Research Methods in Business Studies
This accessible guide provides clear and practical explanations of key
research methods in business studies, presenting a step-by-step approach to
data collection, analysis, and problem solving. Readers will learn how to
formulate a research question or problem, choose an appropriate research
fi
method, argue and motivate, collect and analyse data and present ndings in
a logical and convincing manner. The authors evaluate various qualitative
and quantitative methods and the consequences of their use, guiding readers
to a deep understanding of the most appropriate research design for
particular questions. Furthermore, the authors provide instructions on how to
write reports and dissertations in a clearly structured and concise style. Now
in its
fi
fth edition, this popular textbook includes new and dedicated chapters
on data collection for qualitative research, qualitative data analysis, data
collection for quantitative research, multiple regression, and additional
methods of quantitative analysis. In addition, cases and examples have been
updated throughout, increasing the applicability of these research methods
across various situations.
Pervez Ghauri is
Professor of International Business at the University of
Birmingham. Pervez is the Editor-in-Chief of the International Business
Review and Consulting Editor for the Journal of International Business
Studies. Pervez has published more than 30 books and more than a hundred
articles in top level journals.
Kjell Grønhaug is Professor Emeritus, Norwegian School of Economics. He is
Honorary Professor at the Turku School of Economics and Business Adm.,
Gothenburg School of Business Adm. and Stockholm School of Economics.
His publications include 18 authored and co-authored books and some
200 articles in leading European and American journals.
Roger Strange is
Professor of International Business at the University of
Sussex Business School. He is an Associate Editor of the International
fi
Business Review, a Senior Editor of the Asia Paci c Journal of Management,
and on the Editorial Boards of the Journal of World Business, the Global
fi
Strategy Journal, and the Asia Paci c Business Review. His publications
include 12 authored or edited books, and numerous book chapters and
articles in leading international journals.
Research Methods in
Business Studies
Pervez Ghauri
Kjell Grønhaug
University of Birmingham
Norwegian School of Economics and Business Administration,
Bergen-Sandviken
Roger Strange
University of Sussex
University Printing House, Cambridge CB2 8BS, United Kingdom
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Cambridge University Press is part of the University of Cambridge.
It furthers the University’s mission by disseminating knowledge in the pursuit of
education, learning, and research at the highest international levels of excellence.
www.cambridge.org
Information on this title: www.cambridge.org/9781108486743
DOI: 10.1017/9781108762427
© Pervez Ghauri, Kjell Grønhaug, and Roger Strange 2020
This publication is in copyright. Subject to statutory exception
and to the provisions of relevant collective licensing agreements,
no reproduction of any part may take place without the written
permission of Cambridge University Press.
First published 2020
Printed in Singapore by Markono Print Media Pte Ltd
A catalogue record for this publication is available from the British Library.
Library of Congress Cataloging-in-Publication Data
Names: Ghauri, Pervez N., 1948– author. | Grønhaug, Kjell, author. |
Strange, Roger, author.
Title: Research methods in business studies / Pervez Ghauri, University of Birmingham,
Kjell Grønhaug, Norwegian School of Economics and Business Administration,
Bergen-Sandviken, Roger Strange, University of Sussex.
Description: Fifth Edition. | New York : Cambridge University Press, 2020. |
Revised edition of the authors’ Research methods in business studies, 2010.
Identifiers: LCCN 2019037291 (print) | LCCN 2019037292 (ebook) |
ISBN 9781108486743 (hardback) | ISBN 9781108708241 (paperback) |
ISBN 9781108762427 (epub)
Subjects: LCSH: Management–Research. | Business–Research.
Classification: LCC HD30.4 .G49 2020 (print) | LCC HD30.4 (ebook) | DDC 658–dc23
LC record available at https://lccn.loc.gov/2019037291
LC ebook record available athttps://lccn.loc.gov/2019037292
ISBN 978-1-108-48674-3 Hardback
ISBN 978-1-108-70824-1 Paperback
Cambridge University Press has no responsibility for the persistence or accuracy
of URLs for external or third-party internet websites referred to in this publication
and does not guarantee that any content on such websites is, or will remain,
accurate or appropriate.
Contents
List of Figures
List of Tables
Preface
Acknowledgements
Part I Challenges and Ambiguities of Business Research
1 Introduction
page
xii
xiv
xv
xviii
1
3
1.1
Purpose of the Book
3
1.2
Readership
5
1.3
On the Use of the Book
7
2 Research in Business Studies
8
2.1
The Nature of Knowledge Development
9
2.2
Why We Do Research
10
2.3
Research versus Common Sense
12
2.4
Different Research Orientations
16
2.5
Induction, Deduction, and Abduction
18
2.6
What Comes First: Theory or Data?
21
2.7
Knowledge Skills and Research
22
2.8
Why Research Never Stops
23
2.9
Research and Ethics
23
2.10
The Researcher and the Respondent
25
2.11
The Researcher’s Moral Responsibility
27
Further Reading
30
Questions
30
Exercises
30
Part II The Research Process
31
3 The Research Process
33
3.1
The Process Perspective
33
3.2
Levels of Research
36
3.3
Research and Knowledge
37
vi
Contents
3.4 What Comes First: Theory or Research?
38
3.5 Some Important Issues
39
3.5.1 Concepts
39
3.5.2 Definitions
39
3.5.3 Theory
41
3.5.4 Methods
42
3.6 Concluding Remarks
43
Further Reading
44
Questions
44
Exercises
44
4 Research Problems
45
4.1 Research Ideas
45
4.2 Wrestling with Research Problems
47
4.3 Research Problem and Research Purpose
50
4.4 Models in Research
52
4.4.1 General Concepts and Specific Use
4.4.2 Model Purposes
53
54
4.5 The Role of Reviewing Past Literature
57
Further Reading
60
Questions
60
Exercises
60
5 Research Design
61
5.1 The Design Problem
61
5.2 Problem Structure and Research Design
62
5.2.1 Exploratory Research
63
5.2.2 Descriptive Research
64
5.2.3 Causal Research
64
5.3 The Classic Experiment
66
5.4 Validity Threats
67
5.5 Other Research Designs
71
5.5.1 Cross-Section Data
71
5.5.2 Time-Series Data
72
5.5.3 Longitudinal Studies
73
5.5.4 Panel Data
73
5.6 Requirements in Research Design
5.6.1 Research and Choices
74
76
Further Reading
77
Questions
77
Exercises
77
Contents
6 Measurements
78
6.1 The Operationalization of Variables
78
6.2 Scales of Measurement
81
6.2.1
Nominal Level (Scale)
81
6.2.2
Ordinal Level (Scale)
82
6.2.3
Interval Level (Scale)
82
6.2.4
Ratio Scale
83
6.3 Construct Measurement Using Multi-Item Scales
83
6.4 The Reliability and Validity of Measurements
85
6.5 Improving Your Measurements
87
6.6 Measurements in Qualitative Research
88
Further Reading
91
Questions
91
Exercises
91
Part III Implementation
93
7 Data Collection for Qualitative Research
7.1 Qualitative versus Quantitative Methods
95
96
7.2 Different Qualitative Methods and When to
Use Them
98
7.2.1
Historical Review
99
7.2.2
Grounded Theory
100
7.2.3
Case Studies
101
What Is a Case Study?
101
When to Use a Case Study
103
Preparing for a Case Study
104
How to Select the Cases
106
Sampling in Qualitative Research
108
Theoretical Sampling
110
Different Types of Case Study Design
110
7.3 Observations
111
7.4 Communication
114
7.5 Interviews
115
7.5.1
Preparing for an Interview
117
7.5.2
Pre-Interview
120
7.5.3
The Interview
121
7.5.4
Post-Interview
123
7.6 Focus Groups
7.6.1
7.6.2
124
Advantages and Disadvantages of Focus
Groups
126
Conducting Focus Groups
126
vii
viii
Contents
Further Reading
128
Questions
128
Exercises
128
8 Qualitative Data Analysis
129
8.1 Characteristics of Qualitative Research
129
8.2 Qualitative Data
130
8.3 Analytical Procedures
131
8.3.1
Analytical Activities
8.3.2
Data Reduction
134
139
8.4 Interpretation
139
8.5 Strategies for Qualitative Analysis
140
8.5.1
Departure from Theory
140
8.5.2
Departure from Data (Observations)
142
8.5.3
Analysing Case Studies
142
8.6 Quantifying Qualitative Data
143
8.7 Validity in Qualitative Research
146
8.8 The Use of Computers in Qualitative Research
146
8.8.1
A Word of Warning
147
8.9 Case Studies and Triangulation
147
8.10 Case Studies and Generalization
150
Further Reading
152
Questions
152
Exercises
152
9 Data Collection for Quantitative Research
9.1 Secondary Data
153
154
9.1.1
The Advantages of Secondary Data
158
9.1.2
The Disadvantages of Secondary Data
158
9.2 Primary Data
9.2.1
The Advantages of Primary Data
9.2.2
The Disadvantages of Primary Data
159
160
160
9.3 Why Take Samples?
161
9.4 Probability Sampling
163
9.4.1
Simple Random Sampling
163
9.4.2
Systematic Sampling
164
9.4.3
Strati ed Random Sampling
164
9.4.4
Cluster Sampling
164
9.4.5
Non-Response Bias
fi
9.5 Non-Probability Sampling
165
166
Contents
9.6 Sample Size
168
9.7 Survey Research
169
9.7.1
Survey Administration
171
9.7.2
Questionnaire Design
172
9.7.3
Guidelines for Constructing Questionnaires
175
9.7.4
Improving Response Rates
178
9.8 Experimental Studies
179
Further Reading
181
Questions
181
Exercises
181
10 Description and Preliminary Analysis of Quantitative Data
10.1 How to Describe the Data
182
183
10.1.1
Bar Charts, Histograms, and Scatter Plots
183
10.1.2
Measures of Central Tendency
187
10.1.3
Measures of Dispersion
188
10.1.4
Measures of Skewness
190
10.2 How to Analyse the Data
191
10.2.1
Hypothesis Testing
191
10.2.2
Comparison of Means (Independent Samples)
195
10.2.3
Comparison of Means (Paired Samples)
197
10.2.4
Correlation Coef
199
10.2.5
Correlation Coef cient (Ordinal Variables)
204
10.2.6
Correlations between Nominal and/or Ordinal Variables
206
ficient (Continuous Variables)
fi
10.3 The Use of Computers in Quantitative Research
206
Further Reading
208
Questions
208
Exercises
208
11 Multiple Regression
210
11.1 The Determinants of Export Performance
211
11.2 The Regression Model
212
11.2.1
Operationalization of the Variables
213
11.2.2
Functional Form
215
The Linear Relationship
215
The Semi-Logarithmic Relationship
216
The Quadratic Relationship
217
Time Lags
219
11.2.3
11.3 Estimation Methodology
11.3.1
OLS Estimation
219
220
ix
x
Contents
11.3.2 The Coefficient of Determination
221
11.3.3 The Assumptions Underpinning OLS Estimation
223
The Omission of Relevant Explanatory Variables
224
Multicollinearity
226
11.3.4 The Residual Plot
228
11.3.5 Estimation Methodology with Nominal or Ordinal
Dependent Variables
11.4 Hypothesis Testing
11.4.1 Testing for the Significance of Individual Regression
230
231
Coefficients
231
Explanatory Variables
232
11.4.2 Testing for the Joint Significance of Several
12
11.4.3 Testing for Common Method Variance
234
11.4.4 Statistical Significance and Effect Sizes
234
11.5 The Determinants of Export Performance Redux
236
11.6 What Constitutes a Good Regression Model?
239
Further Reading
241
Questions
241
Exercises
241
Additional Methods of Quantitative Analysis
243
12.1 Factor Analysis
243
12.1.1 Exploratory Factor Analysis
244
12.1.2 An Example of Factor Analysis: Burnout on Oil Rigs
246
12.2 Structural Equation Modelling
12.2.1 Estimation of Structural Equation Models
247
250
12.2.2 An Example of Structural Equation Modelling: Green
Innovation
12.3 Event Studies
252
253
12.3.1 The Estimation Procedure
254
12.3.2 The Applicability of Event Studies
256
12.3.3 An Example of an Event Study: Cross-Border
Acquisitions
13
258
12.4 Final Comments
259
Further Reading
261
Questions
261
Exercises
261
Cross-Cultural Research
262
13.1 The International Dimension
262
Contents
13.2
Data Collection in Some Cross-Cultural Research
265
13.3
Collecting Cross-Cultural Data
267
13.4
Analysis of Cross-Cultural Data
270
13.5
Enhancing Trustworthiness of Cross-Cultural Research
273
Further Reading
275
Questions
275
Exercises
275
14 Writing the Final Report
276
14.1
Purpose and Importance of a Written Presentation
276
14.2
Guidelines
277
14.2.1
Audience
277
14.2.2
Good Communication
278
14.2.3
Language and Flow
279
14.2.4
Form and Style
280
14.2.5
Headings
14.3
281
Structure of the Report
281
14.3.1
Title Page
282
14.3.2
Table of Contents
282
14.3.3
Executive Summary
282
14.3.4
Introduction and Problem Statement
283
14.3.5
Theoretical Background
283
14.3.6
Methodology
284
14.3.7
Findings
284
14.3.8
Conclusions
285
14.3.9
Footnotes
286
14.3.10
Bibliography and References
287
14.4
Oral Presentation
288
14.5
Writing for Publication
290
Further Reading
292
Questions
292
Exercises
292
Appendix A:
Sample of Manufacturing Firms, 2018
293
Appendix B:
Simple Comparative Tests
299
Appendix C:
Formulae
300
References
302
Index
319
xi
Figures
page 22
2.1
Theory or data as starting point
2.2
The wheel of research
24
2.3
Factors in
29
3.1
The research process
34
3.2
From research topic to research problem
35
3.3
Two levels of research
37
3.4
Production and use of theory
38
3.5
Example
41
3.6
Use of methods
42
4.1
Problem, methods, and research practice
50
4.2
Organizational chart (a model)
54
5.1
The classic experiment
69
6.1
Responses and sense-making
89
7.1
Quantitative and qualitative methods and techniques
98
7.2
Number of
7.3
The data structure
102
7.4
A process model for case research
105
7.5
Basic design for case studies
110
7.6
Choices for collecting primary data through observations
112
7.7
A typology of interviews
115
7.8
Steps in conducting a focus group
127
8.1
Components of data analysis: interactive model
132
8.2
Problem recognition
– raw
141
8.3
Case study research: theory building and theory testing
151
9.1
Planning a survey
170
9.2
An example of an open question
172
9.3
An example of a category question
173
9.4
An example of a multiple-choice question
174
9.5
An example of a ranking question
174
9.6
An example of a rating question
175
fluencing research
firms in the US transistor industry
materials (last purchase)
10.2
firm sizes by region
Distribution of fi rm sizes by sector
10.3
Symmetric and skewed distributions
10.1
Distribution of
100
184
184
186
Figures
10.4
Scatter plot of export intensity against firm size
187
10.5
Strong and weak correlations
201
11.1a
A positive linear relationship
215
11.1b
A negative linear relationship
216
11.2a
A positive semi-logarithmic relationship
217
11.2b
A negative semi-logarithmic relationship
217
11.3a
A U-shaped relationship
218
11.3b
An inverse U-shaped relationship
218
11.4
The sample regression line
221
numbers
225
11.5
11.6
The relationship between fire damage and fire fighter
A residual plot from a correctly specified regression model
11.7
A residual plot showing an incorrectly specified model
11.8
Residual plots indicating heteroskedasticity
11.9
Scatter plot of the residuals from the OLS regression of export
228
229
229
performance
239
12.1
A path diagram for structural equation modelling
249
12.2
PLS-SEM estimation of green innovation
253
12.3
The time line for event studies
255
12.4
Share price reactions to earnings announcements
257
13.1
Managing the international dimension in the research
13.2
process
263
Steps in operationalizing emic and etic approaches
269
13.3
De fining the unit of analysis
13.4
Cross-country data analysis
272
13.5
Ensuring equivalence in cross-cultural research
274
14.1
An example of a table of contents
283
271
xiii
Tables
page 18
2.1
Different research orientations
2.2
Ethical issues in the researcher–participant relationship
5.1
Cross-table
65
5.2
Reading of advertisement and purchase
71
5.3
A cross-sectional design
72
5.4
A time-series design
72
5.5
A panel design
74
6.1
Example of the operationalization of nominal variables
82
6.2
Example of the operationalization of ordinal variables
83
6.3
Summary of scales of measurement
84
7.1
The difference in emphasis in qualitative versus quantitative
methods
26
97
7.2
Strategies for case selection
109
7.3
Different qualitative approaches
112
8.1
An example of a matrix display in case study analysis
133
8.2
Concept list
144
8.3
The advantages of using NVivo in IB research
148
9.1
Selected publicly available secondary data sources
155
9.2
Selected commercially available secondary data sources
157
10.1
10.2
Summary statistics for the sample of 100 manufacturingfirms
Summary statistics for two sub-samples of manufacturingfirms
10.3
Decision criterion for the skewness coefficient
10.4
Possible errors in hypothesis testing
188
189
191
193
10.5
Comparison of firm export intensities by sector
10.6
Correlation matrix
199
11.1
OLS estimation of the export performance model
237
12.1
Example of exploratory factor analysis: job-related burnout
248
196
Preface
We are very pleased to present the
methods. The
fifth
edition of our book on research
first edition of this book, published in 1995, received consider-
able recognition and attention, mainly from scholars in the United Kingdom,
Scandinavia, and the Netherlands.
The second, third, and fourth editions, published in 2002, 2005, and 2010,
were used in several American and Asian schools, in addition to many
European schools, and have been translated into several languages, such as
Chinese,
Portuguese,
and
Estonian.
The
fourth
edition
in
particular
was
widely praised for its direct and concrete approach to research methods not
only for business studies but also for social sciences in general. Over the past
fifteen years, we personally received many positive comments on the accessibility and directness of the text from our colleagues and the students who
used it. But at the same time, we also received a lot of feedback on what was
missing from the book and valuable input on how it could be improved.
About a year ago we approached Cambridge University Press about a
possible
fifth edition and at that time we started a more systematic collection
of comments from colleagues who have been using the book. The publisher
also sent the fourth edition to ten anonymous reviewers and asked for their
comments as to the strong and weak points of the book and their recommendations as to how it could be improved. We have thus received an
abundance of comments on the previous editions, how the book should be
improved, and what a
fifth edition should include. In this respect, we would
like to thank the following colleagues for their valuable advice and comments: Jose-Pla Barber, Peter Buckley, Tamer Cavusgil, Agnieszka Chidlow,
Jeremy Clegg, Ulf Elg, Philippe Gugler, Amjad Hadjikhani, Maria Karafyllia,
Saba
Khalid,
Jorma
Larimo,
Jean-Paul
Lemaire,
Leigh
Anne
Liu,
Ulrike
Mayrhofer, Niin Nummmela, Rebecca Piekkari, Stefan Schmid, Rudolf Sinkovics, Veronika Tarnovskaya, and Sari Wahyuni.
Following the comments from the above-mentioned colleagues, we have
worked on the
fifth
edition and have considerably changed and, hopefully,
improved it. Not only have we replaced and added three new chapters and
about 50 pages of new text, we have also reorganized the whole book. It is
now divided into three parts: (I) Challenges and Ambiguities of Business
xvi
Preface
Research, (II) The Research Process, and (III) Implementation. We have made
the
language
and
approach
more
neutral
and
have
provided
additional
examples in almost all the sections. We have also invited a third co-author,
Professor Roger Strange, for his 25+ years of experience in teaching research
methods, particularly quantitative methods.
This
fifth edition has therefore been thoroughly reworked and reorganized
and now has clear guidelines for doing qualitative and quantitative research.
This approach is considered useful for students as they can go directly to
parts that are relevant to them. We have made several additions to the
qualitative and quantitative data analysis sections and how researchers can
make their research more trustworthy and reliable. Moreover, new sections
have been added on ontological and epistemological considerations while
doing research.
Part III provides concrete guidelines for designing and conducting quantitative
and
qualitative
methods in our
field.
and explain which
research,
We
first
the
two
most
commonly
used
research
discuss these two types of research method
type is more appropriate
for which type of research
question. Then, qualitative and quantitative data collection and analysis
are dealt with in separate chapters, instead of covering both in the same
chapters, as was done in the fourth edition. Special attention has been given
to international and cross-cultural business research, and the sections on
ethical issues have been expanded. A number of examples have been added
to make the text more accessible and easy to understand. The last chapter
gives
section-by-section
guidelines
for
report
writing
depending
on
the
purpose of the report.
A
number
of
new
features
have
been
introduced.
(1) Boxed
examples
provide illustrations and help make the book readable to students without
a considerable background in statistics. (2) At the end of each chapter, a short
list of further reading has been provided for those researchers who want to
pursue their research using statistical/qualitative methods. These may be
particularly useful to teachers and students who want to probe deeper into
a particular topic. These additional readings have been selected with great
care to present a balanced and up-to-date view (classical as well as new
perspectives)
on
the
different
issues
discussed
in
a
particular
chapter.
(3) More questions are posed at the end of each chapter. These can be used
to test the reader’s knowledge and could also be used as exam questions. (4)
There are exercises at the end of each chapter to encourage discussion and
debate in the class.
We believe the
fifth edition is more comprehensive and better structured,
but still to the point and focused, and provides a set of guidelines for research
methods in business studies.
Preface
We take this opportunity to thank our families who afford us time and
stimulate us in these endeavours. Diletta Pegoraro, Emily Pickering, and Saad
Ghauri deserve our special thanks for helping us in preparing the manuscript
and for typing and retyping several versions of the book. Finally, we are
grateful to Valerie Appleby at Cambridge University Press, for her enthusiasm for this project, her professional help, and for sending us encouraging
emails to
fi
nish the manuscript on time.
Pervez N. Ghauri
Birmingham
Kjell Grønhaug
Bergen
Roger Strange
Brighton
xvii
Acknowledgements
We are grateful to the following for permission to reproduce copyright
material.
Figures
Figure 2.3 adapted from Social Research Methods, Pearson Education, Inc.
(Forcese, Dennis P. and Richer, Stephen 1973) p. 22 © 1973, reprinted by
permission of Pearson Education, Inc., Upper Saddle River, NJ; Figure 7.1
adapted from Business Research Projects for Students, Thomson Learning
(Jankowicz, A.D. 1991) p. 159; Figure 7.2 fromMastering the Dynamics of
Innovation , Harvard Business School (Utterback, J.M. 1994) p. 41, based on
data in E. Braun and S. MacDonald,Revolution in Miniature: The History and
Impact of Semiconductor Electronics (Cambridge, UK: Cambridge University
Press 1978); and J.E. Tilton, International Diffusion of Technology (Washington DC: The Brookings Institution 1971), reprinted by permission of Harvard
Business School Press, Copyright © 1994 by the Harvard Business School
Publishing Corporation; all rights reserved; Figure 7.4 adapted from Case
research in marketing: Opportunities, problems and a process, Journal of
Business Research, 12, p. 205 (Bonoma, T.V. 1985); Figure 9.1 fromResearch
Methods for Managers, Sage Publications Ltd. (Gill, J. and Johnson, P. 2002)
pp. 76 77; Figure 13.2 adapted from How to Write a Research Paper, 2nd
edn., Pergamon Press (Berry, R. 1989), reprinted with permission of Ralph
Berry; Figures 13.3, 13.4 from International Marketing Research, 2nd edn.
(Craig, D.S. & Douglas, S.P. 2000) Copyright© John Wiley and Sons Limited,
reprinted with permission; Figure 13.5 from Data equivalence in crosscultural research: A comparison of classical test theory and latent trait theory
based approaches , Australasian Marketing Journal, 7(2): 23 38, (Salzberger,
T., Sinkovics, R., and Schlegelmich, B. 1999).
‘
’
–
‘
’
–
Acknowledgements
Tables
Table 8.2 from ‘Can computerised market models improve strategic decision
making?’,
Journal of Socio-Economics,
Grønhaug, K. 2003), Copyright
©
32: 503– 20
(Fuglseth,
A.M.
and
2003, with permission from Elsevier.
Text
Epigraph on page 3 from Older
ture of the Aging,
People and Their Social World: The Subcul-
F.A. Davis Company (Rose, A.M. and Peterson, W.A. 1965)
p. 11, ISBN: 9780803676206; Epigraph on page 8 from ‘Latent structure
analysis’ (Lazarsfeld, P. 1959)
(Koch,
S.
(ed.)
1959),
Psychology: A Study of a Science,
McGraw-Hill,
reproduced
with
3, p. 480
permission
of
The
McGraw-Hill Companies; Epigraph on page 54 from ‘The analysis of consumer actions’ (Kornhauser, A. and Lazarsfeld, P.F. 1955), The
Social Research,
right
©
Language of
pp. 392–403 (Lazarsfeld, P. and Rosenberg, M. (eds.)), Copy-
1955 by the Free Press. Copyright renewed
©
1983 by Patricia
Kendall Lazarsfeld, all rights reserved, reprinted with the permission of The
Free Press, a division of Simon and Schuster, Inc.; Epigraph on page 103 from
‘Case research in marketing: Opportunities, problems and a process
’,
of Marketing Research,
Journal
12: 199 (Bonoma, T.V., 1985), reprinted with permis-
sion by the American Marketing Association; Example 9.1 from Business
Research Methods,
Oxford University Press (Bryman, A. and Bell, E. 2003)
p. 93; Example 8.1 from
The Management of Innovations
(Burns, T. and
Stalker, G.M. 1961) pp. 120–1, by permission of Oxford University Press;
Box 13.1 from ‘International comparison of labour disputes’,
Trends:
Labour Market
195– 201 (Davis, J. 2001), Source: UK Statistics Authority website
www.statistics.gov.uk and Business Research Methods, Oxford University
Press (Bryman, A. and Bell, E. 2003) p. 227 by permission of Oxford University Press. Crown Copyright material is reproduced with the permission of the
fice of Public Sector Information (OPSI).
controller, Of
In some instances we have been unable to trace the owners of copyright
material, and we would appreciate any information that would enable us to
do so.
xix
PART I
Challenges and Ambiguities
of Business Research
..................................................................................
P
art I (Chapters 1 and 2) explains the purpose of the book and the challenges
related to doing research in business studies. It sets the scene for the rest of the
book and explains its perspective, target group, and the purpose of doing research
in general. It also takes up the ethical responsibilities of the researcher.
1
Introduction
Facts do not simply lie around waiting to be picked up. Facts must be
carved out of the continuous web of ongoing reality, must be observed
fi
within a speci ed frame of reference, must be measured with precision,
must be observed where they can be related to other relevant facts. All of
this involves methods.
R o s e a n d P e t e r s o n (1 9 6 5 : 1 1 )
Purpose of the Book
1.1
The purpose of this book is to help students get rid of the myth that research
is ‘too
fic
scienti
’
or
‘too
abstract’
and
unnecessary
in
business
studies.
It is also intended to help them understand the language and approach of
science and research. It
will assist them in understanding how properly
structured and argued reports can be more convincing and reliable than
reports based on a random approach or on common sense. In our opinion,
fi
a scienti c approach and common sense have much in common. The scien-
fic
ti
approach, however, is a more systematic and controlled treatment of
common
sense.
A
layperson
uses
theories
and
concepts
but
in
a
loose
manner. Often, people accept whatever sounds consistent with their beliefs
and values: an increase in unemployment is because of immigrants or, in a
business setting, the more money you spend on advertising, the more you
fi
can sell, etc. A scienti c approach, on the other hand, systematically examines assumptions and hypotheses before either believing or discarding them.
We have written this book to give practical guidelines for students and
researchers. It is based on our own experiences as researchers and as supervisors for numerous PhD and Master’s theses, and that of many colleagues
and students in business studies. The
fifth
edition has also been improved
and expanded with help and assistance from our colleagues and students
who
have
been
using
the
first
four
editions.
In
this
edition,
particular
attention has been paid to three important issues: the use of qualitative
research in business studies, the impact of cross-cultural issues, and ethical
issues in research. These issues are dealt with in each and every chapter.
A new chapter examines qualitative data analysis, and new chapters and
4
Introduction
sections have also been added on quantitative data collection and analysis.
Moreover,
several
examples
have
been
added;
these
are
now
boxed
to
enhance readability.
This book is designed to help students understand that a conscious (scien-
fic)
ti
approach is the most appropriate for research and problem-solving
projects in business studies. This means helping students understand that
before beginning research on a project, they must be aware of what they are
doing and what they are not doing. They have to clarify for themselves and
for the reader the perspective they have chosen, and they must identify who
fit from the study. In other words, researchers must learn how to
should bene
formulate a problem, how to choose a particular method, and how to argue
and motivate. They must also learn how to write a valid and reliable report,
which is useful for the purpose of research and for managers or decision
makers. We provide students and others involved in research in business
studies with clear, hands-on guidelines for doing research. The book deals
with the following:
•
•
•
the importance of systematic research and problem solving;
how to cope with problems associated with doing research;
different types of research, the role of the researcher, and the importance
of methods and models;
•
the practicalities of research, such as problem formulation, relating the
research to previous studies, choosing a suitable methodology, presenting results and
•
findings, and drawing conclusions;
different methods of data collection and analysis, qualitative as well as
quantitative, and their advantages and disadvantages;
•
which type of method is suitable for which type of research problem and
conditions;
•
•
•
case study research;
the role of the researcher and ethical issues in doing research;
how to test the assumptions necessary for the method and techniques
being used, and whether these assumptions are valid: in other words,
validation of methods and models and not only validation of hypotheses;
•
the impact of cross-cultural (international) issues on the research process
and how to handle these issues;
•
the practical issues around research in business studies, providing some
practical guidelines for questionnaire development, interviewing, and
data analysis;
•
step-by-step guidelines for report writing, the format and writing of a
report as a thesis and for publication purposes;
•
concrete guidelines for oral presentation.
1.2 Readership
1.2
This
Readership
book
is
primarily
meant
for
Master
of
Science
(MSc),
MBA,
and
advanced undergraduate students in business studies. It is also meant for
PhD students and other researchers at the earlier stages of their research
endeavours. Most schools and universities require their graduate and under-
fi
graduate students to write a thesis or a research report at the nal stage of
their studies. These students are the primary target for this text. Students
often
find
the books available either too general or too narrow, dealing
with only one aspect of research, such as surveys, interviews, or case studies,
fi
or else at a higher philosophical level that is often irrelevant and dif cult to
comprehend. This book, on the other hand, has an integrative approach and
is especially adapted to research in business studies. It will also be highly
useful for consultants and business people working with research projects,
problem solving, and report writing. Considering the above-mentioned target
groups, we have kept the language and discussion simple and accessible.
A rather direct and to-the-point stance is taken and a number of examples
are given to help the reader understand the point under discussion.
–
The book is organized in three parts: Part I (Chapters 1 2) dealing with
–
the challenges of business research, Part II (Chapters 3 6) dealing with the
–
research process, and Part III (Chapters 7 14) dealing with data collection
and analysis, implementation, and report writing
In Chapter 2 we discuss the meaning of research with special reference to
business
studies.
The
focus
here
is
the
difference
between
research
and
practical problem solving or common sense, and the different research orien-
final
tations and knowledge and skills required for research. The
part of the
chapter deals with ethical issues and responsibilities. In Chapter 3 the role of
theory in business research is discussed. Here the focus is on the research
process and on the explanation of important concepts, such as theory, models,
and knowledge. Chapter 4 deals with the research problem. It is our observa-
fi
tion that most students of business studies face dif culties in formulating the
research problem, and in differentiating between a research problem and a
research topic. In this chapter we also discuss the importance of models and
systematic thinking in research and the role of reviewing past literature.
Chapter 5 deals with research design and problems related to the choice
of research design: how the research problem is, and should be, related to
the design. Different types of research designs are presented and their use
discussed. Examples are used to illustrate the importance and relevance of
research design. Problems related to validity and reliability are also dealt
with in this chapter.
5
6
Introduction
Chapter 6 handles the problems of measurement and the operationalization of research concepts and data. Measurement of empirical research is
fi
a dif
cult task as the quality of the results depends to a large extent upon
the measurement procedures used in gathering and analysing the data. The
chapter takes us through different types, levels, and scales of measurement.
Validity
and
reliability
in
measurement
are
particularly
stressed.
Some
guidelines are provided to improve the measurement. A special section is
devoted to measurements in qualitative research.
In this new edition, data collection and data analysis for qualitative and
quantitative methods have now been clearly separated and are dealt with in
different chapters in Part III. While Chapters 7 and 8 deal with qualitative
data collection and analysis, Chapters 9, 10, and 11 deal with quantitative
data
collection
qualitative
and
research
analysis.
and
Chapter
Chapter
8
7
thus
provides
discusses
data
guidelines
on
collection
designing
in
and
conducting a case study and other types of qualitative research. It discusses
when to use qualitative methods and how to analyse data collected through
qualitative methods such as case studies and interviews.
Chapter 9 thoroughly explains data collection methods for quantitative
research methods. It explains the main methods of data collection, such as
surveys. Chapter 10 discusses different ways of analysing quantitative data.
It explains the role of statistics in data analysis and presentation.
Chapter
regression
11
provides
can
be
used
guidelines
for
and
a
quantitative
practical
data
demonstration
analysis,
while
of
how
Chapter
12
presents a number of other techniques that can be used for quantitative data
analysis. The chapter explains and gives examples of the most commonly
used statistical techniques for data analysis.
Chapter
13
discusses
the
extra
care
needed
in
cases
of
cross-cultural
research, as research involving an unfamiliar environment may complicate
the understanding of the research problem, data collection, and data analysis. The chapter also provides guidelines for how to handle multi-country
research projects. In these projects, comparability of the data and equivalence
of understanding, both of the researcher and the respondents, are of utmost
importance.
Researchers
have
to
be
extra
careful
in
categorization
and
measurement of cross-cultural data. The chapter deals with each stage of
the
research
project,
how
it
is
complicated
because
of
the
cross-cultural
setting and how each stage can be handled to eliminate items and concepts
fl
that may cause cultural bias and thereby in uence the
fi
ndings of the study.
Finally, Chapter 14 provides guidelines for writing a report. The process of
writing up the
fi
nal report is often viewed as tiresome work. The report has to
be concise, consistent, and convincing. The writing style of the report is also
important in order to convince the reader that the report is valid and reliable.
1.3 On the Use of the Book
In this chapter the structure of reports is discussed section by section, with
examples and illustrations. Guidelines for form and style, for use of footnotes, and the provision of a bibliography are also provided. Different types
of reports are discussed, such as a thesis, an oral presentation, and a report
written for publication.
1.3
On the Use of the Book
A thorough understanding of the objective and the role of theory in practical
fi
research is needed, so it is recommended that the reader rst scans the whole
–
text and thoroughly reads Chapters 1 5 before starting work on a business
research project. When actually working on a research project, researchers
–
should read Chapters 6 12, chapters that are relevant for the research project
at hand, as this will enable them to decide which research design is the most
suitable and which data collection method is most appropriate for their
research problem. At this stage they can also get help on how they should
go about sampling and collecting data and on which type of analysis will
be the most appropriate. While conducting the actual data collection and
analysis, the researcher is advised to read the respective chapter from Part III,
whichever is relevant. If the researcher is working on an international or
cross-cultural research, s/he should go through the chapter on cross-cultural
fi
research. Finally, before sitting down to write up the report and ndings, the
researcher should read Chapter 14, which provides concrete guidelines for
report writing for different purposes.
An added feature in this
fifth edition is that at the end of each chapter a list
of further reading is provided. Researchers who want to look more deeply
into a particular issue (e.g. on different opinions on the study of management, on ethical issues on secondary data, or how to handle a particular
problem caused by cross-cultural data collection) can quickly look up these
readings to enhance their understanding.
7
2
Research in Business Studies
first, the reader could have
If we have mentioned the actual results of investigation
labelled these obvious also. Obviously, something is wrong with the entire argument
of obviousness. It should really be turned on its head. Since every kind of human
reaction is conceivable, it’s of great importance to know which reactions actually
occur most frequently and under what conditions; only then will a more advanced
social science develop.
L a z a r s f e l d (1 9 5 9 : 4 8 0 ) , r e p r o d u c e d
with permission of the McGraw-Hill Companies
The purpose of this chapter is to explain what we mean by research in
business studies and to discuss differences between systematic research and
common sense or practical problem solving. Different research orientations
fl
are also discussed to illustrate the in uence of researchers’ backgrounds and
basic beliefs surrounding research methods and processes. We believe that
research papers or theses at the Master
’s level, when successfully completed,
should demonstrate that the candidate can systematically handle and analyse
a problem, arriving at valid conclusions. In other words, it is a professional
training process through which we can learn to think and work systematically
and develop analytical capabilities. The advantage of systematic thinking and
analytical capability is that it contributes to accuracy and a more orderly
approach and is reliable in handling research as well as business problems.
EXAMPLE 2.1
A business
firm experiences declining sales and – of course – managers and
employees feel frustrated. Through systematic observations and thinking, it
becomes clear that the declining sales are caused by a newly introduced
substitute. This results in a systematic effort to develop and introduce a new
and improved product offering.
The
increasingly
complex
nature
of
business
operations
and
decision
making demands a systematic and thoughtful approach. The importance
of research in business studies, in schools, and in businesses has therefore
2.1 The Nature of Knowledge Development
EXAMPLE 2.1
(CONT.)
increased. Practical problem solving and decision making are (or at least
should
be)
becoming
more
and
more
similar
to
research.
Business
and
marketing research are common activities in medium-sized and larger companies. And most of the decision making in these companies is based on
research. For example, whether a company is launching a new product
fi rst
or trying to enter a new market, it has
decide
which
segment
to
to undertake some research to
target, whether
there will
be demand
for its
product (sales forecast), and how to develop a marketing plan for the new
product or new market. All this requires research that has to be undertaken
by the company itself or by a market research company.
2.1
The Nature of Knowledge Development
Debate on the nature of knowledge is not new and dates back to Plato and
Aristotle and other ancient cultures; all cultures have been interested in
differentiating truth from falsehood or right from wrong. Philosophers therefore often debate the conditions of knowledge, the reliability of sources of
knowledge, and the limits of knowledge (see e.g. Moser et al., 1998). In this
section, the idea is not to be too philosophical about knowledge development
and delve into extensive debate on several ‘isms ’. Our intention is instead
to explain what knowledge is, how we acquire it, and how we differentiate
between ‘knowledge’ and ‘mere opinion’.
We acquire knowledge on our own, in school, at home, and at work, and
we believe that it is valuable to have knowledge. However, we also know that
we cannot believe what everybody says and that we should have a critical
approach to acquiring knowledge. In other words, we need some training
in differentiating false from true or what we can believe from what we
cannot believe.
The theory of knowledge is calledepistemology, which comes from Ancient
Greek episteme for knowledge and logos for theory and explanation (Moser
et al., 1998). Epistemology is thus the source and the limit and the nature of
knowledge. While developing knowledge it is useful to question our own
assumptions in addition to ontology (the nature of reality) (Tsoukas and Chia,
2011). Ontology and epistemology together form research philosophies and
have fundamental methodological implications (Isaeva et al
., 2015). When
choosing research methodologies, we draw upon different ontological and
9
10
Research in Business Studies
epistemological assumptions. These assumptions thus underpin our choice of
methods. However, in management studies, there is no conclusive agreement
on the best philosophy.
Philosophically, we cannot be certain that an individual knows what he
claims to know. This is called scepticism. According to epistemology, therefore, instead of outright accepting or rejecting knowledge, we seek explanation to be sure what a particular piece of knowledge consists of and how it
has
been
acquired.
Methods
of
acquiring
knowledge
and
explanations
thus take a prominent role in the nature of knowledge development. Western
definition of knowledge that it should
fi
ositional knowledge is a justifi ed true belief (Moser et al., 1998), or knowledge
is a justifi ed true belief. However, it is still debated whether all three componepistemology
philosophers
offer a
have three components: justi cation, truth, and belief. This means that prop-
ents have to be there and whether these components are acquired by sense or
experience. In epistemology we use different terminology for knowledge based
on sense and knowledge based on experience. Knowledge based on sense is
called ‘a priori’ knowledge, while knowledge based on experience is called‘a
posteriori’ knowledge, in other words, knowledge acquired after experience.
fi
This distinction helps in the justi cation of known propositions.
The debate about contemporary epistemology and sense (common sense)
started during the period of Bertrand Russell (1872
–1970) and G.E. Moore
fi
(1873–1958). Although there is no common de nition of ‘common sense’, it
was believed that a common sense is the beliefs of a group of people about a
certain thing. Connecting sense and experience, Russell (1972: 75) believed
that ‘all knowledge that asserts existence is empirical, and the only a priori
knowledge concerning existence is hypothetical giving connexions among
things that exist or may exist’. However, our common sense about knowledge
can change; it can be corrected or rejected with the development of knowledge.
2.2
Why We Do Research
The basic purpose of research in education is to teach students to work
systematically
and
for
them
to
learn
to
analyse
issues
critically
before
believing in them or acting upon them. However, research is essential for
understanding even basic everyday phenomena that need to be handled by
individuals and organizations. If we want to buy a car, we do some research,
finding
out which
car
fi
satis es our
needs
and where
it is
available;
compare prices at different dealers, or among different cars that
fit
we
our
criteria, and so on. In the same manner, a company has to do research while
making important decisions, whether it is to reorganize its structure or to
2.2 Why We Do Research
merge or take over another company (Hassan and Ghauri, 2014). Hassan and
Ghauri believe that many mergers and acquisitions fail due to the fact that
proper research was not undertaken while evaluating the target company or
each other. Businesses these days are doing systematic research to handle
their day-to-day activities (Sekaran, 1992).
Businesses are beginning to develop a strategic monitoring program to identify
and
understand
strategies. Any
competitors ’
firm
strengths,
weaknesses
and
overall
business
can establish a competitor-analysis system that provides
management with essential information about a wide range of strategies that
rivals
are
likely
information
pro
and
to
pursue.
how
to
The
key
combine
is
knowing
separate
pieces
where
of
to
data
gather
into
a
relevant
coherent
file of each competing operation.
(Svatko, 1989: 59)
We really cannot take decisions on important issues unless we investigate
(research) the relevant information more deeply, gathering more information
on the particular aspect we are interested in. Then we analyse all this information to make a judgement about the right solution to our problem or
answer to our question. In business studies we normally work with problems
faced by managers and companies. For example: How to enter a particular
fl
market? What are the factors that in uence performance in joint venture
fl
relationships? What are the factors that in uence the successful launch of a
new product? Is advertising necessary, or how much advertising is necessary
fi
to market a certain product? Would acquiring a particular company ful l
fi
fluence its
our strategic objectives? Would the ethical behaviour of a rm in
competitive position or not? And so on.
Without research, we cannot answer the above or similar questions. As
well as learning about systematic information collection and critical analysis, we also need to learn how things work, through research done by others,
and then perhaps assess that knowledge to see whether it is applicable to our
own problems or situations. Sometimes we need to make or suggest changes
to apply what we have found to our own problems. When this process has
been done by several researchers, the ideas and theories are tested. Once
theories are properly tested, we can even predict the future. We can say with
con
fidence that in this type of situation/problem, this should be the solution/
answer. Research, therefore, makes our life easier, not only in business but in
general. Research can thus be considered as a process of problem solving
fi
fi
for a speci c problem under speci c conditions (Kuhn, 1970). The fundamental purpose of research, however, and we hope everybody can agree, is to
improve social life. In business research, the purpose is to understand how
and why things happen.
11
12
Research in Business Studies
EXAMPLE 2.2
Through systematic research it was found that serious stomach and digestion problems were caused by salmonella bacteria. Research also showed
±
that the salmonella bacteria did not survive at a temperature of 80C or
higher, resulting in the practice of heating food to that temperature when
the presence of such bacteria was suspected.
2.3
Research versus Common Sense
There is a common belief that research is an academic activity undertaken
by researchers who are not at all familiar with managerial culture and the
nature of the problems faced by business managers. At the same time, several
studies
have
revealed
that
managers
do
not
know
how
to
use
research
findings and therefore cannot utilize the results and conclusions of research
(Gill and Johnson, 2002; Whitley, 1984). In our opinion, research in business
studies and managerial problem solving are not much different from each
other. Managers need to have some knowledge and evaluation capabilities to
understand the consequences of their decisions. In other words, managerial
decision making or problem solving, if done systematically, should lead to
better decisions and results than those decisions made exclusively through
intuition or personal likes and dislikes. Managers must be able to analyse
their situations and use investigative approaches to decision making and
problem solving. The systematic procedures and approaches to advancing
knowledge suggested by the research process also serve as a disciplined and
systematic procedure for managerial problem solving.
EXAMPLE 2.3
In business life,
firms
and their managers experience negative surprises, for
fi
example that customers become dissatis ed and frustrated. An ability to
obtain systematic insights into what causes dissatisfaction and frustration is
crucial to solve the problem and improve. This can only be done via research.
Companies
that
try
to
resolve
their
problems
without
undertaking
any
research and understanding the factors that are causing customers to be
fi
dissatis ed often end up in bankruptcy. We have witnessed several successful companies disappearing from the business landscape: Motorola, Blackberry, and Enron are good examples.
2.3 Research versus Common Sense
As a
first step, actors in both management and research activities need to
decide what they want to achieve. This is followed by collecting relevant
information and facts that can help in achieving the
first
objective. The
information collected needs to be analysed and put into a structure that
helps to achieve a purpose or initiate different actions. This process, deciding
what to do, collecting information, discarding irrelevant information, analysing the relevant information, and arriving at a conclusion or decision in a
systematic procedure, is useful for building cumulative knowledge as well
as in the personal development of the researcher and manager alike (Ghauri
and
Firth,
2009;
Revans,
1971).
Although
some
scholars
differentiate
between academic research and research done by companies, for practical
problem solving, we believe in ‘trans-disciplinarity ’ in research, where the
boundaries of a single contributing discipline can be crossed. The production
of knowledge is thus not restricted to academic research. It involves academics, policy makers, and managers, and useful knowledge is developed and
exploited more quickly in collaboration than if different types of knowledge
were to be developed by different parties (Gibbons et al., 1994; Hadjikhani
fi
et al., 2012; Tran eld and Starkey, 1998).
The purposes of doing research are manifold, such as to describe, explain,
analyse, understand, foresee, and criticize already existing knowledge or
phenomena in social sciences. The job of a researcher is often that of an
observer, and each observation is prone to error; therefore, we go out and
research to
find a better
‘truth’ or answers to our questions.
EXAMPLE 2.4
It is costly to perform transactions. In a classic article,‘The Nature of the
Firm’, R.H. Coase (1937) raised the ‘obvious’ question: Why do
firms
exist?
Coase was trained as a neo-classical economist. In neo-classical economics,
fi rms
do not exist in highly competitive markets. Rather, transactions are
fi
mediated through markets. Coase explained the existence of rms as being
due to ‘frictions’ – or transaction costs. This insight is useful to understand
why in some cases
firms use the market to perform transactions, whereas in
other cases transactions are conducted internally.
If the role of a researcher is that of an observer, then what is the difference
between an observer who can draw conclusions with common sense and a
researcher? The difference is that observations made by the researcher should
be systematic, arguable, and challengeable. The researcher explains to us
how s/he collects information, argues for the methods used to obtain the
13
14
Research in Business Studies
results, and explains their limitations. In an ideal situation, if anybody else
made observations using the same methods, they would come up with more
or
less
the
same
results.
The
role
of
the
researcher
thus
becomes
very
important. When we look and observe, we see differently depending upon
our background and what we know and expect. Two different people observing the same object may see two different things. It is thus very important to
discuss the object, the observer, and the biases. For this reason, the observer
has to explain and convince the reader of the purpose and methods of
observation so that the reader can make a judgement about the validity of
the results.
EXAMPLE 2.5
Research
fi ndings show
that employees tend to see problems and solutions
from the position and tasks they are involved in. For example, a marketing
person tends to see marketing problems, a person involved in production
sees most problems as production problems. The main reason is that individual actors are constrained by their limited cognitive capacity, that is their
limited capacity to notice, make sense of, store, retrieve, and make use of
information (data) that is not accessible to them.
The usefulness of research is often discussed, especially when it comes to
things that seem self-explanatory and common sense. But the very same
common sense and self-explanatory beliefs have been proven to be wrong
fl
through research. Common sense and beliefs, in uenced by our society and
culture, provide us with a non-conscious ideology, and we believe in things
fl
without being aware of the reality. Our common sense is thus in uenced by
our background, education, and beliefs. Common sense is thus the most
uncommon thing. As Bem (1979: 89) said, ‘Only a very unparochial and
intellectual
fish
is aware that his environment is wet.
’ This is further illus-
trated by the following:
A man and his son are involved in an automobile accident. The man is killed and
the boy, seriously injured, is rushed to the hospital for surgery. The surgeon takes
one look at him and says, ‘I am sorry, but I cannot operate on this boy. He is
my son.’
(Selltiz et al., 1976: 4)
Whenever we tell this story to our students, many of them do not understand the catch. In many societies, the unconscious belief is that a surgeon is
2.3 Research versus Common Sense
always a man and therefore we do not even consider that the surgeon might
be the mother of that boy.
Scienti
fic
research often challenges these non-conscious ideologies and
beliefs by scrutinizing them. Challenging old beliefs, turning things upside
down, and creating new beliefs is not always comfortable. For example,
in a number of somewhat conservative states, it is not allowed to include
Darwin’s evolution theory in the school curriculum, as it challenges the belief
promoted by religion about the creation of mankind. Research allows us to
correct our misbeliefs, generate new concepts, and broaden our perspectives
and perceptions. This is particularly true because research often goes beyond
common sense, while common sense considers most things as given. The
fundamental difference here is, as mentioned earlier, that research involves
systematic
methods.
The
conclusions
drawn
from
research
lead
to
new
theories and beliefs. These new theories can challenge our existing beliefs,
as illustrated by the following story:
A well-known scientist (some say it was Bertrand Russell) once gave a public
lecture on astronomy. He described how the earth orbits around the sun and how
the sun, in turn, orbits around the center of a vast collection of stars called a
galaxy. At the end of the lecture, a little old lady at the back of the room got up
flat
and said, ‘What you have told us is rubbish. The world is really a
plate
supported on the back of a giant tortoise.
’ The scientist gave a superior smile
before replying, and asked ‘What is the tortoise standing on?’ ‘ You are very
clever, young man, very clever,’ said the lady, ‘ But it’s turtles all the way down.’
(Hawkins, 1988: 1)
fi
The above discussion makes it clear that the difference between a scienti c
fi
observation and a layperson’s observation is that scienti c research is done
systematically and is based on logic and not beliefs: therefore, we stress a
logical relationship between cause and effect.
Most students in business studies get confused by the terminology used
in books on traditional research methods. Although the language and the
fic
scienti
approach
fi
is somewhat
different
from common sense, it
is not
strange or dif cult to comprehend. In fact, quite the contrary, as, when
understood, it seems logical and natural. As Whitehead (1911: 10) stated,
common
sense
judgement is
is
not
that the
the
right
new ideas
start
for
research: ‘its
shall look like
sole
the old
criterion
for
ones
’. Research
prepares us for new realities.
According to one idea, science is a systematic and controlled extension of
common sense, as common sense is a series of concepts and conceptual
schemes satisfactory for the practical uses of mankind (Conant, 1951: 32
–3).
Others believe that these concepts and conceptual schemes can be misleading
15
16
Research in Business Studies
in modern times. For example, up until the twentieth century, it was selfevident
common
sense
for
many
to
use
punishment
as
a
basic
tool
of
pedagogy. It has, however, been proved that this old view of‘common sense’
may be quite wrong, as rewards seem more effective in aiding learning
(Kerlinger, 1964: 4). According to this belief, science and common sense
differ in several ways (ibid.):
1.
The
first
difference is that laypersons use
They often accept explanations that
fit
concepts
and
theories loosely.
easily with their beliefs and
values, for example that illness is due to sinfulness. Scientists, on the
other hand, systematically build up theories and test them for internal as
well as external consistency. Moreover, they believe that the concepts
they are using are human-made terms that may or may not exhibit a
close relation to reality.
2.
Laypersons often select theories and
test hypotheses,
but their evidence
fi
comes from their own hypotheses. They believe in the evidence as it ts
their assumptions. Scientists, on the other hand, test their assumptions
and
hypotheses
systematically
and
tend
to
be
more
careful
in
their
selection and drawing of conclusions.
3.
Laypersons
do
not
bother
to
control
phenomena. They do not try to
their
explanations
of
control external influences ,
observed
and they
accept those assumptions that are consistent with their preconceived
biases. They do not try to relate different phenomena. Scientists, on the
other hand, are constantly looking for relations among different phenomena. They systematically try to study and control these relations.
4.
Finally, laypersons often believe in
metaphysical explanations,
such as
‘some people are poor because God wants them to be poor
’. Scientists,
on the other hand, do not accept metaphysical explanations. They are
concerned with things that can be observed and studied. In other words,
research is concerned with studying things that can be observed and
fi
tested. If things can be tested, they can be falsi ed. For example, a person
may believe that drinking coffee at night keeps one awake. This assumption (assertion) can be tested. Assuming that people tend to be equally
awake whether they drink coffee during the night or not, the stated belief
is wrong (cf. Section 12.1).
2.4
Different Research Orientations
fl
The research process and the research method used are in uenced by the
researcher ’s background when it comes to research orientation. A particular
2.4 Different Research Orientations
research orientation prescribes the relationship between the methods, data,
theories, and values of the researcher. Social knowledge builds one upon
fic observations provide new theories, correcting, modifying,
another. Scienti
extending, and clarifying the older and existing ones. Most methodology
books
describe ‘originality ’ or ‘original
contribution
fi
to
knowledge’ as
a
basic condition for a scienti c study. Although the demand for originality
is perhaps the most controversial, its importance and meaning should not be
misunderstood. Students normally believe that topics used by others in their
theses should not be studied, because by doing so they would lose originality.
We believe ‘originality ’ can describe studies that create a new dimension to
already existing knowledge. It implies that there is some novel twist, fresh
perspective, new hypothesis or assumption, or new and innovative methods
of handling existing knowledge that makes the study a distinctive contribution. In business studies, it is equally possible, or perhaps more useful, to
direct research projects towards more sharply delineated tasks.
Researchers do not preach or ask whether the social activity observed is
good or bad; they just analyse, present, and explain it. In fact, that is the
starting point of research: that we have a number of assumptions/speculations,
but we should not accept or reject them unless we study them critically and
unless we
find logical and reliable explanations to accept or reject them. The
researcher thus tries to be as objective as possible. It is, however, not always
possible to be objective, as put forward by Burrell and Morgan (1979: 225):
fi
What passes for scienti c knowledge can be shown to be founded upon a set of
unstated conventions, beliefs and assumptions, just as every day common-sense
knowledge is. The difference between them lies largely in the nature of rules and
community which recognises and subscribes to them. The knowledge in both
cases is not so much ‘objective ’ as shared.
Scholars
involves
often
discuss
fic,
scienti
five
objective,
research
and
orientations:
measurable
(1) Positivism,
facts
that
can
which
predict
the
future (Donaldson, 1997); (2) Critical realism, which asserts that a variety
of data and methods are acceptable as long as the researcher is as objective
and realistic
as possible
(Bhaskar,
1986);
(3) Pragmatism, seeking
know-
ledge through a variety of methods and their combination in providing a
solution to a problem (Elkjaer and Simpson, 2011); (4) Interpretivism, which
aims to present full richness with diverse interpretational stories along with
contextual
factors
to
enhance
our
understanding
(Crotty,
1998);
and
fl
(5) Post-modernism, where the researcher is radically re exive and critically
sensitive
in
his
or her
own
disciplinary nature of our
different
orientations,
role
field,
from
(Cunliffe,
2003).
Considering
the
inter-
researchers in business studies may have
objective
generalizable
problem
solving
to
17
18
Research in Business Studies
Table 2.1 Different research orientations
Orientation
Explanations and beliefs
Positivism
A single truth exists that can be explained by law-like causal
generalization. Knowledge is objective and is based on
observable and measurable facts and relationships. It can
predict behaviour.
Critical realism
Knowledge about social interactions versus physical facts
should be approached differently. Researchers can thus use a
variety of methods while staying as objective as possible. It
leads to explanatory explanations and provides insights into
antecedents and causes. It encourages methodological
diversity.
Pragmatism
Knowledge should be relevant and useful to practice. Research
should provide solutions to practical problems. It encourages a
variety of methodological approaches.
Interpretivism
Context is important and knowledge is thus subjective.
Concepts and theories cannot provide richness and are too
simplistic. A variety of explanations and interpretations are
encouraged where meanings are more important. It provides
fic and in-depth understanding.
context-speci
Post-modernism
Truth and knowledge based on dominant ideologies and
fl
discourses. Researchers are encouraged to be re exive and
examine dominant discourses and reveal the hidden
meanings.
Source:
speci
fic
Based on Isaeva et al. 2
( 015).
practical problem solving for a company. These different orienta-
tions are further explained in Table 2.1. Our epistemological assumptions
lead us to a particular research question and methods of data collection and
analysis
(Burrell
and
Morgan,
1979).
Scholars
often
logical pluralism, saying that diversity enriches the
advocate
epistemo-
field, and as researchers
we should have open minds, in addition to questioning our own assumptions (Isaeva et al., 2015; Knudsen, 2003).
2.5
Induction, Deduction, and Abduction
A researcher observes and faithfully records what is seen without any prejudice. Some of these statements of observation are established as true and serve
as the basis for theories and laws. There are two ways of establishing what is
2.5 Induction, Deduction, and Abduction
true or false and to draw conclusions: induction and deduction. Induction is
based on empirical evidence, while deduction is based on logic.
induction
Through
vations.
analysis
into
!fi
In
this
existing
!
type
ndings
!
we draw general conclusions from our empirical obserof
research
the
process
findings
theory building, as
knowledge
goes
(literature/theories)
to
from
observations
are incorporated back
improve
theories.
In
this
research, thus, theory is the outcome of research (Elg et al., 2017). This type
of research is often associated with qualitative research. The process goes
from assumption to conclusions and is illustrated as follows:
Assumption
:
Psychiatrists have found that psychological problems in
patients depend on their experiences in childhood.
Conclusion
:
All psychological problems are founded in experiences in
childhood.
It is, however, important to note that we can never be 100 per cent sure about
the above inductive conclusions, as these conclusions are based on some
empirical observations. Sometimes conclusions based on hundreds of observations can also be wrong.
This can be explained by the prediction of election results in a general
election. Although the prognosis might be that the Labour Party is going to
fi
win a UK election, we cannot be sure until we have seen the nal results. For
the UK Brexit referendum, all the polls were indicating that the Remain side
was going to win, even after the polling stations had closed. However, when
the
final results came, the Leave side had won. In other words, we can arrive
at more or less probable results, but not with 100 per cent certainty.
By
deduction
we
mean
that
we
draw
conclusions
through
logical
reasoning. In this case, it need not be true in reality, but it is logical. The
researcher
in
this
type
of
research
builds/deduces
hypotheses
from
the
existing knowledge (literature), which can be subject to empirical scrutiny
(testing) and thus can be accepted or rejected. The researcher
’s main job is not
only to build hypotheses from existing knowledge but also to present them in
operational
terms
(operationalization),
to
show
how
information
can
be
collected to test these hypotheses and the concepts being used (Bryman
and Bell, 2003; Chalmers, 1982; Merton, 1967). In this type of research,
theory, and hypotheses built on it, comes
first
fl
and in uences the rest of
the research process. This type of research is often associated with quantitative research. The process of deduction goes as follows:
Assumption
Assumption
Conclusion
:
:
All metals expand when heated.
:
Rail tracks are built of metal.
Rail tracks will expand when heated.
19
20
Research in Business Studies
The above examples explain the difference between induction and deduction. The difference is that in induction, facts acquired through observations
lead us to theories and hypotheses, while with deduction (logical reasoning)
we
accept
or
reject
these
theories
and
hypotheses.
This
acceptance
and
rejection then helps us to explain or predict.
In the process of research, methods begin with the ideas and facts that
we know so far, which lead us to propositions, theories, and predictions. New
theories and predictions lead us to new ideas and facts, and a new cycle begins,
leading us to new theories. When we use observed facts in generating a theory
that is consistent with these facts, we are doing induction. In other words,
induction is the process of observing facts to generate a theory, and it is
perhaps the
first step in scientific methods. While doing research we formulate
propositions after observing the relationships between different the variables of
our study. Most researchers in business studies go through this method, observing facts or earlier results, that lead them to propositions and later to theories.
On the other hand, in deduction we look at the consequences of a theory.
There is an established school of thought which believes that the entire
research process is initiated by theories. Deduction involves the gathering
fi
of facts to con rm or disprove hypothesized relationships among variables
that have been deduced from existing knowledge.
As we can see, discussion on induction and deduction presents us with
alternative ways or stages of building theories. Most researchers and scientists believe that they use both types of reasoning in their research. The
processes of induction and deduction are not totally exclusive of each other,
and induction includes elements of deduction, and vice versa. In both cases,
researchers need to know the nature of the existing knowledge. In both
cases, however, a great deal of creativity and imagination is demanded from
the researcher or investigator. Both induction and deduction demand that we
fi
go beyond statistical signi cance to systematic data collection, and that
we are aware of the sensitive question of the relevance of data to theory or
study. Moreover, both demand that the investigator keeps up to date with
fi
theories and ideas and with scienti c methods.
EXAMPLE 2.6
Deductive reasoning – the
logical process of deriving a conclusion from a
known premise or something known as true.
Inductive reasoning
–
the
systematic
process
of
establishing
proposition on the basis of observation or particular facts.
a
general
2.6 What Comes First: Theory or Data?
Lately,
Abduction
often
several
scholars
have
been
advocating
an
abductive
approach.
is not just a combination of induction and deduction, as it is
understood.
Abduction
refers
to
a
theoretical
interpretation
of
an
empirical problem that can lead to development of new theories. In other
words, abduction is a continuous process that takes place in all phases of the
research process where the researcher’s
observations are
confronted with
theoretical assumptions and generate a new view of the phenomenon (Dubois
and Gadde 2002; Van Maanen et al., 2007). This means that the original
framework and theoretical assumptions of the researcher are continuously
fied
modi
findings. Normally we would develop a
field before undertaking research that leads us to one
as a result of empirical
theoretical view of the
or more theoretical lenses through which we observe the reality. In this
fi
process and in case of unexpected or novel ndings, we need to re-examine
our frameworks. Dubois and Gadde (2014) refer to deep probing that leads
fi
back and forth between framework, data, and research and to context-speci c
flexibility and allows us
explanations. This abductive approach thus brings us
to direct our research towards theoretical insights and learning.
2.6
What Comes First: Theory or Data?
It is often assumed that theory should precede data, that is, observations. This
impression is easily supported by the way the‘research process’ is illustrated
(see Chapter 3), and research often takes place this way. For example, this is
the case when a researcher carefully reviews relevant literature and‘sees’ a
research opportunity, that there is a gap, a weakness, or unanswered question
in present insights. In this case, the researcher usually (but not always) has a
clear research problem: for example, do variations in X explain variations
in Y?
Often, however, this is
not the
case. The
researcher may observe
something s/he does not understand, typically resulting in questions such
as ‘Why? ’ or ‘Why does this happen?’ For example, the researcher may
fi
observe a speci c practice, a way of doing things that is counterintuitive or
flict
in con
with what s/he has been exposed to previously. This is often the
case in ‘qualitative’ research with a prime emphasis on gaining understanding. A key purpose in this latter case is to‘construct theory’, that is, to come
up with an adequate explanation (see Figure 2.1).
It should be emphasized that when doing research, interactions between
theory and data take place. For example, a researcher conducting a structural
fi
survey to test a speci c hypothesis gets an unexpected
finding,
triggering
speculations as to why this might be so: that is, the researcher tries to
fi
theorize to come up with an adequate explanation. The unexpected nding
21
22
Research in Business Studies
Theory
(1)
(2)
Data
Figure 2.1
shows
Theory or data as starting point
that
the
hypothesis
(theory)
may
be
wrong,
and
thus
forces
the
researcher to rethink prior belief.
2.7
Knowledge Skills and Research
Basically, we do research because we want to know more about ourselves and
the world around us. A number of scholars have explained different ways of
knowing (Buchler, 1955; Cohen and Nagel, 1934; Kerlinger, 1964: 6
–9):
1.
Method of tenacity
, where we hold
firmly
to the truth or the truth we
fi
know to be true, as we have always known it to be true. We may nd
ways to strengthen our belief even if the proposition or new develop-
flat.
ments may show otherwise. For example, the world is
2.
Method of authority
, where it has been established that this is the case.
For example, the Bible states that there is a God. In fact, even if such
knowledge
seems
unsound
in
certain
situations,
we
live
our
lives
according to this authoritative knowledge.
3.
A priori method
propositions
or
that
method of intuition
, where knowing is based on
are
self-evident
or ‘agreeable
to
reason’.
However,
something that is self-evident to one person might not be self-evident
to another. For example, is it self-evident that US education is inferior to
European education – is this ‘agreeable to reason’ (Kerlinger, 1964)?
4.
Method of science
body ’s
final
, where we
find
a way of knowing in which every-
conclusion is the same. In other words, there are things
whose characters are entirely independent of our opinions about them
(Buchler,
1955).
The
fi
scienti c
approach
to
obtaining
knowledge
has
self-correction as one of its characteristics. We must, however, point
out that this is a rather deterministic view of science. In our opinion,
there are several methods of acquiring knowledge that can be called
scienti
fic,
but this is the most widely accepted view. Some scholars call
it objectivity (Kerlinger, 1964). As Polanyi (1958: 4) states, scientists
systematically
and
consciously
use
the
self-corrective
aspect
of
the
2.9 Research and Ethics
fic
scienti
approach. Considering the above, as expressed by Braithwaite
(1955: 1):
The function of science . . . is to establish general laws covering the behaviours of
the empirical events or objects with which the science in question is concerned,
and thereby to enable us to connect together our knowledge of separately known
events and to make reliable predictions of events as yet unknown.
2.8
Why Research Never Stops
As mentioned in an earlier section, when solving problems we need to look at
what is already known about that type of situation or problem. In some cases,
for example in exploratory research, we make some observations to clarify
our research topic or
field. What is known in thefield and these observations
help us to understand our problems better. They also help us to ask the right
questions. Once we have better knowledge of our problem area, we try to
look at our
field in a rather systematic manner. With the help of this clarified
picture, we then build hypotheses or assumptions. Hypothesis building or
deriving assumptions leads us to the concepts and constructs we should
study to get answers to our questions. These concepts, when put together
in relation to each other, lead us to our model.
Once we have a clear understanding of our problems, assumptions, and
finding
concepts, we start thinking about how to go about
answers to our
questions. At this stage, we have to come up with a research design. And we
need to consider how we are going to collect the information needed and
what we are going to do with it. In other words, how are we going to analyse
that information? Once we have the design, we can proceed to actually
collect and analyse data. The analysis will lead us to our interpretation, what
we understand from the information we have collected and analysed.
Through
this
interpretation
we
draw
our
conclusions,
thus
improving
existing knowledge or helping in practical problem solving. This also means
that researchers coming after us, working on the same topic, will start where
we left off. In other words, when classifying their problem they will review
earlier knowledge, including our study. The never-ending research activity is
illustrated in Figure 2.2.
2.9
Research and Ethics
fl
Ethics are moral principles and values that in uence the way researchers
conduct their research activities. In fact, ethics apply to all situations and
23
24
Research in Business Studies
Improvement in
Observation,
theory or problem
literature review
solving
Interpretations
Problem
and conclusions
clarification
Assumptions
Data analysis
and hypotheses
Concepts, constructs,
Data collection
and models
Research design
Figure 2.2
The wheel of research
activities in which there may be actual or potential harm of any kind to
anybody (Churchill and Iacobucci, 2015).
fi
As researchers we have a moral responsibility to explain and nd answers
to our questions honestly and accurately. We can point out the strengths of
our methods and models but we also have to be open about the weaknesses
and reliability of our results. The readers and users of our research reports are
often less analytical than we are and believe what we say. They might not
understand
therefore,
the
underlying
inform
our
uncertainties
readers
when
our
fi
and
complexities.
results
or
We
conclusions
should,
might
be
misleading. Also, if not properly clari ed, our results and reports might
be misunderstood in a way that creates more problems than solutions to
existing problems (Ross and Harris, 1994).
The researcher’s ethical responsibility starts with problem formulation.
For
example,
if
we
want
to
study
the
role
of
subsidies
fi
performance of companies, it might not be dif cult to
find
in
the
export
data sources,
do an analysis, and come up with some conclusions. On the other hand, if
we want to study the role of bribes or other illegal methods in the export
fi
performance of companies, we might face dif culty in
and doing analysis. Moreover, we might
finding data sources
find ourselves in a moral dilemma
2.10 The Researcher and the Respondent
if our study shows unexpected or drastic results. We would then have to
ensure that the research did not cause embarrassment or any other disadvantage to people who have provided us with data (information). This leads
to another important matter: the extent to which we should collect data
from a research population that is unaware they are the subject of our
research and who have not given their consent (Saunders et al., 2016). This
is a tricky issue. One way of coping – of particular use in experimental
research – is debriefing, that is, the subjects are told about the study and its
purpose afterwards.
Many researchers have problems in deciding whether it is morally appropriate for them to continue doing research while ignoring ethical issues,
mainly because they are difficult, time consuming, or do not fit into their
research plans. They quite often believe that if they are not doing anything
illegal, it is morally acceptable. There is a difference between whether
something is legal and whether it is ethical. Many researchers do not even
consider evaluating the ethical dimensions of their research. In business
studies, awareness of the importance of ethical issues and the responsibilities
of the researcher is growing, with increasing debate on social responsibility
and consumer well-being, so that there is a danger that business research
that is not aware of ethical issues might lose respect or credibility. Companies
are also becoming more aware of the impact of our research and can sue
researchers for misleading results. For example, Beecham Products sued
Yankelovich Clancy Shulman, a research company, for more than $24
million for negligent misrepresentation of research findings, because its
market share forecasts were not upheld during the launch of Delicare, a
detergent for fine fabrics (Churchill, 1999: 42). The use of the Internet for
data collection has also raised several ethical questions regarding invasion of
privacy and using information about people without their consent.
2.10
The Researcher and the Respondent
The researcher–participant (subject) relationship is the most sensitive one
in business studies research. Quite often a researcher struggles to decide
whether to inform the participant about the real purpose of the research.
They believe that telling the whole truth might result in the participant
refusing or being reluctant to give full cooperation. For example, a student
doing an internship in a company and working on a thesis about that
company’s problem may want to conduct a competition analysis. Where
information on competition is not available, s/he wants to interview one or
more of the company’s competitors, but is concerned that if s/he reveals that
25
26
Research in Business Studies
Table 2.2
Ethical issues in the researcher–participant relationship
1.
Preserving participants’ anonymity
2.
Avoiding exposing participants to mental stress
3.
Not asking participants questions detrimental to their self-interest
4.
Being responsible in our use of special equipment and techniques,
e.g. tape-recorder, video, or health-hazardous equipment
5.
Allowing participants to withdraw at any time during the research process /
data collection
6.
Not involving participants in research without their consent
7.
Being honest with participants
8.
Not using coercion to get information
9.
Ensuring we don’t deprive participants of their rights, e.g. of self-determination
10.
Allowing participants to see the results of the research
s/he is doing an internship in one of the competing companies, the others
will not give her/him access. In our opinion, we have to be honest and
open with the participants and convince them that their position will not
be endangered.
The violation of ethics is often associated with disguised observations
and the use of deception while collecting data. One way to avoid this is to
consider whether the participant has a right to know the whole truth or not.
Churchill and Iacobucci (2015) present ethical concerns on ten areas of the
researcher –participant relationship (see Table 2.2).
Ethical issues thus require attention at an early stage of the research process,
otherwise a researcher might waste a lot of time and resources only to realize
final stage, that the project cannot
halfway through, or sometimes even at the
be completed due to ethical problems. It is the responsibility of the researcher
to assess carefully each and every point mentioned in Table 2.2, and in case of
doubt, s/he must take all reasonable precautions to inform and safeguard the
respondents.
The
issue
of
fi
con dentiality
is
fi
particularly
important.
If
the
researcher has promised anonymity and con dentiality, s/he has to take extra
care that in no way can the identity of the participant or organization be
detected. This may involve legal repercussions for the researcher. Deception is
ficult
another dif
issue: this is when researchers intentionally present their
research or its objectives as other than what they are.
Although
these
days
all
universities
demand
that
approval
is
sought
from the university’s ethical approval committee before data collection is
started, the best way to improve the researcher
– participant relationship in
this respect is to consider the following:
1.
Evaluate ethical issues right from the beginning of the research project,
at the problem-formulation stage.
2.11 The Researcher’s Moral Responsibility
2.
If you suspect problems, discuss these with your supervisor, fellow
researchers, and potential participants.
3.
Provide participants with a complete picture of your research project, its
purpose, objectives, and the type of information and access needed.
4.
fi
Assure participants of anonymity and con dentiality (if required), and
that they will not suffer any harm.
5.
Use appropriate and simple language when interacting with participants. They might not be aware of the terminology or detail of the
research topic, and they might be reluctant to ask.
6.
Facilitate understanding and answering of questions.
7.
Establish a trustworthy and credible relationship with participants.
8.
Where there are any costs involved in answering your questions or
providing you with information, you should be responsible for meeting
these costs.
9.
Assure participants that they will be able to comment on the report
before it is made public.
10.
fi
Assure participants that they will get a copy of the nal report when it
is ready.
Ethical
issues
arise
particularly
at
the
data
collection
stage,
where
a
participant cannot be forced or coerced to answer questions. Depending
upon
the
participant
method
to
of
data collection,
answer.
When
the
conducting
researcher
surveys,
has
to
persuade
researchers
are
the
happy
if they get a 30–40 per cent response rate. In interviews, they may have to
contact tens of potential participants before they get an interview.
Reporting results objectively and honestly is the most important aspect of
ethics. The results should be presented in such a way that they do not cause
embarrassment, disadvantage, or harm to any of the participants (Saunders,
2011; Zikmund, 1997). Moreover, they must not be distorted to
fit
your
purpose or favour a particular participant/target group or be presented in a
fl
way that does not re ect reality. Any misinterpretation of data will lead to
misleading results and is ethically wrong.
2.11
The Researcher s Moral Responsibility
’
The moral responsibility of the researcher relates to the social guidelines and
constraints upon research techniques and measurements. The researcher has
to make a moral judgement about the appropriateness of the research procedures. Although value judgement of research depends on the researcher
’s
own perception and interpretation of the
findings,
this evaluation is also
27
28
Research in Business Studies
fluenced
in
by the researcher’s environment and time period. This has to do
findings that conflict with
research findings are fi nal, the
with whether or not to accept and reveal certain
one’s beliefs, customs, or religion. As no
researcher has to make a decision on whether the evidence is strong enough
to draw certain conclusions from the
findings. Exactly how strong is strong
enough can be an ethical issue, as results based on trivial information could
be misleading (Forcese and Richer, 1973).
findings might lead to action that is against the principles of the
Research
researcher or the funding organization. They may suggest a certain treatment
of a labour force or a certain method of decision making to achieve optimal
ficiency which is against todays management ethics. The results might
infl uence an important decision to be made by policy makers, for example
ef
’
regarding mergers and acquisitions, anti-trust measures, or standards setting
for
a
particular
industry.
Or
they
might
discourage
subsequent
research
on the topic. In these cases, the researcher has to be ethically correct in
reporting his or her results and also the methods, techniques, and instruments used, so that readers can make a judgement about the reliability of the
findings.
fl
Figure 2.3 summarizes the factors that may in uence how research is
conducted
and
the
acceptance,
rejection,
concealing,
or
revealing
of
its
results.
As
we
can
see,
public
interest
in
a
particular
issue
might
encourage
a researcher to reveal or conceal results. For example, a study on racial
discrimination in the recruitment or promotion policies of companies or
public organizations might be a sensitive issue at a particular time, for
instance close to elections or when there is a risk of riots. Studies on the
labour– management relationship, where the results might be in favour or
against
the
interests
of
the
management
conduct. A study might lead to
findings
might
fl
in uence
a
researcher’s
that are against government rules
and regulations but ethically and morally correct. In a similar way, most
researchers have their own interests and biases. Some research is funded by
industrial
sectors,
government
institutions,
or
companies.
In
many
such
cases, researchers are dependent on continuous support from these organizations. If case
findings
are against the interests of these organizations, the
researcher may be reluctant to reveal them for fear of losing funding. In some
cases, the researcher is under peer pressure to use a certain method (e.g.
quantitative methods) even if it is not suitable or reliable for a particular
research problem. Peer pressure may also induce a researcher not to reveal
the
findings
of his/her research, especially at a particular time. All these
fl
factors carry ethical implications for researchers and may in uence procedures, methods, and analysis. Researchers need to be aware of these issues and
2.11 The Researcher’s Moral Responsibility
Public interests
and biases
Company interests
and biases
Conduct and
Government rules
findings of research
and regulations
(tools, procedures,
and analysis)
Researcher’s own
interests and biases
Peer pressure
fl
Figure 2.3 Factors in uencing research
Source:
Based on Forcese and Richer (1973: 22). © 1973. Reprinted by permission of Pearson Education, Inc.,
Upper Saddle River, NJ.
should take an ethical stance. Most universities and a number of academic
associations have drafted codes of ethical conduct in research. Some of these
are listed below:
Academy of Management (AoM) www.aomonline.org/aom.asp?ID=&page_
ID=54
Market Research Society (MRS)www.mrs.org.uk/code.htm
Social Research Association (SRA) www.the-sra.org.uk/ethical.htm
American Sociological Association (ASA)www.asanet.org/about/ethics.cfm.
29
30
Research in Business Studies
........................................................................
Further Reading
Dubois, A. and Gadde, L.-E. (2014)‘ Systematic combining: A decade later’, Journal of
Business Research, 67(6): 1277– 84.
Elg, U., Ghauri, P., Child, J., and Collinson, S. (2017)
‘ MNE micro-foundations and
routines for building a legitimate and sustainable position in emerging
markets’, Journal of Organizational Behavior, 38(9): 1320–37.
Van Maanen, J. (1995) ‘ Style as a theory’ , Organizational Science, 7(4): 641–52.
Malinowski, B. (1916/1948) Magic, Science and Religion and other Essays, New York:
Natural History Press.
Pettigrew, A. (1985) ‘ Contextual research: A natural way to link theory and practice
’,
in Lawler, E.E., Mohrman, A.M., Mohrman, S.A., Ledford, G.E., Cummings, T.G.,
and Associates (eds.), Doing Research That Is Useful for Theory and Practice
, San
Francisco, CA: Jossey-Bass.
........................................................................
Questions
1.
A
researcher
people.
The
is
interested
individuals
in
are
sensitive
unwilling
information
to
give
this
from
a
group
information.
of
This
information can, however, be obtained through others or by the use of
speci
fic
techniques (projective techniques – i.e. individuals tend to pro-
ject on others what is symptomatic for themselves). Do you think this
raises ethical dilemmas?
2.
How does research differ from practical problem solving?
3.
What are the main differences between inductive and deductive types of
research?
4.
Abduction
is
not
just
a
combination
of
induction
and
deduction.
Comment on this statement.
5.
‘Research never stops.’ Comment on this statement.
........................................................................
Exercises
1.
As a marketing manager, you are asked to launch a product (pick a
product) in China. Make a plan of how you would collect and analyse
information that could help you make a decision for this launch.
2.
You have been asked by a company to do a competition analysis for its
product in a particular market. As there is no secondary information
available, you have also to collect information from competitors of your
company. How would you handle the ethical issues in this case? Draft a
letter to send to competing companies asking for their assistance.
PART II
The Research Process
..................................................................................
P
art II (Chapters 3 to 6) covers the theoretical foundations of research and the
important activities involved in conducting research. In the present edition, we
have added sections on the ontological and epistemological background and how it
fluences our research approach. We talk about identifying a research topic and
in
framing and structuring the research problem, as well as determining the research
design, including a strategy for where to
find data and information and how it can
be operationalized in the most appropriate way. Thus, we cover the development of
adequate measurements and a plan for data collection. The role of the literature
review is also discussed.
3
The Research Process
fied true belief. If you have good reasons in support of
Knowledge is justi
the truth of your beliefs, and your belief is true and is based on good
reasons, then you have knowledge, according to traditional analysis.
M o s e r e t a l . (1 9 9 8 : 1 6 )
This chapter deals with some conceptual (theoretical) foundations of research.
Practical business research is often thought of as collecting data from various
statistical
publications,
constructing
questionnaires,
and
analysing
data
using computers. Research, however, also comprises a variety of important,
non-empirical tasks, such as
finding or
‘constructing’ a precise problem and
developing perspectives or models to represent the problem under scrutiny. In
fact, such aspects of research are often the most crucial and skill demanding.
The quality of the work done at the conceptual (theoretical) level largely
determines the quality of the
final
empirical research. This is also the case
in practical business research. Important topics focused on in this chapter are
the research process and the role of concepts and theory.
3.1
The Process Perspective
Research is often thought of as aprocess, that is a set of activities unfolding
over time. A main reason for considering it so is that research takes time and
fi
consideration. Insights may be gained gradually, and may also be modi ed
and changed over time. It is also useful to look at research as a process
with distinct stages, as different stages entail different tasks. This can help
researchers to perform these tasks systematically and to understand what is
fi
fi
to be done at a particular stage. For example, we rst have to clearly de ne
our research problem and objectives before starting to collect information/
data. Also, we have to think and state what type of data are needed and how
best they can be collected before actually doing it.
Figure 3.1 illustrates a typical research process or cycle. The illustrated
fi
process is a simpli ed one. In reality, however, the process is not so orderly
and sequential and is rather messy (see e.g. Morgan, 1983; Pettigrew, 1985).
34
The Research Process
1. Choice of research topic
2. Research problem
2a. Problem presentation
3. Research design
Literature
review
4. Measurement
5. Data
6. Sample/respondents
7. Analyses
8. Writing (completing the report/thesis)
9. Actions
Figure 3.1
The research process
Researchers should therefore not be surprised or worried if their research
process is not as systematic as presented in Figure 3.1, and if in practice they
have to go back and forth in the process all the time. For example, at one stage,
such as when doing observations, something unexpected may be discovered,
resulting in a return to an earlier stage, such as modifying the research
problem. Thus feedback loops between the various stages are more common.
It should also be noted that the starting point could be some observation that
triggers theorizing about the actual problem (see Section 2.6). Research may
also lead to new questions, which is why research seemingly never stops (see
Section 2.8).
The starting point in Figure 3.1 is theresearch topic, that is the phenomenon or theme to be studied (1). For example, you may be interested in
how firms organize their activities, how business firms conduct R&D, or how
firms enter foreign markets. Choice of research topic is important for several
reasons. For example, is the topic worth pursuing, and is it practicable?
A research topic is not a research problem. It is usually broader and more
general than a (good) research problem, such as what organization structure
is most efficient.
When moving from the more general research topic to a research
problem (2), a more specific question is addressed. For example, you may
ask (after having reviewed the literature): Are firms organized in a
3.1 The Process Perspective
Research topic
Research problem
Figure 3.2
From research topic to research problem
bureaucratic way less innovative than
firms organized in an
‘organic’ way?
1
The relationship between research topic and a research problem is illustrated
in Figure 3.2.
From the above discussion we see that a research problem is a question.
When we have really established what we want to know and how this relates
to present insights, we have a clear research problem. This is the point of
departure for further research activities.
Any problem must be captured or represented. This is done by a set of
interrelated concepts, or a ‘model’, implicitly or explicitly (2a in Figure 3.1).
fl
The way the problem is captured in uences how it is framed and understood.
fl
How the research problem is captured in uences:
•
choice of research design;
•
measurements;
•
data collection;
•
sample;
•
data analysis;
•
recommendations.
fi
In Chapter 4 we discuss more fully how to de ne and capture research
problems.
Research design (3) relates to the choice of strategy to collect the data
needed to answer the stated research problem. As will be discussed later,
research problems are multiple, and they come in many forms. In some cases
fi
the purpose is to understand a speci c phenomenon. This will often be the
case in ‘qualitative’ research. In other cases, the purpose may be to determine
the most adequate action, best mode of market entry, and so on.
1
Inspired by Burns and Stalker (1961).
35
36
The Research Process
Inspection of Figure 3.1 shows that after the choice of the overall strategy,
the choice of research design follows a series of activities. Data are carriers
of
information.
A
variety
of
data
sources
are
often
available;
however,
the various sources have both advantages and disadvantages. One can also
use several data sources in the same research project, which is known as
‘triangulation’. More recently, modern information technology, for example
the Internet, has become an important source for gathering the data needed.
Choice of data and how to collect them, from whom, and in what way, is
important. Such choices are dependent on the type of problem, the information needed and, not least, the data possibilities.
Empirical
measurements
relate
to
theoretical,
unobservable
constructs
(concepts). For example, ‘power game’ is a concept. How can/should this be
captured? Another example is the concept of ‘friend’. How do we recognize
that a person is a friend? Good measurements are a prerequisite for highquality empirical research. It is a demanding task to develop good measures.
Measurement problems will be dealt with more fully in Chapter 6.
Data must be handled, analysed, and interpreted to become meaningful
information
that
can
fl
in uence
subsequent
actions
and
results.
Various
aspects and methods of analysing data are dealt with in the data analysis
chapters for the respective type of research in Part III. Most research efforts
are reported in written form, for example as research reports, but also as
theses. Craftsmanship is needed to write a good research report (thesis). This
is dealt with in Chapter 14. In business, the outcome of research efforts often
fl
results in or in uences actions. This, however, is beyond the scope of this
book, and thus is not dealt with here.
3.2
Levels of Research
Going back to Figure 3.1, a distinction can be drawn between activities at the
theoretical (conceptual) level (2, 2a) and those at the measurement (empirical)
level (4, 5, 6, and 7). Choice of research design may be seen as the‘bridge ’
between activities at the conceptual and empirical levels.
The following should be noted:
requires
activities
at
the
all
conceptual
research – irrespective of discipline –
level.
So-called ‘theoretical
studies’
deal only with this level. For example, studies in mathematics and pure
fi
(theoretical) economics primarily relate to speci c problems without seeking
empirical evidence. Also, in business studies, important contributions have
been made that are primarily ‘theoretical ’ (even though inspired by empirical
fl
observations) such as the in uential contributions by J.D. Thompson (1967)
and J.G.
March and H.A. Simon (1958),
which have greatly shaped
the
3.3 Research and Knowledge
Conceptual
1
(theoretical)
2
LEVEL
Measurement
(empirical)
Figure 3.3
Two levels of research
thinking of and research in business administration disciplines. Theoretical
studies correspond to Circle 1 in Figure 3.3.
However, an empirical study – even a study for practical business purposes – requires efforts at the conceptual level. See Circle 2 in Figure 3.3.
Bypassing such activities and jumping to the ‘raw empirical data’ is seldom
very successful. The fact that this is often done in business does not mean
fl
that such research is good; rather it re ects lack of insight.
3.3
Research and Knowledge
Even if it is not the prime purpose of doing particular research, the main
purpose of research is to produce insights or knowledge. Knowledge implies
that we ‘know’ something, and that what we know ‘holds true’, that is, the
add
produced knowledge is valid. Doing research also implies that we
to
present knowledge: that is, research is done to create new insights. For
example,
if
a
business
firm
conducts
a
study
to
examine
what
buyers
emphasize when buying a particular product, this is done to create new
insights believed to be important to the
firm,
so that it can improve its
marketing efforts.
fi
Knowledge can be classi ed in various ways (Naegel, 1961):
•
theories/models;
•
concepts;
•
methods/techniques;
•
facts.
New insights can be acquired in any of the above categories. For example,
the researcher may develop a new theory to describe and explain how buyers
behave. New methods or techniques can be developed to assist business
managers in their decision making, and new facts may be uncovered. For
example, before entering a new market, the
firm needs knowledge to assess
the size of the market and the competitive situation in it; these are prerequisites to developing a marketing plan for the new market.
37
38
The Research Process
New insights may be acquired by demonstrating new practical implications of a theory as well: by testing hypotheses derived from theory, and
by
applying
a
method
to
a
any research should have an
new
problem.
The
important
intended contribution,
point
is
that
that is to bring or add
something new.
3.4
What Comes First: Theory or Research?
In the research literature, a distinction is often made between the following
two strategies:
1.
theory before research;
2.
research before theory.
In the
first
case, present knowledge allows for structuring the research
problem so that the researcher knows what to look for, what factors are
relevant, and what hypotheses should be tested empirically. From the above
discussion it follows that, when wrestling with problems, the researcher also
makes (or at least
should make) use
of available knowledge (earlier studies on
the topic and its related areas).
Figure 3.4
illustrates
the
two research
strategies.
In the
first
case
(1),
important tasks are to identify relevant concepts, theories, and so on, and
to adjust the concepts (theory) to the problem under scrutiny (which also
requires
a
clear
understanding
of
the
research
problem).
In
the
second
case (2), the prime task is to identify relevant factors and construct explanations (theory). This relates to different contexts of research, that is the
KNOWLEDGE (‘THEORY’)
Theories/models
Concepts
Methods/techniques
Facts
(2)
(1)
Problem
Observations/
assumptions
Figure 3.4 Production and use of theory
3.5 Some Important Issues
fi
‘context of justi cation’ (1) and the ‘context of discovery’ (2) (see Popper
(1961) for a lucid discussion). An interesting observation is that route 1 also
corresponds to the use of ‘theoretical’ knowledge for practical problems. The
user must select adequate theories and methods and adjust them to the actual
problem, which is a demanding task.
Figure 3.4 shows a broken line between the two strategies, indicating that
fi
when applying present insights to speci c problems, new observations and
new questions may give rise to a search for new explanations, methods, or
techniques.
3.5
Some Important Issues
3.5.1
Concepts
Concepts are the building blocks of any theory or model (see also Chapter 4).
A concept is an abstraction representing an object, a property of an object,
or a certain
phenomenon. ‘Cost’, ‘income’, ‘market share’,
and ‘business
strategy’ are all examples of common concepts in business and management
disciplines.
Concepts are crucial in the researcher’s tool bag. They serve a number of
important functions:
Concepts are the foundation of communication. Without a set of agreed
•
concepts, meaningful communication is impossible.
Concepts introduce a perspective – a way of looking at the empirical
•
world.
fi cation and generalization.
•
Concepts are means of classi
•
Concepts serve as components of theories (models) and thus of explanations and predictions.
Concepts are the most critical element in any theory, because theydirect
what is captured. For example the concepts ‘cognitive’ and ‘dissonance’
direct the theory of cognitive dissonance, and ‘supply’ and ‘demand’ are
key
concepts
in
economic
theory.
Even
though
many
concepts
fluence ),
everyday life are ambiguous (e.g. ‘democracy’ and ‘in
’
used
in
they must
be clear and agreed upon to be useful in research.
3.5.2
Clari
Definitions
fication
and precision of concepts are achieved through de
fi
finitions.
Here we will distinguish between two types of de nition, conceptual and
operational.
39
40
The Research Process
EXAMPLE 3.1
fi
The concept of ‘market’ as de ned in marketing literature, that is:
all the potential customers sharing a need or want who might be willing
and able to engage in exchange to satisfy that need or want.
fi
In this de nition, ‘customers’ and ‘need ’/‘want’ are among the concepts
fi
used to de ne the concept of market (Kotler, 1997).
Another
example
is
the
concept
fi ned
of ‘industry’ de
in
the
strategy
literature as:
the group of
fi rms
producing products that are close substitutes for
each other.
fi
Here ‘ rms’ , ‘products’, and ‘substitutes’ are key concepts to explain
industry (Porter, 1980).
1.
fi
fi
De
nitions that describe concepts by using other concepts are conceptual
de
nitions. A useful de nition is that concepts should:
•
•
fi
unique attributes or qualities of whatever is defined;
not be circular, i.e. must not contain any part of the thing
point out
fi
de
fi
ned; de
ning ‘market exchange’ as ‘exchange taking place in the
market’ does not enhance communication;
2.
•
be stated
•
use
being
positively ,
fi
i.e. contain the properties of the concept de ned;
clear terms.
fi
An operational de
nition is a set of procedures that describe the activities
to be performed to establish empirically the existence or degree of existence
fi
of what is described by a concept. Operational de nitions are crucial in
measurement. They tell us what to do and what to observe in order to bring
fi
the phenomenon de
ned within the range of the researcher
’s experience.
EXAMPLE 3.2
fi
‘Market share’ may be de ned operationally as:
A company’s sales of products in category X in area A during timet/Total
sales of product category X in area A during timet, which also requires
fi
speci cations of ‘sales’, product category X, area, and time period.
fi
fi
In accounting, ‘sales’ during a speci c time interval is often de ned
operationally, as shown in Figure 3.5.
3.5 Some Important Issues
EXAMPLE 3.2
(CONT.)
Inventory at t 0
Sales
Purchases
Figure 3.5
Inventory at t
1
Example
Or:
Sales = Inventory at t0 + Purchases during the period (t0 . . . t1)
À
Inventory at t1.
fi nition gives sales as measured in cost (purchase) prices or in terms of
volume (quantity). If measured in sales prices, profit will hopefully be
This de
present.
fi
Note that the value de ned differs depending on whether it is measured
in volume or value and, if it is based on value, whether the cost or sales value
fi
is used for ‘sales’ . Operational de nitions will be dealt with in more detail
when discussing measurements (see Chapter 6).
When we move from the conceptual to the empirical level in research,
concepts
are
converted
into
variables
by
mapping
them
into
a
set
of
values. For example, assigning numbers to objects involves the mapping
of a set of objects into a set of numbers. A variable is a property that takes
two or more values and is subject to change, while a constant has only
one value.
EXAMPLE 3.3
Construct (concept)
Variable
Height
. . . 150, . . . , 180, . . . cm
Gender
1 (= women), 0 (= men)
3.5.3
Theory
Theory may be viewed as a system for ordering concepts in a way that
produces
understanding
or
insights
(Zaltman
et
al.,
1977).
A
theory
41
42
The Research Process
includes more than one concept and how these concepts are linked together.
A theory is:
fi
a set of interrelated concepts, de nitions, and propositions that present a systematic
view of specifying relations among variables with the purpose of explaining and
predicting phenomena.
It is important to note the purposes of theory, that is to explain whether it
is related to understanding or prediction. For example, a researcher holds a
fi
theory of ‘how advertising works’, and uses this theory to allocate the rm’s
advertising budget based on a prediction of an outcome resulting from the
spending of the advertising money. Also note the notion of‘proposition’, that
is an assumed relationship between two concepts, such as between‘performance’ and ‘satisfaction ’.
fi
The above de nition of theory also claims it should present a systematic
view, to enhance explanation and prediction, meaning that the concepts and
relationships involved should represent a coherent ‘whole’.
fi
It is important to notice that theories focus on speci c aspects of the
phenomena or problems studied. This is done to capture the actual problem,
and (hopefully) understand (solve) it better. On the other hand, some aspects are
left out. This is done because human beings have limited cognitive capacity,
making it almost impossible to take everything into account at the same time.
3.5.4
Methods
Research methods are rules and procedures and can be seen as‘tools or ways
of
proceeding
to
solve
problems’.
Research
methods
play
several
roles,
such as:
•
‘logic’ or ways of reasoning to arrive at solutions;
•
rules for communication, i.e. to explain how the
findings
have been
achieved;
•
rules of intersubjectivity, i.e. outsiders should be able to examine and
evaluate research
findings.
Figure 3.6 illustrates the role of methods in arriving at solutions. An
important aspect is that there must be a valid reason (or‘theory’) underlying
the actual method so that it will result in the‘correct’ solution.
Problem/task
Solution/answer
Method
Figure 3.6
Use of methods
3.6 Concluding Remarks
Moreover, choice
of methods requires an
understanding of the
actual
research problem. In addition, command over the methods and the ability
to adequately choose among (and combine) methods are needed.
Concluding Remarks
3.6
Research is often associated with constructing and designing questionnaires,
measurements, statistical procedures, and so on, which can be subsumed
under the umbrella concept ‘research methodology’. Research methodology
can
be
conceived
as
a
system
of
rules
and
procedures.
Such
rules
and
procedures are important in research for several purposes:
Research methodology can be conceived as rules for
1.
speci
fic logic to acquire insights.
Research methodology is important for
2.
(in
detail)
how
the
researcher
has
intersubjectivity,
obtained
his
or
reasoning,
i.e. a
i.e. by reporting
her
finding,
the
researcher enables others to evaluate it.
communication.
Research methodology can also be considered as rules for
3.
By reporting on the rules and procedures used, others may try to replicate,
fi
or they can criticize the approach chosen and the reported ndings.
Qualifying research requires competence in logical reasoning and analysis.
The researcher thus needs to have command over the concepts and research
methodology to be used. Research methodology is thus an important tool in
fi
the researcher’s tool box. Research, however, is also closely related to nding, selecting, structuring, and solving problems. In order to grasp, represent,
and understand problems, the construction of concepts, theories, and models
is crucial. Theoretical knowledge and the ability to think conceptually are
fi
important and a prerequisite for doing quali ed research. Problems represent
the
point
problem
of
departure
fluences
in
in
research.
subsequent
The
perception
research
activities.
and
structuring
Qualifying
of
a
empirical
research therefore requires both conceptual and methodological insights.
Skills
related
to
topics
dealt
with
in
relevant use of other tools in research.
this
chapter
are crucial
to
making
43
44
The Research Process
Further Reading
........................................................................
Douglas, S.P. and Craig, C.S. (1997) ‘The changing dynamic of consumer behavior:
implications for cross-cultural research’ , International Journal of Research in
Marketing, 14(4): 379– 95.
Frankfort-Nachmias, F. and Nachmias, D. (1996) Research Methods in the Social
Sciences, 5th edn., London: Edward Arnold.
Locke, L.F., Spirduso, W.W., and Silverman, S.J. (2000)Proposals That Work:
A Guide for Planning Dissertations and Grant Proposals, 4th edn., Thousand
Oaks, CA: Sage.
Questions
........................................................................
1.
Why is research often considered as a process– and not just an outcome?
And what is a research process?
2.
What do you think are major sources of research problems?
3.
Explain and point out the differences between ‘concepts’ and ‘theories ’.
4.
How do theories differ from methods?
Exercises
........................................................................
1.
A
firm wonders whether to increase advertising expenditure for its core
products. What do you consider to be the research problem that should
fied and answered before it does so?
be identi
2.
A
firm
has designed a market test to assess the effect on sales of a
particular discount scheme. What do you consider to be the decision
problem?
4
Research Problems
What we see depends mainly on what we look for.
Sir John Lubbock
Problems, that is ‘questions’, drive research. Without research questions there
would hardly be any research at all. Research problems are not‘given’. They
are
detected
and
constructed.
How
research
problems
are
captured
and
framed drives subsequent research activities. In normal research situations,
we
fi
rst select a topic and then formulate a research problem within that
topic. The process of constructing a research problem is not quite straightforward
and
often
involves
a
lot
of
back-and-forth
adjustments.
In
this
chapter we particularly focus on how to construct and adequately capture
research problems.
4.1
The
Research Ideas
starting point of any research effort is some
attracts
attention
and
initiates
speculation.
The
idea or observation that
researcher
might
have
a
particular interest in a certain topic, such as consumer behaviour towards
imported products, or understanding mergers and acquisitions. However, to
make the topic researchable it must be turned into a research question. Because
a key purpose of research is to create new insights, there must be something
‘new’, that is, something we do not already know. Even when replicating a
previous study there is an aspect of novelty. For example, in a study conducted
among large
fi
fi
rms, a speci
c budgeting procedure has been observed to be
very effective. Is this procedure also effective in middle-sized and smaller
fi
fi
rms? Or do companies perform better after merging with a competing rm?
An
important
source
of
research
ideas
is
the
past
literature.
Reading
the past literature is often also necessary to determine whether the intended
research will contain an element of novelty. A common way to construct
a
research
problem
is
to
spot
a
gap
in
the
existing
literature.
This
may
involve resolving existing controversies, integrating different approaches, or
46
Research Problems
challenging existing beliefs (Campbell et al., 1982; Alvesson, 2011). Several
scholars suggest that formulating a research question should, in addition to
fining the domain and topic of the question, include contextual issues and
stakeholders infl uence (Silverman, 2001). By presenting the existing literade
’
ture as inadequate, incomplete, or as ignoring a certain perspective, researchers create an opportunity to contribute by showing that the present study
would add something that is missing in the current literature (Locke and
Golden-Biddle, 1997). Returning to Figure 3.1, it is seen that the literature is
consulted throughout the whole research process. For example, the literature
is consulted to identify a research problem, to plan sampling, to formulate
questions, and to choose a method of analysis. In Section 4.5 we will return to
the role of reviewing the past literature in more detail.
EXAMPLE 4.1
A researcher is interested in a particular type of decision, captured by the
so-called ‘garbage-can
models
or
decisions’,
characterized
by
situations
where ‘solutions are searching for problems’. This is similar to the situation
where the researcher has a tool, for example linear programming, and is
searching for a problem to apply the tool to. Review of the literature reveals
that such decisions have primarily been studied and found in public organizations. The researcher raises the question: Do such decisions also take
place in private
fi rms
(organizations)?
Once the researcher knows the topic, it is a good idea to look at earlier
theses and journal articles on that topic. Many of these present ideas for
future research in that particular area. Reading the literature is not the only
way to get good research ideas. Reading may not even be the most important
way at a very early stage. The newsletter for ACR (Association of Consumer
ACR News
Research) had an interesting piece (
, 1995) suggesting,
first,
that
good research ideas relate to creativity. To foster creativity one may brainstorm with other people interested in the topic. A common observation is also
fl
that one idea may generate new ones. So let ideas ow. The
first ideas are not
always the best ones.
Important sources for good research ideas are to:
•
Observe
the
real
world.
Look
at
how
people
and
organizations
are
working (or not working) to generate questions as to why things happen
the
way
they
do
(e.g.
go
to
a
shopping
first time).
everything as if it was for the
mall
and
actively
observe
4.2 Wrestling with Research Problems
Look for ‘missing holes’ in the literature to see what has really been
•
addressed and what has been neglected (e.g. Do the studies on mergers
and acquisitions
deal
with
between two competing
performance?
firms?).
Do
they deal
with
mergers
To improve your research idea as input to your research problem, get
feedback. Discuss your research idea with friends, colleagues, your supervisor – and other people who are interested in similar topics.
Wrestling with Research Problems
4.2
Wrestling with a research problem involves the search for structure and
fication
identi
of the ‘real ’ problem; that is trying to answer questions such
as: What do I want to know? and How do I want to map the problem?
first
As emphasized, such questions are important in research. The
step in
(practical) research is therefore to ask the followingtwo questions:
1.
What is the actual problem?
2.
What is the best way to solve the problem?
Although some questions seem trivial in the beginning, the more we read
and
brainstorm,
Answering the
the
first
more
we
realize
that
this
is
not,
in
fact,
the
case.
question implies that the researcher really knows what
s/he wants to know. A common mistake is to go ahead with data collection
fi
and other ‘practical ’ activities before knowing the speci c research problem.
Such an approach often ends up in a situation where ‘a bunch of data
is searching for a problem’; time runs out and money is wasted. To avoid
misunderstanding, this does not mean that no observations should be done
prior to the research. It is often very useful to conduct some preliminary
observations (a pilot study) in order to explore and get acquainted with the
phenomenon (problem) and arrive at the actual research question.
A useful strategy to get hold of the research problem is to ask questions.
Good questions have the following characteristics:
•
They express relationship(s) between two (or more) variables (e.g. Would
fi
an increase in marketing budget increase our pro ts?).
•
They are clear, that is what is asked is understood (e.g. Is culture the main
problem in the post-merger integration of two competing
The
advantage
of
expressing
relationships
between
firms?).
variables
is
that
they can be tested. For example, a marketing manager wonders whether
the marketing effort should be directed towards large or small households,
47
48
Research Problems
depending upon where the propensity to purchase the
firm s
’
product is the
highest. A possible question is thus:
Is there a relationship between household size and propensity to purchase
the particular product?
fically:
Or more speci
Are large households more likely to buy the particular product than smaller
households?
Note that in the above case the following two concepts are included:
household size and propensity to purchase. The relationship between the
two concepts is a
finitions
de
to
hypothesis.
measure
As discussed in Chapter 3, we use operational
concepts
example scores on scales, become
(constructs).
The
actual
measures,
for
variables.
Given available data, for example data gathered through test marketing in
fic area of household size and whether the item was bought or not, the
a speci
hypothesis can be tested. Suppose that a test marketing programme followed
up by a survey study based on a random sample of households shows the
following:
1
Household size
No. of households
Purchase
Large
200
50
Small
300
30
Total
500
80
Here it is evident that the propensity to purchase, meaning purchase/
number of households, is higher for large households, that is 50/200 = 0.25
2
and 30/300 = 0.10, respectively. This also allows for statistical testing.
An important point is that by expressing relationships as in the above
question (hypothesis)
falsi fication is allowed (which is at the heart of hypoth-
3
esis testing).
An
initial
research
problem
is
often
rather
fi
vague
and
general.
For
example, a small business manager has dif culty in understanding what
fluences
in
1
2
3
the
firm s
’
performance in ‘good ’ and ‘bad’ times. How can this
Survey studies are dealt with in Chapter 12.
For statistical testing of hypotheses, see Chapter 11.
falsifying,
A key assumption is that researchers advance knowledge not by verifying, but by
i.e. by letting the assumed hypotheses be tested so that they can be rejected.
4.2 Wrestling with Research Problems
ambiguous problem be approached? From cost accounting we know the
following:
fi
Pro t = (Price
– Variable
costs)
± Quantity – Fixed costs
Based on this simple equation (model), we may ask several questions,
such as:
•
Do the prices for the
•
Does
the
firm
use
firm s product fluctuate?
specifi c raw material
’
a
in price?
•
Does the demand for the
which
fluctuates
highly
firm s product fluctuate?
’
By asking such questions, we can narrow the problem down, and concentrate our effort on solving the real problem. But there is more to this, such as
what initiates the questions? In the above examples, the questions are all
theory driven, that is, existing theory (cf. the above model) is used as the basis
for the questions raised. In fact, a prime value of theory is to identify factors
(variables) and relate them to each other and examine such relationships to
provide explanations.
In the above examples, questions were used to‘structure’ the problems. By
using existing knowledge the researcher will often be able to structure the
problem, so that hypotheses may be derived and tested, for example.
Problems may be more or less understood. A distinction is often made
between ‘structured’ and ‘unstructured ’ problems. It should be noted that it is
not the problems per se, but the understanding of the problems that is more
or less structured. The structuredness of the research problem has implications for choice of research design and research methods. Research practice is
fluenced by the researcher s perspective on the philosophy of science
also in
’
(see Chapter 2), training, and so on. In most cases, a multi-method strategy is
used while formulating a research problem, which varies depending on the
literature
review,
managerial
practices,
and
the
researcher
’s
orientation
fl
(Brewer and Hunter, 1989). How understanding of the problem in uences
choice of research design is dealt with in Chapter 5. The notions of
‘quantitative’ and ‘qualitative’
methods
relate
partly
to
differences
in
problem
structure, but also to differences in the philosophy of science perspective
held by the researcher, as discussed in Chapter 2 (Figure 4.1).
The research problem, that is what the researcher wants to know intuitively, will have an impact on the choice of research approach. For example,
the researcher may be interested in how managers think about strategic
issues,
initiating
the
question:
How can
such
insights
be
obtained?
The
researcher may have some ‘favourite’ methods (e.g. use of surveys) that
fl
may also in uence their choice of approach. The researcher
’s beliefs about
49
50
Research Problems
Philosophy
Problem
of science
Method
Research
practice
Figure 4.1 Problem, methods, and research practice
what constitute relevant insights and how such insights may be obtained will
fluence the choice of research approach.
also in
4.3
The
Research Problem and Research Purpose
first
step towards
finding
answers is to discover a problem/question. If
you are not able to formulate a problem clearly, your answer cannot be
correct. In other words, you need to
find
a problem in your reality (topic/
field). Questions do not exist, they have to be asked: If we drop an apple, it
‘
falls to the ground. This is obvious for everybody – except for Newton’
(Wiedersheim-Paul and Eriksson, 1998: 38).
Strauss and Corbin (1990: 34–6) suggest that there are several sources
of researchable problems, such as:
assigned research problem,
as perhaps
suggested by the supervisor or professor. This is often useful for students, as
it will provide them with a ‘do-able’ and ‘relevant ’ problem. Another source
is
the technical literature, which can point out a relatively unexplored area or
a topic that needs further development. It may also suggest a new approach
to
solve
stimulate
an
old
the
problem.
researcher
The
and
and professional experiences
reading
of technical
enhance his/her
literature
curiosity.
would also
Finally,
personal
are often the source of a research problem.
Professional experience often leads to the judgement that existing practice
fi
is not ef cient, fair, or equitable. Research into the issue would then correct
this situation.
While the research problem explains what you are going to investigate,
the purpose explains why and how extensive the investigation will be. It
also explains what you really want to
find out and how your answer can be
used. The purpose therefore cannot be stated in one sentence and has to
4.3 Research Problem and Research Purpose
be explained in a paragraph or two. It should be clear whether the purpose of
the research is to describe, explain, understand, or predict, and for whom.
In other words, who can make use of your results? This is important because
the reader/supervisor/examiner will check from your result whether you have
fi
achieved the stated purpose. If the purpose is not clear, then it is dif cult to
make that judgement.
The researcher needs to revisit the purpose of the research while presenting
results
and
drawing
conclusions
and
explain
whether
the
purpose
was
achieved as suggested in the earlier sections. In case the original purpose
has
how
not
the
been
achieved,
original
then
purpose
the
was
researcher
adjusted
or
needs
fi
modi ed
to
explain
during
the
why
and
research
process, perhaps due to problems in data collection, time constraints, or costs
involved.
Students often have problems understanding the importance of theory and
methodology in writing a good business report or thesis. This frustration is
further aggravated by the absence of a suitable text that they can use as a
guide to methodological issues in their project work. The message of this
fi
book is that scienti c methods are a question of consciousness and aware-
fi
ness, and should not be seen as dif cult, strange, or unnecessary.
Research is a process of planning, executing, and investigating in order to
find answers to our specific questions. In order to get reliable answers to our
questions, we need to do this investigation in a systematic manner, so that
it is easier for others to understand the logic of our research and to believe in
our report.
Students
need
to
understand
the
basic
methodological
approaches
to
management and business research. This understanding helps them in the
fi
initial stages of their thesis and project work, providing them with con dence and purpose. We do not intend to give students ready-made tools or
advocate a particular approach. The idea is to let students understand that
there is not one ‘best’ method for business research and that the choice of
method depends upon the research problem, the research design, and the
purpose of the research. Moreover, it is also dependent on the capabilities
and resources available for the particular research. It is more important to
first understand what you really want to know than to look for a systematic
and easy or quick way to do it. Although there are several schools and
opinions on management and on the study of management and other social
sciences, our purpose is not to get involved in this discussion (see e.g. Pfeffer,
1993, 1995; Van Maanen, 1995; Holland, 1999). Instead, we want to synthesize and simplify this research to provide practical guidelines to students
fi
so that they can ef ciently handle their research projects and write good
dissertations and project reports.
51
52
Research Problems
4.4
Models in Research
In our discussion above, we showed how research problems should be –
through
questions
–
mapped
into
a
limited
set
of
factors
(variables).
When dealing with a research problem, somerepresentation of the problem is
needed. This is also the case ‘in real life’. For example, when a business
manager instructs employees to smile, the following representation ormodel
may be in his mind:‘When my employees smile, the customers feel comfortable
and are more willing to buy.’ The manager’s model can be illustrated as follows:
Smiling employees
)
Customers feel well
)
Willingness to buy
We all hold such representations/models. Often, however, they are implicit
and ambiguous.
firms (organizations) advertise
quali
fications required, such as
Another example is the following. When
for employees, they usually indicate the
educational background, experience, and so on. Firms seek employees to
perform. The implicit assumptions or model underlying such ads are thus:
fi
Criteria (quali cations) e.g. education, experience
)
Performance
Models play a dominant role in research. They are closely related to the
notion of theory, implying a systematic organization of, and relationships
between, concepts. Key characteristics of a model are:
1.
Representation: the object or phenomenon is represented by the model.
The model is not the object or phenomenon itself.
2.
fi
fi
Simpli cation: a model simpli es by reducing the number of factors
included. This is done to make it manageable because taking everything
into account often becomes impossible.
3.
Relationship(s) between the factors included. In research one is very
often interested in relationships between factors (constructs), and, as will
be discussed later, in causal relationships in particular, because if one
fl
knows what the cause– effect relationships are, one may in uence the
outcome.
Above we used the ‘pro
fi
Pro t = (Price
fit planning
– Variable
’ model known to business students:
cost)
±
Quantity
– Fixed
cost
First, this model is a general representation to capture economic aspects
firms (and is definitely not the firm itself ). Second, it is clearly a
simplifi cation, as a variety of other factors that may in
fluence the firm and
important to
its performance are left out. Note howfew concepts (variables) are used, that
4.4 Models in Research
is ‘costs ’ (variable and
fixed),
‘quantity ’, ‘price’, and ‘pro
fit . Third, it is easily
’
seen that the various factors are related. By changing, say, price, and keeping
fi
the other factors to the right of the equal sign constant, pro t will change.
In research (and this is also the case in practical research), a prime task is to
‘structure’ the problem. This, to a substantial degree, relates to identifying
relevant factors and relating them to each other to
map
and
frame
the
problem under scrutiny.
As noted above, research problems may be more or less structured. This
has implications for the choice of research strategy. In the research literature,
a distinction is often made between ‘theory before research’ and ‘research
before theory’.
In the
first case, important tasks are to identify relevant concepts, theories,
and so on, and to adjust the concepts (theory) to the problem under scrutiny,
which also requires a clear understanding of the research problem. When this
is the case, the researcher knows what to look for, what factors are relevant,
and what hypotheses to test empirically.
The second strategy (research before theory) starts with observations or
gathering of data. A couple of things should be noted before choosing such a
strategy, however:
1.
There should be a
reason
for the chosen approach. If relevant knowledge
already exists, this easily ends up as ‘reinventing the wheel’ (see the
above discussion of understanding the research problem).
2.
This
approach
‘theory
implies ‘theory
testing ’.
The
construction’,
knowledge/skill
which
requirements
is
different
for
doing
from
such
research are different but equally demanding, as doing structured theory
testing includes use of statistical methods. Most students in business
administration have almost
no
training in such research. If for some
no
reason the student dislikes, say, statistics, this in
way guarantees that
s/he can do a good ‘theory-constructing’ study; rather, the opposite will
be the case.
In
the
latter
case,
the
prime
task
is
to
identify
relevant
factors
and
construct explanations (theory). This, however, does not mean that inspecting the literature is irrelevant. Past insights will often allow for ideas or
‘hunches’ on what to look for and how to understand the research problem.
4.4.1
General Concepts and Specific Use
It is also important to be aware that theories/methods and concepts are
general, meaning that they allow for subsuming a variety of specific research
problems, which of course is useful. On the other hand, being general, the
53
54
Research Problems
various
theories,
concepts,
or
models
possess
almost
no
content
about
the actual problem. The researcher’s task is to give the concepts/theories/
models some content.
The notion of the ‘value chain’ is known to any students who have taken a
fi
fi firm in question. This may include the
of the actual firm s activities as well as
course in business strategy. To become of any use for the rm, this general
term must be adjusted to the speci c
identi
fication
fication
and classi
’
linkages between the activities. In order to make use of the general concepts
(theories) the researcher must be able toselect, adjust, and apply such tools
fi
to her or his speci c problem. This is a demanding task, which requires
insight and training. Misuse and non-use of relevant knowledge – as fre-
flect
quently observed – re
lack of such skills, and not, as is often believed,
‘practicality’.
4.4.2 Model Purposes
Models may be used for a variety of purposes. At the general level, we may
distinguish between:
1.
description;
2.
explanation;
3.
prediction/forecasting;
4.
guidance of activities.
1.
A model may describe how‘things are’. An example of a descriptive
model is the organizational chart. Figure 4.2 represents a (naive) description
of the formal organization. Note that this general model only containsone
A
C
Figure 4.2 Organizational chart (a model)
B
4.4 Models in Research
class of variables, that is positions (A, B, and C). The direction of the lines
indicates authority–responsibility relationships. A has formal authority over
B, B is responsible to A, and B and C are at the same authority level.
Are such simple models useful in business research? Description of the
firm may, for example, be combined with analysis and
formal authority in a
assessment of the knowledge and skill requirements of the various positions
fi
and can thus be used for identi cation of knowledge gaps to be
filled.
Alternatively, description of the actual formal structure may be compared
with some ‘ideal’ structure, which may be used for improving the organizational structure, or such descriptions conducted at different points in time
4,5
may be used to study structural changes.
Descriptions come in many forms. Suppose that a researcher wants to
describe the
informal
fi
fi
organization of a company. This requires a speci ed
and detailed de nition of the concept ‘informal organization’, including
what aspects should
be addressed. For example, the researcher may end up
flow in the company, that is, who is interacting
fl
communication fl ow or from power relationships reflected in
studying the communication
with whom; or s/he may study who in uences decisions, to uncover potential
deviations in
the formal organizational chart.
Another example is the researcher who wants to study the buying behaviour of a
firm s business customers. Depending on the purpose of the study,
’
this might be framed in several ways. For example, the study may focus on
what
the business
firms
are buying, or on
how the
firms
business
buy. This
may include mapping how buying processes are triggered, events taking
place and how they are related in the buying processes.
2.
A model may explain phenomena. The model of ‘pro
fit
planning’
fi
used earlier may be used for explanation purposes. In this model, pro t is
explained by the difference between unit price and variable cost per unit
multiplied by quantity sold minus
fixed costs.
Suppose a researcher wants to explain why some
fi
firms
succeed while
others fail in an industry. This requires a de nition of what is meant by
fi
success. Moreover, it requires an identi cation of the factors and processes
that may produce success and failure.
4
fi
This relates to different contexts of research, i.e. the‘context of justi cation ’ and ‘context
of discovery’ respectively. See Popper (1961) for a lucid discussion.
5
t St , St
Comparisons of structural description(s) at different points in time (),
1
2
...
can be
seen as a special case of comparative static analysis as frequently used in economics.
Changes are inferred by comparing the static descriptions from different points in time.
55
56
Research Problems
Lave and March (1993) have proposed the following approach to mapping
(model) problems:
•
Observe some facts (e.g. success or failure offirms).
•
Look at the facts as if they were the end results of some unknown
process
(model). Then speculate about the processes that might have produced
such a result.
deduce other
Then
•
results (implications/consequences/predictions) from
the model.
Then ask yourself whether these other implications are true and produce
•
new models if necessary.
The main advice from the recommended approach is:
•
Think ‘process’.
•
Develop interesting implications.
•
Look for generality.
When working with problems and models it is useful to keep them as
simple as possible. More variables should only be included when it is useful,
that is, to improve the quality of the explanation. A good model– or, more
correctly, its implications – should be
testable . For example, a football team
has just lost a game. The coach explains the loss as follows:‘I ’m sorry. The
problem was that our players didn’t have enough
fighting spirit, enough will
to win.’ The coach’s (implicit) model can be depicted as follows:
f fi
Outcome (win/loss) = ( ghting spirit)
This model is
falsi
3.
circular as
it explains any outcome. The model can never be
fied as it cannot be tested.
A model may be used to predict an outcome.Many business studies
are preoccupied with prediction/forecasting, for example forecasting of sales,
prices, and so on. In their simplest form, such predictions are based on
extrapolation of past behaviour (development). For example, the following
model has been developed to predict population size in a given area:
Pti = Pt
0
± 1.007 ti ² t
(
0)
where:
Pti = population size at time ti
Pt = population size at a specific point in time, e.g. 1 January 2005
0
Closer inspection of the model shows that it contains only one variable,
ti t
i.e. time (
–
0).
Both
Pt
0
and 1.007 are constants.
4.5 The Role of Reviewing Past Literature
When calculating different values for (ti – t0), 0, 1, 2, . . . ,n , a smoothly
growing curve emerges. Is this a good or a bad model? It only maps a speci
fic
pattern (depending on the size of the constants), unable to capture the impact
of sudden changes, for example unemployment due to emigration or people
flocking to the area due to new business opportunities.
either a descriptive or an explanatory model must be complemented with arule of choice.
For example:
4.
•
•
•
A model may be used to guide business decisions:
Drop product if (price ² variable cost) < amount k.
Drop product if (price ² variable cost) ± quantity < contribution c.
After describing market size ² enter market if total sales greater than $X.
EXAMPLE 4.2
fl
A study shows that students’ satisfaction with a programme is in uenced by:
(1) the attention given by the instructor, (2) whether they feel the grading is
fi
fair, and (3) exam score. The study also reveals that the students have difculties
in
research
understanding
how
their
performance
is
evaluated.
Here
the
findings indicate that an important task is to clarify and make explicit
the evaluation criteria used, and explain how these criteria are applied.
4.5
The Role of Reviewing Past Literature
When a business student is conducting a research project as a part of their
degree, they are supposed to use ‘relevant theory’, that is they must demonstrate that they can apply relevant parts of the knowledge that they were
exposed to in the actual programme. There is nothing‘unpractical’ in this, as
all qualified research builds on prior knowledge. To most business students,
nagging questions are what to include and how should the literature be
reviewed? In order to answer such questions, it should be noted that above
we have emphasized the word ‘relevant ’. This means that what is included
should be of importance for the study. For researchers using quantitative
methods, existing literature has a more specific use as it allows them to
discover gaps in understanding. It also helps them to develop theoretical and
conceptual frameworks and models and to identify important variables
and test the relationships between them. For qualitative studies, quite often
57
58
Research Problems
the researcher wants to discover relevant variables and relationships between
them and to put together these variables in a new way to enhance our
understanding. In this case, the researcher needs to develop the theoretical
framework that evolves during the research itself. As the research evolves,
relevant elements from previous studies need to be incorporated (Strauss and
Corbin, 1990: 51). Moreover, the following should be taken into account:
1.
purposes of
The prime
•
•
•
the literature review are to:
frame the problem under scrutiny;
identify relevant concepts, methods/techniques and facts;
position the study. (Any study should add something ‘new ’.)
A useful strategy at the initial stage of the project is for researchers to expose
themselves to a variety of sources dealing with the topic, for example by
reading journal articles and textbooks, and having discussions with‘experts ’
(such as a supervisor or people from the industry). It should be noted that
broad exposure to information and ‘incubation’ are considered important in
most creative techniques.
Hint: To
get quick insights, start with recent state-of-the-art or up-to-date
reviews done by others, which are available in most disciplines.
Then, after this exposure to information, they should start active questioning to frame the problem. The more precise the problem statement, the
better direction for the research activities to follow. This will also help clarify
what the
2.
the study is supposed to be.
Based on the activities in (1) above, the researcher should be able to state
the
3.
intended contribution of
criteria
for inclusion in the written literature review.
Based on (2), a
systematic search for relevant contributions, e.g. by using
computerized
library
services,
can
be
undertaken,
and
the
various
sources gathered to supplement the initial search (1).
4.
In
writing
up
the
literature
review,
conscious
decisions
on
what
to
emphasize should be made. If the focus is on mapping the problem,
the emphasis may be on prior conceptualizations. If, however, the main
focus
is
on
how
the
variables
should
be
measured,
the
emphasis
should be mainly on measurement procedures used in prior studies. This
implies that the literature inspected need
the
written
literature
review.
What
to
not necessarily be
include
should
be
included in
determined
by the problem and the criteria used.
5.
A literature review should also include
evaluation
and
critique
of the
sources reviewed. To evaluate and critique, some criteria are needed.
4.5 The Role of Reviewing Past Literature
Such criteria can be thought of as ‘ideals ’ with which to compare
past research, and they must be found or constructed. For example, a
researcher is interested in studying entry modes in foreign markets.
The researcher observes that the great majority of past studies have been
conducted in Western countries, thus identifying the criterion‘global’ or
‘all
countries’. Based on such an evaluation and critique, the researcher
’s
own choice of conceptualization and later research design should be
argued for.
EXAMPLE 4.3
A researcher has observed that the propensity to invest in foreign markets
varies across industries. Many theories and perspectives have been applied
in the study of foreign direct investments. In this study s/he is particularly
interested in whether conditions to perform transactions may vary across
industries when they are operating internationally. This gives directions for
reviewing theory of transaction cost economics, and empirical studies of
fl
transaction costs (and in uencing factors) in various industries.
NB: The search for and review of literature takes time, not least because it
is demanding and time consuming to get hold of the research problem.
and thinking often produce fruitful
results. Here are some good sources to help youfi nd relevant literature:
Therefore, try to get a head start! Reading
•
state-of-the-art articles/reviews;
•
databases (the Internet);
•
conference proceedings/journals;
•
conference participation/networking;
•
‘experts ’;
•
computer search using keywords
–
this can be done effectively on the
library website of your school or university.
59
60
Research Problems
Further Reading
........................................................................
Churchill, G.A. and Iacobucci, D. (2015) Marketing Research: Methodological
Foundations , 11th edn., Mason, TN: Earlie Lite Books (Chapter 3).
Lilien, G.L., Kotler, P., and Moorthy, K.S. (1992)Marketing Models, Englewood Cliffs,
NJ: Prentice Hall.
McCaslin, M.L. and Scott, K.W. (2003) ‘The
five question method for framing
qualitative research study’, The Qualitative Report, 8(3): 447– 61.
Questions
........................................................................
1.
What are the roles of ideas and speculation in research?
2.
What are the key characteristics of models?
3.
Explain the differences between descriptive and explanatory models.
4.
Are decisions in business based on models? Give an example.
5.
Why do researchers review existing literature when conducting research?
Exercises
........................................................................
1.
You are in charge of a
financial
institution offering credit. Naturally,
you are interested in limiting losses for your institution. Develop a model
that can guide your decisions as to whether or not applicants should be
allowed credit.
2.
You are employed by a
firm
selling your product directly to private
households. It is costly to visit private homes. You have also observed
that some households buy at the
first
visit, some at the second one,
and that some households never buy in spite of several visits to their
homes. Suggest a model to determine number of visits. Specify your
assumptions.
5
Research Design
Research designs are master techniques . . .
K o r n h a u s e r a n d L a z a r s f e l d (1 9 5 5 : 3 9 6 ) , r e p r i n t e d
with permission of The Free Press
The research design is the overall plan for relating the conceptual research
problem to relevant and practicable empirical research. In other words, the
research design provides a plan or a framework for data collection and
analysis. It reveals the type of research (e.g. exploratory, descriptive, or
causal) and the priorities of the researcher. The research methods, on the
other hand, refer to the techniques used to collect and analyse data.
5.1
The Design Problem
Empirical research is conducted to answer or elucidate research questions.
Poorly formulated research questions will lead to misguided research design.
Some
advocate
an
open
approach,
with
no
research
questions.
This
is,
however, a very risky approach (Bryman and Bell, 2003; Hammersley and
Atkinson, 1995). Strategic choice of
research design should
come up with an
approach that allows for solving the research problem in the best possible
way – within the given constraints. In other words, a research design should
be
effective in
producing the wanted information within the
constraints put
on the researcher, for example time, budgetary, and skill constraints. This
last point is important, even though too frequently overlooked. In business,
research
results
must
often
be
produced
within
strict
time
constraints,
limiting the number of possible research options. What is more, the business
student usually has a limited amount of time to produce a research report for
her or his degree. In most cases, the amount of money available for doing the
research is also limited, and the student’s research competence and experience (usually) have their limitations as well.
Choice of research design can be conceived of as the overall strategy to
fl
get the information wanted. This choice in uences the subsequent research
62
Research Design
activities, for example what data to collect and how they should be collected.
The respected social scientists Arthur Kornhauser and Paul Lazarsfeld once
claimed that research design played the role of ‘master techniques’, while
statistical analysis of the data collected was termed ‘servant techniques’
(Kornhauser and Lazarsfeld, 1955).
Design errors occur too often, often through neglect of the design problem.
The typical approach: ‘Let ’s prepare a questionnaire and get some data
’ easily
ends up with ‘a bunch of data’, which – after time has run out and the money
has
been
used – leaves
the
researcher
(student)
with ‘a
bunch
of
data
searching for a problem’ and a research design.
Other common mistakes are making wrong or irrelevant design choices, for
example by examining a badly understood problem with a very structured
design or, as seems even more common as‘qualitative methods’ have become
increasingly
popular,
by
examining
structured,
well-understood
problems
fi
by ‘unstructured ’ methods, making it dif cult to solve the research problem
adequately. The importance of the problem–research design relationship is
discussed below.
EXAMPLE 5.1
Firms are often interested to know what consumers think about their product
or service offerings. The research challenge is to design a study that allows
access to such information. One way to proceed is to ask consumers directly.
However, consumers may not hold explicit views about a
firm’s
products.
Thus a study that gradually uncovers whether the consumers know the
products at all and whether they have tried or used the product or service,
will probably be preferable.
5.2
Problem Structure and Research Design
fi
Research problems are in nite, and they come in many forms. Consider the
following examples:
1.
A political party wants to conduct a poll to examine its share of voters.
This is a structured problem. The political party knows what information
is wanted, that is, the fraction (or percentage) of voters.
2.
An advertising company has produced two sets of copy and wants to
know which is the most effective in an advertising campaign. Again, the
5.2 Problem Structure and Research Design
research problem is structured. The company wants to know which (if
>
either) advertisement copy (A or B) is the better, that is whether A
B
>
B,
A, or A = B. Moreover, in this case the advertisement is seen as a
‘cause’ that may produce some effect (e.g. awareness, interest, or sales).
3.
Company X’s sales have dropped in the last three months. The management does not know why. In this case, the management has made an
observation, that the sales are dropping. The management does not know
what
has
caused
the
decline
in
sales.
This
is
a
more
unstructured
problem.
The above examples show (among other things) that problems may vary in
structure. Based on problem structure, we may distinguish between the three
main classes of research design:
5.2.1
Research design
Problem structure
Exploratory
Unstructured
Descriptive
Structured
Causal
Structured
Exploratory Research
When the research problem is badly understood, a (more or less) exploratory
research design is adequate. An example will illustrate this. Consider your
favourite Friday-night TV detective series. Most such stories start with a
phone call leading the detective to a dead person, apparently murdered. The
problem the detective is confronted with is: Who did it, who is the guilty
person (if any)? How should the detective proceed? He (or she) collects data
and tries to
find a lead. As new information comes up,
the picture becomes
clearer, and at the end, the detective has found the answers.
A key characteristic of the detective’s approach to solving the problem is
its
fl exibility.
As new pieces of information are available, the search for the
solution may change direction. But there is more to this:
•
no
Research problems may be more or less understood. There is
reason
not to use available a priori information. As the detective does, so too
may the researcher have ‘suspects’. This is often the case in medical
research, where potential causes are examined in a experimental laboratory
setting.
(This
indicates
that
even
experiments
can
be
used
in
exploratory research: see Section 5.4.)
•
As with other types of research, exploratory research should be conducted in the best possible way.
63
64
Research Design
Exploratory research requires skills, as do all types of research, but the
•
skill requirements differ. Key skill requirements in exploratory research
are
often
the
ability
to
observe,
collect
information,
and
construct
explanation, that is theorizing.
5.2.2
Descriptive Research
In descriptive research the problem is structured and well understood. Consider the case where a
firm
wants to look at the ‘size of market M’. The
first
fi
problem as such is clear. What is needed is
a classi cation of what is
meant by ‘market ’. Is it the number of people – the actual and potential
fi
fi
fi
buyers of a speci c product group within a speci c area, within a speci ed
time period? Assume agreement on the latter interpretation, that is actual
fi
fi
and potential buyers of a speci c product group (e.g. X) within a speci ed
time period (say one year). The researcher
’s task now is to produce this
information. What would be the best research strategy?
Assume that there is no relevant data publicly available. The researcher
plans to collect the data by a survey using personal interviews. A detailed
plan must be made with regard to how many and who to interview, that is, a
sampling plan. The researcher must also construct questions, that is measurements, to get information about purchase (or use) of the product. Good
measurements are crucial in research. Then procedures must be developed for
how the interviews should be conducted, questions reported, and so on. All
interviews should be conducted in the same way, meaning that the variation
in the data collection should be as small as possible. Thus key characteristics
of descriptive research are
structure, precise rules,
and
procedures.
A good
example is the procedure used by medical doctors when examining a person’s height. The person has to take his shoes off, stretch his legs, and look
all
straight ahead. The same procedure is used for
persons measured.
Descriptive studies may include more than one variable. For example, for
some reason the researcher wants to describe smokers by social class. Again,
the
researcher
is
confronted
with
conceptual
and
fi
de nitional
problems.
When solved, procedures on how to collect the data must be determined to
produce the data needed to answer the research question. In this case, the
task can be conceived of as completing the cross-table in Table 5.1. (Preparation and analysis of cross-tables are dealt with in Section 11.4.)
5.2.3
Causal Research
In causal research, the problems under scrutiny are also structured. However,
in contrast to descriptive research, the researcher is confronted with‘causeand-effect ’ problems, as illustrated in the advertising example earlier. The
5.2 Problem Structure and Research Design
Table 5.1 Cross-table
Social Class
Yes
No
Total
n=
Smoke
I
II
III
IV
Total
100%
( )
100%
( )
100%
( )
100%
( )
100%
( )
main tasks in such research are to isolate the cause(s), and assess whether and
to what extent these cause(s) result(s) in effect(s). Examples of questions in
causal research are:
Is the medical drug effective?
What dose is the most effective?
Does the advertising help in achieving greater market share?
Such problems are discussed more fully in the following sections.
‘
’
•
•
•
EXAMPLE 5.2
A
sample
of
middle-aged
people
is
randomly
assigned
to
four
weight-
reducing programmes: (1) diet, (2) exercise, (3) education about nutrition,
or (4) control group (the group not following any of the programmes). The
people were weighed on 1 February 2019 and again
The
findings
five
months later.
show:
Groups
Diet
Exercise
Education
Control
À 4.1
À 6.1
À 1.5
Weight change
À5.2
Standard deviation
(2.3)
(1.5)
(3.5)
(1.2)
n =
(30)
(30)
(30)
(30)
kg
kg
kg
kg
The data report average weight losses, standard deviations, and number of
participants in each group.
The data show that all groups
diet,
exercise,
and
education
–
on average
groups
lost
–
more
have lost weight, but the
than
the
control
group.
Here diet, exercise, and education are seen as potential causes of weight loss.
65
66
Research Design
The Classic Experiment
5.3
The ‘classic’ experiment is a useful starting point for thinking about research
design, even though – as will be discussed below – it is little used in business
studies
research.
In
the
simplest
form
of
the
classic
experiment,
the
researcher randomly assigns subjects to one of two groups: an experimental
group
or
a
control
group.
The
researcher
then
gives
a ‘treatment’
to
the
experimental group but not to the control group. Subsequently s/he measures
some
attribute
of
each
of
the
subjects,
and
compares
the
post-treatment
mean value for the experimental group with that of the control group:
•
If the former is larger than the latter, then the researcher may conclude
that the treatment has had a positive impact.
•
If the former is smaller than the latter, then the researcher may conclude
that the treatment has had a negative impact.
•
If the two means are similar, then the researcher may conclude that the
treatment has had little or no impact.
One
possible
complication
is
that
the
subjects
in
each
group
may
have
different values of the attribute being measured even before the treatment
is administered, hence a more sophisticated design would measure the values
of the attribute both before and after the treatment. The researcher would
then compare the changes in the mean values of the attribute for the experimental group and for the control group – see Example 5.3.
EXAMPLE 5.3
In a medical experiment, the subjects in the experimental group were given
a new drug to reduce cholesterol, while those in the control group were
given a placebo. The cholesterol levels of both groups of subjects were
measured (a) before the drugs were administered, and (b) after one month.
The results are shown below (lower values indicate better cholesterol levels,
as measured in millimoles per litre).
Group
Mean cholesterol level (mmol/L)
Pre-treatment
Post-treatment
Change
Experimental
5.25
5.10
À 0.15
Control
5.12
5.08
À 0.04
5.4 Validity Threats
EXAMPLE 5.3
(CONT.)
It is apparent that the post-treatment mean cholesterol level in the control
group (5.08) is marginally lower than that in the experimental group (5.10).
But this comparison is not helpful because the pre-treatment mean cholesterol levels in the two groups were markedly different (5.12 and 5.25). It is
more useful to compare the changes in the cholesterol levels between the
two groups: this shows that the subjects taking the new drug experienced a
mean drop in cholesterol of 0.15 mmol/L, while those taking the placebo
only experienced a mean drop of 0.02 mmol/L.
The intuition underpinning the design of the classic experiment is straightforward:
the
researcher
is
able
to
manipulate
the
treatment,
and
then
observes the effects upon the attribute. In Example 5.3, the treatment takes
one of just two values (drug or placebo), but it would also be possible to
consider various dosages of the drug (e.g. zero, low, medium, high, very
high). The drug dosage is referred to as the explanatory variable, while the
measured cholesterol level would be termed the dependent variable.
We noted above that ‘classic ’ experiments are little used in business studies
research. There are three reasons for this. First, the‘subjects’ are sometimes
firms,
people, but are more often
may be recruited
ex ante
organizations, or even countries. People
for experiments, but it is generally not feasible to
firms etc. Researchers thus typically have to rely onex post
data. Second, fi rms etc. typically have quite different characteristics (e.g. size,
age, industry, location, internationalization), hence it is extremely dif
ficult to
do that with
construct
matched
samples
for
use
as
experimental
and
control
groups.
Third, most dependent variables in business research are affected by many
possible explanatory variables, which the researcher can observe but cannot
manipulate. It is thus impossible to assess directly from
effects
on
variables,
the
as
Fortunately,
dependent
typically
these
all
variable
of
variables
problems
may
changes
will
be
be
in
individual
changing
overcome
by
ex post
at
the
explanatory
the
use
data the
same
of
time.
statistical
techniques such as multiple regression analysis – see Chapter 11.
5.4
Validity Threats
A key purpose of the experimental design is to isolate and estimate the effect(s)
of potential cause(s) (see Section 5.3). The experiment is a‘strong’ design as it
67
68
Research Design
allows for manipulation of treatment (cause) before and after measurements
fi
and thus for identi cation of covariation between treatment (cause) and effect,
fi
determines time order (cause precedes effect), and offers some con dence in
ruling out the effect of other explanations (randomization). The idea of experimental
design
is useful
in many studies.
Some examples are
pre-test
of
alternative advertisement copy, studying the effectiveness of various selling
strategies, and
field tests of marketing programmes.
The researcher wants to obtain
valid
knowledge (see Section 4.2), that is,
wants results that are ‘true’. For example, if a study shows that advertisement
fi
A is more effective than advertisement B, the researcher should be con dent
1
that this is the case. There are many types of validity. In the above adver-
internal validity, the question
tisement case, the question of validity refers to
of whether the results obtained
within the
fi
study are true. In other words, for
internal validity we have to be con dent that the causal variation among
x is really causing the variations
variables suggested by our study is true, that
y, at least as one of the influencing factors (Bryman and Bell, 2003). On the
external validity refers to the question of whether thefindings
can be generalized, for example to other populations, settings, or periods,
in
other hand,
beyond the study at hand. This becomes extremely important in quantitative
research. The sampling procedures in this type of research thus become very
important, to ensure that the researcher has a representative sample (for
a good sample, see e.g. Scase and Goffee, 1989), as only in the case of a
representative sample can he or she claim the generalizability of the results.
There are several
threats
to validity (for further discussions see Campbell,
1975 and Cook and Campbell, 1979).
1.
fic events external to the study (experiment) that occur at
History, speci
the same time and which may affect the response (criterion variable).
EXAMPLE 5.4
Consider a TV store that reduces prices by 10 per cent and observes a sales
increase of 20 per cent. A potential external threat is the announcement of a
price increase for TV sets the following month. Note that the experiment
(see Figure 5.1), by including one (or more) control group(s), allows for
controlling the impact of such effects.
1
For an excellent overview, see Cook and Campbell (1979), Chapter 2.
5.4 Validity Threats
Experimental group
Pre-test
Post-test
Difference
Figure 5.1
2.
Control group
R
R
O1
O3
X
X
O2
O4
O 2 - O1
O4 - O3
The classic experiment
Maturation, processes which are operating within the test units in the
study as a function of the passage of time per se. For example, the patient has
received a medical drug and recovers. Often patients recover without such
treatment. Thus what is the cause of the patient
’s recovery, the medical drug
or their immune system? Maturation is a serious threat to validity in many
studies.
EXAMPLE 5.5
A company recognizes the need for reorganization to enhance performance
and survive. It succeeds in doing this. Is the success due to the reorganization per se, or might it be explained by the fact that the reorganization has
made the employees aware of the serious situation and motivated them to
perform better in order to keep their jobs?
3.
Test effect indicates that the experiment itself may affect the observed
fi
response. For example, a group of employees is chosen for a speci c programme
and
obtains
superior
results
after
the
programme
period.
Is
their performance caused by the programme or by the fact that they are the
chosen ones and thus motivated to perform? Test effects have been found to
be a serious threat to validity, for example in the recognized‘Hawthorne
studies’ (see Example 5.6), aimed at explaining the impact of various work
conditions.
4.
the
Selection
subjects
bias (self-selection)
are
illustrates this.
not
(or
cannot
is
be)
a
serious
assigned
threat
to
randomly.
validity
when
Example
5.7
69
70
Research Design
EXAMPLE 5.6
During the 1920s and 1930s a number of investigations were carried out in
the Hawthorne factory of Western Electric Company in the USA. Some of
the workers doing manual tasks were taken away and asked to work in a
separate room. A number of changes were introduced (e.g. in heating,
fl
lighting) to see whether these would in uence workers’ productivity.
As the lighting was increased day by day the workers were asked whether
they liked it or not. The workers said that they did. Then the investigators
changed the lights, but put back the same size bulbs, thus in reality not
changing the lighting. The workers perceived that the lighting had been
improved and replied favourably. After some days, the investigators started
to decrease the light day by day, letting the workers know about the change
and asking for their comments. After some further days, the investigators
repeated the earlier experience of changing the bulbs but not changing the
intensity of light/illumination. Again the workers complained that they did
not like the lesser light.
As the experiment went on, it was realized that productivity did increase,
but irrespective of the changes introduced. It was eventually recognized that
the workers were reacting positively to the attention and special treatment
accorded to them. The investigators thus concluded that the changes in
productivity
were
not
due
to
changing
working
condition
(increase
or
decrease in light) but due to favourable circumstances/perceptions created
by
the
experiment
itself.
These
fi ndings
contributed
to ‘stimulating
the
human relation’ approach and emphasized how important it is to provide
‘psycho-socio’ support at the workplace.
Source:
Based on Roethlisberger and Dickson (1939: 17); Van Maanen and Kolb (1985: 6)
EXAMPLE 5.7
In assessing advertisement effects, the following procedure has frequently
been applied (Colley, 1961). Assume the producer of the shampoo‘Z’ wants
to know whether its advertising campaign has been effective. A random
sample of people are asked the following questions:
Q1.
‘Have you seen any advertisements for shampoos during . . . ’?
‘For what shampoos?’
5.5 Other Research Designs
EXAMPLE 5.7
Q2.
(CONT.)
‘Have you bought shampoo during . . . ?
’
If yes, ‘ What brands?’
From the results shown in Table 5.2 it could be argued that 20 per cent of
those who have seen the advertisement bought the product, while only
5 per cent of those who did not see the advertisement bought it. Thus the
advertisement has ‘contributed’ (20
Is the observed
À 5) = 15
per cent.
finding valid? It may be, but the result may equally well be
explained by other factors, such as selective perception, that is, people who
regularly buy brand ‘Z’ are more inclined to see the advertisement for‘Z’.
Table 5.2 Reading of advertisement and purchase
Seen advertisement for Z
Yes (%)
No (%)
‘ ’
Bought Z
‘ ’
Yes
20
5
25
No
80
95
175
100
100
200
Total
5.5
Total (%)
Other Research Designs
The simplest research design involves a single unit of observation (individual,
firm, country) at one point in time. Such a one-shot case study may be
useful in exploratory research of a new phenomenon, but it suffers from
two main weaknesses. First, the focus on a single unit of observation raises
questions about the generalizability of any research
findings
to a wider
population. Second, the focus on a single point in time does not allow
–
for in-depth consideration of cause effect relationships. So what are the
alternatives?
5.5.1 Cross-Section Data
One possibility is to collect information on many units of observation. This
might involve the collection of qualitative information from several case
studies, so that the researcher is able to compare and contrast the experiences
of the different units and to identify patterns. Or quantitative data on a
71
72
Research Design
selection of variables may be collated from a sample for observational units.
Table 5.3 depicts such a cross-section design where 2018 data have been
collated on five variables (X1, X2, X3, X4, and X5) for a sample of 100firms.
The main advantage of a cross-section design is that any researchfindings
apply not just to a single observational unit but to a larger sample and may be
generalizable to a wider population. The main disadvantage is that the data
apply to just one point in time (2018) so it is not possible to track the changes
in each variable over time, or the effects of such changes on other variables.
5.5.2 Time-Series Data
A second possibility is to collect information on one unit of observation but
over a number of time periods (e.g. years). This allows changes to be tracked
over time, and possible estimation of cause– effect relationships. Table 5.4
depicts such a time-series design where data have been collated on thefive
variables (X1, X2, X3, X4, and X5) for firm #53 over the 20-year period
2000–2019.
The researcher might have hypothesized that changes in X1 (the dependent
variable) are caused by changes in the other four variables (i.e. X2, X3, X4,
and X5), and that these changes take place over time. The time-series design
Table 5.3 A cross-sectional design
Firm
Variable (2018)
X1
X2
X3
X4
X5
1
54.3
3.6
234.8
76.4
23.9
2
46.8
3.4
145.7
75.3
45.8
100
63.6
4.5
122.9
78.3
52.3
X4
X5
…
Table 5.4 A time-series design
Year
Variable (Firm #53)
X1
X2
X3
2000
45.8
4.6
212.0
80.1
45.2
2001
47.3
4.4
193.6
87.3
45.1
2019
65.4
5.8
176.5
115.3
22.5
…
5.5 Other Research Designs
allows these hypotheses to be tested. The main disadvantage of a time-series
design is that only one firm (#53) is being considered. Is firm #53 representative of the other 100 firms, or is it a special case?
5.5.3
Longitudinal Studies
The third possibility is to collect information on several units of observation
over a number of time periods (e.g. years). This is generally referred to as a
longitudinal design if the information is primarily qualitative and the
number of observational units is fairly small. Lorenzoni and Lipparini
(1999) provide an excellent example of a longitudinal study. They were
interested in how inter-firm networks with suppliers influence lead firms’
knowledge access and transfer. They focused on the networks of three Italian
packaging machine manufacturing firms in Bologna, and looked at structural changes in the inter-firm networks of these firms between two points in
time (1988 and 1995). More specifically, they were interested in how various
activities and competences were distributed over firm boundaries, why network evolution occurred, and how leveraging occurred. They concluded that
lead firms can potentially lower overall production and coordination costs
through multiple, repeated, trust-based relationships with key suppliers, and
that such relationships may have positive effects on the networks as a whole.
The main advantages of longitudinal designs are that they allow the
researcher to track processes, to identify time-ordered antecedents and consequences, and to distinguish between short-term and long-term phenomena. The main disadvantages are that longitudinal studies take a long period
of time to gather results and are labour-intensive (hence the number of
observational units may be small), participants may drop out (subject attrition), time effects may be confounded with time-of-measurement (period)
effects, and it may be difficult to pick up causal relationships (because there
is no manipulation of explanatory variables).
5.5.4
Panel Data
The final possibility is to collect primarily quantitative data on many units
of observation over a number of time periods (e.g. years).2 The use of panel
data has expanded in recent years with the improved availability of large
datasets and developments in estimation methodology. Panel data blend the
inter-individual differences (revealed in cross-section data) and the intraindividual dynamics (revealed in time-series data). The main advantages
(Gujarati and Porter, 2009) are that panel designs give the researcher greater
2
Panel data often involve two dimensions, as here (a cross-section dimension and a time-series
dimension), but additional dimensions (e.g. a country dimension) may also be added.
73
74
Research Design
Table 5.5
A panel design
Firm
Year
1
Variable
X1
X2
X3
X4
X5
2000
33.3
3.4
276.5
65.3
44.4
2001
35.4
3.4
266.9
64.2
44.5
2019
54.3
3.6
234.8
76.4
23.9
2000
21.7
2.5
165.3
60.2
87.3
2001
24.3
2.8
165.2
61.7
85.4
2019
46.8
3.4
145.7
75.3
45.8
2000
55.4
3.4
154.2
58.4
76.7
2001
53.2
3.5
155.3
59.8
75.3
2019
63.6
4.5
122.9
78.3
52.3
human
behaviour
...
2
...
…
100
capacity
section
for
or
capturing
time-series
the
data,
complexity
and
also
of
involve
larger
datasets
than
cross-
(which
has
fits in terms of statistical hypothesis testing– see Chapter 10). Table 5.5
depicts such a panel design where data have been collated onfi ve variables
(X1, X2, X3, X4, and X5) for 100fi rms over the 20-year period 2000–2019.
The panel is balanced as each of the 100fi rms has the same number (20) of
bene
time-series observations, but panels may be unbalanced if data are missing.
The main disadvantages with panel designs are that they require much
more data collection, and the methodologies for the analysis of the data are
more complicated
5.6
– see
Gujarati and Porter (2009) for more details.
Requirements in Research Design
When moving from the research problem at the conceptual level to empirical
research, questions arise such as: how to proceed? and how to do it? As noted
at
the
outset
of
this
chapter,
the
research
design
represents
the
overall
strategy for gathering the information needed to answer the research problem under scrutiny.
5.6 Requirements in Research Design
After thinking through what the research problem is, and if possible how it
should be represented (see Section 5.2), and the potential hypotheses derived
(if any), the
first question to answer is: What requirements should the actual
research design satisfy? A few examples will illustrate this point.
EXAMPLE 5.8
A study was conducted to explore whether
fi rms
(managers) within the
same industry perceive and interpret their surrounding environments differ-
fl
ently and, if so, whether this might in uence organizational actions and
performance (Grønhaug and Haukedal, 1989). A case study was chosen for
the research purpose. The following
for the selection of the cases, that
•
•
•
criteria were, however, established
firms
should:
belong to the same industry, and be embedded in similar environments;
be of approximately the same size;
be in an industry where it was possible to identify major environmental
change(s);
•
•
at the outset have approximately the same economic resources; and
have demonstrated different response(s) to the environmental change(s).
The above requirements also demonstrate that even in‘qualitative’ (case)
research a priori theorizing can be useful in structuring the research problem
(see Chapter 4).
Assume a study involving a set of hypotheses, for example:
H1:
The higher the consumer’ s knowledge about her/his rights, the more likely it
is that s/he will complain.
Inspection of the above hypothesis shows that two constructs (variables)
are included: (1) knowledge about rights and (2) propensity to complain. The
first requirement to test this hypothesis is information on these two variables.
Moreover,
variation
in knowledge and propensity is needed. Reading the
hypothesis also shows that nothing is said about causation; only covariation
between the two constructs (variables) is indicated. Thus a cross-sectional
(correlational) design is appropriate.
Consider the following hypothesis:
H2: An increase in advertising expenditure will lead to an increase in the
probability of getting more orders.
Again, two variables are present: (1) advertising expenditure and (2) probability of getting orders. Information on these two variables is needed. The
75
76
Research Design
hypothesis also expresses a causal relationship, ‘will increase’. In order to
demonstrate
causality,
a
design
taking
the
time
order
between
change
(increase) in advertising expenditure and change in probability of getting
orders is needed.
5.6.1
Research and Choices
Research involves
choices,
problematic choices. Once the design require-
fi
ments have been speci ed, decisions must be made on how they should be
met, and how the information needed should be collected. Important decisions in this respect are:
•
How should the concepts (variables) be measured (operationalized)?
•
What type of data are needed? Secondary or primary?
•
If secondary: what secondary data sources are available?
•
If primary: how should the data be gathered? Through observation or
3
interviewing?
•
If interviewing: personal interviews, by phone or face to face?
•
If interviewing: how should the questions be formulated; structured or
unstructured?
•
Who should be interviewed? How should they be selected (sampling
plan)? How many should be included (sample size)?
This list of questions is in no way complete, but clearly indicates that
research involves choices. Quality research implies conscious, reasonable
choices, and the needed skills in performing the activities involved. Several
of these questions will be dealt with in the following chapters.
3
fi
Primary data refers to data/information that has been collected by the researcher speci cally
for the research at hand. Secondary data refers to data/information that has been collected by
others for a different purpose, but which the researcher can use for her/his research. The pros
and cons of primary and secondary data are discussed in more depth in Chapter 9.
Exercises
Further Reading
.................................................................
Altheide, D.L. and Johnson, J.M. (1998) ‘Criteria for assessing interpretive validity
in qualitative research’, in Denzin, N. and Lincoln, Y.S. (eds.),
Interpreting Qualitative Materials,
Collecting and
London: Sage, pp. 283–312.
Ghauri, P.N. and Firth, R. (2009) ‘ The formalization of case study research in
international business’ ,
Der Markt,
48(1): 29– 40.
Isaeva, N., Bachmann, R., Bristow, A., and Saunders, M.N. (2015)‘ Why the
epistemologies of trust researchers matter’,
Journal of Trust Research,
5(2): 153–69.
Strauss, A. and Corbin, J. (1994) ‘Grounded theory methodology: an overview’,
in Denzin, N.K. and Lincoln, Y.S. (eds.),
Handbook of Qualitative Research,
Thousand Oaks, CA: Sage, pp. 273–85.
Questions
.................................................................
1.
Describe the key elements of the classic experimental design.
2.
What do you consider to be the basic uses of exploratory research?
3.
Explain what is meant by: (1) dependent and (2) independent variables.
4.
What is meant by: (1) validity and (2) validity threat?
5.
How would you decide whether a factor can be considered to be a
‘cause’
of something?
Exercises
.................................................................
1.
An advertising agency has developed two advertisements to be used in a
magazine. It wants to select the most effective before the ad is printed.
Design a study allowing the agency to select the most effective one.
2.
An internet bank is concerned with improving its services. In particular,
fi
the bank wants to know if its customers are dissatis ed with current
services and the nature of this dissatisfaction. How would you design a
study allowing the bank the desired insight?
3.
A researcher has observed that some
firms
in the furniture industry
outsource activities such as transportation services while others do not.
S/he
wants
to
study:
(2) whether outsourcing
firms outsource/do not outsource and
infl uences fi rms performance. Suggest a study
(1) why
’
to enquire into these research questions.
77
6
Measurements
The GIGO principle: garbage in, garbage out.
Empirical research requires the collection and analysis of data and other
information. The quality of the research (and the conclusions derived therefrom) depend upon the collection of appropriate data, the quality of the data
collected, and on how well the data are analysed. Quantitative research
requires the measurement and enumeration of the variables to be used in
the analysis.
In this chapter, we will
first
explain the process of operationalization, by
which researchers decide how to measure the theoretical concepts they use.
The second section considers different scales of measurement, and highlights
some of the implications for empirical analysis. The third section focuses
on the measurement of multi-dimensional variables, and the generation of
latent constructs. The fourth section addresses how to assess the reliability
fifth section
and validity of variables and multi-dimensional constructs. The
offers some practical suggestions for improving the measurement of the
variables used in quantitative research, while the
final
section is concerned
with measurements in qualitative research.
6.1
The Operationalization of Variables
In most research problems, we
first
start with theoretical concepts. These
concepts may be straightforward attributes of individuals (e.g. age, gender),
firms
(e.g. size, industry), countries (e.g. population, living standards), etc.
But sometimes the concepts are more complex. Marketers are often interested
in customer satisfaction. Many organization scholars ponder the determinants
of
organizational
culture.
HR
researchers
may
be
concerned
about
attitudes to work. Those interested in international business debate the merits
of
firms
pursuing
international
and/or
product
fi
diversi cation
strategies.
All disciplines make frequent reference to national culture, and to cultural
differences between countries.
6.1 The Operationalization of Variables
Operationalization involves rules for assigning numbers to the theoretical
concepts, and these rules should involve (a) a clear mapping between the
theoretical concepts and the corresponding empirical variables; and (b) clear,
unambiguous (and ideally objective) definitions of the variables. Sometimes,
these mappings and definitions are fairly easy: the age of an individual may
be measured in terms of years since birth (or months, or days). But the
process is often more problematic. For example:
•
•
•
The size of a firm may be assessed in terms of the number of employees
(at which point in time?), the value of its assets (again at which point in
time?), or its sales/revenues over a given period. Each of these variables
is a measure of firm size, but they are not equivalent. What units are
being used (numbers of employees; $m; €’000)?
The living standards of a country may be assessed in terms of per capita
gross domestic product (GDP), per capita gross national income (GNI),
or by using broader measures such as the human development index
(HDI). All three measures are averages, and may mask important differences between individual citizens. What units are being used ($ or local
currency for GDP/GNI; how is the HDI measured)?
Customer satisfaction is even more tricky. This is clearly very subjective
and also multi-dimensional in that satisfaction may involve considerations of price, functionality, appearance etc. How can data be obtained
on customer satisfaction?
EXAMPLE 6.1
How
could
the
following
theoretical
concepts
be
operationalized
and
measured?
•
the gender of an individual?
•
the industry of a
•
the extent of a
•
a country’s national culture?
fi rm?
fi rm’s
fi
international diversi cation?
EXAMPLE 6.2
The theoretical concept of absorptive capacity (ACAP) refers to the ability
of a
from
firm
to identify, assimilate, transform, and exploit knowledge gained
external
sources
(Teece,
2007).
Most
researchers
have
typically
79
80
Measurements
EXAMPLE 6.2
(CONT.)
operationalized absorptive capacity using simple proxy measures such as
R&D intensity (R&D expenditure as a % of sales), R&D investments (number
of R&D employees), or R&D outputs (number of patents). However, Flatten
et al. (2011) queried whether any single measure can capture the complexity
of absorptive capacity, and recommended the use of a multi-dimensional
construct. They initially identified 53 items which reflected the different
shades of meaning of the ACAP concept, but reduced this list to 36 items
after three rounds of pre-testing. A questionnaire survey of 285 German
executives was then carried out, in which the importance of the various
items was assessed using 7-point Likert scales (see Section 9.7.2 for details).
Confirmatory factor analysis then established that ACAP did indeed involve
four dimensions (i.e. identification; assimilation, transformation, and exploitation), and confirmed that the following 14 items were required to capture
these four dimensions adequately:
ACAP dimension Questionnaire items
Acquisition
Please specify to what extent your company uses external
Assimilation
resources to obtain information:
▪ The search for relevant information concerning our
industry is everyday business in our company
▪ Our management motivates the employees to use
information sources within our industry
▪ Our management expects that the employees deal
with information beyond our industry
Please rate to what extent the following statementsfit the
communication structure in your company:
▪ In our company ideas and concepts are communicated
cross-departmentally
▪ Our management emphasizes cross-departmental
support to solve problems
▪ In our company there is a quick informationflow, e.g.
if a business unit obtains important information it
communicates this information promptly to all other
business units or departments
▪ Our management demands periodical crossdepartmental meetings to interchange new
developments, problems, and achievements
6.2 Scales of Measurement
EXAMPLE 6.2
Transformation
(CONT.)
fi
Please specify to what extent the following statements t
the knowledge processing in your company:
▪
Our employees have the ability to structure and to use
collected knowledge
▪
Our employees are used to absorb new knowledge as
well as to prepare it for further purposes and to make it
available
▪
Our employees successfully link existing knowledge
with new insights
▪
Our employees are able to apply new knowledge in
their practical work
Exploitation
fit
Please specify to what extent the following statements
the commercial exploitation of new knowledge in your
company:
▪
Our management supports the development of
prototypes
▪
Our company regularly reconsiders technologies and
adapts them accordant to new knowledge
▪
Our company has the ability to work more effective by
adopting new technologies
A variety of tests were reported to assess the reliability and validity of
these four constructs, including tests for internal consistency and convergent
validity. The authors suggest that these questionnaire items and constructs
provide managers with a useful tool to assess their
fi rms’
strengths and
weaknesses with regard to ACAP, and to compare their ACAP with those of
their competitors.
6.2
There
Scales of Measurement
are
four
measurement
scales
(or
types of
data):
nominal,
ordinal,
interval, and ratio. It is important to understand the differences between
them because they have implications for the choice of appropriate techniques
for statistical analyses (see Chapter 10 for further discussion).
6.2.1 Nominal Level (Scale)
fi
A nominal scale classi es the subjects into mutually exclusive categories.
An example would be nationality (Belgian, German, French, Italian, etc.).
81
82
Measurements
Table 6.1 Example of the operationalization of nominal variables
Nationality
Values of dummy variables
DB
DG
DF
Belgian
1
0
0
German
0
1
0
French
0
0
1
Italian
0
0
0
Note: Nationality has j = 4 different categories, hence the variable may be operationalized using
(j
±
1) = 3 dummy variables. The values of all three dummy variables (i.e. DB, DG, and DF) are all set
to zero for the fourth category (Italian).
fi
Subjects are classi ed into categories, and these categories are mutually
exclusive. Such nominal variables are also called categorical variables, and
there is no intrinsic order or ranking to the categories. If there are two
possible categories (e.g. gender = male or female), then the variable may be
operationalized using a dummy variable (e.g. gender = 1 if female; = 0 if
male) that takes one of two possible values. If there are more than two
possible
categories,
then
additional
dummy
variables
are
required
–
see
Table 6.1. Four nationalities are considered, and three dummy variables are
fined:
de
DB = 1, DG = 0, and DF = 0 for Belgian; DB = 0, DG = 1, and DF = 0
for Germans; DB = 0, DG = 0, and DF = 1 for French; and DB = 0, DG = 0, and
DF = 0 for Italians. Each of the four categories thus involves a different
combination of the three dummy variables.
6.2.2 Ordinal Level (Scale)
An ordinal scale provides numerical ratings for the variable under consider-
fi
ation: it is the order of the values that is important and signi cant, but the
differences between the values do not have a clear interpretation. Variables
such as customer satisfaction that have been assessed using Likert scales in
a questionnaire survey
–
see Table 6.2
–
are measured on ordinal scales.
Clearly, a rating of 3 indicates a higher level of satisfaction than a rating of 2,
and a rating of 5 indicates a higher level of satisfaction than a rating of 4.
But it is not possible to say how much higher in each case, or whether
the difference between the 2 and 3 ratings is greater than, or less than, the
difference between the 4 and 5 ratings.
6.2.3 Interval Level (Scale)
Interval scales involve continuous variables in which similar differences
between the values are equivalent, but the scale does not have a true zero.
The
oft-cited
example
is
temperature
in
degrees
Celsius.
The
difference
6.3 Construct Measurement Using Multi-Item Scales
Table 6.2 Example of the operationalization of ordinal variables
fi
Question: how satis ed are you with the product you purchased?
fi
1 = very unsatis ed
fi
2 = somewhat unsatis ed
3 = neutral
fi
4 = somewhat satis ed
fi
5 = very satis ed
²
²
²
²
²
²
20 C and 30 C. But is 20 C twice as warm as 10 C? The answer is no, as zero
between 10 C and 20 C degrees is 10 degrees, as is the difference between
1
on the Celsius scale is not a true zero where there is no temperature. An
alternative example would be measures of intelligence (e.g. IQ). Ratios have
no meaning when there is not a true zero: someone with an IQ of 200 is not
twice as intelligent as someone with an IQ of 100.
6.2.4 Ratio Scale
Ratio scales also involve continuous variables, but the scale does have a
natural zero, and hence values can be meaningfully added, subtracted, and
firm size. A firm with 60 employees is three
firm with 20 employees, it has 40 more employees, and the
divided. An example would be
times the size of a
two
firms together employ 80 people.
Table 6.3 summarizes these different scales and their characteristics.
6.3
Construct Measurement Using Multi-Item Scales
The use of single-item measures has many practical advantages, notably
simplicity and ease of data collection. Single-item measures are thus typically favoured when the variable to be operationalized has a clear interpretation
(it
is
narrow
in
scope,
unambiguous,
and
unidimensional).
Many
attributes meet these criteria: ‘age’, for instance, may be measured by the
fi
number of years since birth (individuals) or establishment ( rms). But singleitem scales have often been criticized (notably in the marketing and consumer
psychology
literatures)
for
over-simplifying
complex
constructs.
Hence it has become standard practice in many business disciplines to use
multi-item scales to measure complex constructs. For example, ‘customer
1
Absolute zero is at
±
²
273.15 C, or zero on the Kelvin scale.
83
84
Measurements
Table 6.3
Summary of scales of measurement
Features
Scale of Measurement
Nominal
Variables
Ordinal
categorical
Best measure of central
tendency
ordinal
1
Interval
Ratio
continuous
continuous
mode
median
mean
mean
no
yes
yes
yes
no
no
yes
yes
no
no
no
yes
no
no
no
yes
Data from
IQ
Age, size
2
Order of values is
known
Quantifiable
differences between
values
True zero
Meaningful ratios
between values
Typical example
Industry
fi cation
classi
questionnaire
surveys
Notes: 1. Ordinal variables involve numerical values but should not be considered as continuous variables.
fi
2. See Section 10.1.2 for de nitions and details of how to calculate means, medians, and modes.
fl
satisfaction ’ is a complex multi-dimensional construct that is in uenced by
many
factors.
Rather
than
simply
asking
customers
about
their
overall
satisfaction with a product, it may be preferable to dig deeper and seek views
on multiple items such as:
fied are you with the price of the product you purchased?
satisfied are you with the functionality of the product you
•
How satis
•
How
purchased?
•
How
fied
satis
are
you
with
the
appearance
of
the
product
you
purchased?
fied are you with the service provided by the sales staff?
•
How satis
•
Does your product provide value for money?
‘Customer satisfaction’ may then be measured by combining the responses to
these
separate
items
(and
maybe
other
items
2
too)
into
a
single
multi-
item scale.
2
The combination may be effected simply by summing the responses, or through more
sophisticated techniques such as factor analysis – see Chapter 12 for further discussion.
6.4 The Reliability and Validity of Measurements
The use of such multi-item scales to measure complex constructs has two
main advantages. First, it avoids the need to choose one item from a set of
alternative items. Rather than choosing one item rather than another, why
not combine them all into an aggregate measure? Second, the use of multiple
fi
items tends to average out inaccuracies and speci cities that relate to single
items, and is thus associated with increased reliability and construct validity.
As Churchill and Iacobucci (2015) note, in valid measures, items should have
a common core (which increases reliability) but should also contribute some
fi
unique variance which is not tapped by other items. Each of the ve items
listed above is clearly related to the core construct of customer satisfaction,
but each is tapping into a different aspect of satisfaction. In combination, the
responses to these
five items provide a more holistic assessment of customer
satisfaction.
The use of multi-item scales has been further encouraged by the widespread adoption of structural equation modelling techniques (see Chapter 12
for further details). Diamantopoulos et al. (2012) provide some helpful practical guidelines for researchers in choosing between single-item measures
and multi-item scales.
6.4
The Reliability and Validity of Measurements
Measurement involves assigning values to variables/constructs. But how do
researchers know that these measurements are meaningful? There are two
distinct criteria by which researchers evaluate their measures: reliability and
3
construct validity.
Reliability refers to the degree to which measurement produces consistent
outcomes. A reliable measure is one with the following three attributes:
1.
Stability. Suppose the researcher obtains responses to the questions
about customer satisfaction this week, and then again next week under
similar circumstances from the same respondents. Stability may be
–
assessed by calculating test retest reliability scores. If the two sets of
ratings are similar, then this is an indication of stability over time. If the
two sets of ratings are very different, however, this casts doubt on
the reliability of both sets.
3
The discussion in this section is primarily concerned with internal validity (i.e. the extent to
which the research is properly designed and analysed) rather than external validity (i.e. the
extent to which the research
findings may be generalized to the real world). Research may
–
have high internal validity but very low external validity at the same time see Cooper and
Schindler (2001) for further discussion.
85
86
Measurements
2.
Equivalence . This requires that there is a level of consistency across the
fl
respondents, so that the measured values are not unduly in uenced by
the views of individual respondents. Suppose that the researcher asks
fi
several experts to assess the growth prospects of a sample of rms. If
these assessments are very different, then this casts doubt not just upon
the reliability of the responses from each individual but also upon the
reliability of the whole exercise as a way of measuring growth prospects. If the assessments are similar, then the researcher will have more
con
fidence
in
the
overall
ratings.
Equivalence
may
be
assessed
by
calculating inter-rater reliability scores.
3.
Internal consistency. This requires that the variables used to generate a
construct should be measuring similar (though not identical) concepts,
and should be highly correlated. In the customer satisfaction example
discussed in Section 6.2, it is clear that the various questions cover
different dimensions of satisfaction and may be usefully combined to
form a customer satisfaction construct. The internal consistency of this
ficient
construct may then be assessed by calculating Cronbach
’s coef
alpha (see Section 12.1.1).
The
attributes
of
stability
and
equivalence
are
important
when
the
researcher is collecting primary data through experimental studies. Internal
consistency is important whenever the researcher has developed multi-item
scales, whether using primary or secondary data (see Chapter 9 for further
discussion).
Reliability
is
a
necessary but not a sufficient condition
for
validity. A reliable scale may not be valid: my stopwatch may tell me that
my walk to work always takes about 15 minutes (and is hence reliable), but it
may be slow as the walk takes me 20 minutes (and hence is not valid).
Construct validity refers to ‘the extent to which an operationalization
measures the concept it purports to measure’ (Zaltman et al, 1977: 44). There
are three main aspects of construct validity:
1.
Content
validity.
This
refers
to
the
extent
to
which
the
construct
adequately includes all relevant information. In the example of the
measurement of the customer satisfaction construct, do the
tions
related
to
satisfaction
with
price,
functionality,
five
ques-
appearance,
service, and value for money cover all dimensions of customer satisfaction? Or have important dimensions been omitted, with the result
that the construct is a poor measure of the theoretical concept? Content
validity may be improved by using (if available) questions that have
been used in previous published work, and whose validity has already
been established. An additional
ex ante
check on content validity is
to ask a panel of experts for their opinions on whether or not all the
6.5 Improving Your Measurements
relevant dimensions have been considered (this is sometimes referred to
as face validity).
2.
Convergent validity. This refers to the extent to which the construct is
correlated with other constructs measuring similar theoretical concepts.
Suppose two researchers are both measuring customer satisfaction, but
each is using different items in their questionnaires. If the two constructs are highly correlated, then this would be evidence of convergent
validity. One way of establishing convergent validity is to carry out
robustness tests in empirical studies, using alternative measures of key
variables.
3.
Divergent validity. This refers to the extent to which the construct is
distinguishable
from
other
constructs.
Suppose
that
the
researcher
wants to assess the factors that affect the choice of country location
by US
firms
investing in the European Union. S/he might hypothesize
that both average living standards and average wages in the potential
host
countries
are
important
determinants:
higher
living
standards
mean a more attractive market, while lower wages mean lower production costs. Average living standards might be measured by GDP per
capita, but such a measure is likely to be highly correlated with average
labour costs. In other words, GDP per capita does not have divergent
validity,
and
alternative
measures
for
both
variables
will
need
to
be found.
6.5
Improving Your Measurements
As noted above, empirical research requires the collection and analysis of data,
and the quality of the research depends critically upon the quality of the
measurement of the variables and constructs used in the analysis. A necessary
prerequisite for good research is thus good measurement. This simple requirement is expressed more pithily in the‘garbage in, garbage out’ quote used at
the start of the chapter. So how can measurements be improved?
1.
Start by elaborating the theoretical concepts to be used.
2.
Consider how each of these theoretical concepts may be operationalized. Here it is useful to consult previously published work because
(a) this may provide some useful ideas; (b) it is easier to justify the use of
a measure if it has been used in previous studies; and (c) the work may
also
provide
hints
to
potential
sources
of
data.
Be
critical
in
your
assessment of this previous work, and do not simply accept measures
without
question.
Consider
alternative
and
possibly
better
87
88
Measurements
3.
4.
5.
6.
7.
operationalizations of the concepts, and think carefully about the
advantages and disadvantages of each option in relation to your
research question. For example, if you are researching employmentrelated issues, then the number of employees might be the most appropriate measure of firm size. But if you are researching the determinants
of profitability, then a financial measure of firm size such as total
revenues might be more appropriate.
Are values for the variables readily available in secondary sources?
If not, how will the values be generated (e.g. by a questionnaire
survey)? Develop the questionnaire instrument, and pre-test it to
ensure that the questions are understandable and unambiguous. This
is particularly important in the context of cross-cultural studies (see
Chapter 13).
Can each of the variables be measured directly, or do multi-dimensional
constructs need to be calculated? How are the constructs generated?
Assess the reliability (stability, equivalence, internal consistency) and
validity (content, convergent, divergent) of each of the constructs,
and include these assessments in the written report.
What scales of measurement are used for each variable/construct? Are
they nominal, ordinal, interval, or ratio? In general, it is better to retain
as much information as possible in the measures. For example, measure
firm size using number of employees (a continuous variable)
rather than simply classifying firms as ‘large’ or ‘small’ (a categorical
variable).
Make a careful note of the units in which your variables are measured,
as this will be important in the interpretation of the data and any
empirical analyses. For example, is firm size measured by total revenues
expressed in $ (e.g. $5 678 123) or in $m (e.g. $5.678123m)?
The acid test for any measure is whether it is useful in predicting an
outcome that it should be able to predict on the basis of theory, and
whether it predicts this outcome better than alternative measures. This
is termed predictive validity (Diamantopoulos et al., 2012).
6.6
Measurements in Qualitative Research
The research literature deals with measurement problems when it comes to
exploratory/qualitative research only to a modest degree. But is the
question of measurement irrelevant in such research? The answer is probably no.
6.6 Measurements in Qualitative Research
Understanding/
explanation
?
?
Conceptual
LEVEL
Empirical
R1, R2,
. .
.
n
Figure 6.1 Responses and sense-making
1. In Chapter 5, a key characteristic of exploratory research is that the
problem under scrutiny is only partly understood. If the problem is only
modestly understood, a prime purpose is to obtain an understanding. Various
approaches can be used (see Chapter 8 for more detailed discussion). Assume
that the researcher approaches the problem by using semi-structured questions, based on an interview guide (developed by surveying previous studies
and so on). As noted above, a key purpose of measurement is to map
‘reality’.
When
the
researcher
asks
her
or
his
questions
s/he
gets
responses
(see
Figure 6.1). A detailed example is given in Chapter 8 on qualitative data
analysis reporting the study by Burns and Stalker (1961) to capture characteristics of innovative and non-innovative organizations, which they termed
‘organic’ and ‘mechanic’, respectively.
The responses,
R1 , R2,
. . ., are empirical manifestations that the researcher
is trying to understand. In this process the researcher will try to relate them
to her or his knowledge base, and hopefully produce a reasonable explanation. This may partly be seen as ‘data-driven’ problem solving. However,
without
use
of
emerge.
Thus
concepts
a
mapping
and
theory,
between
an
explanation
empirical
(theory)
observations
and
will
never
concepts/
theory is taking place. Besides noting that such research requires considerable conceptual skills, which is often overlooked, it should also be noted
that
the
findings.
researcher
In
order
4
supply evidence.
should
to
be
handle
able
such
to
demonstrate
validity
claims
the
the
validity
researcher
and
validity claims
must
infer-
have much in common with measurements as
discussed above.
4
the
He or she should report the questions, responses, infer-
ences made, and what supports these inferences. Thus, themapping ,
ences,
of
For an excellent discussion, see Kirk and Miller (1986).
89
90
Measurements
EXAMPLE 6.3
s
Assume a medical doctor is examining a patient with symptoms
fi
1,
. . .
sn .
During the examination s/he arrives at a speci c diagnosis and decides on
treatment.
Has
the
doctor
made
some
sort
of
measurement?
S/he
has
observed the symptoms and related them to her or his knowledge base,
and
thus
theory
conducted
(diagnosis).
mapping
An
expert
mistakes constructs
valid
toms)
(relevant
and
theory
between
the
observer
observable
who
seldom
symptoms
makes
and
diagnostic
mappings between empirical observations (sympdiagnosis),
which
corresponds
to
excellent
construct validity.
2. In business studies the researcher often makes use of secondary data.
Such
data are gathered
measurements
have
by
been
means
used
secondary data, one should
as
of
fi
speci c
well.
This
always inspect
procedures,
indicates
where
that,
fi
speci c
when
using
and evaluate the data gathering
and measurement procedures used.
3. Analysis of written texts, such as annual reports and business maga-
zines,
and
research.
taped
Even
exploratory,
and
here
the
transcribed
measurement
arguments
put
interviews,
problems
forward
at
is
are
the
often
used
present.
outset
If
of
in
the
this
business
study
is
section
fi
apply. If the research is structured (descriptive or causal), conceptual de n-
fi
fi
itions must be developed, and speci c procedures (operational de nitions)
speci
fied.
The above discussion indicates that measurements are important and must
be properly dealt with in qualitative research, where measurement problems
have usually been given less attention.
Exercises
Further Reading
.................................................................
Churchill, G.A. and Iacobucci, D. (2015) Marketing Research: Methodological
Foundations, 11th edn., Mason, TN: Earlie Lite Books, Inc. (Chapter 7).
Ghauri, P.N. (2004) ‘ Designing and conducting case studies in international business
research’, in Marshan-Piekkari, R. and Welch, C. (eds.),A Handbook of
Qualitative Research Methods for International Business, Cheltenham, UK:
Edward Elgar, pp. 109– 24.
Golafshani, N. (2003) ‘Understanding reliability and validity in qualitative research’ ,
The Qualitative Report, 8(4): 597–606.
Questions
.................................................................
5.
In Table 6.1, three dummy variables were used to operationalize the
nationality
variable.
Why?
Why
not
just
use
one
variable
with
the
following values: Belgian = 1; German = 2; French = 3; and Italian = 4?
6.
Suppose I rank all the 44 students in my class on the basis of their
ability, with the best student being rated 1 and the worst student being
rated 44. Is this an ordinal scale or an interval scale?
7.
What are the main attributes of reliable measures?
8.
What is the difference between convergent validity and divergent validity?
9.
Give examples of measurements in qualitative research.
Exercises
.................................................................
1.
Determine whether each of the following variables is measured on a
nominal, ordinal, interval, or ratio scale:
a.
prices on the stock market;
b.
marital status (married or not married);
c.
level
of
education
(school,
undergraduate
degree,
postgraduate
degree, PhD);
personal income;
d.
e.
2.
scores derived from Likert scales.
fine
De
fi
each of the following concepts, and develop operational de n-
fi
itions for the de ned concepts:
3.
a.
a ‘workaholic’;
b.
market share;
c.
product diversi cation.
fi
How might you measure ‘job satisfaction’ using a questionnaire survey
of employees?
91
PART III
Implementation
..................................................................................
P
art III deals with the actual processes of conducting data analysis, examining case
–
studies, and writing up the report. The chapters 7
( 14) take you through step by
step, providing examples and guidelines. As such, this part can be used as a manual
while carrying out the respective activity. This part also provides guidelines for handling cross-cultural research.
7
Data Collection for Qualitative
Research
Since Hippocrates
first
presented 14 classic case studies of disease some
2300 years ago, science has proceeded along two divergent knowledge
paths. One involves formulating a tentative theory of a phenomenon
‘ writ large’ , deducing implied empirical consequences, and controlling
situational events in order to observe the validity of empirical deductions.
The second path, less frequently used but equally valid, is to reason from
individual and naturally occurring but largely uncontrollable observations towards generalizable inductive principles.
B o n o m a (1 9 8 5 : 1 9 9 ) , r e p r i n t e d w i t h p e r m i s s i o n b y t h e
American Marketing Association
In business studies most researchers need to collect some primary data to
answer their research question. Once the researcher has decided to collect
information/data through primary sources, s/he has to decide what kind of
data collection method to use. As discussed earlier, s/he could do an observation, experiment, interview, or survey. However, the choice of data collection method will depend upon an overall judgement on which type of data
are needed for a particular research problem. One important aspect is to
identify the unit of analysis. Even in one-country research, where data are
firms
compared and contrasted, we need to be sure that the
consulted are
comparable. This is also important for segmentation, new product development, demand forecast, or country analysis for entry strategy studies.
fi
While de ning the unit of study, several dimensions are important. One is
the
scope:
whether one study will cover a region (e.g. the EU), a country, or a
particular area in a city or a country (e.g. north-west England or central
fi
characteristics of the unit. When we are
firms, we should have clear criteria, such as: age, size, revenue, and
London). Also, we need to de ne the
studying
industrial sector. Say we want to study‘Export behaviour of SMEs (small and
medium-sized enterprises)’, should we study all
firms in that category or only
firms? Should we study all firms or only those that have been
for at least fi ve years? Moreover, the type of answers we are
manufacturing
in business
looking for will determine which type of analysis we need. At the outset, a
researcher has to decide whether s/he wants to use a qualitative or quantitative data collection and analysis method.
96
Data Collection for Qualitative Research
Qualitative versus Quantitative Methods
7.1
Alternatives to the standard approach, like unstructured interviewing, tend to
be viewed as faulted variants. . . I am arguing, instead, that the standard survey
interview is itself essentially faulted and that it therefore cannot serve as the
ideal ideological model against which to assess other approaches.
(Mishler, 1986: 29)
In the literature on research methods there is some discussion on which
fic . It is sometimes stated
fic and thereby
methods or techniques are more suitable or‘scienti
’
that structured and quantitative methods are more ‘scienti
’
better. But in our opinion, methods or techniques are not‘better ’ or ‘scien-
fic
ti
’
only
because
they
are
quantitative.
As
mentioned
earlier,
which
methods and techniques are most suitable for which research project depends
on the research problem and its purpose (Jankowicz, 1991). Several scholars
also assert that it depends upon the ontological orientation of the researcher.
If the researcher believes in objectivism, that social phenomena and their
meaning have an existence that is independent of the researcher, s/he would
be inclined to do a positivistic and quantitative type of research. However,
in
case
a
researcher
believes
in
constructionism,
that
social
phenomena
fi
and their meanings are continually being revised and are not de nitive, the
researcher will be inclined to use an interpretive and qualitative type of
research (Bryman and Bell, 2003).
Research methods refer to the systematic, focused, and orderly collection
of data for the purpose of obtaining information from them, to solve/answer
a particular research problem or question. Methods are different from techniques of data collection. By methods we mean data collection through
historical review and analysis, surveys,
field
experiments, and case studies,
while by techniques we mean a step-by-step procedure that we follow in
order to gather data and analyse them to
find
the answers to our research
questions. These are concerned more with how to do things than what to do
or why to do it. In business studies, we normally use techniques such as
structured, semi-structured or unstructured interviews, surveys, and observations (Bennett, 1986; Jankowicz, 1991).
The main difference between qualitative and quantitative research is not
one of ‘quality’ but of procedure. In qualitative research,
arrived
at
by
statistical
methods
or
other
procedures
findings
of
are not
fi
quanti cation.
Normally, the basic distinction between quantitative and qualitative research
is
that
quantitative
researchers
employ
measurement
and
qualitative
researchers do not (Bryman and Bell, 2003; Layder, 1993; Sinkovics et al.,
2008).
The
difference
between
quantitative
and qualitative
methods
and
7.1 Qualitative versus Quantitative Methods
fi
approaches is not just a question of quanti cation, however, but also a
flection of different perspectives on knowledge development and research
re
objectives. We can do research on behaviour, events, organizational functioning, social environments, interaction, and relationships. In some of these
fi
studies, data may be quanti ed, but the analysis itself is qualitative, such as
with census reports. It is quite common for researchers to collect their data
through
observations
and
interviews,
which
are
the
methods
normally
related to qualitative research. But the researcher may code the data collected
in such a manner that would allow statistical analysis. In other words, it is
quite
possible
to
quantify
qualitative
data.
Qualitative
and
quantitative
methods are therefore not mutually exclusive. The difference is in the overall
form and in the emphasis and objectives of the study. These differences in
emphasis are illustrated in Table 7.1.
Qualitative research is a mixture of the rational, explorative, and intuitive,
where the skills and experience of the researcher play an important role in the
analysis of data. It is often focused on social process and not on social
structures, which is frequently the focus in quantitative research. The skills
needed to do qualitative research are thinking abstractly, stepping back and
Table 7.1
The difference in emphasis in qualitative versus quantitative methods
Qualitative methods
Quantitative methods
• Emphasis on understanding
• Emphasis on testing and veri cation
• Focus on understanding from
• Focus on facts and reasons for social
respondent’ s/informant’ s point of view
fi
events
• Interpretation and rational approach
• Logical and critical approach
• Observations and measurements in
• Controlled measurement
natural settings
• Subjective ‘ insider view’ and closeness
to data
• Explorative orientation
• Objective ‘outsider view’ distant from
data
• Hypothetical-deductive; focus on
hypothesis testing
• Process oriented
• Result oriented
• Holistic perspective
• Particularistic and analytical
• Generalization by comparison of
• Generalization by population
properties and contexts of individual
membership
organism
Source :
Based on Reichardt and Cook 1
( 979).
97
98
Data Collection for Qualitative Research
TECHNIQUES
TECHNIQUES
Qualitative
Conversation
Structured observation
Unstructured and
Structured interview
semi-structured
Structured surveys
Attitude scaling
interviews, etc.
Quantitative
Historical
Group
Case
review
discussion
study
Survey
Field equipment
Experiment
METHODS
Figure 7.1 Quantitative and qualitative methods and techniques
Source :
Adapted from
Business Research Projects for Students,
Thomson Learning (Jankowicz, 1991: 159).
analysing situations, recognizing and avoiding biases, obtaining valid and
reliable information, having theoretical and social sensitivity, being able to
keep analytical distance while utilizing past experience, and having a shrewd
sense of observation and interaction (Hagen et al., 2019; Strauss and Corbin,
1990; Van Maanen, 1983). Although most researchers emphasize one or the
other, qualitative and quantitative methods can be combined and used in the
same study. Many scholars claim that the two approaches are complementary
and cannot be used in isolation from each other (for this type of discussion see
Jankowicz, 1991; Jones, 1988; Martin, 1988). According to this view, no
method
is entirely qualitative or quantitative. However, the techniques can
be either quantitative or qualitative. Figure 7.1 illustrates this point further.
In
qualitative
methods,
although
the
number
of
observations
is
low,
several aspects of the problem area can be analysed. Low numbers are also
fied
justi
because we often want to do in-depth studies or provide ‘thick
description ’, which is not possible in cases of numerous observations. Qualitative methods are, therefore, most suitable when the objectives of the study
demand in-depth insight into a phenomenon.
7.2
Different Qualitative Methods and When to Use Them
Research problems focusing on uncovering a person’s experience or behaviour,
or when we want to understand a phenomenon about which little is known, are
typical examples of qualitative research (Ghauri, 2004; Marshan-Piekkari and
fi
Welch, 2004). Moreover, when an event or social process is dif cult to study
using quantitative methods, qualitative methods are most suitable and can
provide
intricate
details
and
understanding.
Qualitative
research
is
thus
common in social and behavioural sciences and among practitioners who
want to understand human behaviour and functions. It is suitable for studying
organizations, groups, and individuals (Strauss and Corbin, 1990).
7.2 Different Methods and When to Use Them
There are three major components of qualitative research (Becker, 1970;
Miles and Huberman, 1994; Strauss and Corbin, 1990):
1.
data:
2.
interpretative or
often collected through interviews and observations;
analytical procedure:
and analyse the data to arrive at
report:
3.
the techniques to conceptualize
findings or theories;
written or verbal. In the case of students, the report is written in
the form of a thesis or project.
In spite of claims that relatively few studies use qualitative methods, it is
ficult to find support for the use of qualitative data:
not dif
Qualitative data are attractive for many reasons: They are rich, full, earthly, holistic,
flow
real; their face validity seems unimpeachable, they preserve chronological
where that is important, and suffer minimally from retrospective distortion; and
they, in principle, offer a far more precise way to assess causality in organizational
affairs than arcane efforts like cross-lagged correlations.
(Miles, 1979: 117)
It
is
generally
qualitative
accepted
methods
are
that,
most
for
inductive
useful,
as
they
and
can
exploratory
lead
us
to
research,
hypothesis
building and explanations. According to this view, qualitative and quantita-
fi
tive methods are suitable at different stages or levels of research. At the rst
level, the problem is of an unstructured nature and qualitative methods
are suitable. At the second level, quantitative methods are most useful, as
we want to test different hypotheses that were arrived at through level 1.
Quantitative methods allow us to accept or reject these hypotheses in a
logical
and
consistent
manner.
At
the
third
level,
both
qualitative
and
quantitative methods can be used. Often a combination of the two methods
is used at this level.
As we can see in Figure 7.1, the methods from left to right become more
quantitative and use more quantitative techniques. Historical review, group
discussions, and case studies are mostly qualitative research methods. These
qualitative methods use relatively more qualitative techniques, such as conversations and in-depth, unstructured, or semi-structured interviews.
7.2.1
Historical Review
In cases of historical reviews, our job is to describe what happened in the past
so that we can understand the present or plan for the future. Here we go
through existing records and reports and talk to different people to get as
true a picture as possible. The archives are reviewed in an interrogative
manner
with
a
particular
research
question/problem
in
mind.
The
main
problem in using such a method is that we have to trust human memory,
99
100
Data Collection for Qualitative Research
35
30
25
smrif fo rebmuN
Key
20
Entry
Exit
15
Total
10
5
0
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
Year (from 1951 to 1968)
Figure 7.2 Number of
firms
in the US transistor industry
Source : Utterback, J.M. (1994: 41). Reprinted by permission of Harvard Business School Publishing.
Copyright © 2004 by the Harvard Business School Publishing Corporation; all rights reserved. Based on
data in E. Braun and S. MacDonald, Revolution in Miniature: The History and Impact of Semiconductor
Electronics (Cambridge, UK: Cambridge University Press, 1978) and J.E. Tilton,International Diffusion of
Technology (Washington DC: The Brookings Institute, 1971).
which records selective parts of reality. It is quite possible that two different
people, while going through a certain situation or experience, will record or
remember different things; sometimes they make mistakes or misunderstand
things. It is therefore important that, when using such a method, we should
cross-check one written source with another, or a written source with an
interview, or two interviews with each other. In other words, we have to be
critical and compare different explanations of the situation or event. For
further insight into historical reviews as a research method, we recommend
Orbell
(1987).
Figure
7.2
presents
an
example
of
historical
review
in
a
particular industry.
7.2.2 Grounded Theory
It is often asked whether it is possible to apply a systematic and analytical
approach through studying the reality without any pre-assumed understanding,
and whether this type of approach can provide us with credible interpretations
and
conclusions
towards
theory
development.
Researchers
using
grounded
theory suggest that instead of developing constructs that can be measured,
first
speci
fied
for example in quantitative research, we
need to develop some concepts.
Concepts are more general and less
and can describe and explain a
7.2 Different Methods and When to Use Them
phenomenon better. In other words, concepts are precursors to constructs in
making sense of our reality (Gioia and Pitre, 1990; Gioia et al., 2012).
In
this
approach,
it
is
considered
better
to
develop
concepts
before
developing constructs for the purpose of theory development. This can be
done using
the grounded
theory approach
(Gioia
and Chittipeddi,
1991;
Glaser and Strauss, 1967). In this type of research, credible interpretations
first-order analysis based on
can be achieved by systematically presenting a
informants’ understanding and terms and then presenting a second-order
analysis based on the researcher’s concepts and themes (Hagen et al., 2019;
Van Maanen, 1979).
This type of research demands clear research question(s) that can lead to
some semi-structured questions/interviews to obtain real-time information on a phenomenon or a process. Here the informants are free to talk
about
their
experiences
and
their
actions
(Gioia
et
al.,
2012;
Morgan,
1983). In this case, the researchers do not provide leading questions and
fi
should not be afraid of getting too much information. As a rst stance, the
informants are leading us to several concepts, while it is in the secondorder analysis that the researcher interprets these concepts into themes.
These
themes
can
further
lead
to
aggregate
dimensions
(Hagen
et
al.,
2019), as suggested in Figure 7.3. This allows the researcher to build a
data structure.
In this process the researcher can go back and forth between concepts,
themes,
and
dimensions
to
see
whether
some
new
concepts
have
been
discovered. However, it should be noted that the researcher cannot totally
ignore earlier research (literature). Particularly at this stage, the researcher
needs to compare and contrast his/her analysis with the earlier literature. In
other words, at this stage, the data and existing theory work hand in hand
(see Hagen et al., 2019 as a good example). This also means that a researcher
needs
to
be
informed
about
earlier
research;
this
is
what
Gioia
et
al.
(2012: 21) mean by the ‘willing suspension of belief or witting (as opposed
to un-witting) ignorance of previous theorizing in the domain of interest
’.
This allows for discovery and not reinventing the wheel.
7.2.3
Case Studies
What Is a Case Study?
The case study approach is often associated with descriptive or exploratory
research, but it is not restricted to these areas (Bonoma, 1985; Ghauri, 2004;
Yin, 1994). In business studies, case study research is particularly useful
when
the
phenomenon
under
investigation
is
fi
dif cult
to
study
outside
its natural setting and when the concepts and variables under study are
dif
ficult
to quantify. Often this is because there are too many variables to
101
102
Data Collection for Qualitative Research
Strategy
Concepts
Themes
Aggregate
dimensions
Continuous interaction with key partners
(e.g. customers, experts) for development and
refinement of offering
Identification of alternatives/broader scope of
Selective customer/partner
offerings through customers
intimacy and intensity for
Discovery of threats and potential
market sensing
drawbacks through regular contacts
Important to listen but avoid being overwhelmed,
Selective
selection of signals is vital
Responsiveness
Regular assessment and feedback from customers
regarding future and current offering reduces
risk of failure/need of adaptation
Selective experimentation
Better and quicker development/refinement of
& testing for innovation and
(more) offerings
business development
Co-creation with customers and key partners
Experimentation is a resource-conserving and
rapidly resource-generating way to innovate
Selection of ‘right’ partners
Coordination, cultivation,
Trust-based intensive relationships
and harmonization of
Mobilization of complementary resources
multiple stakeholders and
Migration and sharing of risks
resources
Flexibility
‘Doing more with less’
Find new ways to realize objectives (stretch
Creative management and
resources)
extension of resources
Flat hierarchy, quick communication, passion,
and involvement
Figure 7.3 The data structure
Source :
Based on Hagen et al. 2
( 019).
be considered, which makes experiment or survey methods inappropriate
(Bonoma, 1985; Yin, 2009).
As mentioned earlier, research in business studies emphasizes the role of
deduction, where the validity of all
findings depends solely on the quality of
logic employed in the study and precise measurement. However, the trade-off
between precision and reduced generalizability is not a useful one in many
situations. Many phenomena cannot be understood if removed from their
social context. In these cases, inductive qualitative approaches are alterna-
fi
tive methods to scienti c investigation.
Case research is based on a process model as suggested by Bonoma (1985).
A case study is a description of a management situation and often involves
data collection through primary sources such as verbal reports, personal
interviews, and observation. In addition, case methods involve data collection through sources such as
financial reports and archives, as well as budget
and operating statements, including market and competition reports. The
case method is not suitable for all types of research. It is the research problem
and the objective that decide whether the case method is suitable or not. With
the above proviso, the case method is useful for theory development and
7.2 Different Methods and When to Use Them
testing. The main feature is the intensity of the study of the object, individual, group, organization, culture, incident, or situation. The researcher needs
fi
to have suf cient information to characterize, to explain the unique features
of the case, as well as to point out the characteristics that are common in
several cases. Finally, this approach relies on integrative powers of research:
the ability to study an object with many dimensions and then to draw an
integrative interpretation.
When to Use a Case Study
A case study is a preferred approach when‘how’ or ‘why’ questions are to be
answered,
when the
researcher
has little
control over
events,
and when
the focus is on a current phenomenon in a real-life context (Yin, 2009). Case
studies
are
often
of
an
explanatory,
exploratory,
or
descriptive
nature.
According to Eisenhardt, case studies are:
particularly
well-suited
to
new
research
areas
or
research
areas
for
which
existing theory seems inadequate. This type of work is highly complementary
to incremental theory building from normal science research. The former is
useful in early stages of research on a topic or when a fresh perspective is
needed, while the latter is useful in later stages of knowledge.
(Eisenhardt, 1989: 548– 9)
When to use which research approach depends upon:
•
the type of research questions;
•
the control of the researcher on behavioural events;
•
the focus on a current as opposed to a historical phenomenon;
•
what information is needed;
•
how this can be obtained.
When research questions concern only ‘what?’, for example, ‘What are the
ways
in
which
fied. Here
justi
an
effective
firm
is
operated?’,
an
exploratory
study
is
the objective is to develop hypotheses or propositions, which
can later be studied. For an exploratory study, any of the
five
research
strategies can be used. If the questions relate to‘how many?’ or ‘how much?’,
survey or archival strategies are favoured. But when ‘how? ’ and ‘why?’
questions are asked, a case study method is favoured as a research strategy.
It is quite often stated that the case study method is used when we want to
study a single organization and we want to identify factors involved in some
aspects or behaviour of an organization or smaller unit, such as a marketing
or
finance
department.
However,
it
is
equally
possible
to
study
several
fi
organizations with regard to a set of variables we have already identi ed
or assumed. Such case studies are called
comparative case studies.
In this
103
104
Data Collection for Qualitative Research
type of study we ask or study the same questions in a number of organizations and compare them with each other to draw conclusions.
The purpose of data collection in the comparative case study method is to
compare the phenomenon (e.g. strategy formation) in different cases in a
systematic way, to explore different dimensions of our research issues, or to
examine different levels of research variables. In a survey, on the other hand,
we are more concerned with the sampling of different organizations as we
want
to
generalize
our
findings
to
all
other
organizations
of
the
same
type (Jankowicz, 1991). Yin (2009) compares the case study method with
experiments, and suggests three situations where case study is the preferred
method:
1.
fi
If we want to follow a theory that speci es a particular set of outcomes in
some particular situation, and if we
find a firm which finds itself in that
particular situation, we can use the case study method for a critical test
of theory and its applicability to the organization.
2.
fi
If we want to study some speci c characteristics of a rare or extreme
situation in which an organization
finds itself, we can use the case study
method to compare and contrast.
3.
If we want to study a situation or an organization which has rarely been
studied and is unique in its nature, we can use the case study method. In
this case, we hope to learn something new and important.
As most case studies are done through a review of existing historical
material and records plus interviews, the case study method is quite similar
to historical review, but it is different in the sense that here we have a
possibility of direct observation and interaction. As mentioned earlier, we
would like to make it explicit that the case study method is not synonymous
with qualitative research or methods. A case study may very well involve
quantitative methods or even be entirely quantitative.
In many cases, especially in business studies, students
method to use
first
decide which
– for example, case study or survey – and then formulate their
problem. We have been advocating that it is the research problem and the
research objectives that dictate the type of method that should be used. Here
fi
lies a dilemma: should we decide the method rst, or should our problem lead
us to the method? Of course, the latter should be the case, as most problems
and research objectives clearly suggest one form of data collection over
another.
Preparing for a Case Study
When preparing to undertake the case study method for research, a theory/
–
data/theory revision cycle, as suggested by Bonoma (1985: 204 6), is quite
7.2 Different Methods and When to Use Them
Disconfirmation
gnidnatsrednu fo segatS
Model/theory creation
Prediction
Design
Drift
Early
Middle
Late
Stages of the research project
Figure 7.4 A process model for case research
Source:
Based on Bonoma (1985: 205). Copyright © 1985, with permission from Elsevier.
useful (see Figure 7.4). This revision process goes through four stages:‘drift ’,
‘design’, ‘prediction ’, and ‘discon
firmation .
’
At the beginning of a research project the researcher is in a
‘drift ’ stage and
is trying to learn the area of research and the concepts and terminology in
the
field.
This drifting leads him/her to a priori notions about the phenom-
enon and how it operates. This stage widens the perspective of the researcher
fi
and often leads to modi cation in the basic research questions (Van Maanen,
1983). In fact, most research methods involve this ‘drift’ phase in the early
stages of the research project.
A researcher moves to ‘design ’: the choice of strategy to collect the data
needed to answer/elucidate the research question plus the stage when s/he
starts developing tentative explanations of the observations so far. At this
fi
stage, the researcher assesses and re nes major areas of the research project
as suggested by the drift stage. This facilitates the conceptualization of the
research problem. At some time, s/he may have to go back to the drift stage,
to get a better understanding.
‘Prediction’ occurs in the middle-to-late stage of the project. By this stage,
the researcher has a good understanding of the factors according to which
the case information may be grouped and can proceed with further case
fi
construction and analysis. The researcher proceeds con dently, compiling
more cases with the purpose of drawing conclusions. As a result, s/he can
even develop some tentative explanations. Similarly, s/he may realize that
105
106
Data Collection for Qualitative Research
some generalizations are not very general and are valid only in particular
circumstances, settings, industries, or
The
final
firmation ,
stage, ‘discon
’
firms.
refers to further testing and analysis of
the results suggested by the prediction stage. This can be done by applying the
results to other or broader sets of cases. The idea is to apply the concepts to
totally different cases or situations to test the generalizability of the results.
As we
can see, these four stages do not really form a rigid
fi
or xed
hierarchy but rather represent an iterative evolution towards understanding
(Bonoma, 1985: 205).
Coming to more practical details, once our research problem suggests case
study as the preferred approach, we should deal with questions such as: What
are the skills needed? What types of a priori assumptions do we have? How
do we select the cases? How many cases shall we include in our study?
How shall we conduct the case study?
As far as the skills required are concerned, this is not one of the easiest
types of research to do, as it demands special skills from the researcher. Some
fic
training for speci
cases is necessary, especially if the researcher has no
previous experience of conducting case studies, or a research assistant is used
when there are several cases. Unlike in surveys, where data collection is more
routinized, in case studies the researcher has to be skilled in the dynamics of
a case and should be able to take advantage of opportunities offered during
the data collection.
While
collecting
data
through
semi-structured
interviews
a
researcher
must be able to control the situation, ask the right questions, adapt to new
or unexpected situations, and develop trust. All these skills can be learnt if
the researcher is aware of them beforehand. Yin (1994: 67) recommends case
study training as a seminar experience, especially when many cases and
several researchers are involved. Moreover, he recommends a protocol to
arrive at the
final
version. One purpose of these seminars and protocols
should be to discuss potential problems and how to handle them. Such
training may also reveal weaknesses in the research problem, in the study
design, and even in the capabilities of the researchers. All these can be
improved if detected. It is also recommended that a pilot study is conducted
before the data collection starts. This serves as a rehearsal for the data
collection procedure, indicating the time it takes and any problems it can
cause or that may arise.
How to Select the Cases
As in other methods of data collection, it is important to decide the target
fi
population that is to be used for the investigation. It includes those rms,
individuals, groups, or elements that will be represented in the study. The
7.2 Different Methods and When to Use Them
next stage is to assess the accessible population, the population to which we
can get access (Cooper, 1984). Out of this accessible population we have to
firms
select one or a few cases, objects, or
for the study,
financial
for the study. The time available
resources for travelling, and other practical issues
are of great importance. For example, depending upon how much time we
have, the type of organization or company we select for our study would be
different. If we have very little time available, we should perhaps study a
firm, as in these firms the communication lines are shorter and faster,
they are more fl exible, and it is easier to get overall or in-depth information.
On the other hand, if we are studying a speci
fic and complex issue, we
should perhaps study a bigger fi rm, because these firms experience complex
smaller
problems and have in-house expertise that can provide us with in-depth
information on the particular issue (van der Meer-Kooistra, 1993). The cases
should also correspond to our theoretical framework and the variables we are
studying. For example, if we are studying the behaviour of industrial buyers,
we
have
to
firms
select
that
are
dealing
purchasing. Once we have selected a
firm,
with
industrial
marketing
and
we should select a manager who
is involved in the process of marketing and purchasing. An interview with
the
firm s
’
public relations manager or an accountant would not provide us
with the information we are looking for.
In bigger organizations it is very important to select the right department,
section, or individual. It is not a question of interviewing the most important
individuals, but of interviewing the
right person from
an organization: right
from the point of view of our research questions and study variables. Finding
the right person is sometimes a long process. However, when we are able to
establish contact with a key, or highly placed, manager, our goal should be
to take his or her help in identifying the right person.
Students often ask how many cases they should include in their study. The
fi
answer to this question is very dif cult as there is no upper or lower limit to
the
number.
Often
one
case
is
enough.
As
Mintzberg
says, ‘What,
for
example, is wrong with a sample size of one? Why should researchers have
to apologize for them? Should Piaget apologize for studying his own children, a physicist for splitting only one atom?
’ (1979: 583). It is the research
fl
problem and the research objectives that in uence the number and choice of
cases to be studied. Moreover, what is meant by‘a case’? For example, if we
fi
are trying to understand the process of decision making in a rm, we may
study several decisions on different issues, important/unimportant, novel/
routine decisions, etc., in the
among
important
factors
same organization.
(see,
e.g.,
Campbell,
This will provide variability
1975,
who
argues
for
the
richness of detail within a single case by looking for many implications of
the ideas under study).
107
108
Data Collection for Qualitative Research
Hint: There is no upper or lower limit with regard to the number of cases to
be included in a study.
Sampling in Qualitative Research
Sampling is primarily associated with quantitative research, emphasizing
estimation of various parameters, testing hypotheses, and so on. Samples
consisting of one or more units of observations (and analysis) are, however,
always applied in qualitative research. In quantitative empirical research an
important purpose is to arrive at statistically valid conclusions, for example
that on a statistical basis we can conclude that‘X’ and ‘Y’ covary. In other
words, statistical conclusion validity plays a major role. For example, if a
study of the effectiveness of a new medical drug cannot demonstrate that the
drug is effective (or more effective than another drug), no conclusions can be
drawn on either internal or external validity.
In qualitative research the purpose is seldom to arrive at statistically valid
conclusions (even though it is possible), but rather to understand, gain insights,
and create explanations (theory). Therefore, in qualitative research, sampling
issues are also important, such as who and how many should be included.
EXAMPLE 7.1
fl
In a qualitative study the objective is to gain insights into who inuences
purchase decisions. To obtain these insights the researcher has to consider
who is involved, and who possesses (and is willing to share) the needed
information, which implies selecting the most relevant respondents (subjects). In this effort the researcher may start with one person, for example
fi
the manager of the research department, and by asking about speci c
purchases, and also asking: ‘Were other people involved?’, will thus grad-
fl
ually uncover participation and in uences in buying decisions.
EXAMPLE 7.2
Focus groups are often used to gain insights into various perspectives and
opinions. How many focus group interviews should be conducted? Let us
assume that the researcher starts with one focus group, and the data are
transcribed
and
analysed.
Then
s/he
conducts
another
focus
group
7.2 Different Methods and When to Use Them
EXAMPLE 7.2
(CONT.)
fi
interview, and that also uncovers points of view not present in the rst one.
The researcher continues the procedure until no new opinions/points of
view are uncovered.
Most scholars suggest that in qualitative research‘purposeful sampling’ is
most suitable. This means that we should select information-rich cases for
in-depth study to achieve insight and understanding rather than to achieve
generalizability (Patton 2014). Some of the most commonly used purposeful
case selections are: critical case, comparative cases, criterion-based cases,
heterogeneous cases, homogeneous cases, typical case, and snowball case
selection. These most common case selection strategies and what they entail
are presented in Table 7.2.
Table 7.2
Strategies for case selection
Selection
strategy
Explanation
Critical case
Where an insight into a single critical case can provide
maximum and logical explanation that can be true for all other
cases in that category
Comparative
When cases are selected so that they can be compared and
cases
contrasted to achieve maximum understanding
Criteria-based
When cases are selected based on pre-decided criteria, often to
cases
compare the cases that meet criteria with those that do not meet
criteria
Heterogeneous
When we select cases that are clearly different from each other,
cases
for example to understand the impact of a certain factor on the
success of manufacturing versus services
firms
Homogeneous
When we select cases that are clearly similar, for example, cases
cases
from manufacturing as well as the same industry such as
automobiles
Typical cases
When we select cases that are considered normal/average to
achieve better insight and understanding
Snowball case
When we select cases that can lead us to further cases, suggested
selection
by respondents, that are dif cult to approach on our own or are
fi
connected to earlier cases
Source:
Based on Patton (2014).
109
110
Data Collection for Qualitative Research
Theoretical Sampling
In any study, variability is needed. Consider a study designed to examine the
(potential) relationship between organizational form and innovativeness. In
order to study this research question,
firms
(organizations) representing a
range of organizational forms need to be included. Let us also assume that
F
the researcher, based on a review of the literature, knows that forms,
F
2
fi
1
,
. . . exist. This insight is then useful when deciding on which rms (organ-
izations) should be included. We see here that the sample units are chosen for
theoretical reasons.
Different Types of Case Study Design
Yin
2
(1994)
provides
four
types
of
case
study
design
and
presents
a
 2 matrix (see Figure 7.5), suggesting that single and multiple case studies
re
flect different design considerations.
The four types of research design are as follows:
1.
single case design, holistic;
2.
single case design, embedded;
3.
multiple case design, holistic;
4.
multiple case design, embedded.
As we can see, the primary distinction is between single and multiple case
designs. We should therefore decide, at an early stage, whether we are going
to use single or multiple case design.
Single case
is appropriate when a
particular case is critical and we want to use it for testing an established
theory. It is a critical case because it meets all the conditions necessary to
con
firm, challenge, or extend the theory. Another situation is when a single
case is an extreme or a unique case. Finally, a single case design is appropriate when a case is revelatory. This means that we can observe and study a
phenomenon that was previously not accessible, and which can provide
useful insight. We can also use single case design in other situations, such
Single case design
Multiple case designs
Holistic
(single unit
of analysis)
Type 1
Type 3
Embedded
(multiple units
of analysis)
Type 2
Type 4
Figure 7.5 Basic design for case studies
Source :
Yin (1994: 46). Copyright © 1994 by Sage Publications. Reprinted by permission of Sage
Publications, Inc.
7.3 Observations
fi
as a pilot study or an exploratory study that serves as a rst step to a later,
more comprehensive, study (Yin, 2009).
Multiple case
study design is
considered
more
appropriate for
studies
which do not involve rare, critical, or revelatory cases. In this approach we
should be clear that every case must serve a particular purpose in the study.
In other words, we have to justify the selection of each case. However, as
flexible
pointed out earlier, case study design is often
fi
fi
and can be modi ed
with proper justi cation.
The use of a case study method depends also upon the type of study we are
doing, whether it is inductive or deductive, and also upon whether we are
fi
looking for speci c or general explanations. In the case of an inductive
fi
approach and speci c explanation, we may use the single case. On the other
hand, if we are doing a study with a deductive approach, we can use a case
study at an early stage to develop our hypotheses or propositions. If we are
doing a study with an inductive approach but are looking for general explanations, then we should use a multiple case method. However, if we are doing
a study with a deductive approach and are looking for generalizations, then
the case study is a less recommended method. We suggest that students using
this
method
should
consult
Yin
(2003)
for
further
guidance.The
above-
mentioned approaches to qualitative methods are summarized in Table 7.3,
where we can see the characteristics of these different approaches.
7.3
Observations
Observation as a data collection tool entails listening and watching other
people’s behaviour in a way that allows some type of learning and analytical
interpretation. The main
advantage
is that we can collect
first-hand
infor-
mation in a natural setting. Moreover, we can interpret and understand the
observed behaviour, attitude, and situation more accurately, and capture the
dynamics of social behaviour in a way that is not possible through questionnaires and interviews.
The main
disadvantage
is that most observations are made by individuals
fi
who systematically observe and record a phenomenon, and it is dif cult
fi
to translate the events or happenings into scienti cally useful information.
This is particularly important when the purpose is to generalize from these
observations.
Here
questions
about
validity
and
reliability
become
very
important and need to be answered satisfactorily. When we collect data
through observations, we have to make a number of choices regarding, for
example, participatory versus non-participatory, laboratory versus
tings. This is further illustrated by Figure 7.6.
field set-
111
112
Data Collection for Qualitative Research
Table 7.3
Different qualitative approaches
Features
Historic review Focus group
Grounded theory
Case study
Focus
Description of
Gathering
Developing a theory
Developing in-depth
events
information from a
from the data in the
description through
group of people
fi eld
one or more cases
Type of
To tell stories of
Explaining a lived
Grounding a theory
Providing an in-depth
research
experiences
phenomenon
in the view of the
understanding from a
participants
case or cases
Discipline
Humanities,
Philosophy,
Sociology,
Psychology, law,
history,
psychology,
management
political science,
sociology,
education
medicine,
literature
management
Unit of
Studying one
Studying several
Study a process,
Study an event,
analysis
or more
individuals who
action, or
programme, activity
incidents/
share the
interaction between
more than one
individuals
experience
individuals
individual
Data
Documents and
Group discussion
Interviews with
Multiple sources,
collection
interviews
and observations
several individuals
interviews,
–
observations,
documents
ficant
Data
Storytelling,
Seeking signi
Generating a theory
Developing detailed
analysis
developing
statement,
illustrated by data
analysis from one or
themes,
structure, and
chronologies
description
more cases
Source:
Based on Creswell (2007).
Observations
Human
Laboratory
Figure 7.6
Mechanical
Field
Laboratory
Field
Choices for collecting primary data through observations
In participant or
fi eld observation,
the observer is a natural part of the
situation or event. The researcher is part of a company or organization and
decides to study the same organization in one way or another. Sometimes
fi
a researcher speci cally joins an organization to be able to observe it as a
7.3 Observations
participant. In this case, the observation is not hidden or disguised: the
people who are being observed know that they are being observed and by
whom. In business studies, participant observation can enable researchers to
have access to what people actually do instead of what they might claim they
do. One danger of participatory observation is that the observers can be so
fluenced
in
by the event, situation, culture, or everyday lives of the subjects
that they become unable to take a neutral view of events and situations. On
the other hand, observers may be so ethnocentric that they are not able to
observe or analyse the situation because they believe the subjects have an
inferior culture or that the observer knows best (Douglas, 1976).
A
contrived setting
refers to a method where reactions are observed in a
controlled setting in a laboratory or in other virtual reality. In this method,
the researcher is better able to control the observation without any disturb-
fi
ances. Moreover, it could be more ef cient and less time consuming.
In
non-participant observations,
natural
setting
but
is
not
part
of
the
the
observer or researcher
situation
her/himself.
fl
observes a
It
has
been
reported in several studies that people’s behaviour may be in uenced by a
non-participatory observer, but only in the beginning; people get used to it in
a very short time. One way to overcome this problem is to observe under
disguise. For example, in a buying or selling situation an observer can act as
a potential customer or salesperson. Observations for research should be
planned systematically in direct relevance to research questions. However,
these observations can be simple and straightforward, quite often in a natural
setting, event, or occurrence. An example is observing customers in a supermarket while they make their choices in picking up a particular product
category or brand.
In the case of a mechanical method of observation, the same observation
can be done by placing a video camera overlooking a particular section of
the supermarket. Another way employed by a number of companies is to use
their hotline statistics to understand consumer behaviour and future trends.
These calls are recorded and later analysed in terms of the type of questions
asked and the types of problems mentioned by the customers. However, a
researcher has to consider the ethical aspects of this method of data collection. Do the customers/subjects know that they are being observed? If not,
then is it ethically correct, or might the recording of their behaviour harm
them in some way or violate their personal integrity?
In the case of
human
observation, an observer follows his or her own
values and expectations and is also limited by his or her own constraints. The
subjects can catch those values and constraints by placing the researcher in
some class or category. Most subjects who are alert to these issues can do so
by the dress, manner, or language used. For example, while studying the
113
114
Data Collection for Qualitative Research
fi
behaviour of a labour force, if the observer is classi ed as anti-working class,
fl
this can in uence the behaviour of the observed (Phillips, 1966).
Some scholars agree that data collected through observation are more
objective and accurate, as this method is independent of respondents
’ unwillingness or inability to provide the information needed by the researcher. For
example, respondents are often careful in replying to sensitive or embarrassing
questions.
Observation
of
the
actual
behaviour
as
it
occurs
fl
re ects
respondents’ behaviour more accurately (Churchill and Iacobucci, 2015). As
mentioned earlier, observation as a data collection method should not be
fl
selected just because it is easier. This choice is highly in uenced by the
research problem and the research design as well as the researcher
’s skills,
capabilities, and nature, and the characteristics of the subjects to be observed.
7.4
Communication
For primary data, the researcher has to decide whether to communicate with
the respondents/subjects or just to observe them.
Communication
does not
have to be direct or face to face. We could send our questions by post or
email and ask for answers to be sent back to us in the same manner. We
could
also
use
more
personal
methods
and
meet
with
the
respondents/
subjects face to face and ask questions.
The instrument used for this type of data collection is called a survey
or questionnaire. A questionnaire can be structured, unstructured, or semistructured.
advance
In
and
a
structured
respondents
questionnaire,
have
to
the
answer
questions
from
a
set
are
of
decided
in
predetermined
answers. In the case of an unstructured questionnaire, the questions are only
roughly scripted in advance and there are no predetermined answers; the
respondent
can
reply
in
his
or
her
own
words.
A
questionnaire
where
the questions are predetermined but the respondents can use their own words
to answer is a semi-structured questionnaire (Churchill and Iacobucci, 2015).
Communication thus refers to collection of data by asking those who have
experienced a particular phenomenon to explain it to the researcher. In
qualitative research, there are three main ways to collect data through this
method:
1.
personal interview;
2.
telephone interview;
3.
email interview.
Many
students
interviews.
and
business
researchers
collect
their
data
through
7.5 Interviews
7.5
Interviews
Interviews demand real interaction between the researcher and the respond-
fi
ent. To be able to run the interview ef ciently and without any disturbances,
the researcher needs to know the respondent and his/her background, values,
and
expectations.
At
present,
we
have
fi
enough
accumulated
experience
available to be able to carry out ef cient interviews. Interviews are often
considered the best data collection methods (see Figure 7.7).
first
In research we use two types of interview. The
structured
is survey research or
interviews, where a standard format of interview is used with an
emphasis on
fixed response categories and systematic sampling and loading
procedures combined with quantitative measures and statistical methods.
The second type is
unstructured
interviews, where the respondent is given
almost full liberty to discuss reactions, opinions, and behaviour on a particular issue. The interviewer is there just to give lead questions and to record
the responses in order later to understand‘how’ and ‘why’. The questions and
answers are often unstructured and are not systematically coded beforehand.
In this section, we deal with this type of interview for qualitative research.
In the literature there is some discussion on semi-structured interviews,
which differ from both unstructured and structured interviews. They differ
from the former in the sense that the topics and issues to be covered, sample
sizes, people to be interviewed, and questions to be asked have been determined beforehand. They also differ in how we plan to minimize bias arising
from the sequence in which we address subject matter, from any inadvertent
omission of questions, from unrepresentative sampling, and from an uncontrolled over- or under-representation of sub-groups among our respondents.
In semi-structured interviews we handle this bias by careful design of the
technique
itself.
Semi-structured
and
unstructured
interviews
differ
from
structured interviews in that they demand greater skills from the interviewer,
as in semi-structured and unstructured interviews we often obtain information
about personal, attitudinal, and value-laden material, which calls for social
sensitivity. An advantage of structured interviews lies in the uniformity in the
behaviour of interviewers, as those other than the researcher can replicate
the interview in similar situations. Unstructured interviews are considered
Interviews
Interview by mail
Figure 7.7
A typology of interviews
Interview by phone
Personal interview
115
116
Data Collection for Qualitative Research
advantageous in the context of discovery because the researcher/interviewer
who is well acquainted with the research questions/area can ask follow-up
questions and enrich the data so collected (Alvesson, 2011; Phillips, 1966).
There is an abundance of literature available on structured vs. unstructured or semi-structured interviews when it comes to the question form
and respondent understanding (e.g. Alvesson, 2011; Beza, 1984; Bryman
and Bell, 2015; Fowler and Mangione, 1990; Mishler, 1986; Schuman and
Presser, 1976). Anybody interested in this discussion should consult any of
the references cited, as our purpose here is to provide some guidelines for
interviewing. For the purpose of this book, interviews refer to face-to-face
verbal exchanges, in which one person, the interviewer, attempts to obtain
information or opinions or beliefs from another person, the interviewee.
The
advantage
of in-depth unstructured interviews is that we can gain a
more accurate and clear picture of a respondent
’s position or behaviour. This is
possible because of open-ended questions and because respondents are free to
answer according to their own thinking, as we have not constrained answers to
only a few alternatives. This is also true in the case of complicated or sensitive
issues, where the interviewer can ask for further elaboration of answers and
attitudes. This method of data collection is highly suitable for exploratory and
inductive types of study as it matches their purposes very well.
EXAMPLE 7.3
An interviewer starts by asking: Could you please tell me about your experiences with your new computer?
fi rst
Respondent:
At
I had a lot of trouble.
Interviewer:
What trouble?
Respondent:
It was dif cult to operate, it was different from my old
fi
computer.
Interviewer:
How? Could you please explain?
fl
This hypothetical example re ects that the respondent feels unsure and
uncomfortable learning to use a new computer.
The
disadvantage of
in-depth interviews is that they demand a skilled and
cautious interviewer. The interviewer should have a complete understanding
of the research problem, its purpose, and what information is being sought.
The course of the interview is decided by the skills of the interviewer when
asking questions and probing further with supplementary questions. The
7.5 Interviews
know-how and skills of the interviewer are thus of the utmost importance.
Interviews can also take a long time – longer than
questionnaires –
and
may
even
require
several
filling
sessions
in structured
with
the
same
fi
respondent (Churchill and Iacobucci, 2015). In addition, interviews are dif cult to interpret and analyse. The researcher
’s own background may greatly
fluence
in
the
interpretations,
thereby
causing
problems
of
objectivity.
Depending upon which type of analysis we want to do, coding of in-depth
fi
interviews is a dif cult task in spite of improved techniques and systems.
7.5.1 Preparing for an Interview
The
first steps in preparing for an interview are to: (1) analyse your research
problem, (2) understand what information you really need to have from an
interviewee, and (3) see who would be able to provide you with that information. The clearer the problem statement is, the easier it is to know what
to ask. It is understood that the purpose of data collection through interviews
is to obtain valid information from the most appropriate person. In other
words, you should clearly know what you want to ask as well as who can
provide the most relevant and valid information on those issues. Moreover,
the interviewee should be willing to answer your questions truthfully. For
example, can you expect everyone to be honest on very sensitive matters?
The next step is to draft an interview
guide
or
interview questions.
These
questions should be compared with the research problem several times, partly
to
test
the
consistency
between
the
two
and
partly
to
see
whether
find
questions are thorough and precise enough to allow you to
the
out what
you want. It is very useful to let somebody else (perhaps your supervisor) see
the problem statement and the questions to be asked in the interview to check
this congruence.
After the above scrutiny, a
first draft of the interview questions should be
prepared. This draft has to be pre-tested as a pilot
study.
While the above
scrutiny dealt with the researcher’s understanding of the research problem
and the interview questions, this test checks the understanding of the interviewee regarding the research problem and interview questions. Such pilot
research
also
provides
first-hand
insight
into
what
might
be
called
the
‘cultural endowment’ of the informants. After this pilot study, where a few
respondents (say three to
five) have been asked to read the research problem
and the interview questions and have also answered the questions and have
fi
commented on their understanding of them, you can prepare the nal draft
of the interview guide and questions.
At this point, particular attention has to be given to the approach you are
going to use: for instance, before contacting the interviewee you have to
decide
how much time the
interview should take. The pilot study can help you
117
118
Data Collection for Qualitative Research
to determine the time needed for your questions. You must consider that
business executives work with the belief that ‘time is money’ and might
refuse to give an interview because of the shortage of time. Our experience
shows that an interview should not take more than one and a half hours, and
ideally it should take around an hour. However, the total meeting time may
be two hours or more. In many cases, the interview is preceded or followed
by a factory visit or lunch. In open-ended interviews you can get a lot of
information during these interactions.
In this respect, it is very important to realize that the interviewer has to
create a situation where
the respondent willingly offers time. If the respond-
fi
ent is not suf ciently motivated to provide you with time, there will be little
motivation to answer your questions and to be an interviewee. This can
jeopardize the whole purpose of the interview.
Once you have considered all aspects and
prepared the interview guide,
you should approach the person(s) you want to interview. Here you can use
the telephone or write a letter/email, perhaps both. In the letter/email, for
example, you can explain the purpose of your study, provide a short problem
statement, and describe the type of information you are interested in collecting. In this letter you may also mention that you will be calling very soon
(next Monday, next week, etc.) to request an appointment for an interview.
You should also mention how much time you think the interview will take.
You must remember that you cannot demand a certain time, day, or week for
the interview: you have to adapt to the interviewee
’s schedule, and not the
other way around. Here is an example of the conversation for getting an
appointment for an interview:
EXAMPLE 7.4
‘Hello, my name is . . . ’Im working on my Master’s thesis on new product
development. Last week I sent you a letter with some information on my
project, which I think will be of interest to your company, and I would like
very much to have an interview with you.
’
Manager:
It sounds interesting, but it is very hectic here, and ’m
I
short
of time.
Student:
I understand the situation, but the interview will not take more
than an hour and I can adjust to your time table.
Manager:
fi
Well, let me see, can you meet me at my of ce on Monday
at 9am?
Student:
Yes, thank you very much. I will be there on time.
7.5 Interviews
One important issue that you must be clear on before you contact the
record the information you
interviewee is how you are going to
will get. It is
widely accepted that voice-recording is a useful method. The disadvantages
with tape-recording are that the respondent might be reluctant to answer
sensitive questions, or might not even answer some questions. There is also a
risk that the interviewer might cease to listen carefully, believing that all the
information is going on to the tape to be listened to later in a more relaxed
environment. It is therefore recommended that you also take notes even if
you will be tape-recording the interview (Alvesson, 2011; Ghauri, 2004).
inform
When you make the approach for an appointment, you will have to
the respondent
whether you will be using a voice-recorder or not. In fact,
instead of informing, you should ask whether you can use the recorder. This
issue automatically leads to the question of
fi
confidentiality.
You will have
to ask if the interview is to be treated con dentially and you will have to give
your
promise
(undertaking
or
personal
fi
guarantee)
that
all
information
you receive will be kept con dential. If necessary, you may send a written
fi
assurance of con dentiality, signed by yourself and your supervisor or any
other responsible person from the school, for example the director of research.
When making an appointment, you should remember that you have to
create a reason or a reward for
the respondent – why should they answer
your questions? What is in it for them? For example, you can mention that
the results of the study will be provided to the respondent, or that you hope
the results will help in analysing a competitive/managerial position that
fi
he/she/the company in question would bene t from. Or perhaps the study
would help the industry or the country or it would help policy makers in their
job and thereby indirectly the
firm
in question. Another way to show your
appreciation is to take a token gift such as a pen or a coffee mug with
your school’s logo.
Now that you have made the appointment, it is time to decide who is going
to conduct the interview: One person or more? Will the same person be
conducting all the interviews or will different people interview different
companies/managers?
If
several
interviewers
are
used,
they
have
to
be
trained. Once you have taken an appointment and cleared matters such as
the amount of time to be used, recording or not, etc., you should send a
confirmation letter
about
the
appointment,
thanking
the
interviewee
for
giving you the opportunity to visit them and saying you look forward to
seeing them on the agreed date and time. This is necessary to avoid any
misunderstandings on date or timing, and to remind the interviewee.
Before closing the preparation section, we would like to discuss what you
need to do before you start making appointments and interviewing. It is very
important to analyse, discuss, and consider
resources available
to you for
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Data Collection for Qualitative Research
performing these interviews. You have to consider all the costs: for example
travelling costs, the time necessary for the interviews, and also the time you
need to process the interviews. Sometimes researchers start a very ambitious
interview process and after a couple of interviews, or after about 50 per cent
of their planned interviews, they give up due to travelling expenses, the time
consumed, or overload of information. We have seen several students start
their research interviewing, spending two to four hours at each interview,
recording everything,
and ending up
with several
hours of information.
But when they started listening to the recorded material (which is a very
time-consuming and tedious job), they did not know what to do with all the
information,
and
in
fact
reported
that
they
had
used
less
than
half
of
the interviews they recorded. The best way to handle this issue is to discuss
these matters with your supervisor before you start interviewing and stick to
a model or a plan for the interviews.
7.5.2 Pre-Interview
The appointment has been made with the respondent and now, together with
your
supervisor,
consider
the
you
data
have
to
collection
review
the
dimension,
questionnaire.
how
you
are
Here
going
you
to
must
use
the
responses for the analysis in your report, and how you want to present the
information you are gathering. You also have to schedule your time properly.
plan your
If you are having more than one interview per day, you should
time. This is particularly important when you are in a foreign environment or
if you are in a strange city, as you must also consider how much time you
need to arrive at the location from your hotel, especially in rush hours. The
interviewees have limited time and are always very busy. If you have already
informed them that you need one hour of their time, you cannot arrive half
an hour late and give the excuse that the journey took longer than you
fi
thought, or that you were stuck in traf c, or you could not
Another important issue is one of
social conventions,
find a taxi.
meaning how you
behave in the interview, how you should dress, and so on. It is advisable to be
reasonably formal. If you go in shabby clothes or worn-out jeans you may
not give a positive impression to the interviewee about your seriousness and
the fact that you can do worthwhile research and thereby might be of some
help to the company. It is also wise not to‘party ’ the night before. It would
not be a good idea to go for an interview smelling of alcohol or half-asleep.
Finally, we believe that interviewing is a skill you should
practise
rehearse
or
with regard to understanding the time needed and your own argu-
ments and questions, etc. Find a ‘victim ’, such as a friend, and
practise.
If
there is the slightest chance that the respondent might misunderstand something, then they probably will. Moreover, it goes without saying that you
7.5 Interviews
must check and recheck the equipment you are going to use in the interview,
such as the voice- or video-recorder.
Hint:
Dress
properly
and
check
your
timing,
questions,
and
recording
material.
7.5.3 The Interview
The
fi
to
rst important issue here is to introduce the study and its purpose and
orient the respondents.
The interviewer should be able to answer clearly all
fi
the questions the respondents might have, such as: Who will bene t from the
study? When will the
fi
nal report be ready? Will they get a report or not?
fi
Moreover, the interviewer should reinforce the con dentiality, if required, to
the respondent’s satisfaction. At this stage, it is important to realize that the
respondent is asking the questions and the interviewer must provide satisfactory answers. These introductory
fi
ve to ten minutes can be a determining
factor in how the rest of the interview goes.
The language used in these early minutes and in the subsequent inter-
simple and under-
action is of great importance. The interviewer must use
standable language,
being extra careful when using certain terminology or
concepts from a particular discipline, such as
quite
normal
that
working in the
fi
business
executives,
fi
nance or management. It is
although
they
might
have
been
eld for several years, will not be familiar with textbook
terminology. This point is even more important when the interview is taking
place in another environment or country. In that case, it is not only the
terminology you have to be careful about, but also the language. You must
be
sure
that
the
language
and
the
level
of
language
you
are
using
is
compatible with the respondent’s knowledge and use of the same language.
The
interviewer,
irrespective
of
questioning
entirely to the informant to provide answers to
technique,
must
leave
it
questions. In other words,
the questions should not be asked in a leading or directive manner, as this
puts pressure on the respondent to give the answer s/he thinks you want to
hear. For example, do not pose questions such as:‘You must have realized
that . . .’ or ‘How could you . . .?’ Moreover, it is important that every now
and then the interviewer expresses an understanding of what the interviewee
is saying. A nod or a‘hmm’ from time to time gives the impression that the
interviewer understands what is meant. For the interviewee to keep on asking
and answering questions, the interviewer must show interest and enthusiasm
in the respondents and their ‘story’.
121
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Data Collection for Qualitative Research
Although
it
is
advised
that
the
interviewees
are
given
full
freedom
to
express personal meanings and give their own answers, it is quite important
that the interviewer controls the situation to get the relevant information.
Therefore,
control with some care is
necessary, not only to get the relevant
information but also to manage the time. The interviewee must be given
reasonable time for each question and should not be interrupted. However,
business executives or other respondents often like to talk at length about
their
experiences
and
know-how,
especially
about
positive
events,
and
should therefore be controlled, but with care. Here is a conversation example
to start an interview:
EXAMPLE 7.5
Interviewer:
You have allowed one hour for this interview. ’ve
I
read annual
reports and inspected your homepage. Could you
fi rst tell me
about what you consider to be the major reasons for your
company’s success in the past?
firm’s
Manager:
[starts explaining in detail the
Interviewer:
This is interesting, but could you tell me a little more about the
dif
ficult
history and his role.]
period you just mentioned?
And so on . . .
Controlling time
is very important, as the interviewee has given you a
certain time and may be interested in talking a lot on each issue. You must
fi
ensure that you have answers to all your questions within the speci ed time.
If the time agreed upon beforehand was between 9 and 10.30 am, it is quite
possible that at 10.30 the interviewee has to go to a meeting and just stops
talking and asks you to leave, or a number of people enter the room to have a
meeting, giving you only one option, to leave.
You also have to, in a way,
That
is
why
we
stress
taking
develop a relationship with
great
care
in
the
the interviewee.
opening
minutes
of
the
interview. You must be able to give the impression of a serious, trustworthy,
and friendly person. The relationship can also be developed by expressing
interest in the interviewee’s position and opinion and by appreciating his/
her point of view. The better the relationship between the interviewer and
the
interviewee,
the
more
open
the
responses
and
the
more
useful
the
information you will get. This is also important in case you have not been
able to get all the information due to time or any other reason, or if you
would like to obtain some additional information later. If the interviewee
7.5 Interviews
enjoyed
with
talking
you
or
to
you,
offering
s/he
you
a
should
factory
not
mind
visit
or
having
lunch.
another
These
meeting
are
excellent
opportunities to develop trust, friendship, and a positive relationship with
the interviewee.
You should be
careful about sensitive questions.
Many times it is just a
question of phrasing or using the right language to make the questions less
sensitive. Sometimes, the questions are of a sensitive nature but must still be
fi
asked. Here the interviewee should not be pressured to provide a de nite yes
or no (admit it . . . ) answer. Questions regarding why a certain strategy or
plan failed, about competitors and their success, or about some
financial
issues can be of this nature. For example, when interviewing a bank manager, the following question may be quite sensitive:‘Who is responsible for
fi
all the bad debts reported by your branch/of ce? ’ The same question can be
asked in another way; for example: ‘What, in your opinion, are the factors
fi
that caused the bad debts reported by your branch/of ce?’ It is advisable to
avoid any direct questions on who was responsible for a certain blunder
fl
or miscalculation. Questions regarding intra-organizational con icts should
be asked with some care and with indirect language.
7.5.4 Post-Interview
After coming back from the interview, you should write
ant points
down the import-
that were discussed as well as notes on the practical details. This
can include whether you were able to get all the answers you wanted or
how much time it took, some opinions on the respondent (such as very
open
or
reserved
person),
and
your
perception
of
your
interaction
and
relationship with the interviewee. All these details will help you later when
you
listen
to
the
recorded
tape
or
when
you
sit
down
to
write
up
the
information you collected. Most of all, it will help you in case you need
to get additional information. In case the interview was not recorded, it
is
recommended
that
you
go
through
your
notes
and
write
a
complete
descriptive report of the interview immediately (or as soon as possible) after
the interview.
The
second
thing
you
must
do
is
to
write
a thank-you letter
to
the
respondent. You might also send some further information on your research
project which you might have promised or realized in the interview that they
would like to have.
Reporting or transcribing an interview is an important and tedious job. As
mentioned earlier, interviews that are not recorded should be written as a
narrative story as soon as possible. Recorded interviews always need some
supporting material to remind you of the situation and the feeling of the
123
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Data Collection for Qualitative Research
interview. The best way is
first
to write down all the information recorded
in the same order, and later develop a descriptive report of the interview
relevant for the study. In this second stage you can discard all irrelevant talk
and information.
Sometimes it is useful to send this descriptive report to the interviewee for
comments. You might have misunderstood something or perhaps you are not
sure what the respondent really wanted to say. Depending upon your relationship with the interviewees, they might like to see what they said and,
quite often they will give additional information or clarify their message
voluntarily.
Hint: Go through your interview notes promptly.
7.6
Focus Groups
Another qualitative method mentioned above is group discussion (also called
a focus group). In this research method, the researcher can get together with
several respondents at the same time and initiate discussion on a small
(focused) number of issues (Bryman and Bell, 2003; Stewart and Shamdasani,
1990). The opinions of respondents are considered as information and analysed later. This method differs from other methods such as in-depth interviews in that here the interaction is not only between the interviewer and the
respondent but also among the respondents. It is also considered a relatively
cheap and convenient way of gathering information from several respondents in a short time.
In
business
studies,
focus
groups
can
be
particularly
useful
in
the
following types of study (Stewart and Shamdasani, 1990: 15):
obtaining general background about a topic;
generating research hypotheses;
stimulating new ideas and creative concepts;
diagnosing
problems/success
factors
for
a
new
product,
service
or
programme;
generating impressions of products, programmes, services or institutions/
firms;
learning how respondents talk about the phenomenon, which may help in
designing questionnaires or other instruments;
interpreting previously obtained quantitative data.
7.6 Focus Groups
fl
Here we should be aware of the in uence the group itself will have on
fl
the discussion and information that is exchanged. The discussion is in uenced by the size of the group, its composition, the personalities of the
people
involved,
geographical
the
the
roles
arrangement
interviewer
and
the
they
of
are
the
group
asked
meeting,
or
to
and
individuals.
play,
the
physical
the ‘chemistry’
It
is
thus
and
between
apparent
that
information gathered by this method will be different from information
gathered through historical review and case studies. However, this method
is widely used in some research cultures, such as consumer behaviour (Kent,
1993).
As a data collection method, focus groups take many different forms, such
as
discussion
groups,
focused
interviews,
group
interviews,
and
group
research, and they are often used in business studies, for example for new
product development, programme evaluation, marketing, advertising, and
communication.
The size of the group interviewed should be small, normally from six to
around ten people, who will discuss a particular topic or issue under the
direction of a moderator, who keeps the discussion on track (focused). Too
small (e.g.
<
>
5) or too large (e.g.
10) numbers can make the focus group
ineffective as the participation of individuals can become too fragmented or
too little. These groups are arranged and the discussion may last from half an
hour to around two hours. The moderator can then observe the interview or
group, sometimes without disturbing the discussion.
The moderator plays an important role in keeping the discussion on the
focus issue and in ensuring that it goes smoothly. The role of the moderator is
to secure interaction between the focus group members and see that they
address the topics believed to be important. Prior to running a focus group, a
list of topics or keywords is usually prepared. The moderator
’s task is to
ensure the discussion covers all the topics, but not necessarily in the listed
order. The amount and the nature of direction provided by the moderator will
fluence
in
the quality and depth of the data collected. In the worst case, the
group discussion might not cover the topic or issue at all. The moderator
must therefore carefully consider the structure and nature of direction in
relation to the research questions and purpose.
Although focus groups can provide quantitative data, they are mostly used
for collecting qualitative data. One of the conditions is that there should be
some homogeneity among the individuals in each focus group, as this will
encourage more in-depth and open discussion (Cowley, 2000; Merton et al.,
1956). In the case where data are to be collected from different groups of
people, it is often advised that separate focus groups be arranged for different
subsets of the population.
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Data Collection for Qualitative Research
Advantages and Disadvantages of Focus Groups
7.6.1
One
advantage
of the focus group is that it produces very rich and in-
depth data expressed in respondents’ own words and reactions, which is
ficult
normally dif
to obtain using other methods such as surveys. It allows
the researcher to understand why people feel or behave the way they do.
It allows him or her to probe into respondents
’ views through discussion
and reasoning, as in a focus group people can argue with each other
’s
views.
This
reveals
how
people
really
think
about
main advantage, however, is that it is a quick,
different
flexible,
issues.
The
and inexpensive
method of data collection. It gives the researcher a chance to observe the
reactions of people in open and free conversation with each other. Further
advantages
include
the
fact
that
it
allows
the
researcher
to
interact
directly with respondents, and to react and build upon the discussion as
it goes.
The
disadvantages
are that this type of data collection makes it very
ficult to summarize and categorize the information gathered. And in the
case of an unskilled moderator, it may be dif
ficult to get really useful
information. Other disadvantages include the fact that it can be dif
ficult
dif
to gather people at a location, and the small numbers who are willing might
not be representative of the population under study. The responses of the
fl
group members are not independent of one another and might be in uenced by each other or by a dominant group member. The live participation
and
observation
warranted.
The
may
lead
moderator
the
researcher
may
bias
the
to
have
greater
respondents,
faith
than
knowingly
is
or
unknowingly.
7.6.2
Conducting Focus Groups
Once a clear statement of the problem has been generated (see Figure 7.8), it
is important to identify the sample from those who are representative of the
larger population. Representativeness is most important in this case, as we
fi
will be observing only a few individuals. Having done this, we need to nd
an appropriate moderator and, with his or her assistance (or keeping that
individual in mind as well as our research objectives), formulate an interview
guide and questions, and decide the structure of the focus group and exactly
how it will be run.
Normally in focus groups researchers record data on paper or in a notepad.
It is, however, becoming quite common to use video recorders that enable
researchers to view and analyse the data later. In the case of several focus
groups, the records and notes are analysed; they then provide a good picture
to the researcher.
7.6 Focus Groups
Formulation of research questions
Identification of the sample
Identification of the moderator
Generation of interview
guide/questions and structure
Recruiting the sample
Conducting the group
Interpretation and analysis
Writing the report
Figure 7.8 Steps in conducting a focus group
Source:
Stewart and Shamdasani (1990: 20). Copyright © 1990 by Sage Publications. Reprinted by permission
of Sage Publications, Inc.
127
128
Data Collection for Qualitative Research
Further Reading
........................................................................
Fontana, A. and Frey, J.H. (1994) ‘Interviewing: The art of science’, in Denzin, N.K.
and Lincoln, Y.S. (eds.),
Handbook of Qualitative Research
, Thousand Oaks, CA:
Sage, pp. 361–76.
Hagen, B., Zucchella, A., and Ghauri, P. (2019)‘ From fragile to agile: Marketing as a
key driver of entrepreneurial internationalization’ ,
Review
International Marketing
, 36(2): 260–88.
Mason, J. (2002) ‘ Qualitative interviewing: asking, listening and interpreting’ , in
May, T. (ed.),
Qualitative Research in Action
, London: Sage, pp. 225–41.
Questions
........................................................................
1.
When are: (1) quantitative and (2) qualitative research approaches most
appropriate?
2.
What
do
you
consider
to
be
the
major
reasons
for
doing
historical
reviews?
3.
For what research purposes do you consider focus group interviewing is
more appropriate?
4.
What
is
(are)
the
difference(s)
between
single
case
and
multiple
case study designs? When do you consider: (1) single, (2) multiple case
designs to be most appropriate?
5.
‘Develop a relationship with the interviewee.’ Comment.
Exercises
........................................................................
1.
You want to understand consumer behaviour towards use of mobile
telephones. You have decided to do that through focus group interviewing. Prepare a focus group interview plan to gain insights into how a
group of people experience and use a product (e.g. a mobile phone). Draft
a letter to explain your plan to send to the target group.
2.
Draft a letter to send to companies you want to interview/study using the
case study method for your research. Formulate a research problem and try
to explain it to companies, also convincing them to participate in your study.
3.
Referring to Exercise (1) above: suppose you are trying to understand
consumer behaviour around mobile phones through a survey. Formulate
a precise research problem and develop a questionnaire that you can use
for this data collection.
4.
Visit a store or a marketplace. Observe how people behave and shop.
8
Qualitative Data Analysis
Quality, not quantity is my measure.
Douglas Jerrold, quotation from L. Henry,
Quotations for all Occasions,
Best
New York: Fawcett
Premier, 1995
Qualitative
research
poses
fic
speci
analytical
challenges.
This
chapter
addresses important characteristics of qualitative research and qualitative
data. Strategies and procedures to handle the analytical challenges are also
dealt with, as well as validity and reliability issues in qualitative research.
8.1
Characteristics of Qualitative Research
Previously we have emphasized that a key purpose of qualitative research
is to understand and gain insights (see Section 2.5). Qualitative research is
particularly
relevant
when
prior
insights
about
the
phenomenon
under
scrutiny are modest, implying that qualitative research tends to be exploratory and
flexible
because the problems tend to be ‘unstructured ’. Even
though qualitative research may allow for tests of hypotheses, the main
emphasis is usually on gaining insights and constructing explanations or
theory. In the terminology of Popper (1961), qualitative research is particularly relevant in the ‘context of discovery’ (see Sections 5.2, 8.2). Qualita-
fi
tive research approaches are multiple and no agreed-upon classi cation of
the various approaches or methods exists. Van Maanen (1979: 520) also
claims that
[the] label qualitative methods has no precise meaning in any of the social
sciences.
It
is
at
best
an
umbrella
term
covering
an
array
of
interpretative
techniques which seek to describe, decode, translate, and otherwise come to
terms with the meaning, not the frequency, of certain more or less naturally
occurring phenomena in the social world.
130
Qualitative Data Analysis
The distinction between quantitative and qualitative data is not an easy
one –
and
disagreements
prevail.
As
noted
above,
qualitative
research
tends to be more explorative and unstructured, with emphasis on understanding, while quantitative research tends more to emphasize descriptions
and
testing
of
derived
hypotheses.
In
‘traditional
quantitative’
research, data collection and analysis are usually sequential. By contrast,
in qualitative research, data collection and analysis are often conducted
simultaneously in an interactive way: collected data are analysed, which
raises
new
(evidence)
clari
fied.
questions,
are
initiating
collected
and
further
analysed,
data
the
collection.
problem
As
more
becomes
data
gradually
fi
An analogy is the effort of a detective trying to nd the person
guilty of a crime.
Qualitative Data
8.2
Data are carriers of information. To become information they must, however, be interpreted. A common observation is that in qualitative studies,
the researcher easily becomes overwhelmed by the masses of data. One
reason
for
this
is
that
when
the
research
problem
is
badly
(modestly)
understood, many data are collected, of which a large proportion may be
irrelevant. Thus, redundancy of data is often the case. Miles (1979: 590)
has
even
termed
qualitative
data
as ‘attractive
are
usually
associated
with
numbers.
data
fic
possess speci
As
nuisance’.
noted
Quantitative
above,
numbers
characteristics that make them very useful for analytical
fi
purposes. They can be seen as an ef cient way to represent information
and meanings. Often, however, the research problem is less well understood and the underlying concepts may be ambiguous. Assigning numbers
to something we do not know and understand makes no sense. In such
situations, qualitative data may be useful to gain insights and understand
concepts.
Qualitative data come in many forms, such as pictures, texts, and verbal
reports. Such data are rich, full, earthy, holistic, and‘real’ (Miles, 1979:
590).
Such
data
are
in
many
ways
closer
to
the
problem/phenomenon
studied.
In a famous study, Burns and Stalker (1961) were interested to understand
why some
firms were very innovative while others were not, despite thefirms
belonging to the same industry, being of approximately the same size, and
located in the same region. In this case they had adequate information about
fi
the dependent variable, innovativeness, that is, the extent to which the rms
8.3 Analytical Procedures
EXAMPLE 8.1
Management System
‘Mechanistic’
‘Organic’
– specialized differentiation of
– contributive nature of special knowledge
functional tasks
and experience to common tasks
– abstract nature of individual
– ‘realistic’ nature of individual task set by
tasks
the situation adjustment and continuous
fi
– immediate superiors
rede nition of individual tasks
fi
– precise de nition of rights and
obligations
– spread of commitment
– network structures of control, authority,
– translation of rights and
and communication
obligations into responsibilities
– hierarchic structure of control,
– lateral direction of communication
– information and advice rather than
authority, and communication
instructions
– vertical interaction
– instructions by superiors
Source:
Burns and Stalker (1961: 120–1)
introduced novel products and patents. Their insights regarding why the
firms
differed in this way were, however, highly limited. Detailed study of
a sample of highly innovative and non-innovative
two groups of
firms differed
firms
revealed that the
flow of
innovativefirms as
greatly in organizational structure and
information (see Example 8.1). They termed the highly
‘organic’ and the non-innovative as ‘mechanistic ’. These terms are summary
labels of the characteristics shown in Example 8.1.
Inspection of Example 8.1 shows that meanings are expressed through
fi
words: the non-standardized collected data here are classi ed into two broad
categories, ‘organic’ and ‘mechanistic’, and the analysis is conducted through
conceptualization. The two labels ‘organic’ and ‘mechanistic’ are used as
flow of
‘shorthand’ to capture the differences in organizational structure and
information for the two groups of
firms,
innovative and non-innovative
respectively.
8.3
Analytical Procedures
A key – if not the key – purpose of analysis is to understand and gain insights
from the collected data, or, as stated by Marshall and Rossman (1995: 111),
131
132
Qualitative Data Analysis
Data
collection
Data
display
Data
reduction
Conclusions:
drawing/verifying
Figure 8.1 Components of data analysis: interactive model
Source :
Miles and Huberman (1994: 23)
‘data analysis is the process of bringing order, structure and meaning to the
mass of collected data’. As noted above, in qualitative studies, the researcher
is often overwhelmed by the mass of data. A key characteristic of analysis
is the dividing up or breaking down of some complex whole into its constituent parts (i.e. from the Greek, ana-, ‘up, throughout’, and
lyein,
‘to loosen’).
Through analytical operations the researcher dissects, reduces, sorts, and
reconstitutes
data.
Researchers
use
analysis
to
manipulate
data
to
gain
understanding, clarify problems, and test hypotheses. No single, agreedupon approach to qualitative data analysis exists. However, it is possible to
give some rather general and helpful advice. For example, in their recognized
‘source book’ Miles and Huberman (1994) distinguish between the following
components in qualitative data analysis (see Figure 8.1):
•
data reduction;
•
data display;
•
conclusion drawing and veri cation.
Data reduction
fi
refers to the process of selecting, focusing, simplifying,
fi
abstracting, and transforming the data that appear in writing up eld notes
or transcriptions.
For example, to be
manageable
and to
give meaning,
lengthy transcripts from focus group interviews must not only be read, the
researcher must also focus, simplify, and abstract to create meaning from the
mass of words.
8.3 Analytical Procedures
Table 8.1
133
An example of a matrix display in case study analysis
Atmosphere
Cooperation and
con
Process
Power and
flict
Distance
dependence
Expectation
Offer
Informal meetings
Final offer
Planning for
negotiations
Formal
negotiations I
Internal meetings
Formal
negotiations II
Source: Ghauri and Firth (2009)
fi
In this phase the researcher generates categories and identi es themes
and patterns. As categories and patterns between them in the data are
gradually uncovered, understanding or explanation of the phenomenon
studied may emerge. However, the researcher should be extremely careful
and critical in this phase, because other explanations are in most cases
possible. This is important, because the key purpose is to arrive at valid
explanations (see Kirk and Miller, 1986, for an excellent discussion). Ghauri
and Firth (2009) suggest that matrices can be used to display data and to
explain
the
relationship
between
different
variables
(see
Table
8.1
in
Section 8.5.3).
Data display
is an organized, compressed assembly of information that
permits drawing conclusions and taking action. Data display can be in the
form
of
strategy
a
data
process
matrix,
may
figures,
display
and
the
so
on.
major
For
events
example,
and/or
a
study
actors
in
of
a
that
process.
In the following sections such activities will be dealt with in greater detail.
We
first describe various analytical activities, and then turn to interpretation,
which is at the heart of qualitative research.
134
Qualitative Data Analysis
8.3.1
Analytical Activities
Here we distinguish between:
•
categorization;
•
abstraction;
•
comparison;
•
dimensionalization;
•
integration;
•
iteration;
•
refutation.
1
Categorization
is
the
process
of
classifying
units
of
data.
Qualitative
researchers categorize data during the process of coding. The essence of
categorization is identifying a chunk or unit of data (e.g. a passage of text
of any length) as belonging to, representing, or being an example of some
more general phenomenon. Categorization involves naming, or giving labels
to, instances of the phenomenon found in the data. A passage categorized
fi
with a speci c label may be a few words or many pages long. A passage
may
also
exemplify
different
categories
of
interest
to
the
analyst
and
thus have many labels. Usually some part of the text, for example a transcribed
interview,
will
contain
no
meaningful
information
and
remain
uncategorized.
Codes serve as shorthand devices to label, separate, compile, and organize
data. Coding of qualitative data differs somewhat from the coding of quantitative data, for example survey data. When handling quantitative data,
coding is mostly a way of managing the data, while in qualitative data
analysis – in particular in situations where the analysis departs from inspection of the collected data (see Section 8.5.2)– coding is an important
first
step in arriving at understanding and generating theory.
Several approaches to coding exist. Probably the most elaborate is that
developed by Strauss and Corbin (1990) drawing on the‘grounded theory’
approach (see Section 8.5). These authors distinguish between three types of
coding: open coding, axial coding, and selective coding.
•
Open coding –
‘the process of breaking down, examining, comparing,
conceptualizing
and
categorizing
data’
(1990:
61);
this
process
of
coding yields concepts, which are later to be grouped and turned into
categories.
1
This section builds on Spiggle (1994).
8.3 Analytical Procedures
•
Axial coding
– ‘a set of procedures whereby data are put back together in
new ways after open coding, by making connections between categories
’
(1990: 96). This is done by linking codes to contexts, to consequences, to
patterns of interaction, and to causes.
•
Selective coding
– ‘the procedure of selecting the core category, system-
atically relating it to other categories, validating those relationships, and
filling
in
categories
(1990: 116). A
that
need
core category
further
fi
re nement
and
development’
is the central issue or focus around which
all other categories are integrated. It is what Strauss and Corbin call the
storyline that frames the account.
The three types of coding are really different levels of coding, and each relates
to a different point in the elaboration of categories in grounded theory.
Qualitative
researchers
do
not
generally
specify
the
unit
of
analysis
(although it is done in some cases). Rather they categorize a chunk of data
on the basis of its coherent meaning– its standing on its own – and not by an
arbitrary designation of grammar. It is also quite common in qualitative
fi
research for initial categories and codes to be changed and re ned during the
research process.
Abstraction
builds on categorization. It surpasses categorization in that
it collapses more empirically grounded categories into higher-order con-
fi
ceptual constructs. Abstraction goes beyond the identi cation of patterns
fi
in the data. It groups previously identi ed categories into more general
conceptual classes. Abstraction includes both incorporating more concrete
categories into fewer, more general, ones, and recognizing that a unit of
data is an empirical indicator of a more general construct of interest. For
example, a researcher studying consumers’ purchasing behaviour might,
based on transcribed interviews, make categories for ‘reading of advertisements ’, ‘brochures ’, ‘ store
visit’,
and ‘asking
for
advice’,
and
then
relate these categories to a more general and abstract one– ‘information
search ’.
Abstract constructs encompass several more concrete instances found in
fi
the data that share certain common features. The theoretical signi cance of a
construct partly depends on its relationship with other constructs. The reason
amount of search
importance of the purchase
is that most research is relational. For example: Is
purchase situation related to the
Comparison
explores
differences
in a
?
and
similarities
across
incidents
within the data currently collected and provides guidelines for collecting
additional data.
135
136
Qualitative Data Analysis
Comparison begins in the initial stage of the analysis as one categorizes
and abstracts data. While categorizing, the investigator notes general simi-
fi
larities in the speci c empirical instances in the data and labels them as
representing the same category. As the analysis proceeds, the investigator
may conduct comparisons in a systematic way.
EXAMPLE 8.2
A researcher is studying purchasing behaviour in a business
firm.
Among
other things, s/he is observing various search and evaluation activities for the
purchase of the same products. To examine whether search and evaluation
activities
are
related
to
purchase
experience,
s/he
makes
the
following
comparison:
Purchase product A:
First
Last
Bid
Yes
No
Price evaluation
Yes
No
Brochures
No Yes
No
Advertisements
No Yes
No
fi
Here we see that more search and evaluation are conducted in the rst than
fi
in the last purchase. This may indicate learning and simpli cation of purchases through experience.
Glaser and Strauss (1967) pioneered the
constant comparative method
as
an analytical procedure in which the analyst compares incidents in the data
with other incidents appearing to belong to the same category, exploring
their similarities and differences. As the analysis proceeds and categories
develop, the investigator compares incidents in the data with appropriate
emergent categories.
Comparison processes also guide subsequent data collection. As investigators
identify
categories,
constructs
and
preliminary
propositions,
and
fi
conceptual linkages from the initial analyses, they may de ne subsequent
individuals
or
groups
to
sample
that
maximize
or
minimize
differences
8.3 Analytical Procedures
between them on variables of interest. This procedure allows them to control
for, or manipulate, similarities and differences in conditions, outcomes, or
information characteristics in a way that is analogous to that in experimental
and survey designs.
EXAMPLE 8.3
A researcher is doing a detailed tracking of budgeting in small, privately
owned restaurants and notes that the restaurants do not prepare formal
budgets (as prescribed in the textbooks). S/he wonders whether this is
something particular to this industry and proceeds to collect the same
information from other small, privately owned service
findings, s/he may proceed
example larger fi rms or fi rms from
on the observed
fi rm,
for
This
way
of
proceeding
is
an
example
of
fi rms.
Depending
to include other types of
manufacturing
purposive
or
industries.
theoretical
sampling.
Dimensionalization
involves identifying properties of categories and con-
fi
structs. Once a category has been de ned, the researcher may explore its
attributes or characteristics along one or more dimensions. The properties
represent conceptual dimensions that vary empirically in the data across the
incidents depicting the constructs. Strauss and Corbin (1990: 72) offer the
following hypothetical example:
Category
Properties
Dimensional range
(applied to each incident)
Watching
frequency
often . . . never
extent
more . . . less
intensity
high . . . low
duration
long . . . short
In this example, the category ‘watching’ is described by four properties, each
one dimensionalized along a continuum.
137
138
Qualitative Data Analysis
Dimensionalization is important for theory construction in the following
ways. By systematically exploring empirical variations across incidents representing a construct, the researcher can clarify and enrich the conceptual
fication
meaning. Identi
the
researcher
to
of properties and their dimensions can
explore
and
fi
de ne
relationships
across
also help
categories
and
constructs.
Integration
is important
in efforts to build theory
based on
data, and
departs by noting in the data that certain conditions, contexts, strategies,
and outcomes tend to cluster together. Integration requires the mapping of
relationships between conceptual elements.
Iteration
involves moving through data collection and analysis in such a way
that preceding operations shape subsequent ones. In doing so, the researcher
fi
does not perform speci c research stages in a sequential order, but moves back
and forth between stages. An example is simultaneous data collection and data
analysis as emphasized above. This is in contrast to quantitative research,
where the various stages tend to be performed sequentially.
Refutation
involves
deliberately
subjecting
one’s
emerging
inferences –
categories, constructs, propositions, or conceptual framework – to empirical
scrutiny. To do so, the researcher may make use of a negative case or negative
fi
incident to discon rm the emerging analysis. A main reason for trying to
firm is to avoid the so-called confirmation bias
discon
Hint:
’ (Bazerman, 1998).
‘
It is important to note that assumptions and hypotheses are not tested
by seeking supporting evidence but by seeking information that allows for
fi
discon rmation.
EXAMPLE 8.4
In their well-known book
In Search of Excellence,
Peters and Waterman
fi
(1982) made an effort to come up with characteristics for excellent rms,
such as that successful
fi rms were headed by managers who walked around
–
talking to the employees. In their study they only included
successful
fi rms,
at that time
–
which makes it impossible to conclude whether the sug-
gested criteria hold true. To hold true, the probability of success should be
higher when a given characteristic is present,
the characteristic is not present,
P(S/C i).
P(S/ci),
compared with when
8.4 Interpretation
8.3.2
Data Reduction
The above analytical operations – categorization, abstraction, comparison,
dimensionalization, integration, iteration, and refutation – provide a way of
managing analysis of qualitative data. Even though the researcher cannot
program these operations in a mechanical fashion, s/he can proceedsystem-
atically.
One way of doing this is to read through all the data before rereading
records for each case. Alternatively, the researcher can read and retract the
record for each case before going to subsequent cases. Also, as one categorizes,
abstracts,
compares,
and
integrates,
one
can
move
through
the
data horizontally (e.g. grouping indicators of categories), or vertically (e.g.
grouping records of cases), or both sequentially. Tabulations can be helpful
in
promoting systematic
comparisons, for
example listing
incidents that
represent a construct across cases.
The purpose of proceeding systematically is to stimulate a complete analysis and minimize potential distortion from selective use of the data.
Record. In analysing qualitative data, the researcher is strongly encouraged
to
keep records
charts,
and
of the analysis in process in the form of memos, journals,
other
documents.
Records
may
be
of
two
types:
those
that
condense, summarize, and integrate data displays and documents thatfacili-
tate
the analysis, and records that
preserve
the construction of inference.
Such records can be memos about insights and ideas, notes, and summaries.
Report. In both quantitative and qualitative research it is common to report
the procedures applied for collecting and analysing data. A reason for doing
so is that others should realistically
be able to follow and evaluate the
underlying logic.
8.4
Interpretation
Analytical procedures are applied to manipulate data. Through interpretation, the researcher makes sense of the collected (and manipulated) data.
Compared with data analysis, interpretation tends to be more intuitive and
subjective. Interpretation may occur in many ways, and involves the use of
some ‘lenses’, that is, some conceptual or theoretical ‘glasses ’. A key point is
that sense is made of data by using some concepts, perspective, or theory.
One example is the use of metaphors. A metaphor requires us to suspend our
ordinary frame of reference for viewing some aspect of the world‘as is’, and,
instead, view it ‘as if’. In using a metaphor as a lens we seek correspondence,
139
140
Qualitative Data Analysis
similarities, and identities between domains. One domain is compared with
or seen in terms of another. The comparison involves a mapping of elements
of
one
domain
on
to
those
of
the
other.
Metaphors
not
only
play
an
important role in research but are also of crucial importance for how people
make sense of their observations and experiences (for an excellent discussion, see Lakoff and Johnson, 1980).
Grasping the meanings of others. In qualitative research the investigator
often attempts to understand informants by grasping a concept, an experience, or an idea in their terms. In doing so, the researcher grasps the informants ’ meaning by ‘translating’ their sayings into her/his own insights and
understanding. This implies seeking correspondence between the informants
’
and the researcher’s experiences and understanding.
Seeking patterns in meaning. Researchers often represent the meaning and
experiences of informants as forming coherent patterns. They do this by
fi
aggregating them into larger wholes and identifying uni ed themes by which
the individual informants construct their worlds and more generalized patterns that characterize their sample.
8.5
Strategies for Qualitative Analysis
From the above discussion, it is apparent that there are several approaches to
analysing qualitative data. A distinction can be made between whether the
research departs from existing theory (deductive), or whether the analyses
mainly depart from the observations/data (inductive).
8.5.1
Departure from Theory
In qualitative research the investigator may – and often does – depart from
some
theory.
exploratory,
This
may
sound
discovery-oriented
strange.
However,
research
with
a
even
focus
when
on
conducting
understanding
and construction of explanations, the investigator often holds prior assumptions and ‘hunches’ about critical factors and relationships. By taking such
assumptions
and
hunches
into
account,
the
research
may
fi
bene t
from
theorizing at the outset, which can be helpful to get some direction for
how to proceed (cf. Zaltman et al., 1982). Two things should be noted. First,
the a priori assumptions and hunches should guide and direct, not dictate, the
research. Second, even though some a priori theory may exist, it is assumed
that a priori insights should not allow for complete structuring and explan-
fi
ation of the research problem. If so, the research could probably bene t from
structured tests of hypotheses (see Section 5.2).
8.5 Strategies for Qualitative Analysis
EXAMPLE 8.5
Firms make use of technologies, and these can be described in various ways.
One way is the
flexibility
of a
firm’ s
production technology, that is, how
fi
the technology allows for by using specific input. In a study, a researcher
asked whether technological fl exibility infl uences fi rms’ buying strategies.
Here the researcher believed that technological flexibility did influence firms’
many types of input can produce a speci c output, and how many outputs
buying strategies, but s/he did not know exactly how, and therefore conducted a discovery-oriented qualitative study.
Inspection
of
Figure
differently in the two
8.2
firms.
shows
that
problem
recognition
occurs
very
In Alpha, information about expected increases
in the prices is the stimulus for the purchasing department to start the search
fi
for raw materials. It should also be noted that in this rm no alternative for
the type of raw materials used exists. In Beta, the buying process is triggered
by a sales order. In this
fi rm,
multiple alternatives for raw materials were
perceived to be possible. Is it possible to explain the two apparently different
fl
buying strategies re ected in Figure 8.2?
The actual study focused on the role of technological
fl exibility,
here
conceived as bindings between input and output possibilities. In Alpha,
the perceived technological possibilities of changing to other combinations
of input and output factors were few. Thus the ‘proactive’ behaviour in
Alpha can be seen as a way of‘buffering’ against environmental changes.
In Beta, a high degree of technological
iour. Its
flexibility
fl exibility allows for ‘reactive’ behav-
allows for immediate adjustments (Grønhaug, 1999).
(1) Alpha
Environmental
information -
Sales budget
expected increase
in prices
(start)
Purchase dept
Order (Sales)
(2) Beta
Headquarters
production
(Specification)
(start)
Purchase dept
Figure 8.2 Problem recognition
–
raw materials (last purchase)
141
142
Qualitative Data Analysis
8.5.2 Departure from Data (Observations)
The opposite strategy is to start from the data and let inspection of the data
result in explanation. In its extreme form this is what has been termed
grounded theory,
that is ‘theory derived from data, systematically gathered
and analysed’ (Strauss and Corbin, 1990: 121). An important point is that
theory is needed in any type of research. To make sense of data (observations), theory is needed. In fact, the quality of qualitative research is highly
dependent on the investigator’s ability to observe, conceptualize, and theorize. As noted above, qualitative research implies intensive interplay between
data (observations) and theory (see Section 2.5).
Over the years, the original grounded theory (Glaser and Strauss, 1967) has
fied.
been modi
It has also been criticized. One objection is that theory-
neutral observations are hardly feasible, and what we‘see’ when conducting
fl
research is in uenced by multiple factors, one of which is what we already
know about the social world being studied. (For more details, see Bryman,
2001; Flyvbjerg, 2006; Gioia et al., 2012.)
8.5.3 Analysing Case Studies
One way to analyse data collected through case study methods is to look for
commonalities and differences, for example in situations where there are
many cases. As stated by Boyd et al. (1985: 51):
In one
study to
improve the productivity
of the sales force
of a particular
company, the investigator studied intensively two or three of the best sales
representatives
and
two
or
three
of
the
worst.
Data
was
collected
on
the
background and experience of each representative and then several days were
spent making sales calls with them. As a result, a hypothesis was developed that
checking the stock of retailers and suggesting items on which they were low were
the
most
important
differences
between
the
successful
and
the
poor
sales
representatives.
find answers to our questions
As illustrated by the above example, we can
by comparing different cases. In some cases, comparing the best and worst
cases is the most suitable approach. In the same manner, we could analyse
the performance of several units/branches of a company by comparing them
with each other. Campbell (1975) and Ghauri (2004) refer to‘pattern matching’
where
information
assumptions. If we can
from
find
several
cases
could
be
related
to
a
priori
fi
a systematic pattern, we can accept or con rm
our assumption or proposition. We do not need statistical testing to come to
fi
the conclusions. The requirement is that the pattern has to be suf ciently
systematic. Patterns can be found through single case analysis and then
8.6 Quantifying Qualitative Data
fi
through case comparison. It can be bene cial to use a matrix to present data
and to show the analysis and patterns in case study research as this helps to
show
the
relationship
between
different
concepts
fluences what. This is illustrated in Table 8.1.
and
to
establish
what
in
In
the
case
of
descriptive
case
studies,
this
does
not
mean
that
the
purpose is to collect and present only the facts. As put by Ferber et al.
(1964: 153):
Facts do not lead anywhere. Indeed facts, as facts, are the commonest, cheapest
and most useless of all commodities. Anyone with a questionnaire can gather
thousands of facts a day
–
and probably not
find much real use for them. What
makes facts practical and valuable is the glue of explanation and understanding,
the framework of theory, the tie-rod of conjecture. Only when facts can be
fleshed
to
a
skeletal
theory
do
they
become
meaningful
in
the
solution
of
problems.
fi
In descriptive research, the researcher has to work with speci c research
problems, propositions, or hypotheses. While exploratory study is characterized
by
requires
a
flexibility,
clear
descriptive
fi
speci cation
study
of
the
can
who,
be
considered
what,
fi
why,
more
and
rigid.
how
of
It
the
research problem. For further details of speci c techniques for data analysis
for descriptive studies, the reader should look into Miles and Huberman
(1994).
Quantifying Qualitative Data
8.6
fication can be relevant in qualitative research. This may be the case,
Quanti
for example, when the researcher wants to capture the frequency of certain
fl
events. To gain insights into whether computer models in uence strategic
decision making, a study was conducted in which a management team was
asked to solve a strategic task with and without a computer tool. The team
was taped and video-taped while solving the task. A part of the analysis was
to examine the concepts used (because concepts held and used relate to
what is captured). Table 8.2 reports the concepts mentioned and used before
(without
the
computer)
and
after
(with
the
computer).
In
this
case,
the
researchers knew that managers hold concepts, but not what concepts they
hold and use.
fi
fi
fi
Also, assume that an identi ed concept is gradually re ned and de ned.
Over time, quantitative measures may be developed to capture the underlying
phenomenon empirically (see Chapter 6). Hypotheses may also be tested
statistically in qualitative research.
143
144
Qualitative Data Analysis
Table 8.2
Concept list
Concepts
Number of concepts
Number of concepts
Mentioned
Used in discussions
Before
After
Before
After
Management of the Norwegian shelf
7
1
7
1
Goals
2
0
2
0
Role of the authorities
5
1
5
1
Producer economic results
4
7
2
7
Pro
1
1
1
1
Price
1
1
1
1
Income
0
2
0
2
Cost
2
3
0
3
Transportation costs
1
1
1
Production costs
1
1
1
1
1
fit
Marginal costs
Supply (production)
18
13
17
13
Producer behaviour
4
3
4
3
Actors
8
7
8
7
1
1
0
1
Volumes
5
2
5
2
Production capacity
1
1
1
1
Production volume
1
1
1
1
Surplus/scarcity
1
1
Seasonal variation (winter sale)
1
1
Take back gas clause
1
1
Demand (consumption)
9
16
7
16
Actors
5
13
3
13
Import quota
0
1
0
1
Growth
2
1
2
1
Gas
fields
8.6 Quantifying Qualitative Data
145
Table 8.2 (cont.)
Concepts
Number of concepts
Number of concepts
Substitutes
2
0
2
0
Volumes
0
1
0
1
0
3
0
3
Trade pattern
– changes
Norway 1/Norway 2
1
1
Norway 1 and 2/other producers
1
1
Market share
1
1
Pipelines
1
4
Landing node 1
1
1
1
Capacity
1
1
Utilization
1
1
Shadow price 1
1
1
Number of superordinate categories
Number of concepts
Source :
0
4
5
6
4
6
39
44
33
44
Fuglseth and Grønhaug (2003). Copyright © 2003, with permission from Elsevier.
EXAMPLE 8.6
fi
An anthropologist observes a speci c behaviour among a group of people
and advances the assumption (hypothesis) that this is due to a particular
condition.
When
the
fi
speci c
condition
occurs,
nothing
happens.
The
researcher drops the initial hypothesis and advances a second one, and so
on. At his
nth
trial to advance a hypothesis, the observation is as expected.
fi
Moreover, every time the expected condition is present, the speci c behaviour is observed. Assume that the researcher has observed the expected
behaviour in
sent.
With
fi ve
fi
subsequent situations when the speci c condition is pre-
no prior
information, this implies the probability
outcome happening by chance is:
of such an
P(B = 5) = 0.031, p = 0.5, n = 5;
that is,
the likelihood of making such an observation by chance is a little higher than
3 per cent.
146
Qualitative Data Analysis
Validity in Qualitative Research
8.7
In
earlier
chapters
we
emphasized
the
importance
of
validity,
and
that
validity is also required in qualitative research (see Sections 5.4 and 6.4).
Validity
concerns
are
challenging
to
handle.
In
qualitative
research
the
following types of validity are often emphasized:
•
descriptive;
•
interpretative;
•
theoretical;
•
generalizable.
Descriptive validity refers
to the degree to which the actual description
holds true. For example, is the description of a strategy process or the buying
process in Figure 8.2 correct: that is, does it capture the buying process in
Alpha and Beta as it took place?
Interpretative validity
refers
to
how
good
the
interpretation
is.
Is
the
expressed interpretation the correct one? For example, is the explanation
regarding technological
Theoretical validity
flexibility suggested above valid?
refers to the adequacy of our suggested ‘theory’ or
explanation. For example, based on a substantial research effort, a researcher
suggests an explanation (a theory) of how
firms
cope with external uncer-
tainties. Does the suggested theory hold true?
By
generalizable validity we mean to what extent thefindings from a study
can be generalized to other settings. For example, the suggested explanation
of how
firms
among small
cope with external uncertainties was
firms
based
on observation
in one industry. Can the explanation be generalized to
hold true also for large
firms or firms in other industries?
Validity claims: It is not enough only to talk about validity. Validity must
also be demonstrated. For example, in a study you claim that a power game
‘
was going on in the company’. The term ‘power game’ must be explained (on
finitions,
conceptual de
power
game
really
see Chapter 3). Moreover, evidence that the claimed
was
going
on
must
be
reported.
Validity
claims
are
responses to the question: How can I trust you?
8.8
The Use of Computers in Qualitative Research
Analysis of qualitative data is tedious and time consuming. However, over
the past 15 years, various software programs have been designed to facilitate
data
analysis,
assisted
known
qualitative
collectively
data
analysis
by
the
acronym
software).
Some
CAQDAS
(computer-
well-known
computer
programs for qualitative research are ATLAS.ti, NVivo and QSR NUD*IST.
8.9 Case Studies and Triangulation
These software packages are able to systematically organize and analyse
qualitative data. NVivo, for example, allows for grouping and linking of
concepts
support
through
the
use
codes,
of
nodes,
NVivo
to
master-lists,
enhance
the
and
trees.
Several
trustworthiness
of
scholars
qualitative
analysis (Kelle, 1997; Miles and Huberman, 1994; Sinkovics et al., 2008). It
not only
ficient,
ef
makes the
coding and retrieval
of text much faster and more
but also enhances the transparency of the analysis and supports
the theory building beyond description, and allows us to avoid the criticism
of anecdotalism in qualitative research (Silverman, 1993).
NVivo has become the most widely used CAQDAS as it provides procedural advantages in the management of textual data (Ghauri and Firth, 2009;
King et al., 1994). The decision to use NVivo or not should be taken at an
early
stage,
before
data
collection.
When
using
NVivo,
data
collection
should be standardized. Data collected through interviews should be digitally
recorded
and
transcribed.
Data
collected
through
multiple
sources,
secondary and primary, should be gathered in one place. This will facilitate
the
data
analysis
when
data
are
organized,
coded,
and
reduced
into
a
manageable system so that interpretations can be made to draw conclusions. NVivo facilitates this process through nodes, and makes it easier to
examine the nodes at any time and bring them together, even from different cases. It shows the frequency of different concepts in all cases. Thus, in
a
final
stage it helps in the interpretation and building of models, propos-
itions, and theories (Ghauri and Firth, 2009; Sinkovics et al., 2008). Table 8.3
summarizes
the
fi
bene ts
of
using
NVivo
in
international
business
(IB)
research.
8.8.1
A Word of Warning
To make proper use of such programs, the researcher must of course know
how to analyse data and have command over how to use the program.
Probably even more important is to have an intimate personal knowledge
of the data, so that appropriate themes, nodes, codes, and responses can
be inserted at the outset of data interpretation. Such knowledge can best
come from personal involvement in the entire process, from data collection
through to analysis. Moreover, the researcher needs to get some training in
the particular program used.
8.9
Case Studies and Triangulation
Triangulation refers to the combination of methodologies in the study of the
same phenomenon. Through triangulation we can improve the accuracy of
our judgements and thereby our results, by collecting data through different
147
148
Qualitative Data Analysis
Table 8.3
The advantages of using NVivo in IB research
Characteristics of IB
research
Implications for analysis of
case studies
Advantage of using NVivo
Data saturation – advances
Case studies can become
All types of data can be
in technology mean the
increasingly large and less
neatly stored and organized
researcher can collect more
manageable
with a single program,
data on a global scale
increasing clarity and focus
of analysis
Research may be undertaken
Interpretations of cases may
NVivo offers a single
by a variety of different
differ between researchers and
platform in which all
researchers based in
ideas may not be
researchers can work with
different countries
communicable across borders
individual passwords. Data
in real time in order to cross-
can then easily be emailed so
check the data
researchers can view/alter
others ’ ideas in an iterative
process which is less
subjective
Language barriers/difference
Case studies may not be
Newer versions of NVivo
in language of data collected
comparable, and validity of
will accept languages other
data may be compromised
than English. Different codes
and categories can be used
to show which language was
used and retain equivalence
of the data
The unit of analysis often
Case studies must recognize
NVivo allows categorization
differs i.e. HQ/subsidiary/
the differences in units of
of units of analysis, and
local
analysis and compare issues
direct comparisons between
according to separate accounts
them are possible on a single
from individual units
screen
Time constraints of
Case study analysis may be put
NVivo allows initial analysis
cross-border research
on hold until ALL data are
of the data even if collection
collected, compromising the
is not totally complete as
quality of analysis
alterations can easily be
firm
made at a later date, rather
than the analysis having to
be repeated
IB research is
Case studies can be rich with
NVivo can store all sources
multidisciplinary and likely
data however, linking different
of data, including text,
to make greater use of a
sources may be dif cult
webpages, electronic
variety of sources of data
fi
files,
videos, pictures, and audio
files, and link them together
in terms of their coding
Source :
Ghauri and Firth (2009: 36).
8.9 Case Studies and Triangulation
methods or even by collecting different kinds of data on the subject matter of
our study. The discussion on validity is particularly relevant here. Sometimes
to enhance the validity of our research we need to collect or analyse data
through triangulation. In cases where correctness or precision is important, it
is quite logical to collect information through different methods and from
different angles. The following story illustrates what we mean.
Three blind men were asked to describe an elephant by touching or feeling
only a part of it. We can well imagine what they might have described by
touching different parts of an elephant, such as a foot, an ear, or a trunk. This
illustrates
that,
in
many
cases,
one
method
alone
cannot
be
enough
to
explain or describe a phenomenon, and we need to use a multi-method
approach to capture the whole reality.
The use of multiple methods or triangulation is not new and can be traced
back to Campbell and Fiske (1959), who argued that to ensure validation one
should use more than one method. The main advantage of triangulation,
however, is that it can produce a more complete, holistic, and contextual
portrait of the object under study. This is particularly important in the case
study method as you need to check and validate the information you receive
fi
from various sources. For example, you can check the performance of a rm
or
a
project
claimed
by
an
interviewee
through
examining
the
annual
accounts or archives or by interviewing another manager or the company
auditors.
However,
there
are
some
fi
problems
with
triangulation.
For
example,
sometimes it can be dif cult to judge whether the results from different
methods are consistent or not. A second problem arises when the different
methods come up with contradictory results. Sometimes researchers may
prefer or emphasize one method over another: for example, quantitative
versus
qualitative.
However,
all
research
methods
have
advantages
and
disadvantages when it comes to different research problems. Our conclusion
is that triangulation or the use of a multi-method approach on the same
study object can be useful even if we do not get the same results. It can lead
us to a better understanding or to new questions that can be answered by
later research.
Triangulation is particularly useful in international business research to
check for method bias. Using multiple methods to examine the same phe-
fi
nomenon and checking for consistencies enhances our con dence in the
analysis and results. Here we could compare the data and results obtained
through an experiment and with those gained through interviews. Convergence of results obtained through these different methods will enhance their
reliability and validity. We could also compare results obtained through
qualitative methods with statistical analysis.
149
150
Qualitative Data Analysis
Case Studies and Generalization
8.10
fi
Case studies are often considered less rigorous because their ndings cannot
be generalized (Hagg and Hedlund, 1979; Hillebrand et al., 2001). Although
fl
this view is widely accepted, a number of fundamental and in uential studies
in our
field
have been based on case studies. For example, Johanson and
fi
Vahlne’s (1977) study on the internationalization process of rms is based on
fl
four cases and has been one of the most in uential studies in international
business. Some scholars also argue that case studies should be judged not on
the basis of generalization, but on the basis of transferability and comparability (Chreim et al., 2007; Sinkovics et al., 2008).
fi
Case studies are generally de ned as ‘an empirical enquiry that investigates a contemporary phenomenon in depth and within its real-life context,
especially when the boundaries between the phenomenon and the context
fi
are not clearly evident’ (Yin, 2009: 18). The generalization is often de ned as
‘a
general statement or proposition made by drawing on inference from
observation of the particular’ (Schwandt, 1997: 57). Generalization is thus
fi
an act of inference from speci c observation to general statements (Cohen
and Nagel, 1934; Tsang, 2014). We follow Tsang (2014) in keeping our
fi
discussion on logic rather than on any speci c philosophical perspective
such as positivism or interpretivism.
According to Tsang (2014), the results of case studies may in fact be more
generalizable than quantitative studies based on large survey data analysed by
statistical methods. He particularly discusses three aspects of generalization:
fi
theoretical generalization, falsi cation, and empirical generalization. Case
studies are particularly suitable for theoretical generalization (theory building)
fi
and falsi cation (theory testing) and not particularly inferior to quantitative
studies in empirical generalization. However, different case study designs may
fluence
in
the strength of generalization claims. For example, single case
versus multiple cases and cross-sectional cases versus longitudinal cases.
The evidence from multiple cases is considered more compelling than from
a single case and a longitudinal case study provides more convincing evidence
for theoretical generalization than a cross-sectional case (Tsang, 2014).
In
the
same
manner,
falsi
fication
concerns
theory
testing:
existing
hypotheses or theory can be rejected on the basis of a case study. The famous
story of ‘all swans are white’ was considered true for some time till just one
fi
observation of a black swan falsi ed the earlier statement/theory (Flyvbjerg,
fi
2006). In empirical generalization, the idea is that ndings from a case can be
applicable to the population from which the case is drawn (within a population). This means that to generate empirical generalization, it is better to
choose
cases
that
are
representative
of
the
particular
population
(Gobo,
2004). Figure 8.3 summarizes the relationship between case studies, theory
8.10 Case Studies and Generalization
Aspect of Generalization
Empirical generalization
Identification of empirical regularities
Theoretical generalization
Theory Building
Falsification
Theory Testing
Figure 8.3 Case study research: theory building and theory testing
Source:
Based on Flyvbjerg (2006).
development, and theory testing. As we can see, the relationship between
theory
building
and
theory
testing
is
quite
close.
However,
case
study
research normally aims to achieve one or the other (Tsang, 2014).
fi
The above discussion deals with and clari es the common objection and a
prevailing view that case studies cannot provide generalizable results. This
view
has
been
a
major
factor
in
many
researchers
avoiding
case
study
research. Our discussion reveals that, in addition to Yin
’s (2009) internal
validity,
discuss
construct
the
type
validity and reliability,
of
generalization
their
the
study
case
can
study researcher
can
provide
and
(Gibbert
Ruigrok, 2010; Tsang, 2014). Moreover, case study researchers can claim
that they seek to investigate phenomena in their context and not independent of context (Gibbert et al., 2008).
151
152
Qualitative Data Analysis
Further Reading
........................................................................
Ghauri, P. (2004) ‘ Designing and conducting case studies in international business
research’ , in Marshan-Piekkari, R. and Welch, C. (eds.),A Handbook of
Qualitative Research Methods for International Business, Cheltenham, UK:
Edward Elgar, pp. 109– 24.
Miles, M.B. and Huberman, A.M. (1994) Qualitative Data Analysis, 2nd edn.,
Thousand Oaks, CA: Sage.
Sinkovics, R.R., Penz, E., and Ghauri, P.N. (2008)‘Enhancing the trustworthiness of
qualitative research in international business’, Management International
Review, 48(6): 689–714.
Tsang, E.W.K. (2014) ‘ Generalizing from research
findings: The merits of case
studies’, International Journal of Management Reviews, 16(4): 369–83.
Questions
........................................................................
1.
What
do
you
consider
to
be
the
key
characteristics
of
qualitative
research?
2.
Explain what is meant by coding. Also explain the role of coding in
analysing qualitative data.
3.
What is meant by suggesting that qualitative data are an ‘attractive
nuisance ’?
4.
What
do
you
think
are
the
main
advantages
and
disadvantages
in
departing from theory in qualitative research?
5.
How can triangulation make our research more reliable?
6.
What is meant by theoretical generalization? Give examples.
Exercises
........................................................................
1.
You
wonder
whether
young
women
and
young
men
differ
in
their
evaluations and behaviours in their purchase of a new car. Suggest
how you would design a study to explore your research problem. Also
explain what data you will gather and how you would do this to solve
your research problem.
2.
Assume you decide to gather information through (modestly) unstructured interviews. Prepare, conduct, and tape-record one such interview.
Transcribe the interview, read, code, and analyse it.
9
Data Collection for Quantitative
Research
The goal is to turn data into information, and information into insight.
Carly Fiorina
In Chapter 6, we considered various issues related to the measurement of the
variables to be used in quantitative data analysis. Here we turn our attention
to the collection of the data. A distinction is usually made between primary
data and secondary data.
Secondary data are data that have already been collected by government
•
agencies,
market
research
agencies,
firms,
or
other
organizations
or
individuals, and which are publicly available. For the researcher, the
main advantage of using secondary data is that they have already been
collected, and the researcher simply needs to collate the data (often this
may just involve downloading from a website) in a suitable format for
subsequent analysis.
Primary data
•
are
data
that
are
collected
by
the
researcher
through
fi
surveys, interviews, or experiments speci cally for the research problem
that is being studied. This means that the researcher is able to specify
exactly
which
data
should
be
collected
and
how
much,
and
is
able
of
the
personally to verify the data.
In
this
chapter,
first
we
provide
a
more
detailed
discussion
advantages and disadvantages of collecting and using secondary data and
highlight some important secondary data sources. The next section then
considers
the
advantages
and
disadvantages
of
collecting
and
using
primary data. The following four sections are devoted to sampling. With
secondary data, the researcher is obliged to accept the data that are publicly
fl
available, and is not able to in uence how the data are collected or how
much data are collected. In contrast, the researcher collecting primary data
needs to decide whether to survey the entire population or just a sample, to
choose an appropriate sampling procedure, and to determine the sample
size
that
will
assure
a
empirical analysis. The
satisfactory
final
level
of
precision
in
the
subsequent
two sections are then devoted to undertaking
154
Data Collection for Quantitative Research
the two most common methods of primary data collection, viz: questionnaire surveys and experiments.
Secondary Data
9.1
The
first step for any researcher should be to look for secondary data sources
relevant to the research project, before going out to collect primary data. As
Churchill (1999: 215) advises, ‘Do not bypass secondary data. Begin with
secondary data, and only when the secondary data are exhausted or show
diminishing
returns,
proceed
to
primary
data.’ Secondary
data
can
help
researchers in several ways:
•
answering research questions or solving research problems;
•
helping in problem formulation and devising more concrete and focused
research questions;
deciding on the appropriateness of particular research methods or sug-
•
gesting better research methods;
providing benchmarking methods and other
•
findings
that can be com-
pared later on with the results from the study at hand.
five broad categories.
1
Secondary data sources may be grouped into
there
is
an
enormous amount
of
data that
is
publicly
First,
available via
the
websites of supranational organizations (e.g. the World Bank, the World
Trade Organization, UNCTAD, OECD) and independent research institutes
(e.g. the Heritage Foundation, Transparency International) – see Table 9.1
for
details.
Most of
these
sources
present
data at
a
fairly
high
level
of
aggregation (i.e. country-level, or industry-level), though the national statistics bureaux of individual countries may be able to provide more disaggregated
data.
Examples
would
fi
include
the
Bureau
of
Economic
Analysis
(United States), the Of ce for National Statistics (ONC, United Kingdom),
the National Bureau of Statistics (China), and the Ministry of Statistics and
Programme Implementation (India). Trade organizations and chambers of
commerce may also be good sources of more disaggregated data. Second,
more detailed
1
firm-level
data may be found in
internal
Some secondary data may be available from
may be undertaking research for a
firms
’ annual accounts, but
sources. For instance, the researcher
firm and may have been provided with data on suppliers,
customers, employees, marketing plans, invoices, minutes, production costs, and perhaps on
competitors. A prudent researcher should treat this data with care, as only favourable data
external
may have been provided. Our emphasis in this chapter, however, is on data from
sources where the researcher is able to collect whatever data s/he requires.
9.1 Secondary Data
Table 9.1
155
Selected publicly available secondary data sources
Organization
Bank for
Website
www.bis.org
Data available
* Country-level data on international
International
banking, liquidity, credit, external debt,
Settlements
foreign exchange, consumer prices,
property prices, etc.
CIA
www.cia.gov
* World factbook
* Country reports
* Administrative, transportation etc. maps
Columbia Center on
http://ccsi.columbia.edu
fi
* Inward and outward FDI pro les for
Sustainable
selected countries
Development
* Data on leading MNEs for selected
emerging economies
European Union
www.europa.eu
* Country-level data for the Member States
* Basic information about life and business
in the European Union
* Public opinion polls
Heritage
www.heritage.org
* International trade freedom index
www.ilo.org
* Country-level data on formal and
Foundation
International
Labour
informal employment, hours of work,
Organization
labour costs, labour productivity, labour
migration, etc.
* Labour, social security and human rights
legislation
International
www.imf.org
Monetary Fund
fl
* Country-level data on trade, FDI, in ation
rates, commodity prices, exchange rates,
etc.
* World Economic Outlook database
* Links to many other relevant databases
MSU-CIBER
www.ciber.msu.edu
* Market potential indices
* Business climate rankings
* The globalEDGE database
OECD
www.oecd.org
* Country-level data for many countries,
not just member countries
* Economic surveys
* Economic forecast summaries
* Labour force statistics
* Activities of multinational enterprises
(AMNE) database
156
Data Collection for Quantitative Research
Table 9.1 (cont.)
Organization
Data available
Website
Property Rights
Alliance
www
.propertyrightsalliance.org
* International property rights index
Transparency
International
UNCTAD
www.transparency.org
* International corruption perceptions
index
* Country-level data for all countries on
economic trends, international trade, FDI,
migrants remittances, population,
commodities, information economy,
creative economy, and maritime transport
* Country profiles
www.unctadstat.unctad.org
’
World Bank
www.worldbank.org
World Economic
Forum
www.weforum.org
World Trade
Organization
www.wto.org
* Country-level data on agriculture & rural
development, aid effectiveness, climate
change, economy & growth, education,
energy & mining, environment, external
debt, financial sector, gender, health,
infrastructure, poverty, private sector,
public sector, science & technology, social
development, social protection & labour,
trade, and urban development.
* Ease of doing business index
* Worldwide governance indicators (voice
& accountability, political stability,
government effectiveness, regulatory
quality, rule of law, control of corruption)
* Global competitiveness rankings
* Country-level indices on environmental
footprints, inclusive development, gender
gap, and income equality
* Country-level data on international trade,
tariff barriers, non-tariff measures, and
global value chains
* Value-added trade statistics
there are several organizations (e.g. Bureau van Dijk) that collate such information into online databases which may be accessed on payment of a
subscription. Bloomberg provides real-time market data for individualfirms.
In addition, there are dozens of private organizations who can provide offthe-peg market research reports on different product sectors in different
countries for a fee see Table 9.2 for a non-exclusive list. Third, useful data
–
9.1 Secondary Data
Table 9.2
Selected commercially available secondary data sources
Organization
Website
Data available
AC Nielsen
Bloomberg
Bureau van
Dijk
www.nielsen.com
www.bloomberg.com
www.bvdinfo.com
* Expertise in consumer measurement
* Firm-level real-time market data
* Orbis database contains fi nancial,
ownership, and M&A activity etc. data on
300m firms worldwide
* Fame database contains fi nancial,
ownership, and M&A activity etc. data on
11m firms in the UK & Ireland
* Zephyr database contains detailed
information on M&A, IPO, private equity
and venture capital deals
Euromonitor
International
Financial
Times
www.euromonitor
.com
www.ft.com
* Market research information for many
products in many countries
* Several firm-level databases (e.g. on
mergers and acquisitions)
Ipsos
www.ipsos.com
Market
Research
.com
Mintel
www.marketresearch
.com
* Market research
* Opinion polls
* Market research reports for many
products in many countries
* Surveys of global market trends
* Market research reports for many
products in many countries
* Surveys of global market trends
www.mintel.com
may be obtainable directly from other researchers, or via their personal
websites. Some academic journals insist that authors publish their datasets
so that others may check or replicate their results. Two examples of personal
websites are (a) the site of Geert Hofstede w( ww.hofstede-insights.com),
which provides data on his dimensions of national culture (i.e. power distance, uncertainty avoidance, individualism vs collectivism, masculinity vs
femininity, long-term orientation, indulgence vs restraint) and enables the
calculation of cultural distances between pairs of countries; and (b) the site of
Pankaj Ghemawat (www.ghemawat.com), which enables the calculation not
just of cultural distances, but also administrative, geographic, and economic
distances between countries according to his CAGE model (Ghemawat, 2001).
Fourth, big data is a fashionable contemporary term. Many firms, banks,
social media sites, and search engines routinely collect huge amounts of data
157
158
Data Collection for Quantitative Research
firms. These data are
about the activities and preferences of individuals and
collated and analysed for their own commercial purposes but may be made
available to bona
fide researchers. Last but not least, it is always possible to
track down additional data sources through the use of search engines such as
Google or Yahoo.
9.1.1
The Advantages of Secondary Data
The major advantage of using secondary data is obviously the enormous
savings in time and money. As noted above, huge amounts of secondary data
are freely available on a wide variety of variables. There may be a cost
involved for access to some commercial data, but this is unlikely to be
signi
ficant
relative to the costs the researcher would incur in collecting the
data by her/himself.
Furthermore, much of the data published by government departments and
supranational organizations will have been compiled and checked by specialists, and may reasonably be assumed to be collected on a consistent basis.
This
is
particularly
useful
when
data
are
presented
for
several
different
figures are comparable.
countries, where it is imperative to ensure that the
9.1.2
The Disadvantages of Secondary Data
There are also possible drawbacks to using secondary data. The
first
and
main disadvantage is that the data were collected for purposes other than
those of the researcher, hence the data may not map exactly on to the
theoretical
concepts
in
which
the
researcher
is
interested.
For
example,
international business scholars often use data on foreign direct investment
(FDI) stocks as proxy measures for the importance of multinational enterprises (MNE) in host countries. But FDI data relate to (net) funds transferred
from the home country of the MNE to the host country, and do not take
account of
finance
(e.g. though local equity or debt, or retained earnings)
raised locally in the host country. Such data thus provide biased measures of
filiate activity (Beugelsdijk et al., 2010; Kerner, 2014).
MNE af
The second disadvantage relates to the accuracy and reliability of the raw
data. The ‘true and fair’ concept in auditing requires that
firms financial
’
financial performance and situation of the
firm, and are free of significant misrepresentations. But financial statements
statements faithfully represent the
can be massaged in both legal ways (e.g. to minimize tax liabilities) and
illegal ways, as numerous
financial scandals (e.g. Enron) testify.
Finally, even when collated data are published by reputable organizations
(such as those listed in Tables 9.1 and 9.2), there is often no way of checking
how (if at all) the raw data were checked and cleaned before being published.
The researcher is thus obliged to take responsibility for the data used (and the
9.2 Primary Data
findings from any analysis) without being fully cognizant of their authenticity (Cooper and Schindler, 2001).
9.2
Primary Data
Sometimes suitable secondary data sources are not available (as with many
of the types of data listed below),
or are inappropriate (for the
reasons
outlined in Section 9.1.2), or the researcher requires data for a new group
of
firms or individuals. Then the researcher is obliged to collect primary data
though surveys, interviews, or experiments. Primary data come in many
types:
•
Data
on
attributes.
This
category
refers
to
demographic
or
socio-
economic data, such as age, education level, profession, marital status,
gender,
classi
income,
fication
or
social
class.
These
data
are
used
for
cross-
of information as, for example, in market segmentation
and positioning. Does the purchase/usage of a product depend upon
age group?
•
Data on personality and lifestyle. This category refers to personality data
fically, to
and, more speci
data that may be related to individual behav-
fi
iour. For example, different types of consumers might be identi ed:
proactive vs reactive shoppers, or traditional vs innovative shoppers.
•
Data
on
attitudes
and
opinions.
This
category
refers
to
individuals
’
fic products, ideas, objects, or issues, or to the individ-
opinions on speci
ual ’s cognitive behaviour. Such data may reveal future usage of particular products.
•
Data on awareness and knowledge. This category includes data on what
is known about particular products, issues, or business activities. Such
data are required if the researcher wants to
find out what factors motiv-
ate consumers to buy products, or the factors that stimulate opinions on
issues, or the factors that motivate different activities.
•
Data on intentions. The data in this category relate to future actions and
might,
for
example,
be
used
to
compare
the
intended
purchases
of
consumers with their actual buying behaviour.
•
Data on motivations. The data in this category may reveal the motives or
fluence
stimuli that in
consumers’ or managers’ behaviour. Motives are
more stable than behaviour, and such data may help the researcher both
understand past behaviour and predict future behaviour.
•
Data on behaviour. The data in this category relate to what the respondents have done or will be doing.
159
160
Data Collection for Quantitative Research
9.2.1
The Advantages of Primary Data
There are three advantages associated with the collection of primary data.
The first and main advantage is that the researcher is able to collect data with
the exact objectives/questions of the project in mind, and hence is able to
generate data that map exactly on to the theoretical concepts in which s/he is
interested. Example #1: government policy makers are often interested in the
factors that attract inward foreign direct investment. Secondary data on
many potential factors (e.g. market size, labour quality, technological capabilities) are readily available, but, crucially, data on the motivations (i.e.
market-seeking, efficiency-seeking, asset-seeking, natural resource-seeking)
of the investing firms (Dunning and Lundan, 2008) are not, and thus require
primary data collection. Example #2: market researchers often want to know
about the attributes, personalities, lifestyles, and buying behaviours of potential customers when evaluating future market demand. Such data are typically not available from secondary sources, hence primary data collection is
required. Example #3: many researchers in international entrepreneurship
are interested in why some SMEs internationalize quickly and/or extensively
(so-called ‘born globals’), while others do not, and believe that part of the
explanation may be linked to the experientialknowledge of the firms and the
personality characteristics of the founders. Again such data can not be
obtained from secondary sources, so primary data collection is required.
The second advantage is that the researcher can choose exactly who to
approach for the required data, and how many units of observation are to be
surveyed. In example #1, the most appropriate respondents would be the
CEOs or board members of the investingfirms. In example #2, the appropriate respondents would be a sample of potential customers. In example #3, the
appropriate respondents would be the founders of the SMEs. The issue of
sample size will be discussed in Section 9.6 below.
The third advantage is that the data will be collected directly by the
researcher, or by her/his trusted nominee, who will be aware of the objectives
of the study and possible nuances of interpretation, and should thus be able to
collect accurate responses and thus have greater confidence in the final results.
9.2.2
The Disadvantages of Primary Data
There are two main disadvantages with primary data collection. Thefirst is
that the process of data collection is often slow and expensive, which can be
a major issue if timely analysis is required or the researcher has a limited
budget. The second is that it may be difficult to obtain suitable access or
build a dataset of suitable size. Manyfirms (especially large, successful, and
well-known firms) receive dozens of requests every year to participate in
questionnaire surveys or to provide interviews with senior executives: most
9.3 Why Take Samples?
are refused or simply ignored because the executives do not have the time, or
because they do not feel the project merits attention. Unfortunately, though
perhaps understandably, access is even more problematic for research students than for more senior researchers. Response rates are often low, and this
may lead to small sample sizes (with problems for statistical inference) and/
or non-response bias (see Section 9.4.5).
9.3
Why Take Samples?
One possibility when carrying out a research project is to survey the entire
population of interest. The term population, as used here, refers to all possible
units of observation, and these units may be people,
countries,
depending
upon
the
context
of
the
firms,
project.
For
products, or
instance,
the
project might involve the problems faced by SMEs wishing to export from
their home country. Now, even if the home country was fairly small (e.g. the
Netherlands),
the
number
of
SMEs
is
likely
to
be
large
(maybe
tens
of
thousands). So, although it might in principle be possible to survey the
population and collect primary data from all the SMEs within the Netherlands, such an approach would have two obvious drawbacks. First, such a
survey would require a considerable amount of resources (including labour
and
money)
to
visit
each
SME
and
collect
the
requisite
data,
and
such
resources may not be available. Second, such a survey would require a
considerable amount of time not just for data collection but for subsequent
data processing, hence the
findings from such a project may be delayed to an
unacceptable degree. Hence it is often preferable to undertake a sample
survey, in which a subset of the units of observation from the population
2
are selected for investigation.
Sample surveys are cheaper and quicker than population surveys, but it is
essential that the sample is selected in such a way that it is representative of
fi
the population from which it is drawn so that any ndings may be generalized to the population. In the example above, it would be important to select
SMEs with a range of sizes, capabilities, and industries if the sample was to
be representative of all Dutch SMEs. In assessing representativeness, ideally
the researcher would like to have access to a sampling frame listing all the
units in the population so that the characteristics of the sample and the
population could be compared. See Example 9.1 for details of the basic
terminology used in sampling.
2
Sample surveys are almost inevitable when collecting primary data, but it is often feasible to
survey many more units of observation when using secondary data.
161
162
Data Collection for Quantitative Research
EXAMPLE 9.1
•
Population – the universe of units from which the sample is to be
selected. The term ‘ units’ is employed because it is not necessarily people
who are being sampled – the researcher may want to sample from a
universe of nations, cities, regions,
firms,
etc. Thus, ‘population’ has a
much broader meaning than the everyday use of the term, whereby it
tends to be associated with a nation’s entire population.
•
Sample – the segment of the population that is selected for investigation.
It is a subset of the population. The method of selection may be based on
a probability or a non-probability approach (see below).
•
Sampling frame – the listing of all units in the population from which the
sample will be selected.
•
fl
Representative sample – a sample that re ects the population accurately
so that it is a microcosm of the population.
•
Probability sample – a sample that has been selected using random
selection so that each unit in the population has a known chance of
being selected. It is generally assumed that a representative sample is
more likely to be the outcome when this method of selection from the
population is employed. The aim of probability sampling is to keep
sampling error (see below) to a minimum.
•
Non-probability sample – a sample that has not been selected using a
random selection method. Essentially, this implies that some units in the
population are more likely to be selected than others.
•
Sampling error – the difference between a sample and the population
from which it is selected, even though a probability sample has been
selected.
•
Non-sampling error – differences between the population and the
fi
sample that arise either from de ciencies in the sampling approach, such
as an inadequate sampling frame or non-response (see below), or from
fl
such problems as poor question wording, poor interviewing, or awed
processing of data.
•
Non-response – a source of non-sampling error that is particularly likely
to happen when individuals are being sampled. It occurs whenever some
members of the sample refuse to cooperate, cannot be contacted, or for
some reason cannot supply the required data (for example, because of
mental incapacity).
Source:
Bryman and Bell (2003: 93)
9.4 Probability Sampling
A sampling frame is essential for probability sampling, so that each unit has
a known non-zero probability of being included in the sample. This then allows
the researcher not only to construct a representative sample, but also to make
fi
statistical inferences about the population on the basis of the survey ndings.
But often such a sampling frame does not exist or may be out of date, in which
case it is not possible to calculate the probability that any one unit will appear
in the sample. In such cases, the researcher has to rely on non-probability
fi
sampling and s/he will have less con dence when deriving inferences about
the population. In our example above, there may be an up-to-date list of all
Dutch SMEs, but such a list will quickly become inaccurate as new SMEs are
established and old SMEs develop, are acquired, or go out of business.
9.4
Probability Sampling
There are several techniques of probability sampling, of which the most
widely used are:
•
•
•
•
simple random sampling;
systematic sampling;
strati
fied random sampling;
cluster sampling.
To illustrate the differences between these techniques of probability sampling,
suppose
the
researcher
wishes
to
interview
some
of
the
(approx)
2 million students studying at UK universities (of which there are about
130) as part of a market research project. Further suppose that an accurate
sampling frame exists of these 2 million students.
9.4.1
Simple Random Sampling
A key characteristic of simple random samples is that all units (here the 2m
students) have the same probability of being included in the sample. Thus the
researcher might select 1 000 students at random from the sampling frame for
interview, with each student having a 0.0005 chance of being selected. Clearly
this procedure results in a substantial reduction in the numbers of students
being interviewed, with commensurate savings in the time and resources
required. However, such a procedure still has two major drawbacks. First,
the random selection of students might still give rise to an unrepresentative
sample in terms of, say, gender or undergraduate/postgraduate. Second, it is
highly likely that most (if not all) of the 130 universities will be represented in
the sample: this may be a good thing, but the downside is that the researcher
still has to visit each and every university to carry out the market research.
163
164
Data Collection for Quantitative Research
9.4.2
Systematic Sampling
fi
A systematic sample involves selecting every nth unit after a random rst
choice. In our market research example, the researcher might visit various
first
halls of residence at the 130 universities, choose the
rooms at random,
and then select every nth room thereafter until 1 000 students have been
interviewed. The procedure would be random (assuming students were randomly allocated to halls), and simple to undertake; indeed, a full sampling
frame would not even be needed. Other applications might involve checking
for defects on production lines by examining every 100th product. The main
disadvantage is the danger of hidden periodicities.
9.4.3
Strati fied Random Sampling
fied
A strati
sample is a probability sample where the population may be
divided into mutually exclusive and exhaustive subsets, and where simple
random samples of units are chosen independently from each subset. The
fi
idea of strati ed sampling is to ensure that every part of the population, that
is every stratum, gets satisfactory representation. This is especially important
if the means (or proportions or whatever we want to estimate) are very
different in the different strata. The result will be a smaller sampling variation: i.e. more stable results in repeated samples than we would get by using
simple random sampling. Sampling from the different strata can be either
fi
proportional or disproportional. A proportionate strati ed sample involves a
uniform
sampling
fraction
in
each
strata.
A
disproportionate
fi
strati ed
sample involves a variable sampling fraction.
In our market research example, the researcher might believe that the
results will depend upon the students’ gender, undergraduate/postgraduate
status, and possibly other characteristics. In this case, the strata are gender
and status, and the researcher can improve the representativeness of the
sample by selecting appropriate proportions of students (based on the sam-
fi
pling frame) in each category. The main advantages of strati ed random
sampling are that the results have greater precision for the same sample size
(or alternatively the same precision with a smaller sample size), and separate
results may be generated for each stratum.
9.4.4
Cluster Sampling
Cluster sampling involves
first
selecting large groups of units (clusters) and
then selecting the sample units from the chosen clusters. If the researcher
examines all units in the selected clusters, the procedure is called one-stage
cluster sampling. If a sample of units is selected randomly from within the
selected clusters, the procedure is known as two-stage cluster sampling.
9.4 Probability Sampling
One advantage of cluster sampling is that the researcher does not need a
complete sampling frame, just knowledge of all potential clusters and sampling frames for the selected clusters. A second advantage is that it will
generally be easier and quicker to survey units in geographically proximate
clusters than in the more dispersed population.
In our market research example, the researcher might select the Universities
of Birmingham and Sussex as two clusters, and then randomly survey 500 students in each. If the student bodies in Birmingham and Sussex are similar to
those at other UK universities in demographic characteristics and attitudes,
then the researcher will maintain the precision of the survey while saving a lot
of time and expense as s/he will only need to visit two universities.
9.4.5 Non-Response Bias
A serious potential threat to the validity of results from sampling surveys is
non-response. When some units that have been drawn for inclusion in the
sample do not respond to our questions, the effective sample size is reduced.
But this is not the main problem, since it can easily be remedied. If we need a
sample of 400 units and we expect a 50 per cent response rate, we could take
a sample of 800 units to counteract the non-response.
The real problem with non-response is that those who do not respond are
usually different from those who do respond, and thus there will be no
– this is
guarantee that the sample is representative of the population
termed
non-response bias. To take an extreme example, suppose we use a mail
questionnaire to learn something about consumption patterns for alcoholic
beverages. The majority of the real drinkers will probably not respond, for
several reasons, but they make up an important part of the whole picture.
Therefore it is very important to get responses also from the drinkers who
have
been
picked
out
by
our
sampling
procedure.
For
further
details
regarding how to deal with the problem of non-response bias, see Churchill
–
(1995: 652 88). Non-response and other sampling errors are often more
problematic than statistical errors.
EXAMPLE 9.2
Non-response is widely believed to be a major reason for the discrepancy
fi
between the survey-based estimates of alcohol consumption and of cial (taxbased) estimates. Lahaut et al. (2002)
first
undertook a mail questionnaire
survey in 1999 on the alcohol consumption of 1 000 people in Rotterdam.
The response rate in this primary survey was 44 per cent after two reminders.
165
166
Data Collection for Quantitative Research
EXAMPLE 9.2
(CONT.)
They then undertook a secondary follow-up study involving house visits. The
fi
key results were that several categories were signi cantly under-represented
in
the
primary
survey.
In
particular,
abstainers
were
under-represented,
fi
whereas frequent excessive drinkers were not signi cantly under-represented.
Furthermore, the under-representation of abstainers was greater for females
than for males, for people younger than 35 years, and for the Dutch than for
the non-Dutch group. They concluded that the results of the secondary study
revealed a serious non-response bias in the primary survey, and that this bias
cannot be corrected by weighting data on the basis of socio-demographic
variables. They recommend that researchers should try to minimize nonresponse bias by, for example, developing more appealing survey materials.
9.5
Non-Probability Sampling
In many practical cases, a comprehensive and accurate sampling frame may
not exist, hence the researcher may be obliged to adopt some form of nonprobability sampling. The disadvantages are that it is then not possible to
calculate the probability for each unit being included in the sample, there is
no way of guaranteeing a representative sample, and hence the researcher
fi
will have less con
dence when drawing inferences about the population. The
main advantages are that non-probability sampling techniques are generally
easier to administer, and may provide valid insights.
There are three major forms of non-probability sampling used by business
researchers:
•
Convenience sampling.
Select whatever units are conveniently avail-
able. In our market research example, the researcher might simply interview those students s/he knows personally or their friends.
•
Judgement (purposive) sampling.
Here the researcher selects units sub-
jectively in an attempt to obtain a sample that appears in her/his judgement to be representative of the population. Thus the researcher might
try to ensure that the sample included a mix of male/female and undergraduate/postgraduate students.
•
Quota sampling.
fl
that re
The main aim of quota sampling is to select a sample
ects the main characteristics of the population. Our researcher
might well have some knowledge about the gender composition to ensure
a proper representation.
9.5 Non-Probability Sampling
EXAMPLE 9.3
Quota sampling is widely used by political polling organizations (e.g. Gallup,
Ipsos MORI, YouGov). The first step is to identify the key characteristics of
the population that might have systematic influences on voting behaviour:
say gender (male; female); age (under 20; 20–39; 40–59; 60 and over); and
annual income level (under £25K; £25–74K; over £75K).
Suppose that it is known that the population is distributed as follows:
Gender
Male
Female
45%
55%
Age (years)
< 20
20
–
39
40
–
59
> 60
Annual income (£)
20%
40%
30%
10%
<
£25K
£25
>
–
74K
£75K
40%
50%
10%
The quotas for each age-gender-income combination could then be
calculated by cross-multiplying the appropriate percentages in the population distribution. For example, 20% of the population is aged under
20 years; 45% is male; and 40% has annual income less that £25K. Hence
our sample should include a quota of 3.6% (= 20%Â 45% Â 40%) of
males under the age of 20 years, with annual incomes less than £25K. The
full quota sample would then be constructed as follows:
Age (years)
Gender
Annual income (£)
< £25K
< 20
Male
Female
20
–
39
Male
Female
40
–
59
Male
Female
> 60
Male
Female
Totals
3.6%
4.4%
7.2%
8.8%
5.4%
6.6%
1.8%
2.2%
40%
£25
–
74K
4.5%
5.5%
9.0%
11.0%
6.75%
8.25%
2.25%
2.75%
50%
Totals
> £75K
0.9%
1.1%
1.8%
2.2%
1.35%
1.65%
0.45%
0.55%
10%
20%
40%
30%
10%
167
168
Data Collection for Quantitative Research
EXAMPLE 9.3
Thus
if
the
(CONT.)
polling
organization
wished
to
survey
a
sample
of
1 000
individuals, it would interview the following:
Age (years)
Gender
Annual income (£)
<
< 20
£25K
£25 – 74K
Totals
>
£75K
Male
36
45
9
Female
44
55
11
Male
72
90
18
Female
88
110
22
Male
54
68
Female
66
83
Male
18
23
Female
22
27
400
500
200
20
–
39
400
40
–
59
1
1
14
300
> 60
Totals
1.
The exact
figures
1
1
1
1
16
1
5
1
100
5
100
1 000
have been rounded to an appropriate integer.
The polling organization would thus need to interview 36 males under the
age of 20 years, with annual incomes less than £25K; 44 females under the
age of 20 years, with annual incomes less than £25K; and so on. The sample
would be ‘roughly’ representative of the population as long as the categories
were independent, and there were no other systematic factors confounding
the results. The main advantage is that the organization does not need a full
sampling frame, just knowledge of the key characteristics of the population
9.6
Sample Size
An important and frequently asked question is: what sample size is needed?
Beware of simplistic answers such as that a sample size of 30 is satisfactory,
or that the sample should be greater than a given proportion (e.g. 5%) of the
population. A more helpful answer is that it depends upon five main
considerations:
How do you plan to analyse the data? If your intention is just to use the
sample mean as an estimate of the population mean (see Chapter 10),
1.
9.7 Survey Research
2.
3.
4.
5.
then a relatively small sample should suffice. But if you want to estimate
the parameters of a multiple regression model with several explanatory
variables (see Chapter 11), then a much larger sample will be required to
generate enough degrees of freedom.
What level of
do you require in your estimates? If greater
precision is required (e.g. Æ2% of the true population mean rather than
Æ4%), then a larger sample size will be needed. Furthermore, a doubling
of the required level of precision (as above) will require a quadrupling of
the required sample size.
What is the
in the values of the sample data (as
measured by the standard deviation – see Chapter 10)? If the values are
expected to be similar, then a relatively small sample will suffice. But the
greater the variability, the larger the sample size that will be required for
any given level of precision.
All samples involve making estimates of the population parameters and
there is always a risk that the sample was unrepresentative and hence the
sample estimate would not be accurate. How much confidence do you
require in your prediction? The greater the confidence, the greater the
required sample size.
What is your budget and timescale? The involved in terms of time,
labour, and other resources typically rise with sample size. Generally
speaking, a larger sample is preferable to a smaller sample in statistical
analysis, but larger samples are more expensive and slow or difficult to
collate, particularly if primary data are to be collected. Sometimes it may
be necessary to forego extensive data collection and settle for restricted
but more timely data collation and analysis.
9.7
precision
expected variability
costs
Survey Research
Survey research refers to the collection of primary data using questionnaires
and/or semi-structured interviews.3 Survey research is an effective tool for
obtaining opinions, attitudes, and descriptions as well as for capturing
cause-and-effect relationships. Sample surveys are popular in business studies research, and may be analytic or descriptive– see Figure 9.1.
According to Simons (1987), analytic surveys may be used to test a theory
by taking the logic into thefield, for example to understand the relationship
3
Semi-structured interviews typically involve a list of pre-determined questions, but the
researcher is also able to digress when further clarification is required or promising issues are
raised.
169
170
Data Collection for Quantitative Research
Conceptualize and structure
the research problem
1. Consider the aims of the research
2. Review the current state of knowledge
3. Assess the various resources available
Analytic survey?
Descriptive survey?
Identify the phenomena
whose variance you
wish to describe
Identify the independent,
dependent, and extraneous
variables
Determine the sampling strategy by defining
the research population and designing a means
of accessing a representative (random) sample
Are the data to be collected through one approach
to respondents? Or does the nature of the research
problem require the repeated contact of a single
sample or several equivalent samples?
Interviewer-administered
questionnaire/schedule
Respondent-completed/
postally administered
questionnaire
Figure 9.1 Planning a survey
Source :
–
Gill and Johnson (2002: 76 7). Copyright Sage Publication Ltd. Reproduced by permission of Sage
Publications Ltd.
between accounting control systems and business performance. In this type
of survey, we have to put the emphasis on specifying the dependent, independent, and control variables. We should also give due attention to and
bene
fit
from existing literature, theory, and research while conceptualizing
and structuring our research. The review of literature is therefore of the
utmost importance. In analytical surveys, dependent, independent, and control variables are controlled through statistical techniques such as multiple
regression (see Chapter 11). The questions and variables included in such a
survey thus need careful conceptualization and measurement scales.
In contrast, descriptive surveys are concerned with identifying the phenomena whose variance we wish to describe. The survey is concerned with
fi
fixed
particular characteristics of a speci c population or subjects, either at a
point in time or at varying times for comparative purposes. Here the focus
is
more
on
a
representative
sample
of
the
relevant
population
than
on
9.7 Survey Research
the analytical design, as we are concerned principally with accuracy of the
findings
and whether they can be generalized. Even in these surveys, a
review of earlier research and literature is important to determine what kind
of questions are to be included in the questionnaire. In business studies,
descriptive surveys are often used to obtain consumer attitudes towards a
certain product and to ascertain views and opinions of employees in an
organization (Reeves and Harper, 1981). These surveys are often used to
understand
the
behaviour
of
employees
with
regard
to
motivation,
job
satisfaction, and grievances.
9.7.1
Survey Administration
One key issue for the researcher is how the survey will be administered. There
are four main methods for obtaining survey data, each of which has advantages and disadvantages:
•
mail questionnaires;
•
email or website questionnaires;
•
personal interviews;
•
telephone/video interviews.
The main advantages of mail questionnaires are that costs are low, espe-
cially
when
the
sample
respondents
are
geographically
dispersed.
The
respondents also enjoy a high level of anonymity and have time to give
considered responses to the questions. The respondents
’ answers will not be
fluenced
in
by the characteristics or technique of the interviewer. The main
disadvantages are that mail questionnaires require simple, easily understood
questions and instructions, and the researcher does not have the opportunity
to clarify answers or request additional information. Response rates are often
low, and the researcher often does not know who actually completes the
questionnaire.
The main advantages of email or website questionnaires are similar to
fi
those of mail questionnaires, with the added bene t that answers may be
coded automatically. The main disadvantages are also similar, with response
rates often even lower. Some questionnaires are rejected as spam, while
others are easily deleted. Respondents must be computer literate while the
researcher must know the email addresses of potential respondents in order
to distribute the
questionnaires. Anonymity may be compromised if
the
completed questionnaires reveal information about the respondents
’ email
addresses. There are many websites on the Internet (e.g. SurveyMonkey)
which you can use to disseminate surveys.
Semi-structured personal interviews typically require answers to questions
as might be found on a questionnaire. The main advantages of such personal
171
172
Data Collection for Quantitative Research
interviews are that they allow greater
flexibility
in the survey process, with
the researcher able to react to answers and to collect additional detail from
respondents, including spontaneous reactions and tangential or background
information. Once agreed, response rates tend to be high, as is the degree
to
which
researcher
the
questionnaire
also
has
control
is
completed
over
who
with
answers
usable
the
information.
questions,
where
The
the
interview is conducted, and the order in which questions are answered.
The main disadvantages are the higher cost, especially when the respondents
are geographically dispersed. Anonymity is also compromised and respondents may not answer the questions honestly, while they may also be affected
by the interviewer’s personal characteristics or technique.
Telephone/video
interviews
tend
to
be
more
structured
than
personal
interviews, with less opportunity for collecting supplementary information.
Costs
are
dispersed
much
lower,
respondents
and
and
respondents
hence
can
undertake
easily
many
reach
geographically
interviews
in
short
a
period of time. The main disadvantages are the lack of personal contact
and a reluctance by many respondents to discuss sensitive issues.
9.7.2
Questionnaire Design
The main objective of a questionnaire survey (whether administered by mail
or in person) is to obtain data that enables the researcher to provide answers
to the research questions posed in her/his project. This objective highlights
the need for the researcher to think carefully about the questions to be
included (content, format, and sequence), about the wording of the questions,
and about how the data will be analysed
before
the survey is administered.
A common mistake by inexperienced researchers is to administer a survey,
and then seek the help of a statistician to analyse the data.
Most questionnaires include a mixture of open and closed questions. Open
questions allow the respondent to provide his/her own answers without any
prompting from the researcher – see Figure 9.2.
Please list up to four features you like about product X
1 …………………..
Figure 9.2
2
…………………..
3
…………………..
4
…………………..
An example of an open question
9.7 Survey Research
Open questions are useful in exploratory research, when the researcher
may not have a clear idea of possible responses, or when the researcher wants
fi
to elicit detailed responses. But it is often dif cult to code the responses to
such questions consistently, as each respondent may use different terminology. Hence open questions should be used sparingly.
In contrast, the general format of closed questions is for the researcher to
list all possible answers, and to instruct the respondent to choose from this
list. The main advantage of using closed questions is that the responses can
be easily coded, and their analysis is straightforward. The main disadvantage
fi
is that the researcher speci es the set of possible answers and may omit other
valid alternatives. Sometimes closed questions also include‘Don’t know’ or
‘No comment’ options to provide an escape route for respondents who wish
to avoid providing a response, either because they do not know the answer or
because they do not want to answer the question because of its sensitive
nature. If no such escape route is provided, then the respondents are obliged
to choose from the list of possible answers and this may bias the results.
Closed questions may have several formats:
•
Category questions. The respondent is invited to select one response from
a
fi
nite
set
of
mutually
exclusive
categories – see
Figure
9.3
for
an
example. The resultant data are measured on a nominal scale.
•
Multiple-choice
questions.
The
respondent
is
invited
to
select
one
or
more responses from a predetermined set of possibilities– see Figure 9.4
What is your age?
(tick one category only)
Under 21
21 – 30
31 – 40
41 – 50
51 – 60
61 or over
Figure 9.3
An example of a category question
173
174
Data Collection for Quantitative Research
Which of the following features attracted you to product X?
(tick as many features as apply)
Low price
Functionality
Appearance
Value for money
After-sales service
Figure 9.4 An example of a multiple-choice question
Rank the following features of product X using the values 1 to 5 where:
1
=
the feature you like the most
5
=
the feature you like the least
Low price
Functionality
Appearance
Value for money
After-sales service
Figure 9.5 An example of a ranking question
for an example. Note that respondents simply list the attractive features
but do not put them in order of importance.
•
Ranking questions. The respondent is invited to list the possible options
in order of preference
– see Figure 9.5 for an example. The resultant
are measured on an ordinal scale.
data
9.7 Survey Research
Strongly
Disagree Neither Agree
Disagree
nor Disagree
Agree
Strongly
Agree
Product X provides
good value for money
Figure 9.6
•
An example of a rating question
Rating questions. The respondent is invited to express her/his opinion
about how much s/he agrees or disagrees with a particular statement– see
fi
Figure 9.6 for an example. The scale typically involves a nite number of
pre-coded levels, each with its own label, and where the middle level is
‘neutral’. In Figure 9.6, there are
five
levels, which are labelled ‘strongly
disagree ’, ‘disagree’, ‘neither agree nor disagree’, ‘agree’, and ‘strongly
disagree ’, hence we have a 5-point scale. The responses can then be
fied with a value of 1 allocated to the lowest level, and 5 allocated
quanti
to the highest level. Sometimes 7-point scales are used, possibly adding
additional levels with labels such as ‘very strongly disagree’ and ‘very
4
strongly agree’. Such rating scales are often referred to as Likert scales,
although there are other types of rating scale (e.g. semantic differential
rating scales). The data obtained from such Likert scales are ordinal,
though researchers often treat them as though they were measured on
interval scales. It is more acceptable to assume that the data are continu5
ous when the responses to several questionnaire items are combined.
9.7.3
Guidelines for Constructing Questionnaires
The precise wording of the questions is crucial for achieving the maximum
validity of the data collected through questionnaire surveys. This is illustrated though the following (possibly apocryphal) story provided by Sudman
and Bradburn (1989: 8):
Two priests, a Dominican and a Jesuit, are discussing whether it is a sin to smoke
and pray at the same time. After failing to reach a conclusion, each goes off to
4
5
The scale is named after the Swedish psychologist, Rensis Likert. Sometimes the scales are
referred to as Likert-type scales.
There is considerable debate as to whether items measured on a Likert scale should be
considered as ordinal variables (Wu and Leung, 2017). Strictly speaking, single items should
be treated as ordinal variables. Often, however, several items are combined and averaged (see
Section 6.3) to form a multi-item scale which allows for non-integer scores, and many
researchers then treat such scales as if they were continuous variables. This is acceptable in
student dissertations, but may not
find favour with the reviewers at top-quality journals.
175
176
Data Collection for Quantitative Research
consult his respective superior. The next week they meet again. The Dominican
says, ‘ Well, what did your superior say?’ The Jesuit responds, ‘ He said it was all
right’ . ‘That ’s funny’, the Dominican replies, ‘my superior said it was a sin’.
Jesuit: ‘What did you ask him?’ Reply: ‘I asked him if it was all right to smoke
while praying’. ‘Oh ’ , said the Jesuit, ‘ I asked my superior if it was all right to pray
while smoking’.
Here we provide some guidelines for the construction of questionnaires.
1.
The questions must be asked in very
simple and concise language.
One
should consider the respondents’ background when it comes to educational
level,
cultural
background,
knowledge,
and
acquaintance
with the subject matter. The questions should then be adjusted and
adapted
to
the
above-mentioned
characteristics
of
the
respondents.
Not only the questions but also the possible answers provided (in the
case of structured questionnaires) should use clear and unambiguous
language.
2.
We should be rather conservative as to the level of knowledge, education, etc. necessary for the respondents to answer the questionnaire. We
should not put
unrealistic demands
on the respondents’ know-how,
memory, and willingness to respond.
3.
dimension or aspect. If we mix
Each question should deal with only one
fi
up several dimensions or aspects in one question, it will be dif cult for
respondents to explain their behaviour or to answer yes or no. In other
words, one cannot ask one question about more than one variable or
dimension of the study. Each variable and dimension should be covered
by a separate question. It is quite common to ask several questions on
one variable or to have each question cover different dimensions of the
subject matter. In other words, avoid ‘double-barrelled’ questions, such
as: ‘What is the turnover of your company and how much of that comes
from export?’ or ‘What is your educational background and how long
have you been working in this position?’ One way to do this is not to
use ‘and ’ in any question.
4.
The questions should be formulated in such a way that there is no
escape route
in the questions. We should not offer an option such as
‘Don’t know’.
5.
Moreover, the questions should be
specific and not too general in nature
so that the respondent does not give several answers. If we must have
general questions, we should check the understanding of these questions
through
another
question.
The
more
fi
speci c
and
concise
the
questions are, the easier it is for us to interpret the questions and
answers in different categories and then draw conclusions.
9.7 Survey Research
6.
The questions should not be of a suggestive nature, directing the
respondent towards an answer or a specific opinion. For example, we
cannot ask a high-tech or a pharmaceutical company the following
question: Do you consider R&D important for your type of company?
Questions should be formulated in a polite and soft language. They
should not irritate, offend, or provoke the respondents. It is very
important to place the sensitive questions, if any, at the right place
in the questionnaire, so that the respondents can understand why
that particular question is being asked. But, in any case, there must
be a logical and systematic sequence of questions to avoid misunderstandings and to ensure a high response rate. We must keep in
mind that the respondents are doing us a favour by answering the
questions.
The language and words used in the questions should bestraightforward and should not have double or hidden meanings, otherwise
respondents may answer the questions with a different understanding
and thereby contribute negatively to the conclusions of the study.
Another risk is that the respondent will leave the question unanswered
if s/he is not sure of the questions meaning. For example, it will be
quite difficult for a respondent to understand what you want to know
by the following question: What type of structure does your company
have for export activities? or What are the major barriers to entry
faced by your company in international markets? If one is using
complex wording or concepts, a note describing or clarifying the exact
meaning of the text should be added to the question. Several authors
(e.g. Cannell et al., 1981) have advocated that an explanation or argument as to why that particular question is being asked gives a better
response rate.
Questions should be placed in a logical order. The easy-to-answer
questions and positive types of questions should be placedfirst. If we
place the complicated or difficult questions first for example questions
for which the respondents need to consult books or managers/colleagues they might get the impression that all the questions are of
that nature and thus refrain from responding at all. The same is true for
sensitive questions. There should also be a logical order from general to
specific questions.
The layout of the questionnaire is also important. It should lookneat
and tidy as this can influence respondents willingness to answer. The
questionnaire should be formatted and printed in a way that does not
look frightening due to its length or complexity to understand and
answer.
‘
7.
8.
’
’
‘
’
‘
’
9.
–
–
10.
’
177
178
Data Collection for Quantitative Research
Last but not least, we should go through the questionnaire critically or
11.
have a friend, colleague, or adviser do this and give comments. The best
five
way to handle this is to do a pre-test on three to
real
firms
or
respondents. In such a pre-test we should check whether the above-
fi
mentioned issues such as understanding, the level of dif culty, the
willingness to answer sensitive questions, and the time it takes to
answer the questionnaire are as we wish.
9.7.4 Improving Response Rates
Poor
response
rates are
commonplace,
particularly
with
questionnaire
surveys undertaken by mail, email, or via websites. There are various practices that the researcher can try in order to improve response rates:
•
Write a cover letter that introduces the researcher and her/his status and
fi
af liation. Explain succinctly the purpose and relevance of the research
fi
first thing that the potential respondents will read, and it
project, and guarantee the con dentiality of any responses. This cover
letter will be the
is vital that its content and tone encourages the respondents to look at
the questionnaire itself.
•
Consider carefully the format of the questionnaire. Limit yourself to only
those questions that are necessary for the research project, and keep the
questionnaire as short as possible. Think about the layout and appearance of the questionnaire, and even the colour of the paper. Perhaps use
images and different colours to make the questionnaire more attractive.
Provide clear instructions to respondents about how to answer each
question.
•
If possible, notify possible respondents in advance in person or by
telephone so that they are more likely to participate when they receive
the survey instrument.
•
Sometimes a sponsor may be useful. This might be a government department
or
an
industry
organization
that
is
willing
to
encourage
its
members to participate. For students, it may be helpful if their supervisor
is willing to supply a letter of support. In other cases, the use of a sponsor
may be counterproductive. Suppose you are trying to elicit views on
fi
corruption, then it would probably be unwise to have any of cial government organization as a sponsor!
•
fi
Offer some form of inducement to participate. This might be nancial (if
funds are available), a promise to provide a summary report of the
research, or simply a reiteration of the importance of the project.
•
Provide
to
clear
minimize
instructions
the
cost
and
for
the
hassle
return
for
of
the
potential
survey
instrument
respondents.
This
9.8 Experimental Studies
might involve a stamped addressed envelope for the return of mail
questionnaires,
or
dedicated
email
addresses/websites
for
online
questionnaires.
Experimental Studies
9.8
Survey research is an important source of primary data based on events and
actors in the ‘real world’. In contrast, experimental studies allow the collection
of
methods
primary
have
data in
long
more
been
fi
arti cial
popular
in
controlled
settings.
psychology
and,
Experimental
more
recently,
in
economics (Bandiera et al., 2016), while applications in strategy and business
studies are becoming more frequent (Chatterji et al., 2016; Croson et al.,
2007; Di Stefano and Gutierrez, 2018; Kraus et al., 2016; van Witteloostuijn,
2015;
Zellmer-Bruhn
et
field
experiments and/or
al.,
2016).
Experiments
may
involve
laboratory
fi
experiments. The former refers to any arti cial
experimental setting, which might involve alternative scenarios, vignettes
(Aguinis and Bradley, 2014), or policy capturing (Connelly et al., 2016), and
fi
which takes place in a computer laboratory or classroom. In contrast, eld
experiments
though
the
involve
novel
situations
laboratory and
field
are
strategies
still
being
contrived,
piloted
hence
the
in
actual
situations,
distinction
between
experiments is often not clearcut (Harrison and List,
2004).
The
advantages
of
controlled
experiments
over
observational
research
based on real-world data are several (Croson et al., 2007). First, the experiments can be designed to generate data that capture the relevant theoretical
constructs, while real-world data tends to be noisy in that often only rough
proxies are available. Second, the experiments can be designed to isolate the
key mechanisms that the researcher wishes to study, and to remove the
confounding effects that are inevitably present with real-world data. Third,
additional controls may be introduced to take into account factors that may
interact with the main effects. Fourth, experimental methods are able to
establish causal relationships more conclusively by comparing effects with
and
without
just
provides
a
particular
evidence
of
stimulus,
whereas
statistical
observational
association.
Fifth,
research
often
experiments
may
be replicated: other researchers can undertake the same experiment and
verify the
findings
independently, or the same experiment may be carried
out in a different setting or with different subjects. Taken together, these
advantages mean that experimental methods can be very useful in testing
the
predictions
predictions.
of
theories,
and
for
comparing
competing
theories
and
179
180
Data Collection for Quantitative Research
first is that
There are two disadvantages with controlled experiments. The
the experimental context is necessarily abstract and unrealistic in that many
fl
potential real-world in uences are not considered. This gives rise to claims
that controlled experiments lack external validity (Falk and Heckman, 2009).
Advocates of experimental methods acknowledge this criticism, but suggest
not only that experimental methods can be useful complements to observational research but also that experimental and observational studies often
show high correlations between estimated effect sizes. As Croson et al. (2007:
176) suggest, ‘when research methods that emphasize internal validity and
those that emphasize external validity provide a consistent set of results, it
increases not only the reliability of the outcome, but also leads to a stronger
set of conclusions.’ The second disadvantage is more practical. Often it is
dif
ficult
to recruit actual managers as subjects, and researchers instead use
MBA or undergraduate students whose experience of, and expertise in, work
settings are typically less than those of managers. This raises issues about
how subjects are selected, and whether they are representative.
Exercises
Further Reading
.................................................................
Brace, I. (2018) Questionnaire Design: How to Plan, Structure and Write Survey
Material for Effective Market Research, 4th edn., London: Kogan Page.
Chatterji, A., Findley, M., Jensen, N., Meier, S., and Nielson, D. (2016)‘ Field
experiments in strategy research’ , Strategic Management Journal, 37(1): 116–32.
Cooper, D.R. and Schindler, P.S. (2013)Business Research Methods, 12th edn., Boston
MA: McGraw-Hill.
Sudman, S. and Bradburn, N. (1982)Asking Questions: A Practical Guide to
Questionnaire Design, San Francisco, CA: Jossey-Bass.
Questions
.................................................................
1.
Explain the difference between secondary and primary data.
2.
What are the advantages and disadvantages of secondary data?
3.
Is all secondary data on a particular topic suitable for a research project
on that topic?
4.
‘First
explore secondary data, only when secondary data sources are
exhausted, then you can plan to collect primary data.
’ Comment.
5.
What do you understand by the terms ‘population’ and ‘sample’?
6.
What are the main considerations involved in choosing an appropriate
sample size for survey research?
7.
Are the data obtained from Likert rating scales ordinal or interval?
8.
What are the advantages of controlled experiments over observational
research based on real-world data? What are the disadvantages of controlled experiments?
Exercises
.................................................................
1.
Suppose you wish to undertake a questionnaire survey of manufacturing
firms
to ascertain the extent of their adoption of 3D printing technologies. Draft a
fi
cover letter to accompany the questionnaire to be sent to the CEOs of the rms.
2.
Design a questionnaire survey to assess customer satisfaction with the
service provided in a local restaurant. Include items related to different
aspects of the service provided, and use Likert scales to provide the
assessments. Include some relevant category questions.
3.
firms may have different motivations (i.e.
In Section 9.2.1, we suggested that
fi
market-seeking, ef ciency-seeking, asset-seeking, natural resource-seeking)
for investing in foreign countries. Suggest some questions that you could
firms to discover their motivations? [Note: you
pose to the CEOs of investing
should not assume that the CEOs will understand the terms‘market-seeking’,
ficiency-seeking ,
‘ef
’ ‘asset-seeking’, and ‘natural resource-seeking’.]
181
10
Description and Preliminary Analysis
of Quantitative Data
The greatest moments are those when you see the result pop up in a graph or in your
statistics analysis: that moment you realise you know something no one else does
and you get the pleasure of thinking about how to tell them.
Emily Oster
The appropriate method of data analysis depends upon a variety of factors
fi
that have been speci ed in the research question and as part of the research
design (see Chapter 5). Indeed, it cannot be stressed too strongly that the
before
researcher should consider how s/he intends to analyse the data
makes
s/he
final decisions about the research design. All too often (especially with
inexperienced researchers), research strategies are decided, data are collected,
and only then is attention given to how the data are to be analysed
– only to
find that the requisite data have not been collected to provide answers to the
research questions. As the renowned scientist and statistician Sir Ronald
Fisher pithily remarked, ‘To consult the statistician after an experiment is
finished
is often merely to ask him to conduct a post mortem examination.
He can perhaps say what the experiment died of.
’
One key issue is whether the data are qualitative or quantitative, and this
depends upon the underlying research approach. If the research approach is
deductive, then most of the data are likely to be expressed as numbers, and
the
key
issue
will
be
selecting
the
appropriate
statistical
techniques
for
describing and analysing the data. In this chapter, we will concentrate on
techniques for describing quantitative data and for providing simple preliminary analyses. Much statistical analysis involves explaining and predicting
relationships
between
variables:
a
comprehensive
review
of
all
possible
techniques is beyond the scope of this book, so we will concentrate on
providing an intuitive primer on the use of regression analysis in Chapter 11.
In Chapter 12, we will provide introductions to factor analysis, structural
equation modelling, and event study analysis. If the approach is inductive,
then most of the data are likely to be qualitative (text, interview transcripts,
pictures, etc.) in nature and the purpose of the analysis will be to bring some
order, structure, and meaning to this mass of data.
10.1 How to Describe the Data
10.1
How to Describe the Data
10.1.1 Bar Charts, Histograms, and Scatter Plots
fi
Often data are collated in a spreadsheet, and it can be dif cult to identify
patterns in the data or to compare the values of two or more variables.
Consider the dataset in Appendix A, which contains data for a sample of
100 imaginary manufacturing
(years since establishment),
firms
CAPS
on
SIZE
(number of employees),
AGE
(capabilities, measured on a subjective
REGION (north, south, east, or west), SECTOR (electronic, automobile, or textile), and export intensity (export sales as percentage of total
sales). REGION and SECTOR are both nominal (categorical) variables while
SIZE, AGE, CAPS, and export intensity are all continuous variables. Two
values for export intensity are provided for each fi rm: one (EXP18) for
2018 and a second (EXP19) for 2019. In addition, a panel of industry experts
have also ranked the 100 firms according to their growth potential (GROW )
over the next fi ve years: GROW is an ordinal variable with the rank 1 indi–
scale: 1 7),
cating the
the
firm judged to have the most potential, and the rank 100 indicating
firm judged to have the least potential.
One way of visualizing the data for the two categorical variables is to
present the data in the form of bar charts, where the categories are presented
on the x-axis of the chart while some metric for each category is presented
1
on the y-axis.
Suppose we are interested in how
firm size varies across the
four regions and across the three sectors. The relative height of each bar
– see
corresponds to the mean value of the chosen variable in each category
Figure 10.1 for the size distribution of the
for the size distribution of the
mean size of
firms
firms
by region, and Figure 10.2
by sector. Figure 10.1 shows that the
firms is greatest in the south region, while the mean size of the
firms in the east region is less than 100. Figure 10.2 shows that the automobile fi rms are larger, on average, than the electronicfi rms, which are in turn
larger than the textile fi rms.
A histogram provides a graphical representation of the distribution of the
sample observations for a single continuous variable. Several discrete inter-
fi
vals are speci ed on the x-axis for the values of the variable, while the
frequencies with which the values fall within these intervals are measured on
the y-axis. Vertical bars then depict how many sample observations fall
within each interval. A histogram differs from a bar chart in that it presents
1
Alternatively, the categories may be presented on the y-axis with the metric on the x-axis.
See Example 10.1 for a sophisticated bar chart in this format. Pie charts are alternatives to
bar charts.
183
184
Description and Preliminary Analysis
150
EZIS naeM
100
50
0
NORTH
EAST
SOUTH
WEST
REGION
Figure 10.1
Distribution of firm sizes by region
150
EZIS naeM
100
50
0
ELECTRONIC
AUTOMOBILE
TEXTILE
SECTOR
Figure 10.2
Distribution of firm sizes by sector
EXAMPLE 10.1
In the United States, government shutdowns occur when the necessary
legislation to allocate funds for the operation of government agencies is
not enacted. This may be because Congress fails to pass the legislation or
because the President vetoes the legislation. In these circumstances, the
federal government is obliged to shut down the affected agencies and put
the employees on temporary unpaid leave.
Shutdowns
typically
occur
when
Congress
and
the
President
cannot
agree on a budget, but may also happen when one or the other is trying
10.1 How to Describe the Data
EXAMPLE 10.1
(CONT.)
to force through other political objectives. Murse (2019) reports that the
Republican majority in the House of Representatives triggered a 16-day
shutdown in 2013 in an unsuccessful attempt to force President Barack
Obama to repeal the Affordable Care Act. More recently, the Democratic
–
majority in Congress triggered a 34-day shutdown in 2018 19 by refusing
to approve funding for the plan of President Donald Trump for a border wall
along the US border with Mexico.
There
1976
have
–2018:
Jimmy
been
21
US
government
shutdowns
over
the
period
–
1 under President Gerald Ford (1974 77); 5 under President
Carter
–
(1977 81);
8
under
President
Ronald
Reagan
–
(1981 89);
1 under President George Bush (1989-93); 2 under President Bill Clinton
(1993
–2001);
none under President George W. Bush; 1 under President
–
Barack Obama (2009 2017); and 3 to date under President Donald Trump
(2017
–).
The bar chart below depicts these shutdowns visually. The chart
might be further enhanced by highlighting the terms of the various presidents, and the periods when Republican (Democratic) presidents are confronted by Democratic (Republican) majorities in Congress.
US Government Shutdowns, 1976–2018
2018 Dec 22
2018 Jan 20
2018 Feb 9
2013 Oct 1
1995 Dec 16
1995 Nov 14
1990 Oct 5
1987 Dec 18
1986 Oct 16
1984 Oct 3
1984 Sept 30
1983 Nov 10
1982 Dec 17
1982 Sept 30
1981 Nov 20
1979 Sept 30
1978 Sept 30
1977 Nov 30
1977 Oct 31
1977 Sept 30
1976 Sept 30
1
16
nwodtuhs fo etad tratS
2
5
3
3
2
11
8
8
10
0
21
5
3
1
1
1
34
3
5
10
18
12
15
20
Days
25
30
Source : Murse, T. (2019) ‘All 21 government shutdowns in US history’, ThoughtCo, 25 May
2019. Available at: thoughtco.com/government-shutdown-history-3368274.
35
40
185
186
Description and Preliminary Analysis
Symmetric Distribution
Skewed Distribution
Mean
Median
Mode
Figure 10.3
Mode
Median
Mean
Symmetric and skewed distributions
the distribution of data for a single continuous variable, while the bar chart
compares metrics for different categorical variables.
A histogram plots the observations on a single variable from a (small)
finite
sample, and may provide a rough idea of the underlying probability
fi
distribution of the variable in a large (possibly in nite) population. Different
2
variables exhibit different probability distributions.
For example, the roll
of a dice gives rise to a uniform distribution where each possible outcome
(1, 2, . . . 6) has an equal probability (i.e. 1/6) of occurrence. In business
–
applications, we often encounter variables that follow a normal distribution
see Figure 10.3. Such variables exhibit a symmetrical bell-shaped distribution around a mid-point which corresponds to the mean, median, and mode
of the variable (see Section 10.1.2). The distribution of some variables is not
symmetric but is skewed (to the right, as depicted in Figure 10.3, or to the
left): in such cases the mean is less than the median which is less than
the mode.
A scatter plot is a graph in which the values of two continuous variables
are measured along the two axes: each observation is plotted as a point
whose x-y coordinates correspond to the values of the two variables. Additional variables may also be added by highlighting the points using different
colours or indicators. Scatter plots are useful in identifying patterns in the
data, such as whether there may be a relationship (linear or non-linear)
between the variables. Figure 10.4 presents a scatter plot of the observations
on
firm size (SIZE) and export intensity (EXP18) from Appendix A. Although
there is considerable variability in the data, the plot suggests clearly that
export intensity tends to fall as
firm size increases.
Such charts and plots provide useful visual representations of the data, but
they are cumbersome and of little use in statistical analysis. We need to be
2
Common distributions include uniform, binomial, Bernoulli, normal, Poisson, and
exponential. For further details: http://blog.cloudera.com/blog/2015/12/commonprobability-distributions-the-data-scientists-crib-sheet/.
10.1 How to Describe the Data
90
80
)%( ytisnetnI tropxE
70
60
50
40
30
20
10
0
0
50
100
150
200
250
300
Firm Size (employees)
Figure 10.4
Scatter plot of export intensity against firm size
able to summarize the key characteristics of each variable in a small number
of summary statistics that measure central tendency (average), dispersion,
and skewness.
10.1.2
Measures of Central Tendency
Three measures of central tendency are commonly used in practice: mean,
n observations on variable X:
± Þ of
these observations may depicted as X , X , . . . X
, X . The mean value ðX
median, and mode. Suppose we have a sample of
1
2
n-1
n
a variable is calculated by adding together all the observations in the sample,
XX
and then dividing by the total number of observations. In formal terms:
X± ¼
1
n
n
i¼1
i¼
1
n
ðX 1 þ X 2 þ . . . . . . . . . . . þ
.
X n±
1
þ XnÞ
(10.1)
where
Xi = value of observation i of the variable
n = number of observations in the sample
eg X
The horizontal bar over the top of a variableð : : ± Þ is used to denote the
mean value. The sigma symbol
Σ
is used as a shorthand way to denote a
n observations on the X variable from
i n shown at the
top of the sigma symbol). Any letter (herei, but t is often used for time-series
summation: here we are summing the
i
X1 ( = 1 shown at the bottom of the sigma symbol) to X
( =
n
data) can be used for the index, and the index can run from any integer to
any other integer to denote a sub-sample.
Mean
values
may
be
calculated
for
continuous
variables
but
not
for
categorical or ordinal variables. As shown in Table 10.1, the mean size of
187
188
Description and Preliminary Analysis
Table 10.1
Summary statistics for the sample of 100 manufacturingfirms
Standard
SIZE
AGE
CAPS
Region
Sector
Export intensity
Coef
ficient of
Mean
Median
deviation
variation
Skewness
114
31
4.3
n/a
n/a
38.0
111.5
29
4.35
n/a
n/a
37.75
59.0
16.1
1.5
n/a
n/a
13.8
0.52
0.52
0.35
n/a
n/a
0.36
0.458
0.335
±0.253
n/a
n/a
0.295
41.4
43.45
15.2
0.37
±0.041
n/a
50
n/a
n/a
n/a
(EXP18)
Export intensity
(EXP19)
GROW
Source: Appendix A
the 100 firms in the sample is 114 employees, the mean age is 31 years, mean
capabilities is 4.3, mean export intensity in 2018 was 38.0%, and mean
export intensity in 2019 was 41.4%.
The median value of a variable is determined by ranking all the observations from smallest to largest, and then selecting the value of the middleranking observation. Median values may be calculated for continuous
variables, and for variables measured using ordinal scales. As shown in
Table 10.1, the median size of the 100firms in the sample is 111.5 employees,
the median age is 29 years, median capabilities is 4.35, and the median
export intensity is 37.75% in 2018. If the variables are normally distributed,
then the median values should be similar to the mean values (Figure 10.3).
The median value of the
variable is 50. Finally, the mode value of a
variable is simply defined as the value that occurs most frequently in the
sample.
GROW
10.1.3
Measures of Dispersion
Two samples may share the same mean value, yet the distributions of the
data may be completely different. In Table 10.2, we reproduce the data
on
for the first 10 firms (sub-sample A) and the last 10 firms
(sub-sample B) in our sample of 100 manufacturing firms. Both samples
have the same mean value (120 employees), but the size distributions of the
two samples is obviously quite different: the 10firms in sample B are all of
similar size, while the 10 firms in sample A have very different sizes.
SIZE
10.1 How to Describe the Data
Table 10.2 Summary statistics for two sub-samples of manufacturing
Sample A
Mean
firms
Sample B
Deviation
Sum
189
Deviation
from the
Squared
from the
Squared
Size
mean
deviation
Size
mean
deviation
243
+123
15 129
122
+2
4
67
±53
2 809
105
±15
225
79
±41
1 681
118
±2
4
134
+14
196
126
+6
36
207
+87
7 569
131
+11
121
114
±6
36
115
±5
25
33
± 87
7 569
117
±3
9
65
± 55
3 025
122
+2
4
163
+43
1 849
128
+8
64
95
± 25
625
116
±4
16
0
40 688
0
508
120
120
Variance
4 068.8
50.8
Standard
63.8
7.1
0.53
0.059
deviation
Coef
fi
cient
of variation
Source: Appendix A
3
One measure of dispersion is the variance. This is derived by taking the
deviations of each observation in the sample from the mean, then squaring
Var) is
all the deviations, and summing the squared deviations. The variance (
then calculated by dividing this sum by the total number of observations in
the sample:
Varð XÞ ¼
3
1
n
XX
n
i¼1
ð i±
X± Þ
2
(10.2)
Many measures of dispersion exist, both for continuous and for categorical variables. See
Weisberg (1992) and Frankfort-Nachmias and Nachmias (1996) for excellent discussions.
Here we will focus on measures of dispersion based on the mean.
190
Description and Preliminary Analysis
How may the variance be interpreted? If all the values in the sample are
similar (as in the case of sample B), then each value is close to the mean,
hence
all
the
deviations
are
relatively small number (here
small
VarB
and
the
variance
in
the
sample
is
a
= 50.8). If, in contrast, the values in the
sample are quite dissimilar (as in the case of sample A), then few values
are close to the mean, most deviations are quite large, and the variance in the
sample is a relatively large number (here
VarA
= 4068.8).
The variance measures the total variability in the sample and is expressed
not in the units of the original variable but in squared units. A second
s
measure of dispersion is the standard deviation ( ) which is calculated by
taking the square root of the variance, and is expressed in the units of the
pffiffiffiffiffiffiffiffiffiffiffiffiffiffi
original variable:
sð XÞ ¼ Var ðXÞ
If
two
variables
have
very
different
means,
(10.3)
then
a
comparison
of
their
standard deviations is not meaningful: a standard deviation of 3 is quite
substantial if the mean of the variable is only 5, but is almost negligible if the
mean is 105.
A
final
fi
CV
measure of dispersion is thus the coef cient of variation (
),
which is calculated by dividing the standard deviation by the mean of the
variable:
CV ð XÞ ¼ sðX±XÞ
(10.4)
The three measures of dispersion are clearly closely related and, notwithstanding the differences in their calculation, all may be interpreted in similar
ways: large values indicate a high degree of variability; small values indicate
a low degree of variability.
10.1.4
Measures of Skewness
Normal distributions are assumed in many (parametric) statistical tests, but
many variables exhibit other distributions which may invalidate these tests
and may require the use of non-parametric tests. Tests for normality are
shown in most basic statistical textbooks, but will not be covered here.
Skewness refers to the asymmetry of the distribution of a variable about its
mean. One simple measure which might indicate a non-normal distribution
fi
SK
is the Fisher-Pearson coef cient of skewness (
4
4
), calculated as follows:
Some computer packages use a variant of this formula which takes sample size into account.
10.2 How to Analyse the Data
Table 10.3
Decision criterion for the skewness coefficient
Data are
Data are
Data are fairly
Data are
Data are
highly
moderately
symmetric
moderately
highly
skewed
skewed
skewed
skewed
±
1.0 – 0.5 + 0.5 + 1.0
SK ðXÞ ¼
Pni
X i ± X± Þ =n
SDð XÞ
¼1
3
ð
(10.5)
3
fi
A normally distributed variable will have a skewness coef cient close to
fi
zero. A large value of the coef cient re
flects
a mean that is quite different
from the median, and is evidence of skewness: a large negative value of the
ficient
coef
indicates the distribution is skewed to the left, while a large
fi
positive value of the coef cient indicates that the distribution is skewed to
the right. Some rough rules of thumb for deciding whether the skewness
ficient is large or small are shown in Table 10.3.
It is apparent from Table 10.1 that all five continuous
coef
variables
fi
in
Appendix A have data that may be described as fairly symmetric as all ve
fi
skewness coef cients fall within the range [± 0.5, +0.5]. However, these
ficients do not confirm normality.
coef
10.2
How to Analyse the Data
10.2.1
Hypothesis Testing
In
most
quantitative
research,
the
intention
is
to
move
beyond
simple
description of the sample data and to test various hypotheses about the
variables and the relationships between variables. At this point, it is important
to reiterate
the
distinction
between a
sample and
a population (see
fi
Section 9.3). A population is the entirety of a de ned set of
firms,
people,
countries etc, and may be described by reference to various parameters. In
principle, the researcher could survey the entire population but, if the population is large, this might involve a considerable expenditure of time, effort,
5
and money.
So the researcher might instead survey a sample, or subset of
the population, and generate sample statistics as described in Section 10.1.
Such sample statistics might then be used to make inferences about the
unknown population parameters. For example, the data in Appendix A relate
5
fi
Some populations are in nite, in which case a full survey is not feasible.
191
192
Description and Preliminary Analysis
firms, and this sample may well be drawn
from a much larger population of, say, 10 000+fi rms in the economy.
to a sample of 100 manufacturing
We can use the sample statistics to make estimates of the population
estimates .
parameters but it is important to emphasize that the statistics are
firms in our sample is 114 employees,
For example, the mean size of the 100
but this sample mean is only an estimate of the population mean size of the
firms. The sample mean will provide a good estimate of the popula-
10 000+
tion mean if the sample is representative of the population, but this requires
that
the
sampling
procedure
has been
done
in
such
a
way
as to
avoid
potential bias and to maximize precision (see Chapter 9). A larger sample
size will generally improve the precision of the estimate, but larger samples
are more costly to collect.
A hypothesis test involves making a statement
hypothesis (H0)
– about
–
referred to as the null
an unknown population parameter, and then seeing
whether the sample data are consistent with this null hypothesis or not. If
not, we reject the null hypothesis in favour of an alternative hypothesis (H
).
A
fi
For instance, we might be interested in comparing the mean sizes of rms in
two samples (see Sections 10.2.2. and 10.2.3 below) to see if they come from
populations of similar-sized
firm
firms: the null hypothesis is thus that the mean
sizes in the two populations are equal, but this will be rejected if the
mean
firm
sizes in the two samples are markedly different. All hypothesis
tests involve the same four basic steps even though, as we will see, the
formulae and other details vary according to the exact nature of the test.
These steps are as follows:
Step 1:
•
Specify the null and alternative hypotheses.
It is vital that an explicit statement is made of the null hypothesis (and
alternative hypothesis) otherwise there is often confusion about the
result of the test.
Step 2:
•
Step 3:
•
Calculate the test statistic.
The test statistic is calculated on the basis of appropriate sample data.
Find the critical value from tables.
The
sampling
distribution
of
the
test
statistic
theoretical distribution (e.g. normal, t, F,
χ
2
will
follow
a
given
), and all statistics text-
books provide tabulated values for the most common distributions.
•
This critical value will typically depend, among other things, upon
fi
(a) the degrees of freedom in the sample, and (b) the signi cance level
speci
•
fied by the researcher (see below).
The degrees of freedom depend in part upon the number of observations in the sample. More observations mean more degrees of freedom,
and more powerful hypothesis tests.
10.2 How to Analyse the Data
Table 10.4
Possible errors in hypothesis testing
Result of hypothesis test
Do not reject H0
State of nature
Reject H0
H0 is true
Correct
Type I error
H0 is false
Type II error
Correct
Step 4: Specify the test criterion.
•
The test criterion is a decision rule for rejecting or not rejecting the null
hypothesis, based upon a comparison of the calculated test statistic
and the critical value.
•
Explain the outcome of the hypothesis test in words.
fi
What is the signi cance level of a hypothesis test? In setting up the test,
the researcher hopes that the sample is representative of the population, and
that the test criterion provides a robust rule for deciding whether or not to
reject the null hypothesis. However, it is important to emphasize again that
there is always the possibility of drawing an ‘unrepresentative’ sample and
making a decision error when using sample data to test a hypothesis about an
(unknown) population parameter. Two types of error are possible:
1.
The researcher rejects the null hypothesis on the basis of the hypothesis
test, but the null hypothesis is in fact true. This is called a Type I error.
2.
The researcher does not reject the null hypothesis on the basis of the
hypothesis test, but the null hypothesis is in fact false. This is called a
Type II error.
The possible outcomes of the hypothesis test are summarized in Table 10.4:
the ‘state of nature’ refers to the true value of the unknown population
parameter. Example 10.2 illustrates the idea of Type I and Type II errors in
the context of a legal trial.
EXAMPLE 10.2
Suppose you (the reader) have been accused of a crime. Probably only you
know whether you are guilty of the crime or innocent. This is the‘state of
nature’ . Nevertheless, you have been arrested and brought to trial for the
crime. In most jurisdictions, the default position at the trial (the null hypoth-
fi
esis) is that the accused person is innocent, unless the evidence is suf cient
193
194
Description and Preliminary Analysis
EXAMPLE 10.2
(CONT.)
to convince the judge/jury that the accused is guilty (the alternative hypothesis). There are four possible outcomes to the trial:
Result of trial
Innocent
Guilty
State of nature
Innocent
Guilty
Correct decision
Type I error
Type II error
Correct decision
Now society would like the judge/jury to
find
you guilty if you did indeed
fi
commit the crime (the south-east quadrant), and to nd you innocent if you
did not commit the crime (the north-west quadrant). But the judge/jury may
fi
make two types of errors. One error (Type I error) is to nd you guilty even
though you are innocent; the other is to
fi nd
you innocent (Type II error)
even though you are guilty. Both these errors are regrettable from the point
of view of society, though you would be happy in the latter case. One way of
minimizing the probability of making a Type I error would be to require (very)
high standards of proof for guilty verdicts in trials, but this is likely to result in
some guilty people being found innocent (i.e. more Type II errors). Similarly,
one way of minimizing the probability of making a Type II error would be to
lower the standards of proof in trials, but this might result in some innocent
people being found guilty (i.e. more Type I errors). It is only possible to
reduce the probability of one incorrect decision at the expense of increasing
the probability of the other.
In hypothesis testing, the probability of making a Type I error is termed the
significance level (denoted as α ) and this significance level is specified by the
researcher as part of the research design. In business research, researchers
typically choose one of the following significance levels:
• α = 0.05. This means that there is a 1 in 20 chance of making a Type I
error. Hypothesis tests that result in the null hypothesis being rejected are
said to be significant, or significant at the 5% level.
• α = 0.01. This means that there is a 1 in 100 chance of making a Type I
error. Hypothesis tests that result in the null hypothesis being rejected are
said to be highly significant, or significant at the 1% level.
• α = 0.10. This means that there is a 1 in 10 chance of making a Type I
error. Hypothesis tests that result in the null hypothesis being rejected are
said to be significant at the 10% level.
10.2 How to Analyse the Data
The probability of making a Type II error in a hypothesis test is denoted byβ,
and the power of the test is then (1 β). Multicollinearity (see Section 11.3.3)
reduces the power of hypothesis tests in regression analysis. For a given
sample size, it is only possible to reduce the probability of one type of error at
the expense of increasing the probability of the other. The only way to reduce
simultaneously both the probability of a Type I error and the probability of a
Type II error is to increase the sample size.
Hypothesis testing can be something of a drudge if the calculations are
undertaken manually, and the researcher has to identify the appropriate
critical values from tables. Fortunately, many computer packages are
readily available that take care of this drudgery (see Section 10.3), and these
packages can be instructed to calculate a range of sample statistics (mean,
standard deviation, skewness, etc.) and to carry out a large array of hypothesis tests. Typically, the output from these packages will report the sample
estimate and also the p-value associated with the chosen hypothesis test: this
p-value is the calculated probability that the null hypothesis is true. A small
p-value indicates a low probability that the null hypothesis is true, and may
be interpreted as evidence against the null hypothesis. If the p-value< 0.05,
this is equivalent to rejecting the null hypothesis at the 5% level of significance (α = 5%); if the p-value< 0.01, this is equivalent to rejecting the null
hypothesis at the 1% level of significance (α = 1%). Generally, it is more
helpful to show p-values (which show the exact probability that the null
hypothesis is true) rather than to report the results of hypothesis tests with
researcher-determined levels of significance.
We will illustrate the mechanics of hypothesis testing below in the context
of (a) comparing the means of two samples; and (b) checking the correlations
between pairs of variables.
–
10.2.2 Comparison of Means (Independent Samples)
One of the most common hypothesis tests is to compare the mean values of
two samples, or of two sub-samples. The appropriate test depends upon
whether or not the data are normally distributed and whether the samples
are independent or paired see Appendix B for details. Here we will illustrate
the procedure (student s t-test) for comparing the means of two independent
samples where the data are normally distributed.
Consider again the data on firm export intensity from Appendix A,
which have been divided into three sub-samples based on the sector of the
firms see Table 10.5. All three sub-samples are independent in that each
firm is categorized to just one of the three sectors, and the three sub-samples
contain different numbers of observations.
The researcher might well be interested in knowing whether the mean
export intensity of the firms in the automobile sector is significantly different
–
’
–
195
196
Description and Preliminary Analysis
Table 10.5
Comparison of firm export intensities by sector
Electrical
Automobile
Textile
29
40.0
14.5
28
28.9
10.0
43
44.6
12.2
Number of observations
Mean
Standard deviation
Total
100
38.0
13.8
from the mean export intensity of thefirms in the textile sector.6 Clearly the
sample means are different (automobile = 29.0; textile = 44.6), but are the
population means significantly different7 from each other? In more formal
terms, we specify the null hypothesis that the two population means are
equal to each other, and set up the test so that we only reject this hypothesis
if there is a small (α = 5%) probability if it is correct.
Step 1: Specify the null and alternative hypotheses
H0 : μ 1 = μ2
HA : μ 1 6¼ μ 2
where
μ1 = population mean firm export intensity in sector1 (automobile)
μ2 = population mean fi rm export intensity in sector2 (textile)
Step 2: Calculate the test statistic
±
±
±
±
± ð ±1 ± ±2 Þ ±
± ð28: 9 ± 44:6 Þ±
±
± ¼±
± ¼ þ5: 69
±
±
±
ð ± 1 ± ± 2Þ ±
2 :76
X X
SE X X
where
X± 1 ¼ sample mean firm export intensity in sector1 ðautomobileÞ
X± 2 ¼ sample mean firm export intensity in sector2 ðtextileÞ
SEðX± 1 ± X± 2 Þ is the standard error of the difference between the two means
The numerator of this expression shows the difference between the two
sample means, while the denominator is the standard error of this difference
(a measure of the precision of the estimated difference).8 Note that the
6
7
8
Or whether the mean export intensity of thefirms in any sector is different from the mean
export intensity of the firms in any other sector.
In this chapter, we are only testing hypotheses about whether two means are significantly
different from each other: these are known as two-tailed tests. There are analogous onetailed tests that test whether one mean is significantly larger (or smaller) than another mean.
More detail about how to evaluate this formula is provided in Appendix C.
10.2 How to Analyse the Data
standard error will be larger the smaller the number of observations in the
two samples and the more variation there is in the data.
If there is a ‘large ’ difference between the sample means, and/or the
•
standard error is small, then the test statistic will have a‘large’ value –
this may be interpreted as evidence against the null hypothesis.
If there is a ‘small’ difference between the sample means, and/or the
•
standard error is large, then the test statistic will have a‘small’ value –
this may be interpreted as evidence in favour of the null hypothesis.
But what is a ‘large ’ and what is a ‘small’ value of the test statistic?
Step 3: Find the critical value from t-tables
We know that the test statistic follows a t-distribution, hence the critical value is:
t∗n
n
1 þ 2 ±2
t∗
¼ 71 ¼
1:99
for α ¼ 5%
where
n = number of sample observ ations in sector1 (automobile)
n = number of sample observations in sector2 (textile)
n + n + 2 = degrees of freedom
α = level of significance
1
2
1
2
Note that the critical value depends upon the combined number of observations
fi
fi
α
(n1 and n2) in the two sub-samples, and upon the speci ed signi cance level ( ).
Step 4: Specify the test criterion
± ±
If SEXX± XX±
±
±
± ð 1 ± 2Þ ±
±
± >
±
ð 1 ± 2 Þ±
1:99
± 1 ± X± 2Þ ±±±
X
If SEðX± ± X± Þ± ² 1 99
1
2
±
± ð
±
±
:
Reject H0
Do not reject H0
firms is
ficantly greater than the mean export intensity of the automobilefirms.
As 5.69 > 1.99, reject H0. The mean export intensity of the textile
signi
10.2.3 Comparison of Means (Paired Samples)
Here we will illustrate the procedure for comparing the means of two paired
samples (paired t-test) where the data are normally distributed.
Consider again the data on export intensity from Appendix A.
EXP18
fi
measures the export intensity of the sample of 100 manufacturing rms in
2018 while
100
firms
EXP19
measures the export intensity of the same sample of
fi
in 2019: i.e. the samples are paired and, by de nition, the two
samples contain the same number of observations.
197
198
Description and Preliminary Analysis
Suppose the home country of these SMEs negotiated a free trade area (FTA)
with its neighbouring countries, and this agreement came into force on
1 January 2019. The researcher might well be interested in knowing whether
the introduction of this FTA had a significant effect upon the export performance of SMEs in the home country. Clearly, the sample means are different
( EXP 18 ¼ 38 0; EXP 19 ¼ 41 4), but are the population means significantly
different from each other?9 In more formal terms, we specify the null hypothesis that the two population means areequal to each other, and set up the test
so that we only reject this hypothesis if there is a small α( = 5%) probability
that it is correct. Please note that the test procedure follows the same steps as
in Section 10.2.2, but that the detail of the test is rather different.
:
:
Step 1: Specify the null and alternative hypotheses
H0 : μ 1 = μ2
HA : μ 1 6¼ μ 2
where
μ1 = population mean export intensity in2018
μ2 = population mean export intensity in2019
Step 2: Calculate the test statistic
±
±
±
±
±d
±
±
± ¼
±
SEðd± Þ
±
± ð38:0 ±
±
±
±
41:4Þ ±±
± ¼
0:63
þ5 :40
where
±d ¼² mean
difference between the paired observations
³
±
SE d ¼ standard error of the mean difference
Step 3: Find the critical value from t-tables
We know that the test statistic follows a t-distribution, and that the critical
value is:
t∗n±1 ¼ t∗99 ¼ 1:98
for α ¼ 5%
Note that the critical value depends upon the number of paired observations
(n) in the two sub-samples, and upon the specified significance level (α).
Step 4: Test criterion
If
9
See footnote 7.
±
±
±
±
±d
±
±
± >
±
SEð±d Þ
1 :98
Reject H0
10.2 How to Analyse the Data
±
±
±
±
±
d± ±±
If
² 1: 98
SEð±d Þ±
Do not reject H0
As 5.40 < 1.98, reject H0. The mean export intensity of the
signi
199
firms
in 2019 is
ficantly greater than the mean export intensity in 2018.
fi
10.2.4 Correlation Coef cient (Continuous Variables)
fi
A correlation coef cient measures the degree of association between two
fi
variables, X and Y. The choice of appropriate correlation coef cient depends
upon
the
scale
of
measurement
for
the
variables.
If
both
variables
are
continuous, then the researcher should use the Pearson (product-moment)
ficient (r):
correlation coef
sXY
r¼
sX ³ sY
where
sXY = sample covariance of X and Y
sX = sample standard deviation of X
sY = sample standard deviation of Y
fi
There will be a different correlation coef cient for each pair of variables
and, if there are several variables of interest, these are normally presented in
the form of a correlation matrix. Table 10.6 shows such a matrix for the
continuous variables in the dataset in Appendix A.
fi
fi
The statistical signi cance of each correlation coef cient may be tested as
follows. Consider the correlation between
firm SIZE and CAPS.
Step 1: Specify the null and alternative hypotheses
H0 : ρ = 0
HA : ρ 6¼ 0
where ρ is the population correlation coefficient between SIZE and CAPS
Table 10.6 Correlation matrix
SIZE
AGE
SIZE
1.000
AGE
0.257**
1.000
CAPS
0.146
0.006
CAPS
EXP18
1.000
EXP18
± 0.507**
±0.064
0.353**
1.000
EXP19
± 0.445**
0.008
0.306**
0.909**
p < 0.05; ** if p < 0.01.
Note: The p-values are denoted as follows: * if
EXP19
1.000
200
Description and Preliminary Analysis
Step 2: Calculate the test statistic
r
where n
rffiffiffiffiffiffiffiffiffiffiffiffi
n±2
1 ± r2
¼ 0 146
:
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
100 ± 2
1 ± 0:021
¼ 1 46
:
= number of paired observations in the sample
Step 3: Find the critical value from t-tables
We know that the test statistic follows a t-distribution, hence the critical
value is:
t∗n±2 ¼ t∗98 ¼ 1 98 for α ¼ 5%
:
Step 4: Test criterion
If r
If r
rffiffiffiffiffiffiffiffiffiffiffiffi
n±2
1 ± r2
rffiffiffiffiffiffiffiffiffiffiffiffi
n±2
1 ± r2
>
1:98
Reject H0
² 1 98 Do not reject H0
:
As 1.46 1.98, do not reject H0 . There is no evidence of a linear correlation
between firm SIZE and CAPS at the 5% level of significance.
How may the Pearson correlation coefficient be interpreted?
• r is a measure of the linear association between two variables, X and Y–
i.e. the degree to which the two variables move together and are
collinear.
• The value of r lies between ±1 and +1:
○ If r ´ +1 then there is a strong positive linear association between
X and Y;
○ If r ´ ±1 then there is a strong negative linear association between
X and Y;
○ If r ´ 0 then there is no linear association between X and Y.
• A value of r ´ 0 implies the absence of a linear association, but the two
variables may have a non-linear (curvilinear) association.
• Is the value of r statistically significant? Check the p-value.
• r is not a measure of the strength of any relationship between the
variables – see Figure 10.5 – which depicts a scatter plot of two variables
(Y1 and Y2) against X. The Y1 values are identified by the darker dots and
the Y2 values by the lighter dots. The values of X and Y1 are highly
correlated, even though there appears to be only a weak relationship
between them. In contrast, the values of X and Y2 are weakly correlated,
though there appears to be a much stronger relationship between them.
<
10.2 How to Analyse the Data
201
300
250
200
Y
150
100
50
0
0
2
4
6
X
8
10
12
Y1 Y2
Figure 10.5
•
Strong and weak correlations
A value of
cause
r = µ1 implies a strong linear association, but doesnot imply a
–effect
relationship between X and Y or vice versa. It may be that
fl
X causes Y, or that Y causes X, or that both are in uenced by other
variables.
•
fi
By themselves, correlation coef cients provide little insight. But the rvalues do provide useful information about the degree of collinearity
between pairs of continuous variables. If
r
is high (say
>
0.7), then
multicollinearity (see Section 11.3.3) may well be a problem in regression
For
this
reason,
regression analysis
analysis.
first
present and discuss the continuous data in a
most
empirical
studies
that
involve
fl
correlation matrix, and also consider variance in ation factors (see
Section 11.3.3).
fi
The correlation coef cient between
signi
ficant
firms
(r = +0.909,
p < 0.01):
EXP18
and
EXP19 is
large and very
this is unsurprising as we would expect
that export a high proportion of sales one year to also export a high
fi
proportion the following year. There is also a positive and highly signi cant
p < 0.01)
firm SIZE and firm AGE, suggesting that older fi rms tend to be larger than younger firms. Interestingly, the
correlations between capabilities (CAPS) and fi rm SIZE (r = +0.146, p > 0.05)
and between CAPS and firm AGE (r = +0.006, p > 0.05) are both statistically
insignificant. Finally, 2018 export intensity (EXP18) is highly positively
correlation (r = +0.257,
between
CAPS (r = +0.306, p < 0.01), highly negatively correlated with
firm SIZE (r = ± 0.507, p < 0.01) reflecting the finding in Figure 10.4, and
uncorrelated with fi rm AGE (r = ± 0.064, p > 0.05).
correlated with
202
Description and Preliminary Analysis
EXAMPLE 10.3
The term ‘institutions’ refers to the formal rules (e.g. constitutions, laws, and
regulations) and informal constraints (norms of behaviour, conventions, and
self-imposed codes of conduct), and together with their enforcement mechanisms, they set the ‘rules of the game’ to which
firms must adapt in different
economies (North, 1990). It is usually assumed that advanced economies are
characterized
by
high-quality
institutions,
whereas
many
emerging
and
developing economies suffer from institutional voids (Khanna and Palepu,
1997) and are characterized by increased transaction costs, market vulnerabilities to large macroeconomic and political instabilities, under-developed or
missing infrastructures, and opportunistic behaviour, bribery, and corruption.
How to measure the quality of a country
’s institutions? One perspective is
provided by the
Worldwide Governance Indicators
(WGI)
project of
the
fi
World Bank, which identi es six dimensions:
•
Voice and accountability (VA) captures perceptions of the extent to
which a country’s citizens are able to participate in selecting their
government, as well as freedom of expression, freedom of association,
and a free media.
•
Political stability and absence of violence (PS) measures perceptions of
the likelihood of political instability or politically motivated violence,
including terrorism.
•
Government effectiveness (GE) captures perceptions of the quality of
public services, the quality of the civil service and the degree of its
independence from political pressures, the quality of policy formulation
and implementation, and the credibility of the government
’s
commitment to such policies.
•
Regulatory quality (RQ) captures perceptions of the ability of the
government to formulate and implement sound policies and regulations
that permit and promote private-sector development.
•
Rule of law (RL) refl ects perceptions of the extent to which agents have
con
fidence
in and abide by the rules of society, and in particular the
quality of contract enforcement, property rights, the police, and the
courts, as well as the likelihood of crime and violence.
•
Control of corruption (CC) reflects perceptions of the extent to which
public power is exercised for private gain, including both petty and grand
forms of corruption, as well as ‘capture’ of the state by elites and private
interests.
The World Bank has reported country-level data on these six dimensions
since 1996 at its website (http://info.worldbank.org/governance/wgi): the
10.2 How to Analyse the Data
EXAMPLE 10.3
(CONT.)
latest data relate to 215 separate countries/territories. Below we reproduce
the 2017 data on these six variables for just 12 countries, and then calculate
fi
the Pearson correlation coef cients between these six variables. It clear that
all six dimensions are highly collinear with the other dimensions: all values of
r
>
fi
0.672, and all are highly statistically signi cant.
Worldwide Governance Indicators, 2017
VA
PS
±
±
Cameroon
±
±
±
±
±
1.05
China
1.50
India
0.39
±
±
Iran
1.30
Iraq
1.05
GE
±
1.08
0.82
0.25
0.42
0.83
0.09
±
±
0.93
2.33
0.19
1.27
RQ
RL
±
±
±
±
±
±
±
0.82
0.15
0.25
1.02
0.26
0.00
±
±
1.20
1.20
0.68
1.64
CC
±
±
±
±
±
1.18
0.27
0.24
0.81
1.37
Italy
1.05
0.24
0.50
0.70
0.32
0.19
Japan
1.01
1.12
1.62
1.37
1.57
1.52
0.16
0.84
0.68
0.41
0.03
0.03
0.20
±
±
Malaysia
0.40
Mexico
±
0.08
United
±
0.65
±
0.57
±
0.93
1.33
0.26
1.41
1.71
1.68
1.84
1.05
0.30
1.55
1.63
1.64
1.38
Kingdom
United States
Zimbabwe
±
Note: Scores range from
±
1.20
±
0.77
1.19
±
±
1.56
1.38
±
1.27
±
2.5 (poor governance) to +2.5 (good governance).
ficients
Pearson correlation coef
VA
PS
GE
RQ
RL
VA
1.000
PS
0.672
1.000
GE
0.760
0.890
1.000
RQ
0.864
0.824
0.945
1.000
RL
0.842
0.869
0.977
0.953
1.000
CC
0.832
0.832
0.945
0.928
0.984
CC
1.000
203
204
Description and Preliminary Analysis
fi
10.2.5 Correlation Coef cient (Ordinal Variables)
fi
As noted above, the choice of appropriate correlation coef cient depends
upon the scale of measurement for the variables. If one or both variables
are measured on ordinal scales, then the researcher should use the Spearman
correlation
ficient
coef
(rS).
Unlike
variables
measured
on
interval/ratio
scales, where equal differences between values have equivalent meanings
(e.g. the difference between 9 and 10 years on the age scale is equivalent to
the difference between 49 and 50 years), no such equivalence exists for the
rankings on an ordinal scale. All that is known is that a ranking of 1 is better
(or worse) than a ranking of 2, and that a ranking of 2 is better (or worse) than
a ranking of 3, but the rankings do not tell us how much better (or worse) in
fi
Pd
each case. The Spearman correlation coef cient is calculated as follows:
rs ¼ 1 ±
6
2
i
n ðn2 ± 1Þ
where
di = X i ± Yi
Xi = rank of ith item with respect to variable X
Yi = rank of ith item with respect to variable Y
n = number of items being ranked
find out if there is any significant correlation between
the export intensity (EXP18) of our sample of 100 manufacturing fi rms
and the growth potential (GROW) of these firms as judged by the panel of
Suppose we want to
industry experts.
GROW
is an ordinal variable, and the calculated value of
fi
±0.155.
the Spearman correlation coef cient is
fi
fi
The statistical signi cance
of this correlation coef cient may be tested as follows.
Step 1: Specify the null and alternative hypotheses
H0 : ρ = 0
HA : ρ 6¼ 0
where ρ is the population correlation coefficient
Step 2: Calculate the test statistic
Z ¼ rS
,rffi ffi ffi ffi ffi ffi
1
n±1
,rffi ffi ffi ffi ffi ffi ffi ffi
¼ ±0: 155
1
¼
100 ± 1
where n = number of paired observations in the sample
±0: 016
10.2 How to Analyse the Data
Step 3: Find the critical value from normal distribution tables
We know that the test statistic follows a normal distribution, hence the
critical value is:
Z∗ = 1.96 for α = 5%
±±± ,rffi ffi ffi1ffi ffi ffi ±±±
If ±± r
n ± 1 ±±
±±± ,rffi ffi ffi1ffi ffi ffi ffi±±±
If ±± r
n ± 1 ±±
Step 4: Test criterion
S
1 96
Reject H0
² 1 96
Reject H0
>
S
:
:
As 0.016 < 1.96, we do not reject H0. There is no evidence of a correlation
firms and their export intensity.
between the growth potential of the
fi
How should the Spearman correlation coef cient be interpreted?
•
r S is a measure of a monotonic association between two variables, X and
Y
–
i.e. the degree to which the two variables move together in either a
positive
linear.
•
or
a
negative
direction.
The value of rS lies between
○
○
If
The
association
is
not
necessarily
10
rS
´
+1
then
there
±
1 and +1:
is
a
strong
positive
monotonic
association
is
a
strong
negative
monotonic
association
between X and Y.
If
rS
´±
1
then
there
between X and Y.
○
•
If rS
´
0 then there is no monotonic association between X and Y.
ficant? Check the p-value.
Is the value of rS statistically signi
GROW is an ordinal variable where small values indicate high potential and
low
values
indicate
low
potential.
The
potential
tend
calculated
Spearman
correlation
ficient is ±0.155: the negative sign suggests that those thefirms judged
coef
to
have
the
best
growth
to
be
those
with
higher
fi
export
intensities, but the correlation is weak and statistically insigni cant (rS =
±
10
0.155,
p > 0.05).
A monotonic association between X and Y is one where the values of Y always increase as
the values of X increase (or always decrease as the values of X increase), though not
necessarily linearly. More formally, the
first derivative of a monotonic function does not
change sign over the range of X values.
205
206
Description and Preliminary Analysis
10.2.6 Correlations between Nominal and/or Ordinal Variables
fi
Neither the Pearson nor the Spearman correlation coef cients have any
meaning if one or both of the variables are measured on nominal scales. In
such cases, the researcher should instead present contingency tables and use
χ
2
tests for independence. The use of contingency tables is not common in
business research and so will not be discussed here, but there is an excellent
introduction in Field (2018).
10.3
Many
The Use of Computers in Quantitative Research
excellent
statistical
analysis
packages
(e.g.
SPSS,
SAS,
Stata,
MATLAB, R) are commercially available or accessible in most universities
for use on personal computers, and spreadsheet packages (e.g. Excel) also
include some basic functionality. These packages cover a wide range of
statistical procedures and permit rapid and comprehensive analysis even of
large datasets.
But,
and
it
is
an
important
but,
this
powerful
functionality
does
not
remove the need for intelligent input from the researcher:
•
At the heart of all statistical analysis, there is a research question often
fi
formulated as a hypothesis test. The researcher speci es this research
question, and this in turn determines the data that s/he needs to collect.
firms, countries, etc. are now readily
Huge amounts of secondary data on
available online, but the researcher must identify which data are necessary for the analysis and whether primary data are also required.
•
The researcher is responsible for selecting, cleaning, and inputting the
necessary data into the package. If the input data are incorrect or are
otherwise imperfect, then any statistical output will be compromised.
Bear in mind the well-known adage ‘garbage in, garbage out’.
•
The researcher must instruct the package on which statistical procedure
to use, with the choice depending upon a variety of factors, including the
type of data (nominal, ordinal, continuous) and the type of analysis
required.
•
All packages provide huge amounts of output and diagnostic statistics,
and the researcher must decide how much of this output is relevant and
fi
how much s/he should usefully include in the nal report. Do not simply
cut and paste entire tables from the computer output into the report, but
be selective:
○
Produce tables for the report that contain only the essential numbers
and statistics from the computer output. Simple tables (and simple
10.3 The Use of Computers in Quantitative Research
diagrams) are much more effective than cluttered tables containing
fl
large amounts of super uous information. The late Hans Rosling was
○
○
a genius at presenting complex statistical data in an accessible form.
Only include diagnostic statistics on which comment will be provided
in the text of the report.
fi
Most packages produce estimates with many signi cant
figures, often
as many as eight to ten. These estimates suggest a level of accuracy
that may not be warranted, so the researcher has to make a judgement
fi
figures should be included. Our suggessignifi cant fi gures are usually suitable in most
about how many signi cant
tion is that three
situations:
thus
an
estimate
of
0.03466974
should
be
reported
as 0.0347, while an estimate of 3 462 974 should be reported as
3.47 million.
•
Last but not least, computer packages can generate large quantities of
output but they do not interpret this output. This is where the expertise of
the researcher is most important, both in discussing the results of the
statistical analysis and in drawing appropriate conclusions. Do not make
the same mistake as the PhD candidate who, when quizzed on the
statistical analysis in his thesis during the oral examination, replied that
he did not know because someone in the Statistics Department had done
the analysis for him. The candidate did not pass the examination!
207
208
Description and Preliminary Analysis
Further Reading
........................................................................
Field, A. (2018)
Discovering Statistics Using IBM SPSS Statistics
Discovering Statistics Using SAS
Business Data Analysis Using Excel
, 5th edn., London: Sage.
Field, A. and Miles, J. (2010)
, London: Sage.
Whigham, D. (2010)
, Oxford: Oxford University Press.
Questions
........................................................................
1.
What are population parameters, and what are sample statistics? How do
they differ?
2.
What is the relationship between the standard deviation, the variance,
fi
and the coef cient of variation of a variable?
3.
When is it appropriate to assess the degree of association between two
fi
variables (X and Y) using (a) the Pearson correlation coef cient, and
fi
(b) the Spearman correlation coef cient?
fi
4.
What do you understand by the level of signi cance of a hypothesis test?
5.
What is the difference between a Type I error and a Type II error in a
hypothesis test?
Exercises
........................................................................
1.
Consider the data in the table below:
X1
X2
X3
X4
X5
X6
2
5
6
11
9
8
a.
XX ¼
Enumerate the following expression:
6
t¼
b.
t
?
1
XX ¼
Enumerate the following expression:
5
t¼
c.
t
?
3
XX ¼
Enumerate the following expression:
6
2
t¼
t
?
1
d.
Express the sum of these X values (3 + 6 + 9 + 12 + 15 + 18 + 21) using
the sigma notation.
Exercises
2.
Enter the data from Appendix A in an Excel spreadsheet (or a statistical
package of your choice). Calculate the means and the standard deviations
for the export intensities (EXP18) of
firms in each of the four regions (i.e.
north, south, east, and west).
3.
Enter the data from Appendix A in an Excel spreadsheet (or a statistical
fi
package of your choice). Prepare a scatter plot of the observations of rm
capabilities (CAPS) and export intensity (EXP18). Does this plot suggest
there is a positive (negative) association between
firm
capabilities and
export intensity?
4.
Consider the data in the table below. Calculate the value of the Pearson
fi
fi
correlation coef cient between X and Y and test for its statistical signi cance. What do you conclude?
X
20
8
12
4
17
10
18
7
14
Y
63
37
31
59
44
32
49
41
33
X
3
11
19
15
6
9
13
5
16
Y
67
31
56
36
46
34
31
53
39
Now prepare a scatter plot of the data. What type of association is there
between X and Y?
5.
In
Section
10.2.6,
the
estimated
Spearman
correlation
ficient
coef
between growth potential (GROW ) and export intensity (EXP18) was
fi
statistically insigni cant.
a.
Calculate the change in export intensity (
2019 (i.e. EXP19
6.
Spearman
correlation
fi
coef cient
between 2018 and
between GROW and
b.
Derive
c.
Produce a scatter plot of the values ofGROW and
d.
What do you conclude?
ΔEXP.
the
– EXP18).
ΔEXP)
ΔEXP.
Suppose your computer output provides you with the following estimates
of means:
a.
Mean = 0.87459313. How would you report this mean to three sig-
ficant figures?
ni
b.
Mean = 1 956 288.26. How would you report this mean to four sig-
ficant figures?
ni
c.
Mean = 0.0000458762.
signi
ficant figures?
How
would
you
report
this
mean
to
two
209
11
Multiple Regression
Models differ from theories in that a theory
’s role is explanation, whereas a model
’s
role is representation.
B l u m b e r g e t a l . (2 0 0 8 )
The most commonly used technique for the analysis of quantitative data in
business research is regression analysis. It is the ‘go-to method’ in data
analytics, according to Gallo (2015). In particular, it is a powerful technique
for understanding the relationships between variables and which variables
have the most impact, and for prediction. For example, suppose you want to
understand the student-related factors that lead to good performance in the
Research
Methods
examination.
Many
factors
are
potentially
important,
such as intelligence, hours of revision, lecture attendance, age, and many
others. Maybe you have a hunch that female students do better than male
first
students.
The
potential
factors
step
is
(variables)
to
collect
and
on
data
final
for
each
student
examination
on
all
performance.
these
But
which of these variables has the greatest impact on examination performance, and which has the least impact? Can I predict my own performance
based upon my own intelligence, hours of revision, lecture attendance, age,
and gender? This is where multiple regression is useful as it allows the
researcher to separate out and quantify the impacts of the multiple potential
variables.
In more formal terms, the basic idea underpinning multiple regression is
that the researcher wants to explain the variation in the values of a depend-
fi
ent variable (Y) across selected observational units (which might be rms, or
individuals, or countries, etc.). We might hypothesize the following model
where the dependent variable is a function (f ) of several explanatory variables (X1, X2, X3 . . . Xk):
Y i = f( X i, X i, X
1
2
i, . . .Xki )
3
where the subscript i (i = 1, 2 . . . n) refers to the observational unit.
(11.1)
11.1 The Determinants of Export Performance
This model envisages a set of causal relationships between the explanatory
variables and the dependent variable rather than simple correlations between
the variables. The likely direction of causation is established on the basis of
theory and of an understanding of the mechanisms through which each
–
cause effect relationship operates. Here the researcher must draw upon the
existing academic literature on the topic, and his/her own experiences of
these mechanisms in practice.
So
far,
so
good
–
but
how
important
are
each
of
these
explanatory
variables in explaining the variation in the dependent variable? In a natural
experiment (as described in Section 5.3), the researcher would simply generate a dataset by altering the values of each explanatory variable (e.g. X
1) in
turn while keeping the values of the other explanatory variables (e.g. X
to
2
Xk) constant, and then recording what happens to the values of the dependent variable. But such a methodology is generally not feasible in business
research because the data on the dependent and explanatory variables are
observed in the real world, and it is simply not possible to set the values of
1
chosen variables while allowing the values of other variables to change.
separate
However, multiple regression analysis permits the estimation of the
effects of
X , X . . .X k upon Y, even though it is not possible to run controlled
2
3
natural experiments.
The Determinants of Export Performance
11.1
Suppose, for example, a researcher is interested in explaining the variation in
2
export performance across a cross-sectional sample of
firms –
a popular
research topic in the marketing and international business literature (see
Bernard et al., 2007; Chetty and Hamilton, 1993; Sousa et al., 2008; Zou
and Stan, 1998 for excellent literature reviews). Now a careful reading of this
literature
suggests
that
several
fl
firm-specific
characteristics
have
been
shown to in uence export performance, including size, age, and a range of
technological and managerial capabilities (Krammer et al., 2018). In addition,
fi
it is likely that exporting will be more common among all rms in some
industrial sectors (e.g. those producing small, high value-to-weight products)
than
1
2
in
other
sectors
(e.g.
those
producing
large,
low
value-to-weight
See Section 9.8 on experimental methods.
In a cross-sectional sample, the data all relate to the same time period. The data in Appendix
A relate to the year 2018.
211
212
Multiple Regression
firms
products), and among all
in certain regions of the exporting country
(e.g. those with access to ports) than in other regions (e.g. those that are landlocked or inaccessible). Finally, various general characteristics of the busi3
ness environment in the exporting country may be important. In formal
terms,
the
researcher
might
hypothesize
the
following
model
of
export
4
performance:
EXP i = f(SIZEi, AGEi, CAPSi, REGIONi, SECTORi)
(11.2)
where
EXPi = export performance of
firm i
= age of firm i
fi rm i
SIZEi = size of
AGEi
CAPSi = the capabilities of
REGIONi = region of
firm i
fi rm i
SECTORi = industrial sector of
firm i
Appendix A contains data on these variables for an imaginary sample of
100 manufacturing
firms. It should be noted thatEXP, SIZE, AGE, and CAPS
are all continuous variables, while
region
and
sector
are both categorical
variables with four (north, south, east, west) and three (electronic, automobile, textile) categories respectively.
11.2
The
The Regression Model
general
model
of
export
performance
set
out
in
equation
11.2
needs further detail before it may be estimated by multiple regression. This
fi
involves (a) the operationalization of the variables, (b) the speci cation of
the functional form of the relationships between the dependent variable
and
each
of
the
explanatory
variables,
and
(c)
the
fi
speci cation
of
any
time lags.
3
fi
These country-level characteristics would be important if the rms in the sample came from
several different countries and/or if the researcher was interested in changes in export
performance over time (i.e. if the researcher was using time-series or panel data). Here the
firms from the same exporting country, and hence
firms are faced with the same home-country business environment.
This is a very simplified model as our intention here is to illustrate the application of multiple
data relate to a cross-sectional sample of
4
all
regression analysis rather than to make a substantive contribution to the literature on the
determinants of export performance.
11.2 The Regression Model
11.2.1
Operationalization of the Variables
Some variables are relatively straightforward to operationalize. For
instance, the AGE of the firm may be measured by the number of
years since the firm was established, though alternatively age might be
measured in terms of months. But some of the other variables are more
problematic:
•
Export performance (EXP) is often measured by export intensity (export
sales as a % of total sales), but some empirical studies use a dichotomous
variable for export propensity (= 1 if the firm exports; = 0 if the firm
does not export).
Firm size (SIZE) has been measured by the number of employees in
Appendix A. Other common measures of firm size are annual sales
revenues or total assets . These measures are not necessarily highly
correlated with each other: firms producing labour-intensive products
will tend to have more employees, and firms producing capitalintensive products will tend to have more assets for the same amount
of sales revenue. The researcher must thus think carefully about the
most appropriate measure to use in any model, as the choice of
measure will have an impact on the results obtained from the regression analysis.
How are firm capabilities (CAPS) assessed? The literature identifies
numerous capabilities that might be useful for exporting firms to
possess, including technological, managerial, marketing, networking,
and organizational capabilities, as well as dynamic capabilities.
But which capabilities are important in the context of exporting?
Furthermore, how are capabilities measured? It is difficult to think
of readily available secondary data that would provide measures for
each of these capabilities, so often researchers derive more subjective
measures from appropriate items in questionnaire surveys. Typically
this results in measures based on Likert scales, where the possible
values are constrained to a limited range and are expressed in
undefined units. The data on CAPS in Appendix A are captured on
a 7-point scale, and do not distinguish between different types of
capability.
Region is a nominal (categorical) variable, which may take four possible
values (north, south, east, west). There is no ordering to these categories,
so it would be arbitrary to convert the categories into a single numerical
variable with four different values. Instead, we have to define a set of
(j ± 1) dummy variables to capture the separate effects of the j regions, as
follows:
‘
‘
•
’
’
‘
’
‘
’
•
•
‘
’
213
214
Multiple Regression
Firms
REGION
DNORTH
DSOUTH
DWEST
1–18
North
1
0
1
19–34
South
0
1
0
35–77
East
0
0
0
78–100
West
0
0
1
Source: Appendix A.
NOTES : 1. REGION may take j = 4 different values, hence we de
fi ne j ± 1 = 3 dummy variables
(DNORTH, DSOUTH, and DWEST).
2. EAST has been selected as the‘base’ region, for which all three dummy variables have a zero value.
•
SECTOR is also a nominal (categorical) variable, which may take three
possible values (electrical, automobile, textile). There is no ordering to
these categories, so it would be arbitrary to convert the categories into a
single numerical variable with three different values. Instead, we have to
define a set of (j ±1) dummy variables to capture the separate effects
of the j regions, as follows:
Firms
SECTOR
DELEC
DTEXT
1–10; 19–28; 35–38; 78–82
Electrical
1
0
11–15; 29–31; 39–45; 83–95
Automobile
0
0
16–18; 32–34; 46–77; 96–100
Textile
0
1
Source: Appendix A.
1. SECTOR may take j = 3 different values, hence we de
fine j ± 1 = 2 dummy variables (DELEC
and DTEXT).
2. AUTO has been selected as the‘base’ region, for which both dummy variables have a zero value.
NOTES :
The model of export performance may thus be respecified as;
EXPi = f(SIZEi, AGEi, CAPSi, DNORTHi, DSOUTHi, DWESTi,
DELECi, DTEXTi)
(11.3)
Finally, it is important to take careful note of the units in which each
of the variables is measured, as both the results from the regression
analysis and their interpretation will vary accordingly. For example, data
on sales revenue might be expressed either as $56 452 785, as $56.45m or as
$0.05645bn, while export intensity might be expressed as 34.5% or as 0.345.
It is advisable to provide a table in the final report that lists the exact
definitions of all variables used in the analysis, and also the units in which
they are measured.
11.2 The Regression Model
11.2.2
Functional Form
fication involves not only choosing the explanatory variables to
Model speci
include in the regression model, but also choosing the functional form of the
relationship between each explanatory variable and the dependent variable
(Y). The choice of functional form should be based on economic theory rather
than on statistical
fit.
The mathematical properties of the functional form
should be examined and compared with what is known about the nature of
the relationship between the variables.
The Linear Relationship
One option is to hypothesize a (positive) relationship, such as that depicted in
Figure 11.1a between Y and one of the explanatory X
i . Figure 11.1b shows a
negative linear relationship.
If linear relationships are hypothesized between the dependent variable
and all the explanatory variables, then the general model in equation 11.1
5
may be written as:
Yi = β
1
X
+ β2
2
i + β3 X3i + β4 X4i + . . .+ βkXki + ε i
(11.4)
where
β is the intercept term
(k ± 1) = number of explanatory variables
β , β , . . .. . .βk are the partial regression coefficients
εi = disturbance term
1
2
3
Y
t
cep
r
e
t
In
Slope = bk
Xk
Figure 11.1a
5
A positive linear relationship
We only consider ‘additive ’ models where each explanatory variable is assumed to have only
a direct and separate effect on the dependent variable. It is also possible to model interactions
between the explanatory variables, but this is beyond the scope of this textbook.
215
216
Multiple Regression
Y
Slope = -bk
Xk
Figure 11.1b
A negative linear relationship
The hypothesized relationship is stochastic (not deterministic), hence the
fi
6
presence of the disturbance term in the model. The partial regression coef -
β
cients (
2.
βk)
..
measure the marginal effects upon the dependent variable (Y)
of unit changes in the corresponding explanatory variables (X
, X3 . . . Xk),
2
assuming all other explanatory variables are held constant.
A linear relationship implies that the
slope
of the relationship between Xk
and the dependent variable is constant whether or not X
takes a small value,
k
a large value, or a value in between.
slope ¼
ΔY
¼ βk
ΔX k
In certain circumstances, this may be a realistic assumption. However,
in
other
linear
circumstances,
relationship
it
may
between
7
non-linear relationships,
Y
be
more
and
Xk.
realistic
There
to
are
hypothesize
numerous
a
non-
possible
but here we will just highlight two: the semi-
logarithmic relationship and the quadratic relationship.
The Semi-Logarithmic Relationship
Often the relationship between the dependent variable (Y) and the explanatory variables will be subject to diminishing returns, in the sense that unit
increases in each explanatory variable will give rise to small and smaller
increments to Y. The relationship between Y and each of the explanatory
variables (say Xk) will be as depicted in Figure 11.2a (positive relationship)
and b (negative relationship).
6
In a deterministic model, the values of the dependent variable may be predicted precisely
from the values of the explanatory variables. In contrast, stochastic models allow for the
fi
fl
possibility of some (unspeci ed) random in uences on the dependent variable, and these
fl
random in uences are represented by the disturbance term.
7
See Gujarati and Porter (2009) for an extensive discussion of alternative functional forms.
11.2 The Regression Model
Y
Slope = k 1
Xk
b
Xk
Figure 11.2a A positive semi-logarithmic relationship
Y
Slope = ± k 1
Xk
b
Xk
Figure 11.2b A negative semi-logarithmic relationship
If semi-logarithmic relationships are hypothesized between the dependent
variable and all the explanatory variables, then the general model in equation 11.1 may be written as:
Yi = β
1
lnX
+ β2
2
i + β3 lnX3i + β4 lnX4i + . . . + βklnXki + εt
A semi-logarithmic relationship implies that the
(11.5)
slope of the relationship
between Xk and the dependent variable varies with the level of X
:
k
slope ¼
β
If
k
<
β
k
>
ΔY
1
¼
βk
ΔX k
X ki
0 then the positive impact of Xk on Y decreases as Xk gets larger. If
0 then the negative impact of X
on Y decreases as Xk gets larger.
k
The Quadratic Relationship
In a quadratic relationship, the shape of the relationship between Y and X
2 is
U-shaped or inverse U-shaped, depending upon the signs of the two partial
fi
β
regression coef cients (
2
and
β – see Figure 11.3a and b respectively).
3
217
218
Multiple Regression
Y
slope =
ΔY
=
ΔX 2
b2
+ 2b3X 3i
X2
Figure 11.3a A U-shaped relationship
Y
ΔY
slope =
=
ΔX 2
b2
+ 2b3X3 i
X2
Figure 11.3b An inverse U-shaped relationship
If a quadratic relationship is hypothesized between the dependent variable
and just one of the explanatory variables (say X
), then the general model in
2
equation 11.1 may be written as:
Yi ¼ β
þ β X i þ β X i þ εt
2
1
2
2
A quadratic relationship implies that the
3
2
slope of the
(11.6)
relationship between X2
and the dependent variable varies with the level of X
:
2
slope ¼
If
β
2
<
0 and
Y, while if
β
2
>
β
3
>
2
¼ β þ 2β X i
2
3
3
0 then there is a U-shaped relationship between X
and
2
0 and
between X2 and Y.
ΔY
ΔX
β
3
<
0 then there is an inverse U-shaped relationship
11.3 Estimation Methodology
Haans et al. (2016) provide an excellent guide to theorizing about quadratic relationships, while Lind and Mehlum (2010) offer a rigorous three-step
procedure for testing for the existence of such relationships.
11.2.3
A
Time Lags
regression
model
depicts
the
–
hypothesized
cause effect
relationships
between the dependent variable and the various explanatory variables. In
many instances, it is appropriate to use data for all these variables for the
same period or at the same point in time. For instance, it is reasonable to use
firm SIZE, AGE, CAPS, REGION,
2018 data for
and
SECTOR
in order to
model export intensity in 2018.
But it will be important to take explicit account of time lags in two sets of
circumstances:
•
When there is an obvious lag between a change in the explanatory
variable (the cause) and a likely change in the dependent variable (the
effect). For instance, a
firm
may decide to invest in a new production
facility based upon its assessment of current market conditions. But there
will be a lag between the investment decision and the actual commissioning of the facility and the generation of output. If this lag were about
one year, then it would be appropriate to model the change in production
capacity in year t as a function of market conditions in year t(
•
±
1).
When the change in the explanatory variable has a prolonged impact
upon
the
dependent
variable
rather
than
just
a
one-off
impact.
For
instance, RandD expenditure tends to have an impact on innovation
over a period of several years.
11.3
Estimation Methodology
A crucial consideration in deciding upon the appropriate estimation methodology is the scale on which the dependent variable is measured. In this
section, we will concentrate on the estimation of regression models where
8
the dependent variable is a continuous variable
– here
the default method-
ology is ordinary least squares (OLS).
8
See section 11.3.6 for a brief discussion of the estimation methods to be used when the
dependent variable is a nominal variable or an ordinal variable.
219
220
Multiple Regression
OLS Estimation
11.3.1
For the sake of simplicity (and so the regression model can be depicted in a twodimensional
figure), consider a simple (population) linear regression model with
just one explanatory variable (X). Equation 11.4 can then be written as:
Yi = β
1
Xi + εi
+ β2
(11.7)
where
Yi = dependent variable
Xi = explanatory variable
εi = disturbance term
β and β are ( unknown)population parameters
1
2
In order to estimate the (unknown) population parameters, the researcher
collects
a
sample
of
n
observations
on
the
dependent
and
explanatory
variables, viz:
Y , X ), (Y , X
(
These
1
1
2
2),
Y ,X
(
3
3),
X n, Yn)
. . .. . .. . ., (
n observations are depicted by the dots in Figure 11.4. Now OLS is a
mathematical procedure to estimate the ‘best line’ through this set of observations:
9
this ‘best line’ is known as the sample
regression line,
and is
depicted as:
Y^ i ¼ β^
1
þβ
^
2
Xi
(11.8)
where
Y^i ¼ predicted value of Yi
Xi = explanatory variable
^
β and ^
β are sample estimates of the parameters
1
2
The sample regression line may be used to generate a predicted value of the
dependent variable
±
Y^ p
²
fi
for any speci ed value of the explanatory variable
Xp), but this predicted value will generally not be the same as the actual value of
Yp) associated with Xp see Figure 11.4. The difference
(
the dependent variable (
–
between the actual and the predicted values is termed the residual:
residual ¼ ei ¼ Y i ± Y^ i
(11.9)
n observations, for the simple
There is a residual associated with each of the
reason
9
that
the
sample
regression
line
does
not
provide
a
complete
OLS will generate the best line when there is a dependent variable and one explanatory
variable, and a
k-dimensional surface when there are (k ± 1) explanatory variables, as
hypothesized in equation 11.4.
11.3 Estimation Methodology
Y
Yp
Yp
Xp
Figure 11.4
X
The sample regression line
explanation of the variation in the dependent variable: it is merely the best
line through the observations, with some observations lying above the line,
some below the line, and few (if any) actually on the line.
The OLS procedure estimates the best line through the sample of observa-
fi
tions by calculating the values of the sample coef cients
^1
ðβ
and ^β
2
Þ
that
SSE). This sum of squared residuals is
minimize the sum of squared residuals (
a measure of how much of the variance in the dependent variable is not
X e X ±Y Y ²
explained by the sample regression model.
SSE ¼
n
n
i ± ^i
2
i¼1
i ¼
i¼1
2
(11.10)
We have provided here an intuitive explanation of OLS estimation in a
simple model involving the dependent variable and just one explanatory
variable. The same principles apply when estimating a multiple regression
model with several explanatory variables (as in equation 11.4), though the
fi
formulae which may be used to calculate estimates of the sample coef cients
fi
will differ. The precision of the coef cient estimates is measured by their
fl
standard errors: smaller standard errors re ect more precise estimates, and
fl
larger standard errors re ect less precise estimates. Generally, standard errors
may be reduced (and hence precision increased) by increasing the sample size
used to generate the regression results or ensuring that the sample includes a
wide range of values of the explanatory variables. Fortunately, all computer
fi
packages routinely provide the coef cient estimates and their standard errors
as part of their output, so we will not present the formulae here.
11.3.2
Recall
the
The Coefficient of Determination
that
variance
one
of
in
the
the
objectives
observed
of
values
regression
of
the
analysis
dependent
is
to
variable
explain
(Y).
This
221
222
Multiple Regression
variance is termed the total sum of squares (SST) and may be calculated as
follows:
SST ¼
where Y±
¼
XY
n
i¼1
±Þ
ð i ±Y
2
(11.11)
mean value of the dependent variableðY Þ
The amount of variance explained by the regression model is termed the
X ±Y Y²
regression sum of squares (SSR), and this may be calculated:
SSR ¼
n
i¼1
^i ± ±
2
(11.12)
where Y^i ¼ fitted value of Yi
It can be shown mathematically that:
SST = SSR + SSE
In
other
words,
the
total
variance
(11.13)
(SST)
in
the
observed
values
of
the
dependent variable can be decomposed into two components: one component is the variance explained by the regression model (SSR), and the other
component is the variance that is unexplained (SSE). Thus a measure of how
well the regression model explains the variance in the observed values of the
fi
R
dependent variable is provided by the coef cient of determination (
R
2
¼
2
):
variation explained by the regression
total variation
R
2
¼
SSR
SSE
¼1±
SST
SST
(11.14)
fi
How may the coef cient of determination be interpreted?
•
The value of
R
2
must lie between zero and one. A higher value implies a
better model.
•
R
2
=1
implies that changes in the values of the explanatory variables
explain all the variance in the dependent variable (Y).
•
R
2
= 0 implies that changes in the values of the explanatory variables explain
none of the variance in the dependent variable (Y). The model is useless.
•
The
inclusion
of
additional
explanatory
variables
in
the
regression
model, even if they have minimal effect upon the dependent variable,
2
2
will never reduce R . At worst, R will remain the same if the additional
variable has zero explanatory power.
11.3 Estimation Methodology
2
Some researchers thus also report the adjusted R :
•
Adjusted R ¼ R± ¼ R ± k ±
n±k
2
The value of
•
R±
2
2
1
2
±
1
±R
2
²
(11.15)
will fall if irrelevant explanatory variables are added to
the regression model, hence it can be used to compare different models
(with the same dependent variable).
11.3.3
The Assumptions Underpinning OLS Estimation
Above we described OLS as the default estimation method when the dependent variable is continuous. But OLS is only an appropriate estimation method
if six basic assumptions about the multiple regression model hold:
A1: The regression model is linear in the parameters, and is correctly
•
speci
A2:
•
fied.
The
explanatory
variables
are
uncorrelated
with
the
terms
(no
disturbance term.
•
A3: The expected value of the disturbance term is zero.
•
A4:
There
is
no
correlation
between
disturbance
autocorrelation).
•
A5: The disturbance terms have a constant variance (homoskedasticity).
•
A6: No explanatory variable is a perfect linear function of any other
explanatory variable(s).
If these assumptions hold, then the Gauss-Markov theorem shows that OLS
10
provides unbiased estimates of the regression parameters.
But if one or
more of these assumptions do not hold, then OLS estimates may be biased
and
alternative
estimation
methods
may
be
preferable.
What
do
these
assumptions mean, and how can we tell if they do not hold?
•
A1 requires that the functional form of the relationship be correctly
speci
fied (see Section 11.2.2), and that all relevant explanatory variables
are included in the regression model (see ‘Omission of Relevant Explanatory Variables’ below). Omission of relevant explanatory variables may
lead to biased parameter estimates – see below.
•
A2 means
that the explanatory
variables must be exogenous in the
regression model. If some of the explanatory variables are endogenous,
10
Strictly speaking, the Gauss-Markov theorem proves that OLS provides the best linear
unbiased estimators – see Gujarati and Porter (2009) for further details. A seventh
assumption, that the disturbance term in normally distributed (A7), is also required for the
hypothesis tests shown in Section 11.3.3 to be valid.
223
224
Multiple Regression
•
•
•
•
and the values of these variables are determined at the same time as the
values of the dependent variables, then the researcher should specify a
simultaneous equation system. An alternative estimation method such as
two-stage least squares (2SLS) should then be used.
A3 is essential, but is met as long as there is an intercept term β(1)
included in the regression model.
A4 requires that there is no systematic relationship between the disturbance terms. This assumption is often violated in time-series models, and
causes OLS to generate imprecise estimates of the parameters. An alternative estimation method such as Cochrane-Orcutt should then be used.
A5 means that the variance of the disturbance terms is constant. This
assumption is often violated in cross-section models, and causes OLS to
generate imprecise estimates of the parameters. An alternative estimation
method such as weighted least squares (WLS) should then be used.
If A6 is violated, then the regression model exhibits perfect multicollinearity and OLS is unable to distinguish the effects of one explanatory
variable from the effects of the others. Perfect multicollinearity means
that it is impossible to generate estimates of the regression parameters,
but even high levels of collinearity (see ‘Multicollinearity’ below)
between the explanatory variables are a problem as this results in imprecise estimates of the parameters. Possible remedies are discussed below.
A comprehensive discussion of these alternative estimation methodologies
is beyond the scope of this textbook, and the interested researcher is encouraged to consult a basic econometrics textbook such as Gujarati and Porter
(2009). Further explanation of the problems caused by omitted variables and
by multicollinearity are, however, necessary.
The Omission of Relevant Explanatory Variables
Suppose the researcher forgets to include all the relevant explanatory variables
when s/he first specifies the regression model. Or s/he does not include a
variable because the data were hard to collect. If this omitted variable is
correlated with one of the included explanatory variables, then the estimated
coefficient of the latter will ‘pick up the effect’ of the omitted variable and will
be biased. If the omitted and included variables are positively (negatively)
correlated, then the bias will be positive (negative). This is omitted variable bias.
Consider the following hypothetical example of the determinants of the
monetary value of damage (measured in $m) arising from a sample of
building fires. The researcher may hypothesize that the extent of thefire
damage will be influenced by the number of fire fighters who attend the
fire and by the scale of the fires. The researcher first graphs the data on
11.3 Estimation Methodology
fire
damage ($m)
Number of firefighters
Figure 11.5
fire
The relationship between fire damage and firefighter numbers
damage and on
firefighter
numbers for a sample of
fires,
and obtains a
scatter plot such as shown in Figure 11.5. Clearly there is a strong positive
fire damage.
Furthermore, OLS estimation of a simple regression model withfi re damage
as the dependent variable and the number offi refi ghters as the explanatory
correlation between
firefighter
numbers and the extent of the
variable would yield a sample regression line such as that depicted in
Figure 11.5. This apparently shows that more
damage
–
firefighters
a surprising conclusion!
led to greater
fi
The problem is that the regression model is incorrectly speci ed, and that a
fi
relevant explanatory variable (the scale of the res) has been omitted from the
firefighters and the scale of thefires are
likely to be highly positively correlated: biggerfi res require more fi refighters to
deal with them. The estimated coeffi cient of the included variable (the number
of fire fighters) in the regression model picks up the effect of the omitted
variable (the scale of the fire), and generates biased parameter estimates.
In this hypothetical example, the incorrect specification is easy to diagnose
regression model. Now the number of
‘
’
and it is clear that the regression model should include both explanatory
variables (i.e. the number of
firefighters
and the scale of the
fire):
this will
result in an estimated positive relationship between the latter and the extent
of the
fire
damage, and an estimated negative relationship between the
former and the extent of the
fire
damage (as would be expected). But, in
fi
fi
many real situations, the incorrect speci cation may only be identi ed by
careful consideration of the relevant literature and of the underlying mechanisms at work (see Section 11.6).
225
226
Multiple Regression
Multicollinearity
Multicollinearity is a matter of degree (rather than being a particular state),
and it is only a problem when there are high levels of collinearity between
several explanatory variables in a regression model. In such circumstances,
the estimates of the parameters of the collinear explanatory variables are
fi
imprecise: i.e. it is dif cult to separate out the individual effects of the
explanatory variables, and these variables may well be judged statistically
insigni
ficant. High values (> 0.7) of the relevant Pearson correlation coeffi-
cients (see Section 10.2.4) provide an indication of potential multicollinearity
problems, and this is why it is always a good idea to present a correlation
matrix (see Table 10.6) as part of the preliminary analysis of a dataset.
fl
Another diagnostic tool is to calculate a variance in ation factor (VIF) for
>
each explanatory variable: a rough rule of thumb is that a VIF
11
10 suggests
serious multicollinearity (O’Brien, 2007).
What can be done about multicollinearity?
•
If possible, try to increase the size of the sample. This will reduce the
size
of
the
standard
errors
and
hence
increase
the
power
of
any
hypothesis tests.
•
A
flawed
remedy would involve dropping one or more of the collinear
explanatory variables from the regression model or, equivalently, estimating several regression models each including just one of the collinear
explanatory variables. For example, we highlighted in Example 10.3 the
fi
high correlations between the six governance dimensions identi ed by
the
World Bank.
Many
researchers
have tried
to estimate
regression
models incorporating all six dimensions, have been thwarted by the high
correlations, have resorted to estimating six separate regressions each
fi
including just one of the dimensions, and have then reported signi cant
fl
effects due to all six dimensions. But such an approach is awed because
these estimates will all suffer from omitted variable bias.
•
Combine the collinear explanatory variables into a single variable or
index. This new variable must have some theoretical rationale, and must
not be a purely arbitrary combination. Thus the mean of the six governance dimensions might be used as a single ‘governance’ indicator: this
would avoid the problem of multicollinearity but would not allow the
estimation of the effects of the individual variables. Sometimes a simple
mean would not be appropriate as, for example, when the variables to
be
11
combined
have
different
orders
of
magnitude
Most computer packages may be instructed to calculate VIFs.
(in
which
cases
a
11.3 Estimation Methodology
weighted mean would be required) or are inversely correlated. A more
sophisticated technique for combining explanatory variables is factor
analysis (see Section 12.1).
EXAMPLE 11.1
A commonly used construct, particularly in the international business literature, is the cultural distance between pairs of countries. Hofstede (2001)
identi
•
fi es
six distinct dimensions of national culture:
The power distance dimension (PDI) focuses on how societies deal with
inequalities in wealth and power between people.
•
The uncertainty avoidance dimension (UAI) focuses on how different
societies socialize their members into accepting ambiguous situations
and tolerating uncertainty.
•
The individualism versus collectivism dimension (IDV) focuses on the
relationship between the individual and the community.
•
The masculinity versus femininity dimension (MAS) focuses on the
relationship between gender and work roles.
•
Long-term orientation (LTO) stands for the fostering in society of
pragmatic virtues oriented to future rewards.
•
fi
Indulgent societies (IND) allow relatively free grati cation of basic and
fi
natural human desires, while restrained societies suppress grati cation of
needs and regulate it by means of strict social norms.
Consider the United Kingdom and China: their scores (scores range from
1 to 120) on these six dimensions are as follows:
United Kingdom
China
PDI
UAI
IDV
MAS
LTO
IND
35
35
89
66
51
69
80
30
20
66
87
24
What is the cultural distance between the UK and China?
•
It would be a mistake to calculate the average scores for each country
(UK = 57.5; China = 51.2) and conclude that the two countries were
culturally similar because of their similar scores. Clearly the countries are
quite different, with China outscoring the UK on the PDI and LTO
dimensions, and the UK outscoring China on the UAI, IDV, and
IND dimensions. Furthermore, there is more variability in the scores
for some dimensions than for others.
227
228
Multiple Regression
EXAMPLE 11.1
•
(CONT.)
Most researchers thus use the Kogut and Singh (1988) formula, the
Mahalanobis formula (Berry et al., 2010), or variance-based measures
(Beugelsdijk et al., 2015) to combine the scores for the different
dimensions. The Kogut and Singh formula considers the absolute values
X h±I
² i
of the differences in the scores, and also their variability:
Dij ¼
where
•
K
k ¼1
ik ± I jk =V k =K
2
Dij = cultural distance between country i and country k
Iik = score for country i on dimension k
Ijk = score for country j on dimension k
Vk = variance of scores for dimension k
K = number of dimensions
This value for the cultural distance between the UK and China (xxx)
should be interpreted in comparison to the distances between other pairs
of countries: each distance by itself has little meaning.
11.3.4
The Residual Plot
±Y ²
One very useful diagnostic tool is to produce a scatter plot of the residuals
from the regression (ei) against the
^i
fitted
values of the dependent variable
fi
. If the regression model has been correctly speci ed, then this plot
–
should show a random pattern around a mean value of zero see Figure 11.6.
But if there is a clearly discernible pattern to the residuals (as in Figure 11.7),
or the spread of the residuals becomes larger (smaller) with larger
laudiseR dezidradnatS
Predicted
Figure 11.6
A residual plot from a correctly specified regression model
fitted
Residuals
11.3 Estimation Methodology
0
Independents
Figure 11.7
A residual plot showing an incorrectly specified model
Residuals that show an increasing trend
slaudiseR
Residuals that show a decreasing trend
slaudiseR
Constant variance
slaudiseR
Fitted values
Figure 11.8
Residual plots indicating heteroskedasticity
values of the dependent variable (as in Figure 11.8), then something may well
be amiss. In the case of Figure 11.7, the problem may be caused by an
fi
omitted explanatory variable, by an incorrectly speci ed functional form,
or
by
autocorrelation
between
the
disturbance
terms.
In
the
case
of
229
230
Multiple Regression
Figure 11.8, the problem is probably caused by a violation of the assumption
of
homoskedasticity
(see
Section
11.3.3).
The
residual
plot
alerts
the
researcher to these possibilities, and to the need for further diagnosis.
Estimation Methodology with Nominal or Ordinal
Dependent Variables
11.3.5
If the dependent variable is a continuous variable, then OLS regression or one
of its variants will be the appropriate methodology. But often the researcher
may want to estimate regression models where the dependent variable is
12
measured on a nominal or an ordinal scale.
Often business decisions involve the choice between discrete alternatives,
such
as
the
decision
whether
to
make
a
fi
green eld
investment
or
to
acquire an existing facility, or the choice between several alternative locations for an investment. In such cases, the dependent variable is measured
on a nominal scale, and the appropriate estimation methodology is multinomial logit regression (if there are more than two alternatives) or binomial
logit regression (if there are just two alternatives). For example, Majocchi
and Strange (2007) modelled the determinants of the choice of location in
Eastern Europe for a sample of foreign direct investment (FDI) projects by
Italian
multinational
enterprises
(MNEs).
Seven
possible
locations
were
considered (i.e. Bulgaria, the Czech Republic, Hungary, Poland, Romania,
Slovakia, and Slovenia), and the authors reported that important determinants were host country market size and growth, the availability of labour,
the quality of infrastructure, agglomeration economies, the extent of trade,
financial and market liberalization, and (negatively) the openness to foreign
banks. The dependent variable in this study was the choice between seven
discrete alternatives, and there was no ordering to these alternatives.
In other cases, there may be an ordering between the alternatives, in which
case the appropriate methodology is ordinal logistic regression. For example,
Carter and Van Auken (2005) modelled the determinants of the use of various
bootstrapping
financing
techniques
(i.e.
delaying
payments,
minimizing
financing, and
firms in Iowa. The extent to which the
accounts receivable, minimizing investment, private owner
sharing resources) in a sample of small
firms used each technique was assessed on a 5-point Likert scale, using data
from a questionnaire survey, and hence was an ordinal variable. The model
related the extent to which the
12
firms used each technique to three categories
We provide here a very short introduction to these estimation methodologies. The interested
reader is referred to the more complete descriptions provided in Gujarati and Porter (2009)
or Hensher et al. (2005).
11.4 Hypothesis Testing
of perceived business constraints and opportunities (risk, ability, effort) as
well as the age of the
11.4
firm.
Hypothesis Testing
fi
OLS (and other estimation methods) provide estimates of the coef cients
in the regression model together with other diagnostic statistics, but these are
just
estimates.
Hence the next step in any analysis is to formulate various
hypotheses about the corresponding population parameters, and test these
hypotheses using the results from the sample regression analysis.
fi
Suppose we have estimated the regression model speci ed in equation
11.4, and have generated the following regression equation:
Y^ i ¼ ^β
þ
1
β 2 X2i þ ^
β 3 X3i þ ^
β 4X 4i þ . . . þ ^
β k X ki
^
SEð^β Þ SEð^β
1
2
Þ
SEð^β
3
SEð^β
Þ
4
(11.16)
SEð^β k Þ
Þ
where
Y^ i ¼ predicted value of Yi
^
β to ^
β k are the estimated regression coefficients
SEð^β Þ to SEð^β kÞ are the estimated standard errors of the regression
coefficients
R = coefficient of determination
SSE = sum of squared residuals
1
1
2
Numerous possible hypothesis tests are available, but here we concentrate
first
on three of the most commonly used. We will
outline each of the test
procedures in principle, and then illustrate and interpret each of the tests in
Section 11.5 using the data on the determinants of export performance.
11.4.1
Testing for the Significance of Individual Regression
Coefficients
fi
Each of the estimated regression coef cients provides an estimate of the true
value of the corresponding population parameter, and the standard errors
provide an idea of how precise these estimates are. This test involves testing
to see whether changes in any single explanatory variable (say X
2) have a
ficant impact upon the dependent variable, in which case, the estimated
regression coeffi cient should have a value that is signi
ficantly different
signi
from zero. The test can, however, be used to test whether the value of the
estimated
regression
fied value (β).
speci
fi
coef cient
ð^
β 2Þ
is
signi
ficantly
different
from
any
231
232
Multiple Regression
Step 1: Specify the null hypothesis
H0 : β2 = β = 0
where
β is the regression coefficent associated with X
where β is the hypothesized value of β
2
2
2
Step 2: Calculate the test statistic
^
ðβ 2Þ
SEð^β 2 Þ
where
^β
¼
estimated coefficient associated with X
SEðβ^ Þ ¼ standard error of ^β
2
2
2
2
Step 3: Find the critical value from t-tables
We know that the test statistic follows a t-distribution, and that the critical
value is:
for α ¼ 5%
t∗n k
±
where
n = number of observations in the sample
k = number of coefficients in the regression model
(n ± k) = degrees of freedom
Step 4: Test criterion
If
If
11.4.2
^
³
³
³
³
³ ðβ 2 Þ ³
³
³
³
ð β 2Þ ³
> t ∗n k
SE ^
^
³
³
³
³
³ ð β 2Þ ³
³
³ ²
³
ðβ 2 Þ³
SE ^
Reject H0
±
t∗n k
±
Do not reject H0
Testing for the Joint Significance of Several
Explanatory Variables
Suppose that we have estimated the general model in equation 11.4, and we
want to test whether a subset of the explanatory variables (say X
2 and X3)
together
have
a
fi
signi cant
joint
impact
upon
the
dependent
variable,
11.4 Hypothesis Testing
whether
^
ðβ 2
or
^Þ
and β
3
not
each
of
the
estimated
individual
regression
fi
fi
coef cients
are signi cantly different from zero.
Step 1: Specify the null hypothesis
In formal terms, we differentiate between the maintained hypothesis (H
m)
fi
–
as speci ed in equation 11.4
–
and the restricted hypothesis (Hr), which
depicts the model if the null hypothesis (H
) is true:
0
Hm : Y i = β
1
X i+ β X i +β X
+ β2
2
3
3
H :β
0
Hr : Y i = β
1
+
2
4
4
i+
...
+ βk
Xki + εt
= β3 = 0
β4X4i +
...
+ βk
Xki + εt
Step 2: Calculate the test statistic
fi
The regression models speci ed in the maintained and restricted hypotheses
are now both estimated and, in each case, a note is made of the sum of
squared residuals (SSE). Recall from Section 11.3.1 that the SSE in any
regression model is a measure of how much of the variance in the dependent
variable is
not explained
by the model: a low value of SSE is thus preferable
fi
to a high value of SSE. Now the model speci ed in Hr must necessarily have a
fi
higher value of SSE than the model speci ed in Hm because it includes fewer
explanatory variables (see Section 11.3.2), but the hypothesis test addresses
fi
the issue of whether the former model has signi cantly less explanatory
power than the latter model.
ðSSE r ±
SSEmÞ=r
SSEm= ðn ± k Þ
where
n = number of sample observations
k = number of parameters in Hm
r = number of restrictions in H (here = 2)
r and (n ± k) are the degrees of freedom
0
Step 3: Find the critical value from F-tables
We know that the test statistic follows an F-distribution, and that the critical
value is:
F ∗r n±k
,
for α ¼ 5%
233
234
Multiple Regression
Step 4: Test criterion
r ± SSE m Þ= r
If ðSSE
> F∗
rn
SSEm=ðn ± kÞ
,
r ± SSEm Þ=r
∗
If ðSSE
² Fr n
SSE m=ðn ± kÞ
,
Reject H
k
±
k
±
0
Do not reject H
0
A rejection of the null hypothesis means that the explanatory variables (here
X2 and X3) excluded in the restricted hypothesis actually have a jointly
signi
ficant
effect upon the dependent variable, hence their exclusion is not
warranted. It may be that these explanatory variables are not individually
signi
ficant
fi
(see Section 11.4.1), but together they have a jointly signi cant
effect. Such a
finding is common when there is a high degree of collinearity
between the variables (see Section 11.3.3).
11.4.3
Testing for Common Method Variance
Common method variance is often a potential problem in business research
when the data on the dependent and explanatory variables are drawn from
self-report questionnaire surveys. Common method variance refers to the
fl
fact that the respondents to such surveys tend to in ate to a similar degree
the ratings they assign to the items measuring the dependent and explanatory
signi
variables,
ficant
and
this
leads
to
correlations between the
spurious
variables.
and
possibly
Most
statistically
authorities (see, for
ex ante
example, Chang et al., 2010; Podsakoff et al., 2003) recommend
measures to minimize possible common method bias, but common method
bias may also be diagnosed
ex post
by Harman’s single factor test. This
involves an exploratory factor analysis (see Section 12.1.1) involving all of
the constructs derived from the questionnaire survey to ascertain whether a
single factor accounts for a sizeable proportion of the covariance between
the constructs. If it does, then common method bias is present and any
conclusions from the analysis of the data must be interpreted accordingly.
11.4.4
Statistical Significance and Effect Sizes
One of the most important objectives of regression analysis is to determine
which of the hypothesized explanatory variables have the greatest effects
11.4 Hypothesis Testing
upon the dependent variable, and to quantity the magnitude of these effects
with statements such as ‘an x% rise in variable X will give rise to an expected
y% rise (fall) in the value of variable Y
’. Such statements in turn lead to
questions about how feasible in practice is an x% increase in X, and how
meaningful in practice is a y% change in Y? If the answers to these questions
are ‘not feasible’ and ‘not meaningful’, then this raises issues about the (lack
of ) contribution of the research.
Often, however, researchers do not comment upon the effect sizes associated with each of the explanatory variables in their regression models, but
fi
simply report the levels of statistical signi cance (the p-values). Cohen
fi
(1994: 1001) noted that most researchers ‘know that statistically signi cant
fi
does not mean plain English signi cant, but if one reads the literature, one
finding reported in the Results section studded with asterisks implicitly becomes in the Discussion section highly signi
ficant, or very
highly significant, important, big! This is regrettable as statistical signifi cance does not necessarily predict effect size, as statistical signi
ficance
depends upon both effect size and sample size. If the sample is suf
ficiently
often discovers a
’
large (and many samples are huge due to the ease of downloading large
quantities of data from commercial databases, and will become ever larger
with the greater availability of big data), then statistical tests will almost
fi
always report signi cant effects
–
even if the effect size is small and unim-
portant. We would strongly recommend that all researchers include com-
fi
ments about both the statistical signi cance and the effect sizes of their
regression results. As Ellis (2010) comments, the relevant questions are how
big is the effect and what does it mean and for whom? Ellis (2010: 1587)
further suggests that ‘effect size indexes are meaningless unless they can be
contextualized against some frame of reference. At a minimum, authors
should interpret their results in the context of current evidence. Does the
observed effect differ from what others have found and, if so, why and by
how much.’
One
final
warning is to be aware of the units in which each of the
variables are measured, and not to be bamboozled by the absolute sizes
of
the
model,
estimated
firm
regression
coef
ficients.
In
the
export performance
size is measured in terms of the number of employees: mean
firm size is 114 employees
coeffi cient (see Table 11.1)
(Table 10.1) and the estimated regression
is approximately
±0.12.
If
firm
instead been expressed in thousands of employees, the mean
fi
size had
firm
size
would have been 0.114 and the estimated regression coef cient would
have been
±120.
235
236
Multiple Regression
11.5
The Determinants of Export Performance Redux
We now return to the export performance model we outlined in Section 11.2,
and discuss the findings that come from its estimation using OLS regression.
The general form of the model was set out in equation 11.2 (repeated here for
convenience).
EXP i = f(SIZEi, AGEi, CAPSi, REGIONi, SECTORi)
(11.2)
where
EXPi = export performance of fi rm i (%)
SIZEi = size of firm i (number of employees)
AGE' = age of firm i (years )
CAPSi = the capabilities of fi rm i (1 ± 7 scale)
REGIONi = region of firm i
SECTORi = industrial sector of firm i
EXP, SIZE, AGE, and CAPS are all continuous variables, while REGION and
SECTOR are both categorical variables with four (North, South, East, West)
and three (electronic, automobile, textile) categories respectively. The
two categorical variables were operationalized using three (DNORTH,
DSOUTH, DWEST) and two (DELEC, DTEXT) dummy variables respectively
(see Section 11.2.1). Furthermore, we hypothesize linear relationships
between the dependent variable (EXP) and each of the explanatory variables,
hence the model to be estimated is:
EXP i ¼ β þ β SIZE i þ β AGE i þ β CAPSi þ β DNORTH i þ β DSOUTH i
þβ DWEST i þ β DELEC i þ β DTEXT i þ εi
1
2
7
3
8
4
5
6
9
(11.17)
where
β1 is the intercept term
β2, β3 ,
β9 are the partial regression coefficients
εi = disturbance term
. . .. . .
Four different variants (1±4) of this model were estimated and, to aid the
interpretation of the empirical results, the estimated coefficients etc. are
normally tabulated as in Table 11.1. Model (1) just includes the three continuous explanatory variables but excludes the dummy variables. The value
of the coefficient of determination (R2) is 0.449, while the sum of squared
residuals (SSE) is 10 443. The coefficient of CAPS is positive (+4.11) and is
highly statistically significant (p < 0.01), while the coefficient of SIZE
11.5 The Determinants of Export Performance Redux
Table 11.1
OLS estimation of the export performance model
Model (1)
Intercept
34.0**
(3.9)
±
SIZE
AGE
CAPS
2
3
Model (2)
Model (3)
Model (4)
35.1**
26.8**
28.9**
(3.9)
(4.3)
(4.5)
±
±
±
(0.019)
(0.019)
(0.019)
(0.019)
0.074
0.070
0.066
0.051
(0.067)
(0.069)
(0.066)
(0.069)
4.11**
4.29**
3.86**
4.04**
(0.72)
(0.71)
(0.69)
(0.70)
0.139**
0.133**
DNORTH
0.114**
±
(2.92)
(3.25)
±
±
(3.12)
(3.34)
±
±
(2.71)
(2.94)
1.02
DWEST
6.08*
DELEC
DTEXT
n
2
SSE
3.16
6.03*
(2.70)
(2.94)
9.05**
7.34*
(2.62)
(2.94)
100
100
100
0.449
0.487
0.512
0.525
10 443
9 727
9 257
9 015
<
fi
0.05; ** if p
<
0.01.
3. The standard errors of the regression coef cients are in brackets.
±
fi
0.139) and also highly signi cant (p
<
ficient of AGE (+0.074) is insignificant (p = 0.276).
0.01). However, the
coef
In model (2), the three dummy variables DNORTH
(
, DSOUTH , DWEST )
related to region are included. The inclusion of three additional explanatory
fi
variables necessarily leads to an improvement in the coef cient of determin2
ation (R ) to 0.487, and to a fall in the sum of squared residuals (SSE) to
fi
9 727. But are these changes statistically signi cant? An F-test (see Section
firms that the fall in the SSE is not significant (F = 2.28, p > 0.05),
11.4.2) con
and that the inclusion of the threeREGION dummy variables has not led to a
ficant
0.31
100
2. The p-values are denoted as follows: * ifp
signi
4.31
5.98*
Notes: 1. The dependent variable is export intensity E
( XP18).
is negative (
0.115**
±
5.31
DSOUTH
R
237
improvement
in
the
explanatory
power
of
the
regression
model. In model (3), the two dummy variables DELEC
(
, DTEXT ) together
238
Multiple Regression
with the three continuous variables are included. In comparison to model (1),
the inclusion of the two dummy variables leads to an improvement in the
ficient of determination (R ) to 0.512, and to a fall in SSE to 9 257. But
are these changes statistically signifi cant? An F-test (see Section 11.4.2)
confi rms that the fall in the SSE is signi
ficant (F = 6.02, p < 0.01), and that
2
coef
the inclusion of the two
SECTOR dummy variables has led to a jointly highly
ficant improvement in the explanatory power of the regression model.
Finally we estimate the full model, as speci
fied in equation 11.17, which
signi
includes the three continuous variables and both sets of dummy variables.
In comparison to model (1), the inclusion of all the dummy variables leads to
fi
2
an improvement in the coef cient of determination (R ) to 0.525, and to a
fall in SSE to 9 015. The model thus explains 52.5 per cent of the variance in
EXP18).
the dependent variable (
SECTOR
The results of the F-tests suggest that the
fi
dummy variables add signi cantly to the explanatory power of
the model, but that the
REGION dummy variables do not. Here the researcher
must make a choice between reporting the empirical results for the full model
(4),
or only
dummy
for the
variables.
grounds that
model
Some
even
(3) which
researchers
fi
fi
excludes the
prefer
fi
insigni cant coef cients
the
REGION
insigni cant
former
approach
are interesting,
on
while
the
others
favour the latter on the grounds of parsimony.
´
fi
µ
Here we consider the full model (4). The possession of rm-level capabilities
CAPS)
(
fi
has a positive and very signi cant effect
export intensity. Recall that
interpretation
of
fi
the
CAPS
coef cient
^
β
4
¼
4:04;
p < 0:01
on
is measured on a 7-point scale, so the
size
is
not
straightforward
other
than
to
state that every 1-point improvement in capabilities is expected to give rise to
a
4
and
per
very
larger
cent
rise
fi
signi cant
´
in
export
effect
^
β
intensity.
2
¼±
Firm
0:115;
size
(
)
µSIZE
p < 0:01
on
has
a
export
negative
intensity:
firms tend to have lower export intensity than smallerfirms, with each
additional ten employees associated with an expected 1.15 per cent lower export
intensity.
Firm
age
AGE)
(
is
statistically
fi
insigni cant,
as
are
the
three
REGION dummy variables. The two SECTOR dummy variables (i.e. DELEC
and DTEXT ) are not ´
only jointly significant µ
as reported
´ above, but also indiµ
^
vidually significant
β ¼ þ6:03; p < 0:05 and ^
β ¼ þ7:34; p < 0:05 .
8
9
Recall from Section 11.2.1 that the automobile sector has been designated as
fi
the base sector, hence these two coef cients estimate that, on average,
firms of
given size, age, and capabilities in the electrical sector export 6.03 per cent more
firms in the
textile sector export 7.34 per cent more of their sales than similarfi rms in the
of their sales than similar
firms
in the automobile sector, while
automobile sector. These empirical results should be compared with those
findings should be
noted while explanations should be provided for dissimilarfindings and results
reported in previous studies of export performance: similar
11.6 What Constitutes a Good Regression Model?
3
Residuals
2
1
0
–1
–2
–3
Figure 11.9
–2
–1
0
Fitted values
1
2
Scatter plot of the residuals from the OLS regression of export performance
that run counter to theoretical preconceptions. Finally, we provide the scatter
plot of the residuals against the
fitted
values of
EXP18
in Figure 11.9: the
observations appear to be randomly distributed with no obvious evidence of
fi
model misspeci cation.
11.6
What Constitutes a Good Regression Model?
OLS is a mathematical procedure, and computer packages will always pro-
fi
duce output (estimated coef cients, standard errors, etc.) no matter how good
or how poor is the underlying model provided by the researcher (the
‘garbage
in, garbage out’ principle). So what are the essential features of a good
regression model? First, the model should be theoretically grounded in that
good reasons should be explicitly provided for the inclusion of each of the
hypothesized explanatory variables, and for the mechanisms by which each
is expected to have an impact upon the dependent variable. If some of the
ficients turn out to have the wrong signs, then the researcher
should investigate possible misspecifi cation of the model and/or provide
convincing explanations for the perverse fi ndings. Second, the model should
estimated coef
‘
’
have good explanatory power and account for a‘considerable’ proportion of
the variance in the dependent variable. There are no hard-and-fast rules as to
what constitutes a ‘considerable’ proportion, but a regression model that has
little explanatory power is not particularly useful. Third, the model should be
parsimonious in that it should be as simple as possible while still providing
fi
satisfactory explanatory power: statistically signi cant variables should be
239
240
Multiple Regression
included explicitly in the model, while variables that have only minor effects
should be excluded and captured by the disturbance term.
The researcher will thus need to take the following into account in deriving
final regression model, discussing the empirical results, and interpreting
the importance of the findings:
the
•
R
determination (
•
fi
the explanatory power of the model as measured by the coef cient of
signi
2
) (see Section 11.3.2);
ficant correlations between the explanatory variables, as reported in
a correlation matrix (see Section 10.2.4);
•
fi
fi
the statistical signi cance of each of the regression coef cients, as tested
by an array of appropriate t-tests and F-tests (see Sections 11.4.1 and
11.4.2);
•
a comparison of the signs and magnitudes of the estimated regression
ficients with a priori theoretical expectations;
coef
•
an
examination
of
the
residuals
from
the
regression
estimation
(see
fi
Section 11.3.4), together with various formal diagnostic tests, to con rm
fi
the correct speci cation of the model (particular attention should be
given to outliers, though removal of such observations from the dataset
is not recommended unless the observations are clearly wrong);
•
a substantive assessment of the marginal effects on the dependent variable of changing each of the explanatory variables (see Section 11.4.4).
Are these effects feasible/likely and/or substantial? What are the implications for policy and for
A
final
word.
Regression
firm management?
analysis
(like
any
statistical
analysis)
involves
plenty of routine calculations and formal tests, but there is art as well as
science in that the researcher has to exercise his/her judgement in deciding
fi
not only the speci cation of the initial model but also how to adapt and
interpret the model in response to the various statistical tests.
Exercises
Further Reading
.................................................................
Gallo, A. (2015) ‘A refresher on regression analysis’, Harvard Business Review,
4 November, online: https://hbr.org/2015/11/a-refresher-on-regression-analysis.
Gujarati, D.N. and Porter, D.C. (2009) Basic Econometrics, 5th edn., New York:
McGraw-Hill.
Sarstedt, M. and Mooi, E. (2014) ‘Regression analysis’ , in Sarstedt, M. and Mooi, E.
(eds.), A Concise Guide to Market Research, 2nd edn., Berlin and Heidelberg:
Springer-Verlag, pp. 193–233.
Studenmund, A.H. (2017) Using Econometrics: A Practical Guide, 7th edn., London:
Pearson.
Questions
.................................................................
1.
Suggest alternative measures to operationalize a concept such as the
‘international experience’ of
a
firm.
What are the strengths or weak-
nesses of the alternative measures?
2.
3.
Suggest examples of relationships between variables that are likely to be:
a.
linear;
b.
positive semi-logarithmic;
c.
quadratic (U-shaped);
d.
quadratic (inverse U-shaped).
What is omitted variable bias, when is it likely to occur, and why is it a
problem?
4.
What is the relationship between the total sum of squares (SST), the
regression sum of squares (SSR), and the sum of squared residuals (SSE)
in multiple regression analysis?
5.
What is common method variance, when is it likely to occur, and why is
it a problem?
Exercises
.................................................................
1.
Enter the data from Appendix A in an Excel spreadsheet (or a statistical
package of your choice). Estimate the export performance model assuming that there are semi-logarithmic relationships betweenEXP18 and the
three continuous explanatory variables (i.e. SIZE, AGE , and CAPS). Has
this improved the explanatory power of the model?
241
242
Multiple Regression
2.
3.
In Section 11.5, we reported that the inclusion of the two SECTOR
dummy variables (i.e. DELEC, DTEXT) in the export performance model
(3) had led to a highly significant (p < 0.01) improvement in the explanatory power of the regression model. Confirm this finding using the
hypothesis test set out in Section 11.4.2.
Consider the estimated export performance model in Table 11.1 (model 4).
What is the predicted value of export intensity E( XP18):
For an automobile firm located in the north that has 200 employees,
is 40 years old, and scores 5.0 for capabilities?
For a textile firm located in the south that has 50 employees, is
15 years old, and scores 2.5 for capabilities?
For an electrical firm located in the east that has 120 employees,
is 25 years old, and scores 4.0 for capabilities?
Consider the estimated export performance model in Table 11.1 (model 4).
EXP18 is measured as a percentage (mean = 38.0%), while AGE
is measured in years (mean = 31 years).
What will be the estimated coefficient of AGE if the variable
is measured in months (i.e. mean = 372)? What will happen to its
statistical significance?
What will be the estimated coefficient of AGE if EXP18 is measured
as a fraction (i.e. mean = 0.38)? What will happen to its statistical
significance?
a.
b.
c.
4.
a.
b.
12
Additional Methods of
Quantitative Analysis
An approximate answer to the right problem is worth a good deal more
than an exact answer to an approximate problem.
John Tukey
A
huge
array
of
statistical
methods
are
available,
of
variable
levels
of
sophistication, and a comprehensive survey would be well beyond the scope
of this textbook. Here we outline three methods which are widely used in
business studies research, viz: factor analysis, structural equation modelling,
and event study analysis. In each case, we explain the key elements of each
method, the underlying intuition, and how to interpret the results, and then
provide an example from the business literature.
12.1
Factor Analysis
Factor analysis is a mathematical technique that may be used to reduce a
large number of (observed) variables into a smaller number of (unobserved)
factors while still accounting for as much of the variance in the observed
variables as possible. Factor analysis has two main applications in business
studies research:
•
The
first
application relates to the analysis of the responses from ques-
tionnaire surveys, when the researcher wants to
fl
find
out whether the
answers to several questionnaire items re ect some underlying latent
variable. Indeed, it is considered good practice in questionnaire design to
include multiple items when trying to measure respondents
’ opinions or
attitudes.
•
1
The second application relates to multiple regression analyses, when the
researcher has included several explanatory variables which are highly
collinear (see Example 10.3 for evidence of the high correlations between
1
fi
Single-item scales have considerable random measurement error, cannot discriminate nely
between attributes, and cannot fully represent complex theoretical concepts. In short, they
are less valid, less accurate, and less reliable than multiple-item scales.
244
Additional Methods of Quantitative Analysis
the six World Bank worldwide governance indicators). Multicollinearity
is thus a potential issue, and factor analysis may be used to combine the
original variables into a lesser number of orthogonal factors.
fi
fi
The factor analysis may be con rmatory or exploratory. In con rmatory
factor analysis, the researcher starts by specifying the number of factors and
which variables load on to (are associated with) which factors, and will
typically be trying to validate or replicate previous studies. In contrast, the
researcher undertaking an exploratory factor analysis will typically only
have some general expectations about the relationships between the vari-
fi
ables, and hence does not want a priori to impose a xed number of factors
or to specify which variables load on to which variables. Rather, the number
of factors, and their interpretation by the researcher, emerge from the factor
analysis.
In
this
chapter,
we
will
outline
the
basic
ideas
underpinning
exploratory factor analysis, and refer the interested reader to Brown (2014)
fi
for further explanation of con rmatory factor analysis.
12.1.1
Exploratory Factor Analysis
Exploratory factor analysis usually involves three steps. The
first
step is
factor extraction through which initial estimates of the factors are obtained
2
from principal components analysis (PCA).
fi
The coef cients that relate the
variables to the factors are displayed in a factor matrix. However, it is usually
dif
ficult to interpret the meaning of the factors based on this matrix because
most
factors
will
be
correlated
with
most
variables,
hence
the
second
step involves transformation of the initial matrix into one that is easier
3
to interpret by a process called factor rotation. The purpose of rotation
is to achieve a simple structure in which each factor has‘meaningful’ nonzero loadings for only some of the variables (so that interpretation of the
factors is possible) and each variable has‘meaningful’ non-zero loadings for
only a few factors (which allows the factors to be differentiated from each
other). Normally a loading is considered ‘meaningful’ if it has an absolute
2
A number of possible methods of factor extraction are available, but principal components
fi
analysis (PCA) is the most common. PCA proceeds by identifying the rst factor (component)
as the linear weighted combination of the variables that accounts for the highest amount of
fi
variance in the dataset. The process is then repeated, with a second factor identied which
accounts for the highest amount of the remaining variance. The process is repeated until the
number of factors is equal to the number of original variables, each accounting for
progressively smaller proportions of the total variance. The researcher then chooses the
number of factors that account for a satisfactory amount of the variance. Other methods of
3
factor analysis include common factor analysis, image factoring, and alpha factoring.
Several different types of rotation are available, but the most commonly used method is
varimax rotation.
12.1 Factor Analysis
value of greater than 0.40 (Field, 2018). The number of factors extracted will
be equal to the number of observed variables being analysed, but typically
fi
most of the variance will be accounted for by the rst few factors. It is for the
researcher to decide how many factors are retained, and s/he will be guided
by (a) whether the factors have substantive conceptual meanings drawing on
what the variables have in common, and (b) whether the solution accounts
4
for a ‘satisfactory ’ proportion of the variance. This ‘satisfactory’ proportion
might
be
proportion
fi
de ned
of
the
in
terms
variance,
of
a
or
minimum
by
(e.g.
stipulating
fi
50
per
that
cent)
each
cumulative
retained
factor
should account for more than a speci ed proportion (say 4 per cent) of
the variation in the dataset. The proportion of the variance in each observed
variable explained by the extracted factors is called the communality of the
variable. Communalities can range from zero to one, with zero indicating
that the factors explain none of the variance, and one indicating that all the
fi
variance is explained by the common factors. The third and nal step is to
calculate factor scores for each observational unit, which may then be used
in further data analysis. These factor scores are obtained by multiplying
the factor loadings by the values of the original variables, and are thus
weighted linear combinations of the original variables that load on each
factor: positive values denote above-average scores, and negative values
denote below-average scores. Finally, each factor is thus a composite derived
from several of the original variables, but how reliable are these composite
scores? This reliability may be assessed by considering the internal consistency of each of the factors, or how well correlated the values of the underlying variables are. A high degree of internal consistency is desirable because
it speaks directly to the ability of the researcher to interpret the composite
fl
score as a true re ection of the variables. The internal consistency of each of
ficient
the factors is typically measured by Cronbach
’s coef
alpha, whose
values fall in the 0–1 range: values over 0.7 are considered ‘good’, while
values over 0.6 are ‘acceptable’ (Field, 2018). Factors with lower values of
5
Cronbach alpha are unreliable, and should not be used.
4
5
Other criteria might involve looking at the eigenvalues or using scree plots.
Robust factor analysis requires large data samples so that the factor solutions are stable
(MacCallum et al., 1999). The literature contains various recommendations about the
minimum size of the sample to obtain stable factor solutions. Some people offer simple rules
of thumb, such as that the sample size should be at least ten times the number of variables.
Others note that the appropriate sample size will depend upon study-related features, such as
the communalities of the variables: if the communalities are high, then a smaller sample size
is acceptable.
245
246
Additional Methods of Quantitative Analysis
EXAMPLE 12.1
fi
The identi cation of personality factors has a long history in the psychology
fi
literature. Cattell (1943a, 1943b) identi ed various personality traits, and
this list was later extended by Tupes and Christal (1992/1961) to 35 trait
variables: they also used
factor analysis to
reduce these 35 traits to only
5 underlying factors, and showed that these factors were replicable. Subsequent empirical research has enlarged the number of trait variables and has
culminated in the widely accepted Five Factor Theory (McCrae and Costa,
1997, 1999), which posits
•
fi ve
structural personality tendencies:
Openness concerns an individual’s willingness to try new things, to be
vulnerable, and the ability to think outside the box.
•
Conscientiousness is the tendency to control impulses and act in socially
acceptable ways that facilitate goal-directed behaviour.
•
Extraversion concerns where individuals draw their energy and how they
interact with others. Extroverts tend to draw energy from interacting with
others, while introverts get tired from interacting with others and
replenish their energy from solitude.
•
Agreeableness concerns how well people interact and get along with
others.
•
fi
Neuroticism concerns an individual’s con dence and being comfortable
in their own skin. It encompasses the individual’s emotional stability and
general temper.
McCrae and Costa (1997) further claim that these tendencies are universal
and transcultural.
12.1.2 An Example of Factor Analysis: Burnout on Oil Rigs
Job-related burnout is a unique type of stress syndrome, characterized by
emotional exhaustion, depersonalization, and a diminished sense of personal
accomplishment.
It
is
fl
a
serious
problem
both
for
employers
and,
more
importantly, for the af icted workers for whom burnout may have negative
impacts upon both their working lives and their family lives. Hellesøy et al.
(2000) examined job-related burnout among the workers on offshore oildrilling platforms in the North Sea. This work is unique in several ways. It
involves working in cycles, two weeks off-shore interrupted by two-week
periods with no job-related obligations on-shore. The working day on the
platforms
lasts
12
hours,
even
on
weekends
and
traditional
holidays.
The working environment is dangerous. Working conditions are rough, and
12.2 Structural Equation Modelling
often so are the weather conditions. Fires and blow-outs with fatal consequences are not unusual. Transportation to and from the platforms is by
helicopter, and there have been fatal transportation accidents.
The authors administered a questionnaire survey to investigate burnout.
The survey included 19 items, each assessed using a 5-point scale, and 2 061
usable responses were received. The authors then conducted an exploratory
factor
analysis
of
the
survey
data
to
uncover
the
latent
dimensions
of
burnout: 5 items were omitted, hence the original 19 items were reduced to
14. The rotated factor matrix is shown in Table 12.1, with only the loadings
for the
first four factors being included and only factor loadings greater than
fi
0.4 being reported. Inspection of the four items that load heavily on the rst
factor shows that these capture the respondents
’ lack of relationships with
others. The authors thus term this factor ‘alienation’. The three items that
fl
load heavily on the second factor re ect lack of focus and motivation, and
the authors term this factor ‘focus loss’. The four items that load heavily on
fl
the third factor re ect the respondents’ feelings of vulnerability and depression, and the authors term this factor ‘depression ’. Finally, the three items
that load on the fourth factor relate to worrying about home, and this factor
is termed ‘worry ’. Hence the authors conclude that job-related burnout has
four distinct dimensions (factors). The
first factor accounts for 34.9 per cent
of the total variance, with the other three factors accounting for 4.6, 4.1, and
2.2 per cent of the variance. Together these four factors thus account for
almost 46 per cent of the total variance.
The authors note that some of the communalities are modest, and they
suggest this is due to the skewness of the responses to some of the question-
fi
naire items. The computed values of Cronbach
’s coef cient alpha for all four
factors were above the ‘good’ level of 0.7, suggesting that these dimensions
were internally consistent.
12.2
Structural Equation Modelling
Structural equation modelling (SEM) is a general modelling technique which
may be viewed as a combination of factor analysis and regression analysis.
SEM allows the researcher to analyse simultaneously all the paths between
observable indicator variables and multiple outcome variables, rather than
estimating a set of regression equations individually. SEM has been described
as
a
second-generation
multivariate
analysis
technique
(Fornell,
1987),
and its use is common in research on strategic management (Hair et al.,
2012a; Shook et
al., 2004), marketing
(Hair et
al., 2012a),
management
information systems (Hair et al., 2012b; Ringle et al., 2012), family business
247
248
Additional Methods of Quantitative Analysis
Table 12.1 Example of exploratory factor analysis: job-related burnout
Communality
Feel lonesome
even together
with other people
Feel everything is
a great effort
Never feel close to
another person
Feel that nobody
understands or
cares
Difficulties in
remembering
Difficulties in
deciding
Difficulties in
concentrating
Become easily
irritated
Factor 1
Factor 2
Factor 3
Factor 4
Alienation
Focus loss
Depression
Worry
0.65
0.74
0.41
0.41
0.41
0.70
0.46
0.59
0.36
0.51
0.45
0.61
0.71
0.76
0.47
0.57
Feel depressed
0.63
0.51
Feel easily hurt
0.47
0.51
Feel tense
0.53
0.52
Worries about
home when away
Worrying about
home affects work
Difficulties in
sleeping due to
worry
Percentage of total
variance
Cronbach alpha
0.62
0.77
0.57
0.69
0.58
0.50
34.9
0.78
4.6
4.1
2.2
0.73
0.80
0.73
Source :
Hellesøy et al. (2000).
12.2 Structural Equation Modelling
V1
V2
LAT1
V3
V4
OUT1
V5
V6
V7
LAT2
MED
V8
OUT2
V9
V10
LAT3
V11
MOD1
V12
MOD2
Figure 12.1
A path diagram for structural equation modelling
(Sarstedt et al., 2014a, 2014b), operations management (Peng and Lai, 2012),
accounting (Lee et al., 2011), and international business (Hult et al., 2006;
Richter et al., 2016).
A SEM model is often initially visualized in a path diagram which depicts
the expected relationships between the variables and consists of circles and/
or boxes connected by arrows. Observed indicator variables are often represented by square or rectangular boxes, while latent (unobserved) variables
are represented by circles or ellipses. Single-headed arrows denote causal
relationships in the model, with the arrows showing the direction of causation. For example, in Figure 12.1, the model includes 12 observed indicator
V1–V12 ) which are assumed to load on three latent constructs
(LAT1 , LAT2 , and LAT3): V1 –V3 load on LAT1, V4–V8 load on LAT2 , and
V9– V12 load on LAT3. These latent constructs are assumed to have an
impact upon the two outcome variables O
( UT1 and OUT2) but (a) these
impacts are mediated by an intervening variable M
( ED), and (b) the relationships between LAT1 –LAT3 and MED are moderated by two moderating
variables (MOD1 and MOD2). Mediation here refers to a situation where
the path relationship between two variables (e.g. between LAT1 and
variables (
6
6
A SEM does not necessarily include mediating and/or moderating variables. The outcome,
mediating, and moderating variables may be latent constructs (as depicted in Figure 12.1), or
may be observed variables.
249
250
Additional Methods of Quantitative Analysis
OUT1 )
is effected through the intervention of a third variable MED
(
). In
contrast, moderation refers to a situation where the strength of the path
relationship between two variables (e.g. LAT1 and MED ) depends upon the
values of a third variable (or variables). Direct relationships are typically
depicted by solid arrows, while the moderating effects are depicted by dashed
arrows. The path diagram then has to be converted into a set of equations
which specify the model for input into one of the many available computer
programs.
7
In the language of SEM, the relationships between the observed indicator
variables and their corresponding constructs are known as measurement
(outer) models, while the relationships between the constructs are known as
the structural (inner) model. As Hair et al. (2014: 110
–11) emphasize, the
fication
‘sound speci
of the outer models is crucial because the relationships
hypothesized in the inner model are only as valid and reliable as the outer
models’.
12.2.1
Estimation of Structural Equation Models
Now the model set out in Figure 12.1 could in principle be estimated by a
sequential
combination
of
factor
analyses
and regression
8
analyses.
The
main problem with such an approach is that each set of relationships in the
model is estimated separately, and the measurement errors are aggregated in
the residual error term. In contrast, the entire model (including the factor
analyses and all the hypothesized relationships between the variables) are
estimated
simultaneously
with
SEM
modelling.
This
has
a
number
of
advantages:
1.
SEM allows for a more precise estimation of the effects of the latent and
moderating variables on the mediating and outcome variables because
the algorithm can distinguish variance arising from the imperfect meas-
fi
urement of variables from variance arising from the misspeci cation of
the theoretical model.
2.
The impact of the latent, mediating, and moderating variables upon more
than one outcome variable may be assessed simultaneously.
7
8
Popular programs include AMOS, EQS, LISREL and SmartPLS.
The latent constructs and the moderating variables are exogenous in the model (i.e. they are
not affected by other variables in the model)– they do not have arrows pointing at them. In
contrast, the mediating variable and the outcome variables are endogenous (i.e. they are
affected by other variables in the model). The model in Figure 12.1 is also recursive, in that it
does not include any feedback loops between the variables (which would be depicted by
double-headed arrows).
12.2 Structural Equation Modelling
Two
complementary
procedures
are
available
for
estimating
SEM
models, viz: covariance-based SEM (CB-SEM) and partial least squares SEM
(PLS-SEM). The two procedures differ in their statistical methods, have different data requirements, and the choice between them requires an alignment of
the research objective with the amount of existing knowledge (Hair et al.,
2011; Richter et al., 2016; Sosik et al., 2009). Thus CB-SEM is the more
appropriate procedure when there is a solid or strong theoretical foundation
fi
for the proposed model and con rmatory modelling is required. The correctness of the theoretical model is the basic assumption that underpins the CBSEM procedure, model parameters are estimated by minimizing the difference
between the empirical covariance matrix and the covariance matrix deter-
fi fit statistics are computed
to evaluate the extent to which the empirical datafit the theoretical model. In
mined by the theoretical model, and goodness-of- t
contrast, the PLS-SEM procedure is more appropriate when the constructs and
fi
the relationships in the model are not unambiguously de ned in advance and
the approach is more exploratory. PLS-SEM maximizes the amount of variance explained by the observed variables, and is useful for accurately predicting individual scores on the latent variables. Furthermore, PLS-SEM is better
suited to the estimation of complex models or relationships, as long as the
sample
is
of
fi
suf cient
speaking, the use
size.
As
of empirical
Hair
et
methods
al.
(2014:
116)
in business
suggest ‘Broadly
applications has
two
objectives: prediction and explanation. . . Application of CB-SEM typically
overlooks a key objective of empirical studies, which is prediction. The solution to this inherent weakness is the use of PLS-SEM.
’ In conclusion, Richter
fi
et al. (2016: 382) suggest that ‘authors are advised to make use of the bene ts
offered by PLS-SEM in research situations characterized by theorizing and
prediction-oriented goals rather than by strong theory
’.
How should the results from a PLS-SEM be interpreted?
•
The
reliability of each of the latent constructs in the measurement models
should
be
evaluated
using
Cronbach’s
fi
coef cient
alpha
(see
Section
12.1.1) or the composite reliability (CR) measure. The CR statistic provides an alternative and more general measure of reliability than Cronbach alpha, though it is not so widely cited in the literature. CR values
also fall in the 0– 1 range: values over 0.7 are considered to indicate good
reliability (Hair et al., 1998).
•
The
quality of each of the latent constructs should
be assessed by the
average variance extracted (AVE), which measures how much of the total
variance is accounted for by the variance in the construct. The AVE is
equivalent to the communality of a factor, and values above 0.5 are
considered acceptable (Hair et al., 1998).
251
252
Additional Methods of Quantitative Analysis
•
goodness-of-fi t
The overall
the
root-mean-square
comparative
RMSEA
•
>
fit
error
of
approximation
(RMSEA),
and
index (CFI). Recommended thresholds for good
0.08 and CFI
±
by
fit
the
are
0.90.
quality of the paths in
The
of the structural model may be assessed by
the structural model are based on their ability
to predict the endogenous constructs. This may be assessed by the coef-
ficient of determination (R ) and the estimated path coefficients. The
coeffi cient of determination is a measure of the predictive accuracy of
each of the paths in the structural model. The values of the path coef
ficients are standardized in the range from ²
( 1 to +1 ), with coefficients
closer to +1 representing strong positive relationships and coef
ficients
2
²
closer to
1 indicating strong negative relationships. Standard errors
may be obtained using bootstrapping, and then used to test the path
ficients for statistical significance.
coef
12.2.2 An Example of Structural Equation Modelling:
Green Innovation
Kawai
et
al.
(2018)
investigated
the
extent
of
green
innovation
by
the
subsidiaries of Japanese multinational enterprises (MNEs) in overseas host
countries.
They
differentiated between
two
outcome
variables
(i.e.
green
product innovation and green process innovation), and suggested that such
fi
first-mover
innovations brought potential bene ts (e.g. positive image, premium pricing,
improved product design, and
advantages) for the innovating
firms, but that the firms also incurred private costs/obstacles (e.g. uncertain
returns,
competition
from
existing
(dirtier)
products/processes,
customer
reluctance hence additional marketing costs). They argued that the social
fits of innovation often outweighed the private benefits to the innovating fi rm, and thus outside stakeholders have incentives to exert pressure on
firms to undertake more innovation. Furthermore, they suggested that three
bene
types of local stakeholder (regulatory, market, and societal) pressures in the
host countries enhance green innovation, and that these pressures are mediated by the
firms
’ implementation
of formal environmental management
systems (EMS). The authors thus estimated a structural equation model using
PLS-SEM and the results are summarized in Figure 12.2: the two outcome
variables (i.e. green product innovation and green process innovation), the
mediating variable (i.e. EMS implementation), and the predictor variables (i.e.
regulatory stakeholder pressure, market stakeholder pressure, and societal
stakeholder pressure) are all latent constructs using data on multiple items
from
a
Europe.
questionnaire
The
survey
relationships
of
123
between
subsidiaries
the
three
in
types
North
of
America
local
and
stakeholder
12.3 Event Studies
Pressure from
Multiple
Stakeholder Groups
Proactive
Environmental
Initiatives
Green Innovation
Performance
Global
Institutional
Pressure
Macro-level
Micro-level
Regulatory
n.s.
R
b =0.127**
Stakeholder
2
= 0.433
Pressure
Green Product
n.s.
2
n.s.
Market
Stakeholder
Pressure
b=0.429***
R = 0.149
Innovation
EMS
b=0.183**
Implementation
R
2
= 0.484
b =0.615***
Green Process
Societal
Innovation
n.s.
Stakeholder
Direct Effects
Pressure
Moderating Effects
Figure 12.2 PLS-SEM estimation of green innovation
pressure and EMS implementation by the subsidiaries were hypothesized to
be moderated by global institutional pressures.
The authors reported that the CR values for all the latent constructs were
larger than the threshold value of 0.70, and that all the Cronbach alpha
values were larger than 0.64. The quality of each of the constructs was
con
firmed
fi
by all the AVE values being larger than 0.50. The overall t of
2
the model was satisfactory: CFI = 0.958; RMSEA = 0.0628. The R values for
fi
the individual pathways were acceptable. As regards the path coef cients,
β = 0.183, p < 0.05) effect on
fi
market stakeholder pressures have a signi cant (
EMS implementation, whereas the effects of both regulatory stakeholder and
fi
societal stakeholder pressures are insigni cant, and EMS implementation is
β
in turn positively associated with both green product innovation ( = 0.429,
p < 0.01)
β
and green process innovation ( = 0.615,
fi
p < 0.01).
Furthermore,
global institutional pressures have a signi cant positive moderating effect
β = 0.127, p < 0.05)
(
on the relationship between market stakeholder pres-
sures and EMS implementation.
12.3
Event Studies
Event studies are used for assessing the short-term stock market reactions to
firm-speci fic events such as announcements of earnings, new CEO appointments, major acquisitions, etc. The methodology is founded on the ef
ficient
markets hypothesis (Fama et al., 1969), and assumes that thefi rm s share
’
253
254
Additional Methods of Quantitative Analysis
fl
price at any point in time re ects all publicly available information. The
fi
basic idea is that if investors believe that speci c unexpected events are
value-enhancing, then the share price will increase and shareholder wealth
will rise. Alternatively, if investors believe the event is value-destroying (e.g.
a law
suit), then
developed in the
the
share price will
finance
fall. Event
studies
were
originally
literature (Corrado, 2011; MacKinlay, 1997), but
applications are now found in many other areas of business research, including management (McWilliams and Siegel, 1997), marketing (Johnston, 2007;
Sorescu et al., 2017), anti-trust (Cichello and Lamdin, 2006), information
systems (Konchitchki and O’Leary, 2011), and operations and supply chain
management (Ding et al., 2018).
12.3.1
The
first
The Estimation Procedure
fi
step in the estimation procedure is to select a sample of rms that
have experienced the event (e.g. a new CEO appointment) under investigation. This selection is not completely straightforward as it is essential that
none of the
firms in the sample has experienced any other significant value-
enhancing event during the period of the study (see details below), otherwise
the effects will be confounded.
The second step in this procedure is to specify an event window during
which the changes in the share prices resulting from the event are to be
assessed. If the event takes place on day T, then a typical event window
might include both the day of the announcement and the following day: i.e.
the event window would be [T, T+1]. An event window that is too short may
not capture the full extent of the resulting share price increase, while an
event window that is too long may include the effects of other information
becoming available. Sometimes the event window includes days before the
²
announcement – e.g. [T
3, T+1] – in order to capture any possible effects
due to anticipation of the announcement or information leakage about the
announcement.
The third step in the procedure is to specify an estimation window during
which a market model is estimated to predict the expected share prices for
each
firm
window
in
the
absence
be
quite
will
of
long,
fi
signi cant
say
120
events.
days
Typically
from
²
the
[T 130,
estimation
²
T
10],
and
will include a buffer zone (washout period) before the start of the event
window so minimize any possible effects due to information leakage. Longer
estimation windows mean more observations and potentially more precise
estimates of the model parameters, but the window must not include any
ficant
other signi
in Figure 12.3.
events. The time line for a typical event study is depicted
12.3 Event Studies
Estimation window
Buffer
zone
Event
window
T-130
Post-event window
T-10
T
T+1
Time
Figure 12.3
The time line for event studies
fi
The fourth step is the estimation of the market model for each rm,
9
which
is typically expressed in the form of returns (i.e. the share price as a percentage of the amount invested) as follows:
Rit = αi + βi Rmt + εt
(12.1)
where
Rit return for firm i on day t
Rmt return on a market portfolio on day t
αi constant term for firm i
βit captures the systematic risk associated with the stock offirm i
=
=
=
The choice of an appropriate market portfolio is at the discretion of the
researcher and will depend in part upon the empirical context of the study. If
US
firms are being considered, the SandP500 index might be relevant. Other
possible portfolios might include the FTSE100 index in the UK, the DAX
index in Germany, the Hang Seng index in Hong Kong, and the MSCI
emerging markets index.
The estimation (by OLS) of the market model for each
estimation of expected returns
±
²
ARit
) for each
calculate the abnormal returns (
R^ it
on each day. The
firm
fifth
allows the
step is then to
firm, as the difference between
the realized return and the expected return on the same day.
ARit ¼ Rit ²
³
^αi þ ^β i Rmt
´
(12.2)
where ^α i and ^β i are the OLS coefficient estimates
9
Other models may be used to generate expected returns, including the constant mean model,
the capital asset pricing model (CAPM), and the arbitrage pricing model.
255
256
Additional Methods of Quantitative Analysis
The sixth and
CARi
returns (
final
) for each
step is then to calculate the cumulative abnormal
firm
over the event window: this CAR measures the
increase in the market value of the
firm as a direct result of the event.
X AR
t ¼T þ1
CARi ¼
If
the
event
is
perceived
by
it
t¼T
the
(12.3)
market
as
value-enhancing
(e.g.
a
major acquisition), then the CAR will be positive; if the event is perceived
to
be
value-destroying
(e.g.
a
corporate
scandal)
then
the
CAR
will
be
negative.
The usefulness of event studies arises from the assumption that share
flect
prices re
the
economic
ex post
is
all relevant information, and hence it is possible to estimate
no
impact
assessment
need
to
of
any
after
formalize
event
immediately
collecting
a
model
months
of
involving
rather
data.
all
than
making
Furthermore,
possible
there
determinants
fl
an
of
the impact, as it is assumed that all the relevant effects are re ected in
the share prices. Once the estimation procedure has been completed, the
researcher may use the cumulative abnormal return (CAR) data in a variety
of ways:
•
To
test
whether
the
event
has
had
a
fi
signi cant
fi
impact,
by
testing
whether the mean CAR is signi cantly different from zero using the
Corrado rank test (Corrado, 2011).
•
fi
To compare the mean CARs for different sub-groups of rms. MacKinlay
(1997)
compared
the
share
price
reactions
of
firms
reporting
strong
fits, normal earnings, or a loss in the earnings– see Figure 12.4. His
results show clearly that fi rms which reported good news were rewarded
pro
with higher CARs, especially on the event day.
•
To investigate possible determinants of variations in the CARs between
different
12.3.2
firms; see section 12.3.3 for an example.
The Applicability of Event Studies
Event studies are widely applicable in situations where the following conditions are met (McWilliams and Siegel, 1997):
•
fi
Markets are ef cient, so that it may be assumed that share prices react
fi
quickly to all available information related to the pro tability of the
fi
firm.
If markets are less ef cient (as perhaps in developing countries), then
there may be abnormal returns after the event window that capture
second-thought price effects (Zhu and Malhotra, 2008).
12.3 Event Studies
0.025
0.02
0.015
0.01
CAR
0.005
0
–0.005
–0.01
–0.015
–0.02
–0.025
–21 –18 –15 –12
–9
–6
–3
0
3
Event Time
Good News Firms
Figure 12.4
6
9
No News Firms
12
15
18
21
Bad News Firms
Share price reactions to earnings announcements
The event being studied is not anticipated, and hence the information
•
being disseminated is ‘new’. This condition may be problematic in situ-
fl
ations where the event re ects past performance, either good (e.g. the
announcement
of
an
award)
or
bad
(e.g.
the
announcement
of
an
impending law suit).
There are no serious confounding effects during the event window or the
•
estimation window that could distort the results.
The
need
to
have
a
sample
of ‘clean’
data
that
is
clear
of
serious
confounding effects is especially problematic, and this may result in small
sample sizes, with consequent repercussions for assumptions about normality and the choice of hypothesis tests. The results from event studies are
also
dependent
upon
appropriate,
and
to
some
extent
arbitrary,
choices
being made regarding the length of the event and estimation windows, and
the
market
portfolio.
researchers
effects,
to
need
to
justify
McWilliams
report
their
how
choices
and
they
Siegel
control
regarding
the
(1997)
for
emphasize
possible
length
of
that
confounding
the
event
and
estimation windows, and to explain how in theory investors are likely to
react
to
reaction.
the
event
and why
this
behaviour should
deliver
a
stock
price
257
258
Additional Methods of Quantitative Analysis
EXAMPLE 12.2
Porter et al. (2004: 8) suggest that a key role of a company CEO
‘is to sell the
strategy and shape how analysts and shareholders look at the company
’. If
investors are uninformed about the company strategy, the resulting information asymmetries create uncertainty which has a negative impact upon
share prices and the company’s costs of capital. Whittington et al. (2016) thus
argue that CEOs should provide public presentations in which they inform
investors about the broad strategies of their companies. They further suggest
that
the
information
asymmetries,
and
hence
the
potential
fi
bene ts,
are
greatest the more the CEO is unfamiliar to the investors– as in the case of
new CEOs, and when new CEOs come from outside the company. They tested
their hypotheses using event study analysis and data on 876 presentations
made by the CEOs of US companies over the period 2000
– 2010. They found
that share prices rose by 1.6 per cent on average after these presentations, but
that the reactions were stronger the more the CEO was unfamiliar to investors.
Thus the share price gains following presentations by new CEOs were 5.3 per
cent; but they were 9.3 per cent for external, within-industry new CEOs, and
were 12.4 per cent for external, outside-of-industry new CEOs. They concluded that such presentations were not ‘cheap talk’, but effective forms of
impression management, especially in the uncertain months following the
appointment of a new CEO. Furthermore, they report that the timing of
presentations can make a difference, with greater effects if the presentations
take place within the
Park
(2004)
notes
first 100 days than within thefi rst 200 days.
that
most
event
studies
have
analysed
corporate
fi
announcements in a single country, but suggests that stock returns for rms
fi
fluences such as share price movements in other countries as a result
involved in international business to a signi cant degree may be affected by
global in
of
the
global
integration
of
equity
markets.
He
offers
guidance
to
the
researcher on the major challenges and solutions pertaining to the use of
the event study method in multi-country settings: these include the development of a stock return model, the lack of synchronism in stock market trading
hours, and the differences in institutional environments between countries.
12.3.3 An Example of an Event Study: Cross-Border Acquisitions
Ning
et
al.
(2014)
carried
out
an
event
study
to
assess
the
share
price
reactions to 335 announcements of cross-border acquisitions by Chinese
MNEs listed on the Hong Kong Stock Exchange. They used a 90-day estima-
²
tion window (T
²
120, T
31), various (2-day, 3-day, 5-day, and 11-day)
event windows, and the Hang Seng index as the market portfolio. They
12.4 Final Comments
ficant positive CARs of about 1 per cent
reported signi
for each of the event
windows, and concluded that investors perceived the acquisitions as valueenhancing strategies. The main objective of their study, however, was to
investigate the effects on shareholder wealth creation of several dimensions
of MNE ownership structure and corporate control, and of various internal
control mechanisms. They thus estimated the following multiple regression
model with CAR as the dependent variable:
CAR i = β
1
OWNi + β CONTROLi + β ICRi + β INDi + εi
+ β2
3
4
(12.4)
5
where
CARi cumulative abnormal return for firm i
OWNi variables related to the ownership structure offi rm i
CONTROLi dummy variables about the identity of the controlling
shareholder
ICRi variables related to the internal control mechanisms offirm i
INDi independent control variables forfirm i
εi disturbance term
=
=3
=3
=7
=8
=
OWN)
The three variables (
related to the ownership structure were the
percentages of shares held by the largest shareholder, by other blockholders
(with at least 10 per cent shareholding), and by institutional investors. The
CONTROL) related to the identity of the controlling
three dummy variables (
shareholder,
controlled,
signi
or
fied
whether
the
founder-controlled.
firm
The
fi
was
seven
state-controlled,
ICR)
variables
(
foreign-
related
to
internal control mechanisms, and identi ed board size, board independence,
CEO duality, the number of non-executive directors, the size of supervisory
board,
audit
committee
independence,
and
the
fees
paid
to
auditing
firms. The authors concluded that investors react
positively to the presence
of
presence
large
shareholders,
but
negatively
to
the
of
institutional
shareholders. There is also a negative impact if the largest shareholder is
either the state or the corporate founder which, they suggest, is because the
investors
perceive
potential
–
principal principal
agency
flicts
con
in such
ownership/control constellations and discount equity prices accordingly.
They also found that board independence and size had positive effects on
share prices, but that large supervisory boards were associated with negative
reactions.
12.4
Final Comments
We have concentrated in this chapter on three methods that are widely used
in business research, viz: factor analysis, structural equation modelling, and
259
260
Additional Methods of Quantitative Analysis
event
studies.
Other
methods
are
also
available,
including
discriminant
analysis (Eisenbeis, 1977) and cluster analysis (Ketchen and Shook, 1996;
Punj and Stewart, 1983; Sarstedt and Mooi, 2019). The objective in discriminant analysis is to derive a linear combination of two or more independent
variables – the discriminant function – that will best discriminate between
fi
two or more groups that have been de ned in advance. An example might be
the use of
financial
ratios to predict corporate success or failure. A second
example might involve the use of selected
location attributes to predict
firms
firm-specific
characteristics and
’ location choices (Chen and Chen, 1998).
In contrast, the objective of cluster analysis is to discover natural groups
within a sample based on the sample data, with the grouping based upon
similarities or distances (dissimilarities) in n-dimensional space. An example
would be to group customers into market segments based upon their personal
characteristics (e.g. age, gender, ethnicity, social class).
Exercises
Further Reading
.................................................................
Brown, T.A. (2014)
Confirmatory Factor Analysis for Applied Research
, 2nd edn.,
New York and London: Guilford Press.
Chatterji, A., Findley, M., Jensen, N., Meier, S., and Nielson, D. (2016)‘ Field
Strategic Management Journal
Discovering Statistics Using IBM SPSS Statistics
experiments in strategy research’ ,
Field, A. (2018)
, 37(1): 116–32.
, 5th edn., London:
Sage.
Questions
.................................................................
1.
What are the main objectives of factor analysis?
2.
What is the difference between moderation and mediation in structural
equation modelling?
3.
What is the cumulative abnormal return in event studies?
Exercises
.................................................................
1.
Read
the
article
capabilities.
2.
by
Ramaswami
et
al.
(2009)
on
market-based
fi
a.
How do the authors de ne and measure market-based capabilities?
b.
How do they assess the validity and reliability of these measures?
In addition to market-based capabilities, what other capabilities may be
important for
a.
firm performance?
Do a literature review to identify important
firm-level
capabilities
and how they are measured.
b.
What are the limitations of subjective measures of capabilities, and
how may these be resolved?
3.
five-personality-
Use any online facility (e.g. www.truity.com/test/bigtest)
to
assess
your personality scores
according
to the
Five
Factors
model.
a.
Which questionnaire items do you think load on to which factors?
b.
Repeat the analysis (if possible) with friends, and discuss the differences between your scores.
4.
Reese and Robins (2017) investigate whether companies earn abnormal
returns when their shares are added to the SandP500 index. Download
the spreadsheet that accompanies the article (see link on p. 211, note 1),
and carry out the event study as directed.
261
13
Cross-Cultural Research
Collecting information from international markets is by no means a simple matter.
Complexity
of
collecting
data
demands
a
clearer
research
problem,
comparable
sample and equivalence of questions asked.
C r a i g a n d D o u g l a s (2 0 0 0 : 3 )
13.1
The International Dimension
In cases of international or cross-cultural research we need to take extra care
in each and every stage of the process. Research involving unfamiliar environmental and cultural differences may complicate the understanding of the
research problem, and researchers often fail to anticipate the impact of local
cultures on the questions asked. This also has to do with deciding the scope
and limits of the problem. In some cultures, a broader scope is necessary to
cover the necessary variables. For example, concepts such as‘supermarket’
have different meanings in different markets. In Japan, a supermarket usually occupies two or three stories and sells groceries, daily necessities, and
clothing
on
respective
floors.
Some
even
sell
furniture
and
electronics,
stationery, and sporting goods (Ghauri and Cateora, 2014). The availability
of data/statistical information on exports or imports of a particular product
may also be different. Even if it is available, in some countries it might not be
up to date or reliable. Many countries do not have government agencies that
fi
collect and maintain up-to-date data. In some countries private rms collect
and sell data, or the researchers may have to collect primary data themselves.
It is not possible to use data gathered in one market for another market. This
is important for researchers as well as managers doing research in different
markets, as illustrated by Example 13.1.
Comparability of data is, however, the main issue in international/crosscultural research. This is due not just to the availability of data but also to the
manner in which data are collected and analysed (Chidlow et al., 2015).
Researchers have to be extra careful in categorization and measurement of
cross-cultural
data.
The
international
dimension
explained earlier is added in Figure 13.1.
of
the
research
process
13.1 The International Dimension
The process
1. Topic and research
problem
2. Research design
and plan
3. Data collection and
measurement
4. Data analysis and
interpretation
5. Presentation of the
findings and report
The international dimension
Ensure comparability of concepts
Determine scope
Check the influence of culture
Identify comparability differences
Select methodology with minimum
comparability problems
Check practical issues, budget, time, etc.
Attach weight to eliminate cultural
differences
Identify local biases
Check reliability of data
Check understanding of scales for
comparability
Watch for comparability issues
Ensure that unexpected findings are
not due to local biases
Adjust interpretation level to different
countries/cultures/markets
Think of international readers and
communicate accordingly
Watch for language and terminology
Avoid culturally sensitive conclusions
Check implications for different markets
Figure 13.1 Managing the international dimension in the research process
Source:
Based on Ghauri and Cateora (2014: 118)
As illustrated in the
fi
gure, the researcher must be careful and adjust her/
his approach at each and every stage of the research process when doing
international/cross-cultural research. It is appropriate to eliminate items or
concepts that are found to be biased in one or more countries involved in
the research. However, we must still be careful to ensure that a meaningful
comparison
between
the
countries
can
be
made.
This
will
increase
the
263
264
Cross-Cultural Research
probability that the results obtained are comparable and equivalent (Craig
and Douglas, 2000).
EXAMPLE 13.1
The ‘Habits and Practices’ study by Procter & Gamble consisted of home
visits and discussion groups (qualitative research) to understand how the
Egyptian housewife did her laundry. The study wanted to discover her likes,
dislikes, and habits (the company’s knowledge of laundry practices in Egypt
had been limited to automatic washing machines). From this study it was
determined that the Egyptian consumer goes through a very laborious
washing process to achieve the desired results. Among the 95 per cent of
homes that washed in a non-automatic washing machine or by hand, the
process consisted of soaking, boiling, bleaching, and washing each load
several times. Several products were used in the process; bar soaps orflakes
were added to the main wash along with liquid bleach and bluing to
enhance the cleaning performance of the poor-quality locally produced
powders. These findings highlighted the potential for a high-performing
detergent that would accomplish everything that currently required several
products. The decision was made to proceed with the development and
introduction of a superior-performing, high-suds granular detergent.
Once the basic product concept (i.e. one product instead of several to do
laundry) was decided on, the company needed to determine the best
components for a marketing mix to introduce the new product. The company went back to focus groups to assess reactions to different brand
names, to get ideas about the appeal and relevant wording for promotions,
and to test various price ranges and package design and size. Information
derived from focus group encounters helped the company eliminate ideas
with low consumer appeal and to focus on those that triggered the most
interest. Further, the groups helped refine advertising and promotional
wording to ensure clarity of communication through the use of everyday
consumer language.
At the end of this stage, the company had well-defined ideas garnered
from several focus groups, but did not have a‘feel’ for the rest of those in
the target market. Would they respond the same way as the focus groups?
To answer this question the company proceeded to the next step, a research
programme to validate the relative appeal of the concepts generated from
focus groups with a survey (quantitative research) of a large sample from the
target market. Additionally, brand name, price, size, and the product’s
intended benefits were tested in large sample surveys. Information gathered
in the final surveys provided the company with the specific information used
13.2 Data Collection in Cross-Cultural Research
EXAMPLE 13.1
(CONT.)
to develop a marketing programme that led to a successful product introduction and brand recognition for Ariel throughout Egypt.
Source: Based on Ghauri and Cateora (2014)
13.2
Data Collection in Some Cross-Cultural Research
fi
It is sometimes dif cult to classify these data in ways that are consistent with
the
study
at
hand.
The
variables
might
have
been
fi
de ned
differently
or
the measurement unit could have been totally different and would, therefore,
make the comparison invalid. For example, when studying the export behav-
firms, we could use a number of studies undertaken in different
findings. After a closer look,
however, we might realize that smaller firms had been defined differently in
iour of smaller
countries and compare the results with our
‘
’
the different locations – different measurement units had been used to deter-
fi
mine the size (small, medium, or large). Some studies de ned size in terms of
fi
sales, some in terms of number of employees, some in terms of pro t, and some
fi
in terms of square metres of occupied space, as in the case of retailing rms.
Moreover, even if two studies used the same measurement unit, the terms
fi
fi
of de nition were often different. In a study in Norway, for example, rms
fi
with 200– 499 employees were de ned as medium sized, while in the USA
firms with fewer than 500 employees were defined as smaller firms. In such a
comparison, if the US study concluded that smallerfirms depend highly on
unsolicited orders for their initial export, we could not compare thisfi nding
with our fi ndings in Norway by saying, Consistent with the US study,
smaller fi rms in Norway also depend heavily on unsolicited orders for their
initial export, while medium-sized fi rms are much more aggressive and do
‘
not depend upon unsolicited orders for their initial exports.
’
These types of difference are quite common, and researchers using secondary data or comparing and supporting their
findings
with the help of these
data should be aware of the problems and make any comparisons with some
caution. One way to ameliorate the situation is to discuss the differences and
the relevance of secondary data to our own study, looking at the validity of
the comparison and how it should be understood (see Example 13.2).
Although we have mentioned cost saving as one of the advantages of using
secondary data, this cannot always apply. For example, using secondary data
compiled by a commercial organization might be quite expensive. In this case
the
researcher
will
have
to
compare
the
cost
of
collecting
compared with the price of purchasing the secondary data.
primary
data
265
266
Cross-Cultural Research
Another problem with secondary data is that it is the responsibility of the
researcher to ensure that data are accurate; inaccuracies cannot be blamed on
the secondary source. When referring to secondary data, the researcher has to
consult and refer to the original source and not rely on an intermediate or
third-hand report, because it is only the original source that can provide the
required information on the quality of data as it will describe the process of
data collection and analysis. Also, it is the researcher
’s responsibility to check
whether
fi
ndings presented by another researcher are based on primary or
secondary data. This can be checked by examining the internal consistency
of the report being consulted. It is therefore important always to check the
fi
original source of data. Example 13.2 illustrates the dif culties in making
fi
cross-cultural comparison using of cial statistics.
EXAMPLE 13.2
Jackie Davis (2001) carried out an international comparison of labour disputes and stoppages through strike action in 23 OECD countries between
1990 and 1999 using statistical data collected at a national level. However,
the article is careful to point out the limitations of such an analysis for the
following reasons:
•
fi
Voluntary noti cation. In most of the countries, governments rely on
employers notifying them of any disputes, which they are then able to
con
•
firm
through media reports.
Failure to measure full effects. None of the countries records the full
effects of stoppages at work, for example measured in terms of lost
working time in companies that are not involved in the dispute but are
unable to work because of a shortage of materials caused by the strike.
•
Different thresholds for inclusion. In the UK, disputes involving fewer
than ten employees or lasting less than one day are excluded from the
recorded
figures.
In other countries the thresholds for inclusion are
particularly high. For example, in the USA, records include only disputes
involving more than 1 000 workers. This can make comparison of strike
fi
rates between countries particularly dif cult.
•
Exclusion of certain industrial sectors. Some of the countries exclude the
effects of disputes in certain sectors: for example, Portugal omits the
public sector and general strikes.
•
Changes in the way
figures
are recorded. For example, France has
fi
changed the way it records lost working days, thus making it dif cult to
make comparisons over time.
13.3 Collecting Cross-Cultural Data
EXAMPLE 13.2
•
(CONT.)
Indirectly involved workers. There are differences between the countries
in recording those workers who are unable to work because others at
their workplace are on strike. Half of the countries, including France, the
Netherlands, and New Zealand, attempt to include these workers in the
statistics, but the other half, including Italy and Japan, do not.
•
Dispute rates affected by a small number of very large strikes. Some
countries can appear to have very high labour dispute rates in one
particular year because of one strike involving a large number of workers.
In France, for example, there was a strike in 1995 involving all of the
public sector.
These differences lead some countries, such as the USA or Japan, to
record a lower number of working days lost through labour disputes than,
say, the UK or Germany, simply because of the different methods used for
compiling statistics in the individual countries. This means that cross-cultural
comparisons using nationally collected statistics need to be made with a
degree of caution.
Source: Davis (2001); Bryman and Bell (2003: 227). Reproduced under the terms of the
Click-Use Licence.
13.3
Collecting Cross-Cultural Data
Before collecting primary data, however, you need to consider a number of
fi
issues. First you need to de ne the relevant unit of analysis, that is who is the
right person/group/organization to be consulted/studied. This is particularly
important in an international research setting, as the complexity of environmental and contextual factors may have a major impact on the topic/area of
research. According to some views, cross-cultural research can be viewed as
a quasi-experiment in which data are collected from different contexts that
may or may not be equivalent (Campbell and Stanley, 1966; Craig and
Douglas, 2000).
The
unit
of
analysis
and
a
particular
research
question
may
lead
to
different research design and data sources. Here the comparability of data
collected from different countries/cultures also needs to be evaluated. The
data source, and the information collected, should represent the same meaning and interpretation. The data collected from different countries should
also be equally reliable and accurate. Diversity in culture, language, and
267
268
Cross-Cultural Research
methods of communication between the respondents and also between the
researchers (if more than one researcher is involved) can lead to misunderstandings and misinterpretations (see Andreasen, 1990; Craig and Douglas,
2000 and Lonner and Adamopoulos, 1997).
In cross-cultural and international research projects, we need to handle
this issue and develop equivalent or comparable measures and interpret-
find
ations of the data to be collected. In the literature, we
two different
approaches to handling this issue. The emic approach believes that cultures
are different, and issues related to one culture should be understood through
that particular cultural context. Consequently, our questions and interpret-
fi
ations should be adjusted to the speci c culture to arrive at the correct
understanding (Pike, 1966).
The
etic
approach advocates constructing measures that are ‘culture free’
and can be used in cross-cultural studies without posing equivalence or
comparability problems (Triandis, 1972). In this situation, constructs and
measures developed in one country can be used without any, or with minimum, adjustment to cultural differences. These two approaches present two
extremes, and many authors recommend that while doing cross-cultural
research you need to raise this issue and try to use an etic approach as far
as possible, by preparing constructs and measures that are comparable. This
can also be achieved by using already established constructs and measures
(Craig and Douglas, 2000; Schwartz, 1992; Wind and Douglas, 1982).
In many situations a combination of emic and etic approaches is advisable
(Berry, 1989). Here it is suggested that you
first
conduct research in your
own culture and then apply the construct or measurement instrument to
study behaviour in the other culture (imposed etic). Behaviour is then studied
in the other culture within its own context, using an emic approach in
both cultures. These two observations are then brought together towards
an etic approach and are interpreted in a comparable manner. A derived etic
approach
is
thus
possible
based
on
common
aspects
and
features.
This
is further explained in Figure 13.2.
For data collection in a multi-culture or multi-context study, it is important to understand the differences between culture and behaviour. Quite often
when dealing with a culture very different from their own, researchers collect
and interpret data according to their own self-reference criteria (Ghauri and
Cateora, 2006). Qualitative data collection methods can be useful in such
cases, as they often use unstructured questions that can be changed, translated, and reformulated more easily.
Qualitative research allows researchers to explore and probe deeply into
attitudes towards product classes, brands, trends, and behaviour. It also helps
to reduce the psychic distance between the researcher and the respondent,
13.3 Collecting Cross-Cultural Data
Step
Research activity
Culture A (own)
1.
Begin research in own culture
EMIC
A
2.
Transport to other culture
3.
Discover other culture
4.
Compare two cultures
5-1.
Comparison not possible
5-2.
Comparison possible
Culture B (other)
IMPOSED
ETIC
EMIC
B
EMIC
A
EMIC
B
EMIC
A
EMIC
A
EMIC
B
EMIC
B
Derived etic
Figure 13.2 Steps in operationalizing emic and etic approaches
Source:
Based on Berry (1989), reproduced with permission of Ralph Berry
especially in cross-cultural research (Craig and Douglas, 2000). Qualitative
research thus provides a better understanding of a given context and its
underlying motivations, values, and attitudes. Data collected from a small
fi
number of carefully selected samples on relevant issues can be suf cient in
this case (Patton, 1990).
In cross-cultural research, behaviour can be observed quite easily, for
example shopping behaviour in supermarkets, department stores, or bazaars.
The researcher can observe whether bargaining takes place or not, how long
each transaction takes, the conditions of sales and customers
’ reactions,
the reaction of customers to special offers or promotions, and so on. The
observation can be made personally by the researcher or by placing a video
269
270
Cross-Cultural Research
camera
in
the
supermarket.
This
type
of
research
can
provide
useful
insight into how people purchase a certain product in different cultures or
fl
how different environments, cultures, and promotions in uence the buying
behaviour of customers. The data collected from different countries/cultures
can then easily be compared (Craig and Douglas, 2000).
Observations, however, are often criticized because their interpretation can
be rather subjective. In international research it is particularly problematic if
the researcher who is going to interpret the data is not familiar with the
cultural conditions in which the data are collected. This can, however, be
handled
through
collection
of
data
by
several
researchers
familiar
with
different cultures, and through interpretation of data through a common
and systematic analytical framework.
In international or cross-cultural research it is important that the instru-
fi
ment is adapted to the speci c culture in which it is used and not to just one
of the cultures in the study. The design and administration of the instrument
needs adaptation according to the education levels and other background of
respondents. The formulation of questions, the questionnaire as a whole, and
fl
the response format often in uence comprehension and response accuracy
in different cultures (Craig and Douglas, 2000; Malhotra, 1996; Schwartz and
Sudman, 1996).
Concepts such as ‘supermarket’, ‘household ’, ‘occupation’, etc. may have
different meanings in the USA, Sweden, India, or China. All these need to be
explained so that they are correctly understood and interpreted. The response
format should encourage correct and clear answers.
The researcher should check and ensure that everybody
question in the same manner: in other words,
understands the
that everybody draws the same
meaning from the questions. This is particularly important in questions or
questionnaires that are translated from one language to another. One way to
deal with this is to have an expert translate the questions, for example from
English to Norwegian, and then have another expert translate the text back
from Norwegian to English. The researcher can then clearly see if there has
been any change in the meaning. The discrepancies should be corrected with
the help of experts from both sides. There are several methods to handle the
language issue in international research. For more details, see Ghauri and
Cateora’s (2014) chapter on international marketing research.
13.4
Analysis of Cross-Cultural Data
Multi-country or multi-cultural data can be involved in quantitative and
qualitative
research.
Analysis
of
data
in
these
situations
becomes
more
13.4 Analysis of Cross-Cultural Data
complex due to the existence of multiple units of analysis. In this case the
analysis can be conducted in different phases:
first
within-country analysis
and then cross-country analysis. In international research the purpose might
be not only to understand each country/market involved but also to assess
the extent to which strategies or decisions can be standardized across different markets (Craig and Douglas, 2000).
In qualitative research this issue is particularly important, as one way of
handling cross-cultural differences is to collect data through less structured
research. In international business research, the focus shifts from withincountry to between-countries variables. Thus country becomes the unit of
analysis, and it becomes very important that the variables chosen in each
country are representative of that country. Figure 13.3 explains the unit of
analysis discussion.
In cross-cultural or international business research, equivalences of the
unit of analysis need to be considered. Moreover, this should be clearly
fined
de
and explained so that it is not mixed up and is distinct from other
units. Figure 13.3 explains that we need to consider three distinct aspects
–
geographic scope of the
and the situational context,
(Craig and Douglas, 2000: 142 3): the
unit, the
criteria for membership
meaning
in the unit,
the particular socio-cultural setting.
fi
Geographic scope helps us to de ne the geographic boundaries of the unit
we intend to study. In international business the most commonly used unit is
the country, and most secondary data are collected on a country-by-country
Situational context
Micro
Macro
Age
Criteria for
membership
Interest
Gender
Education
Region Country City
Area
Geographic scope
fi
Figure 13.3 De ning the unit of analysis
Source:
Based on Craig and Douglas (2000). Copyright © John Wiley and Sons Limited. Reprinted
with permission.
271
272
Cross-Cultural Research
basis. Increasingly, however, research is being done on a regional basis,
such as the European Union, NAFTA, or ASEAN. Sometimes we also undertake research in cities or in particular industries (e.g. airlines, banking, or
retailing) in a certain country or due to the size of the countries/companies
(e.g. measured as GDP, revenue, or number of employees).
Criteria for membership has to be determined for the unit to be studied.
Whether it is individuals or organizations, we need to decide some criteria
for their inclusion. For individuals it could be age or gender, and for organiza-
fi
tions it could be businesses or non-pro t organizations. For organizations, we
could include criteria such as size, industry, origin, internationality, etc.
Situational context can range from broad socio-cultural settings (macrocontext)
to
a
fi
speci c
situation
(micro-context).
At
the
macro-level
our
geographic unit is a country, and we should identify country characteristics
fl
that might in uence the behaviour of the respondent. In the same manner,
if we are studying cities, we need to differentiate between rural and urban
areas. Moreover, whether we are conducting the study in business settings or
fl
in social (e.g. at home) settings might have some in uence on the data
collected.
This means that we need to analyse data within each country and across
different
countries.
Moreover,
we
need
to
examine
the
structure
or
–
relationship between variables. Craig and Douglas (2000: 292 3) present this
issue as illustrated by Figure 13.4.
first
There are two phases in data analysis. In the
phase we analyse data
within each country. In the second we examine and compare data between
find similarities and differences. In the first phase,
the relationship between different variables is studied (e.g. factors in
fluencing consumer behaviour). In the second stage, we compare thefi ndings from
the countries involved, to
different countries. In qualitative research, the observations in each country
Difference in Within-country Cross-country
analysis
analysis
Level
Structure
Figure 13.4 Cross-country data analysis
Source :
Based on Craig and Douglas 2
( 000: 293). Copyright © John Wiley and Sons Limited. Reprinted
with permission.
13.5 Enhancing Trustworthiness
might be based on subjective judgement and experience. While comparing
data from different countries we can apply relatively objective techniques to
discover the similarities and differences.
Here we can focus either on the level of the variables or on the structure of
For level issues, we need to find out whether there is a
ficant difference in a certain variable in the countries compared. If there
is a difference, then the researcher should try tofi nd out why this is so. In
structural issues, the researcher is trying to find out whether there is a
the
variables.
signi
difference in the relationship between two variables between the countries
fl
compared, for example whether high income and gender in uence a certain
consumer behaviour for a certain product. This type of analysis is more
complex, and the complexity increases with the number of variables. Analy-
fi
sis in this case demands more sophisticated techniques and clear de nition of
the unit of analysis that is consistent across countries (Douglas and Craig,
1997; van de Vijver and Leung, 1997).
The level of variables between countries can be analysed using crosstabulation
and
other
statistical
regression
and
analysis
of
techniques.
variance
are
Techniques
useful
ways
such
to
as
analyse
multiple
data
in
international business research.
Enhancing Trustworthiness of Cross-Cultural Research
13.5
Driven by globalization and relocation of companies
’ activities all over the
globe, there is an increasing emphasis on comparative international research
and how to make it more trustworthy or reliable. Cross-cultural perspectives
that were used in many other disciplines, such as psychology and ethnography,
are
now
adopted and
extended by
management scholars.
In quantitative
research there are established methods to handle and analyse cross-cultural
data and dealing with equivalence and comparability issues, as objectivity and
validity in data collection is easier to enforce (Chidlow et al., 2015).
For qualitative data collection and analysis, it is important to establish
conformability, credibility, and trustworthiness. Some researchers claim that
CAQDAS, computer-assisted methods such as NVivo, can assist researchers
in enhancing this trustworthiness. NVivo facilitates coding and categorization and can assist in preparing matrix graphs. However, if you are planning
to
use
NVivo
for
data
collection
and
analysis,
we
suggest
you
look
at
Sinkovics et al. (2008) and Ghauri and Firth (2009).
For cross-cultural research, however, it is important to be careful right from
the start and at each and every stage of the research process, as suggested by
Figure 13.5. The question of equivalence, that the respondents have understood
273
274
Cross-Cultural Research
Equivalence
of data in
international
research
Comparability
of data
Data
analysis
Equivalence
of data
handling
Response
translation
Response
categories
Data
preparation
Data
collection
Equivalence
of research
methods
Data
collection
Stimuli
Equivalence
of research
units
Definition
Selection
Equiv. of
research
administration
Timing
Interaction
Problem
definition
Equivalence of research topics
Functional equivalence
Conceptual equivalence
Category equivalence
Equivalence of international research
Figure 13.5 Ensuring equivalence in cross-cultural research
Source :
–
Salzberger et al. (1999: 23 8).
your questions in the same way in each and every country/culture and have
responded
researcher.
de
fi
nition
in
the
This
stage.
same
has
to
In the
way
be
across
dealt
same
cultures,
with
right
way, at the
is
the
responsibility
from
the
start,
data collection
at
the
stage,
of
the
problem
if you
are
collecting data/information from several countries, the methods of data collection, the unit of study you have chosen, and the timing have to be comparable.
It is quite common for a researcher to conduct the data collection in the
home country and then decide to collect data from another country after a
year using the same questionnaire. The equivalence and comparability of
data in this case is highly questionable. Also, the method of data collection
is important; for example, was the researcher present at the time of data
collection
through
or
data
not? Later,
reduction
when data
and
are sorted
categorization,
it
and prepared
is
important
for analysis,
to
obtain
the
real meaning. Dictionary translation is often not very helpful as this does
not explain the intensity or the seriousness of the response. This has to be
considered throughout the data analysis stage.
Exercises
Further Reading
.................................................................
Ghauri, P.N. and Firth, R. (2009) ‘ The formalization of case study research in
international business’ , Der Markt: Journal for Marketing, 48: 29–40.
Grisar-Kassé, K. (2004) ‘The role of negative personal experiences in cross-cultural
case study research: failure or opportunity’, in Marshan-Piekkari, R. and
Welch, C. (eds.), A Handbook of Qualitative Research Methods for International
Business, Cheltenham, UK: Edward Elgar, pp. 144
– 61.
Sinkovics, R., Penz, E., and Ghauri, P.N. (2008)‘Enhancing trustworthiness of
qualitative research in international business’, Management International
Review, 48(6): 689–714.
Questions
.................................................................
1.
Is cross-cultural research different from research in the same cultural
setting? Discuss.
2.
What is meant by the equivalence of the unit of analysis? Explain with
examples.
3.
Explain the two concepts emic and etic. How can we handle these issues
in cross-cultural research?
4.
How can a cross-cultural research project be most meaningful? What
adjustments are necessary to accommodate cultural differences?
Exercises
.................................................................
1.
firm wants to go into a new market and is analysing Russia and India
as possible candidates. Do you think the fi rm can collect demographic
A
data on the two markets and compare them to see which market is more
suitable? Advise the company on how it can enhance the comparability
of the data collected in the two markets.
2.
Discuss the emic and etic approach of data collection for a multi-country
research project that you have to undertake. Select three countries of
your choice and explain how you can use these approaches.
3.
You are working on a qualitative research project that involves interviewing several
firms
to understand how companies evaluate a new
market to enter. Suggest ways to enhance the reliability of your results.
275
14
Writing the Final Report
Everything should be as simple as it can be, but not simpler.
Albert Einstein
After completing the data collection and data analysis, you have to put the
research problem, the data collected, and the
ent,
and
persuasive
report.
Fortunately,
findings into a logical, consist-
as
with
research
proposals
and
methodology, research reports conform to a fairly standard format. In many
cases,
an
oral
presentation
is
also
made
to
present
your
findings
and
research. We deal with both of these aspects.
14.1
Purpose and Importance of a Written Presentation
Before starting to write the
final report, you should consider its purpose and
to whom it is addressed. Research reports typically start with an executive
summary providing the important points from the report. After the summary,
a preface is normally written where we explain the course of the study and
thank individuals and organizations who have been helpful in data collection, funding, etc.
If the report is a Master’s thesis, it may look different from a report that is
fi
presented to a company/manager on a speci c issue. In the latter case, the
report can be very concise and to the point, so that the manager can easily
comprehend it and use the results in decision making. On the other hand,
if the report is a thesis or project work, it has to be more detailed and written
in
a
systematic
manner.
In
this
case,
all
the
details
regarding
problem
formulation, data collection, the method of analysis, and conclusion should
be presented. In fact, we would argue that a report written to‘sell’ an idea to
management should also be very detailed and convincing with regard to the
fi
concepts used, comparison with present systems and ideas, and the bene ts
of the new system/ideas (Sekaran, 1992). We believe that a report needs to
be detailed, argumentative, and convincing in both cases. Another type of
report is an academic paper written with the purpose of publication. We will
deal with this type of report separately.
14.2 Guidelines
You must also realize that your report, project work, or thesis will be
assessed according to the criteria set by your institution or supervisor. Before
working on the
(Bloom et
final
report, you must revisit these criteria. Several authors
al., 1971;
Easterby-Smith et
al.,
1991;
Saunders
et
al., 2016)
provide educational objectives for different levels of research and report.
For lower levels, your report should demonstrate understanding and knowledge of the topic. At a higher level, your report should demonstrate not only
that you have knowledge of the topic but also that you can apply it to a
concrete problem, having analysed the situation. At a still higher level, in
addition to the above you have to demonstrate that you can synthesize,
evaluate, and analyse to draw conclusions.
The process of writing up a report is complex and sometimes tedious work.
findings and arguments presented in a
The report should be concise, and the
convincing and consistent way. It is also important to present the research
methodology and results in such a way that the reader can judge the validity
and relevance of the
findings.
Hint: A report needs to be argumentative and convincing, whether it is to be
submitted to a company or as an academic thesis.
We should give an account of our methods
’
weaknesses and strengths and
the necessary details so that readers can make their own judgements on the
validity and reliability of our study and
reader that we have,
certain
problem
findings.
We should convince the
first of all, done our job as a researcher, investigating a
area
with
systematic
data
collection
and
data
analysis,
presented in a logical, easy-to-read and understandable report. Second, we
must show that we have followed the technically correct and consistent
fi
method expected of a quali ed report, that our hypotheses and conclusions
are properly supported by existing literature and empirical evidence, and that
there is a logical congruence between different parts of the report. We should
also be concerned that our report gives due credit to earlier studies we have
used and that we refer to all the sources in a proper manner. These two
aspects
are very
important
in
fi
quali ed
report
writing
and
are therefore
treated separately in this chapter.
14.2
Guidelines
14.2.1
Audience
The
best
reports
are
those
that
are
written
with
a
particular
audience
in mind. The contents, the length, the terminology used, the focus, and the
277
278
Writing the Final Report
presentation of data and results all depend upon the audience. In writing a
fi
report for a company/manager the tone should be concise and ef cient and
the writing and use of terminology should be according to their background,
considering how much they already know about the topic. In this case, an
executive summary becomes very important, as it provides a summary of the
report in a direct and brief manner. Managers can then go into the report
fully or into only those parts of it they feel appropriate.
In the case of a thesis, you need to check the rules and regulations of your
particular school or supervisor. The guidelines provided later in this chapter
on the structure of the report are the most commonly used. The audience
fluences
in
the
overall
presentation,
extensive use of tables and
to decide which tables and
as
figures and
some
audiences
are distracted
by
by footnotes. In this case, you have
figures are necessary in the text and which should
go into appendices, where those who are interested can look at them. Any
fl
restriction on the number of pages also in uences the above. Quite often
there is a maximum page limit but no minimum limit, which shows that a
concise report is more useful than an extensive one. In any case, the audience
determines the type, the level, and the complexity of the report. Researchers
fi
must make every effort to acquaint themselves with the speci c preferences
of their audience. They should not consider these preferences unalterable, but
any deviation from them should be made with reason and not from ignorance (Boyd et al., 1989; Churchill and Iacobucci, 2015).
14.2.2
Good Communication
It goes without saying that the report should be readable. Scholars often
suggest that the report has to be clear, concise, coherent, focused, lively,
exciting,
meaningful,
and
without
pedantry
(Rubin
and
Rubin,
1995;
Sekaran, 1992). The main idea is that the text should provide a clear explanation and should entice the readers, making them interested and curious to
read the whole report. The assumptions and methods should be clearly stated
fl
and explained. There should be a natural and smooth ow from one part of
the report to the next. Many researchers use a pedantic writing style to
impress the reader with their sophisticated concepts and terminology. In
our opinion, such a report does not communicate well. The reader should
flow through the report without any hindrance or being stuck in understanding diffi cult terminology.
The format of the report enhances its readability; the use of headings and
subheadings takes the reader through the report step by step. The reader
should understand what you are saying and where you are going. As a good
communicator, you can ‘program ’ the reader’s mind so that s/he reads your
report as you want it to be read or as you believe readers would like to read it.
14.2 Guidelines
Do not expect your reader to read between the lines; you have to use direct
and simple language.
Hint: You can ‘ program’ a reader’s mind as to how he or she reads your
report.
14.2.3
Language and Flow
It is not easy to write clear and simple sentences. As Elliot (1980: 18
– 19)
says:
Think what you want to say. Write your sentence. Then strip it of all adverbs and
adjectives. Reduce the sentence to its skeleton. Let the verbs and nouns do the
work. If your skeleton sentence does not express your thought precisely, you
have got the wrong verb or noun. Dig for the right one. Nouns and verbs carry
the guns in good writing; adjectives and adverbs are decorative camp followers.
ficult language and terminology does not really impress the reader
Using dif
fluences the readability of the report adversely. Good writing does not
but in
mean setting obstacles for the reader to overcome.
flow
Sentences and paragraphs should be kept fairly short. The
of argu-
ments should run logically through the report. A concept or a terminology
should be explained when
first mentioned.
In our opinion, the fewer the words, the better the report. Several short
sentences are better than one long sentence. Do not torture the reader by
fi
explaining your dif culties in collecting data or interviewing prominent
business executives; just say what you have done and how you have collected your data. If you are using a concept to explain a certain situation, use
the same concept every time you want to explain that situation. In speech,
when we explain a certain situation we repeat facts and stories to make sure
that
the
necessary
listener
in
has
written
heard
and
language,
understood
and
you
what
should
we
mean.
always
This
avoid
is
not
repeating
things. Reports and theses are never judged according to their length or
weight.
Normally we do not start a sentence or a paragraph with‘but ’, ‘because’, or
a number. If it is necessary to start a sentence with a number, it should be
expressed in words: for example ‘Fifty per cent’ instead of ‘50 per cent’.
Whatever rules are used, the most important aspect is to be consistent in
writing, spelling (for example UK English versus US English), terminology,
use of headings, and
figures and tables.
279
280
Writing the Final Report
Hint: Using
fi
dif cult language and terminology does not really impress the
reader.
first
The report, especially the descriptive part, should be written in the
person. Some people believe that impersonal language conveys objectivity
and suggest writing in the third person. We, however, advise that the observer/researchers be referred to as ‘I ’ or ‘we’ and that participants or respondents, etc. be referred to as ‘we ’ or ‘them ’.
14.2.4
Form and Style
The question of form and style is often more a matter of likes and dislikes or
individual style than a matter of rules. However, the following are common
mistakes and should be avoided (Grønhaug, 1985: 71):
Telegraphic style. This means that a report is written in sentences which
•
are not connected to each other with any logical consistency.
Long and complicated sentences. Reports are sometimes written in long
•
sentences that are not comprehensible without being read several times.
Writing short and concise sentences is, however, a good way of writing
reports.
Usage of terminology and differences between spoken and written lan-
•
guage. When using textbook or other terminology, you should be sure
that the reader will understand their use in the same way. It is quite
common for students to use a spoken language form in their report. In
report writing you should follow the rules of writing and avoid using a
spoken or slang version.
Shortage
•
of
tables,
fi gures,
or
other
illustrations.
You
should
try
to
simplify, highlight, and complement the important and interesting parts
figures and tables or other illustrations. It is important
that these tables and figures should not replace the text.
of the report with
to point out
They are used to complement or further explain the text or the point
being made.
Our experience as researchers is that we have to write and rewrite reports
at least four or
five
times
before they are in a
final
shape.
This is the
working draft, and after writing this you should go through the material
again, along with the data and
findings, to see if there is something that has
been missed that should be included in the report. Then read the draft again
and
start
correcting
it
as
you
read.
Add
the
information
you
think
is
missing and delete the information you feel is irrelevant or repetitive. At
14.3 Structure of the Report
the same time, check that the information is correctly placed; if it is not,
reshuf
fle
before
the text. This process will need to be repeated three to four times
you
have
an
almost-ready
report.
Finally,
you
should
check
the
language and ideally have someone else read the report. It is quite common
that, having read and reread the report several times, you become blind to
drawbacks or mistakes.
Hint: Before submitting your report, have someone else read it through.
The appearance is very important. It does not matter how well written a
report is if it does not look good; it leaves a bad impression on the reader.
Here the title page, the margins and the structure of the report are very
important.
It
ones.
common
It
is
is
easier
to
read
practice
to
double-spaced
use
double
reports
spacing
than
and
single-spaced
indent
first
the
word of each paragraph. In the case of single-spaced reports, double-space
between the paragraphs to mark the new paragraphs. Regardless of whether
the
report
is
single-spaced
or
double-spaced,
the
following
documents
should always be typed single-spaced: letters, displayed quotations, footnotes (but use double spacing between them), tables, and
figures
(Murphy
and Hildebrandt, 1988).
14.2.5
Headings
The headings serve as an outline of a report and should be clear, meaningful,
and consistent. Headings are often numbered to highlight different heading
levels, but numbers are not essential. It is possible to use different styles or
type to make these levels explicit. The most important aspect is not to use too
many levels of heading, as the reader should be able to differentiate easily
between different levels of headings and subheadings.
14.3
Structure of the Report
The following outline is a commonly used format for a research report. The
chosen format is, however, dependent upon the reader for whom the report is
prepared. One common format is as follows:
1.
title page;
2.
table of contents;
3.
executive summary;
4.
introduction and problem statement;
281
282
Writing the Final Report
5.
theoretical background;
6.
methodology;
7.
findings or empirical study;
8.
conclusions and recommendations;
9.
footnotes;
10.
bibliography or references;
11.
appendix.
14.3.1
Title Page
The title page should indicate the topic of research and the name(s) of the
researcher(s) (authors). Second, it should indicate the name of the organization where the report has been prepared and for what programme, if appropriate; for example, ‘MSc Thesis for King’s College London’. If it is a research
project ordered or demanded by a company or any other organization, the
company ’s or organization’s name should also appear on the title page.
Moreover, if the project has received any
financial
help from an institute
other than the school or university, this name should also appear on the
title page.
14.3.2
Table of Contents
The table of contents lists the contents of the report with page numbers. Here,
the headings and subheadings are presented. The reader should be able to
differentiate the headings and subheadings both in the table of contents and
in the report (see Figure 14.1). The table of contents should also include tables
and
figures with page numbers.
14.3.3
Executive Summary
A summary provides the important aspects of each part of the report. It is
often stated that a summary should be self-suf
readers,
especially
business
executives,
ficient
often
read
because most of the
only
the
summary
(Churchill, 1999). The summary should thus highlight the whole report. In
addition, it should be based on the main report and should not include any
new material. The data you have collected, presented, and analysed are often
buried in the middle or at the end of the report and have to be brought
forward in the summary so that the reader can quickly and without any
trouble perceive the message of the report (Bolsky, 1988).
The length of a summary depends upon the complexity of the original
material. Normally you should reduce material in a summary to, at the most,
5 per cent of the report’s full length or to a couple of pages. An ideal length is
two to three pages.
14.3 Structure of the Report
Contents
List of figures
ix
List of screenshots
x
List of boxes
xi
List of tables
xii
Preface
xiii
Guided tour of the book
xv
Publisher’s acknowledgements
xvii
PART I CHALLENGES AND AMBIGUITIES OF BUSINESS
RESEARCH
1
1
INTRODUCTION
3
1.1 Purpose of the book
3
1.2 Readership
4
1.3 On the use of the book
5
RESEARCH IN BUSINESS STUDIES
7
2.1 The nature of knowledge development
7
2.2 Why we do research
8
2
Figure 14.1
14.3.4
An example of a table of contents
Introduction and Problem Statement
An introduction to a research report should present what the study is about
and what the purpose of the study is. This background will provide the reader
with the necessary information to understand and comprehend the rest of the
report. The objectives and the purpose of the study should be clearly mentioned
in
this
section.
After
reading
this
section,
the
reader
should
have
complete information on what the report deals with, why you are studying
a particular problem and what can be expected in the rest of the report. Here
fi
all the unfamiliar terms should be clari ed and the concepts of the research
problems
fi
de
ned.
Some
account
of
what
has
already
been
done
in
this
research or problem area is also presented. This section should also explain
how
the
report
is
organized,
so
that
the
reader
’s
mind
is
programmed
accordingly.
14.3.5
Theoretical Background
Here the theoretical background to the problem area as well as to the study
design is presented. If we are using hypotheses or a priori assumptions there
283
284
Writing the Final Report
fi
has to be proper reasoning with the help of previous studies and ndings.
Depending on the research orientation, as covered in Chapters 2 and 3, the
importance of theory and its use is different. It is therefore important to be
consistent
in
your
report,
and
you
should
check
this
section
with
your
research orientation and design.
Methodology
14.3.6
In this section you inform the reader of your research design, whether it is
exploratory, descriptive, or causal, and why a particular design was chosen.
You should state the design requirements and how they are met. Different
research designs require different methods, as explained in Chapter 5. The
research
designs,
exploratory,
descriptive,
or
causal,
would
also
suggest
whether you should use qualitative or quantitative methods for your data
fl
collection and analysis. This then in uences the structure of your report. You
have to ensure these matters are all consistent.
You
should
also
inform
the
reader
about
your
primary
fi
and
secondary
sources of data, along with arguments and justi cations. When discussing
the primary sources, you should explain how you collected information and
discuss
your
studies,
how
population
the
cases
and
were
sampling,
selected
and
and
in
the
why.
case
When
of
in-depth
writing
about
case
data
collection, you have to explain what you have done, how you did it, and
why you have used this particular method. You should also explain which
methods of data analysis have been used: if statistical methods, why these
particular methods; if qualitative methods, then how you operationalized the
different concepts, where the models came from, and what types of conclusion could be drawn through this type of analysis.
Findings
14.3.7
The empirical study, what you have found out from your data collection, is
fi
presented here. This section is often a major part of the report as the ndings
fi
are presented in detail with supportive tables and
gures. Here you have to
fi
refer back to your research questions or hypotheses and present your ndings
in accordance with these in a systematic, structured, and logical manner. The
fi
ndings,
tables,
and
fi
gures
should
follow
a
systematic
chronological
or
psychological order. The most important job is to prune out irrelevant information and
fi
ndings.
How to arrange and present the
fi
fi
ndings of your study is a dif cult issue.
There are no rules for this, but you should consider the purpose of the study
and the report when it comes to what you want to communicate and to
whom.
We
suggest
that
researchers be
following methods of presentation:
systematic
and
choose
one
of
the
14.3 Structure of the Report
•
By order of occurrence.
Here you present the
findings
chronologically.
This is particularly suitable when you are working with case studies or
when you have a process or longitudinal approach: for example, when
fl
explaining the process of negotiations and factors in uencing this process (see, for example, Ghauri, 2004).
•
By criteria or topics. You may use your own headings
– for example from
the questionnaire or problem statement – as a format to present the
findings. You may have some criteria on what affects what
– for example
independent and dependent variables – which can be used as headings
to present the
hypotheses
1,
findings.
2,
You might number your research questions or
3 . . . and
then
discuss
each
of
these
in
the
same
sequence.
•
By order of location. You can present your findings from different parts
of the country or world in different sections and use these as headings:
for example
findings from the south, east, or central parts of the country,
findings from different countries or continents.
By order of importance. Quite often it is advised that you presentfindings
in order of importance. The most important and interesting fi ndings
should be presented first, followed by the less important issues. When
or
•
listing criteria, you can list them according to their importance.
14.3.8
Conclusions
In this section evaluated facts are discussed, but these evaluations should
only be made from the data presented in the earlier sections; the author
’s
fl
biases and desires should not in uence these conclusions. You should state
your conclusions systematically for each study objective, research question,
or hypothesis. The best way is to refer back to the objectives or research
questions and check whether or not you have provided conclusions for each
of these. If the data collected and the analysis do not provide enough information or support for you to draw conclusions, you should clearly state that.
fi
It is important to be speci c and concise. We suggest the following format
for conclusions:
‘Based on our
findings, our conclusions are: . . .
’
or
‘We have found that: . . .’
Recommendations for future research or implications for business executives should be based on the
findings
and conclusions. There should be a
logical congruence between conclusions and implications or recommendations. If recommendations or implications are given throughout the report,
285
286
Writing the Final Report
for example while presenting
findings,
these should be summarized and
highlighted in this section. We suggest the following format:
‘ The implications of C1 are:. . .’
14.3.9
Footnotes
As mentioned earlier, the value of a report also depends upon argumentation
and sources. Proper credit to these sources is given in two ways: the bibliography or references listed at the end, and the footnotes or references given
fi
throughout the paper. The footnotes speci cally and individually document
the facts and opinions referred to in the report (Berry, 1989).
In most reports and theses, footnotes are used primarily for three purposes:
1.
to credit the source or earlier study you have used;
2.
to direct the reader to another section of the same report, also referred to
as cross-references;
3.
to explain, discuss, or provide additional information on a particular
concept or issue.
In research reports, a separate reference list or bibliography is provided. In
that case, you only need to mention the surname of the author and year of
publication or source in footnotes or bracketed within the text at the appropriate point, as in this book.
fi
The type of information that should be documented is a dif cult question.
A fact that a reader already knows need not be documented: for example, the
year when the Second World War ended. However, for facts that are not
common knowledge you have to inform the reader how you arrived at a
certain point. It is also important for the sake of intersubjectivity. For example,
the result of a study on the buying behaviour of a certain segment needs to be
documented with the year of the study because the buying behaviour might
change at different times. Behaviour can change due to awareness about
environmental pollution, oil crises, or any other major incident.
Whenever you bring in and use a paraphrase or quotation, it has to be
documented with a footnote, together with the page number in the book. This
goes for articles or any other source you have used: for example,‘Grønhaug
(1985: 18)’.
The most important rule is to use the same method throughout the report;
different forms should not be mixed up in the same report. When using the
form where footnotes are presented at the end of the report, these should
come before the list of references or bibliography. Some authors advise using
different forms for different types of footnotes: for example, footnotes where
you only want to document and mention the source might be gathered at the
14.3 Structure of the Report
end, while footnotes where you need to explain or discuss a concept should
be mentioned at the bottom of the page (Berry, 1989). It is advised that one
consistent method should be used for all types.
There should also be internal consistency. For example, if footnotes are
given at the bottom of the page, they should be consistently separated from
the main text with double spacing (double the spacing in the text). The
footnotes themselves should be single-spaced, with a double space between
two footnotes. The margins and numbers should also be consistently at the
same place, not only on the same page but throughout the report. For crossreferenced footnotes or references to other parts of the same report, you
should avoid referring to later pages, as the reader is not yet aware of what is
coming. On the other hand, it is acceptable to refer to previous pages: for
example, ‘see p. 10’.
14.3.10
Bibliography and References
Bibliographies are lists of books (and other material) on a particular subject
at least all the sources that have been cited in the report.
A list of references, on the other hand, includesonly those sources cited and
and should include
should not include books and other sources consulted but not used. All
references should be listed in alphabetical order with authors
’ surnames
coming
come
first.
If there is no author, the issuing organization
’s name should
first in the alphabetical order: for example the European Commission,
editorial in a periodical, The Economist. In the case of a
article in a periodical, the author’s name should come first.
or, in the case of an
reference to an
The most popular format is to place the
first
word of the
first
line (e.g.
surname) at the left-hand margin, while the rest of the lines are indented by
several spaces. Remember, however, that for footnotes, especially those at the
fi
foot of the page, the opposite system is often used: the rst word of the
first
line is indented, while the rest of the lines start at the left-hand margin. This
system is used for each and every source. The bibliography or references, like
the footnotes, should be typed in single spacing while giving double spacing
between references. Where there is more than one author in a source, it is
permissible to reverse the names of all authors. However, it is quite common
first author s name for the sake of alphabetical order and
of the authors with their first name (or initials) first. Both
to reverse just the
mention the rest
’
systems are correct, but for the sake of consistency, you should use only one
system in the same report.
The titles of books, names of journals, periodicals, and newspapers, and
titles of published government reports are often underlined, italicized, or
typed with capital letters. In the case of edited books, if the reference comes
from a chapter written by another author, the reference should start with the
287
288
Writing the Final Report
author’s name and not with the editor’s name, and the title of the edited
volume or book should be underlined or italicized. The title of articles and
chapters from edited books and journals should be indicated by quotation
marks (‘. . .’) and be typed exactly as the original. If the title is in a different
language, such as Dutch or Norwegian, it should be mentioned in the original
language. Some common examples of references are provided here:
The Internationalization of the Firm
Buckley, P. and Ghauri, P. (eds.) (1999),
2nd edn., London: Thomson.
Ghauri, P.N. and Usunier, J.-C. (1996),
,
International Business Negotiations
,
Oxford: Elsevier.
Grønhaug, R. and Haukedal, W. (1989), ‘Environmental imagery and strategic
action ’,
Scandinavian Journal of Management
, vol. 4, nos 1–2: 5–17.
Dunning, J. (1980), ‘Towards an eclectic theory of international production:
some empirical tests’,
Journal of International Business Studies
, Spring/
Summer, 1: 9–31.
United Nations Center on Transnational Corporations (1985),
Accounting and Reporting Issues
:
1984
review,
New
International
York:
United
Nations Publications.
As mentioned earlier, there are several methods and styles that are correct
in listing the bibliography. The most important thing, however, is to use one
form throughout a report and not to mix up different styles.
Hint: For the format and structure of the report, also check the rules of your
institution.
14.4
Oral Presentation
Often you have to present your report in front of not only your supervisor or
examiner but also company executives or members of an evaluation committee: people who have not been involved in your project. As in writing a
report, the
first important issue is to know your audience, as that will decide
the level of your presentation. It should not be too technical or academic in
nature, especially if people from outside (companies, etc.) are present.
The presentation must be organized in the same manner as the written
fi
report. First, the main objectives and the speci c research questions should
be presented, as well as why these questions are interesting or worth your
study.
You
should
relate
your
study
to
earlier
work
on
the
topic
and
14.4 Oral Presentation
289
demonstrate how your study differs from that work. You should state the
importance of your study as well as your limitations and constraints.
Second, you have to explain ‘how’ you have done the study: how you have
collected the information and how you have analysed it. Here again you
should explain your limitations and constraints. This will adjust the expectation level of the audience as well as help them to make their own judgement
as to the reliability of your study and its conclusions.
Finally, the
fi
ndings and conclusions can be presented in a simple and
straightforward
unexpected
way.
results
These
and
conclusions
other
should
surprises.
The
be
discussed
conclusions
in
cases
of
also
be
should
related to your objectives and research questions. If you have not been able
to
answer
research
all
your
project
questions,
regarding
this
fi
speci c
should
be
company
explained.
problems,
In
you
the
case
need
to
of
a
also
present your recommendations or solutions and argue why, in your opinion,
these are the best solutions or recommendations.
The most important issue, however, is the presentation itself: how you are
going to use PowerPoint presentations, etc. We recommend that you use a
minimum
amount
fi
Technical or dif
of
text,
preferably
just
points,
and
then
talk
about
it.
cult terminology and concepts should be avoided and, if
fi
used, should be explained. Using illustrations, tables, and
strategy to support your arguments, though
fi
gures is a good
gures and tables with lots of
numbers and characters should be avoided. If used, the part of the table/
fi
explaining
charts
facilitates
gure
your
point
should
understanding.
be
highlighted.
Listeners
in
this
Use
case
of
can
graphics
easily
see
and
the
whole picture and make comparisons more quickly and easily. Otherwise,
fi
they will have to make such evaluations themselves, which is often dif cult,
if not impossible, during the presentation.
Use simple and direct language when presenting, speak slowly, and articulate your words properly – say the whole words. Do not rush through the
presentation in order to say a lot of things. If possible, make copies of your
slides and distribute them. Time management is one of the most important
aspects of your presentation. Rehearse your presentation so that you can
explain the issues clearly without the need to rush. Do not forget that one of
the major purposes of the presentation is for the audience to ask questions, so
you should plan question and answer time into your presentation. In the case
of a 30-minute presentation, we suggest you divide your time as follows:
•
5 minutes to present the objectives and the research question; also, to
relate your study to earlier work and explain its importance;
•
5 minutes to present how you have carried out the study, your information sources and arguments;
290
Writing the Final Report
findings and conclusions;
•
10 minutes to present the
•
10 minutes for questions and answers.
Hint:
Rehearse your presentation and, while presenting, speak slowly and
clearly.
14.5
Writing for Publication
If you are writing a report with the intention of getting it published in a
journal, the report needs a different structure. The
first
thing to consider is
the journal you are aiming at. Most journals demand a certain format for
the
papers
footnotes
submitted
and
to
references,
The starting point is thus
paper
and
look
through
them
with
and
first
the
regard
to
emphasis
length,
on
headings,
use
methodological
of
rigour.
to decide where you want to publish your
your
target
publication’s
criteria
and
format
requirements.
Most academic journals look for well-focused research that can contribute something to existing knowledge in the particular
field.
Many journals
also value contributions towards managerial practice, so-called managerial
implications. In business studies, the knowledge developed by researchers is
rather
more
quickly
applied
to
practice
than
in
many
fields.
other
The research, whether developing theories or testing existing ones, contributes considerably towards the furthering of knowledge. This is the main
objective
particular
of
most
field.
Considering
the
academic
above,
a
journals:
report
to
written
further
for
knowledge
publication
in
needs
establish the objective of study and emphasize the gap it will
fill
new
to
knowledge
knowledge in the
it
will
lead
to.
Second,
it
has
to
be
related
their
first
to
or the
existing
field. Third, the methodology needs much more argumen-
tative explanation to convince the reader that, for the particular research
problem, this is the most appropriate method. Here the discussion on internal
validity, for example of the measurement instrument, and external validity,
for example the generalizability or managerial implications, is much more
important than in other reports (Huff, 1999).
A report written for publication need not have headings and paragraphs
on all the above issues, but nevertheless the argument in the methodology
section and on measurement should satisfy all concerns regarding them.
14.5 Writing for Publication
A paper/report written for publication is normally written with the following
headings:
•
abstract, which explains the research questions, the contribution, the
methods used, and
•
findings;
introduction, which introduces the topic and explains the research problem and objective, why this study is needed, and what contribution it
will make;
•
literature review, either as a separate section or while building/presenting
hypotheses and propositions;
•
method/model/measurement, where you explain how you have done the
study (data collection and data analysis) with all the argumentation
mentioned earlier;
•
findings, where the results are presented according to the method chosen
(quantitatively or qualitatively);
•
discussion and conclusion, where the conclusions are drawn out of the
findings and unexpected or peculiar results are discussed.
Finally, with or without a heading, the contributions (theoretical as well as
practical) of the study are presented in a convincing manner. A reference list
is attached at the end, following the formatting instructions of the journal or
the publisher. All the references mentioned in the report, no more, no less.
Once the report is ready it should be read by at least a couple of colleagues,
especially your supervisor, if you have one, or a senior colleague. These
colleagues should have some experience of reviewing papers for the journal
being considered, or for similar journals. The criticisms and comments given
by these colleagues should be taken seriously and the paper should be revised
before you send it to the journal.
291
292
Writing the Final Report
Further Reading
........................................................................
Birkinshaw, J. (2004) Publishing qualitative research in international business,
in Marshan-Piekkari, R. and Welch, C. (eds.),A Handbook of Qualitative
Research Methods for International Business, Cheltenham, UK: Edward Elgar,
pp. 570 84.
Fisher, C. (2010) Researching and Writing a Dissertation: An Essential Guide for
Business Students, 2nd edn., London: FT & Prentice Hall.
Miles, M.B. and Huberman, A.M. (1994) Qualitative Data Analysis, 2nd edn.,
Thousand Oaks, CA: Sage (Chapter 12).
Rousseau, D.M. (1995) Publishing from a reviewer s perspective , in Cummings, L.L.
and Frost, P.J. (eds.), Publishing in the Organizational Sciences, Thousand Oaks,
CA: Sage, pp. 151 63.
‘
’
–
‘
’
’
–
Questions
........................................................................
1.
2.
3.
What are the differences between writing a company report and writing a
thesis?
What are the most important issues to be considered when writing the
final report for a research project?
Is writing for publication different from writing up a thesis? How?
Exercises
........................................................................
1.
2.
3.
Pick up a Master s thesis from the library and prepare a 15-minute
presentation.
Pick up a Master s/PhD dissertation and write a two-page executive
summary.
Working in groups of two, read each other a report and give critical
comments regarding the structure of the report and the languageflow.
’
’
APPENDICES
APPENDIX A
Sample of Manufacturing Firms, 2018
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
93
89
2
33
52
74
6
91
19
98
53
62
82
001
28
24
13
5.81
9.02
9.57
4.91
3.93
9.71
9.86
3.35
7.82
0.54
8.92
5.24
8.72
7.12
7.51
5.53
1.04
3.21
5.32
5.76
7.61
0.03
6.51
6.45
6.54
7.92
0.54
6.32
2.53
6.32
5.32
4.81
0.43
5.53
CELE
CELE
CELE
CELE
CELE
CELE
CELE
CELE
CELE
CELE
OTUA
OTUA
OTUA
OTUA
OTUA
TXET
TXET
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
5.5
4.1
9.6
4.2
9.4
2.2
5.2
5.6
4.3
7.3
8.3
9.2
6.4
3.5
8.5
7.5
5.3
54
44
91
22
65
24
22
33
43
5
12
43
92
4
5
51
01
342
76
97
431
702
411
33
56
361
59
322
102
781
88
431
55
76
1
2
3
4
5
6
7
8
9
01
11
21
31
41
51
61
71
81PXE rotceS noigeR SPAC
EGA
EZIS
)elacs
)seeyolp
7–1( )sraey( -me(
0
91PXE
mriF
1
WORG
)CELE )TXET )TSEW )HTUOS )HTRON
=
=
=
=
=
)selas )selas
rotceS rotceS noigeR noigeR noigeR )001 – 1 fo % sa fo % sa
fi 1=( fi 1=( fi 1=( fi 1=( fi 1=( gniknar( stropxe( stropxe(
CELED TXETD TSEWD HTUOSD HTROND
Appendix A: Sample of Manufacturing Firms, 2018
294
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
93
89
2
33
52
74
6
91
19
98
53
62
82
001
28
24
13
69
5.81
9.02
9.57
4.91
3.93
9.71
9.86
3.35
7.82
0.54
8.92
5.24
8.72
7.12
7.51
5.53
1.04
4.76
3.21
5.32
5.76
7.61
0.03
6.51
6.45
6.54
7.92
0.54
6.32
2.53
6.32
5.32
4.81
0.43
5.53
3.67
CELE
CELE
CELE
CELE
CELE
CELE
CELE
CELE
CELE
CELE
OTUA
OTUA
OTUA
OTUA
OTUA
TXET
TXET
TXET
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
HTRON
5.5
4.1
9.6
4.2
9.4
2.2
5.2
5.6
4.3
7.3
8.3
9.2
6.4
3.5
8.5
7.5
5.3
8.6
54
44
91
22
65
24
22
33
43
5
12
43
92
4
5
51
01
9
342
76
97
431
702
411
33
56
361
59
322
102
781
88
431
55
76
43
1
2
3
4
5
6
7
8
9
01
11
21
31
41
51
61
71
81
81PXE rotceS noigeR SPAC
EGA
EZIS
)elacs
7– 1( )sraey( )seeyolpme(
0
91PXE
mriF
1
WORG
)CELE )TXET )TSEW )HTUOS )HTRON
=
=
=
=
=
)selas )selas
rotceS rotceS noigeR noigeR noigeR )001 – 1 fo % sa fo % sa
fi 1=( fi 1=( fi 1=( fi 1=( fi 1=( gniknar( stropxe( stropxe(
CELED TXETD TSEWD HTUOSD HTROND
295
Appendix A: Sample of Manufacturing Firms, 2018
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
45
94
68
97
78
11
48
01
42
3
22
06
15
58
73
18
81
63
35
26
37
27
72
9.83
3.91
6.44
7.34
6.83
4.54
3.84
0.66
0.94
3.54
6.32
5.71
5.21
8.44
1.36
6.33
3.54
9.56
8.94
6.43
7.64
3.34
0.83
4.53
3.71
2.34
8.34
6.63
0.32
2.54
5.95
9.34
6.22
8.42
3.81
4.41
6.34
7.45
9.45
6.33
9.95
3.54
8.43
1.14
6.73
8.43
CELE
CELE
CELE
CELE
CELE
CELE
CELE
CELE
CELE
CELE
OTUA
OTUA
OTUA
TXET
TXET
TXET
CELE
CELE
CELE
CELE
OTUA
OTUA
OTUA
HTUOS
HTUOS
HTUOS
HTUOS
HTUOS
HTUOS
HTUOS
HTUOS
HTUOS
HTUOS
HTUOS
HTUOS
HTUOS
HTUOS
HTUOS
HTUOS
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
2.1
9.1
5.2
8.5
2.6
4.4
3.5
3.6
3.3
9.2
8.5
3.1
4.1
5.4
1.5
3.6
3.4
3.5
4.4
0.4
5.3
5
7.5
44
55
32
43
26
44
92
62
45
23
86
23
51
22
55
33
84
82
62
34
22
4
41
211
651
451
76
822
761
561
001
58
941
542
891
551
45
32
67
321
781
33
831
56
431
902
91
02
12
22
32
42
52
62
72
82
92
03
13
23
33
43
53
63
73
83
93
04
14
Appendix A: Sample of Manufacturing Firms, 2018
296
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
38
65
43
09
87
05
54
14
85
44
9
08
51
71
4
79
96
12
0.62
6.72
1.03
6.21
3.56
2.45
2.05
1.17
8.47
2.64
3.24
0.45
1.24
6.54
0.64
4.83
7.83
0.26
5.32
8.32
8.52
9.52
4.56
3.45
6.54
6.46
7.47
0.24
7.53
4.65
7.63
7.93
5.93
8.33
2.54
4.65
OTUA
OTUA
OTUA
OTUA
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
5.2
5.2
6.3
8.3
2.6
3.5
3
1.6
3.6
2.2
8.3
4.3
6.1
4.1
2
3.6
5.2
3.4
3
25
35
12
14
81
83
72
63
33
26
41
21
92
92
13
72
52
85
54
871
651
69
33
54
37
28
22
16
43
65
87
901
321
83
871
24
34
44
54
64
74
84
94
05
15
25
35
45
55
65
75
85
95
81PXE rotceS noigeR SPAC
EGA
EZIS
)elacs
7–1( )sraey( )seeyolpme(
0
91PXE
mriF
0
WORG
)CELE )TXET )TSEW )HTUOS )HTRON
=
=
=
=
=
)selas )selas
rotceS rotceS noigeR noigeR noigeR )001 – 1 fo % sa fo % sa
fi 1=( fi 1=( fi 1=( fi 1=( fi 1=( gniknar( stropxe( stropxe(
CELED TXETD TSEWD HTUOSD HTROND
297
Appendix A: Sample of Manufacturing Firms, 2018
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16
04
25
47
23
07
29
76
49
64
56
17
46
7
57
99
32
02
75
03
34
84
5
9.34
4.95
1.63
1.63
3.04
0.15
3.52
6.54
6.82
6.63
1.05
8.82
5.24
4.55
4.25
3.33
9.35
0.84
9.71
9.74
4.53
5.54
1.56
0.54
0.35
6.53
3.13
6.53
5.45
6.82
7.93
2.23
5.23
0.44
3.33
6.83
4.84
6.74
6.53
0.94
4.24
9.71
3.34
6.33
3.34
4.94
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
TXET
CELE
CELE
CELE
CELE
CELE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSAE
TSEW
TSEW
TSEW
TSEW
TSEW
3.4
8.3
9.2
8.4
2.5
7.5
6.5
7.6
4.2
3.2
4
4
1.6
8.2
3.5
7.4
5.5
3.4
8.4
1.4
3.3
3
3.6
72
41
21
43
82
62
92
24
73
71
81
62
56
56
23
4
5
9
82
63
22
54
56
76
56
221
451
341
65
891
102
38
99
46
111
381
47
57
82
48
56
102
631
44
55
66
06
16
26
36
46
56
66
76
86
96
07
17
27
37
47
57
67
77
87
97
08
18
28
Appendix A: Sample of Manufacturing Firms, 2018
298
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
88
66
36
31
77
67
1
39
59
61
92
86
41
8
83
55
21
95
0.9
1.54
0.03
7.43
7.82
5.44
1.45
0.5
2.32
8.55
1.24
3.64
6.34
5.56
4.35
0.74
1.44
2.43
5.9
1.93
4.03
6.82
2.52
2.93
8.93
2.01
8.82
4.94
3.83
8.04
9.73
5.85
6.74
7.24
3.81
7.33
OTUA
OTUA
OTUA
OTUA
OTUA
OTUA
OTUA
OTUA
OTUA
OTUA
OTUA
OTUA
OTUA
TXET
TXET
TXET
TXET
TXET
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
TSEW
8.4
2.5
6.5
2.6
5
3.3
6.4
4
7.3
6.4
8.3
2.5
8.5
4.4
5.3
2.5
9.3
1.6
45
22
72
32
91
34
74
92
33
73
15
16
33
92
73
14
7
9
842
55
541
781
87
33
441
622
221
501
811
621
131
511
711
221
821
611
38
48
58
68
78
88
98
09
19
29
39
49
59
69
79
89
99
001
81PXE rotceS noigeR SPAC
EGA
EZIS
)elacs
7–1( )sraey( )seeyolpme(
0
91PXE
mriF
0
WORG
)CELE )TXET )TSEW )HTUOS )HTRON
=
=
=
=
=
)selas )selas
rotceS rotceS noigeR noigeR noigeR )001 – 1 fo % sa fo % sa
fi 1=( fi 1=( fi 1=( fi 1=( fi 1=( gniknar( stropxe( stropxe(
CELED TXETD TSEWD HTUOSD HTROND
APPENDIX B
Simple Comparative Tests
Often the researcher will want to compare the means of several populations
by using random samples of data drawn from each. The appropriate method
of analysis depends upon:
the underlying distribution of the data (whether it is normally distributed
or not);
whether the samples contain independent or matched (paired)
observations;
the number of means being compared.
If the data are normally distributed, then the followingparametric tests are
appropriate:
•
•
•
Observations
Number of means
2
Independent
Student s t-test
Matched (paired)
Paired t-test
>2
One-way ANOVA
Randomized block ANOVA
’
Note: The ANOVA will need to be supplemented with an appropriate multiple comparisons
test (e.g. LSD, Tukey). This is because there are 3 possible comparisons if 3 means are being
compared (i.e. A & B, A & C, and B & C); 6 possible comparisons if 4 means are being
compared (i.e. A & B, A & C, A & D, B & C, B & D, and C & D); 10 possible comparisons if
5 means are being compared, etc.
If the data are not normally distributed, or the data are ordinal, then the
following non-parametric tests are appropriate:
Observations
Number of means
2
Independent
Mann-Whitney test
Matched (paired)
Wilcoxon signed-ranks test
>2
Kruskal-Wallis test
Kruskal-Wallis test
Parametric tests are generally more powerful than non-parametric tests, and
hence should be used as long as the underlying assumptions hold.
Many parametric methods are robust if sample sizes are reasonably large
(i.e. n > 30), in that they are valid even if the underlying distributional
assumptions are not met.
APPENDIX C
Formulae
Comparison of Means (Independent Samples)
........................................................................
rffi ffi ffi ffi ffi ffi ffi ffi
The standard error of the difference between two means is:
SEðX± 1 ± X± 2 Þ ¼ sp
where s2p
1
n1
þ n1
2
2
2
¼ ðn1 ± n11Þsþ1 þn2ð±n22± 1Þs2
s21 ¼ var ðX 1 Þ
s22 ¼ var ðX 2 Þ
Comparison of Means (Paired Samples)
........................................................................
The standard error of the mean difference:
ffiffi ffiffi
sffi ffi ffi ffi ffi ffiXffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi
s
0:335
SEð±d Þ ¼ pd ¼ p ¼ 0:137
n
6
where sd
¼
1
n
n ± 1 i¼1
di = X1i ± X2i
ðdi ± ±dÞ2
Appendix C: Formulae
Correlation Coefficient (Continuous Variables)
.................................................................
sXY
¼
XX X Y Y
n
sffi ffi ffi ffi ffi ffi ffi Xffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi
n
1
±1
sX
¼
sY
¼
i¼1
1
n±1
ð i±
n
i¼1
± Þð i ± ± Þ
±Þ
ðX i ± X
2
sffi ffi ffi ffi ffi ffi ffi Xffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi ffi
1
n±1
where
sXY = sample covariance of and Y
sX = sample standard deviation of X
sY = sample standard deviation of Y
n
i¼ 1
2
±Þ
ðY i ± Y
301
References
ACR News (1995) ‘Where’s the big idea?’,
ACR News, December, p. 13.
Aguinis, H. and Bradley, K.J. (2014) ‘Best
practice recommendations for
designing and implementing
experimental vignette
methodology studies’,
Organizational Research Methods,
17(4): 351–71.
Altheide, D.L. and Johnson, J.M. (1998)
‘Criteria for assessing interpretive
validity in qualitative research’, in
Denzin, N. and Lincoln, Y.S. (eds.),
Collecting and Interpreting
Qualitative Materials, London:
Sage, pp. 283–312.
Alvesson, M. (2011) Interpreting
Interviews, London: Sage.
Andreasen, A.R. (1990) ‘Cultural
interpretation: A critical consumer
research issue for the 1990s’,
Advances in Consumer Research,
3: 185–96.
de Saint Antonine’, Lyon: La
Bacconnière.
Bennett, R. (1986) ‘Meaning and
method in management research’,
Graduate Management
Research, 3(3).
Bernard, A.B., Jensen, J.B., Redding, S J.,
and Schott, P.K. (2007) ‘Firms in
international trade’, Journal of
Economic Perspectives,
21(3): 105–30.
Berry, H., Guillén, M., and Zhou, N.
(2010) ‘An institutional approach to
cross-national distance’, Journal of
International Business Studies,
41(9): 1460–80.
Berry, R. (1989) How to Write a Research
Paper, 2nd edn., Oxford: Pergamon
Press.
Beugelsdijk, S., Hennart, J.-F., Slangen,
A., and Smeets, R. (2010) ‘Why and
how FDI stocks are a biased
measure of MNE affiliate activity’,
Bandiera, O., Barankay, I., and Rasul, I.
(2016) ‘Field experiments with
firms’, Journal of Economic
Journal of International Business
Studies, 41: 1444–59.
Beugelsdijk, S., Maseland, R., Onrust, M.,
Perspectives, 25(3): 63–82.
Bazerman, M. (1998) Judgment in
Managerial Decision Making, 4th
van Hoorn, A., and Slangen, A.
(2015) ‘Cultural distance in
international business and
edn., New York: Wiley.
Becker, H.S. (1970) Sociological Work,
Chicago, IL: Aldine.
Bem, J. (1979) Désir et savoir dans
management: From mean-based
to variance-based measures’,
International Journal of
Human Resource Management,
l’oeuvre de Flaubert: ‘La tentation
26(2): 165–91.
References
Beza, T. (1984) Psaumes mis en vers
Français (1551–62), Geneva:
Droz.
Bhaskar, R. (1986) Scientific Realism and
Human Emancipation, London:
Verso.
Birkinshaw, J. (2004) ‘Publishing
Market Research, 4th edn., London:
Kogan Page.
Braithwaite, R.B. (1955) Scientific
Explanation: A Study of the
Function of Theory, Probability and
Law in Science, Cambridge:
Cambridge University Press.
qualitative research in international
business’, in Marshan-Piekkari, R.
and Welch, C. (eds.), A Handbook of
Brewer, J. and Hunter, A. (1989)
Multimethod Research: A Synthesis
of Styles, Sage Library of Social
Qualitative Research Methods for
International Business,
Cheltenham, UK: Edward Elgar,
pp. 570–84.
Research, 175, Beverly Hills, CA:
Sage.
Brown, T.A. (2014) Confirmatory Factor
Analysis for Applied Research, 2nd
Bloom, B.S., Hastings, J.T., and Madaus,
G.F. (1971) Handbook on Formative
and Summative Evaluation of
edn., New York and London:
Guilford Press.
Bryman, A. (2001) Social Research
Student Learning, New York:
McGraw-Hill.
Blumberg, B., Cooper, D.R., and
Methods, Oxford: Oxford University
Press.
Bryman, A. and Bell, E. (2003) Business
Schindler, P.S. (2008) Business
Research Methods, 2nd European
edn., London: McGraw-Hill.
Bolsky, M.I. (1988) Better Scientific and
Technical Writing, Englewood
Cliffs, NJ: Prentice Hall.
Bonoma, T.V. (1985) ‘Case research in
marketing: Opportunities,
problems, and a process’, Journal of
Business Research, 12: 199–208.
Research Methods, Oxford: Oxford
University Press.
Bryman, A. and Bell, E., (2015) Business
Research Methods, New York:
Oxford University Press.
Buchler, J. (ed.) (1955) Philosophical
Writings of Peirce, New York:
Dover.
Burns, T. and Stalker, G.M. (1961) The
Management of Innovations,
Boyd, W.B., Westfall, R., and Stasch, S.F.
(1985) Marketing Research: Text
and Cases, 6th edn., Homewood, IL:
London: Tavistock.
Burrell, G. and Morgan, G. (1979)
Sociological Paradigms and
Irwin.
Boyd, W.B., Westfall, R., and Stasch, S.F.
(1989) Marketing Research: Text
Organizational Analysis, London:
Heinemann.
Campbell, D.T. (1975) ‘Degrees of
and Cases, 7th edn., Homewood, IL:
Irwin.
Brace, I. (2018) Questionnaire Design:
How to Plan, Structure and Write
freedom and the case study’,
Comparative Political Studies,
8(2): 173–93.
Campbell, D.T. and Fiske, W. (1959)
Survey Material for Effective
‘Convergent and discriminant
303
304
References
validation by the multitrailmultimethod matrix’, Psychological
Bulletin, 56: 81–105.
Campbell, D.T. and Stanley, J.C. (1966)
Experimental and Quasi
Experimental Designs for
Research, Chicago, IL: Rand
Journal of International Business
Studies, 41(2): 178–84.
Chatterji, A., Findley, M., Jensen, N.,
Meier, S., and Nielson, D. (2016)
‘Field experiments in strategy
research’, Strategic Management
Journal, 37(1): 116–32.
McNally.
Campbell, J.P., Daft, R.L., and Hulin, C.
(1982) What to Study: Generating
Chen, H. and Chen, T.-Y. (1998)
‘Network linkages and location
choice in foreign direct investment’,
and Developing Research Questions,
Beverly Hills, CA: Sage.
Cannell, C.F., Miller, P.U., and
Oksenberg, L. (1981) ‘Research on
Journal of International Business
Studies, 29(3): 445–67.
Chetty, S.K. and Hamilton, R.T. (1993)
‘Firm-level determinants of export
interviewing techniques’, in S.
Leinhardt (ed.), Sociological
Methodology, San Francisco,
CA: Jossey-Bass, pp. 389–437.
Carter, R.B. and Van Auken, H. (2005)
‘Bootstrap financing and owners’
perceptions of their business
constraints and opportunities’,
Entrepreneurship & Regional
performance: a meta-analysis’,
International Marketing Review,
10(3): 26–35.
Chidlow, A., Ghauri, P.N., Yeniyurt, S.,
and Cavusgil, S.T. (2015)
‘Establishing rigor in mail-survey
procedures in international business
research’, Journal of World
Business, 50(1): 26–35.
Development, 17(2): 129–44.
Cattell, R.B. (1943a) ‘The description of
personality. 1. Foundations of trait
measurement’, Psychological
Chreim, S., William, B.E., and Hinings,
C.R. (2007) ‘Interlevel influences on
the reconstruction of professional
role identity’, Academy of
Review, 50(6): 559–94.
Cattell, R.B. (1943b) ‘The description of
personality: Basic traits resolved
Management Journal,
50(6): 1515–39.
Churchill, G.A. (1995) Marketing
into clusters’, Journal of Abnormal
and Social Psychology, 38(4):
476–506.
Research: Methodological
Foundations, 6th edn., Fort Worth,
TX: Dryden Press.
Chalmers, A.F. (1982) What Is This Thing
Called Science?, 2nd edn.,
Philadelphia, PA: Open University
Churchill, G.A. (1999) Marketing
Research: Methodological
Foundation, 7th edn., Fort Worth
Press.
Chang, S.-J., van Witteloostuijn, A., and
Eden, L. (2010) ‘From the editors:
Common method variance in
TX, London: Dryden Press.
Churchill, G.A. and Iacobucci, D. (2015)
Marketing Research:
Methodological Foundations, 11th
international business research’,
edn., Mason, TN: Earlie Lite Book.
References
Cichello, M. and Lamdin, D.J. (2006)
‘Event studies and the analysis of
antitrust’, International Journal of
the Economics of Business,
13(2): 229–45.
Coase, R.H. (1937) ‘The nature of the
firm’, Economica, 4(16): 386–405.
Corrado, C.J. (2011) ‘Event studies: a
methodology review’, Accounting
and Finance, 51(1): 207–34.
Cowley, J.C.P. (2000) ‘Strategic
qualitative focus group research:
define and articulate our skills or
we will be replaced by others’,
Cohen, J. (1994) ‘The earth is round (p <
.05)’, American Psychologist,
49(12): 997–1003.
International Journal of Market
Research, 37(7): 903–30.
Craig, D.S. and Douglas, S.P. (2000)
Cohen, M. and Nagel, E. (1934) An
Introduction to Logic and
Scientific Method, New York:
American Advertising
International Marketing Research,
2nd edn., Chichester, UK: John
Wiley & Sons.
Creswell, J.W. (2007) Qualitative Inquiry
Association.
Colley, R.A. (1961) Defining Advertising
Goals for Measured Advertising
and Research Design: Choosing
Among Five Approaches, 2nd edn.,
Thousand Oaks, CA: Sage.
Results, New York: American
Advertising Association.
Conant, J. (1951) Science and Common
Croson, R., Anand, J., and Agarwal, R.
(2007) ‘Using experiments in
corporate strategy research’,
Sense, New Haven, CT: Yale
University Press.
Connelly, B.L., Ketchen, D.J., Gangloff,
European Management Review,
4(3): 173–81.
Crotty, M. (1998) The Foundations of
K.A., and Shook, C.L. (2016)
‘Investor perceptions of CEO
successor selection in the wake
of integrity and competence
failures: a policy capturing study’,
Strategic Management Journal, 37:
2135–51.
Social Research: Meaning and
Perspective in the Research Process,
London: Sage.
Cunliffe, A.L. (2003) ‘Reflexive inquiry
in organizational research:
Questions and possibilities’, Human
Relations, 56(8): 983–1003.
Cook, T.D. and Campbell, D.T. (1979)
Quasi-Experimentation: Design and
Analysis Issues for Field Settings,
Davis, J. (2001) ‘International
comparison of labour disputes’,
Labour Market Trends, April,
Chicago, IL: Rand McNally College
Publication Co.
Cooper, D. and Schindler, P. (2001)
pp. 195–201.
Denzin, N.K. and Lincoln, Y.S. (eds.)
(1994) Handbook of Qualitative
Business Research Methods, Boston,
MA: McGraw-Hill.
Cooper, H.M. (1984) The Integrative
Research Review: A Systematic
Research, Thousand Oaks, CA: Sage.
Di Stefano, G. and Gutierrez, C. (2018)
‘Under a magnifying glass: On the
use of experiments in strategy
Approach, Beverly Hills, CA: Sage.
research’, Strategic Organization,
305
306
References
online: https://doi.org/10.1177/
1476127018803840.
Diamantopoulos, A., Sarstedt, M.,
Fuchs, C., Wilczynski, P., and
Global Economy, Cheltenham, UK:
Edward Elgar.
Easterby-Smith, M., Thorpe, R., and
Lowe, A. (1991) Management
Kaiser, S. (2012) ‘Guidelines for
choosing between multi-item and
single-item scales for construct
Research, London: Sage.
Eisenbeis, R.A. (1977) ‘Pitfalls in the
application of discriminant analysis
measurement: a predictive validity
perspective’, Journal of the
Academy of Marketing Sciences,
in business, finance, and
economics’, Journal of Finance,
32(3): 875–900.
40(3): 434–49.
Ding, L., Lam, H.K.S., Cheng, T.C.E., and
Zhou, H. (2018) ‘A review of shortterm event studies in operations and
supply chain management’,
International Journal of Production
Economics, 200(C): 329–42.
Donaldson, L. (1997) ‘Derek Pugh:
Scientific revolutionary in
organization studies’, in Clark, T.
(ed.), Essays in Honour of Derek
Pugh, Aldershot: Ashgate, pp. 23–43.
Douglas, J. (1976) Investigating Social
Research, Beverly Hills, CA: Sage.
Douglas, S.P. and Craig, C.S. (1997) ‘The
changing dynamic of consumer
behavior: Implications for crosscultural research’, International
Journal of Research in Marketing,
14(4): 379–95.
Dubois, A. and Gadde. L.-E. (2002)
‘Systematic combining: An
abductive approach to case
research’, Journal of Business
Research, 55(7): 553-60.
Dubois, A. and Gadde, L.-E. (2014)
‘Systematic combining: A decade
later’, Journal of Business Research,
67(6): 1277–84.
Dunning, J.H. and Lundan, S.M. (2008)
Multinational Enterprises and the
Eisenhardt, K.M. (1989) ‘Building
theories from case study research’,
Academy of Management Review,
14(4): 532–50.
Elg, U., Ghauri, P, Child, J., and
Collinson, S. (2017) ‘MNE microfoundations and routines for
building a legitimate and
sustainable position in emerging
markets’, Journal of Organizational
Behavior, (38(9): 1320–37.
Elkjaer, B. and Simpson, B. (2011)
‘Pragmatism: A lived and living
philosophy. What can it offer to
contemporary organization
theory?’, in Tsoukas, H. and Chia, R.
(eds.), Philosophy and Organization
Theory, Vol. 32, Bradford: Emerald,
pp. 55–84.
Elliot, J. (1980) ‘How hard it is to write
easily’, View Point: The Ogilvy &
Mathew Magazine, 2, pp. 18–19.
Ellis, P.D. (2010) ‘Effect sizes and the
interpretation of research results in
international business’, Journal of
International Business Studies,
41(9): 1581–8.
Falk, A. and Heckman, J.J. (2009) ‘Lab
experiments are a major source of
knowledge in the social sciences’,
Science, 326(5952): 535–8.
References
Fama, E.F., Fisher, L., Jensen, M.C., and
Roll, R. (1969) ‘The adjustment of
stock prices to new information’,
International Economic Review,
10(1): 1–21.
Ferber, R., Blankertz, D.F., and Hollander,
S. Jr. (1964) Marketing Research,
American Marketing Association,
pp. 407–50.
Fowler, F.J. and Mangione, T.W.
(1990) Standardized Survey
Interviewing: Minimizing
Interviewer-Related Error, Newbury
Park, CA: Sage.
New York: Ronald Press.
Field, A. (2018) Discovering Statistics
Using IBM SPSS Statistics, 5th
Frankfort-Nachmias, F. and Nachmias, D.
(1996) Research Methods in the
Social Sciences, 5th edn., London:
edn., London: Sage.
Fisher, C. (2010) Researching and
Writing a Dissertation: An
Essential Guide for Business
Edward Arnold.
Fuglseth, A.M. and Grønhaug, K. (2003)
‘Can computerized market models
improve strategic decision
Students, 2nd edn., London: FT &
Prentice Hall.
Flatten, T.C., Engelen, A., Zahra, S.A.,
making?’, Journal of SocioEconomics, 32: 503–20.
Gallo, A. (2015) ‘A refresher on
and Brettel, M. (2011) ‘A measure of
absorptive capacity: scale
development and validation’,
European Management Journal,
29(2): 98–116.
Flyvbjerg, B. (2006) ‘Five
regression analysis’, Harvard
Business Review, 4 November,
online: https://hbr.org/2015/11/arefresher-on-regression-analysis.
Ghauri, P.N. (2004) ‘Designing and
conducting case studies in
misunderstandings about casestudy research’, Qualitative Inquiry,
12(1): 219–45.
Fontana, A. and Frey, J.H. (1994)
international business research’, in
Marshan-Piekkari, R. and Welch, C.
(eds.), A Handbook of Qualitative
Research Methods for International
‘Interviewing: The art of science’, in
Denzin, N.K. and Lincoln, Y.S.
(eds.), Handbook of Qualitative
Business, Cheltenham, UK: Edward
Elgar, pp. 109–24.
Ghauri, P.N. and Cateora, P. (2006)
Research, Thousand Oaks, CA: Sage,
pp. 361–76.
Forcese, D.P. and Richer, S. (1973) Social
International Marketing: European
Edition, London: McGraw-Hill.
Ghauri, P.N. and Cateora, P. (2014)
Research Methods, Englewood
Cliffs, NJ: Prentice Hall.
Fornell, C.G. (1987) ‘A second generation
International Marketing, 4th edn.,
London: McGraw-Hill.
Ghauri, P.N. and Firth, R. (2009) ‘The
of multivariate analysis:
classification of methods and
implications for marketing
research’, in Houston, M.J. (ed.),
Review of Marketing, Chicago:
formalization of case study research
in international business’, Der Markt:
Journal fur Marketing, 48: 29–40.
Ghemawat, P. (2001) ‘Distance still
matters: The hard reality of global
307
308
References
expansion’, Harvard Business
Review, 79(8): 137–47.
Gibbert, M. and Ruigrok, W. (2010) ‘The
“what” and “how” of case study
rigor: Three strategies based on
published work’, Organization
Research Methods, 13: 710–37.
Gibbert, M., Ruigrok, W., and Wicki, B.
(2008) ‘What passes as a rigorous
case study?’, Strategic Management
Journal, 29(13): 1465–74.
Gibbons, M., Limoges, C., Nowotny, H.,
Schwartzman, S., Scott, P., and
Trow, M. (1994) The New Production
of Knowledge, London: Sage.
Gill, J. and Johnson, P. (2002) Research
Methods for Managers, 3rd edn.,
London: Paul Chapman.
Gioia, D. and Chittipeddi, K. (1991)
Sensemaking and sensegiving in
Silverman, D. (eds.), Qualitative
Research Practice, London: Sage,
pp. 405–26.
Golafshani, N. (2003) ‘Understanding
reliability and validity in qualitative
research’, The Qualitative Report,
8(4): 597–606.
Grisar-Kassé, K. (2004) ‘The role of
negative personal experiences in
cross-cultural case study research:
failure or opportunity’, in MarshanPiekkari, R. and Welch, C. (eds.)
A Handbook of Qualitative Research
Methods for International Business,
Cheltenham, UK: Edward Elgar,
pp. 144–61.
Grønhaug, K. (1985) ‘Problemer i
empirisk forskning’, in NHH/RSF,
Methoder og perspectiver i
okonomisk-administrativ
strategic change initiation,
Strategic Management Journal,
12(6): 433–48.
Forskning, Oslo: Universitetsforlag.
Grønhaug, K. (1999) ‘Technological
flexibility and organisational
Gioia, D.A. and Pitre, E. (1990)
‘Multiparadigm perspectives on
theory building’, Academy of
Management Review, 15, 584–602.
buying strategies’, European
Journal of Purchasing and Supply
Management, 51: 13–22.
Grønhaug, K. and Haukedal, W. (1989)
Gioia, D., Corley, A., Kevin, G., and
Hamilton, A. L. (2012) Seeking
qualitative rigor in inductive
research: Notes on the Gioia
methodology, Organisational
Research Methods, 16(1): 15–31.
Glaser, B.G. and Strauss, A.L. (1967) The
Discovery of Grounded Theory:
Strategies for Qualitative Research,
Chicago, IL: Aldine.
Gobo, G. (2004) ‘Sampling,
representativeness and
generalizability’, in Seale, C.,
Gobo, G., Gubrium, J.F., and
‘Environmental imagery and
strategic action’, Scandinavian
Journal of Management,
4(1/2): 5–17.
Gujarati, D.N. and Porter, D.C. (2009)
Basic Econometrics, 5th edn., New
York: McGraw-Hill.
Haans, R.F.J., Pieters, C., and He, Z.-L.
(2016) ‘Thinking about
U: theorizing and testing U- and
inverted U-shaped relationships
in strategy research’, Strategic
Management Journal,
37(x): 1177–95.
References
Hadjikhani, A., Elg, U., and Ghauri, P.
(eds.) (2012) Business, Society and
Politics, Bingley: Emerald.
Hagen, B., Zucchella, A., and Ghauri, P.
(2019) ‘From fragile to agile:
Marketing as a key driver of
entrepreneurial
tool in business research’, European
Business Review, 26(2): 106–21.
Hammersley, M. and Atkinson, P. (1995)
Ethnography: Principles in Practice,
London: Routledge.
Harrison, G.W. and List, J.A. (2004)
‘Field experiments’, Journal of
internationalization’, International
Marketing Review, 36(2): 260–88.
Hagg, I. and Hedlund, G. (1978) ‘Case
Economic Literature, 42(4):
1009–55.
Hassan, I. and Ghauri, P. (2014)
studies in accounting research’,
Accounting, Organization and
Society, 4(1-2): 135–43.
Hair, J.F., Anderson, R.E., Tatham, R.G.,
Evaluating Companies for Mergers
and Acquisitions, Bingley: Emerald.
Hawkins, S.W. (1988) A Brief History of
Time: From the Big Bang to Black
and Black, W.C. (1998) Multivariate
Data Analysis, 5th edn, Harlow and
Englewood Cliffs, NJ: Prentice Hall.
Holes, Toronto: Bantam Books.
Hellesøy, O., Grønhaug, K., and
Kvitastein, O. (2000) ‘Burnout:
Hair, J.F., Ringle, C.M., and Sarstedt, M.
(2011) ‘PLS-SEM: indeed a silver
bullet’, Journal of Marketing Theory
Conceptual issues and empirical
findings from a new research
setting’, Scandinavian Journal of
and Practice, 19(2): 139–52.
Hair, J.F., Sarstedt, M., Pieper, T.M., and
Ringle, C.M. (2012a) ‘The use of
Management, 16(3): 233–47.
Hensher, D.A., Rose, J.M., and Greene,
W.H. (2005) Applied Choice
partial least squares structural
equation modeling in strategic
management research: A review of
past practices and
Analysis: A Primer. Cambridge:
Cambridge University Press.
Hillebrand, B., Kok, R., and Biemans, W.
(2001) ‘Theory testing using case
recommendations for future
applications’, Long Range Planning,
45(5): 320–40.
studies: A comment on Johnston,
Leach and Liu’, Industrial
Marketing Management, 30: 651–7.
Hair, J.F., Sarstedt, M., Ringle, C.M., and
Mena, J.A. (2012b) ‘An assessment
of the use of partial least squares
structural equation modeling in
marketing research’, Journal of the
Academy of Marketing Science,
40(3): 414–33.
Hair, J.F., Sarstedt, M., Hopkins, L., and
Kuppelwieser, V.G. (2014) ‘Partial
least squares structural equation
modeling (PLS-SEM): An emerging
Hofstede, G. (2001) Culture’s
Consequences: Comparing Values,
Behaviors, Institutions, and
Organizations across Nations, 2nd
edn., Thousand Oaks, CA: Sage.
Holland, R. (1999) ‘Reflexivity’, Human
Relations, 52(4): 463–85.
Huff, S.A. (1999) Writing for Publication,
Thousand Oaks, CA: Sage.
Hult, G.T.M., Ketchen, D.J. Jr, Cui, A.S.,
Prud’homme, A.M., Seggi, S.H.,
309
310
References
Stanko, M.A., Xu, A.S., and
Cavusgil, S.T. (2006) ‘An
assessment of the use of structural
equation modeling in international
business research’, Research
Methodology in Strategy and
Management, 3: 385–415.
Kelle, U. (1997) ‘Theory building in
qualitative research and computer
programs for management of
textual data’, Social Research
Online, 2(2): www.Socresonline
.org.uk/2/2/1.html.
Kent, R.K. (1993) Marketing Research in
Isaeva, N., Bachmann, R., Bristow, A.,
and Saunders, M.N. (2015) ‘Why the
epistemologies of trust researchers
Action, London: Routledge.
Kerlinger, F.N. (1964) Foundations of
Behavioural Research: Educational
matter’, Journal of Trust Research,
5(2): 153–69.
Jankowicz, A.D. (1991) Business
Research Projects for Students,
and Psychological Inquiry, London:
Holt, Rinehart & Winston.
Kerner, A. (2014) ‘What do we talk about
when we talk about foreign direct
London: Chapman & Hall.
Johanson, J. and Vahlne, J.-E. (1977)
‘The internationalization process of
investment?’, International Studies
Quarterly, 58(4): 804–15.
Ketchen, D.J. Jr. and Shook, C.L. (1996)
the firm – a model of knowledge
development and increasing foreign
market commitments’, Journal of
‘The application of cluster analysis
in strategic management research:
an analysis and critique’, Strategic
International Business Studies, 8(1):
23–32.
Johnston, M.A. (2007) ‘A review of the
Management Journal, 17(6):
441–58.
Khanna, T. and Palepu, K.G. (1997) ‘Why
application of event studies in
marketing’, Academy of Marketing
Science Review, 11(4): 1–31.
Jones, M.O. (1988) ‘In search of
focused strategies may be wrong for
emerging markets’, Harvard
Business Review, 75(4): 41–51.
King, G., Kelohane, R.O., and Verba, S.
meaning: using qualitative methods
in research and applications’, in
Jones, M.O., Moore, M.D., and
Snyder, R.C. (eds.), Inside
Organisations: Understanding the
Human Dimension, London: Sage,
(1994) Designing Social Enquiry:
Scientific Inference in Qualitative
Research, Princeton, NJ: Princeton
University Press.
Kirk, J. and Miller, M.L. (1986) Reliability
and Validity in Qualitative
pp. 31–48.
Kawai, N., Strange, R., and Zucchella, A.
(2018) ‘Stakeholder pressures, the
Research, Sage University Paper
Series on Qualitative Applications
in Social Sciences, No. 01, Newbury
adoption of environmental
management systems, and green
innovation in MNC overseas
subsidiaries’, International Business
Park, CA: Sage.
Knudsen, C. (2003) ‘Pluralism, scientific
progress, and the structure of
organization theory’, in Tsoukas, H.
Review, 27(5): 933–46.
and Knudsen, C. (eds.), The Oxford
References
Handbook of Organization Theory:
Meta-Theoretical Perspectives,
Oxford: Oxford University Press,
pp. 262–86.
Lahaut, V.M.H.C.J., Jansen, H.A.M., van
de Mheen, D., and Garretsen, H.F.L.
(2002) ‘Non-response bias in a
sample survey of alcohol
Kogut, B. and Singh, H. (1988) ‘The
effects of national culture on the
choice of entry mode’, Journal of
consumption’, Alcohol and
Alcoholism, 37(3): 256–60.
Lakoff, G. and Johnson, M. (1980)
International Business Studies, 9(3):
411–32.
Konchitchki, Y. and O’Leary, D.E. (2011)
Metaphors We Live By, Chicago, IL:
University of Chicago Press.
Lave, C.A. and March, J.G. (1993) An
‘Event study methodologies in
information systems research’,
International Journal of Accounting
Information Systems, 12(2):
Introduction to Models in the Social
Sciences, 2nd edn., New York:
Harper & Row.
Layder, D. (1993) New Strategies in
99–115.
Kornhauser, A. and Lazarsfeld, P.F.
(1955) ‘The analysis of consumer
Social Research, Cambridge: Polity
Press.
Lazarsfeld, P. (1959) ‘Latent structure
actions’, in Lazarsfeld, P. and
Rosenberg, M. (eds.), The Language
of Social Research, Glencoe, IL: Free
analysis’, in Koch, S. (ed.),
Psychology: A Study of a Science,
vol. 3, New York and London:
Press, pp. 392–403.
Kotler, P. (1997) Marketing Management,
9th edn., Englewood Cliffs, NJ:
McGraw-Hill.
Lee, L., Petter, S., Fayard, D., and
Robinson, S. (2011) ‘On the use of
Prentice Hall.
Krammer, S.M.S., Strange, R., and
Lashitew, A. (2018) ‘The
determinants of export performance
partial least squares path modeling
in accounting research’,
International Journal of Accounting
Information Systems, 12(4):
in BRIC countries: The influence of
firm resources and the institutional
environment’, International
305–28.
Lilien, G.L., Kotler, P., and Moorthy, K.S.
(1992) Marketing Models,
Business Review, 27(1): 218–30.
Kraus, S., Meier, F., and Niemand, T.
(2016) ‘Experimental methods in
entrepreneurship research: the
status quo’, International Journal of
Entrepreneurial Behavior and
Research, 22(6): 958–83.
Kuhn, T. (1970) The Structure of
Scientific Revolutions, 2nd edn.,
Chicago, IL: University of Chicago
Press.
Englewood Cliffs, NJ: Prentice Hall.
Lind, J.T. and Mehlum, H. (2010) ‘With
or without U? The appropriate test
for a U-shaped relationship’, Oxford
Bulletin of Economics and
Statistics, 72(1): 109–18.
Locke, K. and Golden-Biddle, K. (1997)
‘Constructing opportunities of
contribution: Constructing
intertextual coherence and
“problematizing” in organizational
311
312
References
studies’, Academy of Management
Journal, 40(5): 1023–62.
Locke, L.F., Spirduso, W.W., and
Silverman, S.J. (2000) Proposals
That Work: A Guide for Planning
Dissertations and Grant
Proposals, 4th edn., Thousand
Oaks, CA: Sage.
Lonner, W.J. and Adamopoulos, J. (1997)
‘Culture as an antecedent to
behaviour’, in Berry, J.W.,
Poortinga, Y.H., and Panday, J.
(eds.), Handbook of Cross-Cultural
Psychology, vol. 2, Boston, MA:
Allyn & Bacon, pp. 43–83.
Lorenzoni, G. and Lipparini, A. (1999)
‘The leveraging of interfirm
relationships as a distinctive
organizational capability:
A longitudinal study’, Strategic
Management Journal,
20(4): 317–38.
MacCallum, R.C., Widaman, K.F., Zhang,
March, J.G. and Simon, H.A. (1958)
Organizations, New York: Wiley.
Marshall, C. and Rossman, G.B. (1995)
Designing Qualitative Research, 2nd
edn., London: Sage.
Marshan-Piekkari, R. and Welch, C.
(2004) A Handbook of Qualitative
Research Methods for International
Business, Cheltenham, UK: Edward
Elgar.
Martin, C. (1988) Computers and Senior
Managers: Top Management’s
Response to Interactive Computing,
Manchester: NCC.
Mason, J. (2002) ‘Qualitative
interviewing: Asking, listening and
interpreting’, in May, T. (ed.),
Qualitative Research in Action,
London: Sage, pp. 225–41.
McCaslin, M.L. and Scott, K.W. (2003)
‘The five question method for
framing qualitative research study’,
The Qualitative Report,
S., and Hong, S. (1999) ‘Sample size
in factor analysis’, Psychological
Methods, 4: 84–99.
MacKinlay, A.C. (1997) ‘Event studies in
8(3): 447–61.
McCrae, R.R. and Costa, P.T. (1997)
‘Personality trait structure as a
human universal’, American
economics and finance’, Journal of
Economic Literature, 35(1): 13–39.
Majocchi, A. and Strange, R. (2007) ‘The
Psychologist, 52(5): 509–16.
McCrae, R.R. and Costa, P.T. (1999)
‘A five-factor theory of personality,’
FDI location decision: Does
liberalization matter?’,
Transnational Corporations,
in Pervin, L.A. and John, O.P. (eds.),
Handbook of Personality: Theory
and Research, New York: Guilford
16(2): 1–40.
Malhotra, N. (1996) Marketing Research:
An Applied Orientation, 2nd edn.,
Press, pp. 139–53.
McWilliams, A. and Siegel, D. (1997)
‘Event studies in management
Englewood Cliffs, NJ: Prentice Hall.
Malinowski, B. (1916/1948) Magic,
Science and Religion and other
Essays, New York: Natural History
research: Theoretical and empirical
issues’, Academy of Management
Journal, 40(3): 626–57.
Meer-Kooistra, J. van der (1993)
Press.
Coordineren, Motiveren en
References
verrekenen, Wisselwerking
Naegel, E. (1961) The Structure of
tussen omgeving, onderneming en
mensen, Groningen: WoltersNoordhoff.
Science, New York: Harcourt, Brace
& World.
Ning, L., Kuo, J.-M., Strange, R., and
Merton, R.K. (1967) On Theoretical
Sociology, New York: Free Press.
Merton, R.K., Fisk, M., and Kendall, P.L.
(1956) The Focused Interview:
A Manual of Problems and
Procedures, New York: Free
Wang, B. (2014) ‘International
investors’ reaction to cross-border
acquisitions by emerging market
multinationals’, International
Business Review, 23(4): 811–23.
North, D.C. (1990) Institutions,
Press.
Miles, M.B. (1979) ‘Qualitative data as
attractive nuisance: The problem of
analysis’, Administrative Science
Institutional Change and Economic
Performance. Cambridge:
Cambridge University Press.
O’Brien, R.M. (2007) ‘A caution
Quarterly, 24(4): 590–601.
Miles, M.B. and Huberman, A.M. (1994)
Qualitative Data Analysis, 2nd edn.,
regarding rules of thumb for
variance inflation factors’, Quality
& Quantity, 41: 673–90.
Thousand Oaks, CA: Sage.
Mintzberg, H. (1979) ‘An emerging
strategy of “direct” research’,
Orbell, J. (1987) A Guide to Tracing the
History of a Business, Aldershot:
Gower Press.
Administrative Science Quarterly,
24(4): 582–9.
Mishler, E.G. (1986) Research
Park, N.K. (2004) ‘A guide to using event
study methods in multi-country
settings’, Strategic Management
Interviewing, London: Harvard
University Press.
Morgan, G. (1983), Beyond Method:
Strategies for Social Research,
Journal, 25(7): 655–68.
Patton, M.Q. (1990) Qualitative
Evaluation and Research Methods,
2nd edn., Newbury Park, CA: Sage.
Beverly Hills, CA: Sage.
Moser, P.K., Mulder, D., and Trout, J.D.
(1998) The Theory of Knowledge:
Patton, M.Q. (2014) ‘Purposeful
sampling and case selections:
Overview of strategies and options’,
A Thematic Introduction, Oxford:
Oxford University Press.
Murphy, H.A. and Hildebrandt, H.W.
in Patton, M.Q. (ed.), Qualitative
Research & Evaluation Methods, 4th
edn., pp. 264–72.
(1988) Effective Business
Communications, 5th edn., New
York: McGraw-Hill.
Peng, D.X. and Lai, F. (2012) ‘Using
partial least squares in operations
management research: A practical
Murse, T. (2019) ‘All 21 government
shutdowns in US history’,
ThoughtCo, 25 May 2019. Available
at: thoughtco.com/government-
guideline and summary of past
research’, Journal of Operations
Management, 30(6): 467–80.
Peters, T.J. and Waterman, R.H. (1982) In
shutdown-history-3368274.
Search of Excellence: Lessons from
313
314
References
America’s Best Run Companies,
Porter, M.E. (1980) Competitive Strategy,
New York: Harper & Row.
Pettigrew, A. (1985) ‘Contextual
research: A natural way to link
New York: Free Press.
Porter, M.E., Lorsch, J.W., and Nohria, N.
(2004) ‘Seven surprises for new
theory and practice’, in Lawler, E.E.,
Mohrman, A.M., Mohrman, S.A.,
Ledford, G.E., Cummings, T.G., and
CEOs’, Harvard Business Review,
82(10): 62–75.
Punj, G. and Stewart, D.W. (1983)
Associates (eds.), Doing Research
That Is Useful for Theory and
Practice, San Francisco, CA: Jossey-
‘Cluster analysis in marketing
research: Review and suggestions
for application’, Journal of
Bass, pp. 222–74.
Pfeffer, J. (1993) ‘Barriers to the advance
of organizational science: paradigm
development as a dependent
Marketing Research, 20(2): 134–48.
Ramaswami, S.N., Srivastava, R.K., and
Bhargava, M. (2009) ‘Market-based
capabilities and financial
variable’, Academy of Management
Review, 18(4): 599–620.
Pfeffer, J. (1995) ‘Morality,
performance of firms: Insights into
marketing’s contribution to firm
value’, Journal of the Academy of
reproducibility and the persistence
of styles of theory’, Organization
Science, 6(6): 681–93.
Phillips, B. (1966) Social Research:
Strategy and Tactics, New York:
Macmillan.
Pike, K. (1966) Language in Relation to a
Unified Theory of Structure of
Human Behaviour, The Hague:
Mouton.
Podsakoff, P.M., Mackenzie, S.B.,
Lee, J.-Y., and Podsakoff, N.P.
(2003) ‘Common method biases in
behaviour research: A critical
review of the literature and
recommended remedies’, Journal of
Marketing Science, 37(2): 97–116.
Reese, W.A. and Robins, R.P. (2017)
‘Performing an event study: An
exercise for finance students’,
Journal of Economic Education,
48(3): 206–15.
Reeves, T.K. and Harper, D. (1981)
Surveys at Work: A Practitioner’s
Guide, London: McGraw-Hill.
Reichardt, C.S. and Cook, T.D. (1979)
‘Beyond qualitative versus
quantitative methods’, in Cook, T.D.
and Reichardt, C.S. (eds.),
Qualitative and Quantitative
Methods in Evaluation Research,
Beverly Hills, CA: Sage, pp. 7–32.
Applied Psychology, 88(5):
879–903.
Polanyi, M. (1958) Personal Knowledge,
Revans, R.W. (1971) Developing Effective
Managers, London: Longman.
Richter, N.F., Sinkovics, R.R., Ringle,
Chicago, IL: University of Chicago
Press.
Popper, K.R. (1961) The Logic of
Scientific Discovery, New York:
C.M., and Schlägel, C. (2016)
‘A critical look at the use of SEM in
international business research’,
International Marketing Review,
Science Edition.
33(3): 376–404.
References
Ringle, C.M., Sarstedt, M., and Straub,
D.W. (2012) ‘A critical look at the
use of PLS-SEM in MIS Quarterly’,
MIS Quarterly, 36(1): 3–14.
Roethlisberger, F.J. and Dickson, W.J.
(1939) Management and the
Worker, Cambridge, MA: Harvard
Berlin and Heidelberg: SpringerVerlag, pp. 193–233.
Sarstedt, M. and Mooi, E. (2019)
‘Cluster analysis’, in Sarstedt, M.
and Mooi, E. (eds.), A Concise Guide
to Market Research. Berlin:
Springer-Verlag.
University Press.
Rose, A.M. and Peterson, W.A. (1965)
Older People and Their Social
Sarstedt, M., Ringle, C.M., and Hair, J.F.
(2014a) ‘PLS-SEM: Looking back
and moving forward’, Long Range
World: The Subculture of the Aging,
Philadelphia, PA: Davis.
Ross, N.P. and Harris, S. (1994) ‘What are
the ethical responsibilities of model
Planning, 47(3): 132–7.
Sarstedt, M., Ringle, C.M., Henseler, J.,
and Hair, J.F. (2014b) ‘On the
emancipation of PLS-SEM:
builders?’, in Wallace, W.A. (ed.),
Ethics in Models, Oxford: Pergamon
Press, pp. 161–6.
A commentary on Rigdon (2012)’,
Long Range Planning, 47(3):
154–60.
Rousseau, D.M. (1995) ‘Publishing from
a reviewer’s perspective’, in
Cummings, L.L. and Frost, P.J.
Saunders, M.N.K. (2011) ‘The
management researcher as
practitioner’, in Cassell, C. and Lee,
(eds.), Publishing in the
Organizational Sciences, Thousand
Oaks, CA: Sage, pp. 151–63.
W.J. (eds.), Challenges and
Controversies in Management
Research, London: Taylor & Francis,
Rubin, H.J. and Rubin, I.S. (1995)
Qualitative Interviewing: The Art of
Hearing Data, Thousand Oaks, CA:
Sage.
pp. 243–56.
Saunders, M.N.K., Lewis, P., and
Thornhill, A. (2016) Research
Methods for Business Students, 7th
Russell, B. (1972) The Collected Stories,
London: Unwin.
Salzberger, T., Sinkovics, R., and
edn., New York: Pearson Education.
Scase, R. and Goffee, R. (1989) Reluctant
Managers, London: Unwin Hyman.
Schlegelmilch, B. (1999) ‘Data
equivalence in crosscultural
research: A comparison of
Schuman, H. and Presser, S. (1976)
Questions and Answers in Attitude
Surveys: Experiments on Question
classical test theory and latent trait
theory based approaches’,
Australasian Marketing Journal
Form, Wording, and Context, New
York: Academic Press.
Schwandt, T.A. (1997) Qualitative
7(2): 23–38.
Sarstedt, M. and Mooi, E. (2014)
‘Regression analysis,’ in Sarstedt, M.
and Mooi, E. (eds,), A Concise
Inquiry, Thousand Oaks, CA: Sage.
Schwartz, N. and Sudman, S. (eds.)
(1996) Answering Questions:
Methodology for Determining
Guide to Market Research, 2nd edn.,
Cognitive and Communicative
315
316
References
Processes in Survey Research, San
Francisco, CA: Jossey-Bass.
Schwartz, S.H. (1992) ‘Universals in the
content and structure of values’, in
Zane, M. (ed.), Advances in
Experimental Social Psychology,
vol. 25, New York: Academic Press.
Sekaran, U. (1992) Womanpower:
Managing in Times of Demographic
Turbulence, London: Sage.
Selltiz, C., Wrightsman, S., and Cook,
S.W. (1976) Research Methods in
Social Relations, 3rd edn., New
York: Holt, Rinehart & Winston.
Shook, C.L., Ketchen, D.J. Jr., Hult,
G.T.M., and Kacmar, K.M. (2004)
‘An assessment of the use of
structural equation modeling in
strategic management research’,
Strategic Management Journal, 25:
397–404.
Silverman, D. (1993) Interpreting
Qualitative Data: Methods of
overview’, Journal of the Academy
of Marketing Science, 45(2):
186–207.
Sosik, J.J., Kahai, S.S., and Piovoso, M.J.
(2009) ‘Silver bullet or voodoo
statistics? A primer for using the
partial least squares data analytic
technique in group and
organization research’, Group
and Organization Management,
34(1): 5–36.
Sousa, C.D.A., Martínez-López, F.J.,
and Coelho, F. (2008) ‘The
determinants of export
performance: A review of the
research in the literature between
1998 and 2005’, International
Journal of Management Reviews,
10(4): 343–74.
Spiggle, S. (1994) ‘Analysis and
interpretation of qualitative data in
consumer research’, Journal of
Consumer Research, 21
Analysing Talk, Text and
Interaction, London: Sage.
Silverman, D. (2001) Interpreting
Qualitative Data, London: Sage.
(December): 491–503.
Stewart, D.W. and Shamdasani, P.N.
(1990) Focus Groups: Theory and
Practice, Newbury Park, CA: Sage.
Simons, R. (1987) ‘Accounting control
systems and business strategy: An
empirical analysis’, Accounting,
Strauss, A. and Corbin, J. (1990)
Basics of Qualitative Research:
Grounded Theory Procedures and
Organisation and Society,
12(4): 357–74.
Sinkovics, R., Penz, E., and Ghauri, P.
Techniques, Newbury Park, CA:
Sage.
Strauss, A. and Corbin, J. (1994)
(2008) ‘Enhancing the
trustworthiness of qualitative
research in international business’,
Management International Review,
48(6): 689–714.
Sorescu, A., Warren, N.L., and Ertekin, L.
(2017) ‘Event study methodology in
the marketing literature: An
‘Grounded theory methodology: An
overview’, in Denzin, N.K. and
Lincoln, Y.S. (eds.), Handbook of
Qualitative Research, Thousand
Oaks, CA: Sage, pp. 273–85.
Studenmund, A.H. (2017) Using
Econometrics: A Practical Guide,
7th edn., London: Pearson.
References
Sudman, S. and Bradburn, N. (1982)
Asking Questions: A Practical
Guide to Questionnaire Design. San
Francisco, CA: Jossey-Bass.
Sudman, S. and Bradburn, N.M. (1989)
Asking Questions, San Francisco,
CA: Jossey-Bass.
Svatko, J.E. (1989) ‘Working with
consultants’, Small Business
Reports, 14(2): 58–68.
Personality, 60(2): 225–51.
(Originally published as a technical
report in 1961.)
Utterback, J.M. (1994) Mastering the
Dynamics of Innovation, Boston,
MA: Harvard University Press.
Van Maanen, J. (1979) ‘Reclaiming
qualitative methods for
organizational research: A preface’,
Administrative Science Quarterly,
Teece, D.J. (2007) ‘Explicating dynamic
capabilities: The nature and
microfoundations of (sustainable)
enterprise performance’, Strategic
24(4): 520–6.
Van Maanen, J. (ed.) (1983) Qualitative
Methodology, Newbury Park, CA:
Sage.
Management Journal 28: 1319–50.
Thompson, J.D. (1967) Organizations in
Action: Social Science Bases of
Van Maanen, J. (1995) ‘Style as a
theory’, Organizational Science,
7(4): 641–52.
Administrative Theory, New York:
McGraw-Hill.
Tranfield, D. and Starkey, K. (1998) ‘The
Van Maanen, J. and Kolb, D. (1985) ‘The
professional apprentice:
Observations on fieldwork roles in
nature of social organisation and
promotion of management
research: towards policy’, British
two organizational settings’, in
Research in the Sociology of
Organizations, vol. 4, New York:
Journal of Management, 9(4):
341–53.
Triandis, H.C. (1972) The Analysis of
Subjective Culture, New York:
JAI Press.
Van Maanen, J., Sorensen, J.B., and
Mitchell, T. (2007) ‘The interplay
between theory and method’, The
Wiley.
Tsang, E.W.K. (2014) ‘Generalizing from
research findings: The merits of
Academy of Management Review
32(4): 1145–54.
Van Witteloostuijn, A. (2015) ‘Toward
case studies’, International Journal
of Management Review, 16(4):
369–83.
experimental international
business’, Cross Cultural
Management, 22(4): 530–44.
Tsoukas, H. and Chia, R. (eds.) (2011)
Philosophy and Organization
Theory: Research in the Sociology of
Vijver, F. van de and Leung, K. (1997)
Methods and Data Analysis for
Cross-Cultural Research, Thousand
Organizations, vol. 32, Bradford:
Emerald.
Tupes, E.C. and Christal, R.E. (1992/
1961) ‘Recurrent personality factors
Oaks, CA: Sage.
Weisberg, H.F. (1992) Central Tendency
and Variability, Quantitative
Applications in the Social Sciences,
based on trait ratings’, Journal of
83, Newbury Park, CA: Sage.
317
318
References
Whigham, D. (2010) Business Data
Yin, R.K. (2009) Case Study Research:
Analysis Using Excel. Oxford:
Oxford University Press.
Whitehead, A.N. (1911) An Introduction
Design and Methods, 4th edn., Los
Angeles, CA: Sage.
Zaltman, G., Lemasters, K., and
to Mathematics, London: Williams
& Norgate.
Whitley, R. (1984) ‘The scientific status
of management research as a
practically oriented social science’,
Journal of Management Studies,
21(4): 369–90.
Whittington, R., Yakis-Douglas, B., and
Ahn, K. (2016) ‘Cheap talk?
Strategy presentations as a form of
chief executive officer impression
management’, Strategic
Management Journal, 37(12):
2413–24.
Wiedersheim-Paul, F. and Eriksson, T.
(1998) Att Utreda Forska och
Rapportera, Malmo: Liber.
Wind, Y. and Douglas, S.P. (1982)
‘Comparative consumer research:
the next frontier’, Management
Decisions, 4: 24–35.
Wu, H. and Leung, S.-O. (2017) ‘Can
Likert scales be treated as interval
scales? A simulation study’,
Journal of Social Service Research,
43(4): 527–32.
Yin, R.K. (1994) Case Study Research:
Design and Methods, Thousand
Oaks, CA: Sage.
Yin, R.K. (2003) Case Study Research,
3rd edn., London: Sage.
Heffering, M. (1982) Theory
Construction in Marketing, New
York: Wiley.
Zaltman, G., Pinson, C.R.A., and
Angelmar, R. (1977) Metatheory
and Consumer Research, New York:
Holt, Rinehart & Winston.
Zellmer-Bruhn, M., Caliguiri, P., and
Thomas, D.C. (2016) ‘From the
editors: experimental designs in
international business research’,
Journal of International Business
Studies, 47(4): 393–407.
Zhu, P. and Malhotra, S. (2008)
‘Announcement effect and price
pressure: An empirical study of
cross-border acquisitions by Indian
firms’, International Research
Journal of Finance and Economics,
13: 24–41.
Zikmund, W.G. (1997) Business Research
Methods, 5th edn., Fort Worth, TX:
Dryden Press.
Zou, S. and Stan, S. (1998) ‘The
determinants of export
performance: A review of the
empirical literature between
1987 and 1997’, International
Marketing Review, 15(5): 333–56.
Index
a priori method, 22, 75, 140
sampling, 108
a posteriori knowledge, 10
selection of cases, 106
abduction, 21
selection strategies, 109
absorptive capacity (ACAP), 79
single design, 110
abstraction, 135
skills required, 106
accessible population, 107
theoretical sampling, 110
alternative hypothesis, 194
training for, 106
analytic surveys, 169
triangulation, 147
assigned research problem, 50
history, 149
ATLAS.ti, 146
attractive nuisance, 130
average variance extracted (AVE), 251
axial coding, 135
method bias, checking, 149
two-step, 149
when to use, 103
categorical variables, 183, 236
mean values not applicable, 187
bar charts, 183
bias
no intrinsic order or ranking, 82
categorization, 134
common method bias, 234
category questions, 173
method bias, 149
causation
non-response bias, 165
omitted variable bias, 224
semi-structured interviews, 115
bibliography, 287
binomial logit regression, 230
likely direction, 211
classic experiment model, 66
intuition underpinning, 67
limited use, 67
simplest form, 66
bootstrapping, 252
clear instructions for return of survey
capabilities (CAPS), 213, 238
closed questions, 173
CAQDAS (computer-assisted
cluster sampling, 164
instrument, 178
qualitative data analysis software),
Cochrane-Orcutt method, 224
146, 273
codes, 134
analysis, 142
ficient of determination (R ), 252
coef ficient of variation (CV), 190
comparative, 103
collinear explanatory variables, 226
deductive, 111
common method bias, 234
de
common method variance, 234
case studies
fi nition, 101
design stage, 105
discon
firmation stage, 106
2
coef
common sense
de
finition, 10
drift stage, 105
non-conscious ideology, 14
focus groups, 108
research, versus, 12 13
generalization, 150
science, differences between, 16
–
inductive, 111
communality of variables, 245
matrix analysis, 143
communication, 114
multiple design, 111
comparative
prediction stage, 105
comparison, 135
fit index (CFI), 252
320
Index
comparison of means
observation, 269
paired samples, 197
responsibility for accuracy, 266
composite reliability (CR) values, 251, 258
same meaning drawn from question,
computers
270
qualitative research, use in
fi
unit of analysis, de ning, 267
knowledge required, 147
data comparability, 262
program examples, 146
emic approach, 268
concept list, 143
enhancing trustworthiness, 273
concepts
computer assistance, 273
critical role, 39
equivalence, 273
functions of, 39
questionnaires, 274
finitions, 40
conceptual de
etic approach, 268
conclusions, 285
unit of analysis, 270
fidentiality, 26, 119
con firmatory factor analysis, 244
con
criteria for membership, 272
geographic, 271
constant comparative method, 136
situational context, 272
construct theory, 21
cross-sectional design, 71
construct validity, 86
cross-table
content validity, 86
social class, 64
context of discovery, 39
cultural distance between countries, 227
context of justi cation, 38
cumulative abnormal returns (CAR), 256
continuous variables, 183, 236
customer satisfaction, 79, 83
fi
ficient, 301
correlation coef
interval scale, 82
data analysis
median values, 188
ratio scales, 83
case studies,
control of corruption (CC), quality of
case studies
focus groups, 124
institutions, 202
advantages, 126
convenience sampling, 166
conducting, 126
convergent validity, 87
disadvantages, 126
ficient
correlation coef
interviews
continuous variables, 199
ordinal variables, 204
see
data collection
controlling, 122
data display, 133
correlation matrix, 199
data reduction, 132
correlations
deception, 26
Pearson (product-moment) correlation
coef
ficient, 200
strong and weak, 200
deduction, 20
process, 19
deductive case studies, 111
covariance-based SEM (CB-SEM), 251
deductive reasoning, 20
cover letter, 178
de
critical realism, 18
ficient alpha, 245, 247
Cronbach’s coef
cross-cultural research
data analysis
fi nitions, 39
conceptual, 40
operational, 40
degrees of freedom, 192
demonstrated validity, 146
two phases, 272
data collection, 265
descriptive surveys, 170
descriptive validity, 146
costs obtaining, 265
dimensionalization, 137
culture and behaviour, understanding,
disturbance term, 216, 223
268
fi nitions, 265
differing de
constant variance, 224
no systematic relationship, 224
emic and etic approaches, 268
divergent validity, 87
instrument adapted to culture, 270
dummy variables, 213–14, 237
Index
fication, 48, 150
emic approach, 268
falsi
empirical generalization, 150
feedback loops, 34
epistemology, 9
debate, 10
knowledge based on sense and
experience, 10
field experiments, 179
figures
shortage of in report, 280
final report
equivalence, 86, 273
oral presentation, 288
estimation window, 254
structure, 281
ethics, 23
bibliography, 287
legality and illegality, 25
conclusions, 285
moral responsibility, 24, 27
executive summary, 282
fluencing, 28
factors in
fi ndings, 284
negligence and legal action, 25
footnotes, 286
problem formulation, 24
introduction, 283
public interest, 28
methodology, 284
–participant (subject)
relationship, 26–7
researcher
problem statement, 283
references, 287
etic approach, 268
table of contents, 282
event studies
theoretical background, 283
time limits, 254
event study analysis, 253
title page, 282
writing, 276
applicability, 256
appearance, 281
cross-border acquisitions, 258
assessment criteria, 277
estimation procedure, 249
audience, 277
event window, 254
for publication, 290
executive summary, 282
form and style, 280
expected variability, 169
format, 278
experimental studies, 179
headings, 281
advantages, 179
language and
disadvantages, 180
purpose, 276
fl ow, 279
explanatory power, 222, 233, 239
readability, 278
explanatory variables, 211
strengths and weaknesses of methods,
collinear, 226
277
common method variance, 234
findings, 284
effect sizes, 235
Fisher-Pearson coef
multiple regression model, 221
omission of, 224
fi cance, 234
fi cance, 232
statistical signi
testing joint signi
exploratory factor analysis, 244
job-related burnout on oil rigs, 246
export performance, 213
external validity, 68
ficient of skewness
(SK)
calculating, 190
decision criterion, 191
focus groups, 108, 124, 126
advantages, 126
conducting, 126
disadvantages, 126
footnotes, 286
format questionnaire, 178
face validity, 86
frictions, 13
factor analysis
con
firmatory, 244
garbage-can models or decisions, 46
exploratory, 244, 246
Gauss-Markov theorem, 223
two main applications, 243
generalizable validity, 146
factor extraction, 244
factor rotation, 244
factor scores, 245
government effectiveness (GE), quality of
institutions, 202
grounded theory, 100, 142
321
322
Index
heteroskedasticity, 230
structured, 115
histogram, 183
time limits, 117
historical review, 99
time management, 122
homoskedasticity, 223
unstructured, 115
hypothesis testing, 231
introduction, 283
common method variance, 234
inverse U-shaped relationship, 217
effect sizes, 234
iteration, 138
explanatory variables, 232
four basic steps, 192
fi
judgement (purposive) sampling, 166
individual regression coef cients, 231
knowledge development, 9
independent samples
comparison of means, 195
individualism versus collectivism
dimension (IDV), 227
laboratory experiments, 179
Likert scales, 82, 175, 230
–
linear relationship, 215 16
inducement, 178
living standards, 79
induction, 20
long and complicated sentences, 280
process, 19
longitudinal design, 73
inductive case studies, 111
long-term orientation (LTO), 227
inductive reasoning, 20
indulgent societies (IND), 227
mail questionnaires, 171
inferences, 191
maintained hypothesis, 233
integration, 138
mapping, 41, 89 90
intended contribution of research, 38, 58
market model
–
internal consistency, 86, 245, 287
internal validity, 68
international research,
estimation
individual
see
cross-cultural
research
interpretative validity, 146
interpretivism, 18
firms, 255
ordinary least squares (OLS), 255
window, 254
masculinity versus femininity dimension
(MAS), 227
interval scale, 82
matrix display, case analysis, 143
interviews
mean, 187
advantages, 116
measures of dispersion, 188
analysis of research problem, 117
median, 188
con
method bias, 149
contacting subjects, 118
method of authority, 22
controlling, 122
method of intuition, 22
disadvantages, 116
method of science, 22
drafting questions, 117
method of tenacity, 22
initial discussion, 121
methodology, 284
language style, 121
mode, 188
leading questions, 121
moral responsibility, 24, 27
fi dentiality issues, 119
confi rmation letter, 119
pilot study, 117
common, 234
fluencing, 9
factors in
post-interview notes, 123
multicollinearity, 226
pre-interview preparation, 120
multi-item scales, 83
recording information, 119
relationship with interviewee, 122
resource availability, 119
construct measurement
advantages, 84
SEM, and, 85
reward for respondents, 119
multinomial logit regression, 230
semi-structured, 115
multiple choice questions, 173
sensitive questions, 123
multiple design case studies, 111
Index
multiple regression
non-sampling error, 162
estimation methodology, 219
coef
ficient of determination, 221
cultural distance between countries,
227
normal distribution, 186
notify possible respondents in advance,
178
null hypothesis, 192
multicollinearity, 226
nominal or ordinal dependent
observations
variables, 230
advantages and disadvantages, 111
omission of relevant explanatory
variables, 224
contrived setting, 113
field observation, 112
ordinary least squares (OLS), 219, 223
human, 113
residual scatter plot, 228, 239
mechanical method, 113
export performance
non-participant, 113
determinants, 211
omitted variable bias, 224
linear relationship, 215
one-shot case study, 71
operationalization of variables, 213
–
ontology, 9
ordinary least squares (OLS), 236 8
open coding, 134
quadratic relationship, 217
open questions, 172
semi-logarithmic relationship, 216
operational de
time lags, 219
operationalization of variables, 79, 213
fi nitions, 40
features of a good model, 239
oral presentation of report, 288
hypothesis testing, 231
ordinal logistic regression, 230
common method variance, 234
ordinal scale, 82
effect sizes, 234
ordinal variables
ficient, 204
explanatory variables, 232
correlation coef
individual regression coef cients, 231
nominal variables, relationship between,
fi
fi
statistical signi cance, 234
206
operationalization, 82
–
NVivo, 146, 273
ordinary least squares (OLS), 219, 236 8
national culture
individualism versus collectivism
dimension (IDV), 227
paired t-test, 197
panel data, 73
indulgent societies (IND), 227
parameters
long-term orientation (LTO), 227
masculinity versus femininity dimension
(MAS), 227
population, 191
estimates only, 192
null hypothesis, 192
power distance dimension (PDI), 227
parametric tests, 299
uncertainty avoidance dimension (UAI),
parsimonious model, 239
227
parsimony, 238
–
negative linear relationship, 215 16
negative semi-logarithmic relationship,
217
nominal scale, 81
nominal variables
operationalization, 82
ordinal variables, relationship between,
206
non-normal distribution, 190
partial least squares SEM (PLS-SEM), 251
green innovation, 253
interpreting, 251
past literature, 45
importance for study, 57
intended contribution of study, 58
pattern matching, 142
Pearson (product-moment) correlation
coef
ficient, 199–200, 203
non-parametric tests, 299
peer pressure, 28
non-probability sampling, 162, 166
personal and professional experiences, 50
non-response, 162
personal interviews, 171
non-response bias, 161
pilot study, 117
323
324
Index
political stability and absence of violence
(PS), quality of institutions, 202
population
departure from theory, 140
dimensionalization, 137
display, 133
accessible, 107
gaining insights, 131
de
grasping the meanings of others, 140
finition, 161, 191
empirical generalization, 150
grounded theory, 142
linear regression model, 220
integration, 138
non-response bias, 165
interpretation, 139
null hypothesis, 192
iteration, 138
representative sample, 126
problems, 130
sample size, 168
quantifying, 143
strati
record keeping, 139
fied sample, 164
target, 106
reduction, 132
positive linear relationship, 215
refutation, 138
positivism, 18
reporting procedures, 139
post-modernism, 18
seeking patterns in meanings, 140
power distance dimension (PDI), 227
pragmatism, 18
tabulations, 139
data collection, 97
pre-interview preparation, 120
advantages, 99
primary data, 153
case studies,
see
case studies
three advantages, 160
communication, 114
two main disadvantages, 160
grounded theory, 100
types, 159
historical review, 99
–
probability sampling, 162 3
interviews,
see
interviews
cluster sampling, 164
low number of observations, 98
non-probability sampling, 166
observations.
non-response bias, 165
qualitative versus quantitative
simple random sampling, 163
strati
fied random sampling, 164
systematic sampling, 164
problem formulation, 24
see
observations
methods, 96
qualitative versus quantitative
methods, difference in emphasis, 97
qualitative versus quantitative
problem statement, 283
methods, procedure, 96
problem structure, 62
summary, 111
causal research, 64
techniques, 98
descriptive research, 64
three major components, 99
explanatory research, 63
unit of study, 95
public interest, 28
main emphasis, 129
purpose of book, 3
traditional model, 130
p-value, 195
validity, 146
quality of institutions, 202
QSR NUD*IST, 146
Worldwide Governance Indicators
–
quadratic relationship, 217 18
qualitative research
computers, use of
knowledge required, 147
program examples, 146
data analysis
abstraction, 135
categorization, 134
codes, 134
comparison, 135
constant comparative method, 136
–
(WGI), 202 3
quantitative data
comparison of means
independent samples, 195
computers, use of, 206
continuous variables
ficient, 199
correlation coef
data analysis
event study analysis,
see
event study
analysis
factor analysis,
see
factor analysis
Index
–
hypothesis testing, 191 5
representative sample, 162
structural equation modelling (SEM),
research
see structural
equation modelling
(SEM)
abduction, 21
common sense, versus, 13,
data description
–
bar charts, 183
deduction, 19 20
histogram, 183
deductive reasoning, 20
mean, 187
different orientations
measures of dispersion, 188, 190
measures of skewness, 190
median, 188
process, 16
summary, 18
ethics, 23
mode, 188
legality and illegality, 25
–
scatter plot, 186
ordinal variables
see also
common sense
moral responsibility, 24, 27 8
ficient, 204
correlation coef
paired samples
negligence and legal action, 25
problem formulation, 24
public interest, 28
comparison of means, 197
variables, correlations between, 206
quantitative research
induction, 19
inductive reasoning, 20
knowledge skills, 22
data collection
making judgements, 11
experimental studies,
see
ongoing process, 23
experimental studies
primary data, 153,
purpose of, 13
see also primary
data
qualitative,
see qualitative research
see quantitative research
quantitative,
see sampling
see secondary data
research, see survey research
fi rst, 21
sampling,
theory or data
secondary data,
understanding phenomena, 10
survey
techniques, 98
research design
choice of, 36
questionnaires
choice of model, 61
–
construction guidelines, 175 6
classic experiment model, 66
design, 172
intuition underpinning, 67
improving response rates, 178
limited use, 67
types, 171
simplest form, 66
–
quota sampling, 166 7
cross-sectional design, 71
errors, 62
ranking questions, 174
longitudinal, 73
rating questions, 175
panel data, 73
ratio scale, 83
problem structure, 62
readership of book, 5
causal research, 64
record keeping, 139
descriptive research, 64
redundancy of data, 130
references, 287
refutation, 138
a priori method, 75
REGION variable, 213
choices and decisions, 76
regression analysis, 210
explanatory variables, effect of, 234
judegment, and, 240
multiple regression,
explanatory research, 63
requirements, 74
see multiple
regression
time-series data, 72
validity threats, 67
types, 68
research measurement
improving, 87
regression sum of squares (SSR), 222
interval scale, 82
regulatory quality (RQ), quality of
mapping, 89
institutions, 202
multi-item scale, 83
325
326
Index
research measurement (cont.)
advantages, 85
SEM and, 85
theory before research, 38
topic and problem, relationship, 35
researcher
nominal scale, 81
operationalization of variables, 78
ordinal scale, 82
ratio scale, 83
–participant
(subject)
relationship, 25
–
ethical concerns, 26 7
residual scatter plot, 228
OLS regression of export performance,
reliability
239
construct validity, 86
restricted hypothesis, 233
content validity, 86
root-mean-square error of approximation
convergent validity, 87
divergent validity, 87
(RMSEA), 252
rule of law (RL), quality of institutions, 202
three attributes, 85
research problems
capturing, 35
ideas
sample regression line, 220
sampling
basic terminology, 161
past literature, 45
cluster sampling, 164
sources, 46
frame, 162 3
identifying, 47
–
non-probability sampling, 166
asking questions, 47
non-response bias, 165
sources of problems, 50
probability, 163
structured/unstructured, 49
sample size, 168
vague and general, 48
simple random sampling, 163
models in research, 52
descriptive, 54
strati
explanatory, 55
sampling error, 162
guidance, 57
scatter plot, 186
key characteristics, 52
scepticism, 10
predictive/forecasting, 56
secondary data, 153
structured, 53
advantages, 158
value chain, 54
disadvantages, 158
past literature
selected commercially available sources,
importance for study, 57
intended contribution of study, 58
purpose, 50
research process
concepts
157
selected publicly available sources, 158
usefulness of, 154
SECTOR variable, 214
selective coding, 135
–
critical role, 39
semi-logarithmic relationship, 216 17
functions of, 39
semi-structured interviews, 115, 171
cross-cultural dimension, managing, 262
de
fied random sampling, 164
systematic sampling, 164
finitions, 39
bias, 115
sigma, 187
ficance level, 193–4
distinct stages, 33
signi
empirical measurements, 36
simple random sampling, 163
ethical issues, 26
single design case studies, 110
illustrative example, 33
size of
knowledge, producing, 37
skewed distributions, 191
levels of research, 36
skewness, 190
methods, 42
research before theory, 38
theory
de
fi nition, 41
purpose, 42
firm, 79, 213
ficient (SK)
Fisher-Pearson coef
calculating, 190
decision criterion, 191
slope of the relationship
linear, 216
Index
quadratic, 218
semi-logarithmic, 217
time-series data, 72, 74, 187
fi
title page, 282
Spearman correlation coef cient, 204–5
total sum of squares (SST), 222
spoken and written language, differences,
transaction costs,
280
sponsor, 178
stability, 85
standard deviation, 190
see
frictions
trans-disciplinarity in research, 13
triangulation, 36, 147
history, 149
method bias, checking, 149
state of nature, 193
two-step case study, 149
strati
Type I error, 193–4
fied random sampling, 164
structural equation modelling (SEM), 247
Type II error, 193, 195
estimating, 250
advantages, 250
uncertainty avoidance dimension (UAI), 227
complementary procedures, 251
uniform distribution, 186
green innovation, 252
unit of analysis
path diagram, 250
criteria for membership, 272
visualization of, 249
cross-country research, 272
fining, 271
structured interviews, 115
de
structured problems, 62
geographic scope, 271
student’ s t-test, 195
sum of squared residuals (SSE), 221
survey research
analytic, 169
descriptive, 170
situational context, 272
units of observation
cross-sectional design, 71
longitudinal design, 73
panel data, 73
email or website questionnaires, 171
unstructured interviews, 115
mail questionnaires, 171
unstructured problems, 63
questionnaires, 171
use of terminology, 280
construction guidelines, 175–6
use of book, 7
design, 172
U-shaped relationship, 215
improving response rates, 178
semi-structured interviews, 171
telephone/video interviews, 172
validity
qualitative research, 146
systematic sampling, 164
validity threats, 67
table of contents, 282
value chain, 54
tables
variance
types, 68
shortage of in report, 280
average variance extracted (AVE), 251
tabulations, 139
common method, 234
target population, 106
disturbance terms, 224
technical literature, 50
in
telegraphic style, 280
regression sum of squares (SSR), 222
telephone/video interviews, 172
theoretical background, 283
theoretical generalization, 150
flation factor (VIF), 226
total sum of squares (SST), 222
voice and accountability (VA), quality of
institutions, 202
theoretical sampling, 110
theoretical studies, 36
weighted least squares (WLS) method, 224
theoretical validity, 146
World Bank
theoretically grounded regression model,
239
Worldwide Governance Indicators
(WGI), 202–3
327