Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Effectiveness of Business R&D in Emerging Economies, in
Search for Individual Effects
Marek Martin1
In the era of knowledge based economy the problem of optimal allocation of limited
resources allocated to various categories of innovative and R&D effort at the firm level
seems to be of paramount importance especially for emerging, transition and catching up
economies. This paper is the continuation of research work aiming to assess, on the
basis of estimation of transformation of Cobb-Douglas production function the
effectiveness of business innovation and R&D expenditures of manufacturing enterprises
located in Poland.
The main purpose of the research is to deepen the level of understanding of the factors
affecting the effectiveness of business innovation effort in the case of emerging and
transition economies and especially provide an insight, on the basis of estimation of
econometric models, into the area of individual effects of business R&D effort.
The survey is based on relatively large data basis obtained from public statistics (The
Central Statistical Office). The research is based on the sample of firms active in the field
of R&D and cover the period between year 2000 and 2012. The initial sample of firms
was narrowed down subject to availability if data and minimum R&D regularity to 453
firms observed over 13 years period.
The research identified very strong variation of the individual effects of business R&D
effort. In the process of econometric estimations individual effects were estimated in the
case of 123 out of 453 firms (27%). In the case of 330 firm no statistically significant
individual effect was estimated regardless the lag applied. In the case of just over 50% of
companies (62) the overall individual positive effect was identified, negative effect was
found in the case of 61 firms. On top of individualized results, study reviled the positive
and relatively strong overall (not individualized) effect of external business R&D.
JEL Codes: O31, O32 and Q55
1. Introduction
In the contemporary world the potential for creating innovations and taking
commercial advantage of the new technological developments and breakthroughs is
critical for the long term economic good and prosperity of national economies and
individual business units. The ability to generate innovations is to the large extent
based on research and development potential and effort as well as the skills and
abilities to transform results of R&D activities into new improved production
processes and new or superior products. Various studies regarding the issue of
innovation performance and effectiveness of research and development effort at the
firm level deliver rather comprehensive picture and sometimes mixed results.
Scholars often underline firm specific and other than research and development
expenditure itself, factors that determine the effectiveness of innovative and research
and development efforts at the business level. Li and Atuahene-Gima (2001) found
out in the studies on the product innovation and the performance of new high-tech
firms in China that the effectiveness of the innovative activities of Chinese firms is to
the large extent determined by environmental factors and institutional support. On the
1
Dr Marek Martin, Division of Economics, Faculty of Organization and Management, Lodz University
of Technology, Lodz, Poland, e-mail: martin@p.lodz.pl
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
other hand A. Leiponen (2000) in the study related to the finish economy highlights
the fact that profiting from innovation requires strong complimentary capabilities
between R&D and i.e. marketing and manufacturing. The research suggest that even
in the field of micro-econometric studies, the more firm specific and individualised
approach to the problem of effectiveness of business R&D effort seems to be
required in order to identify more firm specific factors that determine the effects of
R&D effort on the microeconomic level.
This study is aiming to identify on the basis of the available data obtained from
national statistics, the individual effects of internal business research and
development of manufacturing companies located in Poland. The analysis and
considerations presented in this article are based on estimation of transformations of
Cobb-Douglas production function. This research seems to address the important
gap in the effectiveness of business R&D territory. As far as the issue of econometric
estimations of individualized effects of business R&D is concerned the research
evidence seem to be very scarce, especially in the case of emerging and transition
economies.
2. Literature Review
Business R&D effectiveness is a very complex and multidimensional issue. Results
of research and development effort are in many cases and aspects indirect and
difficult to measure.
According to The World Economic Forum Global Competitiveness Report 2012-13
(2013) Poland is still in transition between the effectiveness and innovation-driven
stages of economy, which theoretically should limit the general effectiveness of
business innovation and R&D effort. The main reasons for this situation include the
relatively low innovative potential and infrastructure, together with the fact that
Poland‟s economy is to a large extent driven by basic factors of production (factordriven economy). The development level of the local economy might at least in theory
hinder the effectiveness of innovation and R&D undertaken by business entities,
because at this stage of development more effective paths to increased productivity
and competitiveness are related to raising the efficiency of production processes and
higher product quality. Which in turn may result in higher investment in effectiveness
oriented options, rather than accepting the higher risk in the field of innovation and
R&D. According to European Innovation Scoreboard (2006) the level of business
innovation and R&D spending is rather low by international standards, and Poland‟s
economy is classified as the catching up economy in that respect.
The research evidence devoted to the issue of effectiveness of business research
and development effort is fairly comprehensive especially in the case of developed
countries. On the basis of the research evidence it is generally recognized that
innovation, and research and development, improve the competitiveness and
economic wellbeing of probably all national economies. The effects of innovation at
the macroeconomic level are in general unquestionable in today‟s world. In the
process of classical (from the current point of view) research, it has been estimated
that innovation, and especially the commercial applications of science and
technological developments, account for up to 75% of economic growth. According to
E.F. Denison (1962), social wealth is determined by technical progress in up to 90%.
These findings are in line with more recent studies and the economic findings done
by R.M. Sollow (1957) and P.M. Romer (1990), who indicate technical change as a
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
major source of long-term productivity growth. According to L. Sveikauskas (2007),
the overall rate of return to R&D is quite impressive; it is estimated at 25% for private
returns and at a total of 65% in terms of overall social returns.
Nevertheless, the issue of transferring a company‟s innovation expenditure into sales
growth and product development is by no means straightforward at the
microeconomic level. The results of business innovation and R&D are often unclear,
indirect, and difficult to measure. Drake, Sakkab (2006), Jonash (2006), stress the
importance of so called “behind innovation”. In that respect effectiveness of
commercialization strategy is vitally important. The cost of innovation must include
creation of novelty, development and commercialization. The overall effect of
innovation process depends on proper product positioning, well-tailored pricing policy
along the product life cycle. Therefore identification of effective individualized paths
for maximizing return on business innovation seems to be relatively important issue.
On top of their impact on the firm‟s market value, innovation and R&D may have an
influence on the firm‟s financial performance in terms of income and sales growth.
Other research indicate the relationships between R&D and firm‟s financial
performance. According to Brenner (1989) and Rushton (1989) the firms which have
higher R&D spending obtain the higher average sales growth than the market
average. A study done by M. Pianta (2007) and A. Vaona (2007) indicates important
differences in terms of innovation-related factors determining the productivity growth
of European companies in general (represented in the survey by Austrian, French,
Dutch, and British firms) and of Italian companies in particular. This may suggest
important variation regarding factors determining the effects of innovative activities
even among advanced economies representing a similar level of economic
development.
In another study Hashia (2013) and Stojcicc (2013) found that there is a positive
relationship between innovation activities and productivity. In making decisions
regarding innovation activities firms rely on the knowledge accumulated from
previously abandoned innovations and cooperation with other firms and institutions
and other members of their group. Results of the study reveal several differences in
behavior of firms in two groups of countries. Western Europe and advanced transition
economies from Central and Eastern Europe.
Triguero (2013) and Corcoles (2013) found that R&D (input) and innovation (output)
are highly persistent at the firm level. Among external/environmental factors, market
dynamism affects R&D and innovation. Past innovative behavior is clearly more
decisive in explaining the current state of R&D and innovation activities than external
factors or firm-level heterogeneity.
The research done by Pandit (2011), Wasley (2011) and Zach (2011) indicate that
firms which have more productive R&D, exhibit higher and less volatile future
operating performance. One contribution of this study is that it demonstrates that the
relation between R&D expense (input) and future operating performance is better
understood by incorporating information about the productivity (output) of a firm‟s
R&D outlays in the form of patent counts and citations. Gundaya, G (2011), Ulusoya,
G (2011), Kilic K (2011) and Alpkan, L (2011) studied effects of the organizational,
process, product and marketing innovations on various aspects of firm performance
in Turkey. The results indicate the positive effects of all four types of innovation on
firm performance in manufacturing industries.
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
3. The Methodology and Model
The survey presented in this paper, regarding the individualised effectiveness of
business research and development activities in the case of emerging economies, is
based on the estimation of the regression function that is based on the
transformation of Cobb-Douglas production function. The final version of the linear
mathematical regression model was created on the basis of logarithm of both sites of
classical Cobb-Douglas equation. The model is based on relative increases (i.e.
expenditures), instead of levels of capital. This approach is expected to better
capture and reflect small changes of independent variables and is easier to
implement and estimate. The model, despite the fact that it is easy to implement and
estimate, allows for decomposition and examination of the impact of various
categories of independent variables (innovation expenditure) over specified
dependent variable, for details see Martin (2010, 2011, 2012, 2013, 2013a, 2014,
2015). The selection of dependent variable allows for examination of unique and
potentially practical relationships. For the purpose of the study the dependent
variable was defined as the relative growth of sales. The data utilized in the
estimation of the regression functions is obtained from public statistics (The Central
Statistical Office) and represents the time series of innovation and R&D expenditures
and output measures exemplified by sales and sales relative growth. The
econometric estimations are based on the total sample of 453 firms active in the field
of R&D over the observed 13 year period. The data cover the period between year
2000 and 2012. Estimations include the 0 to 3 years lag of independent variables
which results with the maximum of 4077 observations for the sample of firms taken
under consideration in estimations presented in the paper. The impact of innovation
expenditure on sales or profit growth is not contemporaneous. The actual lag
between the expenditure and the observed effect exemplified by sales relative growth
may vary significantly depending for instance on a given branch of industry. In
various cases the lag may extend to more than 10 years, like in some intensively
knowledge based industries i.e. biotechnology. The 3 year maximum lag applied
represent a hopefully realistic trade-off between the data, model limitations and
estimation procedures (individualised estimations require larger number of degrees of
freedom) on one hand and maximum expected lag between innovation expenditure
and its effects in the case of certain firms included in the sample. It seems to be
advisable to further investigate the issues of more lagged effects in the course of the
future research. The firms covered by the survey represent medium and large (by EU
standards) manufacturing companies located in all parts of Poland. Only medium and
large companies, that employ 50+ persons are taken into consideration in the study,
since smaller (employing less than 50 persons) are not covered by the yearly survey
of innovative activities (PNT-02 survey) carried out by The Central Statistical Office in
Poland.
The initial version of the regression function is specified underneath.
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
EMPt  EMPt 1
R & DIntt
R & DExtt
NIEt
Sales*t  Sales*t 1
  0  1
 2
 3
 4
*
EMPt 1
yt
yt
yt
Sales t 1
 5
RIEt
yt
Description of Variables:
Sales – Total sales revenue,
EMP – total employment,
R&DInt – internal research and development expenditure,
R&DExt – external research and development expenditure,
NIE – non-innovation capital expenditure,
RIE – remaining innovation expenditure, that includes all the others categories of
innovation expenditure (innovation expenditure on new technologies, innovation
expenditure on software, innovation expenditure on buildings (associated with
innovative activities or investment), innovation expenditure on domestically made
machinery, innovation expenditure on imported machinery, innovation expenditure on
training (associated with innovative activities or investment), innovation expenditure
on marketing).
On the basis of available data and common standards, business R&D expenditure is
divided into two broad categories:
• Internal business research and development: Activities carried out by a particular
business entity regardless of the sources of funds utilized to finance them.
Internal R&D expenditure includes both running costs and capital expenditure.
• External business research and development: includes R&D activities financed
by a given firm and performed outside of a particular business entity by both
domestic and foreign contractors.
In the process of model testing and refinement independent variables included in the
initial version of the model were proposed to the model in various combinations. The
software package GRETL, that was taken advantage of for econometric calculations
and estimations, that includes, among others features, the procedure for identification
and exclusion of collinear variables from the model.
4. The Findings
In order to estimate the individual effects of internal business R&D a number of
rounds
of econometric calculations were executed. The table 1 presents the results of model
estimation. The table 1 includes only statistically significant non-individualised
variables. Maximum up to three years lag was applied. The research sample consists
of 543 firms and 4077 observations. The detailed statistics (values of the coefficients,
standard errors, T statistics and p levels), for non-lagged and up to 3 years lagged
individualised effects of internal business research and development expenditure are
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
omitted in the table 1 for paper limitation purposes (the inclusion of detailed
individualised statistics would make the table and paper around 5 pages longer).
The transformed individualized effects of internal business R&D are presented in the
table 2.
Table 1: Results of the Model Estimations
Estimated
Value of the Standard
coefficient
coefficient
error
Const
0,0630913
0,00629331
Employment relative
0,759009
0,0266033
growth non-lagged
Employment relative
growth lagged two
-0,0519818
0,0241069
years
R&D EXT to Sales
2,07485
0,566851
lagged one year
NIE to Sales non-0,169109
0,0445149
lagged
NIE to Sales lagged
0,110872
0,0447869
two years
RIE to Sales non-0,171238
0,0416508
lagged
RIE to sales lagged
5,33152e-07 1,58249e-07
two years
T statistics
p level
10,0251
<0,00001
***
28,5306
<0,00001
***
-2,1563
0,03113
**
3,6603
0,00026
***
-3,7989
0,00015
***
2,4755
0,01335
**
-4,1113
0,00004
***
3,3691
0,00076
***
Note: ***, **, and * indicate significance levels of 1, 5 and 10 percent, respectively.
Dependent variable average = 0,059726
Standard deviation of dependent variable = 0,248862
Sum of squares of residual = 119,4740
Standard error of residual = 0,187372
Within R2 = 0,464260
LSDV R2 = 0,526714
LSDV F(673, 3403) = 5,627276
Value of p for F test = 1,1e-252
Log likelihood ratio = 1410,930
Akaika information criterion = -1473,859
Schwarz-Bayesian information criterion = 2781,181
Hannan-Quinn information criterion = 32,98187
Autocor of residual - rho1 = -0,197753
Stat. Durbin-Watson = 2,152028
The statistically significant (at the minimum 10% significance level) non-lagged and
lagged up to three years effects of individualized (individual firm specific) estimations
(values of the coefficients) are presented in the Table 2, values of non-lagged and
lagged up to three years coefficients are added together and in the process the
compound individual effectiveness index was calculated for each individual company,
that allowed to create the ranking of companies subject to decreasing compound
internal business R&D effectiveness index. For the purpose of the paper limitations
values of standard errors, T statistics and p levels are excluded from the Table 2.
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Table 2: Estimated individualised effects of internal business research and
development activities. Ranking of companies subject of compound internal
business R&D effectiveness index
Non lagged
effects for
individual
companies
A
B
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
n313
n181
243,9
700,4
n419
-32,4
n6
316,3
One year lagged
Two years
effects for
lagged effects for
individual
individual
companies
companies
A
B
A
B
n117_2 1201,7
n378_1 1495,9 n378_2 -881,3
n313_1
189,5
Three years
The sum of
lagged effects for
estimated
individual
coefficients
companies
A
B
Sum of B
n117_3
516,7
1718,3
n378_3
607,0
1221,6
n313_3
610,7
1044,0
700,4
n419_1
n419_3
n302_3
n388_3
85,0
n419_2
n275_1
812,5
n397_2
-404,5
41,2
529,2
122,2
n138_1
117,0
117,0
n87_1
n250_1
n119_1
-35,5
-54,4
92,3
n137
41,2
n137_1
39,0
n426_1
48,0
n87_2
105,0
n250_3
n395_1
n298_1
n199_1
n426_2
n135_2
27,9
58,7
250,8
68,9
42,8
n300_2
n227_2
28
29
30
31
71,6
32
33
34
35
n88_3
34,1
n202_3
49,0
n199_3
1,0
40,0
37,5
n214
-21,6
n7_2
n409_1
n214_1
n375_1
32,6
49,0
45,1
44,8
43,9
-37,0
28,2
-14,0
31,9
31,5
28,2
27,9
31,9
24,6
n291_2
n375_2
75,8
58,7
51,6
40,0
37,5
35,6
34,6
n414_3
n7
65,5
110,1
102,8
92,3
80,2
17,5
-205,8
-24,1
239,6
222,1
200,1
170,2
n221_1
n364_1
40,7
91,7
n395
n298
222,1
73,9
170,2
150,7
24
25
26
27
447,5
333,3
332,6
150,7
124,7
122,2
n87
n250
n88
283,1
333,3
332,6
-572,8
n224_3
n275_3
n326_3
n238_2
n221
325,3
316,3
n397_1
n275
-128,5
35,6
n214_3
28,5
n375_3
9,4
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
36
37
38
39
40
41
42
43
n402
-23,0
n209
-14,9
n402_1
n349_1
n295_1
36,3
39,5
44
n242_1
5,2
45
46
47
n213_1
n73_1
n398_1
9,5
137,3
-27,2
49
50
51
n73
n310
n198
-25,9
-66,5
5,0
2,2
60
61
62
63
64
65
66
67
n256
n101
n26
n165
n146
n169
-51,8
-82,3
-16,7
-18,2
-454,4
-3,5
68
n172
n105
-11,2
21,1
69
70
71
n151
n247
n189
-24,3
-11,0
-4,7
72
n23
-4,7
73
74
75
76
n248
-3,0
77
-8,5
n402_3
23,8
n209_3
n76_3
39,9
24,9
n437_2
n255_2
n133_2
n398_2
n198_2
n153
n354
-5,6
-52,2
n292_1
70,3
n97_1
4,3
27,9
25,3
24,9
24,9
13,1
12,5
-57,6
-79,4
53,1
n255_3
n133_3
59,0
50,3
12,5
11,7
10,5
n242_3
4,9
10,1
-61,4
-18,9
9,5
9,4
6,9
n73_3
n398_3
2,6
n107_3
52
53
54
55
56
57
58
59
n402_2
13,1
n255_1
n133_1
48
n255
35,6
25,3
n292_2
4,5
-65,9
n301_3
3,6
n287_3
-10,1
4,3
3,6
3,2
3,0
n445_1
n287_1
-6,8
13,1
n445_2
n256_1
n101_1
n26_1
57,9
84,4
18,1
n256_2
108,5
n256_3
-111,9
n86_2
n363_2
1,2
-7,3
n363_3
8,4
2,7
2,1
1,4
1,2
1,0
-2,0
-0,1
-0,5
-0,5
n165_1
n146_1
n169_1
18,1
453,9
4,9
n131_1
-1,8
n167_1
3,1
n105_1
-27,3
n151_1
n247_1
39,6
12,0
10,0
5,0
4,8
4,5
4,4
n169_3
-1,8
n167_2
n172_2
n105_2
-5,4
8,6
15,5
n151_2
-18,5
n279_3
-2,3
n105_3
-12,4
n247_3
-4,3
-2,3
-2,4
-2,6
-3,0
-3,1
-3,3
-4,7
-4,7
n248_3
n354_1
n82_2
-5,3
n226_2
-6,5
46,1
-2,2
-5,2
-5,3
-5,6
-6,2
-6,5
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
78
79
n60
n203_1
-1,1
-15,7
n60_2
n203_2
-5,4
-6,5
80
81
82
83
n319
-54,7
84
n319_1
n299_1
39,2
-11,2
n126_1
-11,8
85
86
87
n3
n66
11,6
-15,6
n3_1
-26,6
88
n265
-14,6
n265_1
13,6
n197_1
-17,2
n385_1
6,9
89
90
91
n257
n385
-18,7
-17,8
92
n332
-22,0
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
n25
n11
n134
n120
202,6
n442_1
-32,0
n63_2
n3_2
n385_2
n451_2
n65_2
48,3
-24,2
-12,1
30,9
14,6
n264_3
-8,4
-8,4
n58_3
n319_3
-57,3
29,7
-9,0
-10,0
-11,2
n3_3
-30,4
n265_3
-15,1
-8,1
-23,4
-229,5
-36,4
-47,2
-49,9
-67,8
31,7
-75,0
-36,4
-47,2
-49,9
n453_1
-63,8
n453_3
n246_2
n261
n450
-224,0
965,8
112
n124
-153,0
n220
116
n130
n211
-67,0
-38,2
-67,8
-70,2
-75,0
-82,8
-82,8
545,3
-98,7
-110,2
-112,8
n13_1
-655,6
n196_1
-137,3
n13_2
-112,8
n176
n12
113
114
115
-67,0
-98,7
109
110
111
n176_1
-137,3
317,3
n400_1
n220_1
n93_1
-307,0
179,8
-630,5
-4944,9
n130_1
3456,1
-8564,7
n111_1
n211_1
-941,6
-5107,7
-797,6
-16,1
-22,0
-23,4
-26,9
-31,0
-32,0
n70_3
n368
n453
n421
-11,8
-12,1
-14,4
-15,6
-17,2
-18,7
-19,0
-31,0
108
117
118
n65_1
n58_2
n319_2
n203_3
-6,5
-7,5
A - Number of company and lag applied
n93_2
-125,4
n269_3
n176_3
n12_3
-160,1
-275,9
-1192,7
-160,1
-182,6
-226,9
n124_3
-139,2
-292,2
55,5
-307,0
-617,8
-700,5
n93_3
-1488,8
n111_2
n211_2
-1831,4
-7223,8
n211_3
-8157,6
-2772,9
-29053,8
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
B – Value of the estimated coefficient
In the process of extensive estimations of initial version of the model the statistically
significant individual estimations and values of coefficients for 118 companies out of
453 firms initially qualified for the survey sample were calculated. As far as not
individualized results of model estimations are concerned one can draw the following
conclusions: (1) the overall (not individualized) effectiveness of external business
R&D lagged one year is highly statistically significant (at 1% level of significance) and
the value of the estimated coefficient equals 2,07, (2) the statistically significance
values of estimated coefficients for remaining innovative expenditure (RIE) were
calculated for non-lagged effects and two years lagged effects. Both close to zero
effect, slightly negative for non-lagged effect and marginally positive for two years
lagged effect, (3) the statistically significance values of estimated coefficients for noninnovative expenditure (NIE) were calculated for non-lagged effects and two years
lagged effects. The values of both coefficients are also close to zero effect, slightly
negative for non-lagged effect and slightly positive for two years lagged effect. The
buildup of positive effects of various categories of business capital expenditure
(innovative or non-innovative) for more lagged estimations is in line with general
understanding of the problem and other research evidence.
The interpretations of statistics and coefficients for lagged and non-lagged relative
employment growth were omitted as inconsequential from the point of view of the
specified field of research.
5. Summary and Conclusions
The results of the econometric estimations presented in the paper proved to be
promising in terms of the model and method ability to investigate individual (single
firm specific) effects of business R&D effort. In the process of econometric
estimations individual effects were obtained in the case of 123 out of 453 firms
(27%). In the case of 330 firm no statistically significant individual effect was
estimated regardless the lag applied. The research identified very strong variation of
the individual effects of business R&D effort. In the case of just over 50% of
companies 62 out of 123 the overall individual positive effect was identified, negative
effect was found in the case of 61 firms. The study also allowed to identify the
positive and relatively strong overall (not individualized) effect of external business
R&D and statistically significant but marginal in terms of their impact over dependent
variable effects of non-innovative capital expenditure and remaining innovative
expenditure. The slight build-up of positive effects in line with the larger lag applied
was observed in this territory.
The prospects of the future research in the specified field seem to be quite significant
and might include the model modification and application of different dependent
variables. The identification of individual characteristics of selected business units,
although potentially very interesting and promising, might encounter formal issues
related to confidentiality of data.
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Acknowledgments
The research paper was funded by National Science Centre in Poland under the
project number N N112 316238 with the funds allocated to science in the years 20102013 as the research project. Author would like to express special thanks to Prof. Jan
Jacek Sztaudynger from Faculty of Economics and Sociology of University of Lodz
and Prof. Marek Szajt from Faculty of Management of Czestochowa University of
Technology for their support in the field of model specification and refinement.
Without this support and suggestions the work that has led to this and other research
papers would have been impossible. Author would like also to thank the Lodz
Statistical Office for the support and provision of data regarding the input and output
measures of manufacturing enterprises and especially Dr Artur Mikulec for his
important support in the field of data handling and processing.
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