry in early strategy and
population ecology research, barriers of entry have also been investigated in several of
the studies on market success factors. However, only Sandberg and Hofer identified a
significant correlation between barriers of entry and venture performance in terms of
profitability. The findings on barriers to entry might vary strongly according to the
evolutionary stage of a venture. A firm within the first years of existence could still
struggle to overcome the existing barriers of the industry. This firm might be forced to
invest heavily with negative consequences on the profitability of the company. In a
later stage, once the firm has successfully overcome the barriers to enter, it may
benefit from insulation from competition and thereby increase profitability. Potential
competitors may be threatened by the required investment or the risk of failure
resulting from the existing barriers of the market.
High product heterogeneity may exempt a venture from engaging in competition on
prices, with its assumed negative consequences on profitability. Brüderl et al.
demonstrated that competition on prices was negatively correlated to growth in terms
of employee growth, however, the expected negative effect on ROI could not be
demonstrated. On the other hand, Sandberg and Hofer did find a negative correlation
between high product heterogeneity and profitability. A later study by Robinson,
however, could not confirm this finding.
Finally, high buyer concentration was associated with higher profitability for US
ventures in Keeley, Roure et al. and was associated with higher growth in terms of
market share in Tsai, MacMillan and Low. This confirmed the expectation that “higher
levels of fragmentation of immediate customers make it more difficult for the new
firm to reach its end users and is therefore associated with lower munificence”26.
26
Tsai, MacMillan, and Low 1991: 15.
CHAPTER 2 - LITERATURE REVIEW
23
Among the factors related to the structure of competition, the variables of high
competition in the first years and the concentration of competitors have been the most
frequently investigated.
High competition in the first years was found to be negatively related to the
profitability of Japanese firms by Keeley, Roure et al. While Brüderl et al. could not
confirm for their sample of German firms any impact of competition on profitability,
they did relate high competition to a low rate of firm survival. One explanation for the
relatively weak correlation may be that high competition will frequently be found in
markets with an otherwise attractive market environment. Therefore, the competitive
pressure may be moderated for those surviving new ventures by emerging market
opportunities. A high competitive level may also challenge all market participants to
superior performance.
With regard to competitor concentration, none of the four studies found any
relationship between the concentration among competitors and venture performance,
neither in terms of profitability nor in terms of growth measures. The assumption that
it may be more difficult for a venture to reap opportunities in a market that is
dominated by a low number of established competitors could not be confirmed. In fact,
Brüderl et al. even identified a higher firm survival rate among industries with a higher
concentration of competitors.
Studies of entrepreneurship research investigating interactions between market
success factors and strategy
Following Sandberg's findings of the importance of interacting effects, several studies
investigated the interacting effects of industry and strategy on venture performance.
Within these studies the relationship between strategy and venture success was found
to depend on environmental variables such as life cycle stages27, industry dynamics,
heterogeneity and hostility28. These studies stressed the importance of a good
knowledge of the market environment as a prerequisite for strategy formulation.
27
28
Covin and Slevin 1990 found that entrepreneurial strategies were most frequent and most successful in early
industry life cycle stages. Low and Abrahamson 1997 developed a theoretical model to show the different
contextual dimensions in each life cycle stage and stressed the importance of adapting venture strategy to the
stage of the industry life cycle.
Zahra 1996a showed that the success of technology strategies depends significantly on the dynamics,
heterogeneity and hostility of the environment.
24
CHAPTER 2 - LITERATURE REVIEW
Market entry strategies29, competitive aggressiveness30, technology strategy31 in
particular needed to be adjusted to environmental settings. Noteworthy are two studies
in this field, which also investigate the direct impact of market variables on venture
performance and survival.
McDougall et al.32 presented a study that advanced the Hofer/Sandberg model by
systematically investigating market growth and the strategy – market growth
interactions. The authors confirmed a positive correlation of market growth between
both profitability and sales growth. This correlation was even higher for ventures
pursuing broad strategies in high-growth markets.
Stearns et al. investigated the direct impact of four industry sectors on firm survival, as
well as the combined impact of industry sector and strategy on firm survival. The
study tests the assumption that firms in retail and services, downstream in the industry
value chain, have lower chances of survival. The assumption has been justified by the
argument that downstream industries not only “face more diverse competition but also
operate in conditions where buyers and suppliers are more diverse and have greater
levels of turnover. These conditions of downstream industries, in turn introduce higher
levels of uncertainty reducing new firm survival chances”33. Even though a direct
relationship between value chain position and venture survival could not be confirmed,
value chain position, which in this study is equal to the major industry sector, was
found to have a significant influence on the choice of new venture strategies.
Strategies focused on quality were positively related to venture survival in
manufacturing, and yet showed a negative link to venture survival in retail industries.
At first sight this may contradict the common recommendation of quality vs. price
competition for the retail sector. However, low price retailers may more easily achieve
a sufficient customer acceptance and income to maintain their business, while higher
profitability may be more difficult to achieve in the long term.
29
MacMillan and Day 1987 concluded in their investigation of corporate ventures that the ventures most likely
to be successful are those that enter a new market aggressively, with aggressive market share targets, and
those that enter industries where rivalrous responses are less likely.
30
Covin and Covin 1990 came to the conclusion that competitive aggressiveness in hostile environments
promises, in general, more success. However, competitive aggressiveness appeared to be less promising for
young firms, especially in technologically sophisticated environments.
31
Investigated in Zahra 1996a and Zahra 1996b.
32
McDougall et al. 1992.
33
Stearns et al. 1995.
CHAPTER 2 - LITERATURE REVIEW
Author
McDougall
et al
Year
1994
Market factors investigated
industry growth
broad strategies vs. focus strategies
broad strategies & high industry
growth
Stearns
et.al.
1995
25
Significance
(if investigated)
p=0,1 (+ on sales
growth)
p=0,1 (+ on sales
growth)
INDUSTRY
INDUSTRY - STRATEGY
- manufacturing – quality
- retail – quality
- retail – price competitor
- retail – technology value
- service – niche purveyor
- service – technology value
p=0,1 (+)
p=0,001 (-)
p=0,05 (-)
p=0,01 (+)
p=0,05 (+)
p=0,001 (-)
# of
Findings
Unit of analysis
units
investigated
Dependent
variable
Ventures achieved higher sales
growth in growing industries
and highest sales growth for
pursuing broad strategies in
growing industries
123
New independent
ventures in electronic
computing equipment
and radio and television
transmitting industries
- ROS
- sales growth
While no significant relation
between four major industry
categories and firm survival can
be identified, survival strategies
depend significantly on these
major industry categories.
2653
New ventures in
Minnesota and
Pennsylvania
- firm survival
Table 3: Studies of entrepreneurship research investigating market-strategy interactions
Overall, these studies corroborate a general interaction between industry and strategy
as found by Sandberg and Hofer and suggest for future research that strategy – venture
performance relationships should be investigated with regard to industry structure.
2.1.2 Research on VC deal evaluation criteria
In addition to investigating the immediate factors influencing venture success, one
stream of research in entrepreneurship attempts to identify the criteria that venture
capitalists apply in order to select those ventures for which they will provide funding.
This is justified by the observation that ventures, that receive venture capitalist
backing exhibit higher success rates34. Moreover, it is assumed that venture capitalists
possess a tacit knowledge that is honed from a decade-long experience of venture
evaluations. With regard to this study, it might be of special interest to understand
which criteria venture capitalists associate with attractive markets for a new venture.
34
Bruno and Tyebjee 1983.
26
CHAPTER 2 - LITERATURE REVIEW
Ranking
Author
Year
Market factors investigated
(if
# of
Findings
investigated)
Tyebjee and
Bruno
1981
MARKET FACTORS
- market growth potential
- market size
- market access
- freedom from regulation
Ranking only
according to factor
loadings.
UNCONTROLLABLE RISK FACTORS
- protection from competitive entry
- resistance to economic cycles
MacMillan,
Siegel and
Subbanarasimha
1985
MacMillan,
Zemann and
Subbanarasimha
1987
Hall and Hofer
1993
MARKET FACTORS
- market growth
- potential to stimulate existing market
- familiarity of VC with industry
- threat of competition in first years
- creation of new market
1
2
3
4
5
MARKET FACTORS
- competition in first years
- market growth
- stimulation of existing market
- access to distribution channel
- familiarity of VC with industry
- established distribution channels
- creation of new market
1
2
3
4
5
6
7
long-term market growth and profitability
favourable environment to new entrant
access to distribution channels
Zacharakis and
Meyer
1998
MARKET FACTORS
- competitor number
- competitor strength
- market size
- market growth
2 of 18 (in
screening stage)
2 of 18 (in
assessment stage)
8 of 18 (in
screening stage)
1 of 8 (actual) /
7 of 8 (stated)
2 of 8 (actual) /
6of 8 (stated)
3 of 8 (actual) /
3 of 8 (stated)
4 of 8 (actual) /
2 of 8 (stated)
units
investigated
Unit of
Data
analysis
collection
VCs use six dimensions for evaluation
of deals. Ranked by importance with
investment decision these are
1) management quality,
2) profitability,
3) cash-out factors,
4) venture viability,
5) market factors,
6) uncontrollable risk factors
41
Venture
capitalists
Phone survey
and
questionnaire
Among the top ten most important
criteria that had been mentioned by
VCs, five had to do with the
entrepreneur’s experience or
personality, while only one was related
to the market.
100
Venture
capitalists
Questionnaire
The most important consistent criteria
for venture success have been 1)
insulation from competition in first
years and 2) market acceptance of
product
67
Venture
capitalists
Questionnaire
In initial screening key criteria include
fit with venture firm’s lending
guidelines, long-term growth and
profitability of industry. In assessment
stage a favourable industry
environment and recommendations of
ventures played an important role as
well as formal aspects of business plan
and entrepreneurs experience In
general research should investigate
criteria with regard to screening stage.
16
Venture
capitalists
Semi structured
questionnaires
and verbal
protocols
Venture capitalists do not truly
understand their intuitive investment
decisions. With the availability of
more information importance shifted
from the entrepreneur to the market.
51
Venture
capitalist
Computerized
Experiment
Table 4: Studies of entrepreneurship research investigating VC deal evaluation criteria
Early studies were conducted by Tyebjee and Bruno35 at the beginning of the 1980s.
The following deal evaluation criteria were aggregated to the market dimension by
factor analysis: (1) market growth potential, (2) market size, (3) market access, and (4)
freedom from regulation. In addition, other market-related criteria such as (5)
protection from competitive entry and (6) resistance to economic cycles were also
35
Tyebjee and Bruno 1981.
CHAPTER 2 - LITERATURE REVIEW
27
included. The factor analysis did not allow derivations about the weighting of
individual criteria. Comparing the influence of the major criteria dimensions on the
market, the entrepreneur and the product, the authors concluded that the entrepreneur
was the one most important determinant of a positive investment decision from
venture capitalists. Less emphasis was placed on the degree of innovation, product
development or the market.
In 1985, MacMillan, Siegel and Subbanarasimha published a study on venture
capitalist evaluation criteria that intended to replicate the Tyebjee and Bruno study.
With regard to market characteristics, venture capitalists determined the following
ranking of criteria importance for their investment decision: (1) market growth, (2)
potential of venture to stimulate existing market, (3) familiarity of venture capitalists
with market, (4) threat of competition, and (5) creation of new market by venture.
Again comparing the major dimensions for the deal evaluation, the study concluded
that “irrespective of the horse (product), horse race (market), or odds (financial
criteria), it is the jockey (entrepreneur) who fundamentally determines whether the
venture capitalist will place a bet at all”36.
Two years later, MacMillan, Zemann and Subbanarasimha analysed a more
comprehensive model of evaluation criteria. Within this follow-up study they intended
to go beyond identifying applied evaluation criteria and to determine how successful
these criteria predict venture success. The study identified the following ranking of
market-related evaluation criteria: (1) competition in first years, (2) market growth, (3)
stimulation of existing market, (4) market access to distribution channels, (5)
familiarity of VC with industry, (6) existence of established distribution channels, (7)
creation of new market by venture. Among all investigated deal criteria in the study,
only competitive threat and the degree of market acceptance of product were
consistent predictors across several performance criteria. However, it turned out that
these criteria, which have been good predictors of venture success, have not been
frequently applied by venture capitalists, according to their own reports. Therefore,
former studies might be limited in respect to their reliance on the data collection from
venture capitalists’ own reports. As for the major criteria dimensions, it was deduced
36
MacMillan, Siegel and Subbanarasimha 1985: 119.
28
CHAPTER 2 - LITERATURE REVIEW
from the findings that venture capitalists might overestimate the importance of the
entrepreneur compared with that of the market and the product.
Hall and Hofer presented a new research design to avoid the two important limitations
of former studies: Self-reporting of data and ex post facto data gathering. They
collected data from semi-structured interviews and verbal protocols of real deal
evaluations, addressing the errors and biases due to self-reported data found in the
earlier studies. Venture capitalists might have been influenced by their perception of
what is a desirable response. Moreover, the use of ex post facto data gathering could
lead to distortions, since venture capitalists might not recall correctly the criteria that
they applied to select ventures. In addition, they included the screening process stage
as a mediating variable, stating that evaluation criteria might be very different at
distinctive stages of the screening process. As a result, the market-related criteria
“long-term market-growth and profitability” were found to be among the most
important criteria during the initial screening stage. For the later assessment stage, a
favourable industry environment was found to be of high importance. Overall, the
assumption that criteria vary in different life cycle stages has been confirmed. On
comparing the major dimensions, the authors concluded that “the findings of this study
also were surprising for the lack of importance venture capitalists attached to the
entrepreneur/entrepreneurial team and the strategy of the proposed venture during
these early stages of the venture evaluation process”37.
Zacharakis and Meyer conducted an in-depth investigation on these apparent
differences between actual and stated deal criteria. The study found tremendous
differences among the stated and actual evaluation criteria applied by venture
capitalists. “VCs lacked a strong understanding of how they made decisions”38.
Interestingly, VCs seem to rely only on a very small number of criteria in their actual
evaluation process. According to the authors, the evaluation process might be
improved by the application of larger criteria checklists. The study identified the
following ranking of actually applied market-related evaluation criteria: (1) number of
competitors, (2) competitor strength, (3) market size and, (4) market growth.
Remarkably, the four market-related criteria have consistently ranked highest among
37
38
Hall and Hofer 1993: 26.
Zacharakis and Meyer 1998: 57.
CHAPTER 2 - LITERATURE REVIEW
29
all eight evaluation criteria. The authors concluded that “many of the [former] ex-post
studies … would lead entrepreneurs to believe that personal and team characteristics
are the most important criteria, but this study coincides with Hall and Hofer (1993) in
that the entrepreneur factor does not appear that important. Market characteristics
might be better determinants of who gets funding and who does not” 39. The results
suggested that the importance shifted from the entrepreneur to the market, particularly
with the availability of more detailed information.
Tyebjee and
MacMillan
MacMillan
Bruno
et al.
et al.
Hall and Hofer
Zacharakis and
Meyer
1981
1985
1987
1993
1998
MARKET
high market growth*
X
high barriers to entry
X
1 (of 5)
established distribution channels
2 (of 7)
2 (of 18)
4 (of 8) actual /
2 (of 8) stated
6 (of 7)
high seasonal / economical changes
X
market size*
X
access to market / distribution
channels*
freedom from regulation
X
3 (of 8) actual /
3 (of 8) stated
4 (of 7)
8 (of 18)
X
potential to stimulate market*
2 (of 5)
3 (of 7)
creation of new market
5 (of 5)
7 (of 7)
familiarity of VC with industry
4 (of 5)
5 (of 7)
COMPETITION
high competition (in first years)*
4 (of 5)
1 (of 7)
number of competitors
2 (of 18)
2 (of 8) actual /
6 (of 8) stated
1 (of 8) actual /
7 (of 8) stated
* repeatedly investigated factors
Table 5: Summary of VC deal evaluation criteria related to market
The aggregation of identified market-related deal evaluation criteria shows that
consistently high market growth and a low competition within the first years have been
identified as the most important criteria of VCs (venture capitalists) related to market
attractiveness.
Comparing the market criteria applied by VCs with those market factors that
entrepreneurship research associated with higher venture success, there is only limited
congruence. While empirical evidence from research on market success factors in
entrepreneurship supports a negative relationship between high competition and
venture performance. Empirical evidence for market growth has been found to be
39
Zacharakis and Meyer 1998: 72.
30
CHAPTER 2 - LITERATURE REVIEW
contradictory. Notably, the industry life cycle, which was identified among the studies
on market success factors as the one most important factor for venture performance,
was not identified in any of the studies as an evaluation criteria of venture capitalists.
Similarly, market dynamism and buyer concentration, success factors that research has
repeatedly related to venture performance, were not considered as deal evaluation
criteria by venture capitalists.
On the other hand, VCs mentioned some new market criteria that have not been
investigated as market success factors in entrepreneurship research. These criteria are
market size, distribution channels and the potential to stimulate or even create a new
market. Unfortunately, most of the VC studies provide only a few details explaining
the mechanisms of how individual evaluation criteria are assumed to affect venture
performance. While Tyebjee/Bruno (1981) and Zacharakis/Meyer (1998) identify
market size as an important deal evaluation criteria, neither can clarify which specific
market size does favour a positive VC deal evaluation. Given the large share of VCbacked ventures in early market life cycle stages, a large market size has probably
been considered as favourable for future growth potential. It has been shown that new
ventures with appropriate resources may compete successfully with much larger
established companies40. However, targeting relatively small market niches may also
be favourable for venture growth41.
Various studies stressed the importance of distribution channels to VC deal evaluation
criteria. Rather than the degree to which distribution channels are established, VCs
gave importance to the relative accessibility of distribution channels to a new venture.
Considered particularly problematic were situations where established firms had
proprietary distribution channels or they exerted pressure on protected existing
distribution channels against new entrants. The importance of distribution channels has
been widely neglected in market success factor entrepreneurship research.
In contrast to most market success factor research, which in accordance with the
industrial organisation perspective considers the market environment to be given
externally, VC deal evaluation criteria also reflect the potential of individual new firms
to actively stimulate existing markets or to create new markets. In both MacMillan
40
41
Cooper, Willard, and Woo 1986.
David Storey’s research on high growth SMEs in the UK (presentation at the EDP Workshop, March 21-22,
2002 in Barcelona/Spain).
CHAPTER 2 - LITERATURE REVIEW
31
studies, the potential to stimulate a market was among the most important VC deal
evaluation criteria. Especially in early market life cycle stages, new ventures may
stimulate markets by increasing the publicity and legitimacy of new products and
services. Regarding the potential of new ventures to create new markets, VCs are
apparently more sceptical. In both studies this criteria was attributed little importance
and was ranked at the bottom end of all relevant criteria. Potential market stimulations
by new ventures may be difficult to investigate in research that is based on large
organisational populations, since this potential may be highly dependent on the
individual venture’s product and resource configuration. However, on performing an
evaluation of market attractiveness on the individual firm level, the additional
consideration of potential market stimulation by new ventures would lead to more
accurate results.
Looking beyond market-related criteria, the findings indicate that the venture capitalist
decision process is strongly influenced by criteria that do not relate to the potential
success of the venture. These include the venture capitalist’s familiarity with the
industry, cash-out potentials, investment size and, most importantly, a match with the
sectoral focus of the venture capital fund. Consequently, the attempt to deduce from
VC deal evaluation criteria the factors that characterise attractive markets for ventures
is limited to a certain extent by the VCs specific investment interests. In general, the
capabilities of VCs in evaluating new ventures have to be considered more critically in
the light of the widely VC-financed new economy bubble at the end of the 1990s.
Similarly, Gartner et al.42 showed in their investigation of 27 start-ups that venture
capitalists’ prediction accuracy for new venture success was significantly inferior to
the prediction accuracy of industry experts and competitors.
Overall, research on deal evaluation criteria makes a valuable contribution by
suggesting additional market variables that could impact venture success and by
demonstrating that markets may be actively changed to a certain extent by an
individual venture.
42
Gartner et al. 1998 investigated the performance of new ventures that were previously evaluated as cases in the
INC magazine. They concluded that successful new ventures were those that were flexible and that
understood their competitors and customers. Factors related to the management team as experienced staff or
organisational skills were of lower importance.
32
CHAPTER 2 - LITERATURE REVIEW
With regard to the undertaken study, the recent literature on VC deal evaluation
criteria corrobates the importance of investigating market conditions. Contrary to
popular belief and the self-reporting of VCs, market variables turned out to be of most
importance. In the initial screening of new ventures business’ plans, which have been
subject of most VC-related research, market factors seem to be considered more
important than those related to the entrepreneur. However, it should be noted that there
might be a bias due to the fact that business plans, as a primary information base for
the VC deal screening, frequently provide more information on the market than on the
entrepreneur. Moreover, only the first steps of the whole VC selection process have
been investigated. At the end of the selection process and during face-to-face meetings
with the management team, criteria related to the entrepreneur may receive more
consideration.
2.1.3 Conclusions and implications for the study
The results of research in the areas of both market success factors and VC deal
evaluation criteria in the English and German language literature43 will be aggregated
to give an overview of the current findings on market factors in the field of
entrepreneurship research.
43
To undertake the literature review, both English-language and German-language literature was revised.
However, hardly any empirical study concentrating on the impact of market factors on venture performance
in Germany could be found. This is astonishing, although it should be noted that the field of entrepreneurship
research in Germany has gained wide academic importance only after a certain time lapse compared with the
US. Moreover, German language research in entrepreneurship has been very firmly centred on the
characteristics of the entrepreneur, the characteristics of technology-oriented ventures and recently on topics
related to entrepreneurship education. The most important empirical study that also covers market
characteristics is the investigation of Brüderl, Preisendörfer et. al about start-ups in the Munich area.
Noteworthy is also Baaken’s theoretical framework of market factors for new ventures, which was not
investigated empirically and will be discussed in detail within the development of a theoretical framework in
chapter three.
CHAPTER 2 - LITERATURE REVIEW
33
1ST TIER FACTORS: “USUAL SUSPECTS“
EMPIRICALLY CONSISTENT
- early life cycle (+ ROE / + ROI / + MS / o SG)
- high competition (- ROI / - FS / - VC)
- high buyer concentration (+ ROI / + MS)
EMPIRICALLY INCONSISTENT
- high market growth (+/o/- ROI / + VC)
- high market dynamism/disequilibrium (+ ROE /
- subjective / + FS)
EMPIRICALLY MAINLY NO RELATION
- high product heterogeneity (+ ROE / o ROI/ o SG)
- high barriers to entry (+ROE / o ROI / o SG)
- high industry concentration (o ROE / o ROI / o MS / + FS / o SG)
ADDITIONAL IMPORTANT VC EVALUATION CRITERIA
- market size
- potential to stimulate market
- access to distribution channels
2ND TIER FACTORS: “LESS USUAL SUSPECTS“
EMPIRICALLY CONFIRMED
- high change of customer base (- EG)
- high concentration of orders (+ FS)
- high seasonal changes (- EG / -VC)
- high revenue per employee (+ ROI)
- low competitor dependence (+MS)
- competition on prices (- EG)
- competition on quality (+ FS)
- competition on innovation (+FS / + EG)
NOT CONFIRMED EMPIRICALLY
- low segmentation (o ROI)
ADDITIONAL LESS IMPORTANT VC EVALUATION CRITERIA
- number of competitors
- freedom from regulation
+ positively related
– negatively related
o not related
VC=venture capitalist criteria
MS=market share FS=firm survival EG=employee growth SG=sales growth ROI=return on investment ROE=return on equity
Figure 4: Aggregation of research on market factors in entrepreneurship
Among the 1st tier factors all those market factors that have been investigated
repeatedly in at least two independent studies are mentioned. These frequently
investigated factors are categorised into groups of factors with consistent directions of
impact, inconsistent directions of impact and no impact on various success measures.
Consistent factors will frequently affect different venture success measures such as
growth and profitability in the same direction. Empirically consistent first tier factors
include life cycle, competition and buyer concentration. Inconsistent factors might
34
CHAPTER 2 - LITERATURE REVIEW
have caused diverging results due to nonlinear relations or differing impacts on
different success measures. These factors may have generated contradictory findings
that affect profitability and growth measures differently. To this category of
inconsistent factors belong market growth and market dynamism. In addition, there are
the factors for which the majority of revised studies did not find any impact on venture
success measures. Still, it could be that research designs or samples of former studies
have just not been adequate enough in identifying a potential relationship with venture
success for these factors. To this group of factors with empirically mainly no relation
belong product heterogeneity, barriers to entry and industry concentration. Finally,
there are additional market-related deal evaluation criteria that have been considered
as important by VCs in independent studies. These factors include market size,
potential to stimulate a market and access to distribution channels.
Among the 2nd tier factors all those factors that have been investigated in just one
study. Those factors are labelled empirically confirmed for which a statistical impact
on a venture success measure could be demonstrated. On the other hand, there are also
those factors for which an impact on venture success measures could not be confirmed.
The overview is complemented by factors that have been mentioned by VCs as
market-related deal evaluation criteria, but that have only been identified in one study
or have been considered of minor importance.
Especially for market-related factors it can be summarised in accordance with Gartner,
Starr and Bat that entrepreneurship research “on specific venture criterion indicates a
mixed set of results with few consistent findings”44.
In the following paragraph the reasons for the limited findings will be evaluated.
Deficits of studies in entrepreneurship
An overall review of the literature on market success factors in entrepreneurship
research reveals a quantitative lack of studies that empirically investigate the impact of
sets of market factors on venture performance. Although numerous studies provide a
focused analysis of solely the characteristics of the entrepreneur or new venture
strategies on new venture success, studies focusing in-depth on the market–venture
performance relationship are still relatively rare.
44
Gartner, Starr and Bhat 1998:218.
CHAPTER 2 - LITERATURE REVIEW
35
Apart from the restricted number of studies45, various other reasons can be identified
for the limited results gained regarding the market–venture performance interaction.
o Lack of an accepted framework. Results of former findings have only
been considered to a limited extent in subsequent studies. Also attempts
to aggregate the former findings into an integrated environmental
framework are rare46.
o Variety of dependent variables. The comparability of empirical studies
is limited by a vast amount of different measures for the dependent
variable of venture success. In contrast to strategy research, where ROI
and alternatively market share of large-scale enterprises are commonly
accepted as a standard measure of corporate performance, these
measures are generally not available for new ventures, neither are they
unavoidably be considered as meaningful representations of new venture
performance. Dependent variables that have been applied in the context
of market factors comprise: (1) objective performance measures such as
ROI47, ROE48, ROS49, (2) objective growth measures such as sales
growth50, employee growth51 and market share growth52, (3) new venture
foundation53, (4) new venture survival54 and (5) subjective selfevaluations of success by entrepreneurs55 among others as ROI for
venture capitalists56, export performance57, motivation of entrepreneur58,
etc. Some recent studies have responded to this problem by applying sets
of different measures of venture success at the same time.
45
A shortage of in-depth studies was already mentioned in the very comprehensive review of early studies in
entrepreneurship until the mid 1980s by Wortman 1986.
46
See Cooper 1991.
47
Tsai, MacMillan and Low 1991, McDougall, Robinson et al. 1992.
48
Sandberg and Hofer 1987.
49
Hall and Fulshaw 1993, Zahra 1993a.
50
Hall and Fulshaw 1993, Zahra 1993a, Chandler and Hanks 1994a, Shane and Kolvereid 1995, Brüderl et al.
1996, Davidsson 1991.
51
Shane and Kolvereid 1995, Brüderl et al. 1996.
52
Tsai, MacMillan and Low 1991, McDougall, Robinson et al. 1992.
53
Dean and Meyer 1996, Low and Abrahamson 1997, Mitchell 1998.
54
Romanelli 1989, Stearns et al. 1995, Brüderl et al. 1996.
55
Stuart and Abetti 1987.
56
Keeley et al. 1987.
57
Zahra, Neubaum and Huse 1997.
58
Dubini 1989.
36
CHAPTER 2 - LITERATURE REVIEW
o Limited number of variables applied. There is an apparent lack of
studies with the main focus on market success factors59. As a
consequence, research has tended to apply limited sets of variables in
order to measure complex environmental phenomena where the
inclusion of more variables might have been more meaningful.
Frequently, market attractiveness has been measured as one sole
aggregated factor60.
o Small sample size. The review also confirms Robinson’s criticism that
“prior industry structure studies examining independent new ventures
have often utilized relatively small sample sizes”61. As a general
tendency it is noticeable that recent studies aim to provide a wider
generalisability of findings by applying more comprehensive data sets.
In contrast with data on large manufacturing firms, data on small firms,
especially those in the service sector, is generally not publicly available.
Therefore, researchers in the field had to collect their own primary data.
Sample sizes have been limited by the research budget available.
Fortunately, the availability of large data sets for SMEs, including the
service sector may improve as legislative steps have been taken to
improve statistics on the service sector. Moreover, several public and
private organisations have started to build large databases on new
ventures62.
Overall, the current state of research in entrepreneurship can only provide limited
insights into the direct impact of markets on venture performance. The literature
review reinforces the need for studies that analyse the market-venture performance
relationship in more detail and that integrate previous findings. Conceptual models that
take into consideration contextual conditions such as life cycle stages and industry
sectors, may lead to better results than those models that ignore systematic variations
59
Lack of focus is also criticised in Stearns et al 1995 and Low and Abrahamson 1997.
E.g. Stuart and Abetti 1987, Chandler and Hanks 1994b, Low and Abrahamson 1997.
61
Robinson 1998:166.
62
In the US, the Kauffman foundation is, at the time of this study, setting up a database that includes several
thousand small service firms, and in Germany the DtA periodically collects data on several thousand new
ventures.
60
CHAPTER 2 - LITERATURE REVIEW
37
among these contextual conditions. The research design in chapter four of this study
therefore reflects these weaknesses.
2.2
Strategy / Industrial organisation research
2.2.1 Research on measuring industry effects
Measuring the impact of industry versus firm effects on organisational success has
been a central theme in strategy research. Both supporters of the industrial
organisation perspective of strategy and the supporters of the resource-based
perspective of strategy refer to studies on measurement of industry and firm effects for
justifying the importance of their research.
An early study on this topic was conducted by Schmalensee (1985). Using data from
the Federal Trade Commission (FTC), he came up with the surprising result that firm
effects were nonexistent, while industry effects were considered important, explaining
20% of variance in business unit profitability and a 75% share of the variance in
industry returns. Later studies, as conducted by Hansen and Wernerfelt (1989) and
especially Rumelt (1991), who applied the same FTC data, came up with very
different findings.
Hansen and Wernerfelt tested an economic and an organisational model of inter-firm
variance in profit rates. The economic model represented industry effects and the
organisational model the firm effects. As a result, the industry effects of the economic
model explained a mere 14% of variance, while the firm effects of the organisational
model were able to explain 36% of variance.
In 1991, Rumelt replicated Schmalensee’s study using a longer time series of the same
FTC data. He deconstructed firm and industry effects into stable and transient
components. Although again a 20% share of variance was explained by industry
effects, Rumelt argued that only stable industry effects that do not change over time
should be included in the analysis. On eliminating the transient industry effects, only
8% of business unit performance and 40% of dispersion in industry returns could be
explained by stable industry effects. Again, corporate effects from parent organisations
were found to be negligible and accounted for less than 2% of business unit variance.
In addition to industry and corporate effects, Rumelt also investigated business unit
effects and demonstrated that stable business unit effects accounted for a 46% share in
38
CHAPTER 2 - LITERATURE REVIEW
business unit performance. However, the study lacked insights in the nature of these
business unit effects.
All of the three former studies were based on the same FTC data. As pointed out by
Powell, the reliance on this data source does imply some weaknesses. “In examining
industry phenomena, one might hope to gather reliable financial data from a
representative sample of non-diversified firms”63. First, the FTC data focuses on
highly
diversified
companies.
Second,
the
sample
of
industries
lacks
representativeness since all small firms, all privately-held firms, and nearly all service
firms are excluded from the sample. Business units of large-scale enterprises
frequently can not independently determine their strategic posture and “corporate
accounting distortions impede the meaningful interpretation of financial business unit
data”64. Consequently, Powell investigated industry effects on the basis of a different
data set of 54 U.S. firms. Even though his sample included a large share of singlebusiness firms, small firms were again systematically excluded from the sample, and
the median number of employees per firm was 750. This is astonishing since the same
author stressed the obvious advantages of SMEs for the study of industry effects.
Without distinguishing between stable and instable industry effects, his findings
coincided with the previous findings in explaining a 20% variance in firm performance
due to overall industry effects.
Several years later, McGahan and Porter published an empirical study on industry,
corporate parent and business-specific effects. The study was based on data from the
Computstat database, including data from 1981 to 1994. The coverage of a far more
comprehensive time series and the inclusion of data from the service sector
differentiated the analysis from previous studies. Industry effects were found to
explain 19%, corporate parent effects 4%, and business-specific effects 32% of overall
variance in profitability. More importantly, it was shown that the strength of impact of
the respective effects differed substantially across broad economic sectors. Industry
effects were found to account for a far larger share in the service sector than in
manufacturing. Industry effects were found to account for over 40% of overall
variance for the service sector. Contrary to Rumelt's findings of a high instability of
63
64
Powell 1996: 324.
Powell 1996: 325.
CHAPTER 2 - LITERATURE REVIEW
39
industry factors, Porter and McGahan found industry effects more stable than
corporate parent or business-specific effects, reinforcing the importance of industry
effects, even in highly dynamic environments.
A recent study by Chang and Singh investigated the industry, business unit and
corporate parent effects, including the mediating variables of firm size and depth of
industry classification. In accordance with earlier research on large manufacturing
firms, their findings confirmed the dominant importance of business unit effects for
the samples of large firms applying market share as a growth measure. In contrast,
most previous studies had applied business unit profitability as the success measure.
However, for medium-sized and small firms, industry effects turned out to be the one
most important factor, explaining 41% of total variance for medium-sized firms and
54% for small firms. Comparing findings according to a 3-digit SIC industry
classifications with those of a 4-digit SIC classifications, it was shown that the error
margin with the unexplained variance nearly doubled to 50% when only using 3-digit
SIC classifications. With the application of a more detailed 4-digit SIC classification,
the absolute industry effects remained surprisingly stable while corporate and business
unit effects increased significantly. The empirical results strongly corroborate the
finding that industry effects increase in importance if the view is shifted from large,
publicly traded, firms to medium and, in particular, small firms.
Finally, Hawawini et al.(2003) elicited another important aspect by systematically
excluding each industry’s top two leaders and bottom two losers. They concluded that
“industry-specific factors may have different meaning for different types of firms
within an industry. Industry factors may have a large impact on the performance of the
‘also-ran’ firms, while for the industry leaders and losers it is firm factors that
dominate”. Without distinguishing among firm and business unit effects, industry
effects increased by 100% or more when outliers within the industry were removed
from the sample.
Author
Year
Findings
# of
Schmalensee
1985
20% industry effects;
no firm effects
1.775 business units
of 456 companies
Hansen and
Wernerfelt
1989
14% industry effects
36% firm effects
300 business units of
60 companies
Unit of analysis
units investigated
Dependent
variables
Business units of
large US
manufacturing firms
(FTC data)
Business units of
large US
manufacturing firms
(FTC data)
- business unit
profitability
- industry returns
- business unit
profitability
40
CHAPTER 2 - LITERATURE REVIEW
Rumelt
1991
Powell
1996
McGahan and Porter
1997
Chang and Singh
2000
Hawawini et al.
2003
8% stable industry effect (20%
overall industry effects)
2% stable parent organisation
effect
46% stable business unit effect
20% industry effects
10.866 observations
(2810 business units
of 463 companies)
Business units of
large US.
manufacturing firms
(FTC data)
54 companies
Non-diversified US
manufacturing firms
19% stable industry effects
4% stable parent organisation
effect
32% stable business segment
effects
1. large firms:
19% industry effects
48% business unit effect
10% corporate effect
2. medium size firms:
41% industry effects
9% business unit effect
16% corporate effect
3. small size firms:
54% industry effect
9% business unit effect
16% corporate effect
1. full sample
33% firm effects
11% industry effects
2. sample without outliers
17% firm effects
30% industry effects
*results only for TMV/CE which
had lowest error
58.132 observations
(5.196 business
segments /year in
628 industries)
20.161 observations
(7800 business units
in 444 industries)
5.620 observations
(562 firms in 55
industries (3-digit
SIC))
-business unit
profitability
- industry returns
- perceptions of
profitability and sales
growth
Business segments of - accounting
publicly traded US
profitability
companies
(Compustat 19811994)
Manufacturing firms - market share
in US
(Trinet 1981-1989)
Very large
manufacturing and
service firms in US
(Stern Steward 19871996)
- EP/CE
- TMV/CE
- ROA
EP=economic profit,
CE=capital
employed,
ROA=return on
assets
Table 6: Empirical studies on industry effects
Despite the variation of the exact value of each effect, among all of the studies the
strong influence of industry effects on profitability was demonstrated. The majority of
the studies on large-scale firms in the manufacturing sector came to the conclusion that
industry effects rank in importance right after firm or respectively business unit
effects. Recent empirical evidence suggests that the importance of industry effects
could increase if some of the weaknesses of previous studies, such as the data source
and methodology, were resolved.
First, the reliance on data from diverse large-scale enterprises may be inferior to data
from SMEs. SMEs may have a clearer focus on one market and may therefore be more
appropriate for studying industry effects, as pointed out by Powell. Even when the data
is split down to business units, significant distortions resulted from shared overhead
costs and transfer payments that were not taken into account65. The empirical evidence
65
Powell 1996.
CHAPTER 2 - LITERATURE REVIEW
41
from Chang and Singh confirms that industry effects gain substantially more
importance for SMEs. It may be assumed that industry effects are smaller for largescale enterprises since these companies might partly possess the capacity to actively
shape the immediate environment in their favour.
Second, the focus on the manufacturing sector by most former studies, have led to
systematic underestimations of industry effects, as shown by McGahan and Porter.
The service sector is considered in all developed western countries as the most
economically important sector. Therefore, it is desirable that this sector should no
longer be excluded from studies on industry effects.
Third, it may be assumed that industry effects have not been completely captured in
earlier studies due to the application of limited or inappropriate indicators66. More
comprehensive sets of indicators could increase the share of explained variance.
Fourth, a few very successful and very unsuccessful firms might have generated a
strong outlier effect, thereby leading to overestimated firm effects, as demonstrated by
Hawawini et al.
Finally, the “need to rely on the SIC system for industry classification further
diminishes the measured estimates of industry influence because SIC industries err
primarily in being overly broad”67. More in-depth industry classifications and more
precise industry definitions might ensure that competing firms are aggregated under
one industry definition, hence improving the accuracy of the results. However, it must
be noted that Chang and Singh’s findings did show only a small increase in absolute
industry effects with a deeper industry classification.
1
Unit of analysis (firm size): SME vs. large scale enterprises
2
Unit of analysis (sector): service sector vs. manufacturing
3
Industry indicators: more comprehensive indicator sets vs. limited sets
4
Intra-industry variance (outliers): control of outliers vs. no control of outliers
5
SIC: revised 4-5-digit SIC vs. old 3-digit SIC
Table 7: Indicators for former underestimation of industry effects
66
67
Mauri and Michaels 1998: 214.
McGahan and Porter 1997: 16.
42
CHAPTER 2 - LITERATURE REVIEW
Overall research on industry effects has clearly demonstrated the strong influence of
industry on profitability and therefore sustains the importance of studying in detail
how industry affects organisational success. Firm effects, which are emphasised by the
resource-based strategy perspective, also reveal a strong influence on profitability. But
focusing exclusively on the firm level and ignoring the external setting of competition
and industry of a firm might lead to a limited explanation of organisational success.
2.2.2 Research on market success factors in strategy
Since empirical studies in strategy research tend to be based on the data of publicly
traded, large firms, it will be interesting to see which market factors are of importance
independently of firm size, and which factors might be transferable to the new venture
setting. Moreover, industrial organisation research could identify potentially important
market success factors that were overlooked in previous entrepreneurship research.
Even though empirical studies on market success factors are not as prolific as one
might expect68, the literature review has to be limited to some of the most relevant
empirical studies in the field.
Author
Ravenscraft
Year
1983
Factor
buyer concentration
supplier dispersion
supplier concentration ratio
market share
Significance
(minimum)
p=0,10
p=0,10
p=0,10
p=0,10
buyer dispersion
68
adjusted 4- firm concentration
p=0,10
distance shipped
p=0,10
exports / value of all shipments
p=0,10
growth
p=0,10
imports / value of shipment
p=0,10
industry advertising
LOB advertising
p=0,10
industry assets
LOB assets
p=0,10
p=0,10
industry capacity utilization
LOB capacity utilization
p=0,10
industry diversification
p=0,10
LOB diversification
p=0,10
industry R&D
LOB R&D
p=0,10
p=0,10
industry vertical integration
LOB vertical integration
p=0,10
p=0,10
minimum efficient scale
p=0,10
# of
Findings
units
investigated
Statistically the most
important variables were
the positive effect of
higher capacity utilization
and industry growth and
market share. A
significant positive
correlation of buyer
concentration is contrary
to Porter's projectures of
bargaining power of
buyers. Generally higher
returns to advertising and
assets for sellers with
larger market shares
appear to underlie the
positive profit-market
share relationship. Lower
costs rather than collusion
or barriers to entry seem
account for positive
returns of vertically or
diversified lines of
business.
3186
Unit of
Dependent
analysis
variable
lines of
business in 258
manufacturing
categories
- profitability in
terms of line of
business profit
(LOBP)
- on the industry level
price costs margin
(PCM)
Powell 1996: 331 „Although Porter’s Competitive Strategy (1980) is by far the most widely cited publication
in the strategy literature (Hambrick, 1990) the book’s central feature– the industry framework – has attracted
little empirical attention.”.
CHAPTER 2 - LITERATURE REVIEW
Buzzel
(PIMS)
1987
43
high real market growth rate
p=0,01
high rate of price inflation
p=0,01
high purchase concentration
p=0,10
low % unionization
p=0,01
low purchase amount
p=0,01
high % exports- imports
p=0,10
low customized products
p=0,01
low % new products
p=0,01
low marketing, % of sales
p=0,01
low R&D, % of sales
p=0,01
low inventory, % of sales
p=0,01
low fixed capital intensity
p=0,01
high capacity utilization, %
p=0,01
high employee productivity
p=0,01
high vertical integration
low purchase importance to customer
p=0,01
p=0,01
Marshall and
Buzzell
1990
market share
Industry concentration
Fixed capital intensity (GBV/sales)
industry growth
inventory/sales
marketing/sales
P&E/sales
R&D/sales
vertical integration
p=0,000
p=0,000
p=0,000
p=0,000
p=0,000
p=0,000
p=0,051
p=0,007
p=0,000
Powell
1996
industry maturity
entry barriers
competitive power
p=0,05 (p)
p=0,01 (sg)
Mueller and
Rauning
1998
industry concentration
advertising intensity
R&D intensity
import intensity
market growth
p=0,10
p=0,01
p=0,05
p=0,01
Based on this study the
popular PIMS success
factor model has been
derived, stressing the
overall importance of
market share. Regarding
the market dimension, the
most important success
factors are the ones
indicated as "significant".
3000
SBU of large
firms primarily
US
manufacturing
firms
ROI
The two independent
datasets PIMS and FTC
provide remarkably
comparable results for the
investigated industry
factors.
902 observations
(PIMS)
2537
observations
(FTC)
SBU of large
US firms
(PIMS and
FTC line of
business data)
- ROA
Only the entry barrier and
competitive power main
effects explained
significant performance
variance.
54
firms with 50
or more
employees
(median 700
employees)
- sales growth
(sg)
Industry variables provide
high explanation power in
industries which are
homogenous in terms of
profitability and only low
explanation power in
heterogeneous industries.
912
US
manufacturing
firms
(Compustat
data)
- industry profit
- price cost
margin
- firm profit
- profitability (p)
- overall financial
performance (o)
Table 8: Profiles of previous studies in industrial organisation/strategy research dealing with the impact of
individual market factors
The one most influential research project on success factors was the PIMS (“Profit
Impact of Market Strategy”) project, initiated mid 1970s by the Strategic Planning
Institute. The underlying PIMS database contains detailed, annually updated, data
from the business units of large international enterprises. The ROI of individual
business units was compared with descriptive business unit data on product/services,
customers, distribution channels, production processes, market and competition. The
result of this study became widely known as the PIMS model of success factors. This
model stressed the dominant significance of market share for profitability. The
following factors also showed strong influences on profit69:
69
Buzzell 1987: 53.
44
CHAPTER 2 - LITERATURE REVIEW
o Real market growth
o Rate of inflation in selling prices
o Supplier concentration
o Typical customer purchase amounts and importance of the product or
service to the customer
o Extent of employee unionisation
o Magnitude of exports and imports from and to an industry
The PIMS database has received considerable criticism due to sample composition and
measurement methodologies. The main criticism relates to the sampling issue of selfselection, which leads to an overrepresentation of market leaders and large industrial
parent enterprises70.
At the end of the 1970s, Porter’s widely known “five forces industry model”71
provided a theoretical reference for the PIMS research and the whole field of research
on market factors.
Some years later, Ravenscraft72 published a study on the relationships between
industrial structure and performance. The data set applied was extraordinarily
comprehensive, covering more than 3,186 different lines of business. Data was taken
from the Federal Trade Commission (FTC), which collected financial data
disaggregated to the line of business on a vast number of large-scale manufacturing
companies in the US. In accordance with the findings of the PIMS study, high market
share and high industry growth were found to be among the most important profit
influences. The study also confirmed the positive impact of exports and the negative
impact of supplier concentration. Similar to the positive influence of high average
customer purchases found in the PIMS study, Ravenscraft identified a positive
relationship between high buyer concentration and profit. This finding, however,
contradicted other popular explanation models such as the Porter five forces model,
which predicted negative profitability impacts due to buyers’ higher bargaining power.
70
Addressed in Marshall and Buzzell 1990.
Porter 1979. Porter’s conceptual five forces industry model will not be discussed further at this point, since
the concept is not based on empirical research. The model will be treated in detail in chapter three within the
conceptional part of the development of a theoretical model of market attractiveness.
72
Ravenscraft 1983.
71
CHAPTER 2 - LITERATURE REVIEW
45
High capacity utilisation was identified as the most important factor. Furthermore, the
existence of economies of scale was confirmed by the positive impact of larger
minimum efficient scales. The variables advertising intensity, diversification and asset
intensity showed different findings if variables were measured on the basis of industry
averages rather than the data from individual lines of business. Also, an alternative
model of industry profitability, which excluded the strong market share effects, led to
contradictory results. The study was unable to explain why these differences occurred
and simply referred to the strong influence of the market share variable. Overall, the
Ravenscraft study did confirm some of the most important findings of the PIMS model
on the basis of an independent data set.
Marshall and Buzzell (1990) systematically compared the influence of industry factors
based on both the PIMS and FTC datasets and also found remarkably comparable
results for the investigated industry factors.
Among the previously discussed studies that measured industry versus firm effects, it
was only Powell who took into consideration the influence of individual industry
factors on profitability. He came to the conclusion that high entry barriers relate
positively to profitability. High competitive power in terms of bargaining power with
customers and suppliers, low excess capacities and low threats of substitutes were
shown to relate positively to sales growth.
Mueller and Rauning (1998) examined the impact of some of the most frequently
applied variables of the structure-conduct-performance model on industry and firm
profitability. Differentiating industries by the homogeneity of their profit structures,
they concluded that industry variables provide high explanatory power for industries
that are homogenous in terms of profitability and only low explanatory power for
heterogeneous industries.
Ravenscraft
Buzzell
Marshall and Buzzell
Powell
Mueller and Rauning
1983
1987
1990
1996
1998
MARKET
early industry life cycle stage
o ROI o SG
high market growth*
+ LOBP + PCM
high barriers to entry / minimum
efficient scale*
high product customization /
heterogeneity*
high buyer concentration*
+ LOBP + PCM
high segmentation (suppliers)
- LOBP - PCM
+ ROI
-FP o INP (- for
homogeneous industries)
o PCM
+ ROI o SG
- ROI
+ LOBP - PCM
+ ROA
46
CHAPTER 2 - LITERATURE REVIEW
high concentration of orders
+ ROI
long distance shipped
- LOBP - PCM
high export rate
+ LOBP – PCM
+ ROI
high import rate
- LOBP – PCM
- ROI
lb
high industry advertising
- LOBP
+ LOBP + PCM
- LOBP lb
+ LOBP + PCM
+ LOBP lb
+ LOBP + PCM
- LOBP lb
- LOBP- PCM
+ LOPBP lb
- LOBP – PCM
- LOBP – PCM
high industry fix capital / assets
intensity
high industry capacity utilization
high industry R&D
high vertical integration
high supplier concentration
- FP – INP - PCM
- ROI
- ROA
- ROI
- ROA
+ FP + INP + PCM
+ ROI
- ROI
- ROA
+ ROI
+ ROA
high inflation rate of selling prices
+ ROI
low unionization
+ ROI
inventory intensity
- ROI
- ROA
asset newness
+ ROI
- ROA
low purchase importance
+ ROI
low # of new product introductions
+ ROI
high employee productivity
+ ROI
+FP + INP + PCM
COMPETITION
high competition*
high industry / competitor
concentration*
high industry members
diversification
high competitive power
- LOBP + PCM
market share
+ LOBP
o ROA
+/- FP +/- INP +/- PCM
o LOBP + PCM
o ROI + SG
* repeatedly investigated factors
ROI=return on investment
+ ROI
lb
+ ROI
based on line of business data
ROA=return on assets
SG=sales growth
+ ROA
+ positively related
– negatively related
LOBP=line of business profit
o not related
PCM=industry price cost margin
FP=firm profit INP=industry profit
Table 9: Summary of market success factors in industrial organisation/ strategy research
The aggregation of the investigated variables shows that there is a wide acceptance of
the use of profitability indicators for measuring firm performance. This increases the
comparability of results compared to research in entrepreneurship. The approach to
investigating markets in strategy research is, in many aspects, different to the approach
in entrepreneurship research. Some of the most important factors of entrepreneurship
research, such as life cycle stage and market dynamism, have received hardly any
attention in strategy research. These factors, which are particularly related to the
opportunity context of new ventures, may be of less importance for established large
companies. On the other hand, data restrictions may also have led to less attention to
these factors, as it may have been difficult to deduce these variables from the
Compustat and FTC data sets.
CHAPTER 2 - LITERATURE REVIEW
47
The one factor which has been consistently considered of high or even dominant
importance is market share. The positive impact of a high market share on profitability
is attributed to economies of scale, superior purchase prices and exercise of
monopoly73. Taking into account the tendency of the data sets applied to include only
large-scale companies with high market shares, the importance of market share in the
context of SMEs might be different, as the average market shares will be very low.
With regard to the operational measurement there is the potential problem that many
SME managers, especially in the service sector, might not be able to quantify their
market share. Nevertheless, it can be assumed that achievable market share may be a
critical factor for the success of a new venture, particularly in industries with high
economies of scale.
High capacity utilisation is the second market factor that has consistently shown a
strong positive impact on profitability. Excess capacities frequently push companies to
cut prices and, in turn, decrease profitability. While capacity utilisation is an indicator
mainly applied in manufacturing, it may also be adopted for companies in the service
sector. However, measurement and data collection might be more difficult in the
service sector as firms in the service sector may frequently be unaware of their current
capacity utilisation.
Import rate was also consistently related to profitability, even though its importance
was, in general, not considered as high as market share or capacity utilisation. High
import rates reflect strong international competition. A high import rate may result
from a general competitive disadvantage compared with foreign competitors. These
competitors may benefit from better resource configuration or cost structure in their
national context.
Finally, a high inventory intensity was associated with a negative impact on
profitability as high inventories generate high costs and decrease the ability to react
dynamically to changes from the demand side.
The 1st tier factors that have been investigated in two or more independent studies have
been classified into the categories: consistent, mainly consistent and inconsistent. This
new category of mainly consistent factors has been introduced because the factors
73
Marshall and Buzzell 1990.
48
CHAPTER 2 - LITERATURE REVIEW
export rate, fix capital and vertical integration have shown comparable impact on
profitability among several independent studies with inconsistencies only for the
industry profitability measure (PCM) and in a limited industry equation model in the
Ravenscraft study. Ravenscraft’s findings for these factors, based on a more
comprehensive equation model led to results that were consistent with the PIMSrelated studies.
Analogous with the discussed impact of imports on profitability, a high export rate
may indicate competitive advantages for firms in the home market compared with
foreign companies and could indicate additional sales potentials in foreign markets.
High fixed-capital intensity is mainly related negatively to profitability, as high fixedasset intensity has been found to be related to later market life cycle stages, higher
unionisation, vulnerability to decreases in capacity utilisation and an increase in exit
barriers74. By contrast, traditional industrial organisation theory would suggest that
high fixed capital could lead to higher profitability by imposing high barriers to entry.
According to the empirical evidence, the negative impact of fixed capital intensity
seems to be stronger than the positive impact of increasing barriers to entry.
In terms of vertical integration, the majority of the studies confirmed the expected
positive influence on profitability. The positive impact was sustained by presumably
lower transaction costs in vertically integrated firms. To the contrary, modern strategy
concepts that emphasise concentration on core competences and outsourcing, may
suggest opposite impacts of vertical integration. Furthermore, transaction cost
economics only relates profitability gains by vertical integration to markets that exhibit
unfavourable characteristics, such as a small number of transaction partners and a high
risk of opportunistic behaviour.
In strategy research, findings for market growth have not been unanimous. While the
majority of the studies confirm a positive impact for market growth on profitability,
the more recent study by Mueller and Rauning found no impact for the overall sample
and a weak negative impact for the sample of homogenous industries. Nevertheless,
the majority of the empirical evidence confirms the prevalent assumption that market
74
Marshall and Buzzell 1990.
CHAPTER 2 - LITERATURE REVIEW
49
growth does positively affect profitability by facilitating company growth, which must
not be at the expense of competitors but may be driven by increasing demand.
For barriers to entry, industry advertising, industry R&D and asset newness, the
empirical evidence is inconsistent and several studies did not sustain the expected
positive impact on profitability.
Coinciding with the findings on industry fix capital, high barriers to entry / minimum
efficient scale may not solely have positive impacts on profitability. Both advertising
and R&D are traditionally related to markets with high product differentiation, which
is assumed to lead to less price pressure. However, both advertising and R&D intensity
have been negatively related to profitability in both the PIMS-related studies and the
LOB-based Ravenscraft model. The contradictory findings have led to an alternative
explanation in which high advertising and R&D expenses are considered as responses
to high competitive pressure. Moreover, Ravenscraft stated that R&D investments in
particular may lead to visible profitability impacts only after a considerable lapse of
time.
Probably the most contradictory evidence was found for the variable of industry
concentration. Mueller and Rauning suggested that the relationship between
concentration and profitability may not be linear but S-shaped.. Profitability may
increase with industry concentration, but at varying rates of increase. Industry
concentration may be an indicator for high economies of scale, which would give big
market players a costs advantage compared with smaller players. On the other hand, a
highly concentrated market may be more transparent for buyers and therefore may
increase the intensity of competition.
2.2.3 Conclusions and implications for the study
Similar to the previous chapter of literature in the field of entrepreneurship, the
following figure summarises the findings of strategy research on the impact of market
factors on organisational success measures.
50
CHAPTER 2 - LITERATURE REVIEW
1ST TIER FACTORS: “USUAL SUSPECTS“
EMPIRICALLY CONSISTENT
• high market share (+LOBP + ROI + ROA)
• high industry capacity utilisation (+ LOBP lb
+ LOBP + PCM + ROI)
• high import rate (- LOBP – PCM – ROI – FP
-IP – PCM)
• inventory intensity (- ROI – ROA)
EMPIRICALLY MAINLY CONSISTENT
• high export rate (+LOBP + ROI but –PCM)
• high industry fix capital / asset intensity (
- LOBP lb - ROI - ROA but + LOBP + PCM)
• high vertical integration (+ LOPBP lb
+ ROI + ROA but - LOBP – PCM)
• high market growth (+ LOBP + ROI + ROA
+/o PCM but – FP – INP)
EMPIRICALLY INCONSISTENT
• high barriers to entry / minimum efficient scale
(-LOBP + PCM + ROI +ROA – FP o IP o PCM)
• high industry advertising (- LOBPlb
+ LOBP + PCM – ROA – ROI + FP + INP)
• high industry R&D (- LOBPlb - LOBP – PCM
– ROI – ROA + FP + IP + PCM)
• asset newness (+ROI – ROA)
• high industry concentration (- LOBP +/- PCM
+ROI o ROA +/- FP +/- INP)
2ND TIER FACTORS: “LESS USUAL SUSPECTS“
EMPIRICALLY CONFIRMED
• high product customization / heterogeneity
(- ROI)
• high buyer concentration (-LOBP – PCM)
• high segmentation of suppliers (-LOBP
– PCM)
• high concentration of orders (+ROI)
• long distance shipped (- LOBP – PCM)
• high supplier concentration (- LOBP – PCM)
• high inflation rate (+ROI)
• low unionization (+ROI)
• low purchase importance (+ROI)
• low # of new products introduced (+ROI)
• high employee productivity (+ROI)
• high industry member diversification (o LOBP
+ PCM)
• high competitive power (o ROI + SG)
lb
based on line of business data
NOT CONFIRMED EMPIRICALLY
• early life cycle stage (o ROI o SG)
+ positively related
– negatively related
o not related
ROI=return on investment ROA=return on assets SG=sales growth LOBP=line of business profit PCM=industry price cost margin
FP=firm profit INP=industry profit
Figure 5: Summary of research on market factors in strategy
Among the first tier factors, which comprise those factors that were investigated in at
least two empirical studies, three groups of factors are distinguished. The group of
empirically consistent factors comprises market share, capacity utilisation, import rate
and inventory intensity. As mainly consistent have been categorised export rate,
industry fix capital, vertical integration, and market growth. The group of empirically
inconsistent factors comprises barriers to entry, advertising, R&D, asset newness and
industry concentration.
CHAPTER 2 - LITERATURE REVIEW
51
Finally among the second tier factors all those factors are listed which have been just
investigated in a single study among the revised literature.
Research on market factors in strategy can be considered, in several aspects, further
advanced than research in the field of entrepreneurship. Early studies by Ravenscraft
and the PIMS project related studies from Buzzell and Marshall already provide
focused investigations on the market environment that include very comprehensive
factor sets that are still missing in entrepreneurship research. Moreover, the widely
accepted use of profitability measures for company success leads to a higher
comparability of research results. The validity of the findings benefits from the very
large data sets that were applied in most studies.
On the other hand, all of the studies were very limited with regard to the dimensions of
company size and industry sector. The PIMS, Compustat and the FTC databases
contain data on solely large-scale enterprises primarily in the U.S. manufacturing
sector. The applicability of the findings for SMEs and for firms in the service sector
has not been examined. Research in industrial organisation has been facilitated by the
public availability of large data sets. The identification of appropriate measures of firm
performance was also an easier task for researchers in strategy. The widely applied
profitability measure is a far better success indicator in the context of established firms
than in the context of new firms, which have high investments in future growth.
Therefore, research in strategy has not had to face several of the key impediments that
researchers of entrepreneurship have been confronted with, and which have led to the
gaps in entrepreneurship research, as mentioned above.
During the search of relevant literature in the strategy field, a strong emphasis on
research on resources internal to the firm as the principal driver for growth and
profitability has been noticeable. This tendency was spurred by the publication of
studies on industry effects by Hansen and Wernerfelt and later by Rumelt at the end of
the 1980s, which attributed higher importance of firm and business unit effects versus
industry effects on firm performance. As Sampler (1998) noted, “in the current debate
it seems as if the resource-based stream is dominating today”75. However, industry
effects might have been systematically underestimated in previous studies due to the
limited data source on large manufacturing firms. The investigation of industry effects
52
CHAPTER 2 - LITERATURE REVIEW
in the contexts of SMEs and the service sector might supplement earlier findings. It
may be of interest to compare the market success factors as identified by PIMS and
Ravenscraft with the market success factors in the context of SMEs in primarily nonmanufacturing sectors.
In general, the empirical studies have shown that there is little awareness about which
market variables are relevant and which variables are critical. A model of market
attractiveness, which is developed in the following chapter, may guide future research
by enabling a more systematic selection of variables.
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
3
53
Development of a theoretical model of market
attractiveness
3.1
Approach
Within this chapter, a model of the new venture market environment is developed on
the basis of contemplative theory building and the formerly discussed results of
empirical research. This model guides the subsequent empirical analysis in the
following chapters.
In the first step, objective number two of this study76 will be achieved by discussing
those economic theories and research programmes that particularly contribute to the
understanding of the market-venture performance relationship. The underlying
explanatory concepts of each of these research programmes are discussed in the
context of the investigated market-new venture performance relationship. For each
research programme the critical market variables are identified and discussed under
the light of the empirical evidence of the literature review.
In the second step, different approaches towards structuring the complexity of
environments and markets are discussed. For each structuring approach, the strengths
and weaknesses are evaluated with respect to the undertaken study.
Finally, in the third step, objective number three of this study77 will be achieved by
proposing a new proprietary model of market attractiveness from the perspective of
new ventures. Independent variables of the model contain market factors with a
hypothetical influence on the dependent variable of new venture performance.
Departing from a comprehensive list of environmental variables, the number of
variables is narrowed by the elimination of variables that do not seem to be relevant in
the context of new ventures and variables that describe highly related phenomena
under different labels.
76
77
See chapter 1.2, p.3.
See chapter 1.2, p.3.
54
3.2
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
Additional applicable research programs for the theoretical
framework
Following Cooper’s claim (1991: 250) for “better theoretical frameworks and more
theory–driven empirical research” theoretical concepts of related research fields in
economy are incorporated in the process of developing the theoretical model of market
attractiveness. As Cooper (1991: 250) stated, “our understanding may be advanced by
borrowing constructs and theoretical frameworks from other fields”.
3.2.1 Selection of research programs
The undertaken study is primarily related to the research field of entrepreneurship.
However, other adjacent economic research fields may also provide valuable insights
into explaining the interactions between market variables and organisational success.
The lack of integrating former research and the ignorance regarding important market
dimensions has been identified earlier as a major shortcoming in entrepreneurship
research. Therefore, it is the intention of this study to integrate knowledge from a wide
range of research fields in order to expand the understanding of market effects. Those
research programmes that either explicitly postulate a causal relationship between
market factors and organisational success, or that provide concepts that can be applied
to explain such a causal relationship will be included in the theoretical framework. For
the purpose of this study, the presentation of each relevant research programme will be
limited to presenting the basic argument, the identification of the most critical marketrelated variables and an evaluation of the proposed mechanisms of market effects.
3.2.2 Industry economics
Objective and basic concept
The structure-conduct-performance paradigm, which has been developed in the end of
the1930s by Edward S. Mason78 at Harvard university, proposes that the market
structure determines the organisational conduct in terms of strategy, which in turn
determines the economic results. This basic paradigm has been extended by Scherer
and Ross79 to include a group of variables labelled basic conditions, which determine
78
79
Mason 1939.
Scherer and Ross 1990.
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
55
the market structure from the demand and supply perspective, respectively. In another
subsequent expansion of the model, public policy has been included as additional
factor of influence on both structure and conduct. Apart from the principal direction of
causation from structure to conduct and from conduct to performance, it is
acknowledged that a firm’s conduct can also affect market structure and the basic
conditions. Not only dominating large firms may affect the market structure, also small
firms may be able to affect the market structure for example by registering patents
which impact directly the barriers to entry on the market structure level. These lower
importance feedback causalities are indicated in the dotted arrows of the model.
Because in a perfect market environment conduct will primarily be determined by
market structure and its underlying conditions, the structure-conduct-performance
model legitimises an approach that investigates performance directly based on market
structure. The model stresses the importance of market choice as one of the most
important strategic decisions.
Basic Conditions
!
!
!
!
!
!
!
Supply
Raw materials
Technology
Unionization
Product
durability
Value-weight
Business
attitudes
Legal framework
Demand
Price elasticity
Substitutes
Rate of growth
Cyclical and
seasonal character
! Purchase method
! Marketing Type
!
!
!
!
Market Structure
Number of sellers and buyers
Product differentiation
Barriers to entry
Cost structures
Vertical integration
Diversification
Conduct
Pricing behavior
Product strategy and advertising
Research and innovation
Plant investment
Legal tactics
Public Policy
Taxes and subsidies
International trade rules
Regulation
Price controls
Antitrust
Information Provision
Performance
Production and allocative efficiency
Progress
Full Employment
Equity
Figure 6: Structure - conduct - performance paradigm (source Scherer and Ross 1990:5)
56
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
Industry economics has frequently been applied to justify governmental intervention in
highly concentrated markets in the interest of overall economic performance. The
findings of industrial economics had a strong impact on the field of business strategy
at the end of the 1970s when Porter transferred the findings to the field in the widely
known “five forces model”80.
In the ideal model, all firms within one industry would be expected to apply the same
strategies in terms of pricing and investments, and would have similar profit structures.
The model received criticism as apparently firms within one industry differ widely
both in the strategic postures adopted and profitability. Scholars of industry economics
argued that changes could be attributed to differences in information, random
disturbances and organisational inertia. Moreover, some industries could have
provided sufficient profit potential so as to allow suboptimal strategies.
Contribution to assessing market attractiveness
According to industry economics theory, an attractive market is characterised by:
o high barriers of subsequent entry with special attention to scale
economies and product differentiation
o low seller and buyer concentration
o high industry concentration
o high vertical integration
o high diversification
In the past, industry economics probably provided the most important theoretical
concept for empirical research on market-performance relationships. However,
empirical evidence for the critical market factors proposed by industrial economics is
highly inconsistent, as shown in the literature review. None of the above-mentioned
factors could provide consistent evidence in either entrepreneurship or in strategy
research. Entrepreneurship research even suggests that high buyer concentration could
have a positive impact on profitability, contrary to the proposed causalities. The
empirical research in industry economics itself, which has frequently investigated
80
A more detailed discussion of the model is given in chapter 3.3.1, p.67ff.
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
57
market factors related to firm survival, also revealed inconsistent results, which have
been attributed to diverging operationalisations of key variables81.
3.2.3 Organisation ecology
The roots of organisation ecology were planted in Hannan and Freeman's article82 on
the population ecology of organisations in the 1970s.
Objective and basic concept
Organisation ecology questions the adaptability of organisations to their environment.
Large organisations especially are seen as being characterised by strong inertial forces
impeding both organisational change within the company and adaption to new
environmental settings. This inertia arises due to internal and external factors. Internal
factors comprise sunk costs of firms, communication structures, internal politics and
the dominance of institutional norms. The external environmental factors that lead to
inertia comprise barriers to entry and exit, bounded rationality and social legitimacy83.
Consequently, organisational change occurs as a result of the mechanisms of
environmental selection and replacement of existing organisations by new
organisations rather than by internal adaptation within one organisation. Organisation
ecology focuses not on the individual organisation but predicts the probability of births
and deaths within a population of firms in a given industry niche over time. Patterns of
organisational foundings and mortality are investigated as the visible outcomes of
selection processes84.
Contribution to assessing market attractiveness
The complete negation of an organisation’s capacity of internal adaptation may be
difficult to justify, due to the fact that over the last few decades, large companies have
put great effort into improving their responsiveness to environmental changes.
Moreover, small companies are generally considered to be highly flexible with regard
to environmental responsiveness. Still, it might be important to note that degrees of
adaptability vary significantly among organisations and the capabilities of adaptation
81
Woywode 1998, Geroski et al. 1990.
Hannan and Freeman 1977.
83
Hannan and Freeman 1977.
84
Hannan and Carroll 1992.
82
58
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
processes are not unconstrained. Empirical research identified that organisational
mortality is significantly higher for firms that are new and firms of small size, which is
frequently referred to as liability of newness and liability of smallness. However, these
two most popular findings of organisation ecology only contribute a little to the
evaluation of attractive markets. More relevant for the purpose of this study are the
concepts of “carrying capacities” and “organisational density”85. According to the idea
of “carrying capacities”, the availability of resources within a market is restricted. Late
market entrants will be confronted with a limited availability of resources and are
therefore likely to be less successful. The idea of “organisational density”, however,
clarifies that in early market life cycle stages with a low number of competitors, new
ventures will suffer from a lack of legitimacy. Therefore, the entrance of additional
firms in early market life cycle stages increases legitimacy with potential client groups
and consequently increases the market potential. Once a satisfactory level of
legitimacy has been achieved, the entrance of additional firms in later market life cycle
stages will however lead to decreasing firm success due to increasing competitive
forces and limited resources.
Consequently organisation ecology theory suggests that attractive markets for new
ventures are characterised by:
o earlier market life cycle stages
o high degree of technological innovation or changes in regulation that
discriminate established organisations due to organisational inertia
o existence of competing firms to increase legitimacy with potential clients
o abundant availability of remaining resources in markets
o competitors of small size and rather young age, which can be ousted
more easily than long-established large firms
While the theoretical concept and empirical work in the field deals only with survival
and mortality of organisational populations, these factors may also be important for
analysing markets from the perspective of individual firm profitability. The proposed
factors of market life cycle, innovation and regulation, coincide with the most
important empiric findings in entrepreneurship research. The phenomenon of liability
85
Hannan and Carroll 1992.
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
59
of smallness can be related to the factor of market share in strategy research, as both
factors may be highly determined by economies of scale. Overall, empirical research
does confirm some of the most important factors of organisational ecology.
In addition, organisational ecology proposes an interesting framework for structuring
environmental variables, which might prove to be useful for the development of the
theoretical model of market attractiveness. Environmental variables are categorised in
the three main areas of task environment, institutional environment and political
environment. Task environment comprises all factors that are directly related to the
tasks that a firm has to carry out, such as competitive setting and customer structure.
Institutional environment comprises not only the macroeconomic conditions that affect
the firm but also the socio-cultural developments. Finally, the political environment
comprises current economic and tax policies and developments in jurisdiction.
3.2.4 Transaction costs economics
The establishment of transaction cost economics as a scientific research programme
dates back to the publication of "Markets and Hierarchies: Analysis and Antitrust
Implications" by Williamson in 197586, which evolved from the New Institutionalists’
criticism of traditional micro theory.
Objective and basic concept
Transaction cost theory aims to explain the boundaries of the firm. Consequently, firm
size is determined by the efficiency of vertical integration versus market allocation.
Transaction cost theory focuses on the single transaction as a unit of analysis and is
based on the following three key assumptions87:
o Transaction costs occur when goods or services are transferred across
technologically separable interfaces and determines the use of
appropriate governance structures.
o Transactions vary with respect to the three critical dimensions of
frequency, uncertainty and asset specificity.
86
87
The basic idea dates back to Coase’s famous article about the nature of the firm in 1937.
Compare Williamson 1979.
60
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
o Human factors such as bounded rationality and opportunistic behaviour
have to be taken into consideration when dealing with economic
problems.
Casson (1996) complemented this theory by adding the component of information
costs, in order to explain processes within the boundaries of the firm. Casson argues
that as the complexity of the information flows handled by a firm increases,
information costs will occur that will increase transaction costs.
Contribution to assessing market attractiveness
Transaction costs theory defines clear conditions that are proposed to increase
opportunistic behaviour and consequently increase transaction costs. These conditions
can be applied to evaluate the most efficient governance structure, whether it be
market allocation or vertical integration. Since ventures in markets with less exposure
to opportunistic behaviour may face less difficulty in achieving customer acceptance,
one may assume that those markets are more attractive for new ventures from a
transaction cost perspective.
In markets with high transaction costs, involving a high risk of opportunistic behaviour
and only few participants, the large vertically integrated firm is considered as the most
efficient governance structure. Exposure to opportunistic behaviour is greater if the
number of potential transaction partners is limited and at the same time one transaction
partner is in a position to exert power on the basis of asymmetrical dependencies, often
related to conditions of high asset specificity.
By contrast, markets with a larger number of players participating in the market
involve a low risk of opportunistic behaviour. In these markets the price mechanism is
more efficient than vertical integration, since the risk of opportunistic behaviour is less
serious. Therefore, these kinds of market conditions may be considered more
favourable by new ventures and small-sized companies.
It may be deduced from transaction cost theory that attractive markets for new
ventures are characterised by:
o a large number of potential transaction partners in terms of low
concentration of buyers and low concentration of suppliers
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
61
o low asset specificity to minimise the risk of opportunistic behaviour in
transaction partners
o little uncertainty within transaction processes in market
o an existing degree of vertical integration which is higher than justified
from a transaction costs efficiency perspective
o the existence of competing companies offering similar products, which
will decrease risk exposure of potential buyers and therefore reduce their
inclination for vertical integration
o inefficient organisation structure of competing firms with high
information costs
It has to be noted that the above characteristics of attractive markets are mainly limited
to the business to business sector and are less applicable to the business to consumer
sector.
Empirical evidence related to organisational success in the field of transaction cost
economics is scarce88. Entrepreneurship research suggests that high buyer
concentration opposed to low buyer concentration will lead to higher venture success.
Since asset specificity has led to ambiguous results in strategy research, the transaction
costs approach could reveal that product heterogeneity does not necessarily lead to
higher profits through less exposure to price competition, but might well increase the
reluctance of potential buyers. The positive impact of a high degree of vertical
integration coincides with the empirical evidence from strategy research.
Transaction cost economics has received considerable attention within the last decade,
most visibly with the Nobel award for Economics being given to Ronald Coase for his
work on transaction costs. Even though the empirical evidence for the suggested
market characteristics in transaction cost economics is relatively weak, the importance
of transaction costs may be particularly significant in the context of new ventures as
“smaller firms face higher transaction costs because they are more sensitive to
uncertainty, are more vulnerable to opportunism, yield a greater risk of unintended
88
See Picot, Schneider and Laub 1989 for one of the few applications of transaction costs theory in empirical
entrepreneurship research on innovative new ventures.
62
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
discontinuity for their partners and raise higher suspicions of opportunism”89.
Moreover, transaction costs theory provides an alternative explanation for the impact
of some frequently investigated market characteristics and may explain inconsistent
findings in empirical research which cannot be explained by conventional business
theory.
3.2.5 Contingency theory
Objective and basic concept
Contingency theory was established by Lawrence and Lorsch (1969) at the end of the
1960s. The basic argument is that organisational structure is affected by different
context variables such as environment, organisational size and technology.
Contingency theory in the context of environment claims that different environments
place differing requirements on organisations. Organisational design decisions
therefore depend - are contingent- on the environmental conditions. Those
organisations whose internal features best match the demands of their environments
will achieve the best adaptation and consequently the best performance90. Contrary to
the population ecology theory, which assumes limited adaptability for organisations,
contingency theory stresses the importance of adapting organisations to dynamically
changing environmental conditions in order to reach an optimum fit with environment
and therefore a maximum effectiveness.
Even though Scott stated: "contingency theory is dead. There is no longer any
widespread interest in its progress or development"91, it has to be acknowledged that
the basic ideas had a strong impact on the later economic theories of transaction cost
economics, population ecology and industrial economics. The basic causality of the
structure-conduct-performance model was derived from contingency theory.
Contribution to assessing market attractiveness
Contingency theory in the context of the environment stresses the importance of
environmental dynamism. Depending on the degree of environmental dynamism,
either a bureaucratic or organic organisational structure is considered more successful.
89
Noteboom 1993: 291.
Scott 1992: 88.
91
Carroll 1988: 1.
90
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
63
Elements of environmental dynamism comprise uncertainty, technological change, and
change of the market structure. From a new venture perspective, markets with a high
degree of dynamism may be more attractive as established firms will need time to
adapt to changing environments and will therefore have lower efficiency.
Consequently, contingency theory suggests that an attractive market for new ventures
is characterised by:
o a high degree of environmental uncertainty, which impedes established
companies in preparing for future developments
o a high degree of technological change, requiring established companies
to undertake comprehensive organisational adaptation processes
o a high degree of change in the market structure as demand, customer
demographics or competitive structures may change
All these factors lead to a relatively lower efficiency level within a given market as the
fit between the environment and the organisation will in general be lower. Established
firms will not be able to rely on continuous decade-long optimisation of their
processes. Market dynamism has been investigated in several empirical studies of
entrepreneurship with diverging results. A high degree of market dynamism and
uncertainty may, however, not only provide opportunities but it may also increase the
risk of failure. New ventures with limited resources at the time of start-up may run out
of capital and be more likely to fail if dynamic and uncertain market environments
develop unexpectedly. For this study, the concept of dynamic rather than static
environmental settings is considered important and will be adopted.
3.2.6 Game theory
Objective and basic concept
Game theory has had a major impact on economics and its related disciplines in recent
times. The transfer of its findings to the field of business gained most attention with
the publication of Nalebuff and Brandenburger's book on "co-opetition" (1996). In the
field of economics game theory had strong influence on recent research in the field of
industrial economics. Game theoretical thinking is based on anticipating responsive
actions from other participants. The degree of irreversibility of decisions and
investments is considered as the key element to determining the sequence of the game
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CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
process. Irreversibility is related to a higher competitive threat and higher barriers to
entry. Explanation models from game theory in business and economics are primarily
geared towards understanding and analysing the complex structures and dynamics of
competitive processes.
Contribution to assessing market attractiveness
Game theory is of particular interest for evaluating market attractiveness, since a core
application of game theory is the modulation of market entry situations. Especially for
start-ups, it is of great relevance to anticipate the competitive response to their entry
into a new market. This might not only be decisive for the choice of an appropriate
market entry strategy, but also for the appraisal of start-up success in the targeted
market. Game theory cannot provide a theory of market entry, but it can identify
individual critical aspects with regard to interdependences at market entry. Thus,
scholars have proposed that markets with highly differentiated products might provide
higher profit margins as differentiated products reduce the necessity of immediate
competitive reactions. It is even suggested that it may be better to provide an inferior
product than an identical product92. Also, game theory argues that legitimacy in terms
of the existence of competitors will increase the probability of customer acceptance of
a product93. The irreversibility of investments has been found to be a crucial factor for
anticipating competitive responses to the impact on entry and exit barriers94. Although
game theory is limited, in terms of a lacking closed theoretic model as provided by
traditional micro theory, scarce empirical research95, the assumption of perfect rational
decision making and frequently vague and diverse behaviour options, game theory
makes a valuable contribution to broaden strategic planning by incorporating the
perspective of competitors and anticipating future competitive responses.
From a game theory perspective, attractive markets for new ventures are characterised
by:
o high product heterogeneity, since identical products induce strong price
competition
92
McAfee and McMillan 1996: 267.
McAfee and McMillan 1996.
94
The applicability of game theory for explanations of firm foundation and market entries has been discussed
extensively in Schulz 1995.
95
Porter, R.H. 1991.
93
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65
o low fixed costs, which will decrease incentives of competitors to sell
close to variable costs
o low capital requirements and a low degree of irreversibility of
investments decrease the exit barriers and the dependence of competitors
on a market
o low competitive responsiveness and low aggressiveness
While empirical research in entrepreneurship consistently confirmed the negative
impact of high competitive pressure on new venture success, the impact of product
heterogeneity has been less consistent in both entrepreneurship and strategy research.
The variables of fixed costs and capital requirements have been investigated in
entrepreneurship under the broader term barriers of entry, and have been found to have
only a small impact on venture success. Similarly, empirical studies in the field of
strategy also gave inconsistent results regarding barriers of entry and minimum
efficient scale. Solely the related variable of inventory intensity showed the expected
negative relationship with organisational success. Although empirical research in
entrepreneurship and strategy cannot confirm the importance of the postulated market
variables, game theory considerations may offer valuable insights for understanding
competitive interactions.
3.2.7 Summarising consideration of contributions in different fields
The discussed research programs are mainly rooted in economics. The field of
economics tends to provide more comprehensive theoretical postulations and
explanation models than the more fragmented and less theory-guided research in
business administration. The presented theories frequently lack a profound empirical
foundation of research, which might be directly applicable to confirm the impact on
venture success. Also, the identified key market variables will only coincide to varying
degrees with empirical research in entrepreneurship and strategy. The explanation
models frequently adopted parts of another theory or emerged from another. However,
each model concentrated on distinct aspects of the market context. The primary
contribution of these research programmes is seen in the provision of alternative
models for explaining the mechanisms through which market characteristics may
affect venture success.
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CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
For each of the chosen research programmes, the following figure will summarise the
most important market factors that have been associated with market attractiveness
from a new venture perspective.
RESEARCH PROGRAMS
KEY MARKET FACTORS
industry
economics
1. barriers of entry
2. seller and buyer concentration
3. industry concentration
population
ecology
1. market life cycle stage
4. resource availability
2. existence of competitors
5. age & size of competitors
3. technological innovation / change in regulation
transaction costs
economics
1. seller and buyer concentration
4. vertical integration
2. asset specifity
5. existence of competitors
3. uncertainty within transaction process
contingency
theory
game
theory
4. vertical integration
5. diversification
1. uncertainty
2. technological change
3. change in market structure
1. product heterogeneity
4. competitive responsiveness
2. fix costs
3. capital intensity / irreversibility of investments
Figure 7: Key market factors in research programs
Overall, the review of relevant research programmes has identified additional market
characteristics that should be considered in an evaluation of market attractiveness, and
it provides insights into potential ways of explaining the interaction between market
characteristics and venture success.
3.3
Alternative conceptual approaches towards structuring and
classifying the market environment
Different approaches have been developed to structure the complexity of
environmental factors on the market level with regard to organisational success. Most
of these approaches have only been applied in the context of individual investigations.
The one notable exception is Porter’s “five forces model”, which has dominated
analysis of industry and market environments throughout the last two decades. Both
practitioners and researchers in the business field have relied heavily on the
conceptional framework given by Porter. In the following chapter, the Porter model
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
67
and other detailed conceptual approaches towards structuring the market environment
are presented and discussed on the basis of the following criteria96:
o Comprehensiveness: Are the major factors on the market level included?
Does the first hierarchy of the frameworks lead to meaningful
classifications of the overall market environment?
o Empirical support: Is the importance of the main criteria confirmed by
empirical research?
o Applicability to new venture context
o Accuracy in explaining interactions: Does the framework go beyond
listing and classifying variables by explaining relationships and
interactions among variables.
3.3.1 Model of Porter: Five forces model
In his classic book "Competitive Strategy: Techniques for analysing industries" Porter
(1980) transferred the findings of industrial economics to strategy research. The book
introduced two related models. First, a complex model of industry analysis and,
second, a more simplifying model of generic strategies. The industrial organisation
approach in strategy puts industry structure at the centre of strategic choice and the
subsequent organisational success. Porter’s five forces model of industry analysis can
be considered as the focal point of industrial organisation. Within the last two decades,
numerous studies have adopted Porter’s model of industry analysis to investigate
market environments. The model has become one of the most widely used tools of
strategic planning.
Porter’s concept of industry analysis develops a model of five sources of competitive
pressure that should guide strategic positioning.
96
In entrepreneurship research several frameworks have been developed which centre on entrepreneurial
infrastructure such as financial and non-financial assistance to new ventures, education systems, private
financial system, incubators and others. See e.g. Müller-Böling and Klandt 1990, Gnyawaii and Fogel 1994.
These frameworks shall not be discussed here as most of these infrastructure variables may affect new
ventures independent of industry membership and will only to a limited degree contribute to evaluate market
attractiveness.
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CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
Threat of new
entrants
Rivalry
among
existing
firms
Bargaining power
of suppliers
Bargaining
power of buyers
Threat of substitute
products
Figure 8: Porter’s model of five competitive forces
According to Porter, knowledge of these underlying sources of competitive pressure
will reveal the positioning of a company within its industry, and highlight
opportunities and threats. The understanding of competitive forces will be the
groundwork for a strategic agenda. The strongest of the five forces determines the
industry profitability and has to be the foundation for further strategic action. The five
main forces are complemented by sets of elaborated second order determinants:
1. Threat of new entrants
2. Bargaining power of
buyers
3. Bargaining power of
suppliers
4. Threat of substitute
products
Barriers to entry
• Low economies of scale
• Low product differentiation
• Low capital requirements
• Low cost disadvantages independent of size
• Free access to distribution channels
• Government policy impose no direct or indirect restriction to entry
Expected reaction of established rival companies
• Incumbents with low resources to fight back
• Incumbents unlikely to cut prices
• Low industry growth
• High buyer concentration / High purchase volumes (especially in
combination with high fix costs)
• Low product differentiation
• High share of total buyer costs represented by industry product
• Low profitability of buyers
• Low importance to the quality of the buyers products
• Low costs saving potential of product for buyer
• Potential threat of backward integration
• High supplier concentration
• High differentiation of purchased good
• No obligation to contend with other products
• Potential threat of forward integration
• Unimportance of industry for suppliers
• Improving price-performance trade-off of substitute
• Substitutes offered from high-profit industries
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
5. Rivalry among existing
firms
69
• Large number of equal competitors
• Slow industry growth
• Low product differentiation
• High fixed costs
• Capacity augmentation in large increments
• High exit barriers
• Diversity of rivals
Table 10: Porter’s five competitive forces framework including second order determinants
The framework was originally geared towards analysing competition from the
perspective of one individual organisation. However, Porter explicitly states that the
framework can also be applied to predict industry profitability in general.
The Porter model is extraordinarily comprehensive when taking into consideration the
30 second-order determinants that are beyond the five competitive forces. The
majority of the first tier market factors that have been identified within the review of
the entrepreneurship and strategy literature are covered by the model. The model lacks
consideration of the following variables, which have been considered as important in
former empirical research: Life cycle stage, market dynamism, market size and
import/export ratios. These variables, however, are treated in Porter’s complementary
concepts of generic environments97, value chain98 and competition of nations99. Even
though factors such as market growth and product differentiation are repeatedly
mentioned as second order determinants, the classification of market and industry
variables from a competition perspective is consistent and clear.
Direct empirical support for the importance of the competitive forces is rendered
difficult due to the inherent operationalisation and measurement problems related to
competition. In general, competition or competitive forces can only be measured
indirectly via variables of industry and market structure in quantitative research, which
will then be related to competition. The second-order determinants of Porter’s model
are highly consistent with the factors suggested by industrial economics.
Consequently, for Porter’s model the empirical results of the proposed factors of
product differentiation, barriers to entry, buyer and supplier concentration, remain
widely inconsistent in both strategy and entrepreneurship research.
97
Porter 1980. Generic industry environment comprise: Fragmented, emerging, mature, declining and global
industries.
98
Porter 1985.
99
Porter 1990.
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The applicability of the model to the context of new ventures may be limited to some
degree. A bias of Porter’s work towards large companies has been observed by several
scholars100. Some of the determinants of the model may be less relevant in contexts of
non-manufacturing, non-business-to-business industries from a new venture
perspective and other factors that have not been considered may be of importance.
Nevertheless, both the five competitive forces and all the underlying determinants of
the framework can also be applied in the new venture context.
One particular strength of the Porter model is the explanation of interactions between
competition and industry structure within an integrated model. Clear causalities are
drawn from specific industry factors to changes in competition intensity. At the same
time, the five competitive forces provide a clear structure of the phenomenon of
competition. The focus on the competition dimension also imposes, however, a
weakness of the classification, because the interactions between industry factors are
not explained and the underlying drivers that lead to changes in the industry structure
are not integrated.
Comprehensiveness
30 second order determinants cover majority of first tier
factors, however lacking some important variables.
Empirical Support
Inconsistent findings regarding factors, which are
primarily derived from industrial economics.
Applicability for New
Despite large manufacturing firm background, widely
Venture Context
applicable.
Explaining Interactions
Consistent explanation of interaction of competition and
industry/market structure, however lacking explanation
of interactions among industry structure and underlying
drivers of change.
Table 11: Evaluation of Porter's "five forces model"
While Porter’s model of generic strategies has been strongly criticised, the underlying
five forces model for industry analysis has received very little criticism101 and is still
widely in use among both practitioners and researchers. The criticisms concentrate on
the model’s assumption of a static, rational industry structure.
100
101
De Wit 1997.
E.g. Miller and Dess 1993 who empirically tested the Porter model of generic strategies on the PIMS
database, acknowledged the five forces industry model, but criticized the shortcomings of the
oversimplifying model of generic strategies.
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
71
Moreover, some researchers have argued that in today’s highly dynamic industry
environments, the applicability of the model might be limited due to blurring industry
structures102, high degrees of uncertainty103 and increasing rates of change in the
market environment104. By contrast, one may argue that in today’s business
environment, opposing trends towards the limitation on core businesses and
outsourcing may also mark industry borders more than ever before. It should also be
noted that the Porter model is not completely static, as the two determinants
“improving price-performance trade-off of substitute” and “slow industry growth” take
into account inter-temporal dynamics. Nevertheless, the integration of drivers of
change on the underlying industry structure and industry dynamics has been neglected
to a large extent.
Overall, Porter’s five forces model provides a rather comprehensive framework with a
convincing structure according to the distinctive competitive areas represented by the
five forces. Although the model is still frequently applied, it has to be noted that
Porter's model, developed about two decades ago, exhibits several weaknesses. It has
already been mentioned that industry dynamics were neglected. In addition, the model
is oriented towards large scale manufacturing companies. Several market factors such
as life cycle stage and market dynamism are missing in the model, which have been
proven to be of importance in recent research in entrepreneurship and strategy105. The
explanations on interactions are limited to the causalities related to competition
without providing explanations for the industry variables themselves.
3.3.2
Baaken’s market framework
One of the most detailed new venture related frameworks of market environments in
the German language literature was developed by Baaken (1989). He developed a
comprehensive model of criteria to evaluate technology-oriented new ventures,
including relevant variables of the three main dimensions entrepreneur, technology
and market. The market dimension consists of two primary categories of market
attractiveness and competitive position.
102
Levenhagen and Thomas 1993.
Coyne and Subramaniam 1996.
104
Sampler 1998 even attributes the recent transition from industrial organisation to a resource-based view of
strategy to the problems of defining industry boundaries as well as a dramatically increasing rate of change.
105
Compare chapter 2.1 figure 4, p.33.
103
72
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
I. MARKET ATTRACTIVENESS
1. Market growth
•
High growth in industry of customers
•
Low increase of productivity of own products (increasing
productivity of products could compensate increasing demand)
2. Risk of sales
3. Market size
•
Expansion of potential market
•
Low risk of substitute products or processes
•
Early market life cycle stage
•
High dispersion
•
Low dependence on economic situation
•
Niche represents sufficiently high market volume with respect to
firm’s potential
•
Total market size is sufficiently high, but allows a relevant market
position of firm
4. Attractiveness of
procurement market
•
Low economic risks (related to increase in factor prices)
•
High supply security with low dependence on individual suppliers
•
Rapid technological developments (availability of standard
components may decrease costs of formerly proprietarily
manufactured parts)
5. Resistance to market entry
•
Low newcomer phenomenon in terms of customer loyalty
•
Existence of early adopters
•
Low intensity of manufacturer-customer relationship prevalent in
industry in terms of frequency of orders, intensity of consultation,
intensity of service, switching costs
•
Low price elasticity of demand side induces readiness to switch to
lower price supplier
6. Competitive situation
•
Competition on quality vs. competition on price
•
Small number and small size of competitors (or potential
competitors)
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
73
II. COMPETITIVE POSITION
1. Potential relative market
•
share
Potentially growing attainable relative market share within
product/market of range
•
Potentially growing attainable relative market share concerning the
product group
2. Firm strengths
•
High readiness to learn
•
Procurement: Good relationship with suppliers and procurement
flexibility
•
Sales: Strong advertising/promotion, qualified sales team, efficient
and customized order administration, acceptable shipping times,
efficient complaint management
•
Services: Comprehensive services
•
Product range: Ease of product application for user, training
concepts, management of after sales service and replacements,
strong synergy effects in procurement, production and sales, product
range adjusted to market needs, high sales flexibility
•
Customer preferences: High brand potential, high identification of
product with firm, high expert recognition, close relationships to
customers
3. Product strengths
•
Functions and characteristics: High utility for client needs,
technologically up-to-date concept with high reliability, additional
features
•
Variety of types and variants: Variety of products for different
needs, low starting costs with options for expansion
•
Costs of integration: Low costs of integration, acceptable price, low
operating costs
Table 12: Baaken’s framework of the market environment
In order to evaluate the probability of success of a technology-oriented new venture,
the two dimensions of market attractiveness and competitive position are integrated
into an evaluation matrix:
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
MARKET ATTRACTIVENESS
74
Chances Chances Chances
o
+
++
Chances Chances Chances
-
o
+
Chances Chances Chances
--
-
o
COMPETITIVE POSITION
Figure 9: Portfolio matrix of Baaken
Similar to the Boston Consulting Group business portfolio matrix, the Baaken model
suggests that the fit of market attractiveness and the competitive position of the
individual firm will determine venture success.
The variables of the framework have been theoretically derived and, in a further step,
compared with strategic success factors identified from three case studies. All of the
variables for the dimension of market attractiveness and competitive position have
been found valid for the context of the case study firms and have been related to the
identified strategic success factors.
As in Porter’s “five forces” model, Baaken’s model is also geared towards the
evaluation of the market environment from the perspective of an individual firm. The
whole set of competitive position dimension criteria, as well as the market size criteria
from the market attractiveness dimension, can only be applied in the context of an
analysis on the individual firm.
In terms of comprehensiveness, the model contains six primary criteria for the market
attractiveness dimension and three primary criteria for the competitive positioning
dimensions. In total, 28 sub-criteria are considered. Of these 28 sub-criteria, 15 can
also be directly applied for an evaluation in an aggregated industry context. Among the
first tier factors from the literature review106, the following factors are missing in the
framework: Industry capacity utilisation, import/export ratios, capital intensity, capital
intensity and vertical integration.
106
Compare figure 4, p.33 and figure 5, p.50 at end of chapter 2.1 and 2.2.
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
75
Empirical research in entrepreneurship and strategy research consistently confirms the
importance of criteria such as life cycle, market size and market share. The findings
for other proposed criteria such as market growth are, in the context of
entrepreneurship, less consistent and there are indications for an opposite impact of
buyer concentration than that suggested by this model.
The model has been developed specifically for the evaluation of innovative
technology-oriented new ventures. This is reflected in the selection of criteria for the
framework. The products of these innovative technology-oriented ventures have been
assumed to exhibit a high degree of differentiation and even uniqueness. Therefore,
this factor is not applied in the framework. At the same time the framework is geared
towards a business rather than a consumer market, with products that tend to require
an intensive client relationship, which is particularly reflected in the sub-criteria
concerning market growth and barriers to entry. Apart from this sectoral limitation, the
organisational focus of the framework is in coherence with this study on new ventures.
In contrast to Porter’s classification, which categorises market factors according to the
area of competition that is affected, Baaken classifies, in the first step, according to the
aggregation level in market attractiveness and relative competitiveness. In the second
step, the main criteria are identified, which are, in the third step specified by subcriteria. The framework explains a causation from the sub-criteria to the main criteria
and from the main criteria to the main categories of market attractiveness and relative
competitiveness. The explanation of relationships is limited to the grouping of related
sub-criteria under subordinate criteria. Interactions among sub-criteria or among main
criteria are not explained. Moreover, the relationship between market attractiveness
and competitive position in the simplified matrix model remains vague. It is unclear
how individual factors of the competitive position dimension affect or interact with
individual factors of the market attractiveness dimension. Also, contingent variables
are not considered in the framework.
76
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
Comprehensiveness
28 criteria with 15 criteria applicable for analysis on the
industry level. Several important variables missing.
Empirical Support
Inconsistent findings regarding many suggested factors.
Applicability for New
Framework reflects orientation towards technology-
Venture Context
oriented new ventures, with overrepresentation of factors
related
to
technology-oriented
business-to-business
market. Majority of factors of framework applicable to
new venture context in general.
Explaining Interactions
Structuring of factors in three hierarchical levels.
Further explanations of interactions are lacking.
Table 13: Evaluation of the market framework of Baaken
A notable shortcoming of Baaken’s framework is related to the limited consideration
of dynamics and changes in the environment, which are reflected only in relation to
technological development on the procurement market. Especially in the context of
technology-oriented firms, the emergence and acceptance of new technologies in the
overall economy, changes in governmental regulations, and general shifts of consumer
demands may significantly affect the potential and attractiveness of a market. The area
of opportunity recognition is excluded from the model.
3.3.3
Dean and Meyer’s model
Dean and Meyer’s model (1996) is, in several aspects, distinct from the other models.
First, it focuses on firm formation instead of firm performance, and second, it places a
strong focus on environmental dynamics as facilitators of opportunities. Third, it has
been validated in a quantitative empirical investigation, and fourth, it is geared towards
an analysis on an aggregate industry level. The model was developed to perform a
focused analysis of the effects of industry environments on new venture formations in
US manufacturing industries. The selection of relevant market variables and the
overall structure of the model are firmly grounded on the theoretical concepts of
industrial economics and population ecology. The effects of the three major industry
determinants (1) industry dynamism, (2) constraints on new ventures (entry barriers)
and (3) constraints on existing firms (inertia), are tested regarding their effect on new
venture formation. The authors stress the importance of dynamic industries as drivers
for opportunities of entrepreneurial activity. Entry barriers constrain the formation of
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
77
new ventures by prohibiting new ventures from taking advantage of emerging
opportunities. Finally, the inertia of existing firms represents their incapability to seize
new opportunities and thereby increase the potential for new ventures. The following
figure represents the model and includes the variables that were used to measure each
of the four major determinants:
INDUSTRY DYNAMISM
• Demand growth
• Niche dynamism
• Technological intensity
CONSTRAINTS ON NEW
VENTURES (ENTRY BARRI ERS)
• Advertising intensity
• Excess capacity
• Capital requirements
• Industry concentration
CONSTRAINTS ON EXISTING
FIRMS (INERTIA)
• Age of existing firms
• Vertical integration
• Extent of unionization
• Failure to invest in new capital
+
-
NEW
VENTURE
FORMATION
+
+
INDUSTRY SIZE
source: Dean and Meyer 1996: 120
Figure 10: Model of determinants of new venture formations by Dean and Meyer (1996)
The results of the regression analysis confirm a statistical significance for each of the
three factors of industry dynamism: Sales growth, niche dynamism and R&D intensity.
All three showed a significant positive relationship to venture formation. Two of the
entry barrier factors, namely industry concentration and capital requirements, showed
a strong negative correlation with venture formations. Associated with inertia, both
vertical integration and failure to invest in new capital are positively related to venture
formation. Overall, the industry variables have a strong predictive power, explaining
up to 69% of variance in new venture formations.
In terms of comprehensiveness, the model is, with its four main factors and eleven
second-order effects, less comprehensive than the former models. Nevertheless, it is
noteworthy that the majority of the first-tier factors of the literature review107 are
reflected in the model. Missing factors include those such as: industry life cycle,
107
Compare summarizing tables at end of chapter 2.1 and 2.2.
78
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
import/export, buyer concentration, access to distribution channels and, most
importantly, factors related to intensity and type of competition.
Empirical support of the model factors is provided by the authors themselves. A
comparison with the findings of the literature review is not valid as the literature
review concentrated on studies on firm performance and explicitly excluded studies on
firm formation and mortality.
The model has been developed for the new venture context. However, it should be
noted that a limitation on firm formation may have led to an overrepresentation of
factors related to immediate market entry. Factors that could lead to sustainable longterm success may have received less consideration. Factors like product heterogeneity,
intensity of competition and focus of competition, which have frequently been applied
in this respect, are missing in the model. Additionally, one has to take into
consideration that the study exhibits a sectoral orientation towards manufacturing
firms.
The Dean and Meyer model does not provide explanations of the interactions among
factors, and the categorisation within the two hierarchies of factors gives only a limited
explanation.
Comprehensiveness
11 market factors with high congruence with first-tier
variables,
however
particularly
competition-related
factors are missing.
Empirical Support
Study provides empirical support for theoretical model.
Applicability for New
Despite orientation towards venture formation instead of
Venture Context
venture performance and the given sectoral focus on
manufacturing, the model seems widely applicable.
Explaining Interactions
Apart from the basic structure of two levels of hierarchy,
no relations of interactions among factors are explained.
Table 14: Evaluation of the model of Dean and Meyer
3.3.4 Hinterhuber’s framework
Hinterhuber (1995) developed an extraordinarily comprehensive scheme of
environmental factors as a cornerstone for conducting an industry analysis. In
accordance with the industrial organisation theory, Hinterhuber considers industry
analysis as a starting point for strategic planning. The framework is structured around
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
79
the three main categories of developments related to a supra-industry level, structure
and developments on the industry level and the positioning of a company within its
industry. The latter are highly related to the dimensions of market attractiveness and
competitiveness from the portfolio matrix, which has also been applied as a primary
means of classification by Baaken (1989).
I. ANALYSIS OF SOCIO-POLITICAL, ECONOMIC, AND TECHNOLOGICAL DEVELOPMENTS
1. International economic system, national economic structures- and policies
2. Governmental intervention in economy
3. Political, and socio-demographic developments (protection of environment, population growth, labour
market etc.)
4. General technological developments (as far as they concern the present or future markets of the
company)
II. ANALYSIS OF INDUSTRY
1. Demand for products or
services
•
Product function and utilisation, market requirements, social needs
•
Stability of demand (substitution, complementarity, sustainability
etc.)
2. Supply of products or
services
3. Competition
•
Market life cycle stage
•
Market dimension and market growth
•
Segmentation / individualization of demand
•
Bargaining power and behaviour of buyers
•
Average degree of capacity utilisation in industry
•
Capital intensity
•
Labour costs, material costs
•
Stability of supply with raw materials and energy
•
Market segmentation, distribution channels
•
Taxation
•
Bargaining power and behaviour of suppliers
•
Number, size, financial strengths, experience, management
systems, and behaviour of established competitors
•
Risk of entrance of new competitors, and/or substitution products
•
Behaviour of employees and their organisations
•
Organisation of industry sector and its development
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CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
4. Conclusions
•
Governmental intervention, incentives etc.
•
Public opinion about industry sector and production processes
•
Profit- and growth perspectives
•
Critical factors for success of a company in industry
III. POSITION OF COMPANY IN MARKET
1. Market position of
company
2. Analysis of competition
•
Market share (in relation to market size and largest competitor)
•
Quality and characteristics of products and / or services
•
Core competences
•
Alternative structures of products or services
•
Innovation potential (product or process innovations)
•
Sources of competitive advantages
•
Identification of strongest competitor
•
Analysis of competitive differences in relation to strongest
competitor (core competences, quality, resources, quality of
strategies etc.)
•
Analysis of competitive instruments (price, quality, design etc.)
•
Strengths / weaknesses, present / future strategies, motivation and
self-evaluation of each competitor
•
Probability of reaction and capacity to react of each main
competitor
3. Costs structure of firm
•
Analysis of location in relation to supply with energy and raw
material, labour market, sales market
•
Relative efficiency of product- and distribution system (economy
of experience)
4. Specific competitive
advantages
5. Conclusions
•
Relative financial strength of firm
•
Relative capacity of management team
•
Critical resources of success for firms in market
•
Starting point for analysis of strengths and weaknesses of firm
related to critical success factors for companies in industry in
general and for the specific case of the firm
Table 15: Structure for environmental analysis and forecast by Hinterhuber
The framework is strongly centred on the application in practice as guiding structure
for conducting an industry analysis. The conclusions at the end of the industry and
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
81
competitive positioning analysis reflect the fact that individual critical success factors
may vary among different industries. According to Hinterhuber, industry analysis
should not be limited to the collection of data, but also requires critical interpretation
and evaluation related to the specific firm and industry context. The positioning of the
individual company should be evaluated in relation to the strongest competitor in the
respective market. Such a reduction of the competitive environment will increase the
feasibility of competitive positioning analysis, especially for the implementation of an
industry analysis in practice.
With three main categories, 13 second-order categories and 38 third-order criteria, the
framework comprises a larger number of variables than any of the other discussed
frameworks. Hinterhuber states that the quality of the conclusions will increase with
the number of data, phenomena, relationships and their evolution that are
investigated108. The vast majority of first-tier factors of the literature review109 are
considered in the model. Among the few missing variables are product heterogeneity,
vertical integration and import/export intensity. However, the roots of import/export
intensity are considered in the relative costs structure regarding to the location of the
firm.
Hinterhuber’s framework frequently omits to specify the direction of causation
between a variable of the framework and an expected influence on organisational
performance. In contrast with the models formerly discussed, which specify the impact
of each factor on organisational success, the Hinterhuber framework is solely intended
to serve as a structuring scheme without further specification of generic directions of
causations independent of industry. Therefore, no evaluation of consistency with
former empirical research can be given.
The framework is part of Hinterhuber’s book on strategy. Even though the framework
is geared towards conducting portfolio planning in the context of larger firms, the
framework also seems to be widely applicable to the new venture context. However,
some variables, which are of particular importance in the new venture context are, to a
lesser degree, considered as barriers to entry and the dynamics of opportunity creation
through changes within the industry and its external determinants.
108
109
Hinterhuber 1995: 116.
Compare summarizing tables at end of chapter 2.1 and 2.2.
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CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
As has already been mentioned, the Hinterhuber framework is primarily intended to
serve as a structuring scheme. No interactions among variables are explained.
Comprehensiveness
38 criteria cover most aspects of the industry level.
Empirical Support
Not applicable, since frequently directions of causations
are missing.
Applicability for New
Although primarily developed for larger firm context,
Venture Context
widely applicable also to new venture context. Some
particularly important areas for new ventures missing.
Explaining Interactions
No relations of interactions among factors are explained.
Table 16: Evaluation of the framework of Hinterhuber
3.3.5 Summarising consideration of critical aspects for developing
structured models of market environments
The approaches discussed for structuring the market environment provide suggestions
related to content and structure with respect to the development of a proprietary model
of the market environment.
In terms of the content dimension, the different approaches vary significantly in the
number of applied variables. The importance of individual factors within the
frameworks varies among different industries. In some cases the direction of impact
may even be reversed in different industry contexts. As different variables may be
critical in different industry contexts, Hinterhuber’s proposition for including a wide
range of potentially important variables is, in the author’s opinion, appropriate, as it
reflects the complexity of market environments. One frequent proposition of
quantitative research is to limit statistical models, particularly in regression analysis, to
variables with considerable additional explanation power, thereby leading to the
consideration of only a limited number of variables.
In the new venture context, the framework dimensions of opportunity creation and
barriers of entry may be of particular importance. Changes both in the supra-industrial
environment and in the industry itself will create opportunities for new ventures. The
dynamics within the industry have been well reflected in the Dean & Meyer model
with the application of the model dimensions of industry dynamism and inertia.
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
83
The explanation power of a model increases with the degree to which interactions and
relationships are specified. Porter, Baaken, and Dean and Meyer’s models specify the
direction of impact of each factor on organisational success or venture formation.
However, interactions or contingent dependencies among variables of the same level
are not taken into account in any of the models. While most of the presented
frameworks just serve as a structuring scheme to group related factors, Porter’s model
goes beyond this by relating the primary structuring categories of competitive forces to
each other and by relating market factors to the five competitive areas. In order to
develop the model, it is desirable to specify also relationships and interactions among
variables.
In terms of structure, the models suggest that analysis of the market-related
environment has to be conducted on different aggregation levels. On a global or
national level there are various dimensions such as economic, technological, sociopolitical and demographic changes that affect a wide range of industries at the same
time. On an industry level there are other factors related to competitive structure and
market structure that specifically affect the companies within one particular industry.
Finally, on the company level, there are market-related factors that have to be
evaluated with respect to the relative competitive strengths of the individual company.
In the Hinterhuber framework these three levels of aggregation are clearly
distinguished as the primary categories of 1.) analysis of socio-political, economic and
technological developments, 2.) analysis of industry and 3.) position of the company in
the market.
The latter two levels coincide with the dimensions of the portfolio matrix structure of
market attractiveness versus the competitive position of the individual company,
which has been applied as primary structuring element in both the Hinterhuber and
Baaken frameworks. The market attractiveness dimension comprises all factors that
affect all companies within a given industry. The aggregated industry is the unit of
analysis. Changing the perspective to the individual firm as the unit of analysis means
that critical characteristics of an individual firm will be compared with and related to
characteristics of the market and their competitors.
84
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
CONTENT
- Comprehensiveness
Different variables may be important in different
industries, therefore a model should include a wide range
of potentially important variables.
- New venture context
Environmental dynamics and barriers to entry are two
dimensions that seem to be particularly important for a
framework in the new venture context.
- Relationships/
Frameworks that reflect relationships and interacting
Interactions
effects provide higher explanation power and are
desirable.
STRUCTURE
- Levels of aggregation
Market related factors should be investigated on
distinctive levels of aggregation. National/global, industry
and firm levels should be distinguished.
Table 17: Critical aspects for developing frameworks of market environments
3.4
Development of an integrated model for analysing market
attractiveness from a new venture perspective
3.4.1 Objective of model
Within this and the following chapter, a proprietary model for analysing market
attractiveness is developed. The model will reflect the results of the literature review
and potentially relevant variables that have been suggested by different theoretical
concepts. The major objectives in terms of content and structure for the development
of a proprietary model of market environments have been outlined in the summarising
table of the last chapter. Furthermore, the objective is to integrate previous approaches
to market analysis, which dealt with different market dimensions, into one common
structure, which may be relevant for both entrepreneurship and strategy research.
The model considers the specific perspective of new ventures that has been adopted
for this study. However, the overall structure and also the majority of the model
variables should be applicable to evaluate market attractiveness in the context of
established firms as well. For those variables that are assumed to affect the opportunity
structure of established firms and new ventures in a different way, the framework will
specify as a short-term effect the implication from the new venture perspective at the
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
85
time of market entry and as a long-term effect the implication from the perspective of
established firms. It is important to understand that surviving new ventures will
transform over time into established industry members. The barriers that a new venture
has to face upon market entry may increase the probability of failure in the short run,
however, once the venture has successfully overcome the barriers of entering the
market, it may benefit from higher profit potentials in the long run.
For the subsequent empirical investigation the model will serve as guidance for
conducting a structured empirical analysis of the market environment.
Even though the overall relevance of individual factors within the model regarding
venture success might vary greatly, the model does not specify any weighting of
individual factors. Ex-ante weightings of factors are impeded for several reasons. On
the one hand the existing theoretical and empirical basis for justifying even a basic
weighting is considered too weak. On the other hand, it is assumed that the importance
of factors depends on contextual conditions.
3.4.2 Basic structure of the model: Five environmental levels of analysis
The need to investigate market environments on distinctive aggregation levels has
become evident within the review of alternative approaches towards structuring the
market environment110. Variables of a framework to evaluate market attractiveness
may either be applied to all companies in a global or national environment, all
companies within one specific industry or just within the context of one particular
firm.
Baaken (1989) structures the variables of his framework into those which can be
applied to evaluate the whole industry population without information on specific
firms111 and those that can only be applied from the perspective and with information
of one particular firm112. Hinterhuber (1995) made the same distinction, labelling these
two levels clearly as “analysis of industry” and “position of company in market”.
Moreover, Hinterhuber extended the perspective by also including a higher-order level
of what he called “analysis of socio-political, economic and technological
developments”.
110
See table 17, p.84 regarding critical aspects for developing frameworks.
Baaken (1989) labels this industry level as “market attractiveness”.
112
Baaken (1989) labels this firm / venture level as “competitive position”.
111
86
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
The distinction among different aggregation levels is decisive since the choice of an
appropriate level of analysis depends on the aggregation level of the unit of analysis. If
data broken down to the aggregation level of the individual firm is not available, it is
not possible to conduct an analysis on the firm level. Because on the firm level the
characteristics of one particular firm are compared with one competitor or a group of
competitors, it is not possible to analyse variables like reachable market share,
innovation potential or relative product strengths if data is only available for industry
populations. On the other hand, if data is available on a lower aggregation level, the
additional consideration of higher levels may enrich the analysis. The industry level
variables of economies of scale or average gross margin may for example add to the
understanding and evaluation of certain variables like relative growth aspirations on
the firm level. The importance of individual variables on the firm level may vary
according to different industry contexts.
The general criteria by which to determine the level on which a variable is located, is
the highest aggregation level of the unit of analysis to which the variable can be
meaningfully applied. If a variable can be applied to characterise a country as a unit of
analysis, then it should be grouped in the national level. If a variable cannot be applied
to characterise a whole nation, but a whole industry, it should be grouped in the
industry level and, finally, if the variable requires information of a specific firm it
should be grouped on the firm level. Given this basic classification, it is important to
state that higher level analysis will not be conducted without the consideration of the
respective firm or industry context. Rather, the specific firm and industry contexts will
serve to filter and focus on those variables among the multitude of potentially
important factors with relevance in the particular context.
MACRO LEVEL
INDUSTRY LEVEL
VENTURE / FIRM LEVEL
Figure 11: The three aggregation levels of environment
Different aggregation levels of analysis do not only differ with respect to the
aggregation level of the unit of analysis, but also with respect to the underlying
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
87
research question that is addressed by the particular level of analysis. The analysis of
market attractiveness suffers from conceptual ambiguity113 as researchers frequently
use different labels such as resource scarcity, hostility, industry attractiveness, capacity
and munificence when referring to attractive markets. This indicates that there might
be different aspects of market attractiveness associated to specific underlying research
questions.
In order to reflect the differences in the underlying research questions, the three basic
aggregation levels of firm, industry and macro level are complemented with two
additional levels of analysis.
First, analysis of national competitiveness is included. This allows distinction between
variables related to general changes in the macro environment and variables related to
the competitiveness among different countries.
Second, analysis of related industries is included between national competitiveness
and the industry levels, reflecting the impact that members of related industries can
have on the industry under investigation.
In addition, barriers to entry are positioned as a separate section between the industry
and firm levels. Barriers to entry are generally variables that belong to the market
structure of the industry level. However, it is not possible to evaluate whether a barrier
to entry has a positive or negative impact without information on the individual firm.
The particular characteristic of barriers to entry is that they create impediments for
firms that want to enter a market, but at the same time provide benefits and higher
attractiveness for those firms that are able to overcome the barriers to entry as they
subsequently benefit from less competition. A market that is, for example,
characterised by high asset intensity may prevent new firms without the necessary
minimum capital from entering the market, or may doom them to failure. If, however,
a firm with a relatively high financial strength enters such a market and can overcome
this barrier, it could benefit from higher profitability as only those potential subsequent
entries will be able to enter the market, which also have at their disposal the required
minimum capital. Therefore, the impact of barriers to entry on market attractiveness
113
See Castrogiovanni 1991 for a detailed review of the problem of conceptual ambiguity.
88
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
cannot be evaluated and interpreted on the industry level, even though this is where
barriers to entry are generally measured.
The following table summarizes the five levels of analysis of the developed model:
MACRO
OPPORTUNITIES IN GLOBAL & NATIONAL ENVIRONMENT
LEVEL
INTER-COUNTRY
NATIONAL COMPETITIVENESS
LEVEL
INTER-INDUSTRY
OPPORTUNITIES & THREATS FROM RELATED INDUSTRIES
LEVEL
INTRA-INDUSTRY
MARKET & DEPENDENCIES & COMPETITORS
LEVEL
BARRIERS TO ENTRY
VENTURE / FIRM
RELATIVE POSITIONING TO COMPETITION
LEVEL
Figure 12: The five environmental levels of the model
Each level of analysis is characterised by specific research questions and the minimum
aggregation level required for the unit of analysis:
level of
minimum
analysis
aggregation of
research questions
unit of analysis
macro level
none
Opportunities in global & national environment
• Does the global and national environment provide a high
degree of changes with relevance to the targeted industry?
• Do these changes have a positive impact on the opportunity
structure?
• Is this a good moment to start?
inter-country
level
country
National competitiveness
• What is the level of competitiveness for a company of the
targeted industry in the targeted country compared to other
countries in the global economy?
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
inter-industry
industry
level
89
Opportunities & threats from related industries
• How can the current state and expected activities in other
related industries affect opportunities and threats for the
targeted industry?
intra-industry
industry
level
Market & dependencies & competition
• Are the structure and dynamics of the market and competitors
favourable in terms of profit and growth expectations?
• Does the industry pose risks associated to specific
dependencies?
• Which are the industry variables, which are of critical
importance within the industry?
venture / firm
level
firm
Relative positioning to competition
• How is the product, the firm, the management team, the
strategy and the location positioned to the strongest competitor
or a group of main competitors?
• What is the potentially reachable market share of the new
venture?
Table 18: Minimum aggregation level and research questions for five levels of analysis
Each of the five main levels of analysis is divided into five dimensions. Again, for
each dimension, a list of relevant evaluation variables is given. In total, the conceptual
model consists of more than 100 variables. The expected impact of each variable on
the three most frequently applied venture success measures of growth, profitability and
survival will be specified, and positive as well as negative consequences on venture
performance will be summarised in the tables of the conceptual model. In general, the
specific directions of impact of the individual variables on venture performance have
been derived from the literature review and the theoretical concepts presented.
90
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
MACRO
LEVEL
OPPORTUNITIES IN GLOBAL & NATIONAL ENVIRONMENT
macroeconomic legislative technological socio-cultural demographic
changes
changes
changes
changes
changes
NATIONAL COMPETIVITY
INTER-COUNTRY
LEVEL
INTER-INDUSTRY
LEVEL
INTRA-INDUSTRY
LEVEL
production
factors
stability & distance to
industry
new venture
taxation sales market infrastructure infrastructure
OPPORTUNITIES & THREATS FROM RELATED INDUSTRIES
complementary threat of
forward
cooperation substitution integration
backward threat of other
integration new entries
MARKET & DEPENDENCIES & COMPETITORS
market
structure
market dependencies
dynamics
competitor
dynamics
competitor
structure
BARRIERS TO ENTRY
VENTURE / FIRM
LEVEL
RELATIVE POSITIONING TO COMPETITION
products/
services
firm
resources
management
team
strategy
Figure 13: The 5 x 5 model of market attractiveness
In the following chapters details of each level of analysis are presented.
location
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
91
3.4.3 Macro level: Opportunities in global & national environment
Within the analysis on the macro level, the following research questions are addressed.
MACRO LEVEL:
• Does the global and national environment provide a high degree of changes with
relevance to the targeted industry?
• Do these changes have a positive impact on the opportunity structure?
• Is this a good moment to start?
In order to answer these questions, the five most important sources of changes on the
macro level are analysed. These changes may sometimes radically redefine the
conditions of competition within an industry. Examples of such radical changes on the
macro level include the legislative deregulation of telecommunications for firms in the
telecommunication market or the increasing popularity of the internet for mail order
businesses.
OPPORTUNITIES IN GLOBAL & NATIONAL ENVIRONMENT
MACRO
LEVEL
macroeconomic legislative technological socio-cultural demographic
changes
changes
changes
changes
changes
Figure 14: Macro level - Opportunities in global & national environment
The following table of the individual variables on the macro level points to a wide
range of potentially relevant sources of change within an industry. Early recognition of
such sources of change will make it possible to identify evolving opportunities for
start-ups114 and to initiate adaption processes for established firms.
114
Sexton and Bowman-Upton 1991 identify changes in technology, governmental regulations, and in consumer
preferences among the principal sources of entrepreneurial opportunities (p. 92).
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CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
I. MACRO LEVEL – OPPORTUNITIES IN GLOBAL & NATIONAL
ENVIRONMENT
VARIABLE
OPPORTUNITY
RISK
MAJOR
INFLUENCED
IMPACT
BY
1. macro-economic changes
a) former economic
115
• potential supply
v
gap (G)
growth
b) economic growth
116
p
• increase in
v
demand (G)
expectations
c) propensity to
• increase in
117
demand (G)
consumption
d) interest rates118
p
- interest rates
v
p
• increase of
v
investment
p
costs
particularly for
new ventures
in capital
intensive
industries (P)
e) devaluation of
119
home currency
• increasing exports • increasing
of final products
costs if
(G)
reliance on
v
p
imported
resources
(G,P)
115
CIA world fact book: GDP real growth rate http://www.cia.gov/cia/publications/factbook/index.html.
World Economic Forum (Global Competitiveness Report): Growth competitiveness and microeconomic
competitiveness index ranking http://www.weforum.org/ and the deducted indicator of the general national
framework in the GEM report.
117
The World Bank (World Development Indicators): Final consumption expenditure and household final
consumption expenditure http://www.worldbank.org/data/.
118
The World Bank (World Development Indicators): Deposit interest rate, lending interest rate, real interest rate
http://www.worldbank.org/data/.
119
Oanda corporation: International historic currency tables http://www.oanda.com.
116
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
93
2. political / legislative changes
a) environmental
• established firms
• increasing
protection
are forced to
production
regulations120
changes in
costs (P)
v
p
ST
v
equipment and
processes and may
p
have less advantage
LT
in efficiency (P)
• inertia of
b) market
121
regulations
• restrictions
v
established firms to in venture
adapt to new
development
regulations (P)
(G)
p
• decreasing
c) labour market
122
v
flexibility in
regulations
p
personnel
planning (G,P)
d) public spending123
• increasing
v
demand (G)
e) international trade • increasing export
124
deregulation
potentials (G)
p
• increasing
v
foreign
p
competition
(G)
3. technological changes
a) impact of internet
125
usage
• access to new
• risk of
distribution
subsequent
channel and inertia
entry of new
of established firms competitors in
to adapt in the short the long run
run (P,S)
120
(P,S)
v
p
ST
v
p
LT
The World Bank (World Development Indicators): CO2 emissions per capita in relation to state of economic
development http://www.worldbank.org/data/.
121
World Economic Forum (Global Competitiveness Report): Openness rating http://www.weforum.org/ and the
deducted indicator of openness in the GEM report.
122
World Economic Forum (Global Competitiveness Report): Labour market flexibility rating
http://www.weforum.org/ and the deducted indicator of labour market flexibility in the GEM report.
123
The World Bank (World Development Indicators): General government final consumption expenditure
http://www.worldbank.org/data/.
124
The World Bank (World Development Indicators): Taxes on international trade, export duties, import duties
http://www.worldbank.org/data/.
125
GEM internet user per capita.
94
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
b) impact of internal
• inertia of
• increasing
IT applications
established firms to investment
implement new
requirements
applications (P)
(P,S)
c) impact of new
• inertia of
• increasing
production
established firms to investment
technologies
implement new
requirements
production
(P,S)
v
p
v
p
technology (P)
4. socio-cultural changes
a) volatility of
• inertia of
• longer term
consumption
established firms to risk of venture
preferences
adapt to changes of sustainability
demand in the short and
run (P)
v
p
ST
v
investment
p
LT
risk (S)
b) individualisation
• venture
v
opportunities in
p
niche markets (S)
5. demographic changes
a) population
126
growth
• increasing
v
potential demand
p
(G)
b) population age127
• increasing
demand for older
• decreasing
demand for
consumer market
younger
(G)
consumer
- population
v
growth
p
market (G)
success indicators: G=growth, P=profitability, S=survival
figure: v=variable, p=performance, ST=short-term, LT=long-term
Table 19: Model of market attractiveness: Macro level
126
127
US Census IDB: Population growth, growth expectation, age for over 227 countries in May 2003
http://blue.census.gov/ipc/www/idbnew.html.
US Census IDB: Age pyramid, age pyramid expectation for over 227 countries in May 2003
http://blue.census.gov/ipc/www/idbnew.html.
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
95
Most of the mentioned variables of change are relevant for both new ventures and
established firms. Comparative statistical data on basically all variables is available
without any costs from public data sources for a wide range of countries.
3.4.4 Inter-country level: National competitiveness
On this level, factors related to the competitiveness of industries are compared
between one country and another country or a group of other countries that provide
favourable conditions for firms in the respective industry. The following research
question is addressed on the inter-country level of analysis:
INTER-COUNTRY LEVEL:
• What is the level of competitiveness for a company of the targeted industry in the
targeted country compared with other countries in the global economy?
The competitiveness of a country is analysed with respect to the five main sources of
national competitiveness from the perspective of the respective industry. Depending
on the typical geographic extension of the sales market of firms within the industry,
the importance of the analysis of the national competitiveness may vary. Industries
with firms typically operating on a local or regional level within a short distance to
their clients will be less affected by the national competitiveness than industries where
firms typically compete within a global market place.
NATIONAL COMPETITIVENESS
INTER-COUNTRY
LEVEL
production
factors
stability & sales market
industry
new venture
infrastructure infrastructure
taxation
Figure 15: Inter-country level - National competitiveness
Understanding national competitiveness does not only provide useful information for
the decision on where to locate a company, but will also be decisive for make or buy
decisions and decisions of strategic positioning in relation to foreign competitors. A
wide range of variables that could affect national competitiveness is given in the
following table.
96
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
II. INTER COUNTRY LEVEL – NATIONAL COMPETITIVENESS
VARIABLE
OPPORTUNITY
RISK
MAJOR
INFLUENCED
IMPACT
BY
1. production factors
a) availability of
128
qualified labour
• capacity for
v
startup and growth
p
(G,S)
• relative cost
b) costs of qualified
129
disadvantage
labour
v
p
(P)
c) availability of
• capacity for
resources
startup and growth
v
p
(G,S)
• relative cost
d) costs of
130
v
disadvantage
resources
+ tax burden
p
(P)
- competitiveness
of supplying
industries
2. political stability & taxation
a) tax burden131
• relative cost
v
disadvantage
p
and decreasing
shareholder
returns (P)
b) stability of
• decreasing risk of
economical political
investment (S)
environment
128
v
p
CIA world fact book: Unemployment rate and labour force; GME: Management education (item D.5. in 1999
questionnaire).
129
International Labour Office Bureau of Statistics: International wages and labour costs. http://laborsta.ilo.org.
130
The World Bank (World Development Indicators): Consumer price index http://www.worldbank.org/data/.
131
GME - politics: regulation and taxation (item B.4 in 1999 questionnaire); The World Bank (World
Development Indicators): Taxes on goods and services, net taxes on products, taxes on income, profits and
capital gains http://www.worldbank.org/data/.
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
97
3. sales market
a) distance to main
• relative cost
customer markets for
disadvantage
export industries
and difficulty
v
p
in maintaining
customer
relationship
(P)
b) size of national
132
• growth potential
v
(G)
market
p
4. industry infrastructure
a) development of
• capacity for
transportation and
startup and growth
telecommunication
(G,S)
v
p
infrastructure133
b) existence of
• capacity for
dependent supplying
startup and growth
and buying
(G,S)
v
p
industries134
c) competitiveness of
• relative cost
dependent supplying
advantage (P)
v
and buying
p
135
industries
5. new venture infrastructure
a) availability of debt
136
funding
• decreasing
• risk of entry
barriers to entry
of subsequent
and growth and
competitors
decreasing costs of
(P)
financing (G,P,S)
132
v
p
ST
v
p
LT
CIA world fact book: GDP http://www.cia.gov/cia/publications/factbook/index.html; The World Bank
(World Development Indicators): International gross net income per capita http://www.worldbank.org/data/.
133
CIA world fact book: Telephone lines per capita, telephones mobile cellular per capita, internet service
providers, length of railways tracks, length of highways, airports, internet users.
GEM: Physical infrastructure, costs of communication, accessibility of communication (items H.1, H.2, and
H.3 in 1999 questionnaire).
134
GEM: Availability of high-quality subcontractors, suppliers, and consultants (items F.1 and F.3 in 1999
questionnaire).
135
GEM: Costs for subcontractor, suppliers and consultants (item F.2. in 1999 questionnaire).
136
GEM: Availability of debt funding (item A.2. in 1999 questionnaire).
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CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
b) availability of
• decreasing
• risk of entry
equity funding (VC,
barriers to entry
of subsequent
business
and growth (G,S)
competitors
(P)
angels/private
v
p
v
investors)137
p
c) financial assistance • decreasing
• risk of entry
138
barriers to entry
of subsequent
and decreasing
competitors
costs of financing
(P)
for new ventures
p
assistance for new
barriers to entry (S) of subsequent
ventures139
• risk of entry
p
• high
entrepreneurial
entrepreneurial
activity per
activity
country140 (increasing
increases risk
level of competition)
of imitation
=> General
and entry of
international
subsequent
competitiveness level
competitors
(CIA world fact book
(P)
ST
v
p
=> Total
LT
v
competitors
(P)
ST
v
p
• decreasing
LT
v
(S)
d) non-financial
ST
LT
v
p
imports, imports
commodities, exports,
export commodities)
success indicators: G=growth, P=profitability, S=survival
figure: v=variable, p=performance, ST=short-term, LT=long-term
Table 20: Model of market attractiveness: Inter-country level
The variables of the last dimension of new venture infrastructure relate directly to
barriers to entry. A very supportive venture infrastructure will decrease barriers to
137
GEM: Availability of equity funding (item A.1 in 1999 questionnaire); GEM: Business angel prevalence
index (item A.4 in 1999 questionnaire), VC supply (item A.5. in 1999 questionnaire).
138
GEM: Public subsidies for new ventures (item A.3 in 1999 questionnaire).
139
GEM: Public spending in favour of new firms (item B.1 in 1999 questionnaire), GEM: Governmental policy
emphasis on entrepreneurship (item B.2 in 1999 questionnaire), GEM: Incubator organisations (item C.2 in
1999 questionnaire).
140
GEM: Entrepreneurial activity per country; GEM: National culture - entrepreneurial orientation.
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
99
entry. This will be considered positive for all those firms that benefit from the venture
infrastructure and might otherwise not have been capable to enter the market. On the
other hand, established firms or ventures that do not benefit from the offered venture
infrastructure are necessarily faced with more intense competition because of this
easier accessibility. Therefore, data on the industry, but also on the individual firm is
necessary in order to perform a complete analysis of national competitiveness. Most
variables at the inter-country level can be analysed without costs based on publicly
available national and international statistics.
3.4.5 Inter-industry level: Opportunities & threats from related industries
On the inter-industry level, the perspective of analysis is lowered to the industry level.
Starting from the industry under investigation, other industries are identified that could
pose future threats or opportunities for firms within the investigated industry. The
research question on the inter-industry level of analysis is as follows:
INTER-INDUSTRY LEVEL:
• How can the current and expected activities in other related industries affect
opportunities and threats for the targeted industry?
The potential sources of threats and opportunities outside the investigated industry
coincide to a large extent with the major determinants of Porter’s five forces model of
industry analysis. The four dimensions threat of substitution, forward integration
(=bargaining power of suppliers), backward integration (=bargaining power of clients)
and threat of other new entries are identical to the forces in Porter’s model. However,
other industries may not only pose potential threats, they might also provide
opportunities. Opportunities can be reaped by identifying potentially attractive
cooperations and alliances with firms in other industries. These firms may offer
products or services that are complementary to the products of the investigated
industry.
INTER-INDUSTRY
LEVEL
OPPORTUNITIES & THREATS FROM RELATED INDUSTRIES
complementary threat of
forward
cooperation substitution integration
backward threat of other
new entries
integration
Figure 16: Inter-industry level – Opportunities & threats from related industries
100
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
A specific industry may gain attractiveness if firms within this industry have not yet
taken advantage of complementary cooperations with related industries. On the other
hand, an industry may lose attractiveness if threats of an increased intensity of
competition are eminent from related industries. The following table specifies the
dimensions that could lead to threats or opportunities from related industries.
III. INTER INDUSTRY LEVEL – OPPORTUNITIES & THREATS FROM
RELATED INDUSTRIES
VARIABLE
OPPORTUNITY
1. opportunity of
• marketing and
complimentary cooperation
sales potential (G)
2. threat of substitution
RISK
MAJOR
INFLUENCED
IMPACT
BY
v
• risk of
investments
p
v
barriers to entry
p
and higher
- all variables of
intensity of
+ former market
growth
competition
(P,S)
3. threat of forward
• higher
integration
intensity of
- all variables of
v
p
competition
barriers to entry
+ former market
(P)
growth
4. threat of backward
• higher
- all variables of
integration
intensity of
barriers to entry
competition
+ former market
v
(P)
p
growth, buyer
concentration,
average order
volume
5. threat of other new entries
• higher
intensity of
competition
(P)
success indicators: G=growth, P=profitability, S=survival
figure: v=variable, p=performance, ST=short-term, LT=long-term
Table 21: Model of market attractiveness: Inter-industry level
- all variables of
v
p
barriers to entry
+ former market
growth
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
101
The identification of potential threats and, in particular, opportunities is a creative
entrepreneurial process guided by the five main dimensions. The analysis on the interindustry level is facilitated by a broad knowledge of other industries.
3.4.6 Intra-industry level: Market, dependencies & competitors
At this level, the analysis centres on the investigated industry itself as unit of analysis.
INTRA-INDUSTRY LEVEL:
• Are the structure and dynamics of the market and competitors favourable in terms of
profit and growth expectations?
• Does the industry pose risks associated with specific dependencies?
• Which are the industry variables that are of critical importance within the industry?
The analysis on the intra-industry level is divided into the market on the demand side
and the competition on the supply side of the industry. For both market and
competitors the current structure as well as recent developments have to be analysed.
As a fifth dimension, dependencies are analysed on the intra-industry level. An
awareness of the dependencies that industry members are confronted with provides
important insights into the negotiation power of industry members within the value
chain, and may reveal potential risks affecting the long-term sustainability of
entrepreneurial opportunities within an industry.
INTRA-INDUSTRY
LEVEL
MARKET, DEPENDENCIES & COMPETITORS
market
structure
market dependencies
dynamics
competitor
dynamics
competitor
structure
Figure 17: Intra-industry level – Market, dependencies & competitors
The following table comprises, with about 50 individual variables, the central part of
the analysis of market attractiveness. Most of these variables have been discussed in
the literature review and have been applied in the approaches presented previously
towards a classification of the market environment (chapter 3.3) at the intra-industry
level. Also, the quantitative empirical study to follow will concentrate on the intraindustry level.
102
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
IV. INTRA-INDUSTRY LEVEL – MARKET, DEPENDENCIES &
COMPETITION
VARIABLE
OPPORTUNITY
RISK
MAJOR
INFLUENCED
IMPACT
BY
1. market structure
a) market size
• growth potential
+ number of
v
(G)
p
competitors
(legitimacy),
former market
growth
b) heterogeneity of
• tendency to
• production
products
higher margins in
and
market (P)
transaction
+ need for
v
p
specialised
products
costs (P)
c) branding potential
• potential for
+ heterogeneity of
v
higher margins (P)
products
p
d) distance to clients
• extension of
(geographic extension potential client
of sales market)
base (G)
• higher
market
v
transparency
p
(P)
e) economies of scale
• threat of
• cost
- variable costs
v
subsequent entry of disadvantage
competitors in the
or need for
long run (P)
high
p
ST
v
investments
p
for the new
LT
venture (P,S)
f) export-import
• export potential
balance
(G)
g) need for
• tendency to
specialised products
higher margins and
(niche potential)
potential for new
venture to start by
competing on a
limited arena (P,S)
+ national
v
competitiveness
p
v
p
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
h) buyer
• easier
• high buyer
concentration
accessibility of
purchasing
sales market
power and
103
v
p
particularly for new lower margins
ventures and
(P)
tendency to lower
transaction costs
(G,P,S)
i) average order
• decreasing
volume
transaction costs
+ buyer
v
concentration
p
(P)
j) seasonal change of
• tendency to
demand
lower margin
v
due to low
p
capacity
utilization in
low season (P)
k) intensity of price
• tendency to
bargaining from
lower margin
clients
particularly in
+ average order
volume,
v
p
case of low
dependency on
clients
price elasticity
(P)
l) market
• tendency to
transparency
lower margins
- number of
v
p
due to price
sensitivity in
competitors,
heterogeneity of
products
transparent
markets (P)
m) advertising
• indication for
• high capital
intensity
high-margins and
requirements
potential costs
and high risk
advantages for less
of investment
advertising
(G,P,S)
intensive strategies
(P)
+ branding
potential
v
p
- heterogeneity of
products
104
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
n) R&D intensity
• indication for
• high capital
high margins and
requirement
potential costs
and high risk
advantage for less
of investment
R&D intensive
(G,P,S)
+ number of new
products
v
p
introduced
strategies (P)
2. market dynamics
a) former market
• potential supply
growth
gap (G)
b) rel. expected
• growth potential
p
of changes on the
v
of the life cycle)
p
• induce market
growth (G)
• threat of
increasing
+ sales growth
p
(P)
• indication of
macro level
v
competition
d) exits from industry
economic growth
+ industry impact
market growth (stage (G,S)
c) entries to industry
+ overall
v
p
(P,S)
barriers to entry
+ industry impact
v
low margins
- all variables of
of changes on the
macro level,
entries to
industry,
diversification
level in industry
- former market
growth,
dependency on
maintenance on
industry
e) balance of entries
• growth and
and exits
survival potential
+ sales growth
v
p
(G,S)
barriers to entry
f) degree of
• higher initial
• higher risk of
uncertainty about
venture growth
investment
future development
potential as
(P,S)
established firms
have to adapt and
can rely less on
former
experience(G)
- all variables of
+ changing
customer needs
v
p
+ market growth
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
g) customer loyalty
• stability of
• difficulty to
customer base and
establish
lower price
initial
elasticity in the
customer base
long run (P)
for new
105
+ intensity of
v
p
relationship to
ST clients
v
p
venture in the
LT
short run (G,S)
h) changing customer • higher initial
needs
• higher risk of
venture growth
investment
potential as
(P,S)
+ economic,
technological,
v
p
established firms
socio-cultural and
demographic
have to adapt and
changes on the
can rely less on
macro level
former
experience(G)
3. dependencies
a) on clients
• instability of
+ buyer
demand and
concentration
risk of high
- competitiveness
v
bargaining
p
power of
buying industries
clients (P,S)
b) on suppliers
• instability of
supply and
of dependent
- competitiveness
v
of dependent
p
low bargaining
power of firm
supplying
industries
(P,S)
c) on key employees,
• instability
employee institutions,
and limitation
and key knowledge
of growth
+ R&D intensity
v
p
(G,S)
d) on legislation
• risk of
v
investment (S)
e) on business cycle
• low capacity
utilization
during
recessions
(P,S)
+ regulations on
p
the macro level
+ labour market
v
regulations
p
106
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
f) on maintenance in
• irreversibility
industry (barriers to
of investment
exit)
decisions lead
+ degree of
diversification,
v
p
to higher risk
of investment
barriers to entry
related to capital
intensity
and higher
competition
particularly in
decreasing
markets (P,S)
• bargaining
g) relative
power (P)
dependency
+ dependency on
v
compared to earlier
clients,
p
dependency on
suppliers
and later value chain
members
4. competitor dynamics
a) inertia of
established firms due
• opportunity for
+ size of
v
firms with more
to organisational
flexible
structure
organisational
competitors
p
structure (P)
b) inertia of
• opportunity for
established firms due
smaller firms
to legislative
which may be less
restrictions
affected by
v
+ regulations on
p
the macro level
legislative
restrictions (P)
c) inertia of
• opportunity for
- economies of
established firms due
firms with more
to cost structure
flexible cost
productivity level
structure (P)
+ technological
v
p
scale, employee
changes, labour
costs intensity
d) aggressive
• risk of price
responsiveness of
war and abuse
established firms
of market
power (P,S)
- level of capacity
utilisation, market
v
p
growth, variable
costs
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
e) inflation rate in
• potential for high
• increasing
selling prices
margin products
intensity of
(P)
+ aggressive
v
responsiveness of
p
competition
107
(P)
established firms,
number of new
products
introduced
f) number of new
• costs for
products introduced
product
+ aggressive
v
responsiveness of
p
development
established firms,
and instability
changing
of demand
customer needs
(P,S)
g) investment in new
• indication for
• high capital
assets
frequent change in
requirements
production
and high
technology which
productivity
leads to lower
level (P,S)
+ technological
v
change on the
p
macro level
advantages of
established firms
(P)
5. competitor structure
a) concentration
• niche potential in
• indication for
industries with very high economy
large competitors
of scale, with
of low dynamics
difficulty to
(P,S)
survive for
small firms
and
particularly
new ventures
(S)
+ economies of
v
scale, barriers to
p
entry
108
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
b) number of
• pressure on
industry members
margin by
- barriers to entry,
number of entries
v
high
p
competition
+ market size,
number of exits
(P), however
in very new
niche markets
increasing
number of
firms increases
legitimacy
c) heterogeneity of
industry members
• indication for
- general
v
higher margins as
(variance of
also suboptimum
profitability, size,
firm survive (P,S)
efficiency level
p
among industry
members
strategies, age etc.)
d) general efficiency
• high
level among industry
competitive
members
pressure and
- gross margins
v
+ competitor
p
lower margins
dynamics
(P,S)
e) prevalence of
• higher margins
+ heterogeneity of
competitive strategies and diversity of
on other than price
products,
v
market entries for
branding
p
new venture (P,S)
potential, level of
capacity
utilisation
f) capacity utilisation
level
• low price pressure
- seasonal change
v
(P)
of demand,
p
dependency on
business cycle
+ market growth
g) employee
• low pressure of
productivity level
personnel costs on
+ investments in
new assets
v
margin (P) and
facilitation of
p
growth
management (G)
h) labour costs
• high pressure
intensity
on margins (P)
+ costs of
v
p
qualified labour in
country
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
i) gross margin
• higher
(added value)
profitability (P)
109
- dynamics of
v
p
competitors
+ barriers to entry
• vertically
j) vertical integration
integrated
+ dependency on
v
clients,
p
competitors
may be more
dependency on
suppliers
flexible and
more efficient,
in particular
new ventures
will start with
low vertical
integration (P)
k) degree of
• decreasing
diversification
barriers to exit (P)
l) average age of
• older firms may
industry members
lose responsiveness may have
- market size
v
p
and dynamism (P)
• older firms
- firm entries,
aggressive
v
established
p
more powerful
responsiveness
competitive
position (P)
m) average size of
• larger firms may
industry members
lose responsiveness may have a
• larger firms
and dynamism
more powerful
especially in
competitive
respect to niche
position (P)
+ economies of
v
p
scale
markets (P)
n) variable costs
• decrease
propensity to price
war and lead to
higher margins (P)
+ fixed asset
intensity
v
p
success indicators: G=growth, P=profitability, S=survival
figure: v=variable, p=performance, ST=short-term, LT=long-term
Table 22: Model of market attractiveness: Intra-industry level
Depending on the context, the given scheme of intra-industry analysis can be either
applied with regard to the whole overall industry or with regard to a specific market as
part of the industry. For an analysis on the overall industry level, data can be obtained
on a number of variables from publicly available statistics. Within the quantitative
110
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
empirical part it is specified in detail for which intra-industry variables data has been
available from public statistical sources in Germany. Additional industry-specific data
may be obtained from publications in the press and industry magazines, as well as
from industry associations.
MARKET & DEPENDENCIES & COMPETITORS
INTRA-INDUSTRY
market
structure
LEVEL
market dependencies
dynamics
competitor
dynamics
competitor
structure
BARRIERS TO ENTRY
Figure 18: Intra-industry level including barriers to entry
As mentioned before in the discussion of the overall levels of analysis, barriers to
entry generally comprise variables that belong to the market structure dimension of the
intra-industry level. However, they have been separated from the intra-industry level,
since it is not possible to evaluate whether a barrier to entry has a positive or negative
impact without information on the resources of the individual firm.
Further details on the variables of barriers to entry are specified in the table below.
BARRIERS TO ENTRY
VARIABLE
OPPORTUNITY
a) inventory intensity • barrier to
RISK
• high capital
MAJOR
INFLUENCED
IMPACT
BY
+ heterogeneity of
v
subsequent entry in requirement
the long run (P)
p
and high risk
of investment
products
ST
v
in the short-
p
run for new
LT
venture (S)
b) fixed asset and
• barrier to
• high capital
other capital
subsequent entry
requirement
requirements
already in the short and high risk
run for ventures
of investment
that can raise
for new
minimum required
venture (S)
capital (P)
+ technological
changes,
v
economies of
p
scale
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
c) minimum
• barrier to
• high
organisational size &
subsequent entry
requirement of
complexity
already in the short
run for ventures
that fulfil minimum
111
+ R&D intensity,
advertising
v
organisational
p
intensity
skills (S)
size requirement
(P)
d) accessibility of
• barrier to
distribution channels
subsequent entry in reach market
the long run (P)
• capacity to
p
in the shortrun for new
e) loyalty of
• barrier to
• difficulty to
customers
subsequent entry in establish
p
LT
+ intensity of
v
p
initial
customer base
concentration
ST
v
venture (S)
the long run (P)
- buyer
v
relationship to
ST
clients
v
for new
p
venture in the
LT
short run (S)
f) legislative barriers
• barrier to
• capacity to
subsequent entry in fulfil legal
the long run (P)
p
requirements
for new
g) product
• barrier to
• capacity to
sophistication
subsequent entry in fulfil
p
for new
venture (S)
LT
+ R&D intensity
v
p
knowledge
requirements
the macro level
ST
v
venture (S)
the long run (P)
+ regulations on
v
ST
v
p
LT
success indicators: G=growth, P=profitability, S=survival
figure: v=variable, p=performance, ST=short-term, LT=long-term
Table 23: Model of market attractiveness: Barriers to entry
The very nature of barriers to entry implies contrasting consequences on market
attractiveness for different groups of firms. For established firms and new ventures
that possess the resources and capacity to overcome the barriers to entry, the existence
of barriers to entry will increase attractiveness of a market since impeding the entry of
other firms entry may lead to a lower level of competition. For new ventures without
112
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
the necessary resources and capabilities, the existence of barriers may force the
venture to operate on a sub-efficient level and will increase the risk of failure. It is
therefore essential for a new venture to compare the state of their resources with the
requirements of the industry in order to avoid failures.
3.4.7 Venture / firm level: Relative positioning to competition
Finally, on the firm level the unit of analysis centres on the individual firm, its
strengths and weaknesses compared with the most relevant competitor firms. The
questions that are addressed on this level are:
VENTURE / FIRM LEVEL:
• How are the product, firm, management team, strategy and location positioned
against those of the strongest competitor or group of main competitors?
• What is the potentially achievable market share of the new venture?
The analysis on the firm level starts with an evaluation of the actual products and
services offered. In order to evaluate the future potential of the firm within the
competitive setting, firm resources, management team and strategy must also be taken
into consideration as they are crucial to further development. As a final dimension,
location is analysed for the aspects of production costs and accessibility to the client
market.
VENTURE / FIRM
LEVEL
RELATIVE POSITIONING TO COMPETITION
products/
services
firm
resources
management
team
strategy
location
Figure 19: Venture / firm level - Relative positioning to competition
In order to perform an analysis on the firm level, as the first step the most relevant
competitors have to be identified. Usually one may choose competitors with the
highest market shares in the relevant market and with the most prosperous growth
dynamics. If there is no one dominant competitor in the market, it seems advisable to
perform the analysis taking into account up to three of the most relevant competitors.
The following table summarises the evaluation criteria on the firm level:
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
113
V. VENTURE / FIRM LEVEL – RELATIVE POSITIONING TO COMPETITION
VARIABLE
OPPORTUNITY
RISK
MAJOR
INFLUENCED
IMPACT
BY
1. products / services
• higher
a) relative price
relative price
- strategic growth
v
aspirations
p
generally
impedes
market
penetration , in
particular in
markets of low
elasticity of
price (G)
b) relative quality
• higher relative
v
quality facilitates
p
market penetration
and higher margins
in particular in
markets of high
elasticity of price
(G,P)
c) relative design
• better relative
design facilitates
v
market penetration
p
and higher margins
in particular in
markets of high
aesthetic impact
(G,P)
d) innovation
• increasing
potential of product
potential to
stimulate market or
create new market
(G,P)
v
p
114
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
2. firm resources
a) relative financial
• capacity to
strengths
manage capital
requirements and to
+ availability of
debt and equity
v
funding on the
p
threaten
national level
competitors from
- barriers to entry
aggressive
related to capital
response to entry
requirements
(P,S)
b) rel. knowledge /
patents
• capacity to satisfy
demand (G,P,S)
+ relevant
v
industry
p
experience of
management
3. management team
a) rel. capability of
• capacity to build
management team
competitive
v
p
organisation
(G,P,S)
b) relationships key
partners
• integration and
legitimacy of firm
+ relevant
v
particularly in areas
industry
p
experience
where relationships
are difficult to
establish (S)
c) relevant industry
• capacity to know
experience
client requirements
v
p
and adapt firm to
requirements (S)
4. strategy
a) rel. distinction in
• avoiding direct
strategic key
confrontation with
dimensions
competitors (P)
b) relative growth
• earlier
aspirations/aggressive achievement of
ness
critical size (G)
+ gross margin
v
p
+ economies of
v
scale
p
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
115
5. location (regional)
a) rel. distance to
• relative cost
clients
disadvantage
+ distance to
clients in export
v
and difficulty
p
in maintaining
industries
customer
relationship
(P,S)
b) availability of
• capacity for
labour and
startup and growth
production factors
(G,S)
+ availability of
labour and
v
p
production factors
on the national
level
• relative cost
c) rel. costs of labour
and production
disadvantage
factors
(P)
+ costs of labour
v
and production
p
factors on the
national level
=> Potentially reachable market share • competitive
position and cost
advantage (P)
v
p
success indicators: G=growth, P=profitability, S=survival
figure: v=variable, p=performance, ST=short-term, LT=long-term
Table 24: Model of market attractiveness: Venture / firm level
One of the most critical and most difficult to predict variables within each business
plan of a new venture is the specification of a potentially achievable market share.
With the background information of the higher level analysis and the results of the
firm level analysis, a sustained estimation of an achievable market share can be given
by the end of the firm level analysis.
3.4.8 Summary
The developed model distinguishes itself in several characteristics from former
approaches towards structuring the market environment. Deriving from the critical
aspects that were outlined in the review of former approaches141 the particular areas of
differentiation will be pointed out.
141
See chapter 3.3, p.66ff.
116
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
CONTENT OF THE MODEL:
- Comprehensiveness
106 variables in 25 dimensions and 5 levels plus 1
sublevel.
- New venture context
Consideration of environmental dynamics on different
levels and barriers to entry which are of particular
importance in the new venture context.
Inter-temporal
specification
of
impacts
ensures
applicability of model for both new ventures and
established firms.
- Relationships /
Major relations among variables are specified by the
interactions
hierarchical structure of environmental levels of analysis
and the classification according to five main dimensions.
Interactions with other variables are specified as well.
- Direction and detail of
For each variable the major impact on venture / firm
impact
performance is specified. Details are given on how each
variable may impact the opportunity / risk structure.
Also the possibility of opposing directions of impact is
considered.
- Applicability in practice Data sources for most variables on the macro and intercountry level shall facilitate the application of the model
in practice.
- Performance measure
Distinction among impact on performance measures
specification
growth, profitability and firm survival.
STRUCTURE OF THE MODEL:
- Levels of aggregation
5 different aggregation levels of environment under
separate analysis.
Table 25: Summary of the unique characteristics of the developed model of market attractiveness
In terms of comprehensiveness, the developed model considers with a total of more
than 100 variables, a larger number of relevant market variables than the other
previously discussed approaches. The objective was to include a wide range of
potentially relevant variables, since it is assumed that different variables are important
in different industry settings. The multitude of variables also ensures the applicability
of the model in a wide range of contexts, minimising the risk of missing important
market variables for certain contextual settings.
In the context of market analysis from the new venture perspective, the variables
barriers to entry and environmental dynamics have been considered of particular
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
117
importance. On different levels of analysis, dynamics both internal and external to the
industry of investigation are considered. The specific characteristics and importance of
barriers to entry are reflected in their prominent placement as a specific sublevel
between the intra-industry and firm level. In contrast to the other models, the
developed model distinguishes between short- and long-term effects, which ensures
the applicability of the model for both new ventures and established firms, and takes
into consideration opposing directions of impact in the different contexts.
While most previous models were limited in the extent of their explanation of
relationships and interactions among the variables of the model, one of the main
objectives of this model was to go beyond a pure listing of relevant variables.
Relationships and interactions have been pointed out using the following measures:
First, the basic structure of the model divides the total number of variables into five
main levels and one sublevel of variables that belong to a similar perspective of
environmental analysis. These environmental levels of analysis are ordered
hierarchically.
Second, for each environmental level of analysis, variables are related to five main
dimensions of impact. Therefore, the actual market variables are located on the third
hierarchical level, grouped with related variables and situated in an overall framework
that specifies its impact in relation to the other variables of the framework.
Third, interactions among individual variables of the model are specified. Each
variable of the model has been evaluated to detect if it is influenced by another
variable on the same or a higher level of environmental analysis. In those cases where
such an influence has been assumed, the respective impacting variable and the
direction of impact have been specified.
Former models frequently only specify the direction of impact of a variable on
organisational success. Within the developed model, not only the direction of impact
has been specified, but also a short explanation on how this variable influences risks
and opportunities of an organisation has been given. It is recognized that variables can,
on some occasions, not only affect organisational success in one direction, but may
actually affect organisational success in opposing directions. Depending on the
context, the variable may impact organisational success positively or negatively. In
118
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
contrast to all other models, such opposing directions of impact are also considered in
the model.
Special attention has been given to the applicability of the model in order to perform a
market analysis in practice. Where relevant public data sources could be identified, a
reference to the data source has been given together with the associated variable of the
model. For the macro and inter-country level in particular, references for public data
sources have frequently been identified.
Some market variables may have a varying impact on different measures of
organisational success. The impact on growth and profitability may frequently go in
opposite directions. Therefore the model specifies whether the success measure of
growth, profitability or survival will be affected. Within the other models either only
one measure of organisational success has been applied or a specification of the
measure of organisational success has been completely absent.
Finally, with regard to the structure of the model, five aggregation levels of analysis
have been clearly distinguished. Other models have also made distinctions among
analyses on the global, industry and firm level. In the developed model, the
consideration of national competitiveness and the impact of related industries broaden
the comprehensiveness of the phenomenon of market attractiveness investigated.
CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
119
MACRO LEVEL
- OPPORTUNITIES
& NATIONALsocio-cultural
ENVIRONMENT - demographic
macro-economic
legislative IN GLOBAL
technological
changes
changes
changes
changes
changes
• former economic
growth
• economic growth
expectations
• propensity to
consumption
• interest rates
• devaluation of home
currency
• environmental
protection regulations
• market regulations
• labour market
regulations
• public spending
• international trade
deregulation
• impact of internet
usage
• impact of internal IT
applications
• impact of new
production
technologies
• volatility of
consumption
preferences
• individualisation
• population growth
• population age
INTER-COUNTRY LEVEL
distance
to sales
production factors stability & taxation
- NATIONAL
COMPETIVITY
- industry
infrastructure
market
• availability of qualified • tax burden
• distance to main
• development of
labour
customer s for export
transportation and
• stability of economical
industries
telecommunication
• costs of qualified
political environment
infrastructure
• size of national market
labour
• existence of dependent
• availability of
supplying and buying
resources
industries
• costs of resources
• competitiveness of
dependent supplying
and buying industries
new venture
infrastructure
• availability of debt
funding
• availability of equity
funding
• financial assistance for
new ventures
• non-financial
assistance for new
ventures
INTER-INDUSTRY LEVEL
complementary
threat of & THREADS
backward
threat
of other new
forward integration
- OPPORTUNITIES
FROM RELATED
INDUSTRIES
cooperation
integration
substitution
entries
INTRA-INDUSTRY LEVEL
market
structure
• market size
• heterogeneity of
products
• branding potential
• distance to clients
• economies of scale
• export-import balance
• need for specialised
products
• buyer concentration
• average order volume
• seasonal change of
demand
• intensity of price
bargaining
• market transparency
• advertising intensity
• R&D intensity
competitor market
dynamics
dependencies& COMPETITORS
- MARKET
& DEPENDENCIES
dynamics
• former market growth
• rel. expected market
growth
• entries to industry
• exits from industry
• balance of entries and
exits
• degree of uncertainty
about future
development
• customer loyalty
• changing customer
needs
• on clients
• on suppliers
• on key employees,
employee institutions,
and key knowledge
• on legislation
• on business cycle
• on maintenance in
industry (barriers to
entry)
• relative dependency
compared to earlier
and later value chain
members
• inertia of established
firms due to
organisational
structure
• inertia of established
firms due to legislative
restrictions
• inertia of established
firms due to cost
structure
• aggressive
responsiveness of
established firms
• inflation rate in selling
prices
• number of new
products introduced
• investment in new
assets
competitor
structure
• concentration
• number of industry
members
• heterogeneity of
industry members
• general efficiency level
among industry
members
• prevalence of
competitive strategies
on other than price
• capacity utilisation
level
• employee productivity
• labour costs intensity
• gross margin
• vertical integration
• degree of uncertainty
• average age of industry
members
• average size of industry
members
• variable costs
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CHAPTER 3 - DEVELOPMENT OF A THEORETICAL MODEL OF MARKET ATTRACTIVENESS
BARRIERS TO ENTRY
• Inventory intensity
• Fixed asset intensity
• Minimum organisational size & complexity
• Accessibility of distribution channels
• Loyalty of customers
• Legislative barriers
• Product sophistication
VENTURE / FIRM LEVEL
products/
services
management
firm
resources POSITIONING
- RELATIVE
TO COMPETITION
strategy team
• Relative price
• Relative financial
strength
• Relative quality
• Relative knowledge
• Relative design
• Innovation potential of patents
product
• Relative capability of
management team
• Relationships key
partners
• Relevant industry
experience
location
• Relative distinction in • Relative distance to
strategic key
clients
dimensions
• Availability of labour
• Relative growth
and production factors
aspirations/aggressiven • Relative costs of labour
ess
and production factors
Figure 20: The 5 x 5 model of market attractiveness including variables
The above figure summarises the basic structure of the developed model of market
attractiveness specifying the five aggregation levels of analysis and for each level of
analysis the respective five major dimensions of impact. For each dimension the
individual variables are specified.
CHAPTER 4 - METHODOLOGY
4
121
Methodology
Starting from the problem142 stated initially and the research questions that have arisen,
seven specific objectives143 have been derived that will serve as orientation for the
design of the methodology.
The whole research design is divided into two stages144.
The first stage corresponds to the theoretic study and the literature review. It addresses
objectives one to three of the study. This first stage culminates in the proposition of a
new model of market attractiveness.
The second stage contains the empirical study. In stage 2.1, a quantitative study is
conducted in order to achieve objectives four to six. This part of the study investigates
the impact of industry variables on venture success on the basis of hypotheses that are
derived from the model proposed in the first stage. The quantitative study represents
the main part of the empirical study. In stage 2.2, the mechanisms of market effects are
investigated for several new venture cases, guided by the proposed theoretic model of
the first stage. This part comprises the qualitative study, which will allow achieving
the seventh objective of the study.
142
See chapter 1.1, p.2f.
See chapter 1.2, p.3f.
144
See figure 21, p. 122.
143
122
CHAPTER 4 - METHODOLOGY
STAGE 1: THEORETICAL STUDY
(preparation) Objective 1:
Former empirical results
Objective 2:
Theoretical perspectives on
market effects
(culmination) Objective 3:
Model of market
attractiveness
Ad objective 1:
Literature review (chapt. 2)
Ad objective 2:
Discussion of research
programmes (chapt. 3.2)
Ad objective 3:
Development of model
(chapt. 3.4)
STAGE 2: EMPIRICAL STUDY
SUBSTAGE 2.1:
Quantitative study
Objective 4:
Identify markets of highest
venture success
Objective 5:
Relationship between
industry variables and
venture success
Objective 6:
Impact of contingent
variables
Ad objective 4:
DtA sample description on
industry level (chapt. 5.4.2.2)
Ad objective 5:
Hypotheses -guided
correlation and regression
analysis (chapt. 5.5.2)
Ad objective 6:
Correlation and regression
analysis for industry
contexts (chapt. 5.5.3)
SUBSTAGE 2.2:
Qualitative study
Objective 7:
Mechanisms of impact of
industry factors on new
ventures
Ad objective 7:
Case study analysis
(chapt. 6)
Figure 21: Orientation of research design
Given this general overview, the methodology chapter will concentrate on the
empirical study. First, in chapter 4.1, alternative measures of venture success are
discussed and evaluated. In chapter 4.2, the chosen methodology of the quantitative
investigation is presented, industries and standard industry classification codes as the
unit of analysis are discussed, indicating the applied variables, the method of data
collection and the methods of data analysis. In chapter 4.3, the chosen methodology of
the qualitative case study is presented, and the variables applied, the method of data
collection and the method of data analysis are indicated. Finally, in chapter 4.4,
general considerations of validity and reliability are discussed.
CHAPTER 4 - METHODOLOGY
4.1
123
Measurement of venture success on the industry level
The question of appropriate measures of organisational success has received
considerable attention over the last decade145. The use of appropriate success measures
is particularly critical in the context of new ventures. A lack of appropriate success
measures has been attributed to frequently conflicting results in entrepreneurship
research146. The inherent reasons for the difficulty in assessing objective financial
success indicators for new ventures are the following147:
o Lack of availability of data. Since the majority of new ventures are privately
held and do not adhere to regulations of publicly announcing their financial
performance, the availability of this data is highly limited. Data on business
success is considered highly sensitive and most business owners are unwilling
to share this information voluntarily.
o Inappropriateness of some traditional success measures. Due to a small
starting base, the growth rates of new ventures are often erratic, producing
extreme outliers that pose difficulties for further statistical analysis. Moreover,
common profitability measures such as ROE, ROI or ROA can be highly
distorted by relatively high levels of investment, typically found in fast-growing
ventures.
o Different dimensions of success. Different success indicators might capture
very different dimensions of success, which might often exhibit no direct
correlation. As indicated by Venkatraman and Ramanujam (1986), variables
correlated with firm performance change as the definition of success changes.
o Industry-dependence. Absolute performance scores are found to be highly
affected by industry-related factors148. Gross margin, for example, might be a
good indicator of profitability of firms within one industry, however, it will
hardly lead to any meaningful results when comparing firms from different
industries.
145
See Brush and Vanderwerf 1992, Chandler and Hanks 1993, Robinson and McDougall 1998, Robinson 1998
in the field of entrepreneurship research and also Venkatraman and Ramanujam 1986 in the field of strategy
research.
146
Robinson and McDougall 1998.
147
Compare Chandler and Hanks 1993.
148
Miller and Toulouse 1986.
124
CHAPTER 4 - METHODOLOGY
In order to determine the most appropriate success measures, different categories of
potential measures are discussed with special attention to their applicability in the
context of new ventures and cross-industry analysis.
Measures of venture performance can be broadly classified according to the following
structure:
new venture
success measures
objective
growth
volume
profitability
- cash flow
change
- market share
change
- sales growth
- employee
growth
- sales
- employees
- net worth
- market share
- survival /
failure
- cash flow
- earnings
(EBIT, net
profit)
- ROI, ROE,
ROA, ROIC
- ROS, gross
margin
- time to break
even
subjective
performance
(not relative to
competitors)
performance
relative to
competitors
Figure 22: Alternative measures of new venture success
Subjective new venture success measures are highly influenced by the individual
expectations and targets of the founder. Moreover, subjective interpretations on the
meaning of measures may lead to distortions. Therefore, firm data may not be
accurately comparable149 on the firm level. Subjective measures have been found to
provide a lower reliability and validity than objective measures150. One advantage of
subjective success measures for cross-industry analysis is that they are affected to a
lesser extent by different industry characteristics than most objective measures.
Objective new venture success measures are less readily available for non-publicly
traded new ventures and founders might be more reluctant to provide this information.
More importantly, objective firm success measures often reflect different
149
150
Chandler and Hanks1993.
Steinkühler 1994.
CHAPTER 4 - METHODOLOGY
125
characteristics of the industry. Therefore, industry data on some objective success
measures may not be directly comparable among firms in different industries.
With regard to the growth measures mentioned, the following limitations have to be
taken into consideration. Changes in cash flow are probably one of the most
appropriate measures of growth in profitability in the new venture context. Investment
activities in the early stages reflected opposing results to other profitability measures
such as profit or EBIT. The application of measures based on cash-flow is, however,
limited to the availability of data, as many small venture founders will not know their
cash flow and it will also be limited due to differing definitions of the term cash-flow.
Market share has been frequently applied in strategy research as a performance
measure, however, in the context of small ventures, market shares are generally very
low and in hardly any case will a small venture founder be able to give accurate
information about their market share.
Sales growth is the most popular measure of venture growth. Compared with growth
of employees, it is more accurate as, especially in small firms, increasing firm
performance will only be reflected in changes of employee levels after a certain time
lapse. When calculating data on sales growth, it is important that only data of full
calendar years is analysed. Therefore, when dealing with new ventures, the year of
foundation cannot generally be considered when calculating sales growth, since it is
not equal to a full calendar year. Moreover, in early stages of the venture, growth rates
are generally higher than in later stages, as growth rates are calculated on the basis of a
lower absolute level.
Among the volume measures, the level of individual key firm characteristics is
measured after a specific number of years of venture operation. To undertake a crossindustry analysis, volume measures of sales, employees and net worth suffer from
distortions due to the different amounts of initial capital investments, and varying firm
sizes at the time of foundation in different industries. New ventures in the
manufacturing sector may frequently start at a larger size with higher capital
investments than new ventures in retailing, for example. Consequently, they will also
have, after three or four years of operation, significantly higher levels of volume
measures, which can hardly be related to performance differences within different
industries.
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CHAPTER 4 - METHODOLOGY
The dichotic variables of survival and failure are frequently found in entrepreneurship
research. However, survival rates are not a very differentiated estimate of
performance. Furthermore, they have to be used with caution, since generally no data
is available on the reason of failure. Firm exits may occur for various reasons which
should not be interpreted as failure, as for example, mergers or acquisition by another
company may have taken place. Moreover, the performance aspirations of founders
may play an important role. Founders in industries that require little qualification may
be willing to continue ventures at minimum profits, while founders in hi-tech
industries with attractive alternative career perspectives may tend to close ventures
with low profit levels.
In strategy research, relative profitability measures such as ROI are an accepted
standard for measuring firm performance. In the new venture context, this information
is not only frequently unavailable, it also provides a less reliable indication of firm
performance. The salaries of the founder managers represent an important source of
distortion in the new venture context for all measures that are based on reported data
on earnings. For tax reasons, founder managers will frequently try to minimise
reported firm profits by increasing their salaries. Moreover, the high level of initial
investments at the start-up stage affects the applicability of profitability measures.
Probably the most problematic aspect of profitability measures is the negative
relationship to venture growth. Successful fast-growing ventures with sustainable
growth expectations will find it beneficial to invest earnings into further expansion and
exploitation of market opportunities, while firms with reported higher early profits
may lack opportunities to invest these early profits in further venture growth.
Therefore, most profitability measures are only applicable to a limited degree in the
context of early stage ventures with high growth. Outside of the new venture context,
most profitability measures are good indicators of firm efficiency when comparing
firms within one industry. The ROS measure, however which is very industry specific,
is unlikely to serve as an applicable measure of success in the context of cross-industry
analysis. One indicator that is less affected by industry characteristics in a crossindustry analysis is the time to break –even measure, even though this suffers from the
same negative relationship to venture growth as the other success measures.
The following table summarises the evaluation of the applicability of different
measures of venture success.
CHAPTER 4 - METHODOLOGY
127
Accuracy of
Applicability to new
Applicability for
measurement
venture context
cross-industry analysis
-
+
+
o
+
-
Cash-flow change
+
+
o
Market share change
+
-
+
Sales growth
+
+
+
Employee growth
o
o
+
Sales
+
o
-
Employees
o
o
-
Net worth
+
o
-
Market share
+
-
-
Survival / failure
-
+
o
Cash-flow
+
-
-
Earnings (EBIT, profit)
+
-
o
ROI, ROE, ROA, ROIC
+
-
o
ROS, gross margin
+
-
-
Time to break even
o
o
o
SUBJECTIVE
Performance
Performance relative to
competitors
GROWTH
VOLUME
PROFITABILITY
+ high , o medium, - low
Table 26: Evaluation of alternative measures of venture success
The first criteria “Accuracy of measurement” evaluates how accurately small changes
in organisational performance are captured by the respective measure. Financial
measures provide a higher accuracy than measures based on number of employees,
subjective measures, or the dichotic measure of firm survival. The second criteria
“Applicability to new venture context” evaluates whether a measure is applicable to
evaluate success of new ventures within the same industry. Here especially, the
applicability of most profitability measures is limited. Finally, the criteria
128
CHAPTER 4 - METHODOLOGY
“Applicability for cross-industry analysis” evaluates whether measures in different
industries can legitimately be interpreted as differences in venture performance. This
criteria reflects the degree to which measures vary according to characteristics of the
industry in terms of average firm size, average capital intensity or average margins, for
example. In general, all volume measures and growth measures that are based on
absolute numbers are considered highly sensitive to these industry-specific
characteristics.
In general, it is desirable to apply a multitude of these success measures in order to
cover the different facets of success and to facilitate the comparability with previous
studies that were based on a variety of venture success measures.
Overall, for this study, the most appropriate subjective measure of venture success
appears to be the subjective measure of performance without relation to competitors.
Among the growth category, sales growth seems to be the most appropriate volume
growth measure and cash-flow growth the most appropriate profitability growth
measure. Among the different volume measures firm survival seems to be the only
measure which can be legitimately applied for cross-industry analyses. Finally among
the profitability measures time to break even seems to be most appropriate for
measuring venture profitability during the first years of firm operation.
4.2
Quantitative empirical study: Impact of industry factors on
the success of new ventures in Germany
The quantitative part of the empirical study addresses objectives four to six, and deals
with the identification of the most successful markets, the study of industry-venture
performance relationships and the impact of contingency variables.
Unit of analysis
Within the quantitative empirical study, the dependent variables of venture success
will be analysed at the individual firm level. In order to avoid distortions from
industries where only a small number of firms are represented in the sample, both
correlation and regression analysis will be performed at the firm level. The
interpretation of the results refers, however, to the industry as a unit of analysis151. The
151
Only the analysis of the contextual variable of venture performance refers to the firm as unit of analysis.
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independent variables, which represent industry factors, are all aggregations at the
industry level with industries defined by standard industry classification codes.
Standard industry classification schemes
The application of standard industry classification schemes in order to group firms of
related economic activity imposes certain limitations.
Within the quantitative part of the study, data from various sources on the 4-digit
WZ93 industry classification have been used. The WZ93 is the official current
industry classification scheme for Germany, which has had to be used from 1995
onwards for all public statistics containing industry classifications152. The WZ93
directly corresponds to the European NACE Rev.1 classification, which is the official
reference industry classification for all member states of the European Union. The
NACE Rev. 1 was developed in 1990 from the international reference industry
classification ISIC Rev. 3, which was developed by the United Nations. These
international standardisations of industry classifications have dramatically increased
the comparability of inter-country data, but also the comparability of different data
sources within one country, as one single classification scheme is now applied for
basically all statistics. The WZ93 is divided on the 1-digit level into 17 sections, on the
2-digit level into 31 sub-sections and 60 departments, on the 3-digit level into 222
groups, on the 4-digit level into 503 classes and on the 5-digit level into 1062 subclasses. The one to four digits levels correspond to the NACE Rev.1 classification,
which is expanded by the more detailed national differentiation on the 5-digit level.
With regard to the application of standard industry classifications, it is important to
consider the following aspects:
o Lack of consideration of firm diversification. Diversified firms with
economic activity in various industry classification codes are generally
classified solely to the industry code of their one main economic activity,
measured in added brut value. Each firm is classified to only one industry code.
o Susceptibility of accuracy of data collection. The quality of the classification
greatly depends on the accuracy of the information that is provided by the firm
about their activity and the qualification of the person who performs the
152
Regulation (EWG) No 3037/90 from October 9th 1990.
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codification of the activity in the industry classification scheme. For firms that
are active in various industry codes, identification of the one main economic
activity is frequently difficult. Firms in Germany, for example, have an
incentive to give a very broad definition of their economic activity at the time
of registering their company, in order to avoid the bureaucratic efforts of future
reports related to modifications of their activity.
o Incongruence of industries with demand-side perception. Firms that are
perceived by customers to act in a wide variety of markets with highly
heterogeneous characteristics are frequently aggregated under one industry
code. Therefore, firms under one industry code do not necessarily represent an
aggregation of firms that offer similar goods from the demand-side perspective.
Goods may not be close substitutes for each other. Therefore industry codes
only represent industries to a limited degree and only according to a conceptual
definition of industries. The deeper the applied industry classification scheme,
the higher the assumed congruence of goods and the accuracy of aggregation.
However, even at a 5-digit industry classification, industry classifications may
frequently be too broad to represent meaningful market categories. Moreover,
dynamic developments in industry niches will hardly be reflected in industry
classifications.
o Lack of reflection of developments in hi-tech industry segments.
Developments in hi-tech industries are only to a limited degree reflected in the
industry classification, as industry categories even on the 5-digit level are
generally too broad to make reasonable groupings of dynamic and evolving hitech industry segments. Furthermore, industry classifications omit to take into
account emerging new industry segments, since classifications are only revised
after long intervals.
o Differing methods of aggregation of industry classification. Depending on
the process of data collection of a specific data source, higher level industry
classifications may be the sum of the corresponding lower level classifications
or they may include firms that could not be classified to any of the lower level
classifications. Therefore, for every data source, the method of aggregation has
to be identified. This is particularly important when comparing absolute
numbers of different data sources.
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These underlying aspects of industry classifications have to be taken into consideration
when applying data categorized by industries. For large populations of firms errors
within the industry classification process will be of less importance than for small
populations of firms, where individual misclassifications can have a major impact on
the overall value for the whole industry segment.
4.2.1 Variables under investigation
The selection of variables has been conducted on the basis of the evaluation of venture
success measures (chapter 4.1) and the previously developed theoretical model of
industry attractiveness (chapter 3.4). It is important to stress that within the
quantitative part of the study, the selection of variables is limited with regard to the
availability of statistical data. Among the numerous industry variables that have been
included at the intra-industry level of the theoretical model, only those variables for
which statistical data has been available have been chosen for the quantitative study.
This selection does not consider any ranking of importance for these chosen industry
variables. Detailed operationalisations of each variable are specified in the subsequent
chapter of the quantitative study.
Dependent variables: Average success of new ventures in Germany
In order to determine industry success in terms of industry attractiveness, data has to
be collected on the level of individual organisations.
Within the quantitative study the following four dependent variables of venture
success are investigated:
o absolute venture performance as a subjective measure
o venture growth in sales as a measure of growth
o venture profit level as a measure of profitability
o time-to-break even as an additional measure of profitability
These four dependent variables cover many of the venture success measures that have
been identified previously in chapter 4.1 in the evaluation of venture success measures
as the most appropriate for the context of this study.
Because earlier research provided strong evidence that the effect of environmental
variables varies significantly or might even have opposite effects with respect to
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different success measures, it is desirable to apply a set of success measures within the
quantitative study. The additional inclusion of the measure of new venture survival has
been considered appropriate. However, such data on survival with regard to the
applied sample of new ventures could not be obtained. Neither could applicable data
on firm survival specific to new ventures be obtained. Data on survival of the whole
firm population independent of the age of firms has been considered inapplicable as a
measure of new venture success.
Independent variables
Departing from the previously developed model of market attractiveness (chapter 3.4),
those market characteristics for which applicable data is available are included in the
quantitative study as independent variables. Only a fraction of the model’s variables
can be operationalised with secondary data. Therefore, the quantitative model only
allows the testing of individual variables, and not of the model as a whole. The
following independent industry variables are investigated in the study:
o Market size
o Economies of scale
o Export balance
o Distance from clients
o Previous market growth
o Entry to industry
o Exit from industry
o Balance of entries and exits (net entries)
o Degree of uncertainty about future development
o Concentration
o Number of industry members
o Heterogeneity of industry members
o Employee productivity
o Gross margin
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133
o Average size of industry members
o Capital requirements
o Investment intensity
These independent industry variables cover a broad range of dimensions at the intraindustry level and the barriers to entry of the theoretical model.
Control variables
Apart from the dependent variables of venture success and the independent variables
of industry, another four control variables are applied to indicate the characteristics of
the sample that may represent potential sources of distortions in both dependent and
independent variables. The following control variables are applied:
o Number of ventures per industry in the sample
o Average age of ventures per industry in the sample
o Average number of employees of venture per industry in the sample
o Average venture sales per industry in the sample
The inclusion of these control variables ensures that results that depend on differences
in the composition of the sample are identified.
Contingency variables
Finally, four contingency variables are included to investigate to what degree the
relationship among independent and dependent variables vary in different contexts.
The following contingency variables are considered:
o General industry sector
o Intra-industry profitability variance
o Market growth
o Venture growth
The contingency variables serve to split the sample into three to four groups of
industries. For the general industry sector, individual data analysis is performed for
four broad industry sectors independently. For the other three contingency variables,
the overall sample is divided into three groups by the respective contingency variable.
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Individual data analysis is then performed for the upper third, middle third and lower
third ventures according to the value of the contingency variable.
4.2.2 Selection of data sources and sampling criteria
For the undertaken study, applicable data from a wide range of sources has been
identified, complemented, filtered and finally accumulated to a common data file of
coherently comparable variables.
Selection of data sources
In order to analyse the impact of industry factors on new venture performance on the
basis of statistical data, two specific types of data are required. First, data that provides
performance indicators of new ventures. Second, data that provides industry indicators
that characterise all new ventures within one industry or all firms within one industry.
The criteria to select appropriate data sources for each of these types of data are
discussed.
The following criteria were applied when selecting an appropriate data source for the
performance indicators of new ventures:
o Provision of data on year of firm foundation
o Provision of data on relevant success measures of individual firms
o Provision of data on a 4-digit industry classification code of individual firms
o Provision of data for representative scope of new ventures
o Provision of data on ownership at date of foundation153
o Sufficient consideration of small- and medium sized firms
o Sufficiently high number of new ventures per industry classification code
o Sufficient scope of service markets covered
Basically all publicly available data sources with financial data on an individual firm
level are based on data of publicly traded firms. Because new ventures do not normally
publish financial data, data sources on performance indicators of new ventures are
generally not publicly available. With the establishment of new stock exchanges
153
In order to exclude dependent derivative startups.
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135
focusing on young fast-growing firms during recent years, detailed financial data has
become publicly available for an increasing number154 of young firms. However,
publicly traded new ventures are exceptions from the whole population of new
ventures and consequently allow hardly any generalisations on the whole new venture
phenomenon or the whole industry. Publicly traded new ventures have a strong bias
towards extraordinarily high growth rates. They are generally much larger in size than
average new ventures in the industry and are frequently from hi-tech industries. A
major limitation with respect to undertaking this study is the concentration of publicly
traded new ventures on a very limited scope of 4-digit industry codes. Moreover,
common industry classification schemes are limited in their capacity to aggregate hitech firms into groups of similar firms. Although accurate growth and profitability
measures as well as a wealth of additional information are readily available on these
firms, the application of data of publicly traded new ventures has to be rejected for this
study.
After extensive research on non-public data sources the “Existenzgründerpanel” of the
DtA has been identified as the most appropriate data source for the derivation of the
new venture performance indicator with regard to the selection criteria defined. This
data source contains data from several thousands of new ventures that have received
funding from governmental support programmes in Germany.
For general industry indicators, the following criteria have been applied to select
appropriate data sources:
o Provision of data according to the 4-digit industry classification code that was
applied for the new venture performance data source, or an industry
classification that is transformable into such an industry classification.
o Provision of data from a representative scope of firms, including SMEs
o Coverage of a broad scope of industries including the service sector
o Relevance for variables of the model of market attractiveness at the industry
level
o Data for the same country as provided by data source of venture performance
154
At the end of 2001, the German “Neuer Markt” stock exchange listed, at its climax, 327 firms. Even after the
dissolution of this stock exchange in 2003, most of the formerly listed firms remained publicly traded.
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The lack of data on the service sector turned out to be a primary obstacle. Despite the
passing of a European regulation for the establishment of a statistics on service
industries155, the national statistics of service industries in Germany and most other EU
countries are still scarce and provide data on only a limited range of industries.
Therefore, data on individual segments of the service sector is highly fragmented and
incomplete in current statistics from the EU156. At the same time, industry indicators
for a complete range of manufacturing industries are readily available.
Since the majority of new ventures relates to the service sector, the lack of service
sector data implied difficulties for carrying out the study. The unavailability of data on
the service sector also explains the dominant orientation of previous studies in
entrepreneurship and strategy research towards the manufacturing sector.
Because of incomplete industry coverage, a large number of statistical data sets from
market research institutes, public institutions and industry associations relating to
specific industry segments have had to be excluded. These data sets do not allow
comprehensive cross-industry comparisons. Moreover, all data sets that lacked the
required depth of a minimum 4-digit industry classification system157 had to be
excluded.
Finally, the following data sources have been considered most appropriate for the
derivation of industry indicators: DtA (Gründer- und Mittelstandspanel), ZEW
(indicators of firm foundation), Creditreform (industry risk indicators), Statistisches
Bundesamt (value-added tax statistics). A detailed discussion of each data source is
given in chapter 5.1.
Sampling criteria applied to DtA dataset of new ventures
The data on new venture performance is based on survey data from the DtA. The data
source contained data of 6,246 firms which received governmental funding and
responded to a questionnaire that was sent in June 2001. For further investigation, a
155
Regulation (EG,EURAKOM) No 58/97 from December 20, 1996. This regulation was implemented in
Germany only in December 2000, with the law for the introduction of service statistics.
156
A sample extracted from the structural business database from Eurostat, the reference database for corporate
data within the EU, has shown that for more than 70% of all 4-digit service industry codes, not even data on
the most frequent indicator of turnover was available.
157
The application of the balance sheet database of the German federal bank could have provided averages for
the main items of the balance sheet and the income statement per market. Unfortunately, the data is only
available for four major economic sectors.
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137
sample was extracted from the DtA data. The following selection criteria were applied
to generate the final sample:
Selection of new ventures: Firms with a date of foundation of less than seven years
prior to the date of the data report. As the survey was conducted in June 2001, 586
firms have been excluded because they reported a year of foundation prior to the year
1995. An additional 212 firms have been excluded for which no data on the year of
foundation was available.
Exclusion of firms with apparently incorrect data: In order to verify the plausibility
of the respondents’ data, the profit margin was calculated as profit per turnover for any
year from 1998 until 2001. As a consequence, a total of 129 firms with a margin above
100% for any given year were excluded from the sample. As it was considered highly
unlikely that a firm earns profits higher than their turnover, it can be assumed that
most of these respondents were confused about the scale of the turnover and profit
items in the questionnaire.
Exclusion of firms in industries with insufficient number of ventures: From the
resulting sample of 5,448 new ventures, the total number of firms per industry
category was computed. Industry categories with less than three remaining entities
were eliminated resulting in a data set of 5,159 new ventures in 164 industries.
After this sample, the industry segment 9500, which represents private households,
was eliminated, and one venture was eliminated because it indicated its year of
foundation as 2002. This left a total of 5,117 new ventures in 163 industries to
comprise the final sample to be investigated in the study.
4.2.3 Method of data analysis and interpretation: Analysis of correlation and
multivariate regression
The data analysis is guided by hypotheses about the impact of independent industry
variables on different dependent variables of new venture success (chapter 5.2.), as
derived from the previously developed theoretical model. Various steps of data
analysis were performed in order to achieve objective four (see chapter 1.2) of the
study.
First, an analysis of correlation according to Pearson was applied independently for
each variable of venture success with all available industry variables. On the basis of a
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significance level of 0.05, the results of the analysis of correlation either lead to a
confirmation or a rejection of the suggested relationship between industry and venture
success variables. Correlated control variables industry aggregates of the average age
of ventures in the sample, average sales level of ventures in the sample, average
employee numbers of ventures in the sample, and the number of ventures in the
sample. The correlations between the control variables indicate the sensitivity of
variables to factors related to the composition of the sample, which may be potential
sources of distortions. These control variables are not included in later regression
analyses.
Second, a multivariate regression analysis was performed independently for each
dependent variable in order to elicit additional information on the strength of impact of
those industry variables that have been identified previously as correlating to a
dependent variable of venture success. The strength of impact is derived from the
interpretation of the beta coefficient, with special attention given to possible
distortions due to multicollinearity among the independent variables.
Third, the impact of contingency variables such as industry growth, average venture
growth per industry, profit variance in industry and industry sector was investigated.
Consequently, industries were split into groups by the contingency variable and an
independent analysis of correlation and regression was performed for each group. The
results of the different groups per contingency variable were compared with each other
and also compared with the results of the overall correlation and regression analysis.
This allowed an analysis on how the impact of industry variables on venture success
varies in the context of different contingency variables.
Among the various techniques of multivariate analysis, multiple regression is applied
for the analysis of the quantitative data. Hair et al. state: “Multiple regression is the
appropriate method of analysis when the research problem involves a single metric
dependent variable, presumed to be related to two or more metric independent
variables” 158. The independent as well as the dependent variables are available on a
metric scale or alternatively are transformed to a metric scale by dummy variable
coding. Regression analysis is performed independently for each dependent variable.
158
Hair et al. 1998: 14.
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139
Following Hair et al: “The objective of multiple regression analysis is to use
independent variables whose values are known to predict the single dependent
variable. Each independent variable is weighted by the regression analysis
procedure”159. In the context of this study, the correlation analysis based on the
Pearson coefficient was applied to investigate whether the assumed impact of
independent variables on dependent variables of venture success exists. The regression
analysis provides additional information regarding the importance ranking of
individual industry variables with regard to different variables of venture success.
Apart from the condition of the appropriate variable scale, the feasibility of multiple
regression analysis rests on general assumptions about the underlying variables and the
nature of their relationships. In general, the relatively large size of the applied sample
favours a lower sensitivity to deviations from the general assumptions of regression
analysis.
Linearity: Each independent variable of the regression model was evaluated to verify
whether a linear relationship between the independent variable and the dependent
variable could be justified. The linearity of the assumed relationship between
independent and dependent variables is examined by partial regression plots for each
independent variable in the regression analysis.
Normality: Potential violations of normality were to be revealed by normal
probability plots. Throughout the analysis of the residuals, studentised deleted
residuals were applied. This facilitated the identification of potential violations of the
basic assumptions.
Investigating the above-mentioned assumptions allows corrective transformations of
the data to be made, and increases awareness of potential inaccuracies.
The significance of the overall regression model was examined by the F-test. The Ftest tests the hypothesis that the amount of variation explained by the regression model
is more than the variation explained by the average.
Finally, the regression coefficients are interpreted. This interpretation considers the
effects of multicollinearity that occur when independent variables are correlated.
Multicollinearity lowers the levels of unique variance and thus should be minimised.
159
Hair et al. 1998:148.
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4.3
Qualitative empirical study: Case studies of ventures in
telecommunication and e-commerce industries
The qualitative part of the empirical study focuses on objective number seven, which
deals with the underlying mechanisms by which market characteristics impact venture
performance. Even though for the case studies the most relevant market characteristics
are also identified in the context of each individual case, the focus lies in explaining
“how” and “why” market characteristics have had an impact on venture success in
specific industry and firm contexts.
4.3.1 Industrial focus on cases from the telecommunications and e-commerce
sectors
The case studies will be chosen from the telecommunication and e-commerce
industries. Both industries are, for various reasons, particularly interesting for the
qualitative study:
o Dynamic changing market environments and strong competition ensure
that a larger part of the variables from the model of market attractiveness
is relevant in the context of the cases studied.
o A large number of company foundations within recent years ensures a
multitude of case candidates and relevant findings for a large interest
group.
o Both industries are particularly important for future new venture
activities.
o The relatively short existence of these industries leads to limited
knowledge on general recipes of firm success.
o Market environments are characterised by a high degree of complexity.
o Both industries provide a diversity of segments to study.
It is acknowledged that the industry settings of ventures from the telecommunication
and e-commerce industries may be different from the majority of industries in several
aspects. The market environment is assumed to be more rapidly changing and more
competitive than in many other industries. Competitive positions show greater changes
compared with more traditional industry sectors.
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141
For the same reasons telecommunication and e-commerce industries may be more
interesting to study since the effects of market variables on venture success, which are
the subject of this study, may be stronger in these industry settings. As it is not the
objective of the case studies to reveal generalisations on the industry-venture success
interactions identified in all industries, the focus on telecommunication and ecommerce industries appears adequate in order to give examples and details on how
industry variables affect ventures in the given case contexts.
4.3.2 Variables under investigation
One of the major advantages of the qualitative study is that the selection of variables is
to a far lesser extent restricted beforehand. Practically all of the market factors from
the model of market attractiveness are considered. Such a highly comprehensive list of
independent variables is applied to measure the market setting in depth. Highly
aggregated indirect measures are often not sufficiently rich to describe or predict the
phenomena themselves. In-depth data gathering is therefore needed160. As the use of
only a few aggregate variables lends itself to convenience in data collection and
analysis, it often fails to provide insights in more complex phenomena.
In order to evaluate the impact of industry factors within the case studies, measures of
overall venture performance do not appear very appropriate, since overall firm
performance is influenced by a multitude of factors and it will not permit investigation
into the impact of individual industry factors. Instead, within the case studies the effect
of industry factors is evaluated from the subjective perception of the founder with
regard to their venture.
4.3.3 Selection of case studies
The selection of case studies has been guided by the general focus of the study on
market variables and the new venture context. The case studies should be selected
from a range of different industry segments within the telecommunication and ecommerce sector, ensuring the coverage of a broad heterogeneity of market settings.
The following compulsory criteria were therefore applied for a first screening of
potential case candidates:
160
Hofer and Bygrave 1992.
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o Corporate activities focus on the telecommunication or e-commerce
industries161
o Low degree of diversity among the offered range of products/services
o Date of foundation within the last 2 to 7 years162
o SME with less than 10 million Euro of turnover
Firms from both Germany and Spain have been considered for screening. The
selection of cases from different national origins has been intended to ensure a high
heterogeneity of contextual settings. Cases from Spain are especially interesting due to
the fact that the complete annual financial reports of also non publicly traded firms are
published by the commercial register and may provide additional insight in the
performance of competitors of the case study firms.
After the initial screening of potential candidates, ten firms have been selected under
consideration of a high heterogeneity in terms of additional key venture and industry
dimensions:
o Heterogeneity regarding stage of life cycle of the respective market
segment
o Heterogeneity regarding market segments
o Heterogeneity regarding venture success
o Heterogeneity regarding firm size
These ten firms have been contacted and three of them have been willing to cooperate
in the study. The following table indicates relevant characteristics of the three firms
which are investigated in the qualitative study and their industry:
161
162
Compare chapter 4.3.1.
Firms should be seven year’s old or younger, since founders must still be able to remember the market entry
phase of their venture. At the same time, firms should already have been operating for at least two years in
order to report on experienced impacts of industry factors on their firm.
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143
Case
Imente
Open House
Tele-Ruf
Industry
online news
online vacation
payphone operation
aggregation
apartment rental
2000
1997
1997
- number of employees
11
7
16
- sales in Euro (est. 2003)
250,000
1,000,000*
>6,000,000
Profitability
not profitable -
high
high
Date of foundation
Size
break-even projected
for next year
Market
- former growth
slow positive growth
fast positive growth
negative growth
- life cycle
early
medium
late
Competition
very weak
very strong
very weak
Location
Girona /
Barcelona /
Bonn /
Spain
Spain
Germany
TECHNOLOGY
POPULAR AND
RADICAL
INNOVATIONS
EASY TO ENTER
ENVIRONMENTAL
INDUSTRY
CHANGES
- low market
- presumably very
sophistication with
unattractive market
very low barriers to
due to penetration of
entry and easy to
mobile phones
Interesting case features
- technology-oriented
venture perspective
- business-to-business
market
- market in state of
replicate business
emergence
concept
- large number of
competitors offering
similar services
- deregulation of
telecommunication
market
- dominant presence
of former state
monopolist
* data not provided by firm, own estimation
Table 27: Profile of firms from case study
4.3.4 Method of data collection
After the respective case study companies have been identified, in-depth data has to be
gathered. According to Yin (1989), an overriding principle when examining case
studies is that multiple sources of evidence are vital in the data collection effort.
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Initially, publicly available information about the markets and the case study
companies was collected. Moreover, the investigated firms were asked to provide the
market analysis part of their initial business plan and other publications about their
market. Documentation from other sources such as annual reports, analysts’ reports,
online discussion groups and online consumer ratings, information of company,
competitor websites and press releases supplemented the documentation collected
initially. After the review of these documents it has been assumed that the author had
sufficiently familiarised himself with the respective markets in order to proceed with
subsequent interviews.
Interviews can be conducted as (1) open-ended interviews, (2) focused interviews or
(3) structured interviews. For this study, interviews started with a first part of some
open-ended questions about the attractiveness of the market. In the second part,
interviewees were asked to fill in a questionnaire on ratings of market effects, which
was guided by the developed theoretical model of market attractiveness. While filling
in the questionnaire interviewees had to comment on their ratings. The questionnaire
serves two purposes. On the one hand it enables the interviewer to capture data on the
broad range of variables that are given by the underlying model in a limited amount of
time. On the other, it helps the interviewee to reflect on further dimensions of potential
market factors.
In a third part, the interview focussed on those market factors that were considered as
most important from the founder’s perspective and on those factors that have been
shown to be most relevant within the quantitative study. Although the interviewer had
a set of questions worked out in advance, he was free to eliminate, add and modify
questions on the basis of his perception of what seemed most appropriate in the
context of the interview. The complete questionnaire outline is presented in appendix
G.
For each case an initial interview of about four hours was conducted with the leading
founder of the firm. The usage of the questionnaire was limited to these initial
interviews with the founder, while subsequent interviews with additional members of
the management focused more on phenomena specific to the case.
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145
4.3.5 Method of data analysis and interpretation: Pattern-matching and
explanation-building
The analysis and interpretation of case study evidence is grounded on a general
strategy. This strategy might either be explanatory oriented towards theoretical
propositions, or explorative oriented towards the description of a basic framework.
Among these two basic strategies, Yin favours the one based on theoretical
propositions, since clearly defined propositions help “to focus attention on certain data
and … to organize the entire case”163. Within this study, the theoretical propositions as
given by the model of market attractiveness guide further data analysis. After the
general research strategy has been chosen, there are three dominant analytical
techniques that, according to Yin, should be used for analysing case study evidence:
(1) pattern matching, (2) explanation building and (3) time series analysis. Pattern
matching has been identified as one of the most desirable techniques for case study
analysis164. In the theoretical stage of this study, patterns for the industry
characteristic-venture success relationship have been proposed. These model proposals
are used to predict the patterns that might be found in the markets investigated within
the case studies. If the empirical pattern widely coincides with the predicted pattern,
the internal validity of the empirical findings is strengthened. However, the use of this
pattern-matching analysis is limited. Due to the lack of precision of case study
methods, "investigators are cautioned not to postulate very subtle patterns"165. Since
one of the main purposes of the study is to identify exactly these more subtle patterns
of the market-venture success relationship, the use of pattern-matching analysis might
be limited to the validation of a few significant gross patterns. In addition to pattern
matching, the case study can also be used to investigate and enrich the understanding
of the underlying mechanisms of causal relationships, following an explanationbuilding mode of analysis. The case studies provide examples of how market
environment affects venture performance. Also, the explanation-building technique
compares the model propositions with the case study evidence. Special attention is
paid to contradictory or highly unexpected patterns of variables.
163
Yin 1989: 107.
Yin 1989: 109.
165
Yin 1989: 113.
164
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4.4
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General considerations of validity and reliability
The major criteria for evaluating measurement tools are validity and reliability.
Validity
External validity refers to the degree to which findings are generalisable. This issue
has been already discussed within chapter 1.4 delimitations of the study. The
quantitative study aims at an external validity among a broad range of industries,
especially in the service sector in Germany, under the specific perspective of new
ventures. The qualitative study does not claim that the variables that are identified for
the individual case are transferable to other industries. Rather, it aims at providing
examples on how industry effects affect ventures.
Content validity refers to the degree to which the content of items adequately
represents the universe of all relevant items under study. Obviously it is impossible to
measure all the relevant aspects that comprise environments or even markets. Since the
framework of variables to be investigated within this study integrates a broad scope of
interdisciplinary studies on the market-success relationship, a very broad scope of
markets dimensions is covered. Moreover, the framework is very comprehensive in
terms of the large number of variables that have been assessed. Within the qualitative
study, the breadth of model variables that are included in the investigation will ensure
content validity to a sufficient extent. Within the quantitative study, the availability of
data sources restricts the number of variables that can be considered. Here, content
validity for the main sources of market attractiveness might be difficult to sustain due
to the limited number of variables that can be included. Therefore, in the quantitative
study the impact of variables is interpreted with regard to the applied individual
variables and not for the whole model.
Construct validity refers to the degree to which the underlying constructs are
measured. This refers particularly to the problem of operationalising variables for the
quantitative study. The extent to which the operationalised measures capture the
construct of the variables they are supposed to measure is carefully evaluated (see
chapter 5.3.2). Moreover, for each operationalisation potential limitations have been
discussed.
CHAPTER 4 - METHODOLOGY
147
Reliability
Equivalence refers to the degree to which alternative forms of the same measure
produce the same or similar results. In the quantitative stage, mainly unproblematic
objective quantitative data is used. The quantitative data is frequently numeric
financial data, which is generated by the companies’ reporting systems and should
therefore be largely objective. In the qualitative part, the evaluations of market
characteristics in case study interviews will inevitably vary to a certain degree among
the raters. This might be attributed both to different degrees of knowledge among the
raters, but also to contradictory perceptions of market variables. In order to ensure a
high degree of equivalence within the case studies, more than one person was
interviewed regarding the most influential market factors.
Another important issue concerning the reliability of the data is the respondent’s
motivation to give accurate answers. This may be critical, especially when interview
questions touch areas that are considered sensitive by the interviewee or if answers
require the reference to archival data. A large part of the statistical data has been
collected on the basis of a compulsory survey, where firms have been obliged to give
accurate information, such as in tax reports. Participation in the DtA survey, from
which the performance measures have been derived, was not compulsory. However, as
will be further explained in the following chapter 5.1, there was a high willingness to
provide even sensitive information in the DtA survey.
Risk explanation
Validity
External
Measures for reducing risk
Results not generalisable, but only • Consideration of broad range of
context specific
industries
• Consideration of broad range of
context variables
Content
Relevant variables missing
• Inclusion of broad range of
theories and literature
• Large number of relevant
variables in model (for
quantitative study restriction
regarding availability of data)
148
CHAPTER 4 - METHODOLOGY
Construct
Operationalisation of variables not • Operationalisation from large
adequate
data sources
• Careful evaluation of
applicability and potential
limitations of operationalisations
Reliability
Equivalence
Respondents provide different
• Case interviews with more than
evaluations of phenomenon
one person
• Reliance on objective numeric
data
Accuracy of
Respondents provide inaccurate
responses
data
• Reliance on reliable data sources
• Provision of an industry analysis
and strategic recommendations as
incentive for providing accurate
information in case study
interviews
Table 28: Measures to minimise area of concern regarding validity and reliability
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
5
149
Quantitative empirical study: Impact of industry factors
on new venture performance in Germany
Three of the initial objectives of this study will be achieved throughout this chapter.
After the specification of the applied data sources (chapter 5.1), the hypotheses
(chapter 5.2), the operationalisations of variables (chapter 5.3), various analyses are
performed with the data set. In chapter 5.4 the descriptive analysis of the sample will
respond to objective number 4 of this study (compare chapter 1.2) by identifying those
markets in which new ventures have been most successful. In chapter 5.5 the relation
between industry variables and measures of new venture success is dealt with.
Objective number 5 of this study will be achieved by correlation and regression
analysis on the overall sample (chapter 5.5.2). Objective number 6 of this study will be
achieved by also taking into consideration various additional contingency variables,
which may impact the relationship between industry variables and venture success
(chapter 5.5.3).
5.1
Applied data sources
Within this chapter, background information on the process of data collection and a
discussion of its applicability is provided for each data source.
“Gründer- und Mittelstandspanel“ of Deutsche Ausgleichsbank (DtA)
The DtA is a public institution, which consults and distributes funding programmes for
established firms and startups in Germany. In 2001, more than 43,000 loans were
granted, with a total volume of 3,780 million euros. Every one to two years, the DtA
conducts a survey among a large sample of those firms that have received funding. For
the 2001 survey, a 21-item questionnaire was sent to 18,745 firms who received
funding within the years 1996, 1998, 1999 or 2000. Of these firms, 6,247 responded.
The resulting response rate was with 33%, compared with general response rates of
questionnaire surveys very high. The firms contacted were apparently highly inclined
to return the questionnaire and also to provide sensitive financial information about
their firms, despite the fact that participation in the survey was not compulsory. This
may be influenced by the fact that the DtA is known to all firms and all of the
respondents have received funding from the DtA. Moreover, firms already had to
150
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
reveal their financial data within the loan application process, and may therefore be
less suspicious to reveal this information to a bank than to another third party. About
half of the firms contacted received loans for startups, and the other half received loans
for growth or takeover. Each firm is classified according to the 4-digit WZ93166
industry classification scheme, which is the most frequently used industry
classification scheme in Germany and is related to the international NACE
classification scheme. Already in 1999, a comprehensive questionnaire draft had been
prepared by the author and presented to the DtA. Even though the main purpose of the
2001 survey was to evaluate the quality of the DtA’s funding service, several
questionnaire items were included that allowed a specific assessment of venture
performance and industry environment. Among these items were questions regarding
the geographic sales market, the geographic supply market, the development of the
firm according to plan, number of employees (1998-2001), sales (1998-2001),
investments (1998-2001) and profit (1998-2001). From these questions the dependent
variables of subjective venture performance, venture growth, profit level and time to
break even were derived. Moreover, the independent variables of export balance,
distance to clients, degree of uncertainty, heterogeneity of industry members, gross
margin and minimum organisational size could be deduced.
The DtA data source may be biased as it only contains firms that have successfully
received governmental funding. Within the selection process to grant the governmental
funds, the competence of the entrepreneur and the quality of the venture concept were
evaluated. Additionally, 15% private capital resources were required. Therefore, the
funded ventures may over-represent firms with qualified entrepreneurs, feasible
venture ideas, a high share of private capital after successful funding and a high need
for capital as firms which do not require funding are not covered in the data source.
However, the DtA does not require a high minimum investment. In fact, there are
special programmes targeted at providing funding of less than 25,000 Euro, therefore
firms with relatively small capital requirements are also covered in the data source.
Given the large number of firms that actually received governmental funding, the
rejection rate of funding applications and the related bias of the sample are relatively
low. Apart from this potential bias, the DtA data is an excellent source for the
166
Wirtschaftszweig Klassifizierung von 1993 transl. “classification of industries of 1993”.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
151
indicators of new venture performance. The high number of loans that are granted by
the DtA ensures that an important share of startups in Germany is covered. The sample
size of the survey encompasses over 6,000 returned questionnaires and is extremely
comprehensive. Furthermore, due to the special trust relationship between the DtA and
the firms contacted, the potential biases due to self-selection are minimised and data
on sensitive financial information is available. A wide range of industries, especially
from the service sector is covered.
Statistics of value-added tax167 of „Statistisches Bundesamt“
The “Statistische Bundesamt” is the federal statistical office in Germany. It publishes
the statistics of value-added tax, which is an annual aggregate of the monthly and
quarterly value-added tax reports. All those firms in Germany that had a turnover of
more than 16,617 Euro in the previous year or that have an estimated yearly turnover
of more than 51,129 Euro in the current year168, have to submit monthly or quarterly
value-added tax reports, Small firms with a turnover of less than 16,617 Euro are
excluded from the statistics, even though many of these firms submit on a voluntary
basis a yearly value-added tax report. For the year 2000, the value-added tax reports of
more than 2.9 million firms are included in the statistics. Since 1996, the statistics of
value added taxes have been published annually, and before that they were published
in intervals of two years. For privacy purposes, data on those industry categories that
contain less than three firms or that are highly dominated by one firm is eliminated.
For this study data, from the value-added tax statistics from 1998 to2001 is analysed.
The value-added statistics provides information about the number of firms and the
aggregate yearly net turnover169 per industry classification category. The data on net
turnover also includes turnover regarding sales to clients outside of the national fiscal
territory both inside and outside the European Union, despite the fact that these sales
are frequently not subject to value-added tax. For this study, a dataset has been applied
that categorises the number of firms and their total net turnover by a 4-digit WZ93
industry classification for each year from 1998 to 2001. An additional datasheet has
been applied that categorises the number of firms and their total net turnover for each
4-digit industry classification into ten classes of firm size for the year 2000. The first
167
Transl.: Umsatzsteuerstatistik.
§ 19 UStG (Umsatzsteuergesetz transl. “German law for value-added tax“).
169
Turnover excluding value added tax.
168
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CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
class of firm size starts at 16,617 Euro of yearly net turnover. The last class of firm
size starts at 250,000,000 Euro of yearly net turnover. The data of the value-added tax
statistics serves to deduce the independent variables market size, minimum efficient
scale, previous market growth, net entries, number of industry members and average
size of industry members.
The exclusion of very small firms minimises distortions due to firms without actual
economic activity, which have been established by individuals for the main purpose of
obtaining favourable wholesale purchasing conditions or tax benefits. Other statistics
on firm numbers are often distorted by the impact of such irrelevant firms170. The
value-added tax statistics covers only to a limited degree the first and second fiscal
year of new firms, due to the exemption of many small firms from submitting monthly
and quarterly value-added tax reports. New firms with an estimated net turnover of
less than 51,129 Euro generally do not appear in the statistics. The value-added tax
statistics therefore covers small new ventures only with a time lapse of one to two
years. This may lead to distortions in industries with temporarily high rates of smallsize firm formations. However, since the data is applied in the rather broad 4-digit
industry classification scheme, this distortion may be less relevant for the undertaken
study. Unusually, temporarily high rates of small-size firm foundings may generally
occur for certain niches but not among a whole 4-digit industry segment. Therefore
this distortion may be weak on a 4-digit industry classification scale. The value-added
tax statistics is the only public statistics in Germany that provides data on a financial
indicator for the complete range of 4-digit industry segments based on the aggregated
data of a majority of relevant active firms in Germany. Even though net turnover is the
only relevant financial indicator published in this statistics, the value-added tax
statistics is of high relevance for this study as source for the volume of the industry.
Industry risk (insolvency) statistics of Creditreform
Creditreform is a private organisation that primarily provides firm information for the
purpose of informing their clients about the risk of bad debts in business relationships.
According to its own sources, Creditreform maintains the most comprehensive
business database on German firms with continuously updated data on more than 3.5
170
Compare Kirchhoff et al. 1995: 251ff for a discussion of several statistics of firm foundations in Germany.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
153
million companies. Due to the business area of Creditreform, data associated with
insolvency risk is a primary focus in their investigation and collection of firm data.
The industry risk statistics that is applied in this study provides information about the
absolute number of insolvencies per WZ93 industry segment, as well as a relative
insolvency risk indicator calculated as the number of insolvencies per total number of
firms in each WZ93. The data is collected by systematically analysing the commercial
register, newspapers, business reports and published balances. In the case of enquiries
from clients Creditreform additionally complements the firm information in interviews
with representatives of the firm or interviews with associated firms. Annually
Creditreform receives about 10 million enquiries. The response rate to enquiries from
Creditreform is relatively high, as enquiries are generally initiated by business partners
who might be reluctant to continue their business relationship in cases where a firm is
unwilling to provide information. Additional data is collected by the encashment
service provided by Creditreform itself, and the encashment experience of more than
130,000 associated member firms. Firms are counted as insolvent if one or more of the
following criteria apply:
o committal order for the insurance in lieu of an oath
o insurance in lieu of an oath
o request for initiating settlement proceedings
o initiation / completion of settlement proceedings
o request for initiating / initiation of bankruptcy proceedings
o completion of bankruptcy proceedings
o bankruptcy refusal due to lack of resources
o auction by court order
o bankruptcy of an estate
o connection bankruptcy initiated
o bankruptcy proceedings by obligation comparison waived
o abolition of the bankruptcy proceedings
o business insolvency
o consumer insolvency
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CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
o insolvency of an estate
For this study, the number of firms and the number of insolvencies per 4-digit WZ93
industry classification for each year from 1998 to 2001 is analysed. The insolvency
rate is applied as a proxy indicator for economically forced firm exits.
Firm exits can occur for various reasons, including lack of successor, company
takeover, aggregation of firms within restructuring programmes, change of legal form,
health problems of the entrepreneur, death of the entrepreneur, changing personal
preferences of the entrepreneur or missing economic sustainability and insolvency. For
the purpose of this study, firm exits unrelated to the economic situation of a firm, are
considered as potential distortions and may lead to incorrect conclusions when
investigating interactions on an industry aggregation level. In this context, insolvencies
are a good indicator to filter economically forced firm exits. One should keep in mind
that there are also many firm exits resulting from an economically motivated firm
closure where the company or the owners have still been still able to pay any
outstanding debts or where the creditors did not take any legal actions to claim their
outstanding debt. Particularly in industries with high capital requirements,
economically forced exits might more frequently lead to insolvencies. For the purpose
of this study, the insolvency statistics of Creditreform has been considered superior to
the insolvency data of the Federal statistics department, since the data collection
involved more information sources. Additionally the statistics on firm exits from the
federal statistics department could not be applied since data is only available for a 2digit industry classification and the numbers are highly distorted by non-economically
active firms. The insolvency data of Creditreform can be purchased for individual
industries.
The ZEW indicators of firm foundation171
The ZEW (Zentrum für europäische Wirtschaftsforschung172) is a non-profit research
institute engaging in economic research and economic consultancy. Based on data
from creditreform the ZEW provides a database on firm foundations in Germany.The
ZEW performs a number of corrections of the Creditreform data. Firms that are
presumed to be repeatedly included in Creditreform’s data set are identified and
171
172
Gewerbemeldestatistik.
Transl. Centre for European Economic Research.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
155
repeated entries are excluded. Derivative foundations and foundations that are
associated to changes in legal form, changes of shareholder structure, takeovers and
mergers are identified and excluded. Since foundations are only reflected in the
Creditreform database at the time of inclusion in the commercial register or when a
client of Creditreform enquires about a firm, the Creditreform data underestimates the
number of firm formations, particularly for firms where an inclusion into the
commercial register is not required. About 68% of all economically active firm
foundations are included in the Creditreform database 12 months after the date of
foundation173. Taking into consideration the industry, the legal form, the regional area
and the age of foundations in the Creditreform database, the ZEW estimates the actual
number of firm foundations in the current year. The longer a firm is in business the
higher the probability that it is included in the Creditreform database. Therefore, the
Creditreform database already includes nearly 90% of all economically active firms
that have been founded 36 months before the creation of the dataset. The accuracy of
the number of firm formations has consequently increased for previous years. For this
study, data on firm foundation for each year from 1998 until 2001 has been applied in
order to derive the independent variable of entries to industry.
The data is classified by the 4-digit WZ93 industry classification. Industry segments in
agriculture, forestry, holding companies and segments related to non-private
institutions are not covered. Therefore, eleven of the investigated industries had
missing values for the number of firm formations.
The overall under-representation of small-size foundations leads to a decreasing
estimation accuracy for very recent firm foundations. Since the applied ZEW data set
includes only firm foundations of one year or more before the creation of the ZEW
data set, a relatively high level of accuracy can be ensured. Compared with alternative
data sources of new venture foundations, the ZEW database is the only database that
provides data on a 4-digit industry classification level. The statistics on firm
foundations from Germany’s federal statistics department publishes firm registrations
only on a 2-digit level, while the employment statistics from the Bundesanstalt für
Arbeit publishes the number of new establishments with at least one employee on a 3digit level. Neither of these alternative sources excludes derivative foundations. Their
173
Engel and Fryges 2002.
156
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
applied unit of analysis is either firm registrations or establishments with at least one
employee. The number of economically active foundations is overestimated in the case
of the statistics on firm registrations. The number of economically active foundations
is underestimated in statistics of establishments with at least one employee. The
phenomenon that a large share of firm registrations is actually not intended to become
economically active and the fact that many firms are founded only by the owner
without contracting additional employees, render it more difficult to apply these
alternative data sources as indicators for venture foundations in the undertaken study.
The accuracy of the ZEW estimation for the total number of economically active firm
foundations has been confirmed by Fritsch and Grotz (2002) who compared different
data sources for firm foundations.
“Hauptgutachten174” of Monopolkommission
The Monopolkommission is a federal commission with the legally regulated obligation
of monitoring the status and development of firm concentration in Germany175. In
order to fulfil this obligation, the Monopolkommission has to publish a biannual report
on firm concentration, the so-called “Hauptgutachten”. For the undertaken study, the
most up-to-date report, from August 28th 2002, has been applied, which is based on
data from the year 1999. The report publishes indicators related to the evaluation of
firm concentration for most 5-digit WZ93 industries within the broad industry sectors
of manufacturing, trade and transportation. The applied data is primarily based on the
official federal statistics, which is maintained by the Statistische Bundesamt.
Moreover, interviews are conducted with some of the largest firms and additional
private firm databases are analysed. For the official federal statistics a questionnaire
was sent to a sample of firms in different industry sectors in Germany. The sample is
generated according to a division of the overall firm population into counties, each
county is divided into industry groups and within each industry group into groups of
firms with similar turnovers. Firms with higher turnover are included at a higher
probability in the sample. Starting from a certain amount of turnover, all large firms
are included in the sample. The data of smaller firms is estimated based on the
information from the respective sample. In contrast to the value-added tax statistics,
174
175
Monopolkommission 2002.
Compare § 44 GWB (Gesetz gegen Wettbewerbsbeschränkungen transl. law against restrictions of
competition.).
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
157
firms that provide only annual value-added tax reports are also considered as part of
the total population. In the manufacturing and construction industries, all firms with
more than 20 employees are surveyed. Firms with fewer employees are excluded. For
most transportation sectors, all firms are surveyed without any minimum size limit.
Overall, about 50,000 firms in trade, 38,000 firms in manufacturing, 19,000 firms in
construction, 12,500 firms in catering and 20,000 firms in transportation are included
in the annual survey.
For this study, the Herfindahl index is applied as a measure of industry concentration.
Additionally, Monopolkommission data on turnover, investment, number of firms and
employees is applied in order to derive the independent variables of employee
productivity level and investment intensity.
The indicators of firm concentration consider only to a limited degree common
ownerships of legally independent firms. However, interviews and the analysis of
private firm databases are applied additionally to reveal vinculations among
independent firms. One important limitation of the concentration indicators is that the
data is based solely on the national production output without consideration of foreign
trade balances. Therefore, the statistics exhibit a high concentration indicator for
industries with dominant foreign producers and only few national manufacturers176.
The concentration data, particularly for the manufacturing industries, has therefore to
be considered with caution. Another weakness of the Monopolkommission data is its
availability for only a limited number of industries. While basically all industries in
manufacturing, trade, construction, catering and transportation are included, the
following 2-digit WZ93 industry codes are missing:
o 01-05 agriculture, forestry, fishery
o 11-13 mining industries for uranium, ore
o 40 power supply
o 41 water supply
o 60-95 other private and public services
176
For many years a monopoly has been shown in the Monopolkommission data for the motorcycle
manufacturing industry in Germany, with BMW being the only large producer. In fact, BMW’s market share
for motorcycles has been relatively low in Germany due to the high number of BMW motorcycles that have
been produced for export and the high market shares of foreign manufacturers in Germany.
158
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
In particular, the missing information of the industry codes 60-95 poses a major
weakness for the undertaken study since data of 64 industries are missing from the 163
industries to be investigated.
Despite the limitations mentioned, the Monopolkommission data appears to be the
most appropriate data source in Germany, providing indicators of industry
concentration for a broad range of industries.
The following table summarises the details of data collection and details on the applied
data set that have been derived from each data source:
DtA
Statistisches
Creditreform
ZEW
“Hauptgutachten”
”Gründer – und
Bundesamt
”Industry risk /
”indicators of firm
Monopol
Mittelstandspanel”
”Statistics of value
insolvency statistics”
foundations”
kommission
Same as Creditreform
added tax”
DATA COLLECTION
Population
Firms that received
Firms required to
Firms included in the
governmental funding
submit monthly or
commercial register or
between 1996 and
quarterly value-added
firms for which
2001
tax reports and with
Creditreform received
net turnover of more
an enquiry
Firms subject to valueadded taxes
than 16,617 Euro
Sample size
18,745 firms
> 2.9 million firms
Response rate
33%
approx. 100%
> 3.5 million firms
Same as Creditreform
approx. 140,000 firms
information not
Same as Creditreform
approx. 100%
available
Number of available sample
6,247 firms
> 2.9 million firms
> 3.5 million firms
Same as Creditreform
approx. 140,000 firms
Monthly and quarterly
Commercial register
Same as Creditreform
Questionnaire
value-added tax
and interviews
firm data:
Mean of data collection
Questionnaire
reports
4-digit WZ93
5-digit WZ93
5-digit WZ93
5-digit WZ93
5-digit WZ93
4-digit WZ93
4-digit WZ93
4-digit WZ93
4-digit WZ93
4-digit WZ93
Applied years
1998-2001
1998-2001
1998-2001
1998-2001
1999177
Applied indicators
Geographic sales
Number of firms
Number of firms
Number of firm
Herfindahl index of
market, development
(1998-2001), volume
(1998-2001), number
foundations (1998-
industry concentration,
of firm according to
of net turnover (1998-
of insolvencies (1998-
2001)
number of firms,
plan, number of
2001), number of
2001)
employees (1998-
firms per class of firm
2001), sales (1998-
size (2000(, volume of
2001), investments
net turnover per class
(1998-2001), profit
of firm size (2000)
Industry classification
APPLIED DATA SET
Applied industry classification
scheme
(1998-2001)
Table 29: Data sources - Data collection and applied data sets
177
Published on 28.8.2002.
turnover, investment,
employees
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
159
The different underlying definitions of industry populations and the differences in
aggregating data on higher level industry codes pose limitations on the application of
absolute values. Derivations of indicators combining absolute values from more than
one data source are considered particularly problematic. Therefore, the application of
variables based on relative values is considered superior. Apart from the treatment of
very small firms, the underlying data sources of Statistisches Bundesamt, ZEW,
Creditreform and Monopolkommission are quite similar and extraordinarily
comprehensive in size. Therefore, a general comparability of the different data sources
is given.
5.2
Hypotheses on impact of industry variables on venture success
In this chapter, hypotheses about the impact of industry variables on venture success
are formulated, which are examined within the quantitative empirical study. All
hypotheses have been derived directly from the conceptual model of market
attractiveness developed in chapter 3.4. They cover variables located at the intraindustry level and barriers to entry. The model specifies the main direction of impact
for the success measures venture growth, venture profits and venture survival. As no
empirical data is available for venture survival, hypotheses are formulated only with
regard to venture growth and venture profits. No hypotheses are formulated for the
additional success measures of the data set, time to break even and subjective
performance evaluation. Not only that these success measures have not been specified
in the model, but also results of former research do not provide sufficient evidence
regarding the relation between industry factors and these success measures.
The underlying main assumption that is implied in the following list of hypotheses is
that there are certain industry variables that affect growth and profitability for the
overall sample of ventures and not only in limited contextual settings.
All hypotheses are related to objective five of the study, which intents to study the
relationship between industry variables and new venture success.
Market structure
Within the market structure dimension, hypotheses are formulated for market size,
economies of scale, export balance and distance to clients.
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CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
The size of the market may determine the potential growth of a new venture178. In a
small market, the growth dynamics of a new venture may already be affected in early
years by the limited size of the market179. In a large market, ventures may maintain
longer-term growth by simply multiplying one successful concept, while in a small
market the growth potential for a successful concept may be limited to a short period
of time. In small markets, ventures are forced to continuously develop new concepts to
ensure ongoing growth and they cannot rely on the multiplication of one concept.
Hypothesis 1- market size:
The growth rate of new ventures will be positively related to the size of the overall
market.
Porter (1979) has proposed within the five forces model that high economies of scale
will have a positive impact on organisational performance, since new entrants may be
discouraged from entry. Ravenscraft (1983) empirically confirmed the positive impact
of economies of scale on the line of business profits and also on the industry price-cost
margin. From the new venture perspective, which has been chosen for this study, the
assumed impact of economies of scale is contrary to the previously investigated impact
on large established firms. From the perspective of new ventures, a high level of
economies of scale will lead to a costs disadvantage compared with established
industry members, given the fact that ventures generally start at a lower scale. Even
though surviving ventures may, in the long run, benefit from the protection from
subsequent entries associated to high economies of scale, there may be only a few
ventures that grow to the minimum efficient scale level within their first years of
operation.
Hypothesis 2- economies of scale:
The profit level of new ventures will be negatively related with economies of scale
of the industry.
178
Compare Baaken 1989, Chandler and Hanks 1994a, Dean and Meyer 1996, Zacharakis and Meyer 1998,
Hinterhuber 1995.
179
Baaken 1989.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
161
The export balance is a good indicator of the national competitiveness of a specific
industry180. As the variable of economies of scale, the export balance has been
primarily investigated in strategy research181. Due to local know-how, the availability
of resources, costs advantages, or strong national competition, an industry may achieve
a high level of exports. At the same time, high export rates demonstrate that foreign
markets are accessible by the industry’s products. Therefore, high export rates may
increase venture growth, expanding the potential geographical sales market and
providing opportunities for serving export markets.
Hypothesis 3- export balance:
The growth rate of new ventures will be positively related to the export balance of
the industry.
Similar to the export balance, the distance to clients is an indicator of the geographic
extension of the sales market182. However, in contrast to the export balance, the
distance to clients also reflects differences in the geographic extension of the sales
market within the national context, since local, regional and national sales markets are
distinguished.
Accordingly, it is assumed that a larger sales market, as indicated by the longer
distance to clients, will expand the potential sales market and imply venture growth
opportunities. Within the boundaries of one national context, it may be assumed that
the transparency of the market and prices may increase for industries that compete on a
larger geographic basis, with a resulting negative impact on profits. In local sales
markets, criteria other than price may be more frequently applied by clients.
Convenience and social contacts may often be, in the local context, more decisive than
price.
180
The export balance is located on the intra-industry level of the theoretic model of market attractiveness and
not on the inter-country level as it is an indicator of national competitiveness for a specific industry and not
necessarily for a whole economy.
181
Compare Ravenscraft 1983, Buzzell 1987.
182
Compare Ravenscraft 1983, Davidsson 1991, Shane and Kolvereid 1995, Baaken 1989.
162
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Hypothesis 4- distance to clients:
a) The growth rate of new ventures will be positively related to the average
distance to clients in an industry.
b) The profit level of new ventures will be negatively related to the average
distance to clients in an industry.
Market dynamics
Within the dimension of market dynamics hypotheses have been formulated for
previous market growth, entries to industry, exits from industry, net entries and the
degree of uncertainty.
Growth of the overall market is one of the most frequently applied variables of
research on the market environment183. It is assumed that a growing overall market
will also facilitate the growth of the individual venture. For a new venture it will be
easier to grow driven by the dynamics of a growing market than having to grow at the
expense of market shares of other industry members. Moreover, a strong growing
demand may even lead to temporary supply gaps. Even though high market growth
will also attract a higher number of new firm entries, it is assumed that the growth
stimulation by the overall market development is stronger than the potentially negative
impact of additional industry entrants.
Hypothesis 5- market growth:
The growth rate of new ventures will be positively related to the growth of the
overall market.
The number of firms entering an industry has been frequently treated not as a separate
variable184, but has been included in the variable of life cycle stage, assuming that
early life cycles are associated with a high number of firm entries and an increasing
total firm population while late life cycle stages are associated with a low number of
firm entries and a stable or decreasing total firm population. As indicated before, firm
183
Compare Stuart and Abetti 1987, Keeley, Roure et al. 1987, Tsai MacMillan and Low 1991, Tyebjee and
Bruno 1981, MacMillan et al. 1985, MacMillan et al. 1987, Hall and Hofer 1993, Zacharakis and Meyer
1998, Ravenscraft 1983, Buzzell 1987, Marshall and Buzzell 1990, Mueller and Rauning 1998, Dean and
Meyer 1996, Porter 1979, Zahra 1993a, Baaken 1989, Davidsson 1991, Hinterhuber 1995.
184
Shane and Kolvereid 1995 and Zahra 1993a are among the few studies that deal with firm entries separately.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
163
entries will be strongly related to the rate of market growth, since previous and
expected market growth may be the most important attractors of firm entry. Therefore
it will be assumed according to the former hypothesis that venture growth will be more
impacted by the growth of market dynamics than the number of additional firm entries.
The number of entries, however, may affect directly the profit level. With an
increasing number of new firms entering the market and struggling for a share of the
market, profit margins of all firms may be exposed to higher pressure.
Hypothesis 6- entries to industry:
a) The growth rate of new ventures will be positively related to the number of
firm entries into an industry.
b) The profit level of new ventures will be negatively related to the number of
firm entries into an industry.
As with firm entries firm exits have rarely been investigated185 as a separate variable.
However, the number of firm exits provides very crucial information about the
associated risks of a particular industry. Firm exits are caused by insufficient
profitability. Therefore, a high exit rate may indicate a decreasing level of profit
margins within an industry. Decreasing profitability may, in turn, result from
increasing competition. On the other hand, a high number of firm exits may also
indicate that an industry is strongly affected by environmental changes and established
firms may be forced to exit because they lack the ability to adapt to the new
environment186. Such industries in a transformational state may be attractive industries
for ventures with the knowledge on how to adapt to the new environment. Overall,
only a few industries will face an extreme degree of transformation and therefore it is
assumed that the impact of decreasing industry profitability is stronger than the
opportunities in the context of industries in states of great transformation.
185
Among the literature that has been reviewed for this study, not a single investigation including the variable of
venture exits could be found.
186
Compare the discussion of contingency theory in chapter 3.2.5.
164
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Hypothesis 7- exits from industry:
The profit level of new ventures will be negatively related to the number of firm
exits from an industry.
Corresponding to the number of firm entries, the number of net entries is also assumed
to relate positively to venture growth. A high rate of net entries indicates the
sustainability of an industry environment, providing sufficient growth and profit
potential for new industry entrants. For venture profits, no hypothesis has been
formulated as a high venture survival rate may indicate a high profitability. This effect
may be compensated by the pressures on profit margins induced by an increasing
number of competitors.
Hypothesis 8- balance of entries and exits (net entries):
The growth rate of new ventures will be positively related to the number of net
firm entries to an industry.
Several studies in entrepreneurship have stressed the importance of uncertainty and
market disequilibrium for the opportunity structure of new ventures187. A high degree
of uncertainty may reduce the competitive advantage of established firms. These
established firms may be impeded by inertia in adapting to new developments.
Therefore, new ventures may grow more rapidly in uncertain environments where they
can benefit by filling new market niches.
However, uncertainty about the future development of a market increases the risk of
investment and the risk of failure for new ventures. Profitability may therefore be
reduced by investments that turn out to be unprofitable.
Hypothesis 9- degree of uncertainty about future development:
a) The growth rate of new ventures will be positively related to the degree of
uncertainty within an industry.
b) The profit level of new ventures will be negatively related to the degree of
uncertainty within an industry.
187
Compare Sandberg and Hofer 1987, Keeley, Roure et al. 1987 for positive impacts on venture performance.
Stuart and Abetti (1987) found a negative impact, but only on the basis of a very small sample size.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
165
Competitor structure
With regard to the competitor structure dimension, hypotheses have been formulated
for industry concentration, number of industry members, heterogeneity of industry
members, employee productivity, gross margin and average size of firms within an
industry.
Industry concentration has led to inconsistent results in previous research188. From the
new venture perspective it may be assumed that a high degree of concentration has a
positive impact on venture profitability as new ventures may find attractive market
niches outside the core business of the dominant industry players. This positive effect
on venture profits is considered stronger than the potentially negative impact of high
economies of scale, which may correlate with a high degree of industry concentration.
Hypothesis 10- concentration:
The profit level of new ventures will be positively related to the degree of
concentration within an industry.
The number of industry members is related to the variable of concentration, as markets
of low concentration ratios may also frequently have more industry members189. An
industry with a large number of members will frequently have a low industry
concentration.
Predominantly smaller firms may already have occupied market niches. Competition
on the level of smaller firms may be stronger and lead to pressure on profit margins.
Moreover, Zacharakis and Meyer (1998) found that number of industry members has
been the most important actual deal evaluation criteria applied by venture capitalists.
Hypothesis 11- number of industry members:
The profit level of new ventures will be negatively related to the number of
members of an industry.
188
No relationship between performance and concentration was found by Sandberg and Hofer 1987, Tsai,
MacMillan and Low 1991, Robinson 1998, Marshall and Buzzell 1990. A positive relationship was found by
Brüderl et al. 1996, Buzzell 1987. Opposing directions of impact for different success measures were found
by Mueller and Raunig 1998 and Ravenscraft 1983. A negative relationship was proposed in the Dean and
Meyer model 1996.
189
Compare Zacharakis and Meyer 1998, Porter 1979, Baaken 1989, Hinterhuber 1995.
166
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
The importance of heterogeneity has been particularly highlighted by Mueller and
Raunig190. Heterogeneity of industry members is analysed individually for the
measures of heterogeneity in profits, heterogeneity in sales growth and heterogeneity
in the sales volume. A high heterogeneity of industry members indicates that the
industry provides high profit margins, which allow a wide range of diverse firms to
survive. Even firms that deviate greatly from an optimum efficiency level survive in
such an industry.
Hypothesis 12- heterogeneity of industry members:
a) Heterogeneity in profits:
The profit level of new ventures will be positively related to the heterogeneity of
profitability within an industry.
b) Heterogeneity in sales growth:
The profit level of new ventures will be positively related to the heterogeneity of
firm growth within an industry.
c) Heterogeneity in sales volume:
The profit level of new ventures will be positively related to the heterogeneity of
the firm sales volume within an industry.
Employee productivity has been mainly neglected in entrepreneurship research, even
though it has been identified to be of major importance in the PIMS study191. In the
context of growing new ventures with less developed organisational structures,
employee productivity may be even more important than in the context of established
firms. A high level of employee productivity implies that increases in sales involve
less organisational preparation with respect to recruiting, training and integration of
new employees. Therefore, it is assumed that venture growth will be positively
affected by a high level of employee productivity. With regard to venture profits, a
positive impact is assumed, since a relatively low share of personnel costs may result
in high gross margins.
190
Mueller and Raunig 1998 stressed the importance of firm heterogeneity as a contingency variable in studies
that apply industry aggregates. Compare also Porter 1979, Sandberg and Hofer 1987 and Chandler and
Hanks 1994b.
191
Buzzell 1987, Wilson 1998.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
167
Hypothesis 13- employee productivity:
a) The growth rate of new ventures will be positively related to the employee
productivity within an industry.
b) The profit level of new ventures will be positively related to the employee
productivity within an industry.
The average gross margin of an industry may be a major driver of venture profits.
However, because data on industry gross margin is generally not publicly available, it
has not been dealt with in any of the studies reviewed. A high gross margin may be the
result of a relatively low intensity of competition on prices192.
Hypothesis 14- gross margin:
The profit level of new ventures will be positively related to the gross margin
within an industry.
Industries with a high average size of firms may be attractive for dynamic new
ventures193, since large industry players may be inferior in terms of responsiveness and
dynamism. On the other hand, larger firms may have a more powerful competitive
position in order to protect the industry from new entrants and will benefit from a costs
advantage in industries of high economies of scales. Overall it is assumed that the
positive impact on venture profit from the inertia of large firms will be more relevant
to the profit level of new ventures.
Hypothesis 15- average size of industry members:
The profit level of new ventures will be positively related to the average size of
firms within an industry.
Barriers to entry
The above mentioned hypotheses regarding the intra-industry level of the conceptual
model are complemented by two hypotheses on the barriers to entry level relating to
192
193
Brüderl et al. 1996.
Compare Davidsson 1991, Hinterhuber 1995.
168
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
minimum organisational size and investment intensity. In empiric research barriers to
entry are frequently studied as one variable194.
The minimum organisational size defines the smallest firm size at which a venture can
start within an industry. This size will be much smaller than the minimum efficient
scale that defines the average size of those largest industry members that comprise
50% of the total industry sales. Minimum organisational size refers to the number of
employees and the organisational complexity of a new venture. Industries that require
- a large organisational size at startup will be better protected from subsequent firm
entries. Therefore firms in these industries may benefit from a higher profit level. It is
assumed that the majority of the ventures that have entered a market do this after
ensuring that they possess the minimum size and resources required by an industry.
Therefore new ventures may benefit similarly to established firms from a higher
profitability resulting from restricted access to the industry.
Hypothesis 16- minimum organisational size:
The profit level of new ventures will be positively related to the minimum
organisational size at startup within an industry.
The impact of investment intensity corresponds to the impact of minimum
organisational size, with a focus on the minimum capital requirements instead of the
minimum number of employees. A high investment requirement will protect those
ventures that possess the required capital from subsequent venture entries, inducing a
higher profit level for all firms within the industry195.
Hypothesis 17- investment intensity:
The profit level of new ventures will be positively related to the investment
intensity within an industry.
For the other dimensions of the intra-industry level within the conceptual model,
namely dependencies and competitor dynamics, no appropriate indicators can be
194
Compare Sandberg and Hofer 1987, Keeley, Roure et al. 1987, Wilson 1998, Robinson 1998, Tyebjee and
Bruno 1981, Ravenscraft 1983.
195
Compare Ravenscraft 1983, Porter 1979, Dean and Meyer 1996, Davidsson 1991, Hinterhuber 1995.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
169
derived from the data sources available. These other dimensions therefore cannot be
studied in this quantitative study.
5.3
Operationalisation of variables
5.3.1 Operationalisation of venture success
Subjective absolute venture performance measure
A subjective venture performance measure can be derived from the item in the DtA
questionnaire where respondents are asked whether their venture performed better than
plan, according to plan or less well than plan. Since most ventures had prepared a
financial plan within the funding process, respondents generally should have been able
to give a knowledgeable response to this item. Moreover, by relating the subjective
performance to the initial plan, the risk of distortions due to personal, non-monetary
aspects of subjective venture success are reduced, which otherwise would make it
more difficult to compare results. The measure is limited with regard to the extent to
which differentiations are reflected. The three possible response categories capture
only a general state of performance.
For the statistical analysis the three possible response options are converted into
dummy variables from -1 for performed less well than plan to +1 performed better
than plan, and with 0 for all ventures that indicated that they performed according to
plan. This ordinal scale dummy variable transforms into a metric scale variable when
performing the aggregation of the venture data on the industry level. In order to
decrease distortions from firm-specific outliers, the value is excluded for all industries
where data on less than three ventures has been available.
Subjective performance:= d
d
dummy variable for performance according to plan
Only within the descriptive analysis in chapter 5.4.2. the performance measures are
aggregated on the industry level. For this aggregation on the industry level the dummy
variable coding from -1 to +1 is not applicable. Instead an alternative coding has been
applied in this context with 0 for performed less well than plan to +1 performed better
than plan and with +0,5 for all ventures that performed according to plan.
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CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Objective growth measure - venture sales growth
Both growth of employees and growth of sales were available as measures of venture
growth from the DtA data. Due to the relatively small size of the new ventures in the
DtA sample, growth of employees may be a less accurate measure than growth of
sales. An increase in the number of employees only occurs after the sales volume has
increased by a large increment. Since both measures are strongly related, only the
more accurate measure of sales growth will be applied.
Values of turnover from the year of foundation are excluded since they do not
generally cover a full twelve-month period and are therefore not directly comparable
to other years. Moreover, in the first months after foundation, ventures may be
dedicated to preparational tasks before they can actually start their economic activity
and generate turnover. The differences in the age of the ventures in the sample pose a
potential weakness. In general it can be assumed that in early years of venture activity
a higher percentage growth can be achieved more easily as the absolute level of sales
is relatively low. Moreover, a step-wise development of the venture concept facilitates
early venture growth in sales. However, the distribution of firm age is assumed to be
approximately equal for all industries. Therefore, the extent of potential distortions is
limited.
The venture growth measure is calculated as the mean of the annual percentage growth
in sales per venture. Data on sales was available for the years 1998 until 2001. The
data from 2001 is an estimate of the respondents for the year of the survey. Sales from
the year of foundation have been eliminated before calculating the sales growth rate,
since most ventures will not have operated for a full calendar year in the year of
foundation.
2000
x j +1
j =1998
xj
∑
Venture sales growth:=
a
xj
sales in year j
a
number of years for which data on growth rate is available
The value is only given for industries where at least three ventures have provided
information on this item.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
171
Objective volume measures - venture sales level and number of employees
In the context of the measurement of venture size, other aspects of the absolute sales
and employee measures have to be considered. Volume measures on absolute sales are
highly distorted by the margins within an industry. Volume measures on absolute
numbers of employees are highly impacted by employee productivity and an
industry’s labour intensity. In both cases this poses an important limitation for a crossindustry analysis. In addition, the inter-industry differences in initial firm size and
capital investment make it difficult to deduce venture performance from levels of firm
size. Within the data set, both the indicators of venture sales level and the number of
employees per venture are correlated to the average age of the venture. The differences
in venture age lead, therefore, to distortions in the volume indicators. As a result of
these weaknesses and limitations, the volume measures will not be applied as
measures of venture success. Rather, they will be calculated as control variables in
order to investigate possible venture volume-related dependencies.
2001
Sales level:=
∑x
j =1998
a
2001
Employees (incl. owners):=
∑ (e + o )
j =1998
a
x
sales corrected by sales of year of foundation
e
number of employees
o
number of owners
a
number of years for which data is available
Data for industries with less than three venture observations are excluded.
Objective profitability measures – venture profit level & breakeven
Venture profit and time to break-even are applied as objective profitability measures,
since both indicators measure different dimensions of profitability.
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CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Venture profit level
The level of venture profits can be derived form the DtA questionnaire item on profits.
This question did not, however, specify whether the profits given were before or after
taxes. Moreover, many small venture owners are able to manipulate to a large extent
the generated taxable profits by varying their own salaries in the first years of the
venture. For tax reasons, owners of capital corporations in Germany have an incentive
to decrease profits by increasing their own salaries. However, some owners may also
decide to grant themselves lower salaries during the first years in order to increase the
liquidity of the venture. The accuracy of the data on profits may therefore be limited to
a certain extent. Another possible source of distortion is the range of venture age. One
may assume that profits increase more rapidly within the first years of operation after
firm foundation. Despite the acknowledged limitations, venture profits were identified
in the evaluation of success measures (chapter 4.1) as one of the most appropriate
measures.
The profit level is calculated as the average annual profit per venture from 1998 until
2001. The data of 2001 is based on respondents’ estimates for the year of the survey.
The year of foundation is excluded, since most ventures did not operate for a full
calendar year in their year of foundation.
2001
Profit level:=
∑p
i =1998
a
p
profit of venture
a
number of years for which data is available
Data for industries with less than three venture observations is excluded.
Time to break even
The break-even indicator applied here refers to operational break-even, which is
measured as the number of years from foundation until the first year of positive
earnings. This indicator does not give any information on the time that is needed until
the initial investments are recovered. Nevertheless, the operational time to break even
is a useful indicator on how rapidly a stable economic situation can be achieved.
Industries in which ventures need a long time to reach operational break-even, may
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
173
indicate a higher risk of failure and more difficulty to enter the market. Since the
measure of time to break even is based on the profit measure, the same limitations that
were mentioned for profits could also apply for the break even indicator. However, the
possible impact of manipulation of owner salaries and other tax-related reasons is far
less important for time to break even. The impact of taxes does not generally turn a
profit into a loss, due to the progressive system of taxation with exemptions for small
earnings, but does lower positive earnings.
For the calculation of the break-even indicator, a sub-sample of the DtA sample had to
be generated. First, ventures with a year of foundation before 1998 had to be excluded.
After this exclusion of older ventures 4,006 of the previous 5,117 ventures remained.
Another 766 ventures had to be excluded as they did not provide data on profits for
any year. Of the remaining 3,240 ventures, 532 ventures equalling about a 16% rate,
did not have positive results until the year 2001. 46 ventures had to be excluded due to
contradictory data. The break-even indicator was finally calculated as the average time
from the year of foundation until the first year of profits from the remaining 2,662
ventures with positive profits.
Time to break even:= Yp - Yf
Yp
first year of positive earnings (profit)
Yf
year of firm foundation
The fact that ventures of different years of foundation are included in the sample may
lead to a decreased time to break even for industries with ventures of a lower average
age. The correlation analysis between the break-even indicator and the average age of
ventures does not, however, confirm such a dependency.
D data for industries with less than three venture observations was excluded.
Indicator
Computation of
Data source
Availability
DtA
163 of 163 industries
Limitations
indicator
SUBJECTIVE
Absolute performance
Differentiation of
Subjective
Subjective
performance in
performance dummy
success limited to
relation to plan
variable
three response
categories.
GROWTH
Sales growth
Venture sales growth
Average annual
growth of venture
sales
DtA
135 of 163 industries
Different years of
venture foundation.
174
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
VOLUME * (not applied as
dependent variable)
Sales
Venture sales level
Average annual
DtA
156 of 163 industries
venture sales
Different years of
venture foundation.
Distortions by margin.
Impact of industry
conditions as
minimum organization
size, firm size, margin
level.
Employees
Number of employees
Average annual
DtA
146 of 163 industries
number of employees
Different years of
venture foundation.
Impact of industry
conditions as
minimum organization
size, firm size,
employee productivity
and labour intensity.
PROFITABILITY
Profit
Venture profit level
Average yearly
DtA
152 of 163 industries
venture profits
Missing specification
whether profits are
before taxes. Potential
manipulations of
taxable profits by
owner salaries.
Different years of
venture foundation.
Time to break even
DtA
121 of 163 industries
Only ventures founded
Year of venture break
Year of foundation
even
until first year of
between 1998 and
positive venture profit
2001 considered.
Different years of
venture foundation.
Table 30: Operationalisation of dependent variables of venture success
5.3.2 Operationalisation of industry variables on the intra-industry level and
barriers to entry
The measures of venture success mentioned are investigated in relation to a number of
industry variables that are located on the intra-industry level of the conceptual model.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
MACRO
LEVEL
OPPORTUNITIES IN GLOBAL & NATIONAL ENVIRONMENT
INTER-COUNTRY
NATIONAL COMPETITIVENESS
LEVEL
INTER-INDUSTRY
LEVEL
INTRA-INDUSTRY
LEVEL
175
OPPORTUNITIES & THREADS FROM RELATED INDUSTRIES
MARKET & DEPENDENCIES & COMPETITORS
market
structure
(1) Market size
(2) Economies of
scale
(3) Export balance
(4) Distance to
clients
market
dynamics
depen
-dencies
competitor
dynamics
/
/
(5) Market
growth
(6) Entries to
industry
(7) Exits from
industry
(8) Net entries
(9) Uncertainty
competitor
structure
(10) Concentration
(11) Number of firms
(12) Heterogeneity of
industry members
(13) Employee
productivity
(14) Gross margin
(15) Average firm
size
BARRIERS TO ENTRY
(16) Minimum organisational size
(17) Investment intensity
VENTURE / FIRM
LEVEL
RELATIVE POSITIONING TO COMPETITION
Figure 23: Investigated independent variables on the intra-industry level
The number of investigated variables was restricted by the availability of data. For the
intra-industry dimensions of dependencies and competitor dynamics, no statistical data
was available.
5.3.2.1
Operationalisation of variables of market structure
Market size
Market size is operationalised by the turnover in 1,000 Euro per industry as defined by
the industry classification segment. The turnover is derived from the average yearly
turnover per industry of the value-added tax statistics. It has been mentioned in the
methodology part that firms under one industry code do not necessarily represent
aggregations of firms that offer similar goods from the demand-side perspective. Each
industry code may include a variety of markets. Therefore, indicators based on
absolute numbers per industry code have to be interpreted with caution and pose
176
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
important limitations. Nevertheless, the turnover on a 4-digit industry classification
gives an indication of the size of the broader industry category.
2001
Market size:=
∑X
j =1998
i
4
Xi
turnover of all firms within an industry
j
year
Economies of scale
One of the most commonly used indicators of economies of scale is minimum efficient
scale. Minimum efficient scale is defined as the average turnover of the largest firms
that comprise 50% of the turnover of a given industry.
The minimum efficient scale indicator has been estimated on the basis of data from the
value-added tax statistics classified by groups of firm size.
The statistics from the year 2000 has been applied, which was the latest year for which
data with differentiation of groups of firm size was available. The value-added tax
statistics differentiates the number of firms and turnover by twelve classes of firm size.
The smallest class comprises firms with less than 50,000 Euro of turnover and the
largest class firms with more than 250 million Euro of turnover. In order to define the
minimum efficient scale, the size of individual firms within exactly one class of firm
size had to be estimated for each industry. Assuming an even distribution of firms
within the class of firm size, individual firms were distributed at a constant interval
from the bottom of the class size to the top. Even though the assumption of evenly
distributed firms within one class size may not be highly accurate, this will not
generally greatly affect the resulting indicator of minimum efficient scale, as possible
distortions from estimation of firm distribution is limited to exactly one class of firm
size per industry.
The number of largest firms that account for fifty percent of total turnover had to be
calculated. As a first step, the size class variable “z” has to be derived for each
industry from an iterative summation of the total turnovers per size class before 50%
of the total industry turnover is reached, starting from the class of largest firms.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
Derivation of the last class of firm size z from
177
z
∑X
k =0
k
<
X
2
Once this last class of firm size has been identified, the 50% turnover is split in the
following equation. In an iterative process, the number f of firms in the last class of
firms has to be augmented until 50% turnover is reached.
X :2 − Bz + 1
z

Xi
(B − Bz + 2 ) 
= ∑ X k + ∑ B z +1 −  f * z +1

2 k =0
N z +1
f =0


Once the number of firms f in the last class of firms is identified, the number of largest
firms that accounts for 50% of turnover is calculated by the sum of firms in the highest
firm classes until firm class z, plus the additional number of firms in the last class of
firm size f.
 z

N i 50% X =  ∑ N k  + f
 k =0

Finally, the average turnover of these firms is calculated by dividing 50% of turnover
by the above-calculated number of firms, which account for this 50% of turnover.
MES:=
0,5 * X i
N i 50% X
Ni50%X
number of largest firms in industry accounting for 50% of total turnover
Ni
total number of firms in industry
X
total turnover in 1,000 Euro
k
class of firm size starting with 0 for size of largest firms
B
bottom border of class of firm size in turnover per firm
f
number of firms included from last class of firm
The program that was applied to calculate the minimum efficient scale indicator is
given in appendix A. In 22 industries, the class of highest firm size already accounts
for more than 50% of the industry’s total turnover. In these cases, the MES is
estimated by calculating the average size of firms in the class of highest firm size.
Even though the estimate in these cases slightly underestimates the actual MES, the
estimate seems to be legitimate, since the estimated value is generally much higher
178
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
than the average MES over all industries, reflecting the concentration of firms in the
highest class of firm size.
Export balance
Data on foreign trade from the Federal Statistical Office in Germany is not available
for a wide range of the industries that are investigated in this study. Therefore data on
the extension of the sales market in the DtA survey is applied as a proxy indicator for
export intensity. Within the DtA questionnaire, new ventures were asked whether their
main sales market is regional, national, European or worldwide. The indicator is then
calculated by the number of new ventures that indicate that their sales market is
European or worldwide in relation to the total number of new ventures. Since new
ventures engage in export activities to a lesser extent than larger, more mature firms,
the absolute number of the proxy indicator is a poor estimate of the export intensity of
a specific industry. For the purpose of a cross-industry comparison, however, this does
not necessarily pose an important problem, as only relative numbers are compared.
Moreover, a focus on the export activities of new ventures prevents the export
activities of very large firms from leading to distortions. A weakness of the indicator is
that the number of new ventures that indicated their overall sales market to be
European or worldwide was relatively low, only about 5% of all ventures. Therefore,
the indicator is sensitive on a small number of ventures with a European or worldwide
sales market.
Export balance:=
Niex
N iex
Ni
number of new ventures that specified their main sales market as European or
worldwide per industry
Ni
total number of ventures per industry
Distance to clients
The distance to clients is given by the same question on the extension of the sales
market of the DtA survey. The four ordinal categories, regional, national, European
and worldwide sales markets are converted into dummy variables from one to four.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
Distance to clients:=
d
179
∑d
Ni
dummy variable of distance to client of each venture measured in extension of
sales market per industry
Ni
total number of ventures per industry
The operationalisation of the variables related to market structure are summarised in
the table below.
Indicator
Computation of
Data source
Availability
Limitations
indicator
MARKET STRUCTURE
(1) Market size
Taxable turnover
Not needed
Value-added tax
statistics
163 of 163 industries
(2) Economies of scale
Minimum efficient
scale
Average turnover of
largest firms with 50%
of industry code
turnover
Value-added tax
statistics
163 of 163 industries
(3) Export balance
Extension of sales
market of new
ventures
DtA
163 of 163 industries
(4) Distance to clients
Distance to clients
(dummy)
New ventures with
mainly European and
worldwide sales
market divided by
total number of new
ventures
Sum of dummy
variables of distance to
client divided by total
number of new
ventures
DtA
163 of 163 industries
4-digit industry code is
a very broad market
definition
Small inaccuracies
since distribution of
firms within one size
classification has to be
estimated
Relative low number
of new ventures in
export category
Data is limited on new
ventures in DtA
sample
Table 31: Operationalisation of market structure
5.3.2.2
Operationalisation of variables of market dynamics
Previous market growth
Market growth is operationalised by the change of turnover from 1998 to 2001 based
on the value-added tax statistics per industry. Markets are defined by the industry
classification code. The inherent limitation of industry codes in representing markets
has been discussed before. Since relative growth rates and not absolute numbers are
applied, distortions resulting from the industry classification are not critical.
Market growth:=
X i1998
X i2001
Xi1998 turnover of all firms in industry i in 1998
Xi2001 turnover of all firms in industry i in 2001
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CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Entries to industry
The entries to an industry are measured on the basis of the ZEW indicator of firm
foundation. In order to receive an indicator that serves for cross-industry analysis, the
absolute numbers of firm foundation per industry from the ZEW data source have to
be related to the absolute number of firms in the respective industry. First, the average
absolute number of yearly firm foundations per industry from 1998 until 2001 is
calculated from the ZEW data. Afterwards, this number of firm foundation per
industry is related to the total number of firms in 1998 from the Creditreform database.
As the ZEW data is also based on the Creditreform data and consists of similar firm
populations (see chapter 5.1), the data of firm numbers from the Creditreform has been
chosen instead of the data of firm numbers from the value-added tax statistics because
the accuracy of the Creditreform data increases for past years. The 1998 data should
include data of more than 80% of firm foundations in the same year. In general, the
derivation of a time series based on absolute numbers of the Creditreform database is
problematic, as the total number of firms in the database is influenced by the extent of
data collection. The total number of firms in the Creditreform database has increased
by over 22% for the time period of 1998 to 2001. For the same time period, the valueadded tax statistics indicates an increase in the number of firms of only 1.8%.
Therefore, the Creditreform time series heavily overestimates growth rates on the basis
of the absolute number of firms. For the ZEW data, the time series analysis is,
however, applicable, as the corrections and estimates that were made by the ZEW
correct the observed distortions related to the extent of data collection.
2001
Entry ratei:=
Fi
∑F
j =1998
i
4 * N i1998
firm foundations in industry i
Ni1998 total number of firms in 1998 in industry i
j
year
Exits from industry
Exits from an industry are measured according to the insolvency industry risk
indicator, which is calculated as the average yearly relationship between bankruptcies
and total number of firms for the years 1998 to 2001 on the basis of the Creditreform
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
181
data. Even though the Creditreform database applies a very broad definition of
bankruptcy, bankruptcies comprise just a part of total firm exits, as discussed in
chapter 5.1. Nevertheless, the exit of small firms may be slightly underrepresented and
there could be distortions as insolvencies could increase with the investment intensity
of an industry. Overall, the Creditreform bankruptcy indicator appears very adequate
for a cross-industry analysis, since firm exits due to reasons other than financial
performance are excluded.
 Ii

j =1998  N i
Insolvency rate:=
4
2001
∑



Ii
bankruptcies in industry i
Ni
total number of firms in industry i
j
year
Balance of entries and exits (net entries)
Due to the discussed deficits of the Creditreform data with regard to inter-temporal
analysis and the fact that insolvencies do not cover all firm exits, the value-added
statistics is considered a more appropriate source for the operationalisation of net
entries. Net entries are calculated as the division of the number of firms in 2001 by the
number of firms in 1998.
Net entries:=
N
N 2001
N 1998
total number of firms per industry
Degree of uncertainty about future development
The level of uncertainty is measured on the basis of an item in the DtA survey. Firms
were asked whether they considered venture performance had gone according to plan,
better than plan or worse than plan. Even though this indicator provides only little
differentiation and is limited to the new venture perspective, the comparison of
previous plans with actual performance may offer a good reflection on how far the
development of the market and the development of individual firm performance can be
182
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
planned within a market. It can be assumed that all ventures had to provide a financial
plan within the funding process.
Uncertainty:=
n+ + n−
N
n+
number of ventures that performed better than plan
n-
number of ventures that performed worse than plan
N
total number of ventures
Operationalisation of variables regarding the dimension of market dynamics are
summarised in the table below:
Indicator
Computation of
Data source
Availability
Limitations
indicator
MARKET DYAMICS
(5) Previous market growth
Growth in turnover per
industry
(6) Entries to industry
Entry rate
(7) Exits from industry
Insolvency rate
(8) Balance of entries and exits
(net entries)
Net entries
(9) Degree of uncertainty about
future
Deviation of venture
performance from plan
Turnover in 2001
divided by turnover in
2001
Average number of
annual firm
foundations from 1998
to 2001 divided by
total number of firms
Average annual
number of
insolvencies from
1998-2001 related to
total number of firms
Number of firms in
2001 divided by
number of firms in
1998
Sum of share of
ventures that
performed better than
plan plus share of
ventures that
performed less well
than plan
Value-added tax
statistics
163 of 163 industries
-
ZEW & Creditreform
150 of 163 industries
Application of two
data sources
Creditreform
161 of 163 industries
The term insolvency
does not cover all exits
Value added tax
statistics
163 of 163 industries
-
DtA
163 of 163 industries
Data is limited to the
number of ventures in
the DtA sample
Table 32: Operationalisation of market dynamics
5.3.2.3
Operationalisation of variables of competitor structure
Concentration
The Monopolkommission data on industry concentration provides a variety of
indicators of industry concentration. Among these are the Herfindahl index, the
coefficient of variation and the market share of the top 3, top 6, top 10, top 25, top 50
and top 100 firms in each industry. There is a strong correlation among all mentioned
concentration indicators. In the end, the Herfindahl index was chosen as the most
appropriate indicator, since it gives more weight to larger firms, reflecting more
accurately those industries that are dominated by one firm or a small group of firms.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
183
These cases of extreme industry concentration are most likely to have a strong impact
on the level of competition. The Herfindahl index is also used by the US Anti Trust
Department to decide the competitive impact of mergers. The Herfindahl index is
defined as the sum of the squares of the market shares of each individual firm. As
such, it can range from 0 in an industry with a very large amount of small firms to
10,000 in an industry with a single monopolistic producer. The Department of Justice
considers Herfindahl indices between 1,000 and 1,800 moderately concentrated and
indices above 1,800 concentrated.
n
 Xi 
HI := ∑  
i =1  X 
2
n
number of firms in industry
Xi
turnover of firm i
X
sum of turnover of all firms in industry X:=
n
∑(X
i =n
i
)
Number of industry members
Several of the applied data sources provide data on the number of firms per industry
classification code. The value-added tax statistics is chosen as the most appropriate
source, since the data is based on the overall population and not only on a sample.
Moreover, there are only little inter-temporal distortions. Similar to the variable of
market size, it must be noted that absolute numbers per industry classification code
imply weaknesses, as the 4-digit industry classification is a very broad aggregation
criteria and firms may not necessarily offer close substitute products.
Number of industry members:= N i
Heterogeneity of industry members
In order to derive indicators of the heterogeneity of firms within one industry, the data
of individual firms has to be consulted. The DtA data source provides performance
indicators on the level of individual firms. When grouping this information by industry
code, the indicators for venture heterogeneity in terms of profitability, sales growth
and sales per industry can be derived.
184
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
First, intra-industry standard deviation of venture gross margins is applied as an
indicator of the profitability heterogeneity of industry members.
Second, intra-industry standard deviation of venture sales growth is applied as an
indicator of growth heterogeneity among industry members.
Third, intra-industry standard deviation of the venture sales level is applied as an
indicator of size heterogeneity among industry members.
For all indicators it is important to note that data is only based on new ventures and not
on all firms within one industry. Therefore, over all industries the absolute extent of
heterogeneity may be underestimated since new ventures as a group may have more
commonalities than all the firms within one industry. Since it may be assumed that this
underestimation occurs for all industries, this problem may be less critical for the
undertaken cross-industry comparison. Moreover the data is based on a limited sample
of new ventures within one industry. In order to minimise the impact of outliers and to
increase the validity of the indicator, several corrections have been made. Sales of the
first year of operation have not been considered for calculation of the sales and sales
growth indicators. For all three indicators those industries have been eliminated where
data of less than three firms was available for the calculation of the indicator.
Standard deviation of venture profitability per industry with venture profitability
calculated as follows:
2001
Venture profitability:=
∑
j =1998
p
x
t
Standard deviation of venture growth per industry with venture growth calculated as
follows:

 2001
 ∑ ( x j +1 − x j ) 


j =1998


Venture growth:=
t −1
Standard deviation of venture sales level per industry with venture sales level
calculated as follows:
2001
Venture sales level:=
∑ (x
j =1998
t
j
)
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
p
venture profit excluding year of foundation
x
venture turnover excluding year of foundation
j
year
t
number of years for which data on venture turnover is available
185
Employee productivity level
Employee productivity can be computed directly from the data on employees and sales
in 1,000 Euro, as given in the Monopolkommission data source. The data refers to the
year 2000 and is based on large samples of the federal statistics. Since employee
productivity does not vary greatly among different years, the application of solely data
of the year 2000 seems legitimate.
Employee productivity:=
Ei
Xi
E
total number of employees in industry i
X
total number of turnover in industry i
Gross margin
The gross margin level of the industry is estimated from the DtA data on profit and
turnover. It must be noted that new ventures frequently have lower profits than
established firms in the same industry in their early years. Therefore, the gross margin
indicator is derived solely from the venture’s own forecasts of profit and turnover in
the year 2001 as the last available year. For the gross margin indicator the same
limitations apply that have been mentioned before regarding the profit indicator of the
DtA sample. Distortions may occur due to the impact of taxes and owner’s salaries.
Gross margin per venture:=
p 2001
x 2001
p2001 profit of venture in 2001
x2001 turnover of venture in 2001
The gross margins per venture are finally aggregated on the industry level by
calculating an unweighted average of the venture values. In those cases where a
186
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
minimum of three venture values per industry is not given, the indicator is excluded
from the respective industry in order to minimise firm-specific distortions.
Average size of industry members
The average size of industry members can be calculated easily and accurately from
data on turnover in 1,000 Euro and the number of firms in a given industry from the
value-added tax statistics of the year 2000.
Average size of industry members:=
Xi
Ni
Xi
total turnover in 1,000 Euro of all firms in industry i
Ni
total number of firms in industry i
In the following table the operationalisations of variables regarding competitor
structure are summarised.
Indicator
Computation of
Data source
Availability
Limitation
indicator
COMPETITOR
STRUCTURE
(10) Concentration
Herfindahl index
Sum of squares of
market shares of all
firms
-
Monopolkommission
99 of 163 industries
Large number of
missing industries
(11) Number of industry
members
Firms per 4-digit
industry code
Value-added tax
statistics
163 of 163 industries
a) Standard deviation
of intra-industry
venture profitability
b) Standard deviation
of intra- industry
venture growth
c) Standard deviation
of intra- industry sales
level
a) Standard deviation
of sum of profits 1998
to 2001 divided by
sum of turnover 1998
to 2001
b)Standard deviation
of average annual
turnover growth 1998
to 2001
c) Standard deviation
of average annual
sales level 1998 to
2001
DtA
a) 150 of 163
industries
b) 135 of 163
industries
c) 155 of 163
industries
Absolute numbers per
industry codes are
sensitive to fact that
firms within one
industry code are not
necessarily
competitors
Heterogeneity of
ventures and not all
firms is measured.
Data is limited to the
number of ventures in
the DtA sample
(12) Heterogeneity of industry
members
(13) Employee productivity
level
Turnover per
employee
Monopolkommission
99 of 163 industries
Large number of
missing industries
(14) Gross margin
Profit per venture
turnover
Turnover divided by
number of employees
in 2000
Average turnover in
2001 divided by
average profit in 2001
DtA
135 of 163 industries
(15) Average size of industry
members
Average turnover of
firm
Total industry turnover
divided by number of
firms in industry
Value-added tax
statistics
163 of 163 industries
Possible distortions of
profit by owner
salaries and impact of
taxes
-
Table 33: Operationalisation of competitor structure
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
5.3.2.4
187
Operationalisation of variables of barriers to entry
Minimum organisational size
Data on the minimum organisational size is measured on the basis of the number of
employees on startup. The DtA data allows the calculation of the number of employees
at the end of the year of foundation.
n
Number of employees on startup:=
∑e
i =1
s
n
es
number of employees, including the owner, in year of startup
n
number of ventures in industry
Investment intensity
Investment intensity can be calculated directly from the Monopolkommission data on
investment and turnover. The industry investment intensity can be calculated as
follows:
Investment intensity:=
Ii
Xi
Ii
total investments of all firms in industry i
Xi
total turnover of all firms in industry i
The key profile of the two operationalised variables related to barriers to entry is
shown below.
Indicator
Computation of
Data source
Availability
Limitations
indicator
BARRIERS TO ENTRY
(16) Minimum organisational
size
Employees in year of
start up
(17) Investment intensity
Investment rate
Sum of number of
employees on startup
divided by number of
ventures
Investments per
industry divided by
turnover per industry
DtA
155 of 163 industries
Data is limited to the
number of ventures in
the DtA sample
Monopolkommission
96 of 163 industries
Large number of
missing industries
Table 34: Operationalisation of barriers to entry
5.3.2.5
Operationalisation of additional control variables
In the correlation analysis four additional control variables are analysed. All four
control variables are characteristics of the sample derived from the DtA data, which
188
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
may represent potential sources of distortions in both dependent and independent
variables.
Number of ventures per industry in sample
It is important to consider the number of ventures per industry in the sample, as
industry aggregates based on a small number of firms are more sensitive to outliers.
The number of ventures per industry has been automatically generated within the
aggregation of the ventures into industries, by counting the number of ventures for
each industry aggregate generated.
Average age of ventures in sample
It is important to consider the age of ventures per industry in the sample, as it might be
assumed that ventures achieve higher percentages growth rates more easily in early
years. In fact, however, the profit level may be lower for young ventures, as they
operate on a lower sales level and have to cover initial startup costs.
The average venture age is calculated as the average of the difference between the last
reported year of 2001 and the year of foundation for all ventures in the respective
industry.
n
Venture age:=
∑ (2001 − f )
i =1
n
f
year of venture foundation
n
number of ventures in industry
Average number of employees per venture in sample
Venture size can be assessed by looking at the average number of employees per
industry. Variables may be distorted with regard to size in terms of the venture’s
number of employees. The number of employees per venture is calculated as the
average of the number of employees throughout the years for which data is reported
for all ventures in the respective industry.
2001
n
Venture number of employees:=
∑(
i =1
∑e
j =1998
a
n
)
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
189
e
number of employees in year j
a
number of years for which data on employees is available
n
number of ventures in industry
Average sales per venture in sample
The average sales of ventures per industry is applied as another indicator of venture
size. Variables may be distorted with regard to the venture’s sales level in the sample.
Average venture sales are calculated as the average of sales in Euros throughout the
years for which data is reported for all ventures in the respective industry.
2001
n
Venture sales:=
∑(
i =1
∑x
j =1998
a
n
)
x
venture sales in year j (excluding year of foundation)
a
number of years for which data on sales is available (excluding year of
foundation)
n
number of ventures in industry
Indicator
Computation of
Data source
Availability
Limitations
indicator
CONTROL VARIABLES
(C1) Number of ventures per
industry
(C2) Age of venture
Number of ventures
per industry
Average venture age
(C3) Employees per venture
Average number of
employees of venture
(C4) Sales per venture
Average venture sales
Not needed
DtA
163 of 163 industries
Not applicable
Sum of venture age
divided by number of
ventures
Sum of average
number of employees
divided by number of
ventures
Sum of average sales
excluding year of
foundation divided by
number of ventures
DtA
163 of 163 industries
Not applicable
DtA
145 of 163 industries
Not applicable
DtA
155 of 163 industries
Not applicable
Table 35: Operationalisation of control variables
There are no important limitations of the control variables with regard to their purpose
of measuring the sensitivity of other variables to possible sources of distortions.
190
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
5.4
Characteristics and performance of new venture activity based
on the DtA sample
Within this chapter, a descriptive analysis is performed for the sample of new ventures
that has resulted from the sampling process196. The sample includes 5,117 new
ventures in 163 industries.
First, the non-aggregated sample of ventures is described, addressing the structural
characteristics of ventures, means of venture success variables and finally intertemporal developments of success measures for relevant venture sub-samples.
Second, the data on the 5,117 ventures of the sample is aggregated on an industry level
and structural characteristics of industries and their means of success measures are
described, which leads to the presentation of industries according to various success
measures.
5.4.1 Sample description on venture level
The DtA sample offers the rare opportunity to get financial information on a large
number of over 5,000 new ventures in Germany. The applied dataset of the 2001
survey is the only recent DtA dataset which collects data on the development of new
venture financial profit over time. Even though taking into consideration potential
distortions in the selection process of ventures which have been discussed in the
methodology part, the DtA sample reflects a great part of new venture activity in
Germany. Therefore the following descriptive analysis may provide unique insights
into the volume distribution, evolution processes, and performance of new venture
activity in Germany. The descriptive analysis concentrates especially on performance
measures and financials of ventures which are otherwise not available from any public
source.
5.4.1.1
Structure
Year of foundation
The range of years of foundation has been limited to the time span of 1995 to 2001.
About 60% of the ventures in the sample were founded in the years 1999 and 2000, the
196
See chapter 4.2.2, p.136f.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
191
two years prior to the date of the survey. The mean age of the ventures is 3.4 years at
the time of the survey, which corresponds to a date of foundation between 1998 and
1999.
2000
1658
1379
frequency
1500
835
1000
609
500
0
323
179
1995
134
1996
1997
1998
1999
ye ar of founda tion
2000
2001
n=5117
Figure 24: Years of foundation of ventures in sample
Size of ventures at startup by number of employees
For the majority of the ventures founded between the years 1998 and 2001, it has been
possible to derive the number of taxable employees at the year of foundation, as is
shown in the following table.
Cumulative
Frequency
no employees
Percent
Valid Percent
Percent
1555
30.4
39.0
39.0
1 employee
802
15.7
20.1
59.1
2-3 employees
505
9.9
12.7
71.7
3-4 employees
548
10.7
13.7
85.4
5-9 employees
367
7.2
9.2
94.6
10-19 employees
133
2.6
3.3
98.0
20-49 employees
68
1.3
1.7
99.7
50-99 employees
9
.2
.2
99.9
100-199 employees
4
.1
.1
100.0
Total
3991
78.0
100.0
Missing
1126
22.0
5117
100.0
Total
Table 36: Venture size in employees at year of start up
192
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Ventures of the sample started with a mean of 1.5 employees. It is notable that a large
share of 39% started their business without any taxable employees. Less than 15% of
all ventures started with more than four employees.
Venture sales volume
The distribution of sales is analysed on the basis of the average sales volume per
venture. The years 1998 until 2001 are considered as far as data has been provided.
Sales within the year of foundation have not been included for the calculation of the
average sales volume, as sales within the year of foundation do not cover a complete
calendar year.
Sales in 1,000 Euro
Frequency
<10
Percent
Valid
Cumulative
Percent
Percent
53
1.0
1.2
1.2
10< &=>50
532
10.4
12.0
13.2
50< &=>100
770
15.0
17.3
30.5
100< &=>250
1347
26.3
30.3
60.8
250< &=>500
818
16.0
18.4
79.2
500< &=>1,000
509
9.9
11.4
90.6
1,000< &=>2,500
273
5.3
6.1
96.7
>2,500
145
2.8
3.3
100.0
4,447
86.9
100.0
670
13.1
5,117
100.0
Total
Missing
Total
Table 37: Distribution of average venture size in average ventures sales in 1,000 Euro
The average venture sales level is 533,000 Euro with a median of 179,000 Euro. Two
thirds of all ventures had an average sales volume in the range of 50,000 Euro to
500,000 Euro. On the upper edge only about 20% of all ventures reached more than
500,000 Euro in average sales.
Industry sectors
The following figure shows the distribution of the ventures in the sample among four
broad industry sectors. The division has been made based on the NACE industry
classification. All industries within NACE codes below 4,000 are aggregated to the
category of the manufacturing sector. NACE codes from 4,000 to 4,999 are aggregated
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
193
to the construction category. NACE codes from 5,000 to 5,499 are aggregated to the
retail sector and finally NACE codes of 5,500 and higher are aggregated to the service
sector.
The majority of the sample belongs to the service sector, accounting for 2,677
ventures. Both sectors, service and trade account for over 78% of all ventures. The
manufacturing sector, which has been frequently investigated in previous research,
accounts for a mere 8.2% share of all ventures in the sample.
manuf acturing
8,2%
construction
13,4%
serv ices
52,3%
trade
26,1%
Table 38: Distribution of ventures among four major industry sectors
5.4.1.2
Success measures
The key characteristics of the applied venture success measures are summarised in the
table below.
dep
subjective
N
Valid
dep growth
dep profit
dep years to
average
break even
5049
2920
3883
2663
68
2201
1234
2454
Mean
.00
1.26
30.11
1.55
Median
.00
1.10
22.39
1.00
Std. Deviation
.71
1.22
223.29
.69
2
50.00
14.452.53
3
Missing
Range
194
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Minimum
-1
.00
-12.982.54
1
Maximum
1
50.00
1.469.99
4
Table 39: Summarising descriptive analysis of success measures
In the following paragraphs each success measure is treated independently.
Subjective evaluation of performance relative to plan
The measure of subjective venture performance (dep subjective) is available for 99%
of all of the ventures in the sample.
Subjective performance evaluation
Valid
Frequency
Percent
Valid
Cumulative
percent
percent
-1 (performance below plan)
1261
24.6
25.0
25.0
0 (performance according plan)
2534
49.5
50.2
75.2
1 (performance over plan)
1254
24.5
24.8
100.0
Total
5049
98.7
100.0
68
1.3
5117
100.0
Missing
Total
Table 40: Frequency table of subjective performance measure
As shown in the above table, the value is normally distributed with about 25% of all
respondents performing less well than plan, 25% of all respondents performing better
than plan and 50% of all respondents performing according to plan.
0,08
0,06
0,04
subjective
0,02
0
performance
-0,02
-0,04
-0,06
0,08
0,05
0,01
-0,01
-0,03
-0,05 -0,05
1995
1997
(N=177)
(N=322)
1999
(N=1365)
2001
(N=119)
year of venture foundation
(N=5085)
Figure 25: Average subjective performance measure by years of venture foundation
Overall, the mean of the subjective performance measure fluctuates very little among
different years of venture foundation. There is a small tendency of a more negative
subjective performance with increasing venture age. This is astonishing as ventures in
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
195
their early years are generally expected to face more serious problems in the
development of their activity and generate lower profits. Apparently, however, this
does not affect subjective performance evaluations or it may be outweighed by low
expectations for the first years after venture foundation and disappointment of
managers of older ventures about decreasing growth dynamics with increasing venture
age. It is important to stress that the above figure does represent in each category of
year of venture foundation different ventures and does not provide information about
the performance of the same ventures over time. The item of subjective performance
was just evaluated once at the time of filling out the questionnaire.
Sales growth
The measure of venture sales growth (dep growth) is available for only 57% of all
ventures in the sample. The operationalisation of the measure requires that sales of a
minimum of two years have to be available. At the same time the sales data for the
year of foundation has been excluded since it did not represent a full calendar year.
This leads to the exclusion of 1,792 ventures which have been founded in the years
2000 or 2001. An additional 408 ventures have to be excluded since data on sales has
been missing.
Cumulative
Sales growth rate
Valid
Total
Frequency
Percent
Valid percent
percent
<1
487
9.5
16.7
16.7
1
175
3.4
6.0
22.7
1< &=>1.05
393
7.7
13.5
36.2
1.05 < &=>1.10
379
7.4
13.0
49.2
1.10< &=>1.20
534
10.4
18.3
67.5
1.20< &=>1.40
486
9.5
16.7
84.1
1.40< &=>1.60
203
4.0
7.0
91.1
1.60< &=>2.50
198
3.9
6.8
97.9
2.50< &=>5.00
37
.7
1.3
99.1
>5.00
25
.5
.9
100.0
Total
2917
57.0
100.0
Missing
2200
43.0
5117
100.0
Table 41: Frequency table of venture sales growth
196
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
The average venture growth among all ventures in the sample is 26% in annual sales.
As can be seen in the table above, a 16.7% share of all respondents reported a negative
average growth. Another 6% of respondents reported that their ventures stagnated at
an average for the reported period of time. The remaining 73.3% reported increases in
their average sales with 8.9% of all ventures reaching an average sales growth of more
than 60%.
1,4
1,35
1,3
venture growth 1,25
1,2
in sales
1,15
1,1
1,05
1,37
1,28
1,16
1995
(N=166)
1,18
1,18
1997
(N=305)
1999
(N=1134)
year of venture foundation
N=2916
Figure 26: Average venture growth in sales measure, by years of venture foundation
The measure of venture sale growth varies greatly among groups of different years of
venture foundation. The above figure suggests that the sales growth level decreases
with venture age. In the following chapter 5.4.1.3 the inter-temporal development is
investigated in more depth for a sub-sample of ventures.
Profit level
The measure of profit level (dep profit average) is available for 76% of all ventures in
the sample. It has also been decided to exclude data on venture profit for the year of
foundation. Consequently, 139 ventures, which were founded in 2001 are
automatically excluded. Another 1,095 ventures did not provide applicable
information on profits. Considering the reluctance of small business owners to reveal
information on profits, the response rate to this item is surprisingly high.
Profits in 1,000 Euro
Valid
Frequency
Percent
Valid
Cumulative
percent
percent
< -100
22
.4
.6
.6
-100<= & >-50
38
.7
1.0
1.5
-50<= & >-25
66
1.3
1.7
3.2
-25<= & >-12.5
112
2.2
2.9
6.1
-12.5<= & >0
409
8.0
10.5
16.7
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
197
0<= & >12.5
748
14.6
19.3
35.9
12.5<= & >25
797
15.6
20.5
56.5
25<= & >50
903
17.6
23.3
79.7
50<= & >100
548
10.7
14.1
93.8
>=100
240
4.7
6.2
100.0
Total
3883
75.9
100.0
Missing
1234
24.1
5117
100.0
Total
Table 42: Frequency table of average venture profits in 1,000 Euro
The average venture achieved a profit level of about 30,000 Euro. As seen in the above
table, a share of 16.7% of all respondents reported a negative average level of profits
from 1998 to 2001. Of the remaining 83.3% of ventures with positive profits, 20.3%
achieved profits of over 50,000 Euro.
50
40
40
venture profit in 30
1,000 Euro
20
33
40
26
21
31
10
0
1995
(N=155)
1997
(N=289)
1999
(N=1116)
year of venture foundation
N=3883
Figure 27: Average venture profit (in 1,000 Euro) by years of venture foundation
There is no clear recognisable pattern regarding the distribution of average profits
among groups of different age of venture foundation. It has been the intention within
the research design to minimise the effect of venture age on profits by using the
average profit numbers from the years 1998 to 2001. The inter-temporal effect of
profits in individual years is investigated in the next chapter 5.4.1.3 for a sub-sample
of ventures.
Break even (first year of break even)
For 63% of all ventures data is available that specifies whether or not they were able to
break even before the year 2001. For 2,663 ventures, equaling 52% of all ventures in
the sample, information on the actual year of break-even is available. The break-even
measure can only be calculated for ventures that were founded between 1998 and 2001
198
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
since no information on profits was available for previous years. Therefore, 1,111
ventures, which were founded between 1995 and 1997, are not considered. A share of
11% of the overall sample, corresponding to 499 ventures, did not manage to break
even until the year 2001. Of the remaining ventures, 844 did not provide any
information about their profits. Taking into account that only ventures founded
between 1998 and 2001 are considered, the share of 16% of all valid respondents that
did not break even until 2001 appears very low.
Cumulative
Year of break-even
Valid
Frequency
Percent
Valid percent
percent
1
1470
28.7
46.5
46.5
2
947
18.5
29.9
76.4
3
218
4.3
6.9
83.3
4
28
.5
0.9
84.2
2663
-
-
-
499
11.0
15.8
100.0
Total
3162
63.0
100.0
Missing
1955
37.0
5117
5117
Subtotal
No
Break
even
Total
Table 43: Frequency table of years to break even success measure
Apart from the low share of ventures that did not break even until 2001, the average
time period that is needed to break even appears very short. After an average of 1.55
years the ventures of the sample reached break-even. According to the above table,
46.5% of the ventures had already reached break-even within the year of foundation,
and 76.4% reached break-even at the end of the year after their foundation.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
2
199
1,72
1,7
1,32
1,5
1
years to break even
1
0,5
0
1998 (N=646)
1999 (N=958)
2000 (N=996)
2001 (N=63)
year of venture foundation
N=2663
Figure 28: Average years needed to break even by years of venture foundation
Splitting the mean of years to break even into groups of ventures with the same year of
foundation, the duration of time to break even seems to stabilise for the ventures
founded in 1998 and 1999 at about 1.7 years, as indicated in the above figure. For the
group of ventures founded in 2001, the value has to be 1 since the year of their
foundation is also the last year for which data was reported.
23
25
20
% of ventures 15
without break10
even
5
0
20
16
7
1998
(N=52)
1999
(N=184)
2000
(N=244)
2001
(N=19)
year of venture foundation
N=672
Figure 29: Average share of ventures in % without breaking even until 2001 by years of venture foundation
The distribution of shares of ventures that did not break even contrasts with the
development of the years to break even. Of the firms that were founded in 1998, only a
7% share did not break even until their fourth year of operation, in 2001 (see figure
above). It can be assumed that unsuccessful older ventures that closed down before
2001 did not respond to the questionnaire, which may have led to an underrepresentation of unsuccessful older ventures in the sample. However, even for the
group of ventures that was founded in 2001, only 23% of ventures did not break even
in their year of foundation. Comparing this value with the average time-to-break even
among all ventures, the group of ventures founded in 2001 broke even much faster.
200
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Overall, the relatively little time needed for the ventures of this sample to break even
may result from the low level of investment intensity, the relatively small venture size
and the large share of firms in the service sector.
5.4.1.3
Inter-temporal development of success measures for sub-samples
For the described success measures of venture sales growth, venture employee growth
and venture profit, data is available for each individual year from 1998 to 2001. It is of
particular interest to look at the inter-temporal developments of these measures as they
can give valuable information on the evolution patterns and growth dynamics of new
ventures.
Time series can not be analysed in the context of the venture level since different
ventures are included in each year. For the data on the year 1998, only those 1,946
ventures that were founded in 1998 or before can provide information . For the data on
the year 2001, however, all 5,117 ventures can provide applicable information. In
order to ensure inter-temporal comparability, sub-samples of ventures have to be
generated for each individual success measure.
Sales growth
The sub-sample of ventures for the investigation of venture sales growth consists only
of those 913 ventures for which information on sales was given for each year from
1998 to 2001. Ventures founded in 1998 have been excluded, since the indicated sales
number can not be expected to represent a full calendar year and can therefore not be
applied for inter-temporal analysis. Consequently, only ventures that were founded
between 1995 and 1997 are considered. These ventures had been operating at the
beginning of 1998 for an average of about 1.7 years.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
201
1750
1500
sales in 1000 Euro
1250
1000
750
500
250
0
N=
913
913
913
913
1998
1999
2000
2001
Figure 30: Development of venture sales 1998-2001
The above figure shows the interquartil boxplots without outliers, indicating the
median with a horizontal line in the box. The median of sales increases by 30% from
256,000 Euro in 1998 to 332,000 Euro in 2001.
From 1998 to 2001, the level of sales increased by 46 % from an annual mean of
647,000 Euro in 1998 to a mean of 950,000 Euro in 2001.
As can be observed, venture growth dynamics decrease with higher venture age. The
annual growth rate of the sales mean decreases from 22% during 1999 as the second
full calendar year of operation for the average venture in the sample, to 16% during
2000 as the third full calendar year of operation and to only 4% during 2001 as the
fourth full calendar year of operation.
25
22
20
16
annual sales 15
growth in
10
percent
4
5
0
year 2
(19981999)
year 3
(19992000)
year 4
(20002001)
N=913
Figure 31: Sales growth of ventures in percent for years of operation
The development in number of employees per year is only meaningful for ventures
where data for 1998-2001 is available.
202
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
45
40
annual growth of venture sales in %
35
30
25
20
15
ventures founded in
10
1995 (N=151)
5
1996 (N=491)
1997 (N=271)
0
1999
2000
2001
Figure 32: Annual growth of number of employees by year of venture foundation
Comparing the growth rate of ventures of different years of foundation reflects the
assumption that ventures grow more rapidly in their early years. For all ventures,
however, the rate of sales growth drops significantly in the year 2001.
In order to estimate the impact of macro-economic factors on the development of the
venture growth rates, the value-added tax statistics gives information about the
development of sales and rates of sales growth for the whole German economy.
3,5
3,5
3
2,5
annual sales growth 2
in % Germany
1,5
1
0,5
0
3
1,4
1998-1999
1999-2000
2000-2001
N > 2.9 million
Figure 33: Annual growth in taxable sales for Germany from the value-added tax statistics
The ventures of the sub-sample increased their level of sales at growth rates that were
a multiple of the growth rate of the overall economy. At the same time, growth rates of
ventures follow the pattern of the overall economy and are highly sensitive to changes
in the growth dynamics of the overall economy. The data suggests that apart from
macro-economic factors, growth rates are highest in the first years after foundation and
decrease over the years in operation.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
203
Growth in number of employees
Corresponding to the description of venture sales, only those ventures where data on
the number of employees is given for each year from 1998 to 2001 are considered for
deriving information on the employee growth rate. Data from ventures founded in
1998 could also be applied, thereby including another 1,939 ventures, which lead to a
more comprehensive sub-sample than the sub-sample for venture sales growth.
Ventures in the sub-sample were founded between 1995 and 1998. A 43% share of
these ventures was founded in 1998, the first year of data reporting.
12
11
10
number of employees
9
8
7
6
5
4
3
2
1
0
N=
1939
1939
1939
1939
1998
1999
2000
2001
Figure 34: Development of the number of employees 1998-2001
After eliminating outlying values, the above interquartil boxplots indicate that the
median increases from 2 employees in 1998 to 3 employees in 2001. For the same time
span the mean number of employees increases by 38% within the three years, from
3.87 employees in 1998 to 5.46 employees in 2001.
204
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
25
21
20
15
annual employee 15
growth in percent 10
2
5
0
1998-1999
1999-2000
2000-2001
N=1939
Figure 35: Annual employee growth rates1998-2001 in percent
The annual growth rate of the mean number of employees decreases from 21% annual
growth during 1999, as the first full calendar year of operation for the average venture
in the sample, to 15% annual growth during 2000 as the second full calendar year of
operation. Later the figure stagnates at only 2% annual growth during 2001 as the third
full calendar year of operation. The figure shows that the growth rates in number of
employees are nearly identical to the growth rates in sales. In comparison with the
growth rate for ventures of different years of foundation, the impact of the year of
venture foundation is not as strong as it was for sales growth. However, there is a trend
for ventures with a more recent date of foundation to achieve higher employee growth
rates.
35
annual growth of employees in %
30
25
20
15
ventures founded in
1995 (N=177)
10
1996 (N=606)
5
1997 (N=321)
0
1998-1999
1998 (N=835)
1999-2000
2000-2001
Figure 36: Annual growth of number of employees by year of venture foundation
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
205
Profit level
Corresponding to the description of venture sales, only those ventures where data on
the number of employees is given for each year from 1998 to 2001, corrected by the
year of venture foundation, were considered for deriving information on the profit
growth, . Since profit data was less frequently provided in the questionnaire, the subsample for the description of the profit development contains only 767 ventures.
Ventures in the sub-sample were founded between 1995 and 1997.
150
profit in 1000 EURO
100
50
0
-50
-100
N=
767
767
767
767
1998
1999
2000
2001
Figure 37: Development of the venture profits 1998-2001
After eliminating outlying values, the above interquartil boxplots indicate that the
median increases by 46%, from 24,500 Euro in 1998 to 35,800 Euro in 2001.
For the same time span, the mean venture profit of the sub-sample increases by
830%in three years, from 7,300 Euro in 1998 to 60,620 Euro in 2001.
For illustrating the development of profits, they have been related to the development
of sales for the same time period.
206
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
100
89
87
74
80
60
60
61
mean profit in 1,000 Euro
43
40
20
0
mean sales in 10,000 Euro
9
7
1998
1999
2000
2001
Figure 38: Mean of venture profits compared with sales
In comparison, sales of the ventures in the sub-sample increase steadily at a rate of
only 33%, from 600,000 Euro in 1998 to 800,000 Euro in 2001. The large increase of
profits from the year 1999 to the year 2000 is striking, and will be investigated in more
detail by looking at the developments between ventures of different years of
foundation.
80
70
60
Average venture profit in 1000 Euro
50
40
30
20
ventures founded in
10
0
1995 (N=121)
-10
1996 (N=423)
-20
1998
1997 (N=223)
1999
2000
2001
Figure 39: Development of average venture profits by year of venture foundation
While the increase in venture profits is extraordinarily linear for ventures founded in
1995 and 1997, the average for ventures founded in 1996 remains negative for both
1998 and 1999, increasing afterwards much more rapidly to the highest level of profits
in 2001.
The annual growth rate remains relatively stable for the first two years among those
ventures founded in 1995 and 1997, at about 15% annual growth for ventures founded
in 1995 and 33% annual growth for those founded in 1997. The growth pattern of
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
207
ventures founded in 1996 is different. These ventures show increasing losses for the
first years and then making an exceptional recovery in the last year. No value can be
calculated for the growth rate from 1999 to 2000 for ventures founded in 1996.
Venture profits increase within this year from a negative level of 14,000 Euro to a
positive level of 30,000 Euro. Due to this missing value the development of profit
growth is presented in a bar diagram instead of a line diagram.
80
68
60
32
40
20
16
33
14
20
1
0
ventures founded in 1996
((N=423)
ventures founded in 1997
(N=223)
-20
-40
ventures founded in 1995
(N=121)
-27
1998-1999
1999-2000
2000-2001
Figure 40: Annual venture profits in 1000 Euro 1998-2001
It must be assumed that new venture performance measures are also affected by the
development of the overall macro-economic environment. Within the 1998 to 2001
time span, the growth rate of the overall economy slowed down, which may have
reinforced the decline in growth development. For the same time span, there are clear
indications of an evolutionary pattern of a strong growth dynamics for new ventures
during the first three years of operation accompanied by a very low profit level. After
the first three years of operation, venture sales volume stabilised and the level of
profits greatly increased, since startup investments and investments in further growth
decreased. Also the larger size may have led to a better profit structure after the first
three years of operation.
5.4.2 Sample description on aggregated industry level
In the following chapter, the perspective is changed from the venture as unit of
analysis to the industry as unit of analysis. The data sets of the 5,117 individual
ventures are aggregated by industry codes into 163 industries. The structure of this
aggregated industry data set will be revealed and the industries in which new ventures
have been particularly successful will be identified.
208
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
5.4.2.1
Structure
Sector
The shares of the service, trade and manufacturing sectors are more evenly distributed
on the aggregated industry level than on the venture level.
services
36,8%
manufacturing
25,8%
construction
9,2%
trade
28,2%
Figure 41: Distribution of industries among four major industry sectors
The service sector, which represented over half of all ventures in the sample, also
comprises the largest share of all industries, with 36.8%. The 28% share of trade sector
industries corresponds to a 26% share of ventures. Industries in manufacturing are
over-represented with over 25%, while manufacturing counted for only 8% of all
ventures in the sample. The smallest sector on the industry level is construction, with a
9.2% share of industries accounting for 13% of all ventures in the sample.
On average, each industry contained data of about 31 ventures. According to the
industry sector, the average number of ventures per industry is 45 in the service sector,
29 in the trade sector, 46 in the construction sector and only 10 in the manufacturing
sector.
Start-up activity per industry
Since the industry code was not applied as sampling criteria within the process of data
collection, the sample reflects the industry distribution of all ventures that have been
funded by the DtA. The following table ranks startup activity per industry based on the
sample applied in the study.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
Rank
NACE code
Industry
209
# ventures
1
8512 Medical practice activities
333
2
8514 Other human health activities
315
3
5248 Other retail sale in specialised stores
258
4
9305 Other service activities n.e.c.
236
5
9302 Hairdressing and other beauty treatments
195
6
7484 Other business activities n.e.c.
190
7
4545 Other building completion
158
8
5530 Restaurants
151
9
8513 Dental practice activities
151
10
7411 Legal activities
125
11
7420 Architectural and engineering activities and related
119
technical consultancy
12
4533 Plumbing
109
13
5020 Maintenance and repair of motor vehicles
103
14
4534 Other building installation
98
15
5212 Other retail sale in non-specialised stores
90
16
5231 Chemists
77
17
4531 Installation of electrical wiring and fittings
75
18
5241 Retail sale of textiles
73
19
7412 Accounting, book-keeping and auditing activities; tax
73
consultancy
20
5227 Other retail sale of food, beverages and tobacco in
67
specialised stores
21
4542 Carpentry
66
22
5511 Hotels and motels, with restaurant
62
23
5244 Retail sale of furniture, lighting equipment and
60
household articles
24
5211 Retail sale in non-specialised stores of food, beverages or
57
tobacco
25
7414 Business and management consultancy activities
57
Table 44: Top 25 industries with most business startups in Germany according to the DtA sample
210
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Among the 25 industries with most venture startups in the sample, there are four large
groups of industries.
First, there are the professional industries such as doctors, lawyers, architects and
consultants, with an outstanding accumulation of ventures related to health
professionals.
Second, there is a large group of industries in the retail sector.
Third, there is a high representation of ventures in the hotel and catering industries.
Fourth, there are typical craftsmen industries such as plumbing, vehicle repair,
electrical installation and carpentry.
A comparison of the numbers of ventures with the overall number of firms in the
value-added tax statistics and the ZEW data on firm foundations permits specifying of
whether the above-mentioned groups also represent the largest share of firms and
startup activity in Germany. The industry groups retailing, hotel and catering and
craftsmen industries coincide with both the overall firm distribution in Germany,
according to the value-added tax statistics, and with the most frequent industries in the
ZEW data. However, the first group of professional people, especially the industries of
medical practice and human health services, seem to be overrepresented in the DtA
sample. This might be the result of preferences in the DtA funding process. The
complete ranking of industries according to the number of ventures per industry in the
sample is attached in appendix B. It is of interest that many of those industries that
account for large numbers of firm foundations receive only very little attention in
entrepreneurship research. The frequently investigated segments of hi-tech ventures in
the manufacturing sector only account for a very small fraction of entrepreneurial
activity in a given economy.
5.4.2.2
Success measures
For both investors and future entrepreneurs, the identification of attractive markets is
of primary interest. Analysis of the DtA data allows the identification of those
industries where ventures have been particularly successful, and which may also
provide a positive opportunity structure for future new venture foundations. In the
following paragraphs, the industries with the highest average success of new ventures
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
211
in Germany are identified on the basis of four success measures: subjective
performance, sales growth, profit level and years to break even.
Most successful industries by venture sales growth
In the table below, a ranking of those industries where new ventures achieved the
highest average sales growth is shown.
Rank
Sales growth
NACE
rate
code
Industry
# ventures
1
5.38
5261 Retail sale via mail order
5
2
2.56
7260 Other computer-related activities
3
2.33
9234 Other entertainment activities n.e.c.
4
2.03
7484 Other business activities n.e.c.
5
1.84
7483 Secretarial and translation activities
6
1.76
5274 Repair n.e.c.
11
7
1.75
7414 Business and management consultancy activities
57
8
1.71
7230 Data processing
26
9
1.65
5170 Other wholesale
28
10
1.65
4525 Other construction work involving special trades
11
1.63
7031 Real estate agencies
11
12
1.56
1513 Production of meat and poultry products
27
13
1.54
5040 Sale, maintenance and repair of motorcycles and related
22
24
5
190
8
7
parts
14
1.53
2923 Manufacture of non-domestic cooling and ventilation
4
equipment
15
1.49
6024 Freight transport by road
39
16
1.44
4544 Painting and glazing
56
17
1.43
7032 Management of real estate on a fee or contract basis
7
18
1.41
9000 Sewage and refuse disposal, sanitation and similar
3
activities
19
1.41
7220 Software consultancy and supply
17
20
1.40
6330 Activities of travel agencies and tour operators; tourist
32
assistance
212
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
21
1.40
7411 Legal activities
125
22
1.40
5119 Agents involved in the sale of a variety of goods
23
1.40
8042 Adult and other education n.e.c.
35
24
1.38
7470 Industrial cleaning
12
25
1.36
9305 Other service activities n.e.c.
4
236
Table 45: Top 25 industries by venture sales growth
New ventures in the service sector comprise 64% of the top 25 industries, giving them
much more representation than corresponds to their 37% share of all considered
industries. At the same time, ventures in manufacturing are underrepresented in this
list of industries with the highest venture growth rates. Only 8% of the industries in the
top 25 ranking are from the manufacturing sector, compared with a 26% share of the
total number of investigated industries. The higher frequency of the service sector may
derive from the generally smaller venture size and the higher growth dynamics in this
sector. The complete ranking by subjective performance evaluation for all 163
industries is given in appendix D.
Most successful industries by achieved level of venture profits
The following table shows all those industries where new ventures reached the highest
profit level.
Rank
Profit in
NACE
1000 EUR
code
Industry
#
Sales in
ventures
1,000 EUR
1
350.12
4532 Insulation work
5
2.324
2
205.76
3162 Manufacture of other electrical equipment n.e.c.
8
1.451
3
144.23
4525 Other construction work involving special trades
7
3.426
4
135.52
2666 Manufacture of other articles of concrete, plaster
6
3.780
and cement
5
118.62
2862 Manufacture of tools
10
1.115
6
110.52
9261 Operation of sports arenas and stadiums
13
409
7
100.13
4511 Building demolition ; and digging
4
611
8
85.78
8513 Dental practice activities
151
318
9
85.14
2840 Forging, pressing, stamping and roll forming of
11
558
metal; powder metallurgy
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
10
80.83
213
2521 Manufacture of plastic plates, sheets, tubes and
4
1.883
25
1.158
profiles
11
80.55
2875 Manufacture of other fabricated metal products
n.e.c.
12
80.42
7220 Software consultancy and supply
17
2.001
13
76.81
3310 Manufacture of medical and surgical equipment
11
1.886
77
850
auditing
73
412
4523 Construction of highways, roads, airfields and
5
1.574
333
262
6
653
8
338
5
810
12
242
and orthopaedic appliances
14
76.27
5231 Chemists
15
71.74
7412 Accounting,
book-keeping
and
activities; tax consultancy
16
70.93
sport facilities
17
70.88
8512 Medical practice activities
18
68.60
5512 Hotels and motels, without restaurant
19
65.41
3340 Manufacture
of
optical
instruments
and
photographic equipment
20
64.83
2222 Printing n.e.c.
21
58.99
7410 Legal,
accounting
&
auditing
activities;
consultancy; market research
22
57.93
2900 Manufacture of machinery and equipment n.e.c.
4
3.827
23
56.67
3663 Other manufacturing n.e.c.
6
286
24
55.03
7420 Architectural and engineering activities and
119
493
4
340
related technical consulting
25
53.77
4540 Other construction
Table 46: Top 25 industries by achieved level of venture profits
It can be assumed that ventures in industries of a larger average venture size and
higher average venture sales also achieve a higher level of venture profits. Therefore,
the average annual sales volume per venture is given in addition in the above table.
While the average sales level of the top 25 industries is at 1.240,000 Euro, about
double the average sales level of all industries in the sample, the impact is noticeable,
but not as strong as was assumed. As expected, a large share of manufacturing
industries can be found in the top 25 ranking, due to the tendency towards higher sales
volume and venture size in this sector. A 44% share of all industries in the top 25
derives from the manufacturing sector, while an extraordinarily low share of only 4%
214
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
derives from the trade sector. The construction sector represents 20% of the top 25
ranking, but only 9% of all investigated industries. The complete ranking by venture
profits for all 163 industries is given in appendix E.
Most successful industries by years needed to break even
The 25 industries where new ventures needed the shortest time to break even are
shown below.
Rank
1
Years to break
NACE
even
code
1.00
Industry
4523 Construction of highways, roads, airfields and sport
# ventures
5
facilities
2
1.00
2924 Manufacture of other general purpose machinery n.e.c.
3
1.00
4
1.00
2851 Treatment and coating of metals
5
1.07
1513 Production of meat and poultry products
27
6
1.07
4522 Erection of roof covering and frames
47
7
1.09
6603 Non-life insurance
18
8
1.17
7412 Accounting, book-keeping and auditing activities; tax
73
141 Agricultural service activities
11
4
4
consultancy
9
1.17
4541 Plastering
10
1.20
3340 Manufacture of optical instruments and photographic
10
8
equipment
11
1.20
5240 Other retail sale of new goods in specialised stores
11
12
1.20
5147 Wholesale of other household goods
12
13
1.24
5211 Retail sale in non-specialised stores with food, beverages
57
or tobacco
14
1.25
9261 Operation of sports arenas and stadiums
13
15
1.25
2745 Other non-ferrous metal production
16
1.25
5222 Retail sale of meat and meat products
21
17
1.29
2225 Other activities related to printing
16
18
1.29
5243 Retail sale of footwear and leather goods
15
19
1.30
4544 Painting and glazing
56
6
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
215
20
1.33
2862 Manufacture of tools
10
21
1.33
1581 Manufacture of bread; manufacture of fresh pastry goods
40
and cakes
22
1.33
7031 Real estate agencies
11
23
1.33
9212 Motion picture and video distribution
4
24
1.33
5153 Wholesale of wood, construction materials and sanitary
6
equipment
25
1.33
6321 Other supporting land transport activities
4
Table 47: Top 25 industries by years needed to break even
Similar to the ranking of venture profit, both the manufacturing and construction
sectors are much more frequently mentioned in the ranking than corresponds to their
share among the investigated industries.
The complete ranking by subjective performance evaluation for all 163 industries is
given in appendix F.
Most successful industries by subjective venture success evaluation
Finally, those 25 industries are listed where new ventures achieved the highest average
subjective performance evaluation.
Rank
Subjective rating NACE
(0 to 1)
Industry
# ventures
code
1
0,9
4523 Construction of highways, roads, airfields and sport
facilities
5
2
0,88
2923 Manufacture of non-domestic cooling and ventilation
equipment
4
3
0,8
4532 Insulation work activities
5
4
0,8
8531 Social work activities with accommodation
5
5
0,75
2862 Manufacture of tools
6
0,75
6321 Other supporting land transport activities
4
7
0,75
7310 Research and experimental development on natural
sciences and engineer
4
8
0,73
9261 Operation of sports arenas and stadiums
13
9
0,73
2924 Manufacture of other general purpose machinery
n.e.c.
11
10
0,71
8532 Social work activities without accommodation
10
7
216
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
11
0,71
7470 Industrial cleaning
12
0,7
13
0,69
6603 Non-life insurance
18
14
0,68
2811 Manufacture of metal structures and parts of
structures
11
15
0,67
2745 Other non-ferrous metal production
16
0,67
5030 Sale of motor vehicle parts and accessories
17
0,67
5262 Retail sale via stalls and markets
3
18
0,67
7140 Renting of personal and household goods n.e.c.
3
19
0,67
7250 Maintenance and repair of office, accounting and
computing machinery
3
20
0,67
7481 Photographic activities
12
21
0,67
9262 Other sporting activities
12
22
0,67
9271 Gambling and betting activities
23
0,66
2735 Other first processing of iron and steel n.e.c.;
production of non-ECS
24
0,64
8513 Dental practice activities
150
25
0,64
8512 Medical practice activities
330
2625 Manufacture of other ceramic products
12
5
6
12
3
25
Table 48: Top 25 industries by subjective venture success evaluation
With respect to the industry sector, it is remarkable that only two industries from the
trade sector are among the top 25 industries according to the subjective success
measure, even though retailing accounted for about 28% of all industries in the
sample. At the same time, ventures in the service sector are listed with a much higher
frequency among the top 25 than corresponds to their share of investigated industries.
The complete ranking by subjective performance evaluation for all 163 industries is
given in appendix C.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
217
5.4.3 Summary of characteristics and performance of new ventures in
sample
The descriptive analysis provided information on the characteristics of the ventures in
the DtA sample, and may also give more general indications about entrepreneurial
activity in Germany, due to the comprehensiveness of the sample.
Sample characteristics on venture level
The characteristics of the new ventures in the DtA sample can be summarised as a
majority of ventures being founded within the three years before the date of survey
between 1998 and 2000, employing less than two employees, reaching less than
250,000 Euro in sales per year and belonging to the service sector.
Sample success measures on venture level
In terms of success measures, the new ventures in the DtA sample performed on
average according to plan, with about 25% of ventures performing better and 25% of
ventures performing worse than plan. On average, the ventures achieved 26% annual
growth in sales, 30,000 Euro of profit within the last year, and had already broken
even within their second year of operation.
Sample success measures development on venture level
The dynamics of venture success measures is, to a large extent, influenced by the
macro-economic business cycle. For the years from 1999 to 2001, the average growth
in sales and employees started with an initial growth rate of over 20% in 1999, which
decreased in 2000 by 6%, until stabilising in 2001 at nearly zero growth. At the same
time, the profit level increased steadily, with a big leap around the fourth year of
venture operation, increasing from 9,000 Euro in profits in 1999 to 43,000 Euro in
profits in 2000. On average, the first three years of operation were characterised by
high growth rates which slightly decreased over time. During these years, the funds
generated were used to finance venture growth. After the first three years, the size of
the ventures stabilised and profits greatly increased.
Sample characteristics on the industry level
The sample reveals a concentration of startup activity in the areas of retail, hotel,
catering, craftsmen and professionals.
218
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Sample success measures on industry level
For the venture growth and subjective venture performance measures, ventures in the
service sector have been found much more frequently among the top 25 industries than
corresponds to their share of all industries. For the venture profit and years to break
even measures, ventures from manufacturing and construction have been found much
more frequently among the top 25 industries than corresponds to their share of all
industries. The retail sector has been underrepresented for all four measures of venture
performance.
5.5
Analysis of the impact of industry factors on new venture
performance
After the description of venture performance in different industries, this chapter
analyses why ventures performed better in some industries than in others. According
to the initial objective 5197 of the study, the impact of industry factors on new venture
performance is investigated. Particular attention is given to variations of the impact of
industry factors in different contexts.
The identification of industries with high venture success rates provides a first
indication of the potential attractiveness of an industry for the startup of a new venture.
However, the applied 4-digit industry categories are relatively broad. If it is possible to
understand which industry factors are actually relevant for venture performance, the
attractiveness of small niche markets in the individual venture context can also be
analysed more accurately.
5.5.1 Dependencies among variables
As a preparational step of the analysis of the impact of industry factors on venture
performance, it is important to understand how the dependent and the independent
variables are related among themselves. Apart from being a methodological
prerequisite for conducting regression analysis, understanding interactions among the
variables is necessary for the interpretation of results later on.
197
See chapter 1.2, p.4.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
5.5.1.1
219
Dimensions of venture success
A correlation analysis among the applied dependent variables of venture success was
conducted in order to identify whether different dimensions of venture success are
related to each other. The following analysis is based on the DtA data set on the
venture level. The aggregation on the industry level would have led to distortions
because extreme cases and outliers would have levelled out the results. Therefore, the
more complete venture data set is applied. The table below shows the Pearson
correlation among the dependent variables of venture success.
The subjective measure of venture performance correlates significantly with both
profitability measures. Subjective performance is rated higher for increasing venture
profits and shorter for time to break even.
dep
dep growth
subjective
dep subjective
dep growth
dep profit in EURO
dep years to break
even
dep profit
dep years to
in EUR
break even
Pearson Correlation
1
.029
.107(**)
-.153(**)
Sig. (2-tailed)
.
.119
.000
.000
N
5049
2885
3844
2626
Pearson Correlation
.029
1
-.032
.117(**)
Sig. (2-tailed)
.119
.
.101
.000
N
2885
2917
2626
1474
.107(**)
-.032
1
-.181(**)
Sig. (2-tailed)
.000
.101
.
.000
N
3844
2626
3883
2520
-.153(**)
.117(**)
-.181(**)
1
Sig. (2-tailed)
.000
.000
.000
.
N
2626
1474
2520
2663
Pearson Correlation
Pearson Correlation
** Correlation is significant at the 0.01 level (2-tailed).
Table 49: Correlation table of dependent variables of venture performance
Both profitability measures also correlate significantly with each other, with high
profits being related to a short time to break even.
While venture growth is not significantly related to the profit level, it is significantly
related in a positive direction to the time to break even. Fast-growing ventures
220
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
therefore need longer to break even as they may use their generated funds to finance
further venture growth.
5.5.1.2
Dimensions of independent industry variables
All independent industry variables are necessarily collected on the industry level. Even
the independent variables that are based on the DtA data of individual firms are only
considered on the industry level, aggregated by industry classification codes.
Most previous studies that investigated industry variables, concentrated on the
relationship between industry variables and organisational success. The interactions
among industry variables themselves were not investigated in most of these studies.
Therefore, the reviewed literature does not provide explanations for the results of the
correlation analysis among industry variables. The independent industry variables
correspond to the variables for which formerly hypotheses regarding the impact on
venture success have been formulated198.
Market size (v1) is significantly correlated with the number of firms (v11). It can be
assumed that larger industries, measured in overall sales, also tend to have more
industry members. At the same time, the market size variable is also positively
correlated with various variables of firm size, such as minimum efficient scale (MES)
and average firm sales levels. Other significant correlations comprise negative
correlations with investment intensity (v17) and a positive correlation with employee
productivity (v16).
Minimum efficient scale (v2) is positively correlated to market size (v1) and average
firm sales volume (v15). A high average sales volume per firm may lead to a higher
overall sales volume of an industry. Therefore, the correlations identified can be
explained by the fact that a high average firm sales level may be the result of a high
minimum efficient scale, because firms of low sales levels were not able to survive.
Export balance (v3) correlates positively with the distance to clients (v4), as both
variables have been derived from the same questionnaire item and measure, at
different scales, the geographic extent of the sales market.
198
Compare chapter 5.2, p 159ff.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
221
The distance to clients (v4) is as indicated before correlated to the export balance (v3).
The correlation with the average sales volume per firm (v15) suggests that industries
with a larger average firm size in terms of sales tend to have a client base of a
geographically wider expansion. At the same time, relatively low gross margins (v14)
may indicate a high level of national competition, which may have forced firms to
expand abroad. Moreover, with an increasing distance to clients, transparency of the
supply market may also increase and lead to decreasing gross margins. Other
significant correlations include positive correlations with the heterogeneity of sales
growth (v12b) and investment intensity (v17). The correlation with investment
intensity indicates that in industries with firms operating on a larger geographic basis,
investments are also higher.
Total market growth (v5) is positively related to both absolute number of firm entries
(v6) and net entries (v8). On the one hand growing markets attract new ventures and,
on the other, an increasing number of new ventures leads to higher sales in the overall
industry. The significant correlation with the degree of uncertainty (v9) shows that in
high growth industries, future firm development can be planned less accurately than in
industries with low growth rates. Higher total market growth has been found in
industries with less heterogeneous profit levels (v12a), indicating that a relative
coherent range of firm profits within one industry induces higher total growth. A
negative correlation has been found for gross margin (v14). Apparently lower gross
margins pressure firms to achieve higher sales growth. A positive impact of
investments on growth is demonstrated by the positive correlation with investment
intensity (v17). The average firm size (v15) is positively correlated with market
growth. One might have expected that it is easier to achieve a higher percentage
growth for firms on a lower sales base. However, smaller firms may have lower
growth aspirations199 and therefore industries with a smaller average firm size may
have achieved a lower total market growth.
Entries to the industry (v6) correlate positively with the number of net entries (v8).
There are more entries in industries with high heterogeneity of firm size in sales
(v12c). Entries have been more frequent in industries with smaller initial investment
199
Compare Davidsson 1991.
222
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
requirement measured in the number of employees on startup (v16), as the possibility
to start at a low scale increases startup activity.
The variable of exits from industries (v7) does not correlate significantly with any
other independent variable. The theory of organisational ecology would have
suggested that the exit rate correlates with the variable of entries to industry (v6) due
to the concept of liability of newness200. However, this could not be confirmed on the
given 5% significance level.
Net entries (v8), which are calculated as the balance of entries and exits to an industry,
are correlated to market growth (v5) and also correlate to the variable of entries to
industry (v6).
The degree of uncertainty (v9) is positively correlated with market growth (v5), as has
been pointed out before. At the same time it is, as is market growth, negatively
correlated with the heterogeneity of profitability (v12a). Regarding heterogeneity of
the sales level (v12b), there is a positive correlation, indicating that large firm size
differences within an industry relates to higher uncertainty. Furthermore, a higher level
of uncertainty is related to a lower gross margin (v14). Uncertainty is also higher in
industries with a higher average firm size (v15), which might indicate that the
behaviour of large firms is more difficult to anticipate.
The Herfindahl index of industry concentration (v10) is not related to any other
industry variable.
The number of industry members (v11) correlates positively with market size (v1),
which seems understandable as one may assume that an industry with a large total
sales volume may contain more firms than an industry with a low total sales volume.
The number of industry members correlates negatively with the average firm size
(v15).
The three independent variables of heterogeneity correlate individually with different
industry variables.
Heterogeneity of profitability (v12a) correlates negatively with both market growth
(v5) and uncertainty (v9). It correlates positively with gross margin (v14). The latter
200
See chapter 3.2.3, p.57ff on organisational ecology.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
223
can be understood as the average profit level increases with higher gross margin,
which in turn increases the potential heterogeneity of profitability.
Heterogeneity of sales growth (v12b) correlates positively with the distance to clients
(v4). Equivalent to the market growth variable, the heterogeneity of sales growth also
correlates positively with entries to industries (v6) and uncertainty (v9).
Heterogeneity of sales volume (v12c) correlates positively to market size (v1), average
firm size (v15) and average number of employees on startup (v16). All of these
variables are related to each other since they are directly impacted by firm size per
industry. Higher average firm size per industry will have a positive impact on market
size, average firm size in sales and also the number of employees on startup.
Employee productivity (v13) measured in sales per employee correlates positively with
market size (v1). Employee productivity is negatively correlated to investment
intensity (v17). One may have assumed, however, that it is one of the main purposes of
investments to increase the level of employee productivity, which might therefore have
led to higher employee productivity for higher investment intensity. Yet it seems that
industries with low employee productivity provide the highest incentives for individual
firms to increase investments. The improvements in employee productivity that result
from investments might be lower than the differences in employee productivity in
comparison to other industries.
Gross margin (v14) is negatively correlated with the distance to clients (v4) and
uncertainty (v9). A positive correlation can be found for the heterogeneity of
profitability (v12a), which may be explained by the higher levels of profits that may
relate to industries with high gross margins. Finally, gross margin also correlates
positively to investment intensity (v17) since high investment requirements may limit
the extent of competition.
The average firm size in sales (v15) per industry is probably one of the most
influential variables among the set of industry variables under investigation, as the
average firm size is related to more than half of all applied industry variables. Average
firm size in sales is positively correlated to market size (v1), minimum efficient scale
(v2), heterogeneity of sales (v12c), average venture size in number of employees on
startup (v16) and investment intensity (v17). The impact of firm size on all these
variables is explained in the other paragraphs on the respective variables. Other
224
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
variables that correlate with the average firm sales level are distance to clients (v3), the
degree of uncertainty (v9) and the number of industry members (v11).
The startup size in number of employees (v16) positively correlates with the three
variables of average heterogeneity of sales (v12c), average firm size in sales (v15) and
investment intensity (v17), which are all related to higher average firm size.
Furthermore, there is a negative correlation with net entries (v8), which suggests a
higher rate of surviving startups for industries where ventures can start from a lower
scale.
Finally, investment intensity (v17) is positively correlated with the firm size-related
variables of startup size in employees (v16) and average firm sales level (v15), while it
is negatively correlated with market size (v1) as another firm size-related variable.
Higher investment intensity in an industry relates to higher gross margins (v14), as
more financial resources may remain after costs for investments in industries with a
high gross margin. At the same time, investment intensity correlates negatively to
employee productivity(v13) and positively with distance to clients (v3), which has
been already indicated in the previous paragraphs on employee productivity and
distance to clients, respectively.
In the following correlation table, significant correlations among the independent
industry variables at the 0.05 level (2-tailed) are marked with “*” and significant
correlations at the 0.01 level (2-tailed) are market with “**”.
v1
v2
v3
v4
v5
v6
v7
v8
v9
v10
v11
v12a
v12b
v12c
v13
v14
v15
v16
v17
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
v9 uncertainty
v8 net entries
v7 exits from industry
v6 entries to industry
225
v5 market growth
v4 distance to clients
v3 export balance
v2 MES
v1 market size
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
1
.
163
,231(**)
0,003
163
0,1
0,205
163
0,094
0,233
163
-0,104
0,188
163
-0,07
0,393
150
0,043
0,591
161
-0,149
0,057
163
0,052
0,507
163
-0,045
0,659
99
,614(**)
0
163
-0,004
0,962
149
0,002
0,98
134
,373(**)
0
155
,363(**)
0
99
,231(**)
0,003
163
1
.
163
0,047
0,547
163
0,058
0,463
163
0,061
0,439
163
-0,026
0,752
150
0,017
0,83
161
0,015
0,85
163
0,025
0,749
163
0,103
0,312
99
-0,034
0,67
163
-0,003
0,974
149
0,07
0,421
134
0,043
0,598
155
0,007
0,948
99
0,1
0,205
163
0,047
0,547
163
1
.
163
,874(**)
0
163
0
0,996
163
0,082
0,319
150
-0,016
0,839
161
0,014
0,859
163
-0,001
0,992
163
-0,076
0,452
99
-0,012
0,875
163
-0,043
0,605
149
0,068
0,435
134
0,079
0,326
155
0,056
0,583
99
0,094
0,233
163
0,058
0,463
163
,874(**)
0
163
1
.
163
0,058
0,466
163
0,144
0,078
150
-0,021
0,796
161
0,093
0,239
163
0,032
0,685
163
-0,079
0,439
99
-0,044
0,576
163
-0,055
0,506
149
,191(*)
0,027
134
0,08
0,325
155
0,059
0,564
99
-0,104
0,188
163
0,061
0,439
163
0
0,996
163
0,058
0,466
163
1
.
163
,342(**)
0
150
-0,128
0,106
161
,382(**)
0
163
,211(**)
0,007
163
-0,028
0,784
99
-0,091
0,249
163
-,187(*)
0,023
149
0,109
0,211
134
-0,09
0,266
155
0,055
0,591
99
-0,07
0,393
150
-0,026
0,752
150
0,082
0,319
150
0,144
0,078
150
,342(**)
0
150
1
.
150
0,157
0,056
150
,598(**)
0
150
0,107
0,191
150
-0,034
0,744
94
0,05
0,541
150
-0,107
0,208
140
,196(*)
0,028
125
-0,016
0,845
144
-0,048
0,646
94
0,043
0,591
161
0,017
0,83
161
-0,016
0,839
161
-0,021
0,796
161
-0,128
0,106
161
0,157
0,056
150
1
.
161
-0,065
0,41
161
0,102
0,196
161
-0,093
0,361
98
0,005
0,952
161
0,026
0,756
147
0,029
0,744
132
0,133
0,101
153
-0,009
0,932
98
-0,149
0,052
0,057
0,507
163
163
0,015
0,025
0,85
0,749
163
163
0,014
-0,001
0,859
0,992
163
163
0,093
0,032
0,239
0,685
163
163
,382(**) ,211(**)
0
0,007
163
163
,598(**)
0,107
0
0,191
150
150
-0,065
0,102
0,41
0,196
161
161
1
0,132
.
0,093
163
163
0,132
1
0,093
.
163
163
0,03
-0,032
0,769
0,755
99
99
-0,084
0,074
0,289
0,348
163
163
-0,138 -,236(**)
0,094
0,004
149
149
0,139 ,237(**)
0,109
0,006
134
134
-0,059
0,009
0,468
0,916
155
155
-0,033
-0,003
0,743
0,979
99
99
-0,121
0,164
133
,208(**)
0,008
163
0,134
0,104
148
-,220(*)
0,032
96
-0,089
0,307
133
,274(**)
0
163
-0,035
0,674
148
-0,046
0,659
96
-0,144
0,099
133
0,149
0,057
163
-0,003
0,97
148
0,167
0,104
96
-,171(*)
0,049
133
,198(*)
0,011
163
-0,033
0,691
148
,203(*)
0,047
96
0,027
0,758
133
,156(*)
0,047
163
-0,104
0,208
148
-0,002
0,986
96
-0,086
0,342
124
-0,109
0,184
150
-,206(*)
0,015
139
-0,172
0,103
91
-0,145
0,097
131
-0,069
0,385
161
0,054
0,516
146
-0,12
0,247
95
-0,007 -,250(**)
0,937
0,004
133
133
-0,1
,167(*)
0,205
0,033
163
163
-0,067
0,053
0,422
0,523
148
148
0,098
0,074
0,344
0,473
96
96
Table 50: Correlation table of independent industry variables (part 1)
v3
v4
v5
v6
v7
v8
v9
v10
v11
v12a
v12b
v12c
v13
v14
v15
v16
v17
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
-0,045
0,659
99
0,103
0,312
99
-0,076
0,452
99
-0,079
0,439
99
-0,028
0,784
99
-0,034
0,744
94
-0,093
0,361
98
0,03
0,769
99
-0,032
0,755
99
1
.
99
-0,045
0,659
99
0,074
0,484
92
0,023
0,835
85
-0,002
0,981
96
-0,025
0,809
99
0,102
-0,012
0,36
0,891
82
133
-0,07 -,215(**)
0,494
0,006
99
163
-0,095
-0,07
0,373
0,4
90
148
0,03
-0,168
0,769
0,103
96
96
,771(**)
0
133
0,127
0,122
149
0,02
0,812
139
0,206
0,052
89
-,220(*)
0,032
96
-0,046
0,659
96
0,167
0,104
96
,203(*)
0,047
96
-0,002
0,986
96
-0,172
0,103
91
-0,12
0,247
95
0,098
0,344
96
0,074
0,473
96
0,03
0,769
96
-0,168
0,103
96
0,206
0,052
89
-0,154
0,161
84
-0,098
0,349
93
-,231(*)
0,024
96
-0,095
0,294
123
-0,026
0,762
134
-0,112
0,219
123
-0,154
0,161
84
-0,129
0,138
133
,168(*)
0,037
155
,308(**)
0
142
-0,098
0,349
93
-0,047
0,677
82
0,167
0,099
99
0,071
0,505
90
-,231(*)
0,024
96
-0,098
0,271
127
,246(**)
0,003
148
1
.
148
,262(*)
0,014
88
,251(*)
0,025
80
,202(*)
0,048
96
,262(*)
0,014
88
1
.
96
1
.
133
0,043
0,621
133
-0,098
0,271
127
,251(*)
0,025
80
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
Table 51: Correlation table of independent industry variables (part 2)
0,043
0,621
133
1
.
163
,246(**)
0,003
148
,202(*)
0,048
96
v17 investment intensity
0,134
0,104
148
-0,035
0,674
148
-0,003
0,97
148
-0,033
0,691
148
-0,104
0,208
148
-,206(*)
0,015
139
0,054
0,516
146
-0,067
0,422
148
0,053
0,523
148
-0,095
0,373
90
-0,07
0,4
148
0,02
0,812
139
-0,112
0,219
123
,308(**)
0
142
0,071
0,505
90
v16 average number of
employees at startup
,363(**)
-0,121 ,208(**)
0
0,164
0,008
99
133
163
0,007
-0,089 ,274(**)
0,948
0,307
0
99
133
163
0,056
-0,144
0,149
0,583
0,099
0,057
99
133
163
0,059 -,171(*)
,198(*)
0,564
0,049
0,011
99
133
163
0,055
0,027
,156(*)
0,591
0,758
0,047
99
133
163
-0,048
-0,086
-0,109
0,646
0,342
0,184
94
124
150
-0,009
-0,145
-0,069
0,932
0,097
0,385
98
131
161
-0,033
-0,007
-0,1
0,743
0,937
0,205
99
133
163
-0,003 -,250(**)
,167(*)
0,979
0,004
0,033
99
133
163
-0,025
0,102
-0,07
0,809
0,36
0,494
99
82
99
0,04
-0,012 -,215(**)
0,693
0,891
0,006
99
133
163
0,127
-0,009 ,771(**)
0,935
0
0,122
92
133
149
0,028
-0,095
-0,026
0,797
0,294
0,762
85
123
134
0,187
-0,129
,168(*)
0,067
0,138
0,037
96
133
155
1
-0,047
0,167
.
0,677
0,099
99
82
99
v15 average firm size
,373(**)
0
155
0,043
0,598
155
0,079
0,326
155
0,08
0,325
155
-0,09
0,266
155
-0,016
0,845
144
0,133
0,101
153
-0,059
0,468
155
0,009
0,916
155
-0,002
0,981
96
0,037
0,65
155
0,021
0,796
147
0,007
0,933
134
1
.
155
0,187
0,067
96
v14 brut margin
0,002
0,98
134
0,07
0,421
134
0,068
0,435
134
,191(*)
0,027
134
0,109
0,211
134
,196(*)
0,028
125
0,029
0,744
132
0,139
0,109
134
,237(**)
0,006
134
0,023
0,835
85
0,021
0,812
134
-0,103
0,245
130
1
.
134
0,007
0,933
134
0,028
0,797
85
v13 employee productivity
v12a heterogeneity - profitability
variance
-0,004
,614(**)
0
0,962
163
149
-0,034
-0,003
0,67
0,974
163
149
-0,012
-0,043
0,875
0,605
163
149
-0,044
-0,055
0,576
0,506
163
149
-0,091 -,187(*)
0,249
0,023
163
149
0,05
-0,107
0,541
0,208
150
140
0,005
0,026
0,952
0,756
161
147
-0,084
-0,138
0,289
0,094
163
149
0,074 -,236(**)
0,348
0,004
163
149
-0,045
0,074
0,659
0,484
99
92
1
-0,039
.
0,639
163
149
-0,039
1
0,639
.
149
149
0,021
-0,103
0,812
0,245
134
130
0,037
0,021
0,65
0,796
155
147
0,04
-0,009
0,693
0,935
99
92
v12c heterogeneity - sales
variance
v2
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
Sig. (2-tailed)
N
Pearson Corr
v12b heterogeneity - sales
growth variance
v1
v11 number of industry members
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
v10 concentration - herfindahl
226
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
227
5.5.2 Analysis of overall sample
A multivariate analysis is performed for each performance measure201 based on the
overall sample of new ventures. First, a correlation analysis will identify those industry
variables that relate to the respective performance measure. Second, a regression
analysis is performed in order to identify the relative strength of impact and
explanation power of relevant correlated industry variables with significant t values.
5.5.2.1
Sales growth
The following correlation table indicates that several industry variables significantly
correlate with the new venture growth measure, independently of the industry context.
dependent: venture growth
v1 market size in 1,000 Euro
v2 MES in 1,000 Euro of sales
v3 export balance
v4 distance to clients
v5 market growth
v6 entries to industry
201
Pearson Correlation
0.013
Sig. (2-tailed)
0.475
N
2.917
Pearson Correlation
-0.009
Sig. (2-tailed)
0.630
N
2.917
Pearson Correlation
0.048
Sig. (2-tailed)
0.010 *
N
2.917
Pearson Correlation
0.083
Sig. (2-tailed)
0.000 **
N
2.917
Pearson Correlation
0.031
Sig. (2-tailed)
0.093
N
2.917
Pearson Correlation
0.082
Sig. (2-tailed)
0.000 **
N
2.772
Compare discussion of success measures in chapter 4.1, p. 123-128 and selection of dependant variables in
chapter 4.2.1, p. 131.
228
v7 exits from industry
v8 net entries
v9 uncertainty
v10 concentration - Herfindahl
index
v11 number of industry
members
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Pearson Correlation
Sig. (2-tailed)
0.657
N
2.836
Pearson Correlation
0.040
Sig. (2-tailed)
0.032 *
N
2.917
Pearson Correlation
0.068
Sig. (2-tailed)
0.000 **
N
2.917
Pearson Correlation
0.005
Sig. (2-tailed)
0.834
N
1.517
Pearson Correlation
0.010
Sig. (2-tailed)
0.576
N
2.917
v12a heterogeneity - profitability Pearson Correlation
v12b heterogeneity - sales
growth
v12c heterogeneity - sales
v13 employee productivity in
1,000 Euro
v14 gross margin
v15 average firm size in 1,000
Euro
-0.008
-0.028
Sig. (2-tailed)
0.131
N
2.889
Pearson Correlation
0.187
Sig. (2-tailed)
0.000 **
N
2.869
Pearson Correlation
0.012
Sig. (2-tailed)
0.526
N
2.905
Pearson Correlation
0.011
Sig. (2-tailed)
0.673
N
1.517
Pearson Correlation
-0.012
Sig. (2-tailed)
0.518
N
2.852
Pearson Correlation
0.003
Sig. (2-tailed)
0.891
N
2.917
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
v16 average number of
employees on start up
v17 investment intensity
229
Pearson Correlation
-0.032
Sig. (2-tailed)
0.084
N
2.873
Pearson Correlation
-0.040
Sig. (2-tailed)
0.122
N
1.507
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
Table 52: Correlation of industry factors with subjective venture performance
For the regression analysis all those variables that are significantly correlated with
venture growth or for which a correlation has been assumed within the initial
hypotheses are considered for inclusion.
Hyp Significant
Exclusion
Correlation
v1 market size
+
v3 export balance
+
+
v4 distance to clients
+
+
v5 market growth
+
v6 entries to industry
+
+
v8 net entries
+
+
v9 uncertainty
+
+
v12b heterogeneity – sales
growth
+
collinearity with v4
directly influenced by
dependent variable
Table 53: Variables considered for inclusion in regression analysis (venture growth – overall sample)
The highest correlation is found for heterogeneity of sales growth (v12b). However,
this correlation has little explanation power, since v12b is calculated directly from the
dependent variable as the average standard deviation of venture growth within an
industry. Especially in industries with only a low number of ventures in the sample,
the dependent variable may strongly distort v12b. The variable export balance (v3) had
to be eliminated from the regression analysis due to high collinearity with distance to
clients (v4). This is understandable since both export balance and distance to clients
are codifications of the same item of the DtA questionnaire.
230
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
All significant correlations apart from v12b correspond in their direction of impact on
venture growth to the initial hypotheses. The hypothesis of a positive correlation with
market size (v1) and market growth (v5) cannot be confirmed.
Coefficients:
Hyp
Unstand.
B
(Constant)
Std. Er.
Stand.
t
Sig.
Beta
Collinearity
Toler.
0.591
0.125
4.739
0.000
VIF
v9 uncertainty
+
0.631
0.196
0.086 3.223
0.001
0.976 1.025
v4 distance to clients
+
0.226
0.072
0.084 3.134
0.002
0.976 1.025
Model Summary:
R2
0.017
R2 adj.
0.015
F
F Sig
11.957
0.000
Table 54: Regression table of overall sample for venture growth
After eliminating all variables without significant t values, the regression analysis
showed that uncertainty and the geographic extension of the sales market measured in
distance to clients are the most influential among all industry variables that have been
included in the regression analysis. The standardised beta coefficient indicates an
approximately equal strength of impact on venture growth for both variables. The
observed directions of impact correspond to the initial hypotheses. Among the
reviewed literature, no other empirical study has investigated uncertainty (v9) or
distance to clients (v4) in relation to sales growth.
The explained variance of venture growth by the two variables is, despite being
significant, relatively low. This is not of major concern, since it has not been the
intention of this study to build a complete model to explain venture growth. Rather, it
has been acknowledged that firm-specific factors are also important in explaining
venture growth and only a small share of all industry variables of the conceptual model
could be included in this quantitative study.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
5.5.2.2
231
Profit level
Correlations among the industry variables and the dependant variable of venture profit
are shown in the correlation table below.
dependent: venture profit in
Euro
v1 market size in 1,000 Euro
v2 MES in 1,000 Euro of sales
v3 export balance
v4 distance to clients
v5 market growth
v6 entries to industry
v7 exits from industry
v8 net entries
v9 uncertainty
Pearson Correlation
-0.011
Sig. (2-tailed)
0.488
N
3.883
Pearson Correlation
-0.006
Sig. (2-tailed)
0.714
N
3.883
Pearson Correlation
-0.010
Sig. (2-tailed)
0.520
N
3.883
Pearson Correlation
-0.006
Sig. (2-tailed)
0.709
N
3.883
Pearson Correlation
0.023
Sig. (2-tailed)
0.146
N
3.883
Pearson Correlation
-0.033
Sig. (2-tailed)
0.045 *
N
3.682
Pearson Correlation
-0.042
Sig. (2-tailed)
0.010 **
N
3.767
Pearson Correlation
0.041
Sig. (2-tailed)
0.011 *
N
3.883
Pearson Correlation
-0.035
Sig. (2-tailed)
0.027 **
N
3.883
232
v10 concentration - Herfindahl
index
v11 number of industry
members
v12a heterogeneity profitability
v12b heterogeneity - sales
growth
v12c heterogeneity - sales
v13 employee productivity in
1,000 Euro
v14 gross margin
v15 average firm size in 1,000
Euro
v16 average number of
employees on start up
v17 investment intensity
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Pearson Correlation
0.000
Sig. (2-tailed)
0.999
N
1.967
Pearson Correlation
-0.019
Sig. (2-tailed)
0.234
N
3.883
Pearson Correlation
0.066
Sig. (2-tailed)
0.000 **
N
3.857
Pearson Correlation
0.003
Sig. (2-tailed)
0.858
N
3.803
Pearson Correlation
-0.039
Sig. (2-tailed)
0.014 *
N
3.868
Pearson Correlation
0.001
Sig. (2-tailed)
0.965
N
1.967
Pearson Correlation
0.074
Sig. (2-tailed)
0.000 **
N
3.816
Pearson Correlation
0.009
Sig. (2-tailed)
0.576
N
3.883
Pearson Correlation
0.008
Sig. (2-tailed)
0.606
N
3.841
Pearson Correlation
0.038
Sig. (2-tailed)
0.090
N
1.952
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
Table 55: Correlation of industry factors with venture profits
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
233
All those variables with significant correlations in the above table or in previous
hypotheses regarding venture profits are considered for inclusion in the regression
analysis.
Hyp Significant Exclusion
Correlation
v2 MES
-
v4 distance to clients
-
v6 entries to industry
+
+
v7entries from industry
-
-
v8 net entries
+
v9 uncertainty
-
v10 concentration
+
v11 number of industry
-
-
members
v12a heterogeneity -
+
+
dependent variable
profitability
v12b heterogeneity – sales
directly influenced by
+
growth
v12c heterogeneity – sales
+
-
directly influenced by
dependent variable
v14 gross margin
+
+
directly influenced by
dependent variable
v15 average firm size
+
v16 average number of
+
employees on start up
v17 investment intensity
+
Table 56: Variables considered for inclusion in regression analysis (venture profit – overall sample)
The highest correlation is found for gross margin (v14). However, this variable has to
be excluded from the regression analysis since it is also calculated on the basis of a
profit indicator from the DtA sample and may therefore be influenced by the
dependent variable. Correspondingly, heterogeneity of profitability (v12a) has to be
excluded since it also has been calculated on the basis of a venture profit indicator
from the DtA sample. The variable heterogeneity of sales (v12c) will be excluded
234
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
because it can be assumed that, to some degree, the sales level will be related to the
profit level.
Overall, only three of the initial hypotheses regarding the profit level of new ventures
can be confirmed.
Coefficients:
Hyp
Unstand.
B
72.899
(Constant)
V9 uncertainty
-85.710
-
Std. Er.
Stand.
t
Sig.
Beta
19.681
Collinearity
Toler.
VIF
3.704 0.000
38.765 -0.035
-2.211 0.027
1.000 1.000
Model Summary:
R2
0.001
R2 adj.
0.001
F
4.889
F Sig
0.027
Table 57: Regression table of overall sample for venture profits
After eliminating all variables without significant t values, only the variable of
uncertainty (v9) remains for inclusion in the regression model. The direction of impact
of uncertainty on venture profit is negative, as expected in hypothesis 9 and in
congruence with the findings of Stuart and Abetti (1987), who applied a subjective
measure of venture success.
The explained variance of venture growth is very low. The F-test however is
significant on the 5% level.
5.5.2.3
Time to break even
Correlations between the industry variables and the dependent variable of time to
break even are indicated in the following correlation table.
dependent: years of
ventures to breakeven
v1 market size in 1,000 Euro
Pearson Correlation
-0.010
Sig. (2-tailed)
0.614
N
2.663
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
v2 MES in 1,000 Euro of sales
v3 export balance
v4 distance to clients
v5 market growth
v6 entries to industry
v7 exits from industry
v8 net entries
v9 uncertainty
v10 concentration - Herfindahl
index
v11 number of industry
members
v12a heterogeneity –
profitability
235
Pearson Correlation
0.005
Sig. (2-tailed)
0.797
N
2.663
Pearson Correlation
0.064
Sig. (2-tailed)
0.001 **
N
2.663
Pearson Correlation
0.075
Sig. (2-tailed)
0.000 **
N
2.663
Pearson Correlation
0.055
Sig. (2-tailed)
0.005 **
N
2.663
Pearson Correlation
0.079
Sig. (2-tailed)
0.000 **
N
2.530
Pearson Correlation
-0.035
Sig. (2-tailed)
0.076
N
2.588
Pearson Correlation
0.051
Sig. (2-tailed)
0.008 **
N
2.663
Pearson Correlation
0.060
Sig. (2-tailed)
0.002 **
N
2.663
Pearson Correlation
0.004
Sig. (2-tailed)
0.881
N
1.270
Pearson Correlation
0.018
Sig. (2-tailed)
0.358
N
2.663
Pearson Correlation
-0.096
Sig. (2-tailed)
0.000 **
N
2.645
236
v12b heterogeneity - sales
growth
v12c heterogeneity - sales
v13 employee productivity in
1,000 Euro
v14 gross margin
v15 average firm size in 1,000
Euro
v16 average number of
employees on start up
v17 investment intensity
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Pearson Correlation
0.036
Sig. (2-tailed)
0.065
N
2.597
Pearson Correlation
-0.006
Sig. (2-tailed)
0.777
N
2.651
Pearson Correlation
0.004
Sig. (2-tailed)
0.887
N
1.270
Pearson Correlation
-0.050
Sig. (2-tailed)
0.011 *
N
2.618
Pearson Correlation
-0.032
Sig. (2-tailed)
0.102
N
2.663
Pearson Correlation
-0.086
Sig. (2-tailed)
0.000 **
N
2.646
Pearson Correlation
-0.016
Sig. (2-tailed)
0.568
N
1.260
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
Table 58: Correlation of industry factors with years to break even
For the success measures years to break-even and subjective performance, no initial
hypotheses were formulated. Therefore, in a first step all variables with significant
correlations are considered for inclusion in the regression analysis. In a second step, all
those variables with significant t values are inspected for collinearity and validity. In
cases where the variables had to be removed, additional variables were considered for
inclusion.
Significant
Exclusion
Correlation
v3 export balance
+
v4 distance to clients
+
collinearity with v4
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
v5 market growth
+
v6 entries to industry
+
v8 net entries
+
v9 uncertainty
+
V12a profitability
-
v14 gross margin
-
v16 average number of
-
237
employees on start up
Table 59: Variables considered for inclusion in regression analysis (years to break-even – overall sample)
A relatively large number of variables has been found to correlate significantly with
the measure of years to break even. Only v3 had to be excluded for the regression
analysis due to collinearity with v4 distance to clients.
Coefficients:
Corr
Unstand.
B
1.201
(Constant)
Stand.
Std. Er.
t
Sig.
Beta
0.121
Collinearity
Toler.
VIF
9.903 0.000
v12a profitability
-
-0.371
0.090 -0.084
-4.112 0.000
0.887 1.127
v16 employees on start up
-
-0.026
0.007 -0.077
-3.873 0.000
0.942 1.062
v4 distance to clients
+
0.147
0.049
0.061
2.998 0.003
0.906 1.104
v8 net entries
+
0.235
0.094
0.050
2.512 0.012
0.925 1.081
Model Summary:
R2
0.022
R2 adj.
0.020
F
F Sig
14.730
0.000
Table 60: Regression table overall sample for years to break even
After eliminating all variables without significant t values, four variables remained in
the model. The standardised beta coefficients indicate that the strongest impact on
years-to-breakeven was from heterogeneity in profits (v12a), followed by number of
employees on startup (v16), distance to clients (v14) and net entries (v8). Ventures
238
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
reached break-even earlier in industries with heterogeneous profit levels and a larger
number of employees on startup. This second relationship is surprising, as one might
have assumed that ventures that start on a larger scale may require more initial
investment and consequently may need longer to break even. A possible reason for the
identified relationship may be the higher profitability of industries, which require
higher initial firm size. In contrast, the larger the geographic sales market, measured in
distance to clients, and the larger the increase in the total number of firms of an
industry, the longer ventures need to break even. Distance to clients is positively
related to the variables of firm size and investment intensity, which may explain the
longer time needed to break even.
The explained variance of years to break even is higher than the one for venture
growth or venture profits.
5.5.2.4
Subjective absolute venture performance
The correlation analysis shows a very large number of significant correlations between
industry variables and subjective venture performance.
dependent: subjective venture
performance to plan
v1 market size in 1,000 Euro
Pearson Correlation
Sig. (2-tailed)
0.000 **
N
5.049
v2 MES in 1,000 Euro of sales Pearson Correlation
v3 export balance
v4 distance to clients
v5 market growth
-0.085
-0.031
Sig. (2-tailed)
0.025 *
N
5.049
Pearson Correlation
-0.024
Sig. (2-tailed)
0.090
N
5.049
Pearson Correlation
-0.045
Sig. (2-tailed)
0.001 **
N
5.049
Pearson Correlation
0.060
Sig. (2-tailed)
0.000 **
N
5.049
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
v6 entries to industry
v7 exits from industry
v8 net entries
v9 uncertainty
v10 concentration - Herfindahl
index
v11 number of industry
members
v12a heterogeneity –
profitability
v12b heterogeneity - sales
growth
v12c heterogeneity - sales
v13 employee productivity in
1,000 Euro
v14 gross margin
Pearson Correlation
239
-0.054
Sig. (2-tailed)
0.000 **
N
4.785
Pearson Correlation
-0.135
Sig. (2-tailed)
0.000 **
N
4.896
Pearson Correlation
0.112
Sig. (2-tailed)
0.000 **
N
5.049
Pearson Correlation
-0.050
Sig. (2-tailed)
0.000 **
N
5.049
Pearson Correlation
-0.017
Sig. (2-tailed)
0.386
N
2.590
Pearson Correlation
-0.098
Sig. (2-tailed)
0.000 **
N
5.049
Pearson Correlation
0.136
Sig. (2-tailed)
0.000 **
N
5.002
Pearson Correlation
-0.013
Sig. (2-tailed)
0.348
N
4.927
Pearson Correlation
-0.057
Sig. (2-tailed)
0.000 **
N
5.024
Pearson Correlation
0.015
Sig. (2-tailed)
0.458
N
2.590
Pearson Correlation
0.160
Sig. (2-tailed)
0.000 **
N
4.940
240
v15 average firm size in 1,000
Euro
v16 average number of
employees on start up
v17 investment intensity
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Pearson Correlation
0.009
Sig. (2-tailed)
0.503
N
5.049
Pearson Correlation
-0.015
Sig. (2-tailed)
0.293
N
4.998
Pearson Correlation
0.003
Sig. (2-tailed)
0.899
N
2.573
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
Table 61: Correlation of industry factors with subjective venture performance
A total of twelve variables have been identified as being significantly correlated with
subjective venture performance. The following table summarises these significantly
correlated variables, which are considered for the regression analysis.
Significant
Exclusion
Correlation
v1 market size
-
v2 MES
-
v4 distance to clients
-
v5 market growth
+
v6 entries to industry
-
v7 exits from industry
-
v8 net entries
+
v9 uncertainty
-
v11 number of industry
-
members
v12a heterogeneity profits
+
v12c heterogeneity sales
-
v14 gross margin
+
Table 62: Variables considered for inclusion in regression analysis (subjective venture performance – overall
sample)
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
241
None of the considered variables had to be excluded for reasons other than the
significance of the t-value.
Coefficients:
Corr
Unstand.
B
(Constant)
Std. Er.
Stand.
t
Sig.
Beta
Collinearity
Toler.
VIF
-0.243
0.085
-2.866 0.004
1.168 -0.088
-5.712 0.000
0.850 1.176
v7 exits from industry
-
-6.670
v12a heterogeneity profits
+
0.445
0.063
0.103
7.035 0.000
0.926 1.080
v8 net entries
+
0.314
0.070
0.067
4.489 0.000
0.901 1.110
Model Summary:
R2
0.033
R2 adj.
0.032
F
F Sig
55.230
0.000
Table 63: Regression table of overall sample for subjective performance
Among the three variables with significant t values, exits from industry (v7) has the
highest correlation with subjective venture performance. However, heterogeneity in
profits (v12a) has the strongest impact as measured by the standardised beta
coefficient. Entrepreneurs evaluated their performance more positively in industries
with higher heterogeneity in profits. Of interest is the relationship with exits from
industry, which indicates that a high number of exits from an industry represents
unattractive industry conditions, which also affect the subjective venture performance.
If, on the other hand, the total number of firms within an industry increases, a
munificent industry environment relates also to increasing subjective venture
performance.
Overall, the highest share of explained variance with respect to the investigated
industry variables is provided for the venture success measure of subjective venture
performance.
242
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
5.5.3 Impact of contingency variables
It has been one of the main objectives of the study to investigate if and how the impact
of industry factors on venture performance varies in different industry contexts.
Corresponding to the analysis of the overall sample, multivariate analysis is performed
for sub-samples, which are defined by the context variables of industry sector, market
growth and industry profit heterogeneity. Moreover, the sub-samples are distinguished
based on venture success measured in sales growth, in order to investigate whether
industry variables impact successful ventures differently.
5.5.3.1
Industry sector
Research in strategy202 has suggested that industry effects are higher for the service
sector than for any other industry sector. Before investigating the strengths of impact
of industry factors for each sector, the descriptive analysis highlights structural
differences among the four industry sectors.
1 - manufacturing
Mean
dep venture growth
1.209
dep venture profit
44.455
dep years to break
even
dep subjective
v1 market size in
v2 MES in 1,000 Euro of
0.657
Mean
Std Dev.
3 – trade
Mean
1.201
0.660
1.189
85.916 32.936
77.867
9.546
4 - service
Std Dev.
Mean
Std Dev.
1.328
0.707
420.026 36.857
1.518
0.901
1.549
0.742
1.388
0.606
1.561
0.723
1.582
87.492
0.010
0.704
-0.053
0.700
-0.095
0.696
0.057
0.678
18.288 14.024
12.169
31.285
31.086 13.962
15.808
1.289.323 48.926
201.853
11.482
1,000,000 Euro
Std Dev.
2 - construction
164.706
356.820
6.365
19.228
420.851
v3 export balance
0.079
0.115
0.018
0.025
0.049
0.058
0.048
0.072
v4 distance to clients
1.414
0.390
1.177
0.127
1.249
0.243
1.288
0.316
v5 market growth
1.049
0.218
0.969
0.108
1.085
0.124
1.216
0.210
v6 entries to industry
0.048
0.030
0.080
0.025
0.071
0.027
0.097
0.056
v7 exits from industry
0.017
0.005
0.030
0.008
0.019
0.005
0.013
0.008
v8 net entries
1.044
0.139
1.069
0.113
0.989
0.068
1.137
0.161
v9 uncertainty
0.493
0.164
0.492
0.070
0.492
0.100
0.503
0.075
sales
202
McGahan and Porter 1997.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
v10 concentration Herfindahl index
v11 number of industry
members
v12a heterogeneity profitability
v12b heterogeneity sales growth
v12c heterogeneity sales
687
2.983
243
21
28
280
492
43
125
6.927
6.652 28.515
14.383
36.802
39.060 41.568
40.569
0.082
0.102
0.092
0.022
0.004
0.075
0.057
0.208
0.396
0.010
0.615
0.318
0.412
0.628
0.928
1.239
1.214
1
974
800
1.914
1.873
641
809
238
44
164
25
390
945
76
11
0.135
1.549
0.136
0.032
0.061
0.060
0.187
0.133
469
279
1.091
1.093
475
963
2.615
1.129
3.085
2.163
2.285
1.658
v13 employee
productivity in 1,000
Euro
v14 gross margin
v15 average firm size in
1,000 Euro
v16 average number of
employees on start up
v17 investment
intensity
v control - venture age
in sample
v control - venture
sales in sample
2.304
5.295
11.482.4
39
164.705.
507
0.044
0.079
0.022
0.006
0.017
0.006
0.043
0.012
3.667
1.414
3.598
1.704
3.416
1.513
3.318
1.439
1.615
1
1.082
2.562
1.726
5.397
597
2.055
Table 64: Correlation of industry factors with venture performance measures by industry sector
Clear differences can be observed in the above table with regard to venture success
measures among the four industry sectors. Ventures in the service sector had a much
higher growth rate than ventures in other sectors. This may have been affected by the
relatively small sales volume of ventures in the service sector. In terms of profit,
ventures in the retail sector earned much lower profits than ventures in other sectors,
going hand in hand with remarkably low gross margins (v14). Also, with regard to
subjective performance, the trade sector receives by far the lowest evaluations, and in
terms of venture growth the trade sector again has the lowest venture success among
all sectors. As expected, size-related industry factors like MES (v2), average firm size
(v15) and average number of employees on startup (v16) are much higher for the
manufacturing sector. The service sector grows faster than any other sector (v5) with
244
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
more new firm entries (v6) than any other sector and at the same time the lowest exit
rate (v7) among all sectors.
The following table summarises the correlations among each industry variable and
each independent variable of venture performance for all four industry sectors.
industry sector
v1 market
1
dependent:
dependent:
dependent:
dependent:
venture growth
venture profit in Euro
years to breakeven
subjective performance
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
Pears
0.033
0.015
-0.058
0.079
0.028
-0.067
0.016
-0.021
-0.055
0.041
-0.033
0.007
0.020
0.008
-0.027
-0.132
Sig.
0.584
0.771
0.110
0.002
0.617
0.126
0.623
0.346
0.427
0.455
0.406
0.793
0.689
0.839
0.320
0.000
272
389
760
1496
326
528
974
2055
213
327
638
1485
417
674
1313
2645
-0.003
0.011
-0.018
0.031
0.043
0.385
0.006
-0.020
-0.006
0.058
-0.003
0.017
0.090
0.081
-0.037
-0.004
0.963
0.832
0.625
0.226
0.442
0.000
0.847
0.375
0.932
0.299
0.935
0.502
0.067
0.035
0.186
0.827
272
389
760
1496
326
528
974
2055
213
327
638
1485
417
674
1313
2645
Pears
0.038
0.028
0.143
0.033
0.100
0.341
-0.016
-0.080
0.061
0.126
0.083
0.036
0.154
0.034
-0.019
-0.087
Sig.
0.530
0.583
0.000
0.196
0.071
0.000
0.628
0.000
0.374
0.023
0.037
0.169
0.002
0.375
0.487
0.000
272
389
760
1496
326
528
974
2055
213
327
638
1485
417
674
1313
2645
Pears
0.029
-0.008
0.199
0.069
0.120
0.245
-0.010
-0.078
0.100
0.121
0.092
0.044
0.185
0.012
-0.047
-0.115
Sig.
0.634
0.880
0.031 0.000
0.757
0.000
0.144
0.028
0.020 0.088 0.000
0.757
0.088
0.000
272
389
760
1496
326
528
974
2055
213
327
638
1485
417
674
1313
2645
Pears
0.039
-0.022
0.055
0.002
0.059
0.079
0.021
0.020
0.016
0.059
0.081
0.008
-0.042
0.053
-0.040
0.060
Sig.
0.522
0.670
0.128
0.953
0.288
0.070
0.520
0.366
0.817
0.291
0.041
0.755
0.398
0.173
0.152
0.002
272
389
760
1496
326
528
974
2055
213
327
638
1485
417
674
1313
2645
Pears
0.015
-0.073
0.137
0.072
0.029
0.156
0.001
-0.184
-0.027
-0.029
0.097
0.084
0.236
-0.016
-0.061
-0.133
Sig.
0.813
0.155
0.000 0.007
0.621
0.000
0.973
0.000
0.709
0.599
0.015 0.002 0.000
0.680
245
385
752
1390
289
524
963
1906
187
325
632
1386
370
670
1300
2445
Pears
0.014
-0.043
0.024
0.035
-0.080
0.129
-0.032
-0.154
0.082
-0.028
0.024
0.028
0.133
-0.034
-0.105
-0.166
Sig.
0.821
0.396
0.513
0.188
0.150
0.003
0.319
0.000
0.234
0.619
0.537
0.297
0.007
0.372
269
389
760
1418
322
528
974
1943
210
327
638
1413
413
674
1313
2496
Pears
0.012
-0.056
0.122
0.018
0.039
0.092
0.013
0.063
0.008
0.005
0.090
0.040
0.239
0.019
0.018
0.091
Sig.
0.841
0.271
0.001
0.498
0.481
0.035
0.675
0.004
0.909
0.926
0.024
0.121
0.000
0.620
0.510
0.000
272
389
760
1496
326
528
974
2055
213
327
638
1485
417
674
1313
2645
Pears
0.005
0.018
0.154
0.061
0.013
0.088
-0.059
-0.064
0.076
0.043
0.056
0.056
0.014
0.044
-0.045
-0.113
Sig.
0.934
0.719
0.000 0.019
0.809
0.044
0.064
0.003
0.269
0.437
0.158
0.032
0.772
0.255
0.101
0.000
272
389
760
1496
326
528
974
2055
213
327
638
1485
417
674
1313
2645
Pears
0.003
-0.061
0.008
0.096
-0.032
0.038
0.009
0.007
-0.013
0.020
-0.004
-0.014
-0.056
-0.006
-0.020
0.041
Sig.
0.961
0.231
0.828
0.268
0.583
0.390
0.790
0.927
0.860
0.724
0.912
0.873
0.284
0.867
0.483
0.515
249
385
747
136
293
524
959
191
188
325
629
128
374
670
1293
253
size in 1,000
Euro
N
v2 MES in
Pears
1,000 Euro of
sales
Sig.
N
v3 export
balance
N
v4 distance
to clients
N
v5 market
0.000 0.007
growth
N
v6 entries to
industry
N
v7 exits from
industry
N
v8 net entries
N
v9
uncertainty
N
v10
concentration
- Herfindahl
0.028 0.000
0.000 0.000
index
N
v11 number
Pears
0.028
0.017
-0.053
0.016
-0.068
-0.095
0.006
-0.079
-0.165
0.003
0.072
-0.011
-0.137
-0.010
-0.025
-0.167
Sig.
0.645
0.731
0.141
0.539
0.224
0.029
0.852
0.000 0.016
0.957
0.070
0.664
0.005
0.800
0.371
0.000
272
389
760
1496
326
528
974
327
638
1485
417
674
1313
2645
of industry
members
N
2055
213
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
v12a
Pears
-0.050
-0.030
-0.050
-0.031
0.107
0.098
0.009
0.194
-0.032
-0.126
-0.083
-0.111
0.090
0.004
0.422
0.559
0.175
0.228
0.058
0.024
0.773
0.000
0.648
0.022
0.037 0.000
0.074
0.915
260
389
752
1488
315
528
968
2046
207
327
633
1478
399
674
1302
2627
Pears
0.144
0.083
0.403
0.142
-0.050
-0.056
-0.008
-0.004
-0.161
0.016
0.163
0.019
-0.166
0.012
-0.052
-0.029
Sig.
0.022
0.102
0.000 0.000
0.390
0.196
0.801
0.867
0.025
0.770
0.000
0.458
0.001
0.761
0.063
0.145
254
389
746
1480
302
528
947
2026
192
327
618
1460
382
674
1274
2597
-0.043
0.058
0.021
0.070
0.157
0.241
-0.059
0.071
-0.070
0.110
0.001
-0.013
0.028
-0.006
-0.036
-0.032
0.483
0.253
0.567
0.007
0.005 0.000
0.067
0.001
0.314
0.047
0.980
0.620
0.569
0.877
0.191
0.099
268
389
758
1490
321
528
972
2047
209
327
637
1478
410
674
1310
2630
Pears
0.013
-0.070
0.010
0.062
-0.006
0.094
0.007
0.171
0.089
0.025
-0.015
-0.016
0.042
0.021
0.021
0.112
Sig.
0.834
0.169
0.783
0.476
0.919
0.031
0.832
0.018
0.225
0.656
0.699
0.857
0.420
0.584
0.457
0.074
249
385
747
136
293
524
959
191
188
325
629
128
374
670
1293
253
-0.063
0.020
0.002
-0.047
0.147
-0.002
0.000
0.209
-0.026
0.041
0.011
-0.101
0.090
-0.010
0.061
0.178
0.325
0.697
0.951
0.073
0.011
0.959
0.990
0.000
0.717
0.457
0.783
0.000
0.081
0.806
248
382
746
1476
301
520
962
2033
195
325
628
1470
380
663
1293
2604
-0.017
0.017
0.068
0.013
0.108
0.104
0.012
0.001
0.054
0.021
-0.065
-0.053
0.107
0.082
-0.024
0.030
0.777
0.737
0.061
0.613
0.051
0.017
0.714
0.974
0.435
0.710
0.099
0.040 0.029 0.034
0.384
0.119
272
389
760
1496
326
528
974
2055
213
327
638
1485
417
674
1313
2645
Pears
0.003
-0.005
-0.004
-0.040
0.072
-0.048
-0.008
0.068
-0.134
-0.034
-0.089
-0.086
-0.055
-0.008
0.023
-0.001
Sig.
0.961
0.926
0.922
0.122
0.207
0.277
0.795
0.002
0.054
0.538
0.025 0.001
0.275
0.841
0.415
0.967
254
385
751
1483
308
524
966
2043
207
325
633
1481
396
670
1303
2629
-0.089
-0.040
-0.042
0.014
0.035
0.069
0.013
0.180
-0.019
-0.034
-0.018
0.013
-0.107
-0.005
-0.031
0.104
0.169
0.439
0.256
0.867
0.565
0.115
0.683
0.013
0.801
0.537
0.650
0.883
0.043
0.889
0.260
0.100
239
385
747
136
278
524
959
191
178
325
629
128
357
670
1293
253
heterogeneity
- profitability
Sig.
N
v12b
heterogeneity
- sales
growth
v12c
N
Pears
heterogeneity
- sales
Sig.
N
v13 employee
productivity
in 1,000 Euro
N
v14 gross
Pears
margin
Sig.
N
v15 average
Pears
firm size in
1,000 Euro
Sig.
N
v16 average
245
number of
employees
on startup
v17
N
Pears
0.093
0.163
0.001 0.000
0.029 0.000
investment
intensity
Sig.
N
Industry sectors: 1=manufacturing, 2=construction, 3=trade, 4=services
Correlations significant at the 0.05 level (2-tailed) are underlined and marked in bold.
Table 65: Correlation of industry factors with venture performance measures by industry sector
The assumed stimulation of venture growth by higher market size (hypothesis 1)
cannot be confirmed for the overall sample, however, it can be confirmed for the
service sector. The negative impact of MES on venture profits (hypothesis 2) cannot
be confirmed for any industry sector. On the contrary, in construction, which has a
relatively low level of MES, profit is positively related to MES. The relationship
between export balance and venture growth (hypothesis 3) is particularly strong in the
retail sector. The related industry variable of distance to clients (hypothesis 4) is
particularly strong for both the retail and service sectors. The assumed negative
correlation between distance to client and venture profits can be confirmed in the
service sector. However, an opposite positive impact is found for the manufacturing
and construction sectors. In manufacturing and construction, scale advantages may be
of greater importance than in other sectors, which may in part explain the opposite
direction of impact in different sectors. The assumed positive impact of market growth
246
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
on venture growth (hypothesis 5) is not confirmed for any sector. The assumed impact
of entries to industry on venture growth (hypothesis 6) is confirmed for both the retail
and the service sectors. The impact on venture profit is confirmed for the overall
sample and for the service sector. However, for the construction sector the correlation
is negative. The same phenomenon can be observed for the relationship between exits
from industry and venture profit (hypothesis 7), where a negative correlation is also
found for the overall sample and the service sector, but a positive relationship is found
for the construction sector. With regard to the balance of net entries (hypothesis 8),
both the construction and the service sectors are, according to the hypothesis,
positively related to venture profits. The relationship between net entries and venture
growth is only positive for the trade sector. With regard to the relationship between
uncertainty and venture profit (hypothesis 9), again a negative relationship can be
confirmed for the overall sample and the service sector, while for the construction
sector a positive correlation exists. Relating to venture growth, the positive correlation
with uncertainty can be confirmed for both the trade and service sectors. The assumed
positive correlation between concentration and profit (hypothesis 10) cannot be
confirmed for any sector. For no performance indicator in any sector is a significant
correlation to the concentration rate observed. The assumed negative correlation
between number of industry members and profit (hypothesis 11) is only confirmed for
the service sector. The assumed positive impact of industry heterogeneity on venture
profits (hypothesis 12) is confirmed for the construction and service sector when
considering profit heterogeneity. When considering heterogeneity in sales, for the
manufacturing sector as well as for the retail and service sectors, a positive correlation
can be confirmed. However, both industry variables heterogeneity of venture profits
and heterogeneity of ventures sales, as well as the variable of gross margin (v14) have
to be considered with caution, as distortions may result from the fact that these
industry variables are calculated from the DtA sample data and may be influenced
directly by the level of venture profits. For no industry sector can a correlation with
heterogeneity of sales growth be found. Neither can a correlation be confirmed
between employee productivity and sales growth (hypothesis 13). For the construction
and service sectors, the assumed positive relationship between employee productivity
and venture profits can be confirmed. The expected positive correlation between gross
margin and venture profit (hypothesis 14) is confirmed for the manufacturing and the
service sector. However, potential distortions result from the fact that gross margin is
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
247
calculated based on the profits of ventures in the sample. The average firm size is
positively related to venture profit only in the construction sector (hypothesis 15). The
last two hypotheses of a positive correlation between the number of employees on
startup and venture profit (hypothesis 16) and between investment intensity and
venture profit (hypothesis 17) can only be confirmed for the service sector.
The correlation analysis indicates that the impact of industry variables on venture
performance varies greatly with respect to the industry sector. No single industry
variable was found to have equally significant correlation with any venture
performance measure for all industry sectors. On the contrary, on nine occasions
significant correlations of opposing directions are identified for different industry
sectors. In none of these nine cases significant correlations of opposing direction are
identified between the manufacturing and the construction sector or between the trade
and service sectors, indicating that industry variables affect each group
manufacturing/construction and trade/service in a similar way. Opposite directions of
impact with regard to the venture profit measure have been found for export balance,
distance to clients, entries to industry, exits from industry and uncertainty. Similarly,
with regard to the subjective performance measure, opposite directions of impact were
identified for export balance, distance to clients, entries to industry and exits from
industry. No significant correlations with opposite directions of impact were identified
for the measures of venture growth and years to break even.
The number of significant correlations also varies greatly among the different industry
sectors. Considering all performance measures, a total of 37 significant correlations
were identified for the service sector, as opposed to 18 in retail, 17 in construction, and
only 14 in manufacturing. Taking into account only the relations between industry
variables and performance measures for which hypotheses have been formulated the
phenomenon is even more apparent. For the service sector, 15 of the formulated
hypotheses can be confirmed. For the construction and retail sectors, five hypotheses
can be confirmed, and for manufacturing only two of the initial hypotheses can be
confirmed. The correlation analysis by industry sector clearly supports McGahan and
248
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Porter’s (1997)203 conclusion that industry factors are of higher importance in the
service sector than in the manufacturing sector.
In order to gain additional information about the relative strength of impact of the
correlated industry variables, an individual regression analysis has been performed for
each industry sector with each performance measure. The results of these regression
analyses are summarised in the following table.
industry sector
v1 market size in 1,000
Euro
v2 MES in 1,000 Euro of
sales
v3 export balance
v4 distance to clients
v5 market growth
v6 entries to industry
v7 exits from industry
v8 net entries
v9 uncertainty
v10 concentration Herfindahl index
1(+)
dependent:
dependent:
dependent:
venture profit in Euro
years to breakeven
subjective performance
2(+)
3
4
1
Corr.
+
Beta
0.099
t
3.709
2
3(+)
Corr.
+
Beta
0.282
t
5.219
4
1
2
3
Corr.
+
+
Beta
0.155
0.122
t
2.877
2.231
Corr.
+
+
Beta
0.156
0.12
t
4.082
2.171
4
1
2
3
4
Corr.
+
Beta
0.051
t
2.459
Corr.
-
Beta
-0.081
t
-2.565
Corr.
-
-
Beta
-0.091
-0.110
t
-3.197
-4.608
Corr.
+
+
Beta
0.106
0.244
t
2.962
4.462
Corr.
+
-
Beta
0.098
-0.078
t
2.574
-3.533
Corr.
Beta
t
203
dependent:
venture growth
Compare chapter 2.2.1.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
v11 number of industry
members
249
Corr.
Beta
t
v12a heterogeneity profitability
v12b heterogeneity sales growth
Corr.
-
+
+
Beta
-0.123
0.074
0.065
t
-2.240
2.605
2.630
Corr.
-
+
-
Beta
-1.61
0.170
-0.114
-2.252
4.250
-2.087
t
v12c heterogeneity sales
Corr.
Beta
t
v13 employee
productivity in 1,000
Euro
Corr.
Beta
t
v14 gross margin
v15 average firm size in
1,000 Euro
v16 average number of
employees on startup
Corr.
-
Beta
-0.122
t
-4.524
Corr.
+
Beta
0.082
t
2.123
Corr.
-
-
Beta
-0.099
-0.096
-2.464*
-3.583
t
v17 investment
Corr.
intensity
Beta
t
R2.
/
/
0.059
0.100
0.014
0.161
/
0.046
0.026
0.031
0.036
0.022
0.088
0.007
0.017
0.039
R adj
/
/
0.056
0.099
0.011
0.157
/
0.045
0.021
0.025
0.033
0.020
0.082
0.005
0.015
0.038
F
/
/
15.71
13.76
4.71
50.25
/
45.74
5.07
5.16
11.41
15.09
26.92
4.51
11.04
24.59
2
.
Industry sectors: 1=manufacturing, 2=construction, 3=trade, 4=services
Significance level: underline= 0.05 level (2-tailed); double underline= 0.01 level (2-tailed)
(+): no regression analysis could be performed due to lack of correlated variables
Table 66: Summarising regression results of 16 regressions by industry sector
The results of the regression analysis highlight the sensitivity to the industry context
and the performance measures. General patterns relating to specific industry variables,
industry sectors and performance measures independent of the context of the
individual regression analysis cannot be derived. Regarding venture growth, the
highest explanation power is found for the service sector, and regarding venture
profits, the service sector has the second highest explanation power among all sectors.
250
5.5.3.2
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Market growth rate
Market growth and the associated variable of industry life cycle stage have been
identified as some of the most important industry variables in previous
entrepreneurship research204. A large share of studies concentrates only on dynamic,
fast-growing, technology-oriented markets, and it was suggested in previous
research205 that industry variables should be investigated by taking into account the life
cycle stage of the industry. Even though the applied 4-digit industry categorisation
does not allow the identification of industries in their early life cycle stages, it does
allow the identification of industry segments with high rates of market growth, which
is the most critical criteria for the definition of industry life cycle stages.
Consequently, the 163 investigated industries are segmented on the basis of the growth
of industry sales from the value-added tax statistics into low market growth, medium
market growth and high market growth. The analysis will provide information on
whether different industry variables are decisive in markets of different growth levels.
1
v1 market
size in 1,000
Euro
1,000 Euro of
sales
balance
to clients
growth
2
3
1
2
3
1
2
3
1
2
3
0.002
-0.086
-0.026
-0.013
-0.036
0.035
0.006
-0.102
-0.134
Sig.
0.558
0.718
0.305
0.948
0.002
0.362
0.680
0.303
0.300
0.806
0.000
0.000
957
1030
930
1319
1322
1242
941
826
896
1716
1716
1617
Pears
0.018
-0.025
0.077
0.000
-0.050
-0.017
0.067
-0.018
0.020
0.044
-0.049
-0.030
Sig.
0.579
0.423
0.019
0.996
0.066
0.540
0.040
0.615
0.548
0.070
0.044
0.226
957
1030
930
1319
1322
1242
941
826
896
1716
1716
1617
Pears
0.090
0.006
0.115
-0.009
-0.002
-0.091
0.097
0.027
0.052
0.020
0.002
-0.099
Sig.
0.005
0.858
0.000
0.738
0.954
0.001
0.003
0.434
0.117
0.416
0.946
0.000
957
1030
930
1319
1322
1242
941
826
896
1716
1716
1617
Pears
0.097
0.031
0.189
0.010
0.004
-0.120
0.107
0.026
0.075
0.016
0.009
-0.156
Sig.
0.003
0.317
0.000
0.707
0.878
0.000
0.001
0.453
0.025
0.507
0.707
0.000
957
1030
930
1319
1322
1242
941
826
896
1716
1716
1617
-0.010
-0.041
0.035
-0.026
-0.058
0.014
0.010
0.027
0.006
-0.008
-0.053
0.013
0.752
0.191
0.290
0.349
0.034
0.627
0.755
0.441
0.856
0.727
0.029
0.611
957
1030
930
1319
1322
1242
941
826
896
1716
1716
1617
Pears
Sig.
N
204
performance
0.034
N
v5 market
subjective
breakeven
0.011
N
v4 distance
years to
in Euro
0.019
N
v3 export
venture profit
growth
Pears
N
v2 MES in
venture
Compare aggregation table in chapter 2.1.3 and Tyebjee and Bruno 1981, MacMillan, Siegel et al. 1985,
MacMillan and Day 1987, Hall and Hofer 1993, Zacharakis and Meyer 1998.
205
Covin and Slevin 1990, Low and Abrahamson 1997.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
v6 entries to
industry
Pears
0.094
0.051
0.132
-0.003
-0.083
-0.208
0.011
0.031
0.121
0.031
-0.082
-0.142
Sig.
0.004
0.120
0.000
0.913
0.004
0.000
0.751
0.396
0.000
0.203
0.001
0.000
922
949
901
1272
1206
1204
909
751
870
1652
1563
1570
-0.025
-0.032
0.115
0.012
-0.103
-0.183
-0.087
-0.007
0.084
-0.046
-0.127
-0.173
0.442
0.326
0.000
0.650
0.000
0.000
0.008
0.841
0.012
0.055
0.000
0.000
957
949
930
1319
1206
1242
941
751
896
1716
1563
1617
Pears
0.029
0.029
0.031
0.022
0.082
0.066
-0.024
0.028
0.047
0.081
0.109
0.074
Sig.
0.366
0.345
0.342
0.421
0.003
0.020
0.461
0.425
0.159
0.001
0.000
0.003
957
1030
930
1319
1322
1242
941
826
896
1716
1716
1617
Pears
0.074
0.032
0.145
-0.042
-0.022
-0.110
0.047
-0.008
0.101
-0.002
-0.054
-0.127
Sig.
0.021
0.305
0.000
0.132
0.427
0.000
0.147
0.830
0.003
0.922
0.026
0.000
957
1030
930
1319
1322
1242
941
826
896
1716
1716
1617
Pears
0.000
-0.049
0.226
0.005
-0.032
-0.168
0.013
-0.014
0.215
-0.017
-0.014
-0.183
Sig.
0.993
0.211
0.010
0.882
0.356
0.041
0.732
0.753
0.025
0.540
0.642
0.009
724
665
128
987
831
149
665
497
108
1273
1111
206
N
v7 exits from
industry
Pears
Sig.
N
v8 net entries
N
v9
uncertainty
N
v10
concentration
- Herfindahl
251
index
N
v11 number
Pears
0.023
0.004
0.038
0.016
-0.119
-0.126
-0.002
0.015
0.066
-0.001
-0.108
-0.157
Sig.
0.476
0.900
0.253
0.551
0.000
0.000
0.947
0.663
0.047
0.977
0.000
0.000
957
1030
930
1319
1322
1242
941
826
896
1716
1716
1617
Pears
0.028
0.005
-0.075
0.036
0.254
0.217
-0.024
-0.120
-0.128
0.053
0.186
0.192
Sig.
0.396
0.879
0.023
0.188
0.000
0.000
0.474
0.001
0.000
0.029
0.000
0.000
941
1025
923
1304
1318
1235
928
825
892
1688
1709
1605
Pears
0.174
0.150
0.364
0.000
0.016
-0.071
0.031
0.027
0.071
0.025
0.011
-0.109
Sig.
0.000
0.000
0.000
0.997
0.572
0.013
0.351
0.449
0.036
0.307
0.641
0.000
941
1019
909
1292
1303
1208
914
813
870
1672
1689
1566
of industry
members
N
v12a
heterogeneity
- profitability
N
v12b
heterogeneity
- sales
growth
N
v12c
Pears
0.040
0.024
-0.017
-0.060
-0.050
0.116
0.009
0.020
-0.042
-0.024
-0.087
0.032
Sig.
0.215
0.442
0.596
0.029
0.071
0.000
0.794
0.558
0.208
0.312
0.000
0.197
954
1027
924
1314
1318
1236
935
823
893
1706
1710
1608
0.105
-0.001
0.143
-0.029
0.024
-0.037
0.056
-0.021
0.123
0.062
0.023
-0.046
0.005
0.986
0.108
0.370
0.491
0.650
0.147
0.638
0.203
0.027
0.437
0.508
724
665
128
987
831
149
665
497
108
1273
1111
206
Pears
0.041
0.019
-0.096
0.034
0.206
0.217
0.004
-0.028
-0.124
0.037
0.213
0.174
Sig.
0.217
0.549
0.004
0.222
0.000
0.000
0.913
0.431
0.000
0.128
0.000
0.000
924
1018
910
1286
1310
1220
919
818
881
1662
1697
1581
Pears
0.036
-0.021
0.024
0.004
-0.019
0.054
0.060
-0.104
-0.051
0.063
-0.047
0.028
Sig.
0.267
0.501
0.466
0.877
0.487
0.058
0.067
0.003
0.126
0.009
0.053
0.256
957
1030
930
1319
1322
1242
941
826
896
1716
1716
1617
heterogeneity
- sales
N
v13 employee Pears
productivity
in 1,000 Euro
Sig.
N
v14 gross
margin
N
v15 average
firm size in
1,000 Euro
N
252
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
v16 average
number of
employees
Pears
N
v17
Pears
intensity
-0.027
-0.060
-0.006
0.015
0.142
-0.041
-0.140
-0.066
0.016
-0.033
0.020
0.877
0.386
0.070
0.821
0.591
0.000
0.207
0.000
0.048
0.511
0.176
0.433
937
1019
917
1300
1312
1229
931
822
893
1695
1703
1600
-0.034
-0.050
-0.041
0.048
0.044
0.120
0.021
-0.071
0.034
0.010
0.012
-0.051
0.368
0.200
0.654
0.136
0.210
0.152
0.588
0.114
0.732
0.727
0.701
0.472
718
665
124
978
831
143
659
497
104
1262
1111
200
Sig.
on start up
investment
-0.005
Sig.
N
Market growth categories: 1=lower third, 2=middle third, 3=upper third
Correlations significant at the 0.05 level (2-tailed) are underlined and marked in bold.
Table 67: Correlation of industry factors with venture performance measures by market growth categories
Not for a single significant industry variable, opposite directions of impact were
observed for any performance measure. Industry variables do therefore not seem to
impact ventures in fast-growing markets in a fundamentally different way than
ventures with low or declining market growth rates. Ventures in industries of high
market growth, however, seem to react stronger to industry effects. With regard to all
performance measures, a total of 45 significant correlations have been identified for
the high market growth segment, as opposed to 22 in the medium growth segment and
15 in the low growth segment. Only taking into account the relationships between
industry variables and performance measures for which hypotheses have been
formulated, 13 of the formulated hypotheses can be confirmed for the segment of high
market growth, 6 for the segment of medium market growth and 7 for the segment of
low market growth.
dependent:
dependent:
dependent:
venture profit in Euro
years to breakeven
subjective performance
dependent:
venture growth
market growth category:
v1 market size in 1.000 Euro
1
2(+)
3
1(+)
2
3
1
2
3
1
Corr.
Beta
t
v2 MES in 1,000 Euro of
sales
Corr.
Beta
t
v3 export balance
v4 distance to clients
v5 market growth
Corr.
+
Beta
0.125
t
3.311
Corr.
+
+
+
Beta
0.242
0.097
0.368
t
2.721
2.986
3.951
Corr.
-
Beta
-0.084
t
-2.909
2
3
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
v6 entries to industry
v7 exits from industry
v8 net entries
v9 uncertainty
253
Corr.
-
Beta
-0.126
t
-4.021
Corr.
-
-
Beta
-0.174
-0.075
t
-2.138
-2.287
Corr.
+
+
+
Beta
0.109
0.076
0.079
t
3.418
2.714
3.184
Corr.
Beta
t
v10 concentration Herfindahl index
v11 number of industry
members
Corr.
+
-
Beta
0.179
-0.202
t
2.007
-28.827
Corr.
Beta
t
v12a heterogeneity profitability
Corr.
-
-
Beta
-0.103
-0.196
2.965
-2.106
t
v12b heterogeneity - sales
growth
Corr.
Beta
t
v12c heterogeneity - sales
Corr.
Beta
t
v13 employee productivity in
1,000 Euro
Corr.
Beta
t
v14 gross margin
v15 average firm size in
1,000 Euro
v16 average number of
employees on startup
v17 investment intensity
Corr.
+
Beta
0.203
t
8.143
Corr.
+
Beta
0.093
t
3.319
Corr.
-
Beta
-0.127
t
-3.657
Corr.
Beta
t
254
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
R2.
0.016
/
0.107
/
0.027
0.030
0.017
0.030
0.174
0.015
0.051
0.041
R2 adj.
0.014
/
0.092
/
0.025
0.024
0.015
0.028
0.157
0.013
0.049
0.036
F
10.96
/
7.01
/
11.145
4.571
8.046
12.67
10.04
9.392
41.04
7.99
Market growth categories: 1=lower third, 2=middle third, 3=upper third
Significance level: underline= 0.05 level (2-tailed); double underline= 0.01 level (2-tailed)
(+): no regression analysis could be performed due to lack of correlated variable
Table 68: Summarising regression results of 12 regressions by market growth category
In the regression analysis, the highest explanation power of all models is also found
for the highest market growth segment. The explanation power is particularly high for
the measures of venture growth and years to break even. Eight variables do not appear
in any regression model. General patterns relating to the exceptional frequency of
specific industry variables and performance measures independent of the context of
the individual regression analysis, were not observed.
5.5.3.3
Venture growth rate
In the last chapter, a segmentation by growth rate was performed on the industry level.
Correspondingly within this chapter, a segmentation of growth rates shall be
performed on the individual venture level. Research on success factors frequently
concentrates on particularly successful organisations. Ventures of the sample are
divided into groups of low, medium, and high venture growth rate. The analysis will
reveal whether industry variables relate more to successful or less successful ventures.
At the same time, the analysis will provide information on the impact of outliers. The
recent study by Hawawini et al. (2003) has suggested that industry effects are higher
for samples corrected from outliers. Therefore, it may be assumed that the segment of
medium venture growth provides the strongest indicator of industry effects.
v1 market size in
1,000 Euro
Euro of sales
venture profit
years to
subjective
growth
in Euro
breakeven
performance
1
2
3
1
2
3
1
2
3
1
2
3
-0.020
0.004
0.034
-0.033
-0.032
-0.008
-0.006
0.121
0.021
-0.081
-0.110
-0.053
0.533
0.892
0.298
0.326
0.334
0.807
0.891
0.007
0.637
0.012
0.001
0.103
974
994
949
869
901
856
489
488
497
965
980
940
Pears
0.060
-0.009
0.025
-0.024
-0.036
0.002
0.018
0.036
0.092
-0.015
-0.035
0.014
Sig.
0.062
0.766
0.441
0.474
0.281
0.947
0.698
0.423
0.041
0.652
0.268
0.665
974
994
949
869
901
856
489
488
497
965
980
940
Pears
Sig.
N
v2 MES in 1,000
venture
N
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
v3 export
balance
Pears
-0.046
0.048
0.038
0.015
-0.038
-0.009
-0.005
0.126
0.093
0.018
-0.030
-0.014
0.151
0.134
0.247
0.651
0.258
0.800
0.907
0.005
0.037
0.567
0.355
0.660
974
994
949
869
901
856
489
488
497
965
980
940
-0.095
0.073
0.080
0.023
-0.054
0.005
-0.009
0.104
0.121
0.012
-0.045
-0.043
0.003
0.021
0.014
0.502
0.106
0.894
0.845
0.022
0.007
0.705
0.162
0.190
974
994
949
869
901
856
489
488
497
965
980
940
Pears
0.005
-0.005
0.016
0.081
0.072
0.022
-0.014
-0.018
0.123
0.053
0.097
0.036
Sig.
0.888
0.872
0.626
0.016
0.032
0.521
0.755
0.698
0.006
0.099
0.002
0.265
974
994
949
869
901
856
489
488
497
965
980
940
-0.059
0.035
0.078
-0.127
-0.111
-0.005
-0.011
0.108
0.120
-0.019
-0.091
-0.063
0.076
0.286
0.018
0.000
0.001
0.887
0.811
0.019
0.009
0.574
0.005
0.058
904
954
914
806
866
822
458
468
477
896
941
905
-0.119
0.017
0.015
-0.159
-0.107
-0.024
-0.014
0.084
-0.055
-0.125
-0.144
-0.121
0.000
0.589
0.642
0.000
0.001
0.491
0.764
0.068
0.224
0.000
0.000
0.000
929
969
938
827
880
845
470
476
491
921
956
929
Pears
0.011
0.025
0.030
0.086
0.144
0.043
-0.032
-0.095
0.103
0.124
0.109
0.092
Sig.
0.726
0.425
0.354
0.011
0.000
0.210
0.484
0.035
0.022
0.000
0.001
0.005
974
994
949
869
901
856
489
488
497
965
980
940
-0.096
0.021
0.087
-0.083
0.007
-0.052
-0.011
0.060
0.114
-0.064
-0.027
-0.050
0.003
0.502
0.007
0.014
0.843
0.131
0.813
0.186
0.011
0.047
0.399
0.125
974
994
949
869
901
856
489
488
497
965
980
940
Pears
0.087
0.050
0.032
-0.015
-0.037
0.005
-0.023
0.033
0.039
0.024
-0.014
0.077
Sig.
0.033
0.251
0.534
0.723
0.421
0.932
0.706
0.620
0.600
0.555
0.755
0.129
604
522
391
536
469
356
284
233
181
597
517
387
-0.024
-0.015
0.024
-0.047
-0.087
-0.001
-0.005
0.130
0.037
-0.074
-0.112
-0.102
0.459
0.645
0.452
0.169
0.009
0.979
0.915
0.004
0.411
0.022
0.000
0.002
974
994
949
869
901
856
489
488
497
965
980
940
Pears
0.097
-0.022
-0.044
0.161
0.186
0.050
-0.032
-0.150
-0.147
0.124
0.142
0.134
Sig.
0.003
0.499
0.177
0.000
0.000
0.148
0.484
0.001
0.001
0.000
0.000
0.000
964
986
939
863
896
848
487
485
491
955
972
930
-0.115
0.075
0.234
-0.039
0.009
0.009
-0.045
0.042
0.075
-0.004
-0.055
-0.016
0.000
0.018
0.000
0.260
0.780
0.792
0.323
0.360
0.096
0.905
0.089
0.626
957
977
935
855
887
843
478
477
491
948
963
926
-0.045
-0.001
0.031
-0.073
0.023
-0.079
-0.013
0.053
0.029
-0.052
-0.081
0.002
0.159
0.970
0.336
0.032
0.499
0.021
0.776
0.242
0.518
0.105
0.011
0.943
969
990
946
864
897
853
486
485
495
960
976
937
Sig.
N
v4 distance to
clients
Pears
Sig.
N
v5 market
growth
N
v6 entries to
industry
Pears
Sig.
N
v7 exits from
industry
Pears
Sig.
N
v8 net entries
N
v9 uncertainty
Pears
Sig.
N
v10
concentration
N
v11 number of
industry
members
Pears
Sig.
N
v12a profitability
N
v12b sales
growth
Pears
Sig.
N
v12c sales
255
Pears
Sig.
N
256
v13 employee
productivity
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Pears
-0.057
0.097
-0.006
0.004
0.091
-0.007
0.018
-0.005
0.053
-0.018
-0.035
0.063
0.163
0.027
0.907
0.934
0.050
0.897
0.761
0.940
0.478
0.653
0.426
0.218
604
522
391
536
469
356
284
233
181
597
517
387
Pears
0.020
0.014
-0.047
0.224
0.200
0.065
-0.022
-0.168
-0.128
0.156
0.156
0.136
Sig.
0.536
0.653
0.156
0.000
0.000
0.059
0.626
0.000
0.005
0.000
0.000
0.000
946
978
928
847
889
837
478
481
487
937
964
919
-0.026
0.020
-0.009
0.013
0.019
0.007
-0.034
-0.028
-0.001
-0.012
0.024
0.066
0.426
0.532
0.777
0.703
0.569
0.846
0.450
0.539
0.988
0.705
0.458
0.044
974
994
949
869
901
856
489
488
497
965
980
940
Pears
0.064
-0.070
-0.030
0.074
-0.011
-0.010
-0.082
-0.009
-0.114
-0.033
-0.035
0.065
Sig.
0.047
0.029
0.358
0.031
0.739
0.763
0.071
0.844
0.011
0.313
0.282
0.047
959
979
935
855
887
844
483
484
493
950
965
926
Pears
0.028
-0.055
-0.086
0.179
-0.071
0.066
-0.047
0.063
-0.099
0.016
0.034
0.072
Sig.
0.487
0.213
0.091
0.000
0.126
0.219
0.439
0.336
0.186
0.698
0.435
0.158
599
520
388
531
467
353
279
233
180
592
515
384
Sig.
N
v14 gross
margin
N
v15 average firm
size in 1,000
Euro
Pears
Sig.
N
v16 average
number of
employees
N
v17 investment
intensity
N
Venture growth categories: 1=lower third, 2=middle third, 3=upper third
Correlations significant at the 0.05 level (2-tailed) are underlined and marked in bold.
Table 69: Correlation of industry factors with venture performance measures by venture growth categories
Of particular interest is the identification of industry variables with opposite directions
of impact for the groups with high and low venture growth. The two variables distance
to clients (v4) and uncertainty (v9) correlate positively with venture growth for the
group of high venture growth and negatively for the group of low venture growth.
Those ventures with competitive offerings reflected in higher venture growth rates
may grow faster in industries with large distances to clients. On the other hand,
ventures with less competitive offerings may be disadvantaged in industries of large
distances to clients. These ventures may perform relatively better in local markets,
where clients are less intended to perform a meticulous comparison of alternative
providers and may more frequently select the provider that is closest or most
convenient to reach. Uncertainty may lead to high growth rates for ventures that
dynamically reap opportunities in an uncertain environment. However, less dynamic
ventures may be negatively affected by uncertain environments, to which they are
unable to adapt rapidly.
According to the other assumed relationships, the correlation analysis suggests that the
positive impact of entries to industry (v6) on venture growth is strongest for ventures
with high venture growth, while the negative impact of entries to industry (v6) on
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
257
venture profits is highest for ventures of low and medium venture growth. The
assumed negative relationship between exits from industry (v7) and venture profit is
significant, particularly for ventures of medium and low growth rates. The assumed
negative relationship between uncertainty (v9) and venture profits is of particular
importance for ventures with low growth rates.
With respect to the effect of outliers, the segment of medium venture growth, has
according to expectations, the highest number of significant correlations with industry
factors and the highest number of confirmed hypotheses. Still the effect does not
appear very strong since the segment of low venture growth has nearly the same
number of confirmed hypotheses. In total, 24 significant correlations are identified for
the segment of low venture growth, 30 significant correlations for the segment of
medium venture growth and 21 significant correlations for the segment of high venture
growth. Considering only the formulated hypotheses, 7 hypotheses are confirmed for
the segment of low venture growth, 8 hypotheses are confirmed for the segment of
medium venture growth, and 3 hypotheses are confirmed for the segment of high
venture growth.
The regression analysis provides information on the relative strength of the most
important industry factors for each performance measure and group of venture growth.
dependent:
dependent:
dependent:
venture profit in Euro
years to breakeven
subjective performance
dependent:
venture growth
venture growth category:
v1 market size in 1,000 Euro
1
2
3
1
2
3
1(+)
2
3
Corr.
Beta
t
v2 MES in 1,000 Euro of
sales
Corr.
Beta
t
v3 export balance
v4 distance to clients
Corr.
+
Beta
0.124
t
2.072
Corr.
Beta
t
v5 market growth
Corr.
+
Beta
0.127
t
2.834
1
2
3
258
v6 entries to industry
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Corr.
Beta
t
v7 exits from industry
v8 net entries
v9 uncertainty
v10 concentration Herfindahl index
v11 number of industry
members
Corr.
-
-
-
Beta
-0.115
-0.094
0.099
t
-2.717
-2.486
-2.739
Corr.
+
+
+
Beta
0.114
0.123
0.078
t
2.402
2.613
2.269
Corr.
+
-
Beta
0.090
-0.126
t
2.729
-2.816
Corr.
+
Beta
0.088
t
2.086
Corr.
Beta
t
v12a heterogeneity profitability
v12b heterogeneity - sales
growth
Corr.
+
Beta
0.101
t
2.933
Corr.
Beta
t
v12c heterogeneity - sales
v13 employee productivity in
1,000 Euro
v14 gross margin
v15 average firm size in
1,000 Euro
Corr.
-
Beta
-0.079
t
-2.309
Corr.
+
Beta
0.097
t
2.198
Corr.
-
-
+
+
Beta
-0.124
-0.183
0.156
0.104
t
-2.532
-3.885
4.716
2.751
Corr.
Beta
t
v16 average number of
employees on start up
v17 investment intensity
Corr.
-
+
Beta
-0.120
0.104
t
-2.554
3.043
Corr.
+
Beta
0.210
t
4.719
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
R2.
2
.
R adj
F
259
0.022
0.009
0.008
0.071
0.013
0.006
/
0.035
0.049
0.024
0.030
0.041
0.018
0.007
0.007
0.065
0.011
0.005
/
0.030
0.043
0.023
0.028
0.036
6.14
4.83
4.83
12.03
5.77
5.33
/
8.20
8.00
22.24
14.13
9.48
Venture growth categories: 1=lower third, 2=middle third, 3=upper third
Significance level: underline= 0.05 level (2-tailed); double underline= 0.01 level (2-tailed)
(+): no regression analysis could be performed due to lack of correlated variable
Table 70: Summarising regression results of 12 regressions by venture growth category
Within the regression analysis no specific segment of venture growth consistently
provides superior explanation power. The segment of medium venture growth does not
provide higher explanation power for any measure of venture success. Consequently,
outliers do not seem to have significant effects on the explanation power. The results
of Hawawini et al. (2003) can therefore not be confirmed. In general, the strength of
impact of industry variables does not seem to be influenced by venture growth.
5.5.3.4
Intra-industry heterogeneity
It has been suggested in previous research that the "explanatory power of industry
variables strongly depends on whether firms within an industry are homogenous or
heterogeneous”206. When defining industries on the basis of four-digit industry
classifications, heterogeneity may be caused in some broad industry categories by
aggregating distinctive firm activities of differing profitability. However, in very
narrowly defined industries, where each firm offers close substitute products to the
other, a high industry heterogeneity may also be encountered when the success rate of
firms within the industry varies strongly. Apparently, in both cases of heterogeneity,
an analysis of aggregated industry variables may be less accurate and may be more
influenced by factors specific to the firm instead of factors specific to the industry.
Consequently, it is the objective of the following analysis on sub-samples aggregated
by the contingency variable of intra-industry heterogeneity to investigate whether the
suggested stronger impact of industry variables in homogeneous industries can be
confirmed and whether additional relationships may be identified for homogeneous
industries that have not been shown in the analysis of the overall sample.
In accordance with the above cited study by Mueller and Raunig (1998), industry
heterogeneity is measured by the standard deviation of firm profit rates. In the applied
206
Mueller and Raunig 1998.
260
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
data set, non-aggregated profitability data is only available for the ventures of the
sample. The standard deviation of the venture gross margin in the last year of reporting
within each industry has been already calculated in variable 12a “heterogeneity –
profitability”. Based on this variable, all the ventures of the sample have been
classified into three categories of industry heterogeneity.
The following table summarises the correlations among the independent variables and
the four dependent variables for the three categories of industry heterogeneity.
1
v1 market
size in 1,000
Euro
Pears
1,000 Euro of
sales
balance
to clients
growth
industry
industry
performance
2
3
1
2
3
1
2
3
1
2
3
-0.014
-0.038
-0.068
-0.035
-0.045
-0.067
0.038
0.225
0.325
0.622
0.176
0.761
0.691
0.261
0.040
0.153
0.066
0.006
952
944
993
1260
1286
1311
831
892
922
1691
1663
1648
-0.029
0.009
-0.008
0.008
0.005
0.028
-0.021
0.028
-0.019
-0.009
-0.010
-0.004
0.373
0.788
0.803
0.768
0.863
0.311
0.545
0.410
0.563
0.702
0.693
0.884
952
944
993
1260
1286
1311
831
892
922
1691
1663
1648
Pears
0.075
0.034
0.056
-0.003
0.062
0.043
0.090
0.051
-0.016
0.065
0.013
-0.064
Sig.
0.020
0.291
0.078
0.901
0.026
0.118
0.009
0.126
0.633
0.007
0.594
0.010
952
944
993
1260
1286
1311
831
892
922
1691
1663
1648
Pears
0.137
0.074
0.041
0.002
0.075
0.023
0.089
0.099
-0.017
0.038
0.000
-0.092
Sig.
0.000
0.024
0.199
0.944
0.007
0.396
0.010
0.003
0.602
0.123
0.985
0.000
952
944
993
1260
1286
1311
831
892
922
1691
1663
1648
Pears
0.095
0.042
-0.008
0.020
0.030
0.044
0.102
0.114
-0.013
0.023
0.058
0.081
Sig.
0.003
0.199
0.797
0.488
0.286
0.115
0.003
0.001
0.702
0.352
0.019
0.001
952
944
993
1260
1286
1311
831
892
922
1691
1663
1648
Pears
0.135
0.085
0.006
0.014
-0.039
-0.134
0.071
0.068
-0.009
0.027
-0.030
-0.069
Sig.
0.000
0.009
0.846
0.635
0.163
0.000
0.053
0.043
0.782
0.304
0.225
0.006
849
941
962
1114
1282
1267
733
889
894
1497
1659
1594
-0.031
-0.025
-0.003
-0.004
-0.059
-0.083
-0.048
-0.089
-0.065
-0.011
-0.116
-0.142
0.359
0.437
0.920
0.886
0.035
0.002
0.184
0.008
0.049
0.658
0.000
0.000
874
941
993
1148
1282
1311
759
889
922
1542
1659
1648
Pears
0.124
0.089
-0.033
0.016
0.013
0.029
0.104
0.134
0.040
0.067
0.056
0.085
Sig.
0.000
0.006
0.300
0.575
0.653
0.290
0.003
0.000
0.219
0.006
0.021
0.001
952
944
993
1260
1286
1311
831
892
922
1691
1663
1648
Sig.
Pears
Sig.
Pears
Sig.
N
v8 net entries
breakeven
-0.008
N
v7 exits from
in Euro
0.038
N
v6 entries to
growth
0.014
N
v5 market
subjective
0.031
N
v4 distance
years to
0.040
N
v3 export
venture profit
-0.067
N
v2 MES in
venture
N
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
v9
uncertainty
Pears
0.153
0.058
0.018
-0.048
0.029
0.042
0.049
0.089
-0.052
-0.036
-0.030
0.001
Sig.
0.000
0.077
0.574
0.090
0.291
0.124
0.158
0.008
0.117
0.139
0.226
0.962
952
944
993
1260
1286
1311
831
892
922
1691
1663
1648
Pears
0.030
-0.083
0.008
0.006
-0.050
-0.026
-0.003
0.016
0.042
0.002
-0.025
-0.061
Sig.
0.443
0.045
0.899
0.854
0.172
0.632
0.945
0.721
0.541
0.954
0.437
0.202
651
581
267
847
760
345
549
503
209
1151
978
435
-0.080
0.096
0.039
0.016
-0.031
-0.016
-0.019
0.060
-0.048
-0.061
0.042
-0.110
0.013
0.003
0.223
0.572
0.269
0.556
0.593
0.075
0.144
0.012
0.086
0.000
952
944
993
1260
1286
1311
831
892
922
1691
1663
1648
-0.054
-0.053
-0.017
-0.008
-0.007
0.131
-0.042
-0.081
-0.039
0.019
0.018
0.138
0.095
0.103
0.603
0.775
0.791
0.000
0.227
0.015
0.235
0.438
0.461
0.000
952
944
993
1260
1286
1311
831
892
922
1691
1663
1648
Pears
0.361
0.144
0.121
-0.005
0.027
0.004
0.070
0.057
-0.029
-0.065
0.002
-0.025
Sig.
0.000
0.000
0.000
0.870
0.328
0.899
0.046
0.089
0.389
0.008
0.930
0.319
941
938
978
1235
1272
1288
814
882
898
1657
1643
1613
Pears
0.010
0.002
0.035
-0.058
0.042
0.084
0.005
-0.053
-0.026
0.039
-0.096
-0.080
Sig.
0.764
0.954
0.274
0.040
0.137
0.002
0.893
0.116
0.428
0.107
0.000
0.001
950
944
992
1258
1286
1309
830
892
920
1688
1663
1645
0.012
-0.032
-0.111
0.007
0.055
0.046
-0.019
-0.039
0.139
0.037
-0.028
0.092
0.765
0.437
0.071
0.837
0.130
0.399
0.658
0.384
0.045
0.208
0.374
0.056
651
581
267
847
760
345
549
503
209
1151
978
435
-0.059
0.042
-0.017
0.000
-0.001
0.112
-0.046
0.123
0.020
0.012
0.068
0.134
0.070
0.202
0.598
0.999
0.974
0.000
0.184
0.000
0.550
0.625
0.006
0.000
938
935
979
1247
1275
1294
826
884
908
1670
1647
1623
Pears
0.071
-0.019
-0.007
0.008
0.069
0.020
-0.020
-0.023
-0.056
0.069
-0.020
0.034
Sig.
0.029
0.550
0.819
0.772
0.013
0.464
0.563
0.499
0.091
0.005
0.404
0.163
952
944
993
1260
1286
1311
831
892
922
1691
1663
1648
Pears
0.000
-0.063
0.021
-0.006
0.024
0.113
-0.106
-0.099
-0.055
0.022
-0.068
0.045
Sig.
1.000
0.053
0.520
0.833
0.396
0.000
0.002
0.003
0.095
0.369
0.006
0.072
940
935
982
1248
1277
1300
827
888
917
1678
1652
1637
-0.067
0.010
-0.033
0.033
0.033
0.102
-0.053
0.056
0.077
-0.052
-0.014
0.066
0.087
0.810
0.589
0.333
0.372
0.060
0.219
0.210
0.269
0.080
0.673
0.171
649
575
265
844
751
342
548
497
206
1148
967
432
N
v10
concentration
N
v11 number
of industry
members
Pears
Sig.
N
v12a
heterogeneity
profitability
Pears
Sig.
N
v12b
heterogeneity
sales growth
N
v12c
heterogeneity
sales
N
v13 employee Pears
productivity
Sig.
N
v14 gross
margin
Pears
Sig.
N
v15 average
firm size in
1,000 Euro
N
v16 average
number of
employees
N
v17
investment
intensity
261
Pears
Sig.
N
Intra-industry profit heterogeneity categories: 1=lower third, 2=middle third, 3=upper third
Correlations significant at the 0.05 level (2-tailed) are underlined and marked in bold.
Table 71: Correlation of industry factors with venture performance measures by intra-industry profit
heterogeneity categories
262
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
The performance measure of venture profits will not be considered in the following
analysis, since the value of the dependent variable of venture profits is applied in the
calculation of the segmentation variable of heterogeneity in profits and distortions may
occur, especially for industries with a low number of ventures in the sample.
The total number of significant relationships remains stable in the range of 21-23
significant correlations for each group of industry heterogeneity. However, breaking
down the number of significant correlations to the individual venture success
measures, the number of significant relationships varies widely among the three
groups. According to the overall number of observed significant correlations, the
assumed negative relationships between industry heterogeneity and number of
significant correlations cannot be confirmed. With respect to venture growth, the
number of significant correlations is in accordance with the initial assumption, and is
much higher for the group of low industry heterogeneity. A corresponding trend can be
observed for years to breakeven, which is related to venture growth. With respect to
subjective performance, the number of significant correlations is, by contrast,
distinctively lower for the group with low industry heterogeneity.
Het
ALL
venture
venture
years to
subjective
growth
profit*
break even
performance
Low
22
10
1
6
5
Mid
23
6
4
7
6
High
21
1
6
3
11
* venture profit is excluded from former analysis
Table 72: Number of significant correlations for different categories of industry heterogeneity by success
measure
As far as venture growth is considered, the suggested increase in the number of
significant correlations can be confirmed.
In order to identify additional significant correlations that were not identified in the
overall sample, the initial hypotheses are compared to the correlation of the subsample with low heterogeneity for the growth measure. Only for market growth (v5)
an assumed positive correlation with venture growth could not be confirmed by the
overall sample, but could be confirmed for the sub-sample of homogeneous industries.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
263
dependent:
dependent:
dependent:
venture profit in Euro
years to breakeven
subjective performance
dependent:
venture growth
venture growth category:
v1 market size in 1,000 Euro
1
2
3(+)
1(+)
2
3
1
2
3
1
2
3
Corr.
Beta
t
v2 MES in 1,000 Euro of
sales
Corr.
Beta
t
v3 export balance
v4 distance to clients
v5 market growth
Corr.
+
Beta
0.074
t
2.309
Corr.
+
+
Beta
0.076
0.075
t
2.157
2.105
Corr.
Beta
t
v6 entries to industry
v7 exits from industry
v8 net entries
v9 uncertainty
v10 concentration Herfindahl index
Corr.
+
Beta
0.094
t
2.683
Corr.
-
-
-
-
Beta
-0.061
-0.140
-0.099
-0.100
t
-2.207
-4.474
-2.926
-4.000
Corr.
+
+
+
Beta
0.089
0.117
0.099
t
2.742
3.351
2.815
Corr.
+
Beta
0.118
t
3.348
Corr.
Beta
t
v11 number of industry
members
v12a heterogeneity profitability
Corr.
-
-
Beta
-0.060
-0.099
t
-2.272
-3.873
Corr.
+
Beta
0.126
t
v12b heterogeneity - sales
growth
v12c heterogeneity - sales
4.934
Corr.
-
Beta
-0.088
t
-3.514
Corr.
-
Beta
-0.077
t
-3.066
264
v13 employee productivity in
1,000 Euro
v14 gross margin
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Corr.
+
Beta
0.143
t
2.044
Corr.
Beta
t
v15 average firm size in
1,000 Euro
v16 average number of
employees on start up
v17 investment intensity
Corr.
+
+
Beta
0.071
0.065
t
2.554
2.476
Corr.
+
Beta
0.115
t
3.983
Corr.
Beta
t
R2.
0.041
0.008
/
/
0.009
0.028
0.014
0.029
0.020
0.014
0.019
0.032
R adj
0.037
0.007
/
/
0.007
0.026
0.012
0.026
0.015
0.012
0.018
0.030
F
11.91
7.52
/
/
5.51
12.34
11.23
8.73
4.78
7.97
15.88
25.52
2
.
Industry heterogeneity categories: 1=lower third, 2=middle third, 3=upper third
Significance level: underline= 0.05 level (2-tailed); double underline= 0.01 level (2-tailed)
(+): no regression analysis could be performed due to lack of correlated variable
Table 73: Summarising regression results of 12 regressions by industry heterogeneity category
The regression analysis confirms that industry variables provide an explanation power
in homogeneous industries superior to that in heterogeneous industries with regard to
the measure of venture growth. For all other performance measures homogeneous
industries do not provide more explanation power as assumed. Overall the results did
not confirm a general strong impact of industry heterogeneity. Only for the measure of
venture growth were industry effects found to have a stronger effect in homogeneous
rather than in heterogeneous industries.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
5.6
265
Synopsis of results
The following table summarises the results of the 56 correlation analyses that were
performed among the four measures of venture success, the overall sample and the
different contextual sub-samples.
HYP ALL
1
v1 market
growth
R
industry
industry profit
market
venture
sectors
heterogeneity
growth
growth
2
3
+
4
1
+
-
2
3
1
2
3
1
2
3
size in 1,000
Euro
-
profit
-
break even
-
subjective
v2 MES in
-
+
-
-
-
-
-
+
growth
1,000 Euro of
sales
profit
R
-
+
+
break even
-
subjective
v3 export
growth
A
+
+
+
+
-
+
+
+
+
balance
+
profit
+
break even
+
+
subjective
v4 distance
+
-
growth
A
+
profit
R
-
+
+
+
-
+
-
+
+
+
+
+
+
+
+
-
+
+
+
+
to clients
v5 market
+
break even
+
subjective
-
growth
RO/ AC
+
+
+
+
+
+
+
+
-
+
+
+
-
-
+
growth
-
profit
v6 entries to
break even
+
subjective
+
growth
A
+
+
profit
A
-
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
industry
break even
+
subjective
-
+
+
+
+
-
-
-
-
-
-
-
-
-
+
+
-
-
+
266
v7 exits from
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
growth
+
-
-
-
-
industry
profit
A
-
-
+
-
break even
-
subjective
v8 net entries
v9
growth
A
+
+
-
+
profit
+
break even
+
subjective
+
growth
A
+
+
profit
A
-
-
-
+
+
+
-
-
-
-
-
-
-
+
+
-
-
-
-
+
+
+
+
-
+
+
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
-
-
-
+
+
+
+
uncertainty
v10
+
-
break even
+
+
subjective
-
-
+
+
-
-
growth
+
-
-
+
+
concentration
- Herfindahl
index
v11 number
profit
R
+
-
break even
+
subjective
-
growth
+
of industry
members
profit
RO/ AC
-
-
break even
-
subjective
v12a
-
-
-
-
-
-
-
-
+
+
-
-
-
+
+
+
+
-
-
growth
-
-
heterogeneity
- profitability
v12b
+
+
break even
-
-
subjective
growth
profit
NA
+
+
-
-
+
+
+
+
+
+
-
+
+
+
-
-
-
+
+
+
+
+
+
+
+
+
+
-
+
+
heterogeneity
- sales
growth
v12c
profit
R
+
-
break even
-
subjective
-
+
+
+
-
-
+
growth
heterogeneity
- sales
profit
NA
+
+
+
+
-
+
-
+
-
-
+
break even
subjective
+
-
-
-
-
-
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
v13 employee growth
productivity
in 1,000 Euro
profit
RO/ AC
+
RO/ AC
+
267
+
+
+
+
+
break even
+
subjective
v14 gross
+
-
growth
margin
profit
v15 average
NA
+
+
break even
-
subjective
+
+
+
+
+
-
+
+
+
+
+
+
+
+
+
+
+
-
-
+
+
+
growth
firm size in
1,000 Euro
profit
RO/ AC
+
+
+
-
break even
+
subjective
v16 average
+
+
+
+
+
growth
-
number of
employees
on startup
profit
RO/ AC
+
+
-
break even
-
-
-
-
+
-
+
-
-
-
subjective
v17
+
+
growth
investment
intensity
profit
RO/ AC
+
+
+
break even
subjective
-
Hypotheses: A=accepted, R=rejected, RO/ AC=rejected for overall sample / accepted in contexts, NA=not applicable
Table 74: Summary table of significant correlations in different contexts with results of hypotheses
Hypotheses
A total of 17 hypotheses with 23 individual relationships between industry variables
and venture success measures were initially specified in chapter 5.2. Among these
relationships, three are not applicable, since the operationalisations of the respective
industry variable and the respective dependent variable of venture success are based on
similar items from the DtA questionnaire. This may possibly lead to distortions of
results. Eight relationships from the hypotheses can be accepted for the overall sample
based on the results of the correlation analysis. Seven relationships did not correlate
significantly for the overall sample, however, they correlated significantly for various
industry contexts, consistently in the assumed direction. These relationships from the
268
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
hypotheses are rejected for the overall sample, but are accepted for industry contexts.
Finally five relationships from the hypotheses have to be rejected since they neither
correlate significantly for the overall sample, nor consistently correlate significantly
for different industry contexts.
Hypothesis 1 – market size (REJECTED):
Most conceptual models207 and research on venture capitalists’ decision criteria208
have suggested that market size will correlate positively with venture performance.
This study cannot confirm the proposed correlation for the applied performance
measure of sales growth. Hypothesis 1 is therefore rejected for the overall sample.
Hypothesis 2 – economies of scale (REJECTED):
Various studies209 in the industrial organisation stream of strategy have suggested that
established firms will benefit from economies of scale, while new ventures may be
discouraged from entry. Due to the costs disadvantages of new ventures in such
industries of high economies of scale a negative correlation with venture profits has
been assumed in hypothesis 2 for the context of new ventures. This hypothesis has to
be rejected. The MES indicator has been neither correlated for the overall sample, nor
for a larger number of contexts. For the investigated ventures of the sample that had
already made the decision to enter a specific market, economies of scale did not have
significant impact on profits.
Hypothesis 3 – export balance (ACCEPTED):
It has been assumed that the export balance of an industry is positively correlated to
venture growth. This hypothesis can be accepted for the overall sample. The positive
impact of exports on organisational success that were identified by Ravenscraft (1983)
and Buzzell (1987) for large established firms, can be confirmed for the success
measure of venture growth in the new venture context. A high export balance provides
additional growth opportunities.
207
Dean and Meyer 1996, Baaken 1989, Hinterhuber 1995.
Zacharakis and Meyer 1998.
209
Porter 1979 and Ravenscraft 1983.
208
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
269
Hypothesis 4 – distance to clients:
a) (ACCEPTED): Corresponding to hypothesis 3 it has been assumed that a larger
geographic sales market may also provide additional growth opportunities for new
ventures. This hypothesis can also be confirmed for the overall sample.
b) (REJECTED): With respect to profitability it has been assumed that a larger
distance to clients may decrease the profit level, due to higher market transparency.
This hypothesis has to be rejected. No correlation has been found for the overall
sample. For different investigated contexts, a positive correlation is even found. The
relationship between distance to client and profitability that Ravenscraft (1983) found
in his study of established firms cannot be confirmed for the sample of new ventures.
Hypothesis 5 – market growth (REJECTED FOR OVERALL SAMPLE /
ACCEPTED CONTEXT-SPECIFIC):
In accordance with most studies in entrepreneurship and strategy, a positive correlation
between market growth and venture sales growth has been assumed in hypothesis 5.
This hypothesis has to be rejected for the overall sample on the basis of the findings in
this study. However, for the sub-sample of low profit heterogeneity the expected
correlation can be observed. A negative correlation, which was previously found by
Stuart and Abetti (1987), cannot be observed for any contextual setting. The
importance of market growth may not be as high as may be deduced from the frequent
application of this variable in empiric and theoretic research.
Hypothesis 6 - entries to industry:
a) (ACCEPTED) A positive correlation between firm entries and venture growth has
been expected, as high entries to industry are commonly related to early market
lifecycle stages, which provide high venture growth opportunities. This hypothesis can
be accepted for this study.
b) (ACCEPTED) With regard to the venture profit level, a negative correlation has
been proposed, since an increasing number of firm entries is assumed to increase the
competitive pressure on prices. This hypothesis is also accepted for the overall sample.
270
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Hypothesis 7 – exits from industry (ACCEPTED):
The number of exits from an industry has been assumed to be negatively related to
venture profits, since high exit rates may indicate high competitive pressure and low
profitability of an industry. On the basis of the results of the conducted study the
hypothesis can be accepted for the overall sample.
Hypothesis 8 – balance of entries and exits (ACCEPTED):
Corresponding to hypothesis number 6 and the concept of industry life cycles, a
positive correlation between balance of entries and exits and venture growth has been
expected. This correlation has been confirmed for the overall sample.
Hypothesis 9 – uncertainty:
a) (ACCEPTED) Uncertainty has been expected to be negatively related to venture
growth, since established firms cannot secure high advantages compared with new
ventures in uncertain environments. This hypothesis is accepted for the overall sample.
b) (ACCEPTED) Related to venture profits the negative impact of increasing risk of
investments has been assumed to be stronger than the positive impact of lower
competitive disadvantages in relation to established firms. On the basis of the results
of this study, the hypothesis is accepted for the overall sample. This finding coincides
with Stuart and Abetti (1987), who found a negative impact of market dynamics on a
subjective measure of venture performance. The finding contradicts the result of the
new venture study by Sandberg and Hofer (1987) who identified a positive impact of
market dynamics on ROE. However, these studies may only be directly comparable to
a limited degree, since they only measure the related variable of market dynamics and
not directly uncertainty.
Hypothesis 10 – concentration (REJECTED):
It was expected that the profit level of new ventures would relate positively with
industry concentration as was proposed by Buzzell (1987) and Brüderl, Preisendörfer
et al.(1996). This hypothesis has to be rejected for the overall sample. Industry
concentration has no significantly positive correlation with venture profit for any
investigated sub-sample. For the sub-sample of high market growth a negative
correlation is even found. Even though industry concentration has been one of the
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
271
most frequently applied industry variables in previous research, it may not be of much
importance for new ventures, as in many other studies no relation between
organisational success and industry concentration could be identified either210.
Hypothesis 11 – number of industry members (REJECTED FOR OVERALL
SAMPLE / ACCEPTED CONTEXT-SPECIFIC):
The profit level of new ventures was expected to be negatively related to the number
of industry members. For the overall sample, this hypothesis has to be rejected.
However, for the sub-samples of service sector, high market growth, low market
growth and medium venture growth the proposed relationship can be confirmed. At
the same time, for no sub-sample could a significantly positive correlation be
observed. Given the rejection for the overall sample, venture capitalists may
overestimate the importance of the number of competitors211.
Hypothesis 12 – heterogeneity
It was explained in the last chapter that hypotheses 12a and 12c were not applicable
due to the operationalisations of the variables.
b) (REJECTED) It has been proposed that the profit level of ventures will relate
positively to heterogeneity in sales, since a high heterogeneity of firms may also
indicate that firms operating below the optimum efficiency level can survive. This
hypothesis has to be rejected. Heterogeneity in sales growth has not been positively
correlated for the overall sample or for any sub-sample. For the sub-sample of high
market growth, a positive correlation between heterogeneity in sales growth and
venture profit can even be observed.
Hypothesis 13 – employee productivity
a) (REJECTED FOR OVERALL SAMPLE / ACCEPTED CONTEXTSPECIFIC) Regarding the growth rate of new ventures, a positive relationship to
employee productivity has been expected, since growth in industries of high employee
productivity will involve less additional organisational work related to recruiting and
210
211
No relationship was found in the empirical studies by Sandberg and Hofer 1987, Tsai, MacMillan and Low
1991, Marshall and Buzzell 1990, and Robinson 1998.
Zacharakis and Meyer 1998 identified the number of competitors as the number one actual deal evaluation
criteria of venture capitalists.
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CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
training of new employees. This hypothesis cannot be confirmed for the overall
sample. However, for the sub-samples of low market growth and medium venture
performance the assumed relationship can be observed.
b) (REJECTED FOR OVERALL SAMPLE / ACCEPTED CONTEXTSPECIFIC)
Regarding venture profits a positive relationship to employee productivity has also
been expected, since high employee productivity may lead to higher profitability
because of lower personnel costs. This hypothesis cannot be confirmed for the overall
sample. However, for the sub-samples of the construction sector, the service sector and
the medium venture success level, the expected relationship can be observed. For none
of the sub-samples could an opposing, significantly negative, correlation with
employee productivity be observed. This study does, therefore, partly confirm the
results from Buzzell (1987) who identified employee productivity as a major success
factor for established large firms in the PIMS study. Wilson’s results (1998) were
partly confirmed as he found empirical support for the positive impact of employee
productivity in the new venture context.
Hypothesis 14 – gross margin
According to the explanations in the last chapter, hypothesis 14 is not applicable due
to the operationalisations of the variables.
Hypothesis 15 – average firm size (REJECTED FOR OVERALL SAMPLE /
ACCEPTED CONTEXT-SPECIFIC)
The profit level of new ventures has been assumed to relate positively to average firm
size. This has been justified by the concept of liability of size from organisation
ecology212. For the overall sample the hypothesis has to be rejected. For the subsamples of the construction sector and medium profit heterogeneity, the expected
relationship can, however, be observed. Nevertheless, the impact may not be as
important as suggested in population ecology.
212
Compare chapter 3.2.3, p.57ff.
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273
Hypothesis 16 – minimum organisational size (REJECTED FOR OVERALL
SAMPLE / ACCEPTED CONTEXT-SPECIFIC)
In accordance with the general theory on barriers to entry213 it has been assumed that
minimum organisational size as an important barrier to entry will relate positively to
venture profits. The results of this study lead to a rejection of the hypothesis for the
overall sample. But the proposed relationship can be observed for the sub-samples of
the service sector, high profit heterogeneity, high market growth and low venture
growth.
Hypothesis 17 – investment intensity (REJECTED FOR OVERALL SAMPLE /
ACCEPTED CONTEXT-SPECIFIC)
For investment intensity, as second variable of barriers to entry, a positive relation
with venture profits is also proposed. Again the results of the study cannot confirm this
hypothesis for the overall sample, however, for the contexts of the service sector and
low venture growth, they could. It is noteworthy that both contexts for which a
significant correlation with investment intensity can be observed, also have positive
correlations with minimum organisational size. In these contexts in particular, barriers
to entry apparently have strong impacts on venture profits. Therefore, for these
contexts barriers to entry seem to affect new ventures in a similar way to that proposed
by Porter’s model of five forces214 for established firms. For no other sub-sample
could a significantly negative correlation with any of the two variables of barriers to
entry be observed in this study.
Performance measures
The results of the study strongly confirm the necessity of distinguishing among
different venture performance measures, since industry variables have very different
impacts on the applied measures of venture success. Two groups of related
performance measures are identified, with venture growth and years to break even
forming one group and venture profits and subjective venture performance a second
group of related performance measures. For several variables an opposite direction of
impact of industry variables on both groups of performance measures is observed.
213
214
Compare literature of industry economics and Porter 1979.
Porter 1979.
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CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
Such opposite directions of impact are observed in particular for entries to industry
(v6), uncertainty (v9), number of industry members (v11), heterogeneity of
profitability (12a), heterogeneity of sales growth (12b) and gross margin (v14).
Generalisability of industry – venture performance relationships
The study shows, that there are a number of industry variables that affect venture
performance for the overall sample and a wide range of contextual settings with
statistical significance. The following table indicates first those variables that are
significant for the overall sample as generic variables. Second, there are the variables
that are not significant for the overall sample, but are significant for several industry
contexts. Within this group a distinction is made between industry variables that
consistently impact the respective measures of venture success in the same direction as
suggested by the hypotheses, and other industry variables that impact the respective
measures of venture success in different directions for different industry contexts.
Finally, there is a third group of industry variables of low relevance. These variables
are not significantly correlated for the overall sample and correlate to the venture
measure for one or no industry context. Industry variables of group 1 may be
generalised for a wide range of industries, and variables of group 2a may also impact
ventures in different contexts in the same direction, but with varying strength.
Variables of group 2b cannot be generalised since the direction of impact is
inconsistent and varies according to the specific industry context. The variables of
group 3 can be ignored, as they have hardly any correlation with the venture success
measure.
industry variables
venture growth
venture profit
(in hypotheses)
1. Generic (significant for • export balance (v3)
overall sample)
• entries to industry (v6)
• distance to clients (v4)
• exits from industry (v7)
• entries to industry (v6)
• uncertainty (v9)
• net entries (v8)
• number of industry members (v11)
• uncertainty (v9)
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
2a. Context-specific –
consistent (significant for
≥ 2 contexts)
• market growth (v5)
• employee productivity (v13)
275
• employee productivity (v13)
• average firm size (v15)
• employees on start up (v16)
• investment intensity (v17)
2b. Context-specific -
• market size (v1)
• distance to clients (v4)
inconsistent (significant
for ≥ 2 contexts)
3. Low relevance
(significant for < 2
contexts)
• MES (v2)
• concentration (v10)
• heterogeneity – sales growth (v12b)
Table 75: Importance and generalisability of industry variables from hypotheses
Considering also the relationships between industry variables and venture success
measures for which no hypotheses have been formulated, only the relationship
between net entries (v8) and venture profit may be relevant for a wide range of
industry contexts. Net entry has apparently a positive impact not only on venture
growth but also on venture profit. The relationship between heterogeneity of sales
growth (v12b) and venture growth cannot be considered since v12b is the standard
deviation of the dependent variable of venture growth and industries with a small
number of ventures in the DtA sample may therefore be distorted. Apart from these
variables, the hypotheses cover all relationships that may be generalisable for a wide
range of contexts. All other relationships between industry variables and measures of
venture success for which no hypotheses have been formulated, indicate either
context-dependent different directions of impact or low relevance.
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CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
industry variables
venture growth
venture profit
(not in hypotheses)
1. Generic (significant for • heterogeneity – sales growth (v12b)
• net entries (v8)
overall sample)
– but not applicable
2a. Context specific –
/
/
• exits from industry (v7)
• export balance (v3)
• concentration (v10)
• market growth (v5)
consistent (significant for
≥ 2 contexts)
2b. Context specific inconsistent (significant
for ≥ 2 contexts)
• number of industry members (v11)
• heterogeneity – profitability (v12a)
• employees on startup (v16)
3. Low relevance
(significant for < 2
contexts)
• MES (v2)
• market size (v1)
• heterogeneity – sales (v12c)
• gross margin (v14)
• average firm size (v15)
• investment intensity (v17)
Table 76: Importance and generalisability of industry variables not included in hypotheses
The summary table of significant correlations provides information on the specific
contextual settings in which the mentioned context-specific industry variables affect
venture performance.
Strengths of impact of individual industry variables
The regression analyses performed indicate that the strengths of impact of individual
industry variables vary strongly according to the contextual setting. For the overall
sample, uncertainty (v9) and distance to clients (v4) have the highest impact on
venture growth. Uncertainty (v9) has the highest impact on venture profits. For the
individual contextual settings, other variables frequently have the highest impact on
venture performance measures.
CHAPTER 5 - QUANTITATIVE EMPIRICAL STUDY
277
Explanation power of industry variables on venture success
The explanation power of industry variables on different measures of venture success
has been astonishingly low. The adjusted R2 value for the regression models of the
overall sample remain at 0,032 and below. Former studies in strategy research on
measuring industry effects have suggested a higher explained variance from the
regression models (see chapter 2.2.1). With regard to the sub-samples by major
industry segments, the explanation power increases up to a maximum adjusted R2
value of 0,157 for the impact of industry variables on venture profit in the construction
sector. With regard to the sub-samples of market growth segments, the explanation
power increases up to the same maximum adjusted R2 value of 0,157 for the impact of
industry variables on time to break even for the segment of high market growth. The
chosen methodology tends to lead to lower levels of explanation power. Only those
variables for which ex-ante hypotheses have been formulated, and which have been
correlated with the venture success measure, and which had a significant t-value were
included in the regression analysis. The selection of variables has been lmited by the
availability of data. Important variables may have been omitted.
Contingency variable 1: Industry sector
It has been assumed from the study by McGahan and Porter (1997) that industry
variables have a higher impact on venture performance in the service sector than in
other sectors. The correlation analysis by industry sector confirms this assumption.
The total number of significant correlations between industry variables and venture
performance measures, as well as the number of confirmed hypotheses, are much
higher for the service sector than for any other sector. The regression analyses by
industry sector find, with regard to venture growth, the highest explained variance for
the service sector and with regard to venture profit, the second highest explained
variance for the service sector.
Contingency variable 2: Market growth
Previous research suggested the investigation of industry variables with regard to the
contingency variable of life cycle stage. The analyses based on a segmentation of
market growth do not indicate a single case where significant correlations of opposite
directions are found for different groups of market growth. Industry variables do not
278
CHAPTER 5 – QUANTITATIVE EMPIRICAL STUDY
seem to impact ventures in fast-growing markets in a fundamentally different way than
ventures with low or declining growth rates. The industry variables, however, relate
stronger with venture performance for the group of high market growth. The
correlation analyses by market growth identify more significant correlations between
industry variables and venture performance measures, and confirm more hypotheses
for the group of high market growth than for the groups of medium or low venture
growth. Also, within the regression analyses, the highest explanation power with
regard to both venture growth and venture profits is found for the group of highest
market growth.
Contingency variable 3: Venture growth
It has been the intention to apply venture growth as a contingency variable in order to
interpret with more accuracy the effect of industry factors on ventures with different
degrees of success. The variables distance to clients (v4) and uncertainty (v9) are
found to relate positively to venture growth for the group of high venture growth and
negatively for the group of low venture growth. Entries to industry (v6) have a
particular growth impact for ventures with high growth rates. On the other hand,
entries to industry (v6), exits from industry (v7) and uncertainty (v9) have led to low
venture profits, particularly for ventures of low growth. Overall, the impact of industry
factors has not been strongly influenced by venture growth. Moreover, the increase in
explanation power, which has been suggested by Hawawini et al. (2003) for samples
that have been corrected by outliers, has not been confirmed.
Contingency variable 4: Industry heterogeneity
The study by Mueller and Raunig (1998) suggests that the impact of industry variables
is highest for industries of low heterogeneity. Dividing the overall sample into three
groups of industry profit heterogeneity confirms a higher number of correlated
industry variables for the venture growth measure. For the measure of venture profit,
the suggested relationship, however, cannot be investigated. The regression analysis
comes to the same result with the highest explanation power for industries of low
heterogeneity with regard to venture growth. For the other venture success measures
positive impacts of industry heterogeneity are even found.
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6
279
Qualitative empirical study: Case studies of selected
ventures from the telecommunication and e-commerce
industries
The qualitative study is guided by the objective of identifying the mechanisms through
which market factors influence venture success215. In the context of the chosen firms,
not only will the most critical market variables be identified, but it will also be shown
why these variables have an impact on the performance of the venture and the
direction of the impact between critical market factors and venture success.
The explanation of the impact of market characteristics on venture success will be
illustrated based on the structure of the theoretical model. The ratings of individual
market characteristics as given in the tables throughout this chapter are based on the
author’s perceptions and do not necessarily coincide with those of the case
interviewees. The questionnaire guideline for the case interviews, which has been
developed by the author, is printed in appendix G.
6.1
Case study – Imente
6.1.1 Venture profile
Imente Global S.L. (Imente) is interesting to study, as it permits an analysis from the
perspective of a technology-oriented new venture and at the same time serves as an
example of a business-to-business market.
The venture is an online news aggregator specialised on the Spanish language market.
The company has developed a proprietary technology which searches over 7,000 news
websites216 continuously, captures the newest headlines, organises them in multiple
categories and integrates this aggregated content as a kind of online press clipping into
any website. The underlying concept is commonly referred to as “deep linking”. The
service has been online since January 2001. In October 2003, eleven employees were
working for Imente.
215
216
See objective 7 in chapter 1.2, p. 4.
Websites of general interest newspapers such as El país, El Mundo etc. and special interest websites as
Invertia, Futvol.com etc.
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CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
The firm was started with the private funds of the founders. In a first round of external
financing in June 2003, a total of 430,000 Euro was raised for financing the growth of
the firm and the recruitment of additional staff. The firm received 140,000 Euro from
financial investors, 90,000 Euro as a non-returnable technology subsidy from the
government of Catalonia (Generalitat), and 200,000 Euro as a non-interest loan from
the Spanish state.
Founders
The firm was founded in June 2000 in Girona, Spain, by a team of seven founders with
backgrounds in journalism, marketing and IT. The technical director is Qim Pagans,
who developed the basic technology of the firm after being involved in an online
music magazine for which he programmed a tool to capture music-related headlines
from different sources. The general director is Dr. Christian Serarols, who is a
professor of business administration at the Universitat de Girona and the Universidad
de Educación a Distancia.
Products/Services
Imente’s clients are firms that require up-to-date information about their competitors
and their industry (online news clipping service) for internal use , and website owners
who would like to include on their website a section of automatically updated news
about the topic of their website (website news section modules). In the future, Imente
wants to additionally engage in the sale of technology. Since the company started, the
two main services of online press clipping and website news section modules have
been offered nearly identical in the several packages. Meanwhile, the technology has
been developed and the quantity of content which is included has been greatly
increased over the last few years.
Performance
Thanks to a strict cost control, Imente nearly reached break-even in May 2003 before
closing the first round of financing. By October 2003, a total of 50,000 clients were
subscribed to Imente’s services. Over 99% of them use the free service, which only
generates marginal revenues in advertising and is primarily offered to promote the paid
service. In October the firm had 200 paying clients, which generated sales of about
18,000 Euro per month.
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
281
6.1.2 Competition and general market setting
The market of news aggregation in the business-to-consumer segment started around
1998 with some less sophisticated search engines of news headlines. In Spain,
titulares.com was the only competitor of this kind at the time of the Imente start-up. In
contrast to its competitor, Imente positioned itself as provider of business-to-business
services and was the first provider in this segment in Spain. Within a year after Imente
entered the market, a large number of firms started in the same segment. Today
spynews.com and iconoce are considered as their main competitors on the Spanish
market. The service by Moreover.com in the USA is considered as the major
competitor on the international level.
Imente considers its technology superior to the Spanish competitors and equal to
Moreover.com. Despite the relatively low numberpaying customers, they assume that
they still have more clients than their competitors.
The whole market of online news aggregation is relatively young. The market size for
online press clipping is estimated at 50-100 million Euro worldwide.
6.1.3 Explanation of market impacts on venture success
Macro level
Not at all
important
Extremely
important
6.1.3.1
1. macro-economic changes
○
○
○
X
○
○
○
○
○
○
○
○
○
X
X
○
○
○
○
○
○
○
○
○
○
○
○
○
○
○
○
○
○
X
○
X
○
X
○
X
c) development of new production technologies
○
○
○
○
X
○
X
○
○
○
○
○
○
○
X
4. socio-cultural changes
a) changes in consumption preferences (volatility)
○
○
○
○
X
a) changes in the propensity to consumption
b) changes of interest rates
c) changes of currency rates
2. political / legislative changes
a) changes in environment protection regulations
b) changes in market regulations
c) changes in labour market regulations
d) changes in public spending
e) changes in international trade deregulations
3. technological changes
a) development of internet usage
b) development of internal IT applications
282
b) individualisation
5. demographic changes
a) decreasing population growth
b) increasing population age
Other changes:
CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
○
○
○
X
○
○
○
○
○
○
○
○
○
X
X
-
copyright regulations for press
clippings
legal status of deeplinking
online advertising rates
willingness to pay for online content
reputation of internet firms
Table 77: Macro level analysis Imente
The most important change for Imente on the macro level may originate from
legislation. The association Gedeprensa, which currently covers 60% of the major
Spanish newspapers, takes legal action against traditional news clipping services.
These services copy or scan newspapers and send to their clients content-specific
articles without paying royalties to the original media sources. Even though electronic
services like Imente are not yet a target of this legal action in Spain, the consequences
may also greatly affect the venture. Imente takes a very cooperative approach towards
media sources and does not scan the websites of the few sources that did not want to
be included in the service.
In other countries, electronic services like Imente have already been sued by media
sources and have had to shut down as a consequence. The legal situation does not
appear to be very clear. While in Denmark the service newsbooster.com had to cease
deeplinking without permission, in Germany and the USA, the courts decided most
recently in favour of the deeplinking services. For paperboy.de and paparazzi.de, two
formerly leading German news aggregators, the decision of the German courts came
too late. Paperazzi.de could not afford the legal costs to confront the large media
companies. Paperboy.de, which carried the costs of bringing the legal action of the
media companies to the highest court in Germany, finally had to give up their service,
since the whole process took more than two years, during which time the firm was not
allowed to put their service online. The management team of Imente recognises that
these lawsuits may considerably change the market place, but this does not necessarily
have to have negative consequences on Imente. There is an intrinsic interest of those
media companies who understand the internet, for the continuation of these services. It
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
283
all depends on how a final agreement in compensation of media firms would affect the
relative positioning of traditional and online press clipping services.
In the past, the decreasing rates of online advertising have not only forced Imente to
change their originally planned business model, but have also led to the exit of the
initially strongest competitor, titulares.com. Today, all the larger competitors who
started on the basis of an advertising revenue business model in Spain have changed to
a paid business-to-business model. Apart from the general over-supply of online
advertising spaces compared with advertisers, the ventures in the news aggregation
industry had to struggle with the difficulty of relating targeted advertising to news
articles and with a relatively low number of visitors.
In the same context, the general willingness to pay for online services is considered as
a major variable of the macro environment. Especially in Spain, Imente found that the
willingness to pay for online services, even from corporate clients is still very low and
is also lower in comparison with other countries.
Other changes on the macro level that affect the firm are changes in the general
economy and the reputation of internet firms. In times of decreasing business cycles,
many firms first cut their marketing budget, which in turn directly affects the sales of
Imente. With regard to the reputation of internet firms, Imente frequently encountered
that potential clients had doubts about the long-term stability of Imente. An important
opportunity may arise from the inclusion of customised news content in the
management information system modules of SAP and other providers.
6.1.3.2
Inter-country level
Very low
Degree of international competition
Very high
II. INTER COUNTRY LEVEL – NATIONAL COMPETITIVENESS
a) activities of national competitors in foreign markets.
○
○
○
○
X
b) activities of foreign companies in national market.
○
○
○
○
X
c) country of strongest competition in future:
USA
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CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
important
Not at all
competition.
important
competitors from country of strongest future
Extremely
Sources of important competitive advantages of
1. production factors
a) easier availability of qualified labour
○
○
○
○
X
b) lower costs of qualified labour
○
○
○
○
X
c) easier availability of resources
○
○
X
○
○
d) lower costs of resources
○
○
○
○
X
a) lower taxes
○
○
○
○
X
b) higher stability of economical political environment
○
○
○
○
X
a) lower distance to main customers for export industries
○
○
○
○
X
b) larger size of national market
○
X
○
○
○
○
○
X
○
○
○
○
○
○
X
○
○
○
○
X
a) easier availability of debt funding from banks
○
○
X
○
○
b) easier availability of equity funding from venture
○
X
○
○
○
c) more financial assistance for new ventures
○
○
○
○
X
d) more non-financial assistance for new ventures
○
○
○
○
X
2. political stability & taxation
3. distance to sales market
4. industry infrastructure
a) better transportation and telecommunication
infrastructure
b) better access to dependent supplying and buying
industries
c) more competitive dependent supplying and buying
industries
5. new venture infrastructure
capitalists and business angels
other important sources of competitive advantage of firms
from country of strongest competition:
Table 78: Inter-country level analysis Imente
- lower price sensitivity
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
285
The whole industry is very nation-centric. Even though technologically it is very easy
to offer multi-language services, until now no existing foreign c ompetitor has entered
the Spanish market. Among the Spanish competitors tentative efforts to go
international have been limited to the Latin American region. Also, the US market
leader Moreover.com has apparently no interest in expanding internationally. Imente
has contacted Moreover.com in order to build a joint venture to expand to the Latin
American market. Since Moreover.com targets solely fortune 500 companies charging
extraordinarily high prices for their services, they had doubts that firms outside of the
USA would be willing to pay their prices. From Imente’s perspective, international
expansion is considered as an important future direction of expansion.
Despite the limited interest of Moreover.com in internationalisation, the strongest
future competition is expected to come from the USA. Ventures in the USA have a
competitive advantage217 versus Spanish firms in terms of a much larger market size,
easier financing218 and lower price sensitivity. Nevertheless, the Imente team agrees
that the overall danger from current competitors in the USA is limited. These firms
provide their services at prices, which in the opinion of the Imente founders, most
firms in Spain would not be willing to pay. Additionally, US firms would have to
struggle with language and cultural barriers. The Spanish business world still relies
greatly on personal networks, especially with respect to large firms, where, according
to the Imente team, it is very difficult to close deals without personal contacts.
6.1.3.3
Inter-industry level
1. complementary cooperation
X
○
○
○
○
2. threat of substitution
○
○
○
X
○
3. forward integration
○
○
X
○
○
217
218
Compare table 78.
Moreover.com has raised about 100 million dollars of financing according to Imente.
important
Not at all
important
Extremely
III. INTER INDUSTRY LEVEL – OPPORTUNITIES & THREATS FROM RELATED INDUSTRIES
286
CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
4. backward integration
○
○
X
○
○
5. threat of other new entries
X
○
○
○
○
Google!
Table 79: Inter-industry analysis Imente
A vast number of potential complementary cooperation opportunities with firms from
related industries have been identified by Imente. Potential technological applications
of Imente’s services are versatile. Establishing collaborations with related firms in
other industries is currently a major strategic focus of the company. Via a new
commission model, the product is sold by traditional press clipping, web design and
public relation agencies with established relationships to the target customers. In the
medium term, the highest growth expectations of the venture relate to these
collaborations.
The most imminent threat on the inter-industry level for the company is Google’s
recent expansion into the news aggregation sector. With its monopolistic dominance of
the search engine market, its superior technology, its immense financial resources and
its established international sales centres219, Google could easily dominate the market
of news aggregation. Currently, the news aggregation service is already promoted on
Google’s homepages as one of Google’s major four search services and is included as
standard feature in the Google toolbar220. The Google service is currently still very
basic and does not allow the advanced customisation features that are offered by
Imente. Additionally, Google does not currently offer any services for the business-tobusiness segment. Nevertheless, it is clear that Imente’s business model in the future
will have to take into consideration the services offered by Google in order to prevent
direct competition. It appears inevitable that Imente has to focus on the niches that will
not be covered by Google, especially since it will be impossible to offer a
technologically competitive product. On the other hand, the entrance of Google into
the market also represents a major opportunity. It has been mentioned by the
interviewees that most of the potential clients contacted by the company are not aware
that they have a need for the services offered by Imente and that selling the services
requires very intensive explanations of the service. The promotion of a news
219
220
Google just opened its own commercial sales centre in Madrid in the middle of 2003.
Google toolbar version 2.
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287
aggregation service in Google will be an excellent opportunity to increase awareness
of the utility of these services among worldwide internet users. As long as Imente
manages to find profitable business models outside of the service range of Google, the
entrance of Google into the market can be evaluated as a big opportunity.
Current threats from backward and forward integration are not considered particularly
important, even though both cases have occurred in the past. In terms of forward
integration, iconoce, one of Imente’s main competitors in the Spanish market, belongs
to a major media conglomerate and another media group is planning entry into the
market. In terms of backward integration, there is a client of Imente who adds
additional value to Imente’s service by creating summarising statistical and graphical
evaluations. This client is preparing to enter in a future news aggregation market.
6.1.3.4
Intra-industry level
Very low
Very high
IV. INTRA-INDUSTRY LEVEL
1. market structure
a) market size
X
○
○
○
○
potential market
size
b) heterogeneity of products
○
○
X
○
○
c) branding potential
○
○
X
○
○
d) distance to clients
○
X
○
○
○
e) economies of scale
○
○
X
○
○
f) export-import balance
○
○
X
○
○
g) need for specialised products
X
○
○
○
○
h) buyer concentration
○
○
○
X
○
i) average order volume
○
○
○
X
○
j) seasonal change of demand
○
○
○
○
X
k) intensity of price bargaining
○
○
○
X
○
l) market transparency
○
○
○
X
○
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m) advertising intensity
○
X
○
○
○
n) R&D intensity
○
X
○
○
○
TOP factors from market structure
- need for specialised products
-- with positive impact on firm
- potential market size
-- with negative impact on firm
/
2. market dynamics
a) former market growth
○
○
X
○
○
b) rel. expected market growth
○
X
○
○
○
c) entries to industry
○
X
○
○
○
d) exits from industry
○
○
X
○
○
e) balance of entries and exits
○
○
X
○
○
f) degree of uncertainty about future development
○
X
○
○
○
g) customer loyalty
X
○
○
○
○
h) changing customer needs
○
○
○
X
○
TOP factors from market dynamics
- customer loyalty
-- with positive impact on firm
-- with negative impact on firm
/
3. dependencies
a) on clients
○
○
○
○
X
b) on suppliers
○
X
○
○
○
Associations not
individual
suppliers
c) on key employees, employee institutions and key
○
○
○
X
○
d) on legislation
○
X
○
○
○
e) on business cycle
○
X
○
○
○
f) on maintenance in industry (barriers to exit)
○
○
○
X
○
knowledge
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
g) relative dependency compared with earlier and later
○
289
○
○
X
○
value chain members
TOP factors from dependencies
-- with positive impact on firm
-- with negative impact on firm
/
- on legislation
- on business cycle
- on supplier associations
4. competitor dynamics
a) inertia of established firms due to organisational
○
X
○
○
○
b) inertia of established firms due to legislative restrictions
○
○
○
○
X
c) inertia of established firms due to cost structure
○
○
○
○
X
d) aggressive responsiveness of established firms
○
○
○
○
X
e) inflation rate in selling prices
○
○
○
○
X
f) number of new products introduced
○
○
X
○
○
g) investment in new assets
○
○
○
X
○
structure
TOP factors from competitor dynamics
- low responsiveness of firms
-- with positive impact on firm
- high inertia due to organisational structure
-- with negative impact on firm
/
5. competitor structure
a) concentration
○
○
○
○
X
b) number of industry members
○
○
X
○
○
c) heterogeneity of industry members
○
X
○
○
○
d) general efficiency level among industry members
○
○
X
○
○
e) prevalence of competitive strategies on other than price
○
X
○
○
○
f) capacity utilisation level
○
○
○
X
○
g) employee productivity
○
X
○
○
○
potentially
h) labour costs intensity
○
X
○
○
○
currently
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i) gross margin
○
X
○
○
○
j) vertical integration
○
X
○
○
○
k) degree of diversification
○
X
○
○
○
l) average age of industry members
○
○
○
X
○
m) average size of industry members
○
○
○
X
○
n) variable costs
○
○
○
○
X
TOP factors from competitor structure
- low variable costs
-- with positive impact on firm
- potential gross margin
potentially
project team
- low concentration
-- with negative impact on firm
/
Table 80: Intra-industry level analysis Imente
Market structure:
The most relevant variables of the market structure are the large potential market size
and the need for specialised products.
Although Imente is still in the process of identifying which kinds of companies might
be most likely to pay for their services, the potential market size for the news
aggregation service could be extraordinarily large. The customers’ need for specialised
services is considered highly important, since the possibilities of customisation of
Imente’s service are superior to the competition in Spain, and these customisation
features are a major advantage in selling the services to the clients. In general it is
difficult for clients to compare the different services currently offered in Spain. The
basic services are very similar and mainly differ in their pricing models. For clients it
is difficult to evaluate the real quality of the services and the advanced features. It is
recognised that this difficulty of comparison prevents the danger of stronger price
competition for the whole industry. For Imente in particular, this is also negative, since
it considers itself as market leader in terms of service quality with the lowest prices in
the market. At the moment, however, the market is still so young that firms are more
focused on increasing the overall market size than increasing their market share at the
expense of competitors. Even though the whole industry and, in particular Imente, is
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highly technically oriented, advertising intensity is considered as important as R&D
intensity. The maintenance of the free service basically only serves the purpose of
promoting Imente’s brand name and it simultaneously generates considerable costs for
the firm. It has been mentioned by the technological director that the firm with the
strongest sales and marketing team will be most successful rather than the firm with
the most advanced technology.
Compared with the high sales efforts that are required in order to acquire a new client,
the average monthly order volume is relatively low. Imente has therefore changed its
sales strategy and utilises web agencies and traditional press clipping services as
distributors of their services. On the other hand, once clients are acquired, they
generate a steady flow of income for a long time period. The minimum duration of the
contracts is six months.
Market dynamics:
The most relevant variable of the market dynamics is customer loyalty. The Imente
management team does not believe that a single client of theirs has ever changed to a
competitor. By ensuring a good customer support they are generally able to maintain a
large share of their acquired customer base. Customers are very loyal. Only once in
Imente’s history have the prices for their service been increased. As a consequence,
not a single client has quit the service. The clients are not very price sensitive, which
may give Imente the opportunity, in the short-run, to increase profitability by
increasing prices and may prevent stronger price competition in the long-run.
Within the last few years, several firms have entered the industry. Currently the
number of firms is stabilising and the first competitors have already given up. The
entries of new firms have, until now, not directly affected Imente in a negative way. In
fact, these new firms have contributed in convincing potential clients of the necessity
of news aggregation services. When recently one competitor gave up, many of their
clients changed to Imente. The growth of the overall market has been expected to be
higher than it actually was in the last few years. The demand for the service has been
found to depend more on the proactive marketing efforts of the industry members than
on externally given demand drivers.
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Dependencies:
Imente’s dependencies are mainly related to the already mentioned variables of
legislation and business cycle on the macro level. The buyer concentration is relatively
low and Imente is not dependent on a single key content provider. Imente is more
vulnerable if content providers act in associations such as the Gedeprensa. If all media
sources affiliated to this association were to block their websites to Imente, this could
create serious problems.
Competitor dynamics:
The intensity of competitor dynamics is rated as low and in favour of Imente. The
competitors are not very aggressive nor particularly dynamic. One of the reasons
might be that the most important competitors are part of large organisations and
therefore do not have the same business focus nor the same motivation to generate
revenues. For many competitors from large organisations, the news aggregation
service might not be more than a technological experiment. As mentioned before, the
intensity of competition is not very strong. None of the competitors have reacted
directly to special price offers from Imente in the past. Imente enjoys a cooperative
relationship with its main competitors. Discussions have taken place with the major
competitors about increasing the market, and Imente has tried to sell its technology to
other competitors with less developed technologies. So far, no competitor has showed
inclinations of engaging in stronger competition on prices.
Competitor structure:
With regard to the dimension of competitor structure, the most influential variables
belong to the costs structure. Once the technology is developed and the service is
positioned successfully in the market, Imente could attain a very high gross margin.
The variable costs per additional client are negligible. The service is highly automated
and with its current staff and technological resources, Imente could easily serve five
times the current number of customers.
If Imente decided to cease new product improvements and stop all efforts to acquire
new clients, the firm could be maintained with only two employees. Also, the
characteristics of the competitors in Spain are evaluated as positive. No single large
competitor has emerged. Competitors are relatively young, of small size and with a
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293
limited budget to invest. Even though many competitors belong to large firms with
high financial resources, the actual project teams responsible for the news aggregation
service are relatively small and only have limited budgets. This has enabled Imente to
maintain its technological leadership in the market. The diversification of the
competitors’ firm group is considered as an advantage by Imente as it will decrease the
staying power in the market.
6.1.3.5
Barriers to entry
Very low
Very high
BARRIERS TO ENTRY
a) inventory intensity
○
○
○
○
○
b) fixed asset intensity
○
○
○
X
○
c) minimum organisational size & complexity
○
○
X
○
○
d) accessibility of distribution channels
○
○
○
X
○
e) customer loyalty
○
X
○
○
○
f) legislative barriers
○
○
○
○
X
g) product sophistication
X
○
○
○
○
TOP factors from barriers to entry
Not applicable
- technological product sophistication
-- with positive impact on firm
-- with negative impact on firm
- accessibility of distribution channels (in the
future this might turn into an advantage)
Table 81: Barriers to entry analysis Imente
The most obvious barrier to entry is the proprietary technology that is needed to
perform the news aggregation. The Imente team estimates that a firm would need an
investment of about 1 million Euro and about one year to develop the technology from
scratch that Imente possesses today. However, Imente itself intends to sell its own
technology to competitors and acknowledges that their competitor spynews.com can
achieve considerable sales with a far less sophisticated technology. The possibility of
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purchasing the technology therefore decreases the technological barriers. Still, until
now, purchasing external technology has been rare among news aggregators in Spain,
due to the risk of becoming dependent on a competitor.
Access to distribution channels has turned out to be a major barrier, which Imente is
still struggling with. A major share of sales has been prepared by the personal
networks of the founders. Due to the Spanish business culture and the difficulty of
explaining the service, the founders and the commercial director perceived it as
extremely difficult to initiate contacts with large firms without personal contacts. This
difficulty has led to a much slower growth of the company than was previously
projected.
Legislative barriers do not currently exist, but possible changes in the legal attitude to
deeplinking and the copyright laws may create additional barriers in the future.
Since the market is still young, and no dominant player with a strong brand reputation
has emerged yet, a new competitor with strong commercial capabilities may still
successfully enter the market.
6.1.3.6
Venture / firm level
a) relative price (better=lower price)
○
X
○
○
○
b) relative quality
X
○
○
○
○
c) relative design
X
○
○
○
○
d) innovation potential (potential to offer completely new
X
○
○
○
○
a) relative financial strength
○
○
X
○
○
b) relative knowledge / patents
X
○
○
○
○
product/service)
2. firm resources
better
Competitors
better
1. products/services
Imente
V. VENTURE / FIRM LEVEL
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3. management team
a) relative capability of management team
○
○
X
○
○
b) relationships key partners
○
X
○
○
○
c) relevant industry experience
○
X
○
○
○
○
X
○
○
○
a) relative distance to clients (who is closer?)
○
○
X
○
○
b) availability of labour and production factors
○
○
X
○
○
c) relative costs of labour and production factors
○
○
X
○
○
4. strategy
a) relative growth aspirations/aggressiveness
5. location
Table 82: Venture level analysis Imente
In the present industry setting, spynews.com and iconoce are currently identified as
Imente’s strongest competitors in Spain. The Imente team considers their service
superior in quality and in innovation to its competitors, and at the same time they
believe that it is offered at a lower price than their competitor’s services. Imente’s
other advantages can also be found in its internal knowledge, growth aspirations,
industry experience and its relationships with key partners. Imente has been able to
establish a cooperation agreement with Acceso, the largest press clipping agency in
Spain, in order distribute their product.
6.1.4 Excursus: Derived strategic recommendations for Imente
On the basis of the analysis conducted on market attractiveness, several strategic
recommendations have been derived by the author and have been presented to the
management of each case venture. The intention is to test how far the information, that
has been obtained guided by the presented conceptual model, serves to derive valid
recommendations on strategic key decisions.
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General business strategy
Instead of the articulated long-term vision of providing a personalised newspaper, it
might be more appropriate to focus on the vision of a one-stop, automated, up-to-date
monitoring system of relevant company information.
Focus on firm-specific instead of industry-specific information: Imente’s vision
and technological development is currently largely focused on providing customised
industry news content. However, the service of firm-specific online press clipping with
the potential to expand the clipping for additional media contents may be, at the
moment, the more profitable business segment. In the interviews it was mentioned
several times, that clients were asking for more comprehensive news clipping services,
whereas the technological development of Imente goes in a different direction. The
very technological approach of the company may be the reason for the mentioned
problem of commercialisation. It might be too late to reflect on potential customer
applications and ways of promoting the service after the technology has already been
developed. Instead, Imente may achieve greater success by first thinking about the
needs of the clients and the services that they might be willing to pay for and then
afterwards building the corresponding technology. Currently, clients may have a
higher need and a higher willingness to pay for services related to firm-specific press
clipping instead of customised industry news content. Industry-specific news is
already collected by specialised industry magazines and industry-specific websites.
The advantage of both industry-specific magazines and industry-specific websites is
that there is an editorial staff, with knowledge on the industry. They are probably
better able to filter the incoming information than any automated algorithm could ever
do. By contrast, in press clipping the online service could be far more competitive than
traditional services in terms of quality and price221. Traditional press clipping services
suffer from an extremely inefficient and labour-intensive work process. Moreover,
these firms are neither innovative nor dynamic.
Target customers: The company defines SMEs as their target customers. However,
the service might be most useful for large firms, which are frequently mentioned in
online news sources. The example of Moreover.com in the USA demonstrates that
221
The city of Girona, for example, could substitute the work of one employee in the documentation centre by
contracting Imente’s service.
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there is a considerable interest from large firms and that these large firms are willing to
pay very high prices for such services. These large firms may be the most profitable
target group.
Product development strategy
According to the vision of a one-stop service of monitoring firm-relevant information,
Imente may expand its service to monitor not only free online content, but also paid
online content, newsgroups, web forums, external links, TV and radio, and information
from the official firm register:
Paid online content: The possibility of also offering the complete print content of
newspapers as paid content will make it easier to sell the service. Customers can
compare directly the saving potential by using Imente’s service instead of the
traditional press clipping services with which they are already working. Content
providers might also pay for preferred listing of links to their paid content.
Newsgroups and web forums: Unsatisfied clients can generate tremendous damage
to the reputation of a company if they publish their negative experience in frequently
populated newsgroups and web forums. Moreover, these opinions are indexed by
search engines and may damage a firm for many years. Therefore it is critical to be upto-date about such comments in order to react in a timely manner. Firms can respond
directly to the client and offer compensation before they continue to publish the
comment in other places as well. Additionally, the firm may respond publicly in the
forum in order to clarify the issue, apologise, and demonstrate a dedication towards
customer satisfaction. An increasing number of clients is aware of the power of
making negative experiences public.
External web links: External links are not only one of the most critical criteria for the
ranking of a website, it might also be interesting for a firm to keep informed about
which website has published a link and in what context. Such a service could compare,
at monthly intervals, the external web links to a firm’s homepage found in Google or
alltheweb.com, with the links from the month before. A report could be provided about
the external websites that dropped links to the company’s website and those external
websites that added a link to the company’s website. Optionally, it would be helpful to
give additional information on the importance of each of these websites (e.g. by
Google page rank).
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TV and radio: As today many TV and radio stations already broadcast via the
internet, a future service could index the dialogues via third-party voice recognition
technologies and then allow a search of these sources for keywords related to the
client’s firm.
Firm registry: Contrary to most other countries, in Spain the official firm register also
publishes the complete financial information of non-publicly traded firms. Anybody
can request at the firm registry the complete financial details, including annual reports
from basically all firms. Some online database providers such as axesor.com purchase
this data from the firm registry and provide it to their clients in their online database.
Imente could scan the data from the firm registry for changes in the legal situation of
competitors and may inform clients of the dates when their competitors’ annual reports
are available online.
Such a comprehensive service could significantly increase the utility of the service for
the client and would therefore be easier to sell. Additionally, it would be possible to
charge higher prices, since the service can be directly compared with the expensive
services of traditional press clipping agencies, which require a large amount of nonautomated work. This approach would also make the firm less vulnerable to the
emergence of competition in one particular technological segment.
Pricing strategy
The number of potential customers could be greatly increased if small firms were
offered a pay-per-article payment model for the online press clipping service. Small
firms which do not send out weekly press releases are only mentioned a few times a
year in the media. The current payment model, based on monthly fees is too expensive
for these clients. For larger clients, package prices should be maintained. However, the
differentiation of the package prices based on the number of article references might
offer the possibility to charge higher prices to very large clients with respectively
deeper pockets. The pay-per-article payment model should only be offered if the
technological implementation and the processing of small payment amounts can be
widely automated.
Cooperation strategy
Media companies: The current legal actions of media sources against press clipping
services may dramatically change the press clipping industry. Imente now has the
unique opportunity to present to the media companies a compensation model that
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299
provides a fair compensation to the media companies for the articles that are
forwarded from the press clipping service to the final clients. This model could take
the following structure: The media sources publish their complete print content daily
online. In cooperation with a third party provider of micro payments222, a fixed price
has to be paid per article, which is requested in full text by each user and similarly
each final client of the press clipping firm. As soon as the major news sources, who are
members of the Gedeprensa association, publish their complete content online, they
would be able to force traditional press clipping agencies to apply this compensation
model, and Imente could emerge as the leading player for the whole sector of press
clipping. The compensation model between media sources and press clipping services
would also provide the opportunity of additional sources of revenues, as media sources
could be asked for commission on each referral of paid content. It may be critical to
convince media companies that the earning potential through paid online content is
higher than through the sales of CD-Rom archives.
Micro payment companies: The development in Germany has shown that many
media sources have decided to offer their complete print content online. This
development has not yet reached the Spanish market. However, in Spain media
sources have also encountered the difficulty of financing their websites solely from
advertising revenues. Now that third party providers for micro payments with
successful track records in other countries exist, more Spanish media companies will
also try to reap paid content services as an additional source of revenue. Imente can
benefit from the commercial efforts and competence of the micro payment firms, who
are in a much better position to convince media firms to publish their complete content
online. Imente could provide its clients with the “service” of registering them
automatically for the micro payment service, so that clients can access the paid content
without additional registration procedure. Imente’s customer base should be highly
interesting for micro payment providers, since these clients have shown a high
inclination to pay for news content. In return for providing this highly targeted client
base to the micro payment provider and for frequently sending clients to these paid
content sources, Imente should ask the micro payment provider to include in their
contract with the media firm that Imente is allowed to scan all paid content. Moreover,
222
Firstgate (es.firstgate.com) has recently entered the Spanish market and already provides such pay-percontent services for a wide range of media in Germany. Firstgate is the clear market leader in Germany.
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Imente should negotiate a commission with the micro payment provider for all referred
clients who purchase articles.
6.1.5 Summarising evaluation of the impact of critical market characteristics
on venture performance throughout different evolution phases
Imente and the whole market of online news aggregation are still in a very early
evolutionary phase. The exact target market for the service remains vague and Imente
is still in the process of identifying the most profitable market segments for their
service.
The initial opportunity for starting news aggregation ventures has emerged from the
macro-level trend of a growing number of media sources publishing their content
online and on the technological side from the development of tools and knowledge of
search spiders and agent technologies.
At the time of entry, barriers have mainly been imposed in terms of time to
development and financial investment in programming manpower. Imente could
overcome the barriers by making use of the technical director’s expertise and his
experience in former projects. He and his fellow programmers have continuously
developed the service for three years, and it is competitive with the best similar service
on the global market scale. In the meantime, the barriers to entry have changed. With
an increasing number of firms possessing the required technology, it is possible to
purchase the technology instead of having to develop it. Technological barriers have
therefore been replaced by financial barriers. The development of a proprietary
technology requires a minimum of 1 million Euros of investment. On the other hand, it
is now possible to purchase or license the technology from existing competitors at
comparably low costs. Subsequent entries of new firms to the market have been
moderate within the last years and have had hardly any effect on Imente’s growth or
profitability.
Within the first years of operation the critical problem was the slow increase of the
number of clients for the value-added paid service. Several market variables have
contributed to this situation. On the macro level the lack of willingness to pay for
online services has led to the extremely low share of users for the paid service
compared with the dominating share of users of the free service. On the intra-industry
level, the lack of established distribution channels and the intensity of required sales
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301
efforts to sell the service to potential target customer groups turned out to be the major
reason for the slow growth of the paid service segment.
Today, on both the macro level as well the inter-industry level important changes have
started to emerge which have not yet had a direct impact on the business, but may lead
to a radically changed competitive environment in future. Most important among the
changes on the macro level are from legislation the legal actions from media
companies against news clipping services and the future availability of free online
news content together with the acceptance of online micro payment providers. On the
inter-industry level the most relevant factor is the recent entrance of Google into the
segment of news aggregation. It is difficult to forecast the direct implications of these
changes on the future performance of Imente. However, the concepts that were
presented in the previous chapter demonstrate that there is a possibility that Imente
may benefit from such changes. These changes may make Imente’s services more
attractive as it may be possible to process more content and Imente could strengthen its
competitive position compared with traditional news clipping agencies. Further growth
promises a rapid increase in profitability as the service can be scaled at basically no
costs and therefore variable costs are extremely low.
In the future the constellation of high homogeneity of competitor services and low
variable costs may encourage competitors to gain market shares by aggressively
lowering prices at the expense of the profitability of all industry members. Currently,
the intensity of competition is still very low, however, this may change in a later life
cycle stage. The vulnerability to future price competition may depend on the final
client group. Small firms may be more price sensitive than large firms, who may place
more importance on customer service. Due to the small number of industry members
and the high remaining growth potential of the overall market, the danger of intense
price competition is not yet imminent. An already planned future geographic
expansion into South American markets could prolong the growth of the overall
market and therefore delay the need for competition on prices.
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6.2
Case study – Open House
6.2.1 Venture profile
Open House Spain S.L. (Open House) has been chosen as a venture in a relatively
young market with very high increase in competition due to low barriers to entry. The
company is an internet-based agency for vacation apartment rentals in Barcelona and
was founded in 1997. In October 2003 the company employed four permanent staff
members and three temporary student workers.
Founder
Claudia Eleuterio started the firm in 1997 in response to the increasing demand of
shared flats in Barcelona from international students. Previously she worked in a
marketing position for an international fashion company for several years and she has
studied in Brazil, Argentina and Spain.
Products/services
Over 95% of revenues are generated from commissions of vacation apartment rentals.
Commissions from the sales of Spanish language courses account for the remaining
5% of overall revenues.
In 1997, Mrs. Eleuterio started the company by renting flats in Barcelona and
subletting them to foreign students.
In 1998, language courses were included as an additional product segment, which was
considered complementary to the initial target customer group of university students.
In 1999, the company also started to rent third-party apartments based on a new
commission system. Several apartment owners had contacted Mrs. Eleuterio to rent
their apartments as shared flats to students, which generates higher earnings than
traditional long-term rental contracts. At the same time, the maintenance of Mrs.
Eleuterio’s own apartments turned out to be very time consuming, which enforced the
desire to shift the business focus on third party apartments and to concentrate solely on
marketing and sales. In the same year the existing basic web representation was
converted into a professional website with detailed information on the offered
apartments. This shift in the business model enabled an ongoing growth for the
following years.
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303
In 2001, Open House was the first company in Barcelona to offer the service of online
availability checks on their website.
Until today the number of third-party apartments has continuously increased to 150
and large efforts have been taken to further develop the company’s website and to
promote the service online. The target customer has shifted from students to tourists in
the age range of 30 to 40 years.
Performance
Open House is the market leader of vacation apartment rental in Barcelona, with over
5,000 bookings per year and by far the highest number of apartments on offer. Within
the last three years the sales level has increased greatly and is estimated at about 1
million Euros per year. The company has been operating profitably for many years.
6.2.2 General market setting
At the start of the company in 1997, the market for short-term rentals was very limited.
It was a major problem for international students to find accommodation in Barcelona.
Currently the vast majority of students in Spain live with their parents, which meant
that a real estate market for student flats basically did not exist. Over the last few
years, Barcelona has experienced an extraordinary boom in the real estate sector. Rents
and sales prices of apartments have doubled within only five years. This boom has
attracted a lot of speculation. Many people discovered a very profitable business in
buying old run-down flats in the old city centre, renovating them and then renting
them to foreigners and tourists. The earning potential of renting a flat on a daily basis
is frequently 5-10 times higher for property owners compared with renting it by
traditional long-term contracts. Hand in hand with the increasing attractiveness of
Barcelona as a tourist destination from the demand side, the market of vacation
apartment rentals has grown rapidly.
While in 1997 only one or two other companies dealt with short-term rentals, the
number of firms operating in the market has since increased dramatically to more than
30 agencies exclusively focusing on short-term vacation rentals in Barcelona in 2003.
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6.2.3 Explanation of market impacts on venture success
important
Not at all
important
Macro level
Extremely
6.2.3.1
1. macro-economic changes
○
X
○
○
○
b) changes of interest rates
○
○
X
○
○
c) changes of currency rates
○
○
X
○
○
a) changes in environment protection regulations
○
○
○
○
X
b) changes in market regulations
○
○
○
○
X
c) changes in labour market regulations
○
○
○
○
X
d) changes in public spending
○
○
○
○
X
e) changes in international trade deregulations
○
○
○
X
○
a) development of internet usage
X
○
○
○
○
b) development of internal IT applications
○
○
○
○
X
c) development of new production technologies
○
○
○
○
X
a) changes in consumer preferences (increasing volatility)
○
X
○
○
○
b) individualisation
○
○
○
X
○
a) decreasing population growth
○
○
○
○
X
b) increasing population age
○
○
○
○
X
a) changes in the propensity to consume
2. political / legislative changes
3. technological changes
4. socio-cultural changes
5. demographic changes
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
Other changes:
-
305
Attraction of Barcelona as tourist
destination
-
Boom of real estate sector
-
Emergence of low cost airlines flying to
Barcelona
-
High occupancy rate of hotels in
Barcelona
Table 83: Macro level analysis Open House
Three major changes of the macro environment have favoured the development of
Open House over recent years.
First, Barcelona has become one of Europe’s most popular tourist destinations223. The
number of visitors has steadily increased and is expected to reach 4.2 million visitors
in 2003, accounting for 9.2 million over night stays. At the same time Barcelona has
benefited as has no other city from the emergence of low cost airlines. A multitude of
these new airlines are flying to Barcelona. Also, over the next years, projects as the
“Forum 2004”, a new cultural centre with exhibition, conference and entertainment
facilities, which required more than a billion Euro of investment, is likely to maintain
the attraction of Barcelona as a major tourist destination in the future. Open House has
already been in contact with the organisation group of the forum for future
cooperation.
Second, the real estate sector in Barcelona has been booming in recent years and has
attracted a lot of speculation. As a result the offer of renovated city centre apartments
has greatly increased.
Third, the higher intensity of internet usage has opened a new distribution channel.
Especially for travel-related information it has become common to consult offers on
the internet. Open House has been able to benefit from this development by offering
an innovative website and by conducting intensive search engine marketing.
Moreover, several other smaller changes on the macro level have impacted Open
House. A general trend in tourism towards weekend city trips has been notable and has
augmented Open House’s bookings. The number of hotel beds has not increased at the
same pace as the number of visitors to Barcelona. Therefore the occupancy rate of
223
According to the international association European Cities Tourism, Barcelona has been the city with the
highest growth in tourism in Europe within the last decade.
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hotels has been very high, making it frequently difficult to find hotel accommodation.
Renting a vacation apartment may therefore be considered more frequently as a valid
alternative. On the other hand, it has also been shown that geopolitical occurrences can
have an important impact on the business. The terror attacks from 11th of September
2001 have led to a steep drop in visitors from the USA, formerly the strongest client
group. The currency devaluation of the US dollar has been another macro level
variable that has enforced this drop in bookings from the USA.
6.2.3.2
Inter-country level
Very low
Degree of international competition
Very high
II. INTER-COUNTRY LEVEL – NATIONAL COMPETITIVENESS
a) activities of national competitors in foreign markets
○
○
X
○
○
b) activities of foreign companies in national market
○
○
○
X
○
c) country of strongest competition in future
UK
important
Not at all
competition.
important
competitors from country of strongest future
Extremely
Sources of important competitive advantages of
1. production factors
a) easier availability of qualified labour
○
○
○
X
○
b) lower costs of qualified labour
○
○
○
○
X
c) easier availability of resources
○
○
○
○
X
d) lower costs of resources
○
○
○
○
X
a) lower taxes
○
○
○
○
b) higher stability of economical political environment
○
○
○
○
2. political stability & taxation
X
X
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307
3. distance to sales market
a) lower distance to main customers for export industries
○
X
○
○
○
b) larger size of national market
○
○
○
X
○
○
○
○
○
X
○
○
○
○
X
○
○
○
○
X
a) easier availability of debt funding from banks
○
○
○
○
X
b) easier availability of equity funding from venture
○
○
○
○
X
○
○
○
○
X
○
○
○
○
X
4. industry infrastructure
a) better transportation and telecommunication
infrastructure
b) better access to dependent supplying and buying
industries
c) more competitive dependent supplying and buying
industries
5. new venture infrastructure
capitalists and business angels
c) more financial assistance for new ventures (e.g.
governmental funding, tax incentives etc.)
d) more non-financial assistance for new ventures (e.g.
consultancy)
Other important sources of competitive advantage of firms
/
from country of strongest competition:
Table 84: Inter-country level analysis Open House
The market of vacation rentals in Barcelona is still dominated by local competition.
International agencies such as the Swiss-operated interhome provide only a limited
selection of apartments in Barcelona. The increasing intensity of competition has
motivated several local competitors to expand their service to other cities within Spain
and also abroad.
Potential future foreign competitors may be expected, especially from the UK, even
though this danger is not considered particularly high. The UK currently represents the
highest share among all client nationalities. Competitors in the UK are closer to the
final clients. They might find it easier to recognise opportunities for promoting and
distributing the service in the UK market. On the other hand, the labour market in
Barcelona is very attractive from a company’s perspective. A multitude of qualified
foreigners are looking for jobs in Barcelona and face a very limited offer of jobs.
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Therefore it has been easy for Open House to find multilingual staff to support their
international customer base. The salaries, which are paid in Barcelona, are at the same
time among the lowest in Europe. The popularity of Barcelona as tourist destination
has attracted also many qualified foreign students interested in an internship in
Barcelona. Open House has continuously employed several students over the last few
years for four to six months’ internships. Since basically no salaries are paid under
these internship arrangements, Open House has been able to maintain the labour costs
at a very low level.
In case of an expansion abroad, the founder presumes that the location of the company
in Spain may negatively affect the reputation of the service, since clients may have
less confidence in the professionalism and reliability of a service from Spain than from
the UK, for example.
Overall, the situation in Spain and particularly in Barcelona provides a relative
attractive setting for an already established firm such as Open House. The market of
vacation apartment rentals is larger and more competitive than in other cities.
Competitors have been forced to innovate in improving their websites and in
promoting their services. This experience will give them a competitive advantage in
expanding their services to other cities.
6.2.3.3
Inter-industry level
1. complementary cooperation
○
○
X
○
○
2. threat of substitution
○
○
X
○
○
3. forward integration
○
X
○
○
○
4. backward integration
○
○
○
○
X
5. threat of other new entries
X
○
○
○
○
Table 85: Inter-industry analysis Open House
important
Not at all
important
Extremely
III. INTER-INDUSTRY LEVEL – OPPORTUNITIES & THREAT FROM RELATED INDUSTRIES
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309
The industry of vacation apartment rental in Barcelona is expected to further attract
new market players. The forward integration of apartment owners and the emergence
of new competitors pose particular threats.
Most of the apartment owners that Open House cooperate with, also rent their
apartments themselves. Many promote their apartments directly on their own websites.
Clients are frequently more likely to book directly with apartment owners, since they
expect that agencies charge a commission mark-up, which they can avoid paying by
booking directly with the owner. Since Open House relies heavily on search engine
marketing, owner website listings compete directly with Open House’s listings. It is
expected that in the future even more owners will promote their apartments on their
own websites and that their general knowledge about achieving high search engine
placements will increase. Moreover, it is noticeable that the sophistication of these
owner websites improves continuously. Some apartment owners with successful direct
promotion activities may, in the future, consider expanding by also promoting third
party apartments.
Apart from the threat of forward integration by apartment owners, the market is
exposed to various current and potential threats of entry. The number of competitors
continues to increase dramatically. Most of these recent entries to the market are
individual foreign residents in Barcelona, who identify the apartment rental business as
a convenient way of making a living. However, new entries are also emerging from
existing organisations. Many traditional real estate agencies have started to expand
into the segment of short-term apartment rental. In the future, large online travel
agencies may also enter the market and offer apartment accommodation as an
alternative option to established hotel accommodation. Since the vacation apartment
rental market is still relatively small, the entrance of large online travel agencies may
not only pressure the market share of Open House in a negative way. It may also
positively lead to a higher awareness of travellers to consider apartment rental as an
accommodation option.
In general only few competitors actively look for cooperation partners in other
industries. From the perspective of the Open House team, prospective cooperation
partners may be organisers of large events such as the Forum 2004 in Barcelona,
which attract a lot of visitors, as well large international companies who frequently
send their employees on projects outside their home town. In particular, the
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development of the business traveller segment, which currently accounts for only
about 15% of all clients, might be impacted very positively by new partnerships.
Moreover, cooperation with travel agencies could open up an alternative distribution
channel for Open House’s apartments. Potential substitution of the service may derive
from online web services, which give apartment owners the possibility to advertise
their apartments free or at little costs. Operators of such websites could be, for
example, the websites of classified magazines such as Primerama.
Overall, the threat of new entries is rated very high and is expected to have the highest
impact on the future market setting among the variables of the inter-industry level.
6.2.3.4
Intra-industry level
Very low
Very high
IV. INTRA-INDUSTRY LEVEL
1. market structure
a) market size
○
○
○
○
X
If Barcelona
market
b) heterogeneity of products
○
○
○
○
X
c) branding potential
○
○
X
○
○
d) distance to clients
○
○
○
○
X
Local market
in terms of
supply
e) economies of scale
○
○
X
○
○
f) export-import balance
○
○
○
○
X
g) need for specialised products
○
○
○
○
X
h) buyer concentration
○
○
○
○
X
i) average order volume
○
X
○
○
○
Compared to
customer acquisition costs
j) seasonal change of demand
X
○
○
○
○
k) intensity of price bargaining
○
○
○
○
X
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311
l) market transparency
○
○
X
○
○
m) advertising intensity
○
○
X
○
○
n) R&D intensity
○
○
○
X
○
TOP factors from market structure
-- with positive impact on firm
/
-- with negative impact on firm
- limited market size
- seasonal change of demand
- low heterogeneity of products
2. market dynamics
a) former market growth
X
○
○
○
○
b) rel. expected market growth
○
○
X
○
○
c) entries to industry
X
○
○
○
○
d) exits from industry
○
○
○
○
X
e) balance of entries and exits
X
○
○
○
○
f) degree of uncertainty about future development
○
○
○
○
X
g) customer loyalty
○
○
○
○
X
h) changing customer needs
○
○
X
○
○
TOP factors from market dynamics
-- with positive impact on firm
- former market growth
-- with negative impact on firm
- entries to industry & nearly no exits
- lack of customer loyalty
3. dependencies
a) on clients
○
○
○
○
X
b) on suppliers
○
○
○
○
X
c) on key employees, employee institutions and key
○
○
○
○
X
d) on legislation
○
○
○
○
X
e) on business cycle
○
○
X
○
○
knowledge
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CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
f) on maintenance in industry (barriers to exit)
○
○
○
○
X
g) relative dependency compared with earlier and later
○
○
○
○
X
value chain members
TOP factors from dependencies
-- with positive impact on firm
-- with negative impact on firm
- low dependency on clients and suppliers
- dependency on search engine promotion
4. competitor dynamics
a) inertia of established firms due to organisational
○
○
○
○
X
b) inertia of established firms due to legislative restrictions
○
○
○
○
X
c) inertia of established firms due to cost structure
○
○
○
○
X
d) aggressive responsiveness of established firms
X
○
○
○
○
e) inflation rate in selling prices
○
X
○
○
○
f) number of new products introduced
○
X
○
○
○
g) investment in new assets
○
○
○
○
X
structure
TOP factors from competitor dynamics
-- with positive impact on firm
/
-- with negative impact on firm
- aggressive responsiveness
- inflation rate in selling prices
5. competitor structure
a) concentration
○
○
○
X
○
b) number of industry members
X
○
○
○
○
c) heterogeneity of industry members
○
○
○
○
X
d) general efficiency level among industry members
○
X
○
○
○
e) prevalence of competitive strategies on other than price
○
○
X
○
○
f) capacity utilisation level
○
○
○
X
○
g) employee productivity
○
X
○
○
○
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313
h) labour costs intensity
○
○
○
X
○
i) gross margin
X
○
○
○
○
j) vertical integration
○
X
○
○
○
k) degree of diversification
○
○
X
○
○
l) average age of industry members
○
○
○
○
X
m) average size of industry members
○
○
○
X
○
n) variable costs
○
○
○
○
X
TOP factors from competitor dynamics
-- with positive impact on firm
- gross margin
- variable costs
-- with negative impact on firm
- number of industry members
Table 86: Intra-industry level analysis Open House
Market structure
The limited size of the Barcelona market, the high seasonality of the business and the
low heterogeneity has a negative impact among the variables related to market
structure. Considering the high number of agencies in the Barcelona area and the
limited popularity of vacation apartment rentals, the actual market size is very limited.
The potential market size, however, could greatly increase if the geographic market is
expanded beyond Barcelona to the whole of Europe. The seasonal peak demand during
the summer months has affected the company negatively. The nature of the business
requires multilingual staff. At the time of the interview, the customer support was
offered in Spanish, English, German, French and Portuguese. Even though in the offseason only a few enquiries are received in French or Portuguese, staff capable of
speaking all of these languages has to work for Open House throughout the whole
year. Because the number of enquiries from France and Portugal do not reach a
sufficiently high level throughout the whole year to justify the recruitment of a fulltime employee, Open House decided to recruit temporary student workers, who
continuously require a lot of initial training and involve a considerable amount of
recruitment activities. Another disadvantage from the perspective of Open House is the
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low heterogeneity among the service offerings of the different agencies. Now that the
company is established, it is perceived as a disadvantage that small new firms can
basically offer an identical service, which is only distinguished by the number of
apartments offered. A positive aspect is the average order volume, which at about 150
Euros is relatively high compared with the costs of client acquisition and reservation
processing. However, with the increase in competition, the costs of client acquisition
may increase in the future.
Market dynamics
Several aspects of the market dynamics have a critical impact on the industry. The
demand for vacation apartment rental has grown strongly within recent years and has
reinforced the growth of Open House. At the same time, the market growth attracted a
large number of new firms to enter the market. While there were only about two
similar services in Barcelona at the time of the foundation of Open House, today the
number of agencies has increased dramatically. Even with the increasing intensity of
competition over the last few years, basically no firm has quit the industry. Since a
basic vacation rental service does not require large overhead and high fixed costs, the
exit barriers are very low. One could easily maintain a simple service of apartment
rentals as an individual person, even as a part-time job. This has led to a continuous
increase in the total number of firms operating in the market, which further intensified
competition. Another critical aspect is the low degree of customer loyalty. Since at the
time of market entry, no other large established competitors existed, the low loyalty of
customers did facilitate the market entry of Open House. To the contrary, it is a major
problem of the whole industry that many clients will only book their first stay in
Barcelona with an agency, and will book subsequent stays in the future directly with
the owner of the apartment at a lower price. Being aware of this problem, Open House
has started a promotion to offer a premium of 50 Euros for each client referral or
repeat booking.
Dependencies
It is one of the major advantages of the industry that firms are generally neither
dependent on critical suppliers nor on individual clients. Losing the largest client or
apartment owner has only a limited impact on the overall performance of a vacation
rental agency. However, strong dependencies result from heavy reliance on search
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
315
engine placements. The internet and in particular the search engine Google is the one
most important means of distribution and promotion of the service for basically all
vacation rental agencies in Barcelona. The exclusion of a company’s website from
Google would have a devastating effect on sales. In cases where websites apply very
aggressive online promotion activities in order to reach a high ranking, search engines
such as Google occasionally ban websites. Moreover, the prices for paid listings in
search engines have more than doubled within the last year as a result of stronger
competition for the few relevant key words related to Barcelona vacation apartment
rentals.
Competitor dynamics
The increasing number of market players within the last few years has forced firms to
develop their services dynamically in order to remain competitive. The founder of
Open House stressed particularly what she called a war for search engine rankings.
Open House as well as various larger competitors check their ranking every month for
the main keywords “Barcelona apartments”, and make adjustments to their pages in
order to preserve or improve their ranking, which is critical for the monthly sales of
their business. Other competitors pay several thousand Euros monthly for paid listings
in Google. The fierce competition is also notable in the high responsiveness of
competitors. New website features such as online availability verification of
apartments, last-minute offers, off-season discounts, lotteries and city information are
frequently adopted within weeks or months by competitors. In some cases, even text
passages from the website are copied verbally from competitors. Another consequence
of increasing competition, which affects the industry negatively, is a slowly evolving
pressure on rental prices. As the offer of apartments has increased more than the
number of clients, apartment owners are forced to lower their prices. Open House has
also recently started to approach cooperating apartment owners to lower their prices in
order to remain more competitive with the apartment offers from other agencies. Since
Open House earns a fixed commission of 25% on the rental price, the pressure on
rental prices directly affects the earnings per booking. For the future, the pressure on
prices may become even stronger with a continuously increasing construction activity
in the real estate sector in Barcelona.
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Competitor structure
From the supply side of the market, a positive costs-earning relationship combined
with relatively low variable costs characterise the main attractiveness of the industry.
The intentional limitation of Open House on the intermediation service segment,
without offering time-consuming property management services, ensures a high
simplicity and standardisation of the business process, which can be highly automated.
The necessary time needed for Open House staff to consult clients and to process
bookings is very little due to the comprehensive information on the website and the
automated booking procedure. A further increase in the business volume would lead
only to marginal increases in costs and could have immediate impact on profits. The
most important negative aspect of the competitor structure has to be the large number
of highly homogeneous competitors in Barcelona. The management of Open House
mentioned that they would prefer to operate in a highly concentrated market than in
the currently highly fragmented market of numerous small competitors. The sheer
number of competitors leads for potential clients to a confusing information overload.
In order to check all the apartments that are offered from the multitude of agencies in
the market, a potential client can easily spend half a day and more. For Open House, it
is a major challenge to get noticed within this multitude of competitors, especially
since the search engine promotion also makes it easy for small competitors to reach a
wide potential audience.
6.2.3.5
Barriers of entry
Very low
Very high
BARRIERS TO ENTRY
a) inventory intensity
○
○
○
○
○
b) fixed asset intensity
○
○
○
X
○
c) minimum organisational size & complexity
○
○
○
○
X
d) accessibility of distribution channels
○
○
○
X
○
e) customer loyalty
○
○
○
○
X
Not applicable
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317
f) legislative barriers
○
○
○
○
X
g) product sophistication
○
○
○
○
X
TOP factors from barriers to entry
-- with positive impact on firm
/
-- with negative impact on firm
- low minimum organisational size
- low loyalty of customers
- low product sophistication
- low capital intensity
Table 87: Barriers to entry analysis Open House
The industry of vacation apartment rentals is characterised by very low barriers to
entry. This has been a major driver for the continuous increase in the number of
competitors. During the first years of operation, Open House, which started as a onewoman firm without any external financing, also benefited from the low barriers to
entry. The one major barrier is the establishment of an attractive website with a high
ranking in the search engines. However, the difficulty in overcoming this barrier is
decreasing. The costs of developing a website are decreasing, while at the same time
an increasing number of people are capable of developing database-driven websites.
Web editing applications provide out-of-the-box standard solutions which require less
time and knowledge to implement than has been necessary in the past. Moreover, the
awareness about the importance of search engine rankings, and the knowledge on how
to achieve high rankings is growing.
6.2.3.6
Venture / firm level
a) relative price (better=lower price)
○
X
○
○
○
b) relative quality
○
○
○
X
○
better
Competitors
better
1. products/services
Open House
V. VENTURE / FIRM LEVEL
318
CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
c) relative design
○
X
○
○
○
d) innovation potential (potential to offer completely new
○
○
X
○
○
a) relative financial strength
○
○
X
○
○
b) relative knowledge / patents
○
○
X
○
○
a) relative capability of management team
○
○
X
○
○
b) relationships key partners
○
X
○
○
○
c) relevant industry experience
○
X
○
○
○
○
○
X
○
○
a) relative distance to clients (who is closer?)
○
○
X
○
○
b) availability of labour and production factors
○
○
X
○
○
c) relative costs of labour and production factors
○
○
X
○
○
product/service)
2. firm resources
3. management team
4. strategy
a) relative growth aspirations/aggressiveness
5. location
Table 88: Venture level analysis Open House
The one company which has been identified by the Open House team as the most
important competitor is Barcelona On Line S.L. with its website barcelona-online.com. This company was one of the early competitors, who started only one year
after Open House. In 1999, Barcelona On Line S.L. reported for the first time relevant
sales of about 100,000 Euros. In 2000, the firm started to expand rapidly and recruited
four full-time employees. Their sales more than doubled to 250,000 Euros. The
expansion led to an overall reported loss of 130,000 Euros in the same year of 2000. In
the year 2001, the investments made in 2000 paid off and the company managed to
increase their sales by over 150% to more than 640,000 Euros. The annual report of
2002 was not made available by the firm register by the end of 2003. Barcelona On
Line S.L. only offers about 80 apartments in Barcelona compared with the 150
apartments offered by Open House. The firm has expanded their service to include
complementary services such as hotel reservation and bus and boat tour reservations.
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
319
Recently, sister sites for apartment rentals in Madrid and Paris were started. Moreover,
they included various features on their website which Open House is still working on,
such as last-minute offers and the exploitation of their website by the inclusion of
third-party advertising. Another important competitor that has just emerged within the
last years is the firm Visit Barcelona S.L. with its website visit-bcn.com. Within less
than two years, this company has established itself as a major competitor due to a very
dynamic growth strategy. Since the firm has only been operating for a short time, no
financial information is available. Visit Barcelona S.L. has been very successful in
achieving high search engine rankings for their website. In order to increase the
number of apartment offers, Visit Barcelona S.L. has bought apartments in the
Barcelona city centre, renovated them, and later on sold these apartments to investors
who had to agree not to use the apartments themselves but to let the apartments to
Visit Barcelona S.L. for vacation rentals. This way Visit Barcelona S.L. was able to
offer on their website, within a relative short time, over 50 apartments in Barcelona.
Already the firm has expanded geographically with specific websites for vacation
rental in Paris and London.
With regard to the basic service and the ways of promotion, these two competitors and
Open House are nearly identical.
The interviewees from Open House consider the prices of their apartments slightly
lower than the prices of the competitors. Also with regard to the offered apartment
range, Open House leads as it offers many more apartments than other competitors.
However, the growth dynamics of their main competitors have been stronger and
competitors have leaded the introduction of complementary products, innovative
features and the geographic expansion beyond the Barcelona region. Barcelona On
Line S.L. in particular has been very active in offering complementary services. It
appears that in the years 2001 and 2002, Open House mainly concentrated on
organising the growth within their main segment of Barcelona apartment rentals. Due
to the steep increase in sales within this segment, they did not feel a strong need to
expand. However, within the last year the geographic expansion of the service has
become an important priority after several competitors have expanded into other cities.
At the time of the case study interviews, Open House was about to launch a new
service that offers apartment rentals for a wide range of European cities. With this new
offer the managers of Open House are confident that they will be able to take over the
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CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
leadership in the expansion of the vacation rental service beyond Barcelona. The fact
that they are already providing multilingual customer support facilitates the
organisational effort of implementing a geographic expansion. This project and a timeconsuming optimisation of the existing website have been the reason for Open House
lagging behind in the adoption of discount schemes that are already offered by
competitors. Open House may have small competitive advantages with regard to their
relationships with apartment owners and the design of their website. It has been
mentioned in the interviews that Open House might have a better relationship with
apartment owners than their competitors, which is documented in the large number of
apartments offered. Apartment owners have told Open House about other agencies not
treating them respectfully. Many new apartment owners contact Open House due to
the recommendations of apartment owners who already collaborate. Finally, the design
of the Open House website appears more professional than the websites of many
competitors. This reinforces the clients’ trust in the service.
6.2.4 Excursus: Derived strategic recommendations for Open House
The management team of Open House has already identified many promising ideas for
the future development and expansion of their firm. It might be helpful if in more
specific medium- and long-term planning, priorities for the implementation of new
projects are set in accordance to their relevance on firm profits. The orientation on
worldwide apartment rental agencies such as the worldwide leader interhome from
Switzerland may provide new ideas for the development of the company.
Geographic expansion:
Open House’s decision to place first priority on geographic expansion into other cities
and also to implement this new service immediately for a wide range of cities appears
reasonable. Open House will be able to attract many visitors to their new websites for
other cities by applying their proven concepts of search engine marketing. The
attraction of apartment owners in the beginning could be critical. Since the company is
not currently planning to have staff locally present in the different cities, and since the
concept of vacation apartment rental may not be that known in other cities, the
establishment of an initial base of apartments may be difficult. Therefore it might be
recommendable to start the launch of the new website with the standardised marketing
strategy to attract apartment owners. This could be done by putting advertisements in
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
321
the real estate sections of local newspapers or by directly contacting apartment owners
who are themselves offering their apartments for short or long-term rent. Apartment
offers in online bulletin boards, where apartment owners can be conveniently be
contacted by standard emails may also be of particular interest. In the case where their
own recruitment efforts do not give sufficient results or turn out to be too time
consuming, Open House could establish cooperation with similar agencies in other
cities. Those agencies that lack professional website promotion could be of particular
interest. Moreover, these agencies would be obliged to inform Open House
immediately about any changes in the availability of apartments. These agencies could
be offered the opportunity to include their complete apartment offer into the Open
House database and an additional margin could then be added to the price of these
apartments.
Product development:
The offering of additional complementary services should be considered with caution.
The earning potentials of each service should be evaluated carefully. A concentration
on the basic service seems at the moment to offer higher results and the expansion into
complementary services may detract from the main business. Especially with regard to
the limited staff, complementary services could complicate the business process and
slow the growth in the main business.
Organisation:
With the expansion of the service, it might be useful to give apartment owners the
opportunity to include changes in the availability of apartments themselves into the
database. A notification e-mail about the change in availability may be sent to the
respective customer support staff. Moreover, it appears necessary to prepare a plan
about how to delegate responsibilities among the existing staff in case the website
achieves the success expected. The recruitment of additional full-time employees with
clearly outlined responsibilities may be necessary as soon as the new service starts
operating.
Marketing:
Accessing new distribution channels, such as traditional travel agencies, will be
important in order to differentiate Open House from competitors and in order to
decrease the dependency on search engine rankings. However, from the perspective of
traditional travel agencies, cooperation is much more interesting if a broad geographic
322
CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
apartment coverage of the service can be offered. Therefore it might be worthwhile to
focus first on the expansion of the service to other cities and only in a second step start
to access additional distribution channels.
6.2.5 Summarising evaluation of the impact of critical market characteristics
on venture performance throughout different evolutionary phases
Throughout the development of Open House, relevant market characteristics have
been greatly related to barriers to entry.
The initial opportunity for starting Open House has emerged from the formerly
unfulfilled demand of foreign students for short-term apartment rentals in Barcelona.
The market entry was very easy and did not basically require any investments,
organisational overheads or special knowledge. The founder of Open House started to
rent apartments in her own name and offered them for subletting to foreign students.
Until now the barriers to entry have remained extremely low. The case of Open House
illustrates very clearly the opposing short- and long-term effects of barriers to entry as
proposed in the theoretical model. Upon market entry the low barriers facilitated and
enabled the market entry, while in the current venture stage the high number of
competitors resulting from these low entry barriers poses the major threat.
Within the growth phase of the company, the most critical market characteristic has
been the dynamic growth of the overall market induced by macro level factors which
has led to higher numbers of tourist visitors in Barcelona. Attracted by the market
growth and the low barriers to entry, a large number of new competitors entered the
market. It is to be expected, according to the theoretical model and the results of the
quantitative study, that this high number of competitor entries has a negative effect on
the profitability. However, the effect on the profitability of Open House has been
relatively low, since only a low degree of price competition has resulted from the new
entries. Even though the basic service of industry members is quite similar, actual
rental prices from apartments are difficult to compare due to a multitude of differences
in apartment features and locations. While a large number of new firms entered the
market, Open House continued to grow rapidly in the growing market segment.
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
323
Today, in a less dynamically growing environment, the growth of Open House has
further been affected by the large number of competitors, which make it more difficult
for Open House to be noticed, and the limited market size in Barcelona. Together with
the higher professionalism and growth aspirations of current competitors and a low
exit rate, it can be expected that the intensity of competition will further increase.
In the future, further growth dynamics will therefore have to result from geographic
expansion into other cities where the intensity of competition is still lower. This
intensity of competition, which is the major threat to the attractiveness of the
Barcelona market, may actually turn out to be a key advantage in the future for
expansion to other cities, as the intense competition in Barcelona has forced Open
House to continuously improve its service and promotion activities . This will provide
Open House with a favourable competitive position compared with existing services in
other cities. In the context of Open House, therefore, the low barriers to entry had a
positive short-term impact on the market entry, a negative medium- and long-term
impact on profitability and growth potential in the Barcelona market, and a positive
long-term effect for the growth into other geographic markets.
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6.3
Case study – Tele-Ruf Kommunikations GmbH
6.3.1 Venture profile
The Tele-Ruf Kommunikations GmbH (Tele-Ruf) has been chosen for the specific
market conditions of a formerly monopolistic market which has been deregulated. The
company’s core business is the operation of public payphones. In October 2003 the
company employed 14 employees at its headquarter in Bonn, Germany, and four
employees at a sister company in Berlin. Franchising cooperation exist with partners in
six other cities in Germany.
Founder
Stefan Martinstetter installed his first payphone in 1996 close to a snack bar at the
Bonn central railway station. Encouraged by the high revenues of this first payphone,
he decided in 1997 to quit his economics studies and to found the company Tele-Ruf.
Products/services
From 1997 to 2000, Tele-Ruf continuously increased the number of payphone
installations. After the Bonn region, the payphone operations were geographically
expanded to adjacent cities. In 2001, a sister company was founded with a partner in
Berlin in order to cover the new capital of Germany and to prepare further expansion
to the north and east of Germany. In the same year, Tele-Ruf invested heavily to
diversify into the business segment of prepaid phone cards and bought its own
switching centre in Cologne. At the same time, Tele-Ruf started to offer the service of
reselling of phone connections to major clients as hospitals. In 2002, the new business
segment of prepaid phone cards was terminated after encashment problems with major
clients generated losses of over one million Euros, which nearly led to the insolvency
of the whole company. Tele-Ruf focused again on the profitable core business of
public payphones and successfully launched a franchising system to accelerate the
national and international expansion of the service. At the end of 2003, Tele-Ruf will
start to offer first public payphones with broadband internet services.
Performance
It seems that Tele-Ruf has found a profitable niche in the shrinking public payphone
market and the highly competitive telecommunication sector. The sustainable growth
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
325
during the first years ensured that the company already operated profitably in the early
years. For the year 2003, a turnover of 6 million Euros is estimated for the operation of
their 500 public payphones. Additional revenue is generated from the reselling of
interconnections to franchising partners. Since the initiation of the franchising
programme last year, six franchising partnerships have already been established and
current negotiations with several potential partners in Germany and abroad promise
fast growth Over the next few years, Tele-Ruf has declared its objective of acquiring
50 franchising partners and to reach their final target of a total of 5,000 installed public
payphones in Germany.
6.3.2 General market setting
The market of public payphones has been among the first segments of the
telecommunication market that have been deregulated in the beginning of the 1990s.
However, the market attracted only a limited number of private companies. Until
today, the former state monopolist German Telekom dominated the market in
Germany with over 110,000 installed public payphones. Within the last few years
15,000 payphones have been deinstalled and by the year 2005, 15,000 non-profitable
payphones are to be converted into limited emergency phones. By law, German
Telekom is obliged to install at least one public payphone in a radius of 3km.
Consequently, German Telekom has to maintain many payphone locations that are not
profitable. By contrast, private competitors do not have the obligation to provide a
Germany-wide infrastructure of payphones and can pick the most attractive locations.
The largest private competitor is the British firm New World Payphones, which
operates over 6,000 payphones mainly at railway stations. Tele-Ruf is, with 500
installed payphones, the second largest new provider of public payphones in Germany.
With about 100 installed payphones, the firms Gekartel AG and Bytel Kommunkation
GmbH follow.
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CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
6.3.3 Explanation of market impacts on venture success
important
important
Not at all
Macro level
Extremely
6.3.3.1
1. macro-economic changes
a) changes in the propensity to consumption
○
○
X
○
○
b) changes of interest rates
○
○
○
○
X
c) changes of currency rates
○
○
○
○
X
a) changes in environment protection regulations
○
○
○
○
X
b) changes in market regulations
X
○
○
○
○
c) changes in labour market regulations
○
○
○
○
X
d) changes in public spending
○
○
○
X
○
e) changes in international trade deregulations
○
○
○
X
○
a) development of internet usage
○
X
○
○
○
b) development of internal IT applications
○
○
○
○
X
c) development of new production technologies
○
○
○
○
X
a) changes in consumer preferences (increasing volatility)
○
○
○
X
○
b) individualisation
○
○
○
X
○
a) decreasing population growth
○
X
○
○
○
b) increasing population age
○
○
○
X
○
2. political / legislative changes
3. technological changes
4. socio-cultural changes
5. demographic changes
immigration
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
Other changes:
327
-
Intensity of mobile phone usage
-
Penetration of broadband services
-
Immigration
Table 89: Macro level analysis Tele-Ruf
The deregulation of the telecommunication sector has been the major change in the
1990s that opened the opportunity to start the business in the previously monopolistic
market of public payphones. From the technological dimension, changes in the
penetration and usage intensity of mobile phones have led to a decreasing intensity of
public payphone utilisation. However, the impact has been frequently overestimated
since the more expensive calls to mobile phones have compensated for the decreasing
frequency of calls by higher average prices per public payphone call. It can be
expected that the penetration and usage intensity of broadband services such as web
surfing, e-mail, videoconferencing and multimedia messaging will determine the
future growth potentials of the market. Until now, multimedia terminals in Germany
are only operated at airports or in limited pilot projects. Broadband services may offer
the opportunity to generate new sources of revenues and to develop new business
models. Revenues from content providers may add or even replace the traditional
revenues from consumers. Also, demographic changes have had an impact on the
payphone market. A large share of the clients are foreigners and especially illegal
immigrants, who do not have the required papers to contract a mobile phone or a fixed
network phone connection. Changes in immigration regulations may therefore also
have a notable impact on the payphone sales.
328
6.3.3.2
CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
Inter-country level
II. INTER.COUNTRY LEVEL – NATIONAL COMPETITIVENESS
Very low
Very high
Degree of international competition
a) activities of national competitors in foreign markets.
○
○
○
○
X
b) activities of foreign companies in national market.
○
○
○
X
○
c) country of strongest competition in future:
UK (British Telecom and New World
Payphones)
Table 90: Inter-country level analysis Tele-Ruf
The conditions for foreign and national industry members are basically identical, since
independently of the location of the headquarter, all current industry members
purchase telephone minutes and hardware on an international market and have to
perform the acquisition of new locations and the maintenance of phone cells under
same conditions on a regional level. Therefore the country of origin of the company
does not lead to any differences in competitiveness.
Currently German competitors are not operating abroad, and New World Payphones is
the only foreign competitor operating with its German subsidiary in the German
market.
6.3.3.3
Inter-industry level
1. complementary cooperation
○
X
○
○
○
2. threat of substitution
X
○
○
○
○
important
Not at all
important
Extremely
III. INTER-INDUSTRY LEVEL – OPPORTUNITIES & THREATS FROM RELATED INDUSTRIES
Callshops,
mobile phones
3. forward integration
○
○
○
X
○
4. backward integration
X
○
○
○
○
Franchisees
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
5. threat of other new entries
○
329
○
○
X
○
Table 91: Inter-industry analysis Tele-Ruf
The most obvious threat to the payphone business relates to further mobile phone
penetration and callshops. Within recent years, small shops with fixed network phone
booths have been founded throughout Germany, and many internet cafes have started
to complement their product range with phone services. These callshops serve a
similar client group as payphones. Moreover, by offering very competitive pricing,
callshops attract particularly the interesting customer group of frequent international
callers. Tele-Ruf reacted to the trend towards callshops by offering software, hardware
and connection reselling packages specifically to the callshops. Even though Tele-Ruf
is convinced that they can offer their phone service at much lower costs than callshops,
the current connection prices of Tele-Ruf payphones are much higher than the prices
of callshops. This may lead to future pressure on the prices, in order to remain
competitive for the attractive segment of international calls. Another major threat
relates to potential backward integration of current franchising partners. Gekartel AG,
one of Tele-Ruf’s main competitors, only started their company after acquiring
knowledge about the payphone business in negotiations about a franchise contract with
Tele-Ruf. Similarly, current and future franchisees may decide in the future to operate
their payphone business independently of Tele-Ruf. Due to Tele-Ruf’s relatively low
brand recognition, franchisees of a larger size may find it more beneficial to operate
their payphone business independently of Tele-Ruf. The threat from other new entrants
is considered relatively low. Several fixed network carriers such as Netcologne, Isis
and Mannesmann Arcor undertook attempts to enter the payphone market, but finally
decided to terminate their payphone operations. For a large carrier, the market volume
of the payphone market may simply be too small in relation to sales in other segments
of the telecommunication market.
Opportunities may arise from complementary cooperation with firms from other
industries. On analysing the market setting and the offers from competitors, examples
of such cooperation could include the use of the phone booths for advertising,
promotion of third-party content in multimedia phones or the inclusion of WLAN
senders in phone booths. Currently, all the private payphone providers are strongly
focused on the acquisition of new locations.
330
6.3.3.4
CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
Intra-industry level
Very low
Very high
IV. INTRA-INDUSTRY LEVEL
1. market structure
a) market size
X
○
○
○
○
b) heterogeneity of products
○
○
○
X
○
c) branding potential
○
○
X
○
○
Important for
franchising, but
little consumer
preferences
d) distance to clients
○
○
○
X
○
e) economies of scale
○
X
○
○
○
f) export-import balance
○
○
○
○
○
g) need for specialised products
○
○
○
○
X
h) buyer concentration
○
○
○
○
X
i) average order volume
○
○
○
○
X
j) seasonal change of demand
○
○
○
X
○
k) intensity of price bargaining
○
○
○
○
X
l) market transparency
○
○
○
X
○
m) advertising intensity
○
○
○
○
X
n) R&D intensity
○
○
○
X
○
TOP factors from market structure
-- with positive impact on firm
- large potential market size
- high economies of scale
- low advertising intensity
-- with negative impact on firm
/
Regional market
Not applicable
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
331
2. market dynamics
a) former market growth
○
○
○
X
○
b) rel. expected market growth
○
○
X
○
○
Potential from
webphones
c) entries to industry
○
○
○
X
○
d) exits from industry
○
○
○
X
○
e) balance of entries and exits
○
○
X
○
○
f) degree of uncertainty about future development
○
X
○
○
○
g) customer loyalty
○
○
○
○
X
h) changing customer needs
○
X
○
○
○
TOP factors from market dynamics
-- with positive impact on firm
- few entries to industry
-- with negative impact on firm
- uncertainty
3. dependencies
a) on clients
○
○
○
○
X
b) on suppliers
○
○
X
○
○
c) on key employees, employee institutions, and key
○
○
○
X
○
d) on legislation
○
X
○
○
○
e) on business cycle
○
○
○
○
X
f) on maintenance in industry (barriers to exit)
○
X
○
○
○
g) relative dependency compared to earlier and later value
○
○
○
X
○
knowledge
chain members
TOP factors from dependencies
-- with positive impact on firm
-- with negative impact on firm
- low dependency on both clients and suppliers
- high barriers to exit
- potential legislative changes
332
CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
4. competitor dynamics
a) inertia of established firms due to organisational
○
X
○
○
○
b) inertia of established firms due to legislative restrictions
○
X
○
○
○
c) inertia of established firms due to cost structure
○
X
○
○
○
d) aggressive responsiveness of established firms
○
○
○
○
X
e) inflation rate in selling prices
○
○
○
○
X
f) number of new products introduced
○
○
○
X
○
g) investment in new assets
○
○
X
○
○
structure
TOP factors from competitor dynamics
-- with positive impact on firm
- inertia of German Telekom in payphone
segment
- low responsiveness
- low inflation rate in selling prices
-- with negative impact on firm
/
5. competitor structure
a) concentration
X
○
○
○
○
b) number of industry members
○
○
○
○
X
c) heterogeneity of industry members
○
○
X
○
○
d) general efficiency level among industry members
○
○
○
X
○
e) prevalence of competitive strategies on other than price
○
X
○
○
○
f) capacity utilisation level
○
○
○
○
X
g) employee productivity
○
○
X
○
○
h) labour costs intensity
○
○
○
X
○
i) gross margin
○
X
○
○
○
j) vertical integration
○
X
○
○
○
k) degree of diversification
○
X
○
○
○
location
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
333
l) average age of industry members
○
○
X
○
○
m) average size of industry members
○
○
X
○
○
n) variable costs
○
○
○
X
○
TOP factors from competitor dynamics
-- with positive impact on firm
- gross margin (about 70%)
- low number of industry members
- prevalence of strategies on other than price
-- with negative impact on firm
/
Table 92: Intra-industry level analysis Tele-Ruf
Market structure
One of the most attractive industry variables is the large potential market size. With
500 public payphones, Tele-Ruf only covers less than 0.5% of the overall market. The
business model is easily transferable to new regions and cities within Germany, which
is reflected in the launch of the franchising system. Also, the low number of industry
members is a major advantage. It was mentioned in the interview that the limited
number of competitors decreases the pressure on prices and may, in the future,
facilitate a possible coordination of the different competitors. Economies of scale
occur particularly with respect to the purchasing of customised phone cells and call
interconnections. Tele-Ruf already purchases call interconnections with the status of a
telecommunication carrier, which guarantees the lowest possible purchasing prices.
The phone booths are purchased directly from a manufacturer in Scotland. Apart from
the investments in the installation and hardware of the phone booth, basically no other
investments are required for firms entering the market. Neither investment in
advertising nor in R&D is required. Final clients will choose a payphone by proximity
rather than by brand image. It can be deduced that the low importance of brand
recognition has facilitated Tele-Ruf’s market entrance and has led to healthy profit
margins. For the success of the franchising system, however, the lack of brand
recognition may have a negative impact on the long-term commitment of dynamic
franchisees to Tele-Ruf. The low heterogeneity of the payphone services from
different providers does not pose a problem in the context of the payphone market,
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since dependence on location avoids low heterogeneity leading to a greater pressure on
prices.
Market dynamics
The negative overall market growth, given the decreasing usage of payphones, only
affects Tele-Ruf’s growth to a limited degree. Potentially attractive locations for
payphones are still plentiful and growth is more limited by time investments for the
acquisition of new locations. In fact, the decreasing usage of payphones does lead to
lower profitability as the turnover per phone booth decreases. The negative market
growth has had, however, a positive side effect. With German Telekom announcing
the closure of thousands of unprofitable phone booths and the penetration of mobile
phones, only a very limited number of firms have considered the payphone market as
attractive. While after the deregulation of the telecommunication market an immense
number of new firms entered the market for fixed network telephony, no new firms
have successfully entered the public payphone market within the last three years and
only the few existing market players have remained.
The telecommunication sector is among the most rapidly changing markets. The
payphone business has probably never before been exposed to such dramatic changes
from the demand side as today. With the further penetration of mobile phones,
decreasing mobile phone prices, the development of smaller handheld devices and
longer battery life of devices, the market for traditional payphone calls can be expected
to decrease in the long-run. Broadband services may provide future growth
opportunities, however, for now there are no references that show the profitability of
such new broadband services. The uncertainty about the speed of substitution from
mobile phones and the commercialisation of broadband services from public
payphones poses a major investment risk. Costs of the terminal hardware represent a
major share of initial investments. Due to the high costs and long lifetime of the
terminals, all payphone providers have to make long-term investment decisions on a
backdrop of a high degree of uncertainty and changing customer needs in the context
of broadband services.
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335
Dependencies
Tele-Ruf is in the awkward situation of their major competitor, the German Telekom,
being an essential supplier for the installation of the phone booths to the local phone
node. Contrary to other competitors, however, Tele-Ruf has never experienced any
problems with respect to German Telekom’s installation service. German Telekom has
not only complied with their obligations from the deregulation contracts, but attends
Tele-Ruf from their department of key clients with a prompt and professional service.
Apart from the installations to the local node, Tele-Ruf is in the favourable position of
not relying on any critical supplier or client. The deregulation of the
telecommunication market is not expected to change profoundly, although continuous
adjustments are expected to be made. An area where legislative changes may
substantially affect Tele-Ruf’s business is the legislative decision to expand the
responsibility of the maintenance of a Germany-wide payphone infrastructure to all
private providers of a larger size. The exit barriers are particularly high. Not only
would the costs of installation be basically lost for non- profitable locations, also the
phone booths and terminals are difficult to sell and a company which intends to exit
the market would have to face additional costs for the deinstallation of their phone
booths. The dependency on the landlords of the public spaces or the private house
walls where payphones are installed can be considered low, as the contracts are
commonly closed for unlimited or long durations and contracts are rarely cancelled.
Competitor dynamics
The German Telekom, as the only established competitor has paid relatively low
attention to the development of the payphone segment in recent years. Recent changes
have concentrated on improving the cost structure of the loss-making segment.
Unprofitable locations were closed or were converted into low-maintenance
emergency phone columns where it was not possible to close them due to the
deregulation contract. Further attempts to minimise maintenance costs have included
changing to card phones and replacing phone cells by open phone booths. Further
efforts to differentiate their service from competitors and to increase the attractiveness
of the payphone usage have not been undertaken. The German Telekom, as a highly
diversified conglomerate, is apparently not treating the payphone market with priority
in their product portfolio. Moreover, the obligation from the deregulation law to
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CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
provide country-wide coverage of a public phone infrastructure can be considered as a
major competitive disadvantage for German Telekom, which may reduce German
Telekom’s interest in dynamically developing this market segment. Direct actions
against new private competitors have been limited to dissuasions against marketing
campaigns. The price level for payphone calls has remained basically unchanged for
the last six years and has only been adjusted marginally with the introduction of the
Euro currency. Compared with the frequent and strong price cuts in the fixed network
telephony segment, competition on prices has basically been absent for payphones and
has ensured comparably high margins in the payphone business. A one-minute
national call costs, at both Telekom and Tele-Ruf payphones, 10 Cents224, while the
same call only costs the final customer 1-2 Cents from a low-price fixed network
operator.
Competitor structure
Overall, the intensity of competition in the market is very low. It was mentioned in the
interviews at Tele-Ruf that the activities of competitors are hardly relevant for their
business. Frequently, Tele-Ruf phone booths are placed right next to Telekom phone
booths and generate a higher turnover due to the slightly lower costs of calls and the
high level of maintenance, which guarantees that 98% of Tele-Ruf phones are
operational at all times. Also, in relation to private competitors, the intensity of
competition is relatively low since companies operate frequently in different cities and
even in cases where competitors operate within the same city, a multitude of
potentially attractive locations remains. Competition is only notable in cities where the
municipality prohibits the placement of more than one public payphone in a 100 to 300
meter radius on public ground. In these cases, the private payphone providers are
strongly disadvantaged compared with German Telekom with its presence in highly
frequented locations.
The low intensity of competition is manifested in the low number of industry
members, a relatively high gross margin of about 70% and the prevalence of
competitive strategies which focus on location instead of price. The major challenge
for all payphone companies is the identification and acquisition of attractive payphone
224
Tele-Ruf, however, charges the first minute of a call in 30-second increments, which leads to a lower price for
phone calls of less than 30 seconds and a lower minimum fee per call.
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
337
locations, since clients mainly choose payphones for reasons of proximity. Because
companies do not compete on price, low variable costs, low capacity utilisation levels
and low labour costs intensity, which may lead in other industry contexts to pressure
on profitability, do not have a negative impact in the context of the payphone market.
6.3.3.5
Barriers to entry
Very low
Very high
BARRIERS TO ENTRY
a) inventory intensity
○
○
○
○
○
b) fixed asset intensity
○
○
X
○
○
c) minimum organisational size & complexity
○
○
○
○
X
d) accessibility of distribution channels
○
○
X
○
○
e) customer loyalty
○
○
○
○
X
f) legislative barriers
○
○
○
○
X
g) product sophistication
○
○
○
X
○
Not applicable
TOP factors from barriers to entry
-- with positive impact on firm
- low minimum organisational size
-- with negative impact on firm
-
Table 93: Barriers to entry analysis Tele-Ruf
Considering the low number of industry members, one might have deduced that the
barriers to entry may be particularly high. However, in order to start a business in the
payphone market neither large investments, expertise nor organisational overheads are
required. The initial start-up size would be basically only limited by the set-up costs of
each phone booth, which accounts for 5,000 to 10,000 Euros, including installation
and the time-consuming process of location acquisitions, which can take several years
in bureaucratic negotiations with municipalities.
338
6.3.3.6
CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
Venture / firm level
a) relative price (better=lower price)
○
○
○
X
○
b) relative quality
○
○
X
○
○
c) relative design
○
○
○
X
○
d) innovation potential (potential to offer completely new
○
○
X
○
○
a) relative financial strength
○
X
○
○
○
b) relative knowledge / patents
○
X
○
○
○
a) relative capability of management team
○
○
X
○
○
b) relationships key partners
X
○
○
○
○
c) relevant industry experience
○
X
○
○
○
○
X
○
○
○
a) relative distance to clients (who is closer?)
○
○
X
○
○
b) availability of labour and production factors
○
○
X
○
○
c) relative costs of labour and production factors
○
○
X
○
○
better
Gekartel
better
1. products/services
Tele-Ruf
V. VENTURE / FIRM LEVEL
product/service)
2. firm resources
3. management team
4. strategy
a) relative growth aspirations/aggressiveness
5. location
Table 94: Venture level analysis Tele-Ruf
Apart from the German Telekom, Tele-Ruf’s main competitor is Gekartel AG. New
World Payphones as the largest new operator of payphones in Germany is specialised
in equipping airports and train stations, which is not currently Tele-Ruf’s target
segment. Gekartel AG started two years after Tele-Ruf in 1999, and since then it has
CHAPTER 6 - QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
339
grown dynamically with innovative ideas and investments of about 2 million Euros.
For 2003, Gekartel forecasts a turnover of one million Euros with more than 100
installed payphones. The company had sixteen employees at the end of 2003.
The basic service offered by Gekartel and Tele-Ruf is very similar. Gekartel offers
slightly lower prices than Tele-Ruf and German Telekom. Both Tele-Ruf and Gekartel
ensure a higher maintenance frequency of the phone booths than German Telekom and
have their service team visit the booth twice a week. In terms of design, Gekartel
innovated with the provision of phone cells with seats and automatic emission of
scents.
With regard to the firm’s resources and management team, Tele-Ruf has an advantage
over its competitor with a much higher revenue base and nearly two more years of
industry experience. Its larger size in particular gives Tele-Ruf an advantage in
negotiations with key partners and suppliers. Gekartel and Tele-Ruf are strongly
growth oriented. Tele-Ruf may, in the short-term, have the higher growth potential
after the launch of the franchising programme. With regard to location, no relevant
differences between Gekartel and Tele-Ruf can be observed.
6.3.4 Excursus: Derived strategic recommendations for Tele-Ruf
The management of Tele-Ruf has established, with its franchising system, a very
promising vehicle with which to accelerate growth in the future. Parallel to the focus
on growth, additional potential sources of revenues may improve the profitability of
the firm.
Development of a webphone business model
Tele-Ruf lacks a clear idea about how to profit from webphone services and has
delayed the introduction of webphone installations until December 2003. The
provision of webphone services may require the development of a new business
model. These services provide the opportunity to attract a new consumer group to use
the Tele-Ruf terminals and to greatly augment the potential market size. With a
monthly flat fee, the costs for providing broadband internet connections are relatively
low. At the same time, online shops and commercial services could generate additional
revenue. One might even consider changing the whole business model and financing
the webphone service solely from advertising online shops and offering users
websurfing, online games or even videochats at no cost. The management of online
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CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
advertising and the development of online services could also facilitate the attraction
of new franchising partners, as Tele-Ruf would be associated with a growing market
segment. Additionally, the long-term commitment of larger franchisees could be
improved, as the online service could be much more efficiently managed from a
central headquarter.
Increase attractiveness for international phone calls
The emergence of callshops around Germany has demonstrated that there is a large
market for international phone calls from public phones. As clients are more pricesensitive for international phone calls than for national phone calls, Tele-Ruf should
lower its prices for international phone calls. As currently the Tele-Ruf phones are
only marginally used for international calls, the company could reach a new customer
segment. Moreover, clients may associate the low prices of international phone calls
with the Tele-Ruf brand, which might also positively impact the usage for national
calls. International calling rates from Tele-Ruf might not even have to be lowered to
the level of callshops, since many clients may prefer the proximity of a Tele-Ruf
phone over small extra savings in a callshop. Temporary special offers of international
phone calls to specific countries may further increase the attractiveness of the service
and may be an attractive opportunity for the promotion of Tele-Ruf payphones.
Promote advertising space
Contrary to its competitor Gekartel, Tele-Ruf does not yet sell advertising space on
their phone booths. The Tele-Ruf phone booths are generally located in highly
frequented locations, which make them predestined for promotion of advertising
space. Given the large number of installed payphones, it may even be possible to
completely outsource the administration to national outdoor advertising companies
such as, for example, JC Decaux, Stöer, Wall or Deutsche Städte Medien. The
promotion of advertising space may not only generate additional revenue without
additional investments, it might also increase the investment risk and profitability for
Tele-Ruf and its franchisees. Additionally, the central management of advertising
space by Tele-Ruf’s headquarter could increase the long-term commitment of
franchisees, as the promotion of advertising space might be more efficient for a large
number of advertising spaces, and third-party outdoor advertising companies may have
a higher interest in a collaboration.
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341
6.3.5 Summarising evaluation of the impact of critical market characteristics
on venture performance throughout different evolutionary phases
Market factors have been critical for the success of the venture at various stages of the
evolution of Tele-Ruf.
The initial opportunity to create a private business in the payphone market was opened
up by the deregulation of the telecommunication market. The preferential treatment of
private companies was critical for the commercial attractiveness of this opportunity, as
it exempted these companies from the legal obligation to provide a Germany-wide
infrastructure and they could thereby freely choose the most profitable locations. The
core opportunity was therefore the direct result from legislative changes on macro
level.
Upon entering the market, Tele-Ruf benefited from very low entry barriers, which
made it possible to start the firm without large investments and with only two people.
The market did not require large investments in advertising or R&D. Simply by its
physical presence in highly frequented locations Tele-Ruf payphones attracted clients
and were competitive with payphones of the dominant market player German
Telekom. It was subsequently possible to grow continuously from the revenues of the
first payphone installations. As expected by the theoretical model, low barriers to entry
facilitated the entry into the market.
Within the growth phase of the firm, the negative market growth of the overall
payphone market, the losses of German Telekom in the payphone sector and the
penetration of mobile phones have led to the general perception that the payphone
market is not an attractive market for starting a firm. In fact the negative impact of the
mobile phone penetration on the business may have been highly overestimated and the
freedom to pick only the most populated locations has provided a profitable business
model. The negative perception of the attractiveness of the payphone business has
given Tele-Ruf time to grow steadily in their regional market, while at the same time
only a limited number of competitors have entered the market.
Today, with Tele-Ruf having reached a stage that allows nation-wide expansion with
its franchising system, the firm benefits from the ongoing absence of other private
competitors in many regions of Germany. The low presence of competitors together
with the fact that clients choose payphones for location rather than for price has
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CHAPTER 6 – QUALITATIVE EMPIRICAL STUDY: CASE STUDIES
ensured that the margins in the payphone business remained stable, and they have not
been impacted by the threatening price competition in the fixed network phone market.
The high margin is one of the most important factors for the attractiveness of the
market. In the long-term it has been proposed by the theoretical model that low
barriers to entry would lead to lower profitability induced by an increasing number of
competitors. Even though the barriers to entry have not increased within the last few
years, surprisingly only a few companies entered the market and no resulting negative
impact on profitability can be observed. The decrease of the overall demand for
payphone services has not notably slowed Tele-Ruf’s growth, since the potential
market size in terms of attractive payphone locations has provided plenty of growth
opportunities. Instead the decline of the overall payphone market has protected the
firm from a larger number of new firm entries.
In the near future, the constellation of low competition and high market size promises
further growth in particular with the backdrop of the launched franchising programme.
With regard to the low barriers to enter it might be important to evaluate in which
areas Tele-Ruf can generate additional value for its franchisees.
In the medium- and longer-term future, it can be expected that the changes in the
mobile phone segment on the macro level will finally lead to lower sales in pay phone
communication due to a higher availability of mobile phones, longer battery life and
lower mobile phone calling rates. Also, with the dynamic expansion of current
competitors and the potential entrance of new competitors in the future, the intensity of
competition for attractive locations may increase and competitors may start to lower
prices in order to increase market share. There is a great uncertainty about the future
development of the market since telecommunication technology and broadband
services are undergoing rapid changes. These changes may lead to a further
substitution of the use of traditional payphones or may provide growth opportunities
with new services. The uncertainty about future development may, in the present
situation, slow Tele-Ruf’s growth, as franchisees might feel threatened by the risk of
investment, particularly since they are required to make long-term terminal and
installation investments.
CHAPTER 7 - CONCLUSIONS, SIGNIFICANCE OF RESULTS AND PERSPECTIVES
7
343
Conclusions, significance of results and perspectives for
future research
This study has provided a contribution to a better understanding of the influence of
market environment on venture success. In the first part of this last chapter,
conclusions drawn from the results found throughout the study are given. Then, the
significance of the results for both the practical and scientific area are explained.
Finally, the dissertation finishes with the presentation of perspectives for future
investigation for advancing this field of research.
7.1
Summarising conclusions
The conclusions derived from this study are presented according to the seven initial
objectives of the study225, which are repeated for easier reference below.
1. Objective: To analyse the empirical results from relevant studies in the
areas of entrepreneurship and strategy research with respect to marketventure success relationship:
As a result of the comparison and aggregation of previous empirical research,
critical market-related variables that have a consistent impact on venture
success among different investigations and different measures of venture
success were identified. In entrepreneurship research226 early life cycle stage,
low competition and high buyer concentration consistently lead to positive
impacts on venture success. In strategy research227 high market share, high
industry capacity utilisation, low import rate and low inventory intensity were
identified as the variables that consistently lead to a positive impact on
organisational success. Given the findings of the conducted empirical study, it
can be concluded that the inconsistent market variables of the literature review
are either sensitive to specific performance measures or to specific contexts.
The existing literature is too fragmented to deduce the reasons for the
inconsistencies of individual variables.
225
Compare p.3f.
Detailed results in chapter 2.1.3 figure 4, p.33.
227
Detailed results in chapter 2.2.3 figure 5, p.50.
226
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CHAPTER 7 – CONCLUSIONS, SIGNIFICANCE OF RESULTS AND PERSPECTIVES
2. Objective: To study from different theoretical perspectives the market
factors that affect organisational success:
Industry economics, population ecology, transaction costs economics,
contingency theory and game theory were identified to provide relevant
theoretic concepts that could be applied to explain the relationship between
market factors and venture success. The key variables, which were stressed
within each theoretic concept228 together with the variables found within the
review of previous empirical studies, have provided a large number of relevant
market variables which have been taken into account in the development of the
proprietary model of market attractiveness. Overall, the previous theoretic
concepts were useful for providing explanations for isolated mechanisms.
However, the later qualitative study in particular, demonstrated that the
relationship between market environments and venture success is too complex
to be explained by only one theoretical concept with a limited number of
variables.
3. Objective: To propose a new proprietary theoretical model of market
attractiveness that concentrates on the specific perspective of new ventures
and provides a structuring framework for the analysis of the impact of
market variables on venture success:
A model has been developed, which considers over 100 potentially relevant
market variables. These variables are structured into a 5*5 matrix with five
distinctive levels of analysis and five key dimensions of impact for each level of
analysis229. Both the quantitative and the qualitative empirical studies refer to
the model as structuring framework. The proprietary model is different from
previous frameworks for the market environment as a larger number of
variables are considered. From a new venture perspective important
characteristics such as barriers to entry and environmental dynamics were
included to distinguish between short- and long-term effects. Relationships and
interactions among variables were specified. The direction of impact was
specified by explaining potential risks and opportunities. The applicability of
the model in practice was facilitated by referring to publicly available data
228
229
See figure 7 in chapter 3.2.7, p.66 with a list of key factors of each theoretic concept.
See figure 20 in chapter 3.4.8, p.119f for a presentation of the complete model.
CHAPTER 7 - CONCLUSIONS, SIGNIFICANCE OF RESULTS AND PERSPECTIVES
345
sources. Finally, the impact of different measures of organisational success as
growth, profitability and survival were distinguished for each variable. The
model, as applied for the investigation of the case studies, took into
consideration the inevitable limitation in the number of variables that can be
considered by any model and remained open for additions of relevant variables
in each firm’s context. Without this openness, no reasonable analysis of market
attractiveness would have been possible.
4. Objective: To empirically identify markets in which new ventures have
been most successful:
On the basis of the DtA sample of 5,117 new ventures, 163 industries in
Germany were ranked according to the four success measures of average
venture growth per industry230, average venture profits per industry231, average
years needed to break even232 and subjective venture success per industry233.
With regard to the performance measures of average venture profits and years
to break even, industries from the manufacturing and the construction sector
ranked higher. In contrast, with regard to the performance measures of venture
growth and subjective performance, industries from the service sector ranked
higher. Retail, as the fourth sector investigated, ranked low for all performance
measures. This finding has important implications for quantitative empiric
research in entrepreneurship. The frequent approach in empiric research of
relying on firms in the manufacturing sector for reasons of easier data
collection, may lead to results that are not directly transferable to other industry
sectors, as performance measures are impacted differently among industry
sectors. The later study on the impact of contingent variables reinforces this
indication.
5. Objective: To empirically study the relationship between industry
variables and the success of new ventures:
The impact of 17 market variables on the industry averages of venture growth
and venture profits was investigated. High export balance, high distance to
230
Complete ranking in appendix D.
Complete ranking in appendix E.
232
Complete ranking in appendix F.
233
Complete ranking in appendix C.
231
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CHAPTER 7 – CONCLUSIONS, SIGNIFICANCE OF RESULTS AND PERSPECTIVES
clients, high number of entries to industry, high number of net entries and high
uncertainty were found to relate to higher venture growth. With regard to
venture profits, a low number of industry entries, a low number of firm exits
and low uncertainty was found to be positively related. The results underline the
necessity of applying different sets of performance measures in empirical
entrepreneurship research on success factors. Opposite directions of impact
were frequently observed for venture growth and venture profitability.
6. Objective: to empirically study the impact of contingent variables on the
relationship between industry variables and the success of new ventures:
The analysis of the impact of the 17 market variables on venture growth and
venture profits, years to break even and subjective performance was performed
independently for 13 different subsamples, which were derived form the
contingent variables of industry sector, market growth, venture growth and
industry heterogeneity. The market variables that were particularly important in
specific contextual settings were shown. This analysis allowed the identification
of additional variables, which were not significantly related for the overall
sample, but which were significantly related to venture success measures for a
range of contextual settings, as defined by the contingent variables. The
importance of the contingency variable of industry sector, which was suggested
by McGahan and Porter (1997) was confirmed. Several variables were
identified as having opposite impacts on venture success for different industry
sectors. Market growth as a contingency variable was not found to lead to an
opposite impact on venture success in different subsamples. In fact, high market
growth enforced the impact of market variables without changing the direction
of impact. An important impact of the contingency variable of venture growth,
as found by Hawawini (2003), could not be confirmed in this study. Finally, the
importance of the contingency variable of industry heterogeneity, as found by
Mueller and Raunig (1998), could only be confirmed for the performance
measure of venture growth. For other performance measures, industry
heterogeneity did not seem to be very important. Overall, the results of the
study underline the importance of investigating the industry sector in particular
as contingent variable for a better understanding of the complex effects of
market environments on success.
CHAPTER 7 - CONCLUSIONS, SIGNIFICANCE OF RESULTS AND PERSPECTIVES
347
7. Objective: To empirically identify the mechanisms through which market
factors influence venture success:
The qualitative empirical study has reinforced the understanding of the
complexity of industry–venture success relationship. While many market
variables were found to affect the investigated firms, as proposed by the
theoretical model and the results of the quantitative study, some market
variables impacted venture success contrarily to expectations. In particular the
impact of competition seemed highly sensitive to the contextual setting.
Additionally, competition was shown to be an important driver of venture
development and innovation. Changes on the market macro level created
opportunities for a venture’s initial and further growth. From the context of the
case studies one may conclude that the intensity of competition on prices is
critical for venture profitability, and the potential to multiply the business
concept to other geographic regions or related product segments is critical for
the venture’s growth dynamics.
7.2
Significance of results for practice
Several results of this study may be of interest for entrepreneurs, managers and
investors. The literature review and the empirical study highlight, among the multitude
of market variables, those which are most critical with regard to venture success. The
industry rankings according to the four venture success measures give an indication of
the attractiveness of the broader industry context. Of most interest, from the
perspective of practitioners, could be the developed model of market attractiveness,
which provides a clear framework for conducting an industry analysis and interpreting
its implications for a firm.
An understanding of the factors and patterns that contribute to market attractiveness,
which is the purpose of this study, is of high practical value as it allows recognition of
the opportunities and risks from the industry environment. Nevertheless, the recent
explanations and frameworks which research on entrepreneurship and strategy have
yielded, have gone largely unknown to entrepreneurs or investors. Until now, beside
Porter's (1980) model of industry analysis, no other model has been widely applied in
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CHAPTER 7 – CONCLUSIONS, SIGNIFICANCE OF RESULTS AND PERSPECTIVES
practice. It has to be noted that Porter's model, developed about two decades ago,
contains several weaknesses234. This study provides an alternative model with a broad
theoretical base. The model developed should enable practitioners to perform a
structured analysis of the industry environment by taking into consideration a
multitude of potentially relevant factors and allow them to deduce the potential risks
and opportunities for a firm.
In the future, a comprehensive systematic model, as developed in this study, may serve
to develop more advanced tools for industry analysis, which integrate a wide range of
relevant industry information. Moreover, the dataset generated for the quantitative
empirical study with information on 17 market variables for the 163 most popular
industries of new ventures may serve as a starting point for the collection of more
statistical industry data. IT-based models may facilitate the process of analysis,
decrease the required time for conducting the analysis and, finally, increase the quality
of results by processing a higher complexity of data. Possible features of such a future
tool of industry analysis are outlined in the box below.
Excursus - application of results into practice: Industry-analysis.com – Vision of
a web-based tool of industry analysis and industry benchmarking
Basic concept:
A free online service could be developed, which provides a comprehensive, integrated
and guided analysis of market attractiveness for a wide range of industries. On the
basis of the results from previous empirical research, statistical data and the opinions
of other market players, a wealth of information could be provided and attention can
be drawn to the critical areas of the particular industry context.
Specification of the user interface (user input):
o The user is provided a general overview of the different levels of analysis and may
select whether he235 wishes to perform analysis on all or individual levels.
o The user specifies the industry code of the industry that he is interested in.
234
235
See chapter 3.3.1, p.67ff.
It is referred to both female and male users. For easier readability only the male term is used in the text.
CHAPTER 7 - CONCLUSIONS, SIGNIFICANCE OF RESULTS AND PERSPECTIVES
349
o The user specifies whether he would like to (1) evaluate a market that he would
like to enter, or whether he would like to (2) evaluate a market in which he is
already operating in and for which he would like to receive the benchmark values
of his firm.
o The user fills in a questionnaire with questions regarding each variable of the
model of market attractiveness on the chosen levels of analysis. Numeric and likert
scale response fields are provided.
o The user fills in a list of the ten factors that appear to him as the most important for
the given industry.
Specification of the analysis output:
o Summarising profile of all rated market variables, highlighting those variables that
have been rated by the user as highly positive or highly negative.
o Additional highlighting of market variables that have been considered particularly
important by the user for the analysed market.
o Additional average ratings of other users for the same industry or sector.
o Additional statistical industry data is applied from various sources to give
information on the individual market variables of the model of market
attractiveness.
o Explanation of possible opportunities and risks associated to the market variables
that were rated as highly positive or negative by the user, variables that have
extreme values from statistics, variables that were considered very important by the
user, variables that were considered highly important by the average of other users
in the same industry, variables that are suggested by entrepreneurship research as
being important.
o Average firm performance benchmark in industry compared with average firm
performance in other industries (percentage rating of evaluated industry compared
with other industries).
o For existing firms, benchmark of performance of the rated firm compared with
average firm performance in industry for key performance indicators (percentage
rating of evaluated firm compared with average in industry).
350
CHAPTER 7 – CONCLUSIONS, SIGNIFICANCE OF RESULTS AND PERSPECTIVES
Challenges for implementation:
o Reaching critical mass of ratings for comparison:
Financial institutions that provide credit or funding to firms, such as the DtA, could
demand a completed industry analysis, applying the indicated tool from all
ventures that apply for funding . This would facilitate evaluation of venture
concepts, since market analyses for all ventures would apply the same structure.
Thereby the probability of venture success would be increased as founders are
forced to consider risks and opportunities of the market environment that they may
not had recognised previously.
o Reliability of entered data:
(1) Since industry analysis is only conducted for broad industry categories, users in
attractive market niches may not have to be concerned when their specific niche is
identified.
(2) The suggested participation of financial institutions could greatly improve the
data reliability.
(3) Manipulations can be restricted by blocking repeated questionnaire submissions
from the same IP address.
(4) The function of benchmarking the performance data of the individual firm to
the performance of the industry provides an incentive to give accurate information.
o Copyrights for electronic publication of statistical data:
All of the data sources that have been applied within this study do not permit the
electronic publication of data. Those organisations that possess relevant industry
statistics could be offered benefits for cooperating, such as presentation of the
products and services that they offer or providing users with the option to purchase
more in-depth data from cooperating organisations.
Scientific value:
The data obtained on industry ratings and firm performance could be an excellent
source for future empirical research in entrepreneurship as well. It could enable a
higher consideration of not publicly listed SMEs in research, which is currently limited
by the lack of available data sets. Moreover, it may permit the study of the impact of a
higher number of industry variables than has been possible in the undertaken study.
CHAPTER 7 - CONCLUSIONS, SIGNIFICANCE OF RESULTS AND PERSPECTIVES
7.3
351
Scientific significance of results and perspectives for future
research
Significance for entrepreneurship research
The thesis project aims to supply a contribution for a better understanding of the
factors that lead to a higher probability of new venture success. This objective is
approached by narrowing the view on market-related environmental factors, where
former studies provided only limited and often contradictory findings. Thus, the study
contributes to a more complex context-oriented explanation, taking into account the
context variables of industry sector, market growth, industry heterogeneity and venture
growth. Finally, the study might contribute to the development of a general
entrepreneurship theory, by providing a framework for the investigation and
explanation of the environmental dimension. The presented framework, which
integrates insights from a broad scope of disciplines and the findings of previous
empirical studies in entrepreneurship and related fields, may be a valuable guide for
future research in the field.
Although the study is focused on the short-term success of small new venture
companies, the results of the study might be of equal interest to the field of strategy.
Significance for strategy research
Despite the fact that Porter's "Competitive Strategy: Techniques for analyzing
industries and competitors "236 is by far the most widely cited publication in strategy
literature237 and in the history of business administration, the book's central feature the industry framework - has attracted little empirical attention238. Empirical studies on
measuring industry effects often applied similar data on large-scale publicly traded
enterprises. In order to investigate industry impacts, data on highly diversified
multinational companies does not seem to be the most appropriate239. In addition,
McGahan and Porter (1997) identified the focus on manufacturing industries as a
major fault in previous research, and they concluded that manufacturing industries are
highly unrepresentative and understate the importance of industry effects.
236
Porter 1980.
According to a study in Hambrick 1990.
238
Powell 1996.
239
Powell 1996.
237
352
CHAPTER 7 – CONCLUSIONS, SIGNIFICANCE OF RESULTS AND PERSPECTIVES
Perspectives for future research
The results of the study lead to four implications for future research.
First, more application of comprehensive theoretical models of the market
environment are desirable for the future. A major restriction of the undertaken
quantitative study was the fact that a limited number of only 17 market variables from
the previously developed theoretical model could be investigated. Due to the limited
availability of statistical data on variables of the market environment, it was not
possible to verify all of the proposed relationships of the theoretical model. A
questionnaire survey including questionnaire items on all variables of the theoretical
model might allow the empirical verification of the model. However, the collection of
industry data from a range of over 100 industries requires extensive resources240.
Therefore, future research should not only apply more comprehensive models, but also
try to empirically verify a comprehensive model of market factors such as that
proposed within this study.
Second, future research should increase the degree to which the impact of market
variables varies by market context and performance measure. Those generic variables,
which were identified in the literature review as empirically consistent241 and those
variables that showed comparable results for different market contexts within the
quantitative study are the most appropriate for investigating the majority of studies that
do not incorporate the impact of contextual contingencies.
Third, future studies that investigate the relationship between market and
organisational success in more depth should identify the central contextual settings of
market variables and validate their importance empirically. The consideration of four
contingency variables within the undertaken study has already enabled an analysis on a
higher complexity level. However, in order to reach a better understanding of contextsensitive variables, it may be necessary to consider even more complex constellations
of contingency variables. The qualitative study indicated that the current and future
intensity of price competition in particular may be an important contingency variable.
240
241
In the undertaken study, venture success indicators from 163 industries were applied. Given a 10%
questionnaire response rate and the estimation of industry indicators from data of 10 firms per industry, it
would be necessary to send out 16,300 questionnaires.
None of the empirically consistent variables from the literature review could be investigated within the
undertaken quantitative empirical study due to the limited availability of statistical data.
CHAPTER 7 - CONCLUSIONS, SIGNIFICANCE OF RESULTS AND PERSPECTIVES
353
Moreover, the inclusion of strategic variables as contingency variables will permit the
investigation of the connection between market characteristics and strategy.
Fourth, quantitative empirical studies in entrepreneurship research should apply
databases of large numbers of new ventures more frequently. Various organisations in
different countries have recently started to build such databases242. Even though access
to these new venture databases is generally restricted, these databases now offer the
unique opportunity to advance the field of entrepreneurship research with studies of
higher validity. Due to the intensity of firm-specific factors of distortion in the context
of new ventures, the field of entrepreneurship, more than other fields of research, may
benefit from large sample sizes. An alternative approach for obtaining financial
information on a large number of new ventures is given in countries such as Spain,
where financial reports of non-publicly traded firms are also published.
242
E.g. Existenzgründer Pandel of DtA which has been applied within this study; ZEW database of new ventures
in Germany; Kauffmann Foundation`s financial statement database in the USA.
354
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370
APPENDICES
Appendices
Appendix A: Calculations for operationalization of MES variable
The following script has been written in the Visual Basic editor of MS Excel in order
to calculate the value of the MES based on the data of the value added tax statistics in
classes of firm size.
'Private Sub Worksheet_Change(ByVal Target As Range)
Public Sub calculate_values()
debugging = False
debugging_msg = False
warnings = False
'write calculation steps in target
'show msg_boxes with values
'show warning if anz_i0 < k
anz_col = 4
anz_row = 3
sum_anz_col = 4
sum_anz_row = 16
sa_col = 4
sa_row = 19
target_col = 4
target_row = 82
'write output to this position
'output of branchconcentration
data_width = 163
'width for all ranges
gk_col = 3
gk_row = 3
min_i = 0
max_i = 12
' CALCULATION MES
'*******************************************
If debugging Then 'delete content of complete output range
Me.Range(Cells(target_row, target_col), Cells(target_row
target_col + data_width)) = ""
End If
+
max_i
+
2,
For x = 0 To data_width - 1
sum_anz = Me.Cells(sum_anz_row, sum_anz_col + x).Value
total_revenue = Me.Cells(sa_sum_row, sa_sum_col + x).Value
um = 0
' k is number of remaining smaller firms
k = sum_anz
' max_k is total number of firms in industry (constant and corrected by 1)
max_k = k + 1
anz_i0 = Me.Cells(anz_row + max_i, anz_col + x).Value
If anz_i0 < max_k Then
For i = min_i To max_i
With Me
anz_i = .Cells(anz_row + max_i - i, anz_col + x).Value
gk_i = .Cells(gk_row + max_i - i, gk_col).Value
gk_i1 = .Cells(gk_row + max_i - i - 1, gk_col).Value
sa_i = .Cells(sa_row + max_i - i, sa_col + x).Value
APPENDICES
371
End With
If (i = 0) And (anz_i > 0) And (sa_i = "") Then
MsgBox "error: anz(0) = " & anz_i & " but sa(0) = 0 " & sa_i &
Chr(13) & Chr(13) & _
"position: " & x & ":" & i
End If
If debugging_msg And (anz_i > 0) Then
Cells(target_row, target_col + x).Activate
MsgBox ("i:
" & i & Chr(13) & _
"x:
" & x & Chr(13) & _
Chr(13) & _
"anz_i0: " & anz_i0 & Chr(13) & _
"anz_i: " & anz_i & Chr(13) & _
"gk_i:
" & gk_i & Chr(13) & _
"gk_i1: " & gk_i1 & Chr(13) & _
"sa_i:
" & sa_i)
End If
break_total = total_revenue * top_total_revenue_percent / 100
If (um + sa_i) <= (break_total) Then
If anz_i > 0 Then
um = um + sa_i
k = k - anz_i
If debugging Then Me.Cells(target_row + max_i - i + 2,
target_col + x).Formula = "i: " & i & " - k: " & k & " - um: " & Round(um)
End If
Else
If anz_i > 0 Then
'calculation of average sales interval within last size class
Mw = (((sa_i)) / anz_i)
End If
For n = 1 To anz_i
Um = um + gk_i - mw * n
k = k - 1
Cells(target_row, target_col + x).Activate
If debugging Then Me.Cells(target_row + max_i - i + 2,
target_col + x).Formula = "i: " & i & " - k: " & k & " - um: " & Round(um)
If um >= break_total Then Exit For
Next
End If
If um >= break_total Then Exit For
Next
Me.Cells(target_row, target_col + x).Value = Round(um / (max_k - k))
Else
warn_str = "anz(0) bigger than k" & _
Chr(13) & _
"i:
" & i & Chr(13) & _
"x:
" & x & Chr(13) & _
Chr(13) & _
"anz_i0: " & anz_i0 & Chr(13) & _
"k: " & Chr(9) & k
If warnings Then
'activate cell that contains the value that is too high
Cells(anz_row + max_i, anz_col + x).Activate
MsgBox (warn_str)
End If
Me.Cells(target_row, target_col + x).Formula = ""
End If
Next
End Sub
372
APPENDICES
Appendix B: Industry ranking: Startups per industry
Complete ranking in continuation of excerpt in chapter 5.4.2.1. Ranking is based on
venture sample of quantitative study.
rank
1
2
3
4
5
6
7
8
9
10
11
NACE
code
8512
8514
5248
9305
9302
7484
4545
5530
8513
7411
7420
12
13
14
15
16
17
18
19
4533
5020
4534
5212
5231
4531
5241
7412
20
5227
21
22
23
4542
5511
5244
24
5211
25
26
27
28
29
30
31
32
33
7414
8520
4544
5242
5247
5232
4522
7440
1581
34
35
36
6024
9272
5245
37
38
8042
5540
industry
Medical practice activities
Other human health activities
Other retail sale in specialized stores
Other service activities n.e.c.
Hairdressing and other beauty treatment
Other business activities n.e.c.
Other building completion
Restaurants
Dental practice activities
Legal activities
Architectural and engineering activities and related
technical consulting
Plumbing
Maintenance and repair of motor vehicles
Other building installation
Other retail sale in non-specialized stores
Dispensing chemists
Installation of electrical wiring and fittings
Retail sale of textiles
Accounting, book-keeping and auditing activities; tax
consultancy
Other retail sale of food, beverages and tobacco in
specialized stores
Joinery installation
Hotels and motels, with restaurant
Retail sale of furniture, lighting equipment and
household articles n.
Retail sale in non-specialized stores with food,
beverages or tobacco
Business and management consultancy activities
Veterinary activities
Painting and glazing
Retail sale of clothing
Retail sale of books, newspapers and stationery
Retail sale of medical and orthopaedic goods
Erection of roof covering and frames
Advertising
Manufacture of bread; manufacture of fresh pastry
goods and cakes
Freight transport by road
Other recreational activities n.e.c.
Retail sale of electrical household appliances and radio
and television
Adult and other education n.e.c.
Bars
#
ventures
333
315
258
236
195
190
158
151
151
125
119
109
103
98
90
77
75
73
73
67
66
62
60
57
57
57
56
55
53
48
47
41
40
39
38
35
35
33
APPENDICES
373
39
112
40
41
5010
6330
42
43
44
45
46
47
4543
5170
6340
1513
7230
2735
48
49
50
51
2875
5523
7260
5040
52
53
54
55
56
5050
5222
5246
5225
5224
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
6603
2670
6022
7220
9301
2225
2524
5243
5263
8041
2956
2051
9261
2811
3614
4521
73
74
75
5030
5147
7410
76
77
78
79
80
7450
7470
7481
9262
2840
81
82
2924
3310
Growing of vegetables, horticultural specialities and
nursery products
Sale of motor vehicles
Activities of travel agencies and tour operators; tourist
assistance
Floor and wall covering
Other wholesale
Activities of other transport agencies
Production of meat and poultrymeat products
Data processing
Other first processing of iron and steel n.e.c.; production
of non-ECS
Manufacture of other fabricated metal products n.e.c.
Other provision of lodgings n.e.c.
Other computer related activities
Sale, maintenance and repair of motorcycles and
related parts and accessories
Retail sale of automotive fuel
Retail sale of meat and meat products
Retail sale of hardware, paints and glass
Retail sale of alcoholic and other beverages
Retail sale of bread, cakes, flour confectionery and
sugar confectionery
Non-life insurance
Cutting, shaping and finishing of stone
Taxi operation
Software consultancy and supply
Washing and dry-cleaning of textile and fur products
Other activities related to printing
Manufacture of other plastic products
Retail sale of footwear and leather goods
Other non-store retail sale
Driving school activities
Manufacture of other special purpose machinery n.e.c.
Manufacture of other products of wood
Operation of sports arenas and stadiums
Manufacture of metal structures and parts of structures
Manufacture of other furniture
General construction of buildings and civil engineering
works
Sale of motor vehicle parts and accessories
Wholesale of other household goods
Legal, accounting & auditing activities; consultancy;
market res.
Labour recruitment and provision of personnel
Industrial cleaning
Photographic activities
Other sporting activities
Forging, pressing, stamping and roll forming of metal;
powder metallurgy
Manufacture of other general purpose machinery n.e.c.
Manufacture of medical and surgical equipment and
orthopaedic appliance
32
32
32
31
28
28
27
26
25
25
25
24
22
21
21
20
19
18
18
17
17
17
17
16
15
15
15
15
14
13
13
12
12
12
12
12
12
12
12
12
12
11
11
11
374
APPENDICES
83
84
3622
5165
85
86
87
88
89
90
91
92
93
94
5240
5274
7031
2862
4541
9304
5226
2030
3162
3340
95
96
97
98
99
100
101
102
103
7483
2010
4525
5118
7032
7430
8532
9303
2666
104
105
106
2745
3663
5153
107
108
109
110
111
112
113
114
115
5221
5512
5552
8531
9211
2222
2625
3630
4523
116
117
4532
5115
118
119
5142
5143
120
121
122
123
124
125
126
127
128
5223
5233
5261
7020
9234
141
202
1930
2221
Manufacture of jewellery and related articles n.e.c.
Wholesale of other machinery for use in industry, trade
and navigation
Other retail sale of new goods in specialised stores
Repair n.e.c.
Real estate agencies
Manufacture of tools
Plastering
Physical well-being activities
Retail sale of tobacco products
Manufacture of builders' carpentry and joinery
Manufacture of other electrical equipment n.e.c.
Manufacture of optical instruments and photographic
equipment
Secretarial and translation activities
Sawmilling and planing of wood, impregnation of wood
Other construction work involving special trades
Agents specializing in the sale of particular products
Management of real estate on a fee or contract basis
Technical testing and analysis
Social work activities without accommodation
Funeral and related activities
Manufacture of other articles of concrete, plaster and
cement
Other non-ferrous metal production
Other manufacturing n.e.c.
Wholesale of wood, construction materials and sanitary
equipment
Retail sale of fruit and vegetables
Hotels and motels, without restaurant
Catering
Social work activities with accommodation
Motion picture and video production
Printing n.e.c.
Manufacture of other ceramic products
Manufacture of musical instruments
Construction of highways, roads, airfields and sport
facilities
Insulation work activities
Agents involved in the sale of furniture, household
goods
Wholesale of clothing and footwear
Wholesale of electrical household appliances and radio
and television
Retail sale of fish, crustaceans and molluscs
Retail sale of cosmetic and toilet articles
Retail sale via mail order houses
Letting of own property
Other entertainment activities n.e.c.
Agricultural service activities
Forestry and logging related service activities
Manufacture of footwear
Printing of newspapers
11
11
11
11
11
10
10
10
9
8
8
8
8
7
7
7
7
7
7
7
6
6
6
6
6
6
6
6
6
5
5
5
5
5
5
5
5
5
5
5
5
5
4
4
4
4
APPENDICES
129
130
131
132
133
2521
2851
2863
2900
2923
134
135
136
4511
4540
5114
137
138
5119
5144
139
140
141
142
143
5250
6321
7140
7210
7310
144
145
146
147
148
149
150
151
152
9212
1511
2215
2220
2223
3420
3710
5134
5154
153
5220
154
155
156
157
158
5262
5271
6021
6023
7250
159
160
161
8000
8021
9000
162
163
9240
9271
375
Manufacture of plastic plates, sheets, tubes and profiles
Treatment and coating of metals
Manufacture of locks and hinges
Manufacture of machinery and equipment n.e.c.
Manufacture of non-domestic cooling and ventilation
equipment
Demolition and wrecking of buildings; earth moving
Other construction
Agents involved in the sale of machinery, industrial
equipment, ships
Agents involved in the sale of a variety of goods
Wholesale of china and glassware, wallpaper and
cleaning materials
Retail sale of second-hand goods in stores
Other supporting land transport activities
Renting of personal and household goods n.e.c.
Hardware consultancy
Research and experimental development on natural
sciences and engineer
Motion picture and video distribution
Production and preserving of meat
Other publishing
Printing and service activities related to printing
Bookbinding and finishing
Manufacture of bodies (coachwork) for motor vehicles
Recycling of metal waste and scrap
Wholesale of alcoholic and other beverages
Wholesale of hardware, plumbing and heating
equipment and supplies
Retail sale of food, beverages and tobacco in
specialised stores
Retail sale via stalls and markets
Repair of boots, shoes and other articles of leather
Other scheduled passenger land transport
Other land passenger transport
Maintenance and repair of office, accounting and
computing machinery
Education
General secondary education
Sewage and refuse disposal, sanitation and similar
activities
News agency activities
Gambling and betting activities
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
376
APPENDICES
Appendix C: Ranking of industries by subjective success evaluation
Complete ranking in continuation of excerpt in chapter 5.4.2.2. Ranking is based on
venture sample of quantitative study.
rank
1
subjective
rating (0-1)
0,9
NACE
code
4523
2
0,88
2923
3
4
5
6
7
0,8
0,8
0,75
0,75
0,75
4532
8531
2862
6321
7310
8
9
0,73
0,73
9261
2924
10
0,71
8532
11
12
13
14
0,71
0,7
0,69
0,68
7470
2625
6603
2811
15
16
0,67
0,67
2745
5030
17
18
0,67
0,67
5262
7140
19
0,67
7250
20
21
22
23
0,67
0,67
0,67
0,66
7481
9262
9271
2735
24
25
26
27
0,64
0,64
0,63
0,63
8513
8512
141
202
28
29
0,63
0,63
1930
2521
30
0,63
2900
31
0,63
3162
industry
Construction of highways, roads, airfields
and sport facilities
Manufacture of non-domestic cooling and
ventilation equipment
Insulation work activities
Social work activities with accommodation
Manufacture of tools
Other supporting land transport activities
Research and experimental development
on natural sciences and engineer
Operation of sports arenas and stadiums
Manufacture of other general purpose
machinery n.e.c.
Social
work
activities
without
accommodation
Industrial cleaning
Manufacture of other ceramic products
Non-life insurance
Manufacture of metal structures and parts
of structures
Other non-ferrous metal production
Sale of motor vehicle parts and
accessories
Retail sale via stalls and markets
Renting of personal and household goods
n.e.c.
Maintenance and repair of office,
accounting and computing machinery
Photographic activities
Other sporting activities
Gambling and betting activities
Other first processing of iron and steel
n.e.c.; production of non-ECS
Dental practice activities
Medical practice activities
Agricultural service activities
Forestry and logging related service
activities
Manufacture of footwear
Manufacture of plastic plates, sheets,
tubes and profiles
Manufacture of machinery and equipment
n.e.c.
Manufacture of other electrical equipment
n.e.c.
# ventures
5
4
5
5
10
4
4
13
11
7
12
5
18
11
6
12
3
3
3
12
12
3
25
150
330
4
4
4
4
4
8
APPENDICES
377
32
0,63
5114
33
34
35
36
37
0,62
0,62
0,61
0,61
0,61
7220
8520
7411
5231
2956
38
39
0,61
0,6
5170
7412
40
41
0,6
0,6
2222
3310
42
0,6
5240
43
44
45
46
0,58
0,58
0,57
0,57
7430
2051
6340
7032
47
48
0,57
0,57
9303
5040
49
50
51
52
53
54
55
56
0,57
0,57
0,56
0,56
0,56
0,55
0,55
0,55
8041
9272
2225
7483
8514
9304
5050
6330
57
58
59
60
61
0,53
0,53
0,53
0,52
0,52
9302
5243
6022
4522
2875
62
63
0,52
0,51
4544
5232
64
65
66
0,5
0,5
0,5
4533
7484
2030
67
68
0,5
0,5
2215
2220
69
70
71
0,5
0,5
0,5
2851
2863
3420
Agents involved in the sale of machinery,
industrial equipment, ships
Software consultancy and supply
Veterinary activities
Legal activities
Dispensing chemists
Manufacture of other special purpose
machinery n.e.c.
Other wholesale
Accounting, book-keeping and auditing
activities; tax consultancy
Printing n.e.c.
Manufacture of medical and surgical
equipment and orthopaedic applianc
Other retail sale of new goods in
specialised stores
Technical testing and analysis
Manufacture of other products of wood
Activities of other transport agencies
Management of real estate on a fee or
contract basis
Funeral and related activities
Sale, maintenance and repair of
motorcycles and related parts and acce
Driving school activities
Other recreational activities n.e.c.
Other activities related to printing
Secretarial and translation activities
Other human health activities
Physical well-being activities
Retail sale of automotive fuel
Activities of travel agencies and tour
operators; tourist assistance a
Hairdressing and other beauty treatment
Retail sale of footwear and leather goods
Taxi operation
Erection of roof covering and frames
Manufacture of other fabricated metal
products n.e.c.
Painting and glazing
Retail sale of medical and orthopaedic
goods
Plumbing
Other business activities n.e.c.
Manufacture of builders' carpentry and
joinery
Other publishing
Printing and service activities related to
printing
Treatment and coating of metals
Manufacture of locks and hinges
Manufacture of bodies (coachwork) for
motor vehicles; manufacture of t
4
17
56
125
76
14
28
73
5
10
10
6
13
28
7
7
22
15
38
16
8
307
10
21
32
194
15
15
46
25
55
45
105
189
8
3
3
4
4
3
378
APPENDICES
72
73
0,5
0,5
3630
4511
74
0,5
5115
75
0,5
5119
76
77
78
0,5
0,5
0,5
5142
5222
5247
79
0,5
5271
80
81
82
83
84
85
86
87
88
89
90
91
92
0,5
0,5
0,5
0,5
0,5
0,5
0,49
0,48
0,48
0,48
0,47
0,47
0,47
5512
5552
6021
6023
8000
9212
5511
5010
4531
4534
8042
5020
112
93
94
95
0,47
0,46
0,45
2524
3614
2840
96
97
98
99
0,45
0,45
0,45
0,45
5274
4542
9305
5244
100
101
0,45
0,45
5246
7420
102
0,45
7414
103
0,44
5224
104
105
106
107
108
0,44
0,44
0,44
0,44
0,44
5226
5241
4545
5523
3340
109
110
0,44
0,43
6024
4525
111
0,42
5225
Manufacture of musical instruments
Demolition and wrecking of buildings;
earth moving
Agents involved in the sale of furniture,
household goods, hardware an
Agents involved in the sale of a variety of
goods
Wholesale of clothing and footwear
Retail sale of meat and meat products
Retail sale of books, newspapers and
stationery
Repair of boots, shoes and other articles
of leather
Hotels and motels, without restaurant
Catering
Other scheduled passenger land transport
Other land passenger transport
Education
Motion picture and video distribution
Hotels and motels, with restaurant
Sale of motor vehicles
Installation of electrical wiring and fittings
Other building installation
Adult and other education n.e.c.
Maintenance and repair of motor vehicles
Growing of vegetables, horticultural
specialities and nursery products
Manufacture of other plastic products
Manufacture of other furniture
Forging, pressing, stamping and roll
forming of metal; powder metallur
Repair n.e.c.
Joinery installation
Other service activities n.e.c.
Retail sale of furniture, lighting equipment
and household articles n.
Retail sale of hardware, paints and glass
Architectural and engineering activities
and related technical consult
Business and management consultancy
activities
Retail sale of bread, cakes, flour
confectionery and sugar confectione
Retail sale of tobacco products
Retail sale of textiles
Other building completion
Other provision of lodgings n.e.c.
Manufacture of optical instruments and
photographic equipment
Freight transport by road
Other construction work involving special
trades
Retail sale of alcoholic and other
5
4
5
4
5
21
52
3
5
6
3
3
3
4
60
31
75
95
35
99
32
15
12
11
11
65
234
60
20
118
56
18
9
72
155
25
8
39
7
19
APPENDICES
379
112
113
114
0,42
0,42
0,42
5248
5242
2666
115
116
0,42
0,42
3663
4521
117
0,42
5153
118
119
120
121
122
123
0,42
0,42
0,42
0,42
0,41
0,41
5212
5221
7260
9211
2670
1581
124
0,4
5223
125
126
127
128
129
0,4
0,4
0,4
0,39
0,39
5233
5263
7020
5540
5211
130
131
132
0,39
0,39
0,39
7440
4543
5227
133
0,38
5245
134
135
0,38
0,38
5530
5144
136
0,38
7450
137
138
139
140
141
0,35
0,33
0,33
0,33
0,33
7031
1511
2223
3710
5134
142
0,33
5154
143
144
0,33
0,33
8021
9000
145
146
0,33
0,32
9240
3622
147
148
0,3
0,3
4541
5143
beverages
Other retail sale in specialized stores
Retail sale of clothing
Manufacture of other articles of concrete,
plaster and cement
Other manufacturing n.e.c.
General construction of buildings and civil
engineering works
Wholesale of wood, construction materials
and sanitary equipment
Other retail sale in non-specialized stores
Retail sale of fruit and vegetables
Other computer related activities
Motion picture and video production
Cutting, shaping and finishing of stone
Manufacture of bread; manufacture of
fresh pastry goods and cakes
Retail sale of fish, crustaceans and
molluscs
Retail sale of cosmetic and toilet articles
Other non-store retail sale
Letting of own property
Bars
Retail sale in non-specialized stores with
food, beverages or tobacco
Advertising
Floor and wall covering
Other retail sale of food, beverages and
tobacco in specialized stores
Retail sale of electrical household
appliances and radio and televisio
Restaurants
Wholesale of china and glassware,
wallpaper and cleaning materials
Labour recruitment and provision of
personnel
Real estate agencies
Production and preserving of meat
Bookbinding and finishing
Recycling of metal waste and scrap
Wholesale of alcoholic and other
beverages
Wholesale of hardware, plumbing and
heating equipment and supplies
General secondary education
Sewage and refuse disposal, sanitation
and similar activities
News agency activities
Manufacture of jewellery and related
articles n.e.c.
Plastering
Wholesale
of
electrical
household
appliances and radio and television
253
55
6
6
12
6
90
6
24
6
17
39
5
5
15
5
33
56
40
31
66
34
149
4
12
10
3
3
3
3
3
3
3
3
11
10
5
380
APPENDICES
149
150
151
0,3
0,29
0,29
7230
5147
2010
152
0,29
5118
153
0,28
1513
154
0,27
5165
155
0,26
9301
156
0,25
7410
157
0,17
5220
158
159
160
161
162
163
0,13
0,13
0,13
0,13
0,1
0,1
2221
4540
5250
7210
5261
9234
Data processing
Wholesale of other household goods
Sawmilling and planing of wood,
impregnation of wood
Agents specializing in the sale of particular
products or ranges of pr
Production of meat and poultrymeat
products
Wholesale of other machinery for use in
industry, trade and navigation
Washing and dry-cleaning of textile and fur
products
Legal, accounting & auditing activities;
consultancy; market res.; hol
Retail sale of food, beverages and tobacco
in specialised stores
Printing of newspapers
Other construction
Retail sale of second-hand goods in stores
Hardware consultancy
Retail sale via mail order houses
Other entertainment activities n.e.c.
25
12
7
7
27
11
17
12
3
4
4
4
4
5
5
APPENDICES
381
Appendix D: Ranking of industries by venture sales growth
Complete ranking in continuation of excerpt in chapter 5.4.2.2. Ranking is based on
venture sample of quantitative study.
rank
1
2
3
4
5
6
7
sales growth
rate
5,38
2,56
2,33
2,03
1,84
1,76
1,75
NACE
code
5261
7260
9234
7484
7483
5274
7414
8
9
10
1,71
1,65
1,65
7230
5170
4525
11
12
1,63
1,56
7031
1513
13
1,54
5040
14
1,53
2923
15
16
17
1,49
1,44
1,43
6024
4544
7032
18
1,41
9000
19
20
1,41
1,40
7220
6330
21
22
1,40
1,40
7411
5119
23
24
25
26
1,40
1,38
1,36
1,35
8042
7470
9305
5118
27
28
1,35
1,35
9304
5220
29
1,34
5227
30
1,34
9261
31
1,32
2840
32
1,31
3630
industry
Retail sale via mail order houses
Other computer related activities
Other entertainment activities n.e.c.
Other business activities n.e.c.
Secretarial and translation activities
Repair n.e.c.
Business and management
consultancy activities
Data processing
Other wholesale
Other construction work involving
special trades
Real estate agencies
Production of meat and poultrymeat
products
Sale, maintenance and repair of
motorcycles and related parts and
acce
Manufacture of non-domestic
cooling and ventilation equipment
Freight transport by road
Painting and glazing
Management of real estate on a fee
or contract basis
Sewage and refuse disposal,
sanitation and similar activities
Software consultancy and supply
Activities of travel agencies and tour
operators; tourist assistance a
Legal activities
Agents involved in the sale of a
variety of goods
Adult and other education n.e.c.
Industrial cleaning
Other service activities n.e.c.
Agents specializing in the sale of
particular products
Physical well-being activities
Retail sale of food, beverages and
tobacco in specialised stores
Other retail sale of food, beverages
and tobacco in specialized stores
Operation of sports arenas and
stadiums
Forging, pressing, stamping and roll
forming of metal; powder metallurgy
Manufacture of musical instruments
# ventures
5
24
5
190
8
11
57
26
28
7
11
27
22
4
39
56
7
3
17
32
125
4
35
12
236
7
10
3
67
13
11
5
382
APPENDICES
33
1,31
2811
34
1,30
2900
35
1,30
7410
36
1,30
7420
37
38
1,29
1,29
9211
8532
39
1,29
5232
40
41
42
1,29
1,27
1,27
4532
4543
2524
43
44
1,27
1,27
9262
5154
45
1,27
112
46
47
1,26
1,26
7440
2030
48
1,26
7412
49
50
1,25
1,25
5523
4531
51
1,25
9302
52
53
1,25
1,24
8514
5144
54
1,24
2735
55
1,24
5225
56
57
1,23
1,23
6603
2875
58
1,23
2670
59
60
1,23
1,22
4533
5115
61
1,22
5245
62
1,22
6340
63
1,22
5153
64
65
1,22
1,22
4545
2956
Manufacture of metal structures and
parts of structures
Manufacture of machinery and
equipment n.e.c.
Legal, accounting & auditing
activities; consultancy; market res.
Architectural and engineering
activities and related technical
consulting
Motion picture and video production
Social work activities without
accommodation
Retail sale of medical and
orthopaedic goods
Insulation work activities
Floor and wall covering
Manufacture of other plastic
products
Other sporting activities
Wholesale of hardware, plumbing
and heating equipment and supplies
Growing of vegetables, horticultural
specialities and nursery products
Advertising
Manufacture of builders' carpentry
and joinery
Accounting, book-keeping and
auditing activities; tax consultancy
Other provision of lodgings n.e.c.
Installation of electrical wiring and
fittings
Hairdressing and other beauty
treatment
Other human health activities
Wholesale of china and glassware,
wallpaper and cleaning materials
Other first processing of iron and
steel n.e.c.; production of non-ECS
Retail sale of alcoholic and other
beverages
Non-life insurance
Manufacture of other fabricated
metal products n.e.c.
Cutting, shaping and finishing of
stone
Plumbing
Agents involved in the sale of
furniture, household goods
Retail sale of electrical household
appliances and radio and television
Activities of other transport
agencies
Wholesale of wood, construction
materials and sanitary equipment
Other building completion
Manufacture of other special
purpose machinery n.e.c.
12
4
12
119
6
7
48
5
31
15
12
3
32
41
8
73
25
75
195
315
4
25
19
18
25
17
109
5
35
28
6
158
14
APPENDICES
383
66
1,22
4523
67
68
1,21
1,21
8513
2051
69
70
1,21
1,21
8512
3162
71
1,20
2924
72
73
74
1,20
1,20
1,19
2225
7430
5224
75
1,18
3310
76
77
1,18
1,18
4542
5240
78
1,18
2010
79
80
1,18
1,17
5552
202
81
82
1,16
1,16
8520
5223
83
1,16
2521
84
85
1,15
1,15
5010
3340
86
1,15
4521
87
88
89
1,15
1,15
1,14
9303
2745
5248
90
1,14
5147
91
1,14
5030
92
1,13
8531
93
94
95
1,13
1,13
1,13
4534
2863
5244
96
97
1,12
1,12
7481
3622
98
99
1,12
1,12
6022
9301
Construction of highways, roads,
airfields and sport facilities
Dental practice activities
Manufacture of other products of
wood
Medical practice activities
Manufacture of other electrical
equipment n.e.c.
Manufacture of other general
purpose machinery n.e.c.
Other activities related to printing
Technical testing and analysis
Retail sale of bread, cakes, flour
confectionery and sugar
confectionery
Manufacture of medical and surgical
equipment and orthopaedic
appliance
Joinery installation
Other retail sale of new goods in
specialised stores
Sawmilling and planing of wood,
impregnation of wood
Catering
Forestry and logging related service
activities
Veterinary activities
Retail sale of fish, crustaceans and
molluscs
Manufacture of plastic plates,
sheets, tubes and profiles
Sale of motor vehicles
Manufacture of optical instruments
and photographic equipment
General construction of buildings
and civil engineering works
Funeral and related activities
Other non-ferrous metal production
Other retail sale in specialized
stores
Wholesale of other household
goods
Sale of motor vehicle parts and
accessories
Social work activities with
accommodation
Other building installation
Manufacture of locks and hinges
Retail sale of furniture, lighting
equipment and household articles n.
Photographic activities
Manufacture of jewellery and
related articles n.e.c.
Taxi operation
Washing and dry-cleaning of textile
and fur products
5
151
13
333
8
11
16
7
18
11
66
11
7
6
4
57
5
4
32
8
12
7
6
258
12
12
6
98
4
60
12
11
17
17
384
APPENDICES
100
101
102
103
1,12
1,11
1,10
1,10
7020
5231
3614
5243
104
105
1,10
1,09
5511
1581
106
107
108
1,09
1,09
1,09
9272
5241
5222
109
110
1,08
1,07
5050
5271
111
112
1,07
1,07
5530
5212
113
1,06
4522
114
115
1,06
1,06
5242
5165
116
1,06
5020
117
1,06
5211
118
119
120
1,05
1,05
1,05
2222
5540
5247
121
122
123
124
1,04
1,03
1,03
1,03
2223
5263
4541
5246
125
1,02
3420
126
127
128
1,02
1,01
1,00
8041
2862
5233
129
1,00
2666
130
0,99
5512
131
132
133
134
0,97
0,89
0,86
0,70
4540
2221
7210
4511
not available
not available
not available
3710
5221
2625
Letting of own property
Dispensing chemists
Manufacture of other furniture
Retail sale of footwear and leather
goods
Hotels and motels, with restaurant
Manufacture of bread; manufacture
of fresh pastry goods and cakes
Other recreational activities n.e.c.
Retail sale of textiles
Retail sale of meat and meat
products
Retail sale of automotive fuel
Repair of boots, shoes and other
articles of leather
Restaurants
Other retail sale in non-specialized
stores
Erection of roof covering and
frames
Retail sale of clothing
Wholesale of other machinery for
use in industry, trade and navigation
Maintenance and repair of motor
vehicles
Retail sale in non-specialized stores
with food, beverages or tobacco
Printing n.e.c.
Bars
Retail sale of books, newspapers
and stationery
Bookbinding and finishing
Other non-store retail sale
Plastering
Retail sale of hardware, paints and
glass
Manufacture of bodies (coachwork)
for motor vehicles
Driving school activities
Manufacture of tools
Retail sale of cosmetic and toilet
articles
Manufacture of other articles of
concrete, plaster and cement
Hotels and motels, without
restaurant
Other construction
Printing of newspapers
Hardware consultancy
Demolition and wrecking of
buildings; earth moving
Recycling of metal waste and scrap
Retail sale of fruit and vegetables
Manufacture of other ceramic
products
5
77
12
15
62
40
38
73
21
21
3
151
90
47
55
11
103
57
5
33
53
3
15
10
20
3
15
10
5
6
6
4
4
4
4
3
6
5
APPENDICES
385
not available
5114
not available
not available
1930
7450
not available
not available
5226
6321
not available
not available
not available
not available
9212
141
2851
7310
not available
not available
not available
9271
5262
7140
not available
7250
not available
not available
not available
2215
8000
2220
not available
6021
not available
not available
not available
not available
not available
5142
6023
3663
9240
5134
not available
not available
not available
1511
8021
5143
not available
5250
Agents involved in the sale of
machinery, industrial equipment,
ships
Manufacture of footwear
Labour recruitment and provision of
personnel
Retail sale of tobacco products
Other supporting land transport
activities
Motion picture and video distribution
Agricultural service activities
Treatment and coating of metals
Research and experimental
development on natural sciences
and engineer
Gambling and betting activities
Retail sale via stalls and markets
Renting of personal and household
goods n.e.c.
Maintenance and repair of office,
accounting and computing
machinery
Other publishing
Education
Printing and service activities
related to printing
Other scheduled passenger land
transport
Wholesale of clothing and footwear
Other land passenger transport
Other manufacturing n.e.c.
News agency activities
Wholesale of alcoholic and other
beverages
Production and preserving of meat
General secondary education
Wholesale of electrical household
appliances and radio and television
Retail sale of second-hand goods in
stores
4
4
12
9
4
4
4
4
4
3
3
4
3
3
3
3
3
5
3
6
3
3
3
3
5
4
386
APPENDICES
Appendix E: Ranking of industries by venture profit level
Complete ranking in continuation of excerpt in chapter 5.4.2.2. Ranking is based on
venture sample of quantitative study.
rank
1
2
profit in
1000 EUR
350,12
205,76
NACE
code
4532
3162
3
144,23
4525
4
135,52
2666
5
6
7
118,62
110,52
100,13
2862
9261
4511
8
9
85,78
85,14
8513
2840
10
80,83
2521
11
80,55
2875
12
13
80,42
76,81
7220
3310
14
15
76,27
71,74
5231
7412
16
70,93
4523
17
18
19
70,88
68,60
65,41
8512
5512
3340
20
21
64,83
58,99
2222
7410
22
57,93
2900
23
24
56,67
55,03
3663
7420
25
26
53,77
47,38
4540
2956
27
28
46,35
46,02
6603
5030
29
43,34
2924
30
43,32
7450
industry
Insulation work activities
Manufacture of other electrical equipment
n.e.c.
Other construction work involving special
trades
Manufacture of other articles of concrete,
plaster and cement
Manufacture of tools
Operation of sports arenas and stadiums
Demolition and wrecking of buildings; earth
moving
Dental practice activities
Forging, pressing, stamping and roll
forming of metal; powder metallurgy
Manufacture of plastic plates, sheets,
tubes and profiles
Manufacture of other fabricated metal
products n.e.c.
Software consultancy and supply
Manufacture of medical and surgical
equipment and orthopaedic appliance
Dispensing chemists
Accounting, book-keeping and auditing
activities; tax consultancy
Construction of highways, roads, airfields
and sport facilities
Medical practice activities
Hotels and motels, without restaurant
Manufacture of optical instruments and
photographic equipment
Printing n.e.c.
Legal, accounting & auditing activities;
consultancy; market res.
Manufacture of machinery and equipment
n.e.c.
Other manufacturing n.e.c.
Architectural and engineering activities and
related technical consulting
Other construction
Manufacture of other special purpose
machinery n.e.c.
Non-life insurance
Sale of motor vehicle parts and
accessories
Manufacture of other general purpose
machinery n.e.c.
Labour recruitment and provision of
personnel
#
ventures
5
8
sales in
1000 EUR
2.324
1.451
7
3.426
6
3.780
10
13
4
1.115
409
611
151
11
318
558
4
1.883
25
1.158
17
11
2.001
1.886
77
73
850
412
5
1.574
333
6
8
262
653
338
5
12
810
242
4
3.827
6
119
286
493
4
14
340
1.527
18
12
190
466
11
1.341
12
823
APPENDICES
387
31
32
43,19
42,42
5050
5240
33
34
35
41,86
41,76
41,73
7411
141
5143
36
40,08
8532
37
38
39
40
41
42
43
44
45
46
39,77
39,48
39,10
38,09
37,55
36,64
36,39
36,05
35,47
35,35
7484
5147
5511
2051
7470
2625
2225
2745
5010
2811
47
48
34,51
34,41
6024
2735
49
34,11
2923
50
51
33,23
32,95
8531
5114
52
53
54
32,71
31,88
31,67
4544
4534
5144
55
56
57
58
59
60
31,57
31,49
30,92
30,65
29,63
29,36
2670
8514
8041
6340
7481
202
61
62
29,31
28,93
4522
5211
63
64
65
66
67
28,49
28,22
28,14
28,11
28,04
9303
5222
4531
4542
7414
68
69
27,98
27,77
8520
1581
70
71
72
73
27,75
27,25
26,55
26,54
5020
4545
4543
4541
Retail sale of automotive fuel
Other retail sale of new goods in
specialised stores
Legal activities
Agricultural service activities
Wholesale of electrical household
appliances and radio and television
Social work activities without
accommodation
Other business activities n.e.c.
Wholesale of other household goods
Hotels and motels, with restaurant
Manufacture of other products of wood
Industrial cleaning
Manufacture of other ceramic products
Other activities related to printing
Other non-ferrous metal production
Sale of motor vehicles
Manufacture of metal structures and parts
of structures
Freight transport by road
Other first processing of iron and steel
n.e.c.; production of non-ECS
Manufacture of non-domestic cooling and
ventilation equipment
Social work activities with accommodation
Agents involved in the sale of machinery,
industrial equipment, ships
Painting and glazing
Other building installation
Wholesale of china and glassware,
wallpaper and cleaning materials
Cutting, shaping and finishing of stone
Other human health activities
Driving school activities
Activities of other transport agencies
Photographic activities
Forestry and logging related service
activities
Erection of roof covering and frames
Retail sale in non-specialized stores with
food, beverages or tobacco
Funeral and related activities
Retail sale of meat and meat products
Installation of electrical wiring and fittings
Joinery installation
Business and management consultancy
activities
Veterinary activities
Manufacture of bread; manufacture of
fresh pastry goods and cakes
Maintenance and repair of motor vehicles
Other building completion
Floor and wall covering
Plastering
21
11
2.816
590
125
4
5
145
246
860
7
342
190
12
62
13
12
5
16
6
32
12
699
2.121
491
329
312
212
600
175
2.530
525
39
25
607
779
4
1.026
6
4
785
225
56
98
4
333
543
869
17
315
15
28
12
4
553
143
116
385
231
829
47
57
366
2.808
7
21
75
66
57
294
466
352
510
242
57
40
148
1.054
103
158
31
10
1.226
587
556
300
388
APPENDICES
74
26,13
112
75
76
26,11
26,02
5552
5224
77
78
79
25,69
25,59
25,24
7430
2524
1513
80
81
82
24,34
23,95
23,22
1930
3710
6330
83
22,72
3420
84
85
22,67
22,12
4533
5119
86
21,95
3622
87
21,66
5232
88
21,65
9000
89
21,62
5247
90
91
92
93
20,56
20,43
20,00
19,33
5243
7031
3614
5227
94
95
19,31
19,30
8042
2030
96
97
98
99
100
101
18,97
18,96
18,77
18,65
18,38
17,78
5540
5212
5233
5274
5248
5165
102
103
104
17,62
17,33
17,26
5530
5221
5225
105
16,84
5271
106
16,20
5115
107
16,19
5244
108
109
110
111
112
16,15
15,85
15,16
13,88
13,83
5241
9212
9211
5226
7483
Growing of vegetables, horticultural
specialities and nursery products
Catering
Retail sale of bread, cakes, flour
confectionery and sugar confectione
Technical testing and analysis
Manufacture of other plastic products
Production of meat and poultrymeat
products
Manufacture of footwear
Recycling of metal waste and scrap
Activities of travel agencies and tour
operators; tourist assistance
Manufacture of bodies (coachwork) for
motor vehicles
Plumbing
Agents involved in the sale of a variety of
goods
Manufacture of jewellery and related
articles n.e.c.
Retail sale of medical and orthopaedic
goods
Sewage and refuse disposal, sanitation
and similar activities
Retail sale of books, newspapers and
stationery
Retail sale of footwear and leather goods
Real estate agencies
Manufacture of other furniture
Other retail sale of food, beverages and
tobacco in specialized stores
Adult and other education n.e.c.
Manufacture of builders' carpentry and
joinery
Bars
Other retail sale in non-specialized stores
Retail sale of cosmetic and toilet articles
Repair n.e.c.
Other retail sale in specialized stores
Wholesale of other machinery for use in
industry, trade and navigation
Restaurants
Retail sale of fruit and vegetables
Retail sale of alcoholic and other
beverages
Repair of boots, shoes and other articles of
leather
Agents involved in the sale of furniture,
household goods
Retail sale of furniture, lighting equipment
and household articles n.
Retail sale of textiles
Motion picture and video distribution
Motion picture and video production
Retail sale of tobacco products
Secretarial and translation activities
32
219
6
18
205
405
7
15
27
173
1.025
647
4
3
32
.
532
700
3
325
109
4
368
66
11
187
48
281
3
111
53
396
15
11
12
67
242
69
217
1.792
35
8
111
232
33
90
5
11
258
11
508
439
187
729
468
734
151
6
19
251
196
618
3
82
5
179
60
503
73
4
6
9
8
320
91
281
319
38
APPENDICES
389
113
114
115
116
13,64
13,59
13,17
12,82
9302
9262
7440
7032
117
118
12,69
12,18
5242
4521
119
120
11,67
11,31
5263
9301
121
10,99
5223
122
123
124
125
126
127
9,96
9,66
9,65
9,57
9,36
9,23
5246
2223
3630
9272
9305
2010
128
7,85
5040
129
5,11
5118
130
131
132
133
134
135
136
3,23
1,83
1,02
-3,83
-5,68
-6,12
-7,99
6022
2215
9304
5261
7260
9234
5153
137
138
139
140
141
142
-13,21
-18,75
-24,29
-37,21
-40,33
-537,55
5170
5250
7210
5523
7230
5245
not
available
not
available
not
available
not
available
not
available
not
available
not
available
not
available
2863
Hairdressing and other beauty treatment
Other sporting activities
Advertising
Management of real estate on a fee or
contract basis
Retail sale of clothing
General construction of buildings and civil
engineering works
Other non-store retail sale
Washing and dry-cleaning of textile and fur
products
Retail sale of fish, crustaceans and
molluscs
Retail sale of hardware, paints and glass
Bookbinding and finishing
Manufacture of musical instruments
Other recreational activities n.e.c.
Other service activities n.e.c.
Sawmilling and planing of wood,
impregnation of wood
Sale, maintenance and repair of
motorcycles and related parts and
accessories
Agents specializing in the sale of particular
products
Taxi operation
Other publishing
Physical well-being activities
Retail sale via mail order houses
Other computer related activities
Other entertainment activities n.e.c.
Wholesale of wood, construction materials
and sanitary equipment
Other wholesale
Retail sale of second-hand goods in stores
Hardware consultancy
Other provision of lodgings n.e.c.
Data processing
Retail sale of electrical household
appliances and radio and television
Retail sale of food, beverages and tobacco
in specialised stores
Wholesale of hardware, plumbing and
heating equipment and supplies
Manufacture of locks and hinges
7020
Letting of own property
5
1.491
2221
Printing of newspapers
4
735
7310
4
340
6321
Research and experimental development
on natural sciences and engineering
Other supporting land transport activities
4
77
9271
Gambling and betting activities
3
5220
5154
195
12
41
7
89
130
138
175
55
12
218
2.118
15
17
1.327
114
5
194
20
3
5
38
236
7
523
1.054
165
170
261
595
22
2.610
7
234
17
3
10
5
24
5
6
95
186
135
186
251
43
2.613
28
4
4
25
26
35
1.611
35
433
312
412
1.626
3
315
3
364
4
588
.
390
APPENDICES
not
available
not
available
not
available
not
available
not
available
not
available
not
available
not
available
not
available
not
available
not
available
not
available
not
available
5262
Retail sale via stalls and markets
3
7140
Renting of personal and household goods
n.e.c.
Maintenance and repair of office,
accounting and computing machinery
Education
4
98
3
124
7250
8000
2220
3
.
.
3
6021
Printing and service activities related to
printing
Other scheduled passenger land transport
5142
Wholesale of clothing and footwear
5
306
2851
Treatment and coating of metals
4
1.187
6023
Other land passenger transport
3
425
9240
News agency activities
3
5134
3
1511
Wholesale of alcoholic and other
beverages
Production and preserving of meat
3
.
8021
General secondary education
3
.
3
902
.
.
554
APPENDICES
391
Appendix F: Ranking of industries by years ventures needed to break
even
Complete ranking in continuation of excerpt in chapter 5.4.2.2. Ranking is based on
venture sample of quantitative study.
rank
1
years to
break even
1,00
NACE
code
4523
2
1,00
2924
3
1,00
141
4
5
1,00
1,07
2851
1513
Treatment and coating of metals
Production of meat and poultrymeat products
4
27
6
1,07
4522
Erection of roof covering and frames
47
7
1,09
6603
Non-life insurance
18
8
1,17
7412
Accounting, book-keeping and auditing activities; tax
consultancy
Plastering
73
9
1,17
4541
10
1,20
3340
11
1,20
12
Industry
Construction of highways, roads, airfields and sport
facilities
Manufacture of other general purpose machinery n.e.c.
Agricultural service activities
#
ventures
5
11
4
10
5240
Manufacture of optical instruments and photographic
equipment
Other retail sale of new goods in specialised stores
8
11
1,20
5147
Wholesale of other household goods
12
13
1,24
5211
57
14
1,25
9261
Retail sale in non-specialized stores with food,
beverages or tobacco
Operation of sports arenas and stadiums
15
1,25
2745
Other non-ferrous metal production
16
17
1,25
1,29
5222
2225
Retail sale of meat and meat products
Other activities related to printing
21
16
18
1,29
5243
Retail sale of footwear and leather goods
15
19
1,30
4544
Painting and glazing
56
20
1,33
2862
Manufacture of tools
10
21
1,33
1581
40
22
23
1,33
1,33
7031
9212
Manufacture of bread; manufacture of fresh pastry
goods and cakes
Real estate agencies
Motion picture and video distribution
24
1,33
5153
25
1,33
6321
Wholesale of wood, construction materials and sanitary
equipment
Other supporting land transport activities
26
1,36
4534
Other building installation
98
27
28
1,37
1,38
5247
3310
53
11
29
1,38
6024
Retail sale of books, newspapers and stationery
Manufacture of medical and surgical equipment and
orthopaedic appliance
Freight transport by road
13
6
11
4
6
4
39
30
1,38
4542
Joinery installation
66
31
1,38
5274
Repair n.e.c.
11
32
1,39
6340
Activities of other transport agencies
28
33
1,39
5231
Dispensing chemists
77
392
APPENDICES
34
1,40
9303
Funeral and related activities
7
35
1,40
3614
Manufacture of other furniture
12
36
37
1,40
1,40
5226
4533
Retail sale of tobacco products
Plumbing
9
109
38
1,42
4545
Other building completion
158
39
1,43
7450
Labour recruitment and provision of personnel
40
1,43
5530
Restaurants
41
1,44
5244
42
1,46
5050
Retail sale of furniture, lighting equipment and
household articles n.
Retail sale of automotive fuel
43
1,46
5020
Maintenance and repair of motor vehicles
103
333
12
151
60
21
44
1,46
8512
Medical practice activities
45
1,47
2735
25
46
1,48
7440
Other first processing of iron and steel n.e.c.; production
of non-ECS
Advertising
47
48
1,50
1,50
5030
5010
Sale of motor vehicle parts and accessories
Sale of motor vehicles
12
32
49
1,50
2670
Cutting, shaping and finishing of stone
17
50
1,50
4543
Floor and wall covering
31
51
1,50
9301
Washing and dry-cleaning of textile and fur products
17
52
1,50
5246
Retail sale of hardware, paints and glass
20
53
1,50
5245
35
54
1,51
5511
Retail sale of electrical household appliances and radio
and television
Hotels and motels, with restaurant
55
1,53
7420
56
1,53
5241
Architectural and engineering activities and related
technical consult
Retail sale of textiles
57
1,56
4531
Installation of electrical wiring and fittings
75
58
1,56
5224
18
59
60
1,56
1,56
5225
5242
Retail sale of bread, cakes, flour confectionery and
sugar confectionery
Retail sale of alcoholic and other beverages
Retail sale of clothing
19
55
61
1,56
6022
Taxi operation
17
62
1,56
9302
Hairdressing and other beauty treatment
195
63
1,56
5212
Other retail sale in non-specialized stores
90
64
1,57
4521
12
65
1,59
8514
General construction of buildings and civil engineering
works
Other human health activities
66
1,60
2524
Manufacture of other plastic products
67
1,60
7483
Secretarial and translation activities
41
62
119
73
315
15
8
68
1,60
5263
Other non-store retail sale
15
69
1,62
5170
Other wholesale
28
70
71
1,62
1,62
8520
8513
Veterinary activities
Dental practice activities
57
151
72
1,63
8041
Driving school activities
15
73
1,63
6330
32
74
1,63
7484
Activities of travel agencies and tour operators; tourist
assistance
Other business activities n.e.c.
75
1,67
4532
Insulation work activities
190
5
APPENDICES
393
76
1,67
7220
Software consultancy and supply
77
1,67
2222
Printing n.e.c.
17
5
78
79
1,67
1,67
2625
8531
Manufacture of other ceramic products
Social work activities with accommodation
5
6
80
1,67
5114
4
81
1,67
7481
Agents involved in the sale of machinery, industrial
equipment, ships
Photographic activities
82
1,67
7430
Technical testing and analysis
83
84
1,67
1,67
1930
3622
Manufacture of footwear
Manufacture of jewellery and related articles n.e.c.
4
11
85
1,67
5232
Retail sale of medical and orthopaedic goods
48
12
7
86
1,67
5223
Retail sale of fish, crustaceans and molluscs
87
1,67
9304
Physical well-being activities
10
5
88
1,67
7260
Other computer related activities
24
89
1,68
5227
67
90
1,69
9305
Other retail sale of food, beverages and tobacco in
specialized stores
Other service activities n.e.c.
91
1,71
112
92
1,71
5540
Growing of vegetables, horticultural specialities and
nursery products
Bars
93
1,72
7411
Legal activities
125
94
1,74
5248
Other retail sale in specialized stores
258
95
96
1,75
1,78
3162
2051
Manufacture of other electrical equipment n.e.c.
Manufacture of other products of wood
8
13
97
1,78
7414
Business and management consultancy activities
57
98
1,80
7470
Industrial cleaning
12
99
1,80
2811
Manufacture of metal structures and parts of structures
12
236
32
33
100
1,80
5221
Retail sale of fruit and vegetables
101
102
1,83
1,88
2875
2956
Manufacture of other fabricated metal products n.e.c.
Manufacture of other special purpose machinery n.e.c.
25
14
103
1,89
7410
12
104
1,95
8042
Legal, accounting & auditing activities; consultancy;
market res.
Adult and other education n.e.c.
105
2,00
2840
11
106
2,00
8532
Forging, pressing, stamping and roll forming of metal;
powder metallurgy
Social work activities without accommodation
107
108
2,00
2,00
202
3710
Forestry and logging related service activities
Recycling of metal waste and scrap
4
3
109
2,00
5119
Agents involved in the sale of a variety of goods
4
110
2,00
2030
Manufacture of builders' carpentry and joinery
111
2,00
5165
112
113
2,00
2,00
9211
9262
Wholesale of other machinery for use in industry, trade
and navigation
Motion picture and video production
Other sporting activities
114
2,00
5040
115
2,07
116
2,20
117
2,25
6
35
7
8
11
6
12
7230
Sale, maintenance and repair of motorcycles and related
parts and accessories
Data processing
22
26
9272
Other recreational activities n.e.c.
38
7032
Management of real estate on a fee or contract basis
7
394
APPENDICES
118
2,25
2010
Sawmilling and planing of wood, impregnation of wood
7
119
2,67
2923
Manufacture of non-domestic cooling and ventilation
equipment
Other construction work involving special trades
4
6
4511
Manufacture of other articles of concrete, plaster and
cement
Demolition and wrecking of buildings; earth moving
2521
Manufacture of plastic plates, sheets, tubes and profiles
4
5512
Hotels and motels, without restaurant
6
2900
Manufacture of machinery and equipment n.e.c.
4
3663
Other manufacturing n.e.c.
6
4540
Other construction
4
5143
5
4
5552
Wholesale of electrical household appliances and radio
and television
Wholesale of china and glassware, wallpaper and
cleaning materials
Catering
3420
Manufacture of bodies (coachwork) for motor vehicles
3
9000
3
5233
Sewage and refuse disposal, sanitation and similar
activities
Retail sale of cosmetic and toilet articles
5
5271
Repair of boots, shoes and other articles of leather
3
5115
Agents involved in the sale of furniture, household goods
5
2223
Bookbinding and finishing
3
3630
Manufacture of musical instruments
5
5118
Agents specializing in the sale of particular products
7
2215
Other publishing
3
5261
Retail sale via mail order houses
5
9234
Other entertainment activities n.e.c.
5
5250
Retail sale of second-hand goods in stores
4
7210
Hardware consultancy
4
5523
Other provision of lodgings n.e.c.
5220
Retail sale of food, beverages and tobacco in
specialised stores
Wholesale of hardware, plumbing and heating
equipment and supplies
Manufacture of locks and hinges
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available
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available
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available
4525
2666
5144
5154
2863
7
4
6
25
3
3
4
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available
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available
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7020
Letting of own property
5
2221
Printing of newspapers
4
7310
4
9271
Research and experimental development on natural
sciences and engineering
Gambling and betting activities
3
5262
Retail sale via stalls and markets
3
7140
Renting of personal and household goods n.e.c.
4
7250
3
8000
Maintenance and repair of office, accounting and
computing machinery
Education
3
2220
Printing and service activities related to printing
3
6021
Other scheduled passenger land transport
3
5142
Wholesale of clothing and footwear
5
6023
Other land passenger transport
3
9240
News agency activities
3
5134
Wholesale of alcoholic and other beverages
3
1511
Production and preserving of meat
3
8021
General secondary education
3
396
APPENDICES
Appendix G: Questionnaire guideline for case study interviews
PART I: Key Data
Information on interviewee:
o Name:
o Position:
o Years working for firm:
Key firm data:
o Markets in which company is currently operating
o Importance ranking of each of these market in overall firm activities today and
future potential
o Year of firm foundation
o Development of product/service range of firm since startup (each year)
Key market data
o In the moment of starting the company, why did you think that this has been an
attractive market to enter?
o How far did the market change in the meanwhile until today?
o Who has been the strongest competitor at the time of venture foundation and
why this company?
o Who is the strongest competitor now?
o What happened to the strongest competitor from time of startup (question 3.c.)
until now? Why did this happen?
o How did the currently strongest competitor reach his leading competitive
position today?
o What are the TOP5 positive characteristics of the market from your firm’s point
of view?
o What are the TOP5 negative characteristics of the market from your firm’s
point of view?
APPENDICES
397
PART II: Model of market attractiveness
MACRO
LEVEL
OPPORTUNITIES IN GLOBAL & NATIONAL ENVIRONMENT
macroeconomic
legislative technological socio-cultural demographic
changes
changes
changes
changes
NATIONAL COMPETIVITY
INTER-COUNTRY
LEVEL
INTER-INDUSTRY
LEVEL
INTRA-INDUSTRY
LEVEL
production
factors
stability & distance to
industry
new venture
taxation sales market infrastructure infrastructure
OPPORTUNITIES & THREATS FROM RELATED INDUSTRIES
complementary threat of
forward
cooperation substitution integration
backward
integration
threat of
new entries
MARKET & DEPENDENCIES & COMPETITORS
market
structure
market dependencies
dynamics
competitor
dynamics
competitor
structure
BARRIERS TO ENTRY
VENTURE / FIRM
LEVEL
RELATIVE POSITIONING TO COMPETITION
products/
services
firm
resources
management
team
strategy
location
Please mark importance of each variable and comment for the important ones
why they are important!
Not at all
important
Extremely
important
I. MACRO LEVEL – OPPORTUNITIES IN NATIONAL & GLOBAL ENVIRONMENT
Did any of the following changes/developments have an
impact on your main industry and your firm in
particular? Please specify why for the important ones.
1. macro-economic changes
a) changes in the propensity to consumption
b) changes of interest rates
c) changes of currency rates
2. political / legislative changes
a) changes in environment protection regulations
b) changes in market regulations
c) changes in labour market regulations
d) changes in public spending
e) changes in international trade deregulations
3. technological changes
a) development of e-commerce
b) development of internal IT applications
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398
c) development of new production technologies
4. socio-cultural changes
a) changes in consumption preferences (increasing
volatility)
5. demographic changes
a) decreasing population growth
b) increasing population age
APPENDICES
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Other changes in economic, political, technological, sociocultural, demographic environment with important impact
on your main industry and your venture in particular:
Agree
100%
Do not at
all agree
II. INTER COUNTRY LEVEL – NATIONAL COMPETITIVENESS
Introduction – Degree international competition
1. production factors
a) easier availability of qualified labour
b) lower costs of qualified labour
c) easier availability of resources
d) lower costs of resources
2. political stability & taxation
a) lower taxes
b) higher stability of economical political environment
3. distance to sales market
a) lower distance to main customers for export industries
b) larger size of national market
4. industry infrastructure
a) better transportation and telecommunication
infrastructure
b) better access to dependent supplying and buying
industries
c) more competitive dependent supplying and buying
industries
5. new venture infrastructure
a) easier availability of debt funding from banks
b) easier availability of equity funding from venture
capitalists and business angels
c) more financial assistance for new ventures (e.g.
governmental funding, tax incentives etc.)
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important
Will competitors from this country where you expect
strongest competition from have an important
competitive advantage because of
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Extremely
a) Many of your national competitors are also operating in
foreign markets.
b) Many foreign companies compete in our national
market.
c) Which is the country where you expect strongest
competition from in your industry for the future:
=> if international competition is currently and in
foreseeable future of little importance then skip following
paragraph and continue with III.
APPENDICES
d) more non-financial assistance for new ventures (e.g.
consultancy)
399
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Other important sources of competitive advantage of firms
from country of strongest competition:
Agree
100%
Do not at
all agree
III. INTER INDUSTRY LEVEL – OPPORTUNITIES & THREATS FRIN RELATED INDUSTRIES
How do you rate the following statements regarding
opportunities and threats from related industries:
1. complementary cooperation: In my market there are
many unused opportunities of cooperating with firms in
other related industries. Give examples.
2. threat of substitution: New products / services /
technologies from other industries lead to a much lower
demand.
3. forward integration: Current suppliers have great
interest and the capability to sell directly to our
customers.
4. backward integration: Major customers have great
interest and the capability to purchase directly from our
suppliers.
5. threat of other new entries: Many new players are
entering our market and threaten our position.
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Other important opportunities or threats from related
industries:
Agree
100%
Do not at
all agree
IV. INTRA INDUSTRY LEVEL
How do you rate the following statements regarding your
industry:
1. market structure
a) market size: The market for our product/service is
tremendous and we have just covered a small part of the
overall market.
b) heterogeneity of products: Our products/service
features are identical to the ones from our competitors.
c) branding potential: It is easy in our market to establish
an own brand.
d) distance to clients: In my market, firms generally
compete on a national level, not only on a regional or local
level.
e) economies of scale: Large firms can offer much lower
prices than smaller firms in my market.
f) export-import balance: Much more foreign companies
operate in our national market than national companies
operate in foreign markets.
g) need for specialised products: Clients in my market
need a product which is perfectly customized to their
individual necessities.
h) buyer concentration: Less than ten buyers normally
count for over 50% of all firm sales in my industry
i) average order volume: In my market the average
volume of orders is very high (e.g. clients place large bulk
orders).
400
j) seasonal change of demand: A majority of our sales is
generated in only three months of the year.
k) intensity of price bargaining: Basically all clients in
my market do not accept the official price, but try to
bargain.
l) market transparency: It is very difficult for clients to
compare the product/service prices in our market.
m) advertising intensity: You can only compete in my
market if you spend from the beginning a lot of money in
advertising
n) R&D intensity: You can only compete in my market if
you spend from the beginning a lot of money in R&D
TOP 2 factors from market structure
-- with positive impact on your firm
-- with negative impact on your firm
2. market dynamics
a) former market growth: My market grew very rapidly
throughout the last years.
b) rel. expected market growth: My market is expected to
grow very rapidly within the next years.
c) entries to industry: A very large number of new firms
have entered the market in the last year.
d) exits from industry: A very large number of firms in
my market had to close down in the last year.
e) balance of entries and exits: The overall number of
firms in my market is increasing a lot.
f) degree of uncertainty about future development: The
demand varies strongly from year to year and it is very
difficult to forecast the development in our market.
g) customer loyalty: My clients are very flexible and
would change immediately if competitor products are a
little better or cheaper.
h) changing customer needs: The needs of clients have
changed strongly in the last years.
TOP 2 factors from market dynamics
-- with positive impact on your firm
-- with negative impact on your firm
3. dependencies
a) on clients: Firms in my market depend often on a small
number of very large clients.
b) on suppliers: Firms in my market rely on a few critical
suppliers with fix purchase conditions (e.g. suppliers of
brand products).
c) on key employees, employee institutions, and key
knowledge: There are some employees in each company in
my market who are nearly impossible to replace.
d) on legislation: The market is to a very high degree
affected by changes in legislation.
e) on business cycle: The demand of our
products/services fluctuates strongly with the overall
business cycle.
f) on maintenance in industry (barriers to exit): All large
firms in market would stay in market if demand and profits
drop very strongly.
g) relative dependency compared to earlier and later
value chain members: Not a single client or supplier
relationship is so important that ceasing this relationship
would affect strongly our business. Our bargaining power
APPENDICES
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APPENDICES
401
is higher than that of clients or suppliers of us.
TOP 2 factors from dependencies
-- with positive impact on your firm
-- with negative impact on your firm
4. competitor dynamics
a) inertia of established firms due to organisational
structure: Established firms are very sluggish, because the
decision makers do not seem to notice small changes in the
market.
b) inertia of established firms due to legislative
restrictions: Labour law and other legislative restrictions
prevent established firms from reacting quickly to market
changes.
c) inertia of established firms due to cost structure:
Established firms have frequently much higher costs than
new firms entering the market.
d) aggressive responsiveness of established firms:
Competitors react directly with special offers and price
cuts to the new entry of firms into market.
e) inflation rate in selling prices: The prices of newly
introduced product start to decrease rapidly in my market..
f) number of new products introduced: Competitors seek
continuously to improve their products/services and
introduce constantly new products/services.
g) investment in new assets: Everybody in the industry
invests continuously high amounts in new buildings,
machinery and equipment.
TOP 2 factors from competitor dynamics
-- with positive impact on your firm
-- with negative impact on your firm
5. competitor structure
a) concentration: In my industry a few big companies
dominate the whole market.
b) number of industry members: In my geographic sales
market are a large number of other firms offering similar
products / services.
c) heterogeneity of industry members: Firms in industry
are very different in terms of strategies, distribution
channels, and profitability.
d) general efficiency level among industry members:
Most competitors are very efficiently cost-sensitive
managed and would not have a lot of costs saving
potentials.
e) prevalence of competitive strategies on other than
price: In my market NOT the one who offers the lowest
prices is most successful.
f) capacity utilisation level: Most firms in the industry
could increase sales easily by 30% with their current
resources.
g) employee productivity: Turnover per employee is
very high in our industry.
h) labour costs intensity: In our industry the largest share
of costs are labour costs.
i) brut margin: The brut margin is very high in our
industry.
j) vertical integration: Products are bought directly from
creator / manufacturer of product / service or the company
that buys from manufacturer. No more distribution levels
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402
are included in the distribution process.
k) degree of diversification: Most large competitors are
also operating in other markets.
l) average age of industry members is very high
m) average size of industry members is very large
n) variable costs are major share of total costs.
TOP 2 factors from competitor dynamics
-- with positive impact on your firm
-- with negative impact on your firm
APPENDICES
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BARRIERS TO ENTRY
a) inventory intensity: New firms entering the market need
necessarily to invest in a very high inventory.
b) fixed asset intensity: New firms entering the market
need necessarily to invest a lot in building, furniture, or
machinery.
c) minimum organizational size & complexity: It is not
possible to start a company with 2 people in this market.
d) accessibility of distribution channels: In our market is a
clearly defined customer target group which can be easily
reached by advertising in specialised magazines or can be
identified from yellow pages.
e) loyalty of customers: clients do generally not like to
change because of small differences in price or quality
f) legislative barriers: Access to market is restrictively
regulated by law
g) product sophistication: The offered product is very
sophisticated and requires a lot of specialised knowledge
TOP 2 factors from competitor dynamics
-- with positive impact on your firm
-- with negative impact on your firm
You
better
1. products/services
a) relative price (better=lower price)
b) relative quality
c) relative design
d) innovation potential (potential to offer completely new
product/service)
2. firm resources
a) relative financial strength
b) relative knowledge / patents
3. management team
a) relative capability of management team
b) relationships key partners
c) relevant industry experience
Competitors
better
V. VENTURE / FIRM LEVEL
APPENDICES
4. strategy
a) relative growth aspirations/aggressiveness
5. location
a) relative distance to clients (who is closer?)
b) availability of labour and production factors
c) relative costs of labour and production factors
403
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APPENDICES
PART III: Focus on key factors
Key factors
o Is it now easier or more difficult to enter the market than at the moment when
you started? Why?
o Please comment in more detail the impact of the following variables on the
growth of your venture: export balance, distance to clients, entries to industry,
net entries, uncertainty, employee productivity?
o How do you evaluate the impact of the following variables on venture profit:
entries to industry, exits from industry, uncertainty, number of industry
members (employee productivity, average number of employees at startup,
investment intensity?
o Discussion of top5, top5 from former list.
Strategy
o Ad 5.1: How was relative price/quality compared to main competitors upon
startup and how is it today? Why did it change?
o How far is your strategy different from your competitors?
APPENDICES
Appendix H: List of interviewees
Imente Global S.L., Girona/Spain
o Prof. Dr. Christian Serarols
founder and general director
date of interview: 3. October 2003
o Qim Pagans
founder and technical director
date of interview: 3. October 2003
o Oscar Trasbazos
marketing director
working at imente for 2 months
date of interview: 3. October 2003
Open House Spain S.L., Barcelona/Spain
o Claudia Eleuterio
director and founder
date of interview: 7. October 2003
o Jörg Lahmann
co-director
working at Open House for 1 year
date of interview: 8. October 2003
Teleruf GmbH, Bonn/Germany
o Stefan Martinstetter
director and founder
date of interview: 28. October 2003
o Christine Haberland
PR & marketing manager
working at Teleruf for 3 years
date of interview: 28. October 2003
405
Curriculum Vitae
Name:
Daniel Spohn
Born:
29.6.1972 in Leverkusen, Germany
Education
1999
European
Doctoral
Programme
in
Entrepreneurship,
Universitat Autonoma de Barcelona, Spain and Växjo
University, Sweden
1993-1998
Studies of information management at University of St. Gall,
Switzerland, degree: lic.oec.HSG
1996
Exchange semester at MBA program of Rensselaer Polytechnic
Institute New York, USA
1992-1993
Courses in business and law, Fernuniversität Hagen, Germany
1983-1992
Friedrich Wilhelm Gymnasium Cologne, Germany
Practical Experience
Since 1999
Founder manager LanguageCourse S.L. Barcelona, Spain
Since 1999
Co-founder manager Gebr. Spohn GmbH Cologne, Germany
1996-1998
Internships at Mercedes Benz AG (South Africa), Roland
Berger
Strategy
Consultants
(Germany),
(Germany), Euro-Asia Consulting (China)
VTC
Consult