Effectiveness of Information Use for Strategic Decision Making: Direct Effects and Moderating Influences of Perceived Environmental Uncertainty and Cognitive Style

Wolfgang Gänswein Effectiveness of Information Use for Strategic Decision Making

GABLER RESEARCH Entrepreneurship Herausgegeben von Professor Dr. Malte Brettel, RWTH Aachen, Professor Dr. Lambert T. Koch, Universität Wuppertal, Professor Dr. Tobias Kollmann, Universität Duisburg-Essen, Campus Essen, Professor Dr. Peter Witt, Universität Wuppertal

„Entrepreneurship“ ist ein noch relativ junger Forschungszweig, der jedoch in Wissenschaft und Praxis stetig an Bedeutung gewinnt. Denn Unternehmensgründungen und deren Promotoren nehmen für die wirtschaftliche Entwicklung einen zentralen Stellenwert ein, so dass es nur folgerichtig ist, dem auch in Forschung und Lehre Rechnung zu tragen. Die Schriftenreihe bietet ein Forum für wissenschaftliche Beiträge zur Entrepreneurship-Thematik. Ziel ist der Transfer von aktuellen Forschungsergebnissen und deren Diskussion aus der Wissenschaft in die Unternehmenspraxis.

Wolfgang Gänswein

Effectiveness of Information Use for Strategic Decision Making With a foreword by Prof. Dr. Malte Brettel

RESEARCH

Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at http://dnb.d-nb.de.

D 82 (Diss. RWTH Aachen University, 2011)

1st Edition 2011 All rights reserved © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011 Editorial Office: Stefanie Brich | Nicole Schweitzer Gabler Verlag is a brand of Springer Fachmedien. Springer Fachmedien is part of Springer Science+Business Media. www.gabler.de No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the copyright holder. Registered and/or industrial names, trade names, trade descriptions etc. cited in this publication are part of the law for trade-mark protection and may not be used free in any form or by any means even if this is not specifically marked. Cover design: KünkelLopka Medienentwicklung, Heidelberg Printed on acid-free paper Printed in the Netherlands ISBN 978-3-8349-3086-6

Foreword

V

Foreword In practice, strategic decisions are made under very different circumstances by very different characters. As a result, a variety of completely different decision processes can be observed. The continuum ranges from decisions which are perfectly thought out and underlined with complete information to those decisions that merely follow the intuition of a single decision maker. In this context, a material role of one single decision maker is frequently discernible even in large organizations. Economic and management research has already dealt extensively with decisions decision theory may be witness of it. For a long time being, the underlying image of a decision maker was the economic man paradigm, i.e. the image of a perfectly rational decision maker. This image does not hold true in practice such that research has dealt increasingly with the limitations of rationality and the resulting impact on decisionmaking processes. Furthermore however, a number of issues are unexplored: For instance, the connection between the cognitive style of a decision maker and his or her decision making behavior is not adequately explained. Here it may come to quite different information behavior. Moreover, this behavior may again be altered by varying environmental circumstances, so that only a detailed consideration of the impact of all these conditions on decision making behavior creates a complete picture of the practical strategic decision-making process. Wolfgang Gänswein has taken on these questions and seen that as an opportunity to create the following dissertation. His work is exciting in many ways: Through his very thorough theoretic and ambitious empirical approach, Wolfgang Gänswein’s work holds interesting insights for theorists and many suggestions for future researchers. The same way, these findings are exciting and relevant for practitioners. By understanding their own decision making behavior, practitioners can derive many hints to improve their decision making processes. Insofar, this work is to be hoped to reach such a wide audience which it deserves.

Malte Brettel

Acknowledgements

VII

Acknowledgements Effective strategic decision making is important for practitioners, because it involves a substantial commitment of resources and affects a company’s long-term survival and performance. Much practical and academic literature recommends using information in a step-by-step manner for making effective decisions. In contrast to that, cognitive research and my own practical observations show, decision makers are faced with an abundance of information and not all information that might be useful enters into the decision making process. At the same time, individuals have their own ways of making decisions. This might influence which pieces of information they effectively use. These observations are the starting point to investigate how individual level processes and characteristics play out in strategic decision making in organizations. On the basis of 230 various types of decisions in diverse industries, my study shows that individual preferences for information processing (cognitive style) have significant moderating effects on which kind of information is effectively used for strategic decision making. For example, linear thinkers have a preference for factual information and use information from management systems or market reports more effectively than nonlinear thinkers. In addition to this, my research shows that nonlinear thinkers with a preference for personal information are more susceptible to political behavior than linear thinkers. Moreover, cognitive style has even more moderating effects than the perceived environmental uncertainty, which is considered to be a key moderating factor in strategic decision making. Finally, the positive impact of a decision on company performance is stronger for nonlinear thinkers than for linear thinkers. This finding hints to rather indirect effects of individual ways of making a decision on the subsequent implementation success. Overall, my research provides a rich understanding on what drives effective strategic decision making of individuals in organizations and their environments. The cognitive style of individuals seems to be even more influential than perceived environmental uncertainty. Intense discussions with researchers at RWTH Aachen University and the Academy of Management Conference as well as with practitioners show my research opens up a new perspective on the role of individuals in strategic decision making processes. It thus provides specific insights for practitioners who want to improve their personal decision making effectiveness.

VIII

Acknowledgements

I like to thank a number of people who had an important role during my research undertaking. First, I thank Prof. Dr. Malte Brettel for his continuous support. He gave me profound theoretical and methodological advice and direction throughout the whole process of undertaking this study. In addition to this, I very much enjoyed my time at the Aachen Entrepreneurship chair – Lehrstuhl Wirtschaftswissenschaften für Ingenieure und Naturwissenschaftler. With a team of highly motivated and amiable colleagues, I specifically enjoyed the freedom and responsibility of further developing the chair’s activities in university spin-off coaching and support. I also thank all my friends at the chair for their academic support with special thanks to Jens Hutzschenreuter, Uwe Voss and Sven Wilhelm. I also want to thank Prof. Dr. Michael Bastian for dedicating his time as co-corrector of my study. This work was supported and financed by the partners at OC&C Strategy Consultants. I am grateful for this support without which the circumstances of my time of study would not have been manageable so easily. Specifically, I want to thank Gerd Schnetkamp who took his time for dealing with my research topic and pushed me to strive forward. Finally, I want to thank my parents and Patricia, my partner in life. My parents were very supportive, especially in difficult phases of this research. Their own academic background made them understand my situation and the frequent discussions with them motivated me to keep on working diligently. I am very grateful for their mental support and understanding, not only throughout this research work but also throughout my studies and life in general. Last but not least, my largest thank goes to Patricia for her understanding for all the weekends and vacation days that were necessary to finalize this work besides my professional life. In addition, Patricia always backed me up and motivated me to concentrate on this dissertation. I dedicate this work to her.

Wolfgang Gänswein

Overview of Contents

IX

Overview of Contents Foreword Acknowledgements

V VII

Overview of Contents

IX

Table of Contents

XI

List of Figures List of Tables List of Abbreviations 1

2

3

4

5

Introduction

XV XVII XXI 1

1.1

Problem statement

1

1.2

Research objective and questions

5

1.3

Research approach

11

1.4

Outline of this document

15

Conceptual basis

18

2.1

Literature review and basic terminology

18

2.2

Definition of research variables

44

2.3

Summary

57

Selection of a theoretical framework

61

3.1

Alternative theoretical perspectives and theories

61

3.2

Evaluation and selection of theoretical basis

73

3.3

The Upper Echelon View

78

3.4

Summary

85

Theory and hypotheses development 4.1

Theoretical premises

4.2 4.3

Hypotheses development Summary

Research design

89 90 96 119 121

5.1

Unit of analysis

121

5.2

Operationalization of variables

123

X

Overview of Contents 5.3

6

7

8

Data analysis methodology

Data collection and evaluation

146 164

6.1

Data collection

164

6.2

Sample characteristics

170

6.3

Evaluation and preparation of data basis

179

Results

182

7.1

Results of direct effects model

182

7.2

Results of moderating effects models

201

7.3

Triangulation with financial performance variables

212

7.4 7.5

Test for control variable effects Evaluation of hypotheses

214 217

Discussion and implications

220

8.1

Discussion of results

220

8.2

Implications for research

230

8.3

Implications for practice

237

8.4

Summary

240

Appendix

245

Appendix 1: Questionnaire

245

Appendix 2: Partial models – perceived environmental uncertainty

254

Appendix 3: Partial models – cognitive style

261

References

269

Table of Contents

XI

Table of Contents Foreword

V

Acknowledgements

VII

Overview of Contents

IX

Table of Contents

XI

List of Figures

XV

List of Tables

XVII

List of Abbreviations 1

2

Introduction

XXI 1

1.1

Problem statement

1

1.2

Research objective and questions

5

1.3

Research approach

11

1.4

Outline of this document

15

Conceptual basis 2.1 Literature review and basic terminology 2.1.1 Strategic decision making

18 18 18

2.1.1.1

Characteristics of strategic decisions

19

2.1.1.2

Process view of strategic decision making

20

2.1.2

Individual information behavior

26

2.1.2.1

Basic elements of information behavior

26

2.1.2.2

Information from an individual level perspective

30

2.1.2.3

Information use from an individual level perspective

33

2.1.2.4

Facets of information acquisition

34

2.1.2.4.1 Information sources

34

2.1.2.4.2 Modes of information acquisition

35

2.1.2.4.3 Characteristics describing information

36

2.1.2.5 Facets of information processing 2.1.2.5.1 Dual information processing

37 39

2.1.2.5.2 Cognitive functions: Perception, thought, knowledge learning and retrieval

41

XII

Table of Contents

2.2 Definition of research variables 2.2.1 Strategic decision making effectiveness 2.2.2

Information use

2.2.2.2

Political behavior

2.2.3

2.3 3

Strategic decision making process dimensions

2.2.2.1

Contextual factors

2.2.3.1

Perceived environmental uncertainty

2.2.3.2

Cognitive style

Summary

Selection of a theoretical framework 3.1 Alternative theoretical perspectives and theories 3.1.1 Economic perspective and theories

47 50 51 51 52 57 61 61 61

Behavioral perspective and theories

64

3.1.3

Interpretive perspective and theories

67

Evaluation and selection of theoretical basis

3.3 The Upper Echelon View 3.3.1 Introduction 3.3.2 3.4

Main elements and theoretical statements

Summary

Theory and hypotheses development 4.1

Theoretical premises

4.2 Hypotheses development 4.2.1 Direct effects

73 78 78 81 85 89 90 96 96

4.2.2

Moderating effects of perceived environmental uncertainty

105

4.2.3

Moderating effects of cognitive style

110

4.2.4

Interaction effects of perceived environmental uncertainty and cognitive style

4.3 5

47

3.1.2 3.2

4

44 45

Summary

Research design 5.1

Unit of analysis

5.2 Operationalization of variables 5.2.1 Measurement basics and guiding principles 5.2.2

Measurement instrument

5.2.2.1

Information use

116 119 121 121 123 123 128 128

Table of Contents 5.2.2.2 5.2.2.3

Political behavior

134

Perceived environmental uncertainty

135

5.2.2.4

Cognitive style

137

5.2.2.5

Strategic decision quality

138

5.2.2.6

Company performance

138

5.2.2.7

Control variables

141

5.3 Data analysis methodology 5.3.1 Basic methodological considerations

146 146

5.3.1.1

Statistical hypothesis testing

146

5.3.1.2 5.3.1.3

Structural equation modeling Selection of partial least squares

147 149

5.3.2

6

Partial least squares

154

5.3.2.1

Basic functionality

154

5.3.2.2

Measurement model evaluation

155

5.3.2.3

Structural model evaluation

159

5.3.2.4

Testing of causal relationships and significance level

160

5.3.2.5

Testing of moderating effects

162

Data collection and evaluation 6.1 Data collection 6.1.1 Survey design 6.1.2

Sample generation

6.2 Sample characteristics 6.2.1 Response rate and usable answers

164 164 164 168 170 170

6.2.2

Sample representativeness

172

6.2.3

Sample description

176

6.3 Evaluation and preparation of data basis 6.3.1 Biases 6.3.2 7

XIII

Missing values

Results

181 182

7.1 Results of direct effects model 7.1.1 Measurement model evaluation 7.1.1.1

179 179

Reflective constructs

182 182 182

7.1.1.1.1 Unidimensionality 7.1.1.1.2 Reliability

183 187

7.1.1.1.3 Discriminant validity

194

XIV

Table of Contents 7.1.1.2 Formative construct 7.1.2 Structural model evaluation

7.2 Results of moderating effects models 7.2.1 Perceived environmental uncertainty

8

197 198 201 202

7.2.2

Cognitive style

7.2.3

Interaction of perceived environmental uncertainty and cognitive style 207

204

7.3

Triangulation with financial performance variables

212

7.4

Test for control variable effects

214

7.5

Evaluation of hypotheses

217

Discussion and implications 8.1 Discussion of results 8.1.1 Effectiveness of information use

220 220 220

8.1.2

Effects of political behavior on information use

225

8.1.3

Effects of political behavior on strategic decision quality

226

8.1.4

Effects of strategic decision quality on organizational performance

8.2 Implications for research 8.2.1 Theoretical implications 8.2.2

Limitations and avenues of further research

228 230 230 234

8.3

Implications for practice

237

8.4

Summary

240

Appendix

245

Appendix 1: Questionnaire

245

Appendix 2: Partial models – perceived environmental uncertainty

254

Appendix 3: Partial models – cognitive style

261

References

269

List of Figures

XV

List of Figures Figure 1: Outline of this document

17

Figure 2: A simplified model of human information behavior

29

Figure 3: A human information processing model

38

Figure 4: Elements of conceptual basis of this study

44

Figure 5: Overview and selected authors of conceptualization approaches to cognitive styles

55

Figure 6: Conceptual basis of this study

60

Figure 7: Overview of theoretical premises

90

Figure 8: Overview of basic relationships

119

Figure 9: Components of a structural equation model

149

Figure 10: Received and used responses

172

Figure 11: Industry structure of companies in population and sample

174

Figure 12: Size structure of companies in population and sample by number of employees

174

Figure 13: Location structure of companies in population and sample by zip-code 175 Figure 14: Sample structure of companies by manufacturing vs. services sector

176

Figure 15: Sample structure of decisions by type of decision

177

Figure 16: Sample structure of respondents by position of informants

178

Figure 17: Structural model for Partial Least Squares analysis

198

Figure 18: Structural model evaluation of direct effects

199

Figure 19: Overview group comparisons for testing interaction effects

208

List of Tables

XVII

List of Tables Table 1:

Selected phase models of problem solving and decision making

24

Table 2:

Selected definitions of information in management and human information processing literature

31

Table 3:

Information sources used for strategic decision making

48

Table 4:

Overview of theoretical perspectives and relevant strategic decision making theories

72

Table 5:

Summary evaluation of interpretive theories

77

Table 6:

Overview of hypothesized relationships

Table 7:

Measurement instrument for information use from internal, impersonal sources 130

Table 8:

Measurement instrument for information use from internal, personal sources

Table 9:

120

131

Measurement instrument for information use from external, impersonal sources 132

Table 10: Measurement instrument for information use from external, personal sources

133

Table 11: Measurement instrument for political behavior

134

Table 12: Measurement instrument for perceived environmental uncertainty

136

Table 13: Measurement instrument for cognitive style

137

Table 14: Measurement instrument for strategic decision quality

138

Table 15: Measurement instrument for subjective company performance

139

Table 16: Measures of objective company performance

140

Table 17: Measurement instruments for control variables – organizational level characteristics

143

Table 18: Measurement instruments for control variables – individual demographic characteristics Table 19: Measurement instrument for control variable – individual motivation

144 145

XVIII

List of Tables

Table 20: Summary evaluation of structural equation modeling methods

153

Table 21: Results of principal components analysis of reflective indicators

185

Table 22: Reliability evaluation of cognitive style

188

Table 23: Reliability evaluation of motivation

188

Table 24: Reliability evaluation of information use from external, impersonal sources

189

Table 25: Reliability evaluation of information use from internal, impersonal sources

190

Table 26: Reliability evaluation of use of quantitative information from personal sources

190

Table 27: Reliability evaluation of use of marketing related information from personal sources

191

Table 28: Reliability evaluation of use of qualitative information from personal sources

191

Table 29: Reliability evaluation of information use from personal sources (second-order construct)

192

Table 30: Reliability evaluation of political behavior

193

Table 31: Reliability evaluation of strategic decision quality

193

Table 32: Reliability evaluation of subjective company performance

194

Table 33: Discriminant validity on indicator level

195

Table 34: Discriminant validity on construct level

196

Table 35: Reliability evaluation of perceived environmental uncertainty

197

Table 36: Coefficients of Congruence – perceived environmental uncertainty group comparison

202

Table 37: Structural model evaluation – moderating effects of perceived environmental uncertainty

204

Table 38: Coefficients of Congruence – cognitive style group comparison

205

Table 39: Structural model evaluation – moderating effects of cognitive style

207

Table 40: Coefficients of Congruence – interaction effects group comparisons

209

Table 41: Structural model evaluation – interaction effects

210

List of Tables

XIX

Table 42: Structural model evaluation – direct effects including objective performance

213

Table 43: Evaluation of ordinal control variable effects

216

Table 44: Evaluation of hypotheses on direct effects

217

Table 45: Evaluation of hypotheses on moderating effects of perceived environmental uncertainty

218

Table 46: Evaluation of hypotheses on moderating effects of cognitive style

219

Table 47: Evaluation of hypothesis on interaction effects

219

Table 48: Overview implications for strategic decision making practice

240

Table 49: Quality criteria of construct information use from external, impersonal sources (low and high perceived environmental uncertainty groups) 255 Table 50: Quality criteria of construct information use from internal, impersonal sources (low and high perceived environmental uncertainty groups) 255 Table 51: Quality criteria of construct use of quantitative information from personal sources (low and high perceived environmental uncertainty groups)

255

Table 52: Quality criteria of construct use of marketing related information from personal sources (low and high perceived environmental uncertainty groups) Table 53: Quality criteria of construct use of qualitative information from personal sources (low and high perceived environmental uncertainty groups)

256

256

Table 54: Quality criteria of second-order construct information use from personal sources (low and high perceived environmental uncertainty groups)

256

Table 55: Quality criteria of construct political behavior (low and high perceived environmental uncertainty groups) Table 56: Quality criteria of construct strategic decision quality (low and high perceived environmental uncertainty groups)

257 257

Table 57: Quality criteria of construct subjective company performance (low and high perceived environmental uncertainty groups) 257

XX

List of Tables

Table 58: Cross-loadings on indicator level (low perceived environmental uncertainty group) Table 59: Cross-loadings on indicator level (high perceived environmental uncertainty group)

258 259

Table 60: Square roots of Average Variance Explained (diagonal) and latent variable correlations (low perceived environmental uncertainty groups) 260 Table 61: Square roots of Average Variance Explained (diagonal) and latent variable correlations (high perceived environmental uncertainty groups) 260 Table 62: Quality criteria of construct information use from external, impersonal sources (cognitive style groups) 262 Table 63: Quality criteria of construct information use from internal, impersonal sources (cognitive style groups) 262 Table 64: Quality criteria of use of quantitative information from personal sources (cognitive style groups) 262 Table 65: Quality criteria of construct use of marketing related information from personal sources (cognitive style groups) 263 Table 66: Quality criteria of construct use of qualitative information from personal sources (cognitive style groups)

263

Table 67: Quality criteria of second-order construct information use from personal sources (cognitive style groups)

263

Table 68: Quality criteria of construct political behavior (cognitive style groups) 264 Table 69: Quality criteria of construct strategic decision quality (cognitive style groups)

264

Table 70: Quality criteria of construct subjective company performance (cognitive style groups)

264

Table 71: Cross-loadings on indicator level (linear thinkers group)

265

Table 72: Cross-loadings on indicator level (nonlinear thinkers group)

266

Table 73: Square roots of Average Variance Explained (diagonal) and latent variable correlations (linear thinkers group)

267

Table 74: Square roots of Average Variance Explained (diagonal) and latent variable correlations (nonlinear thinkers group)

267

List of Abbreviations

XXI

List of Abbreviations AVE CEO Cf. CI CoSI CSI e.g. EM

Average Variance Explained Chief Executive Officer confer (Latin for compare with) Condition Index Cognitive Style Indicator Cognitive Style Index for example expectation-maximization

et al. H

et alii (Latin for and others) hypothesis

i.e. LNTSP

id est (Latin for that is) Linear-Nonlinear Thinking Style Profile

LT MBTI

linear thinker Myers-Briggs Type Indicator

NLT no. PEU P

nonlinear thinker number perceived environmental uncertainty theoretical premise

p p. PLS

significance level page Partial Least Squares

pp. REI SDM SEM SWOT TMT VIF

pages Rational-Experiential Inventory strategic decision making structural equation modeling strengths-weaknesses opportunities-threats Top Management Team Variance Inflation Factor

vs.

versus

Problem statement

1

1 Introduction 1.1 Problem statement Decision making is “at the heart of our personal and professional lives.”1 While some decisions are small, others are more important and affect whole organizations such as in the two following examples: 1) The take-over of Spar is Alfons Frenk’s master piece. Just two years after taking over the position as Chief Executive Officer (CEO) of Edeka, he accomplished to achieve two strategic goals: Strengthen Edeka’s market position in Germany and expand the cooperation with competitors in foreign markets. Alfons Frenk is a number guy and details are exciting for him. He is a man of thorough analysis instead of quick fixes. Accordingly his take-over decision was prepared well and was based on analyzing and weighing each alternative available against Edeka’s overarching company goals.2 2) At the beginning of the 90s AT&S focused on thin, highly complex multilayer printed boards, whereas the majority of producers dealt with thick, high-layer printed boards. High investments for entering this largely neglected technological field are required, and these investments are only justifiable with prospects towards addressing an attractive market. Ultimately, CEO Willi Dörflinger’s feelings in favor of focusing on this technological expertise resulted in the decision to enter this specific market segment.3 These episodes are just two examples of decisions made within many organizations.4 They demonstrate, even strategic decisions, i.e. decisions with fundamental importance and long-term effects on organizations,5 are made by individuals such as the CEOs in the two examples above. However, making a strategic decision is not an instantaneous act. It comprises a large number of activities, during which a broad

1

Campbell, A./Whitehead, J./Finkelstein, S. (2009), p. 60. Cf. in the internet: Schönert, E. (2005), accessed on December, 12th 2008. Cf. in the internet: Sachs, G. (2006), accessed on December, 12th 2008. 4 Cf. e.g. Campbell, A. et al. (2009), p. 60. 5 Cf. Mueller, G. C./Mone, M. A./Barker Ill, V. L. (2007), p. 853; Dean, J. W./Sharfman, M. P. (1996), p. 368; Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 17; Mintzberg, H./Raisinghani, D./Théorêt, A. (1976), p. 246; Schwenk, C. R. (1988), pp. 473-475. 2 3

W. Gänswein, Effectiveness of Information Use for Strategic Decision Making, DOI 10.1007/978-3-8349-6849-4_1, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

2

Introduction

range of internal and external factors and developments need to be considered.6 Therefore, strategic decision making (SDM) which refers to the “processes involved in choosing a firm’s strategy”7 receives considerable interest of management researchers and practitioners alike.8 Both decision episodes appear to describe cases of successful decision making, whereas the way of how each decision was made differed drastically from each other. How can these differences be explained? From a normative and prescriptive perspective, SDM is depicted as a universal, linearsequential and orderly process.9 According to this perspective organizational goals are given upfront. These goals are translated into strategic decisions by analyzing environmental threats and opportunities, generating and evaluating strategic alternatives and choosing the goal maximizing alternative for the organization.10 Last but not least, prescriptive literature recommends a number of analyses and management accounting techniques to support managers with these activities.11 This theoretical approach seemingly explains the decision making of example one, however falls short of explaining why the decision made in example two was as well successful. As opposed to normative research, descriptive SDM research accounts for the fact, that SDM in real-life settings does not follow a linear model. Instead descriptive research describes SDM as a large number of disorderly or parallel activities which are structured to some degree only12 and which are furthermore constraint by ambiguous, potentially conflicting goals and cognitive limitations of the people involved.13 The market entry decision in the second example rather fits into this theoretical approach.

6

Cf. Mintzberg, H. et al. (1976), p. 250. Fahey, L. (1981), p. 43. 8 Cf. Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 17; Rajagopalan, N./Rasheed, A. M. A./Datta, D. K. et al. (1998), p. 245; Hutzschenreuter, T./Kleindienst, I. (2006), pp. 673-674. 9 Cf. Chaffee, E. E. (1985), p. 90; Dent, J. F. (1990), p. 8. 10 Cf. Dent, J. F. (1990), p. 14; Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 18; Hitt, M. A./Beverly, B. T. (1991), p. 329. 11 Cf. e.g. Bhimani, A./Langfield-Smith, K. (2007), p. 6 for a literature review of such tools and techniques. 12 Cf. Witte, E. (1968), p. 644; Mintzberg, H. et al. (1976), p. 266; Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 35. 13 Cf. Simon, H. A. (1978), p. 2; Schoemaker, P. J. H. (1993), p. 114; Eisenhardt, K. M./Zbaracki, M. J. (1992); Elbanna, S. (2006), p. 2; Das, T. K./Teng, B.-S. (1999), p. 758; Huff, A. S./Huff, J. O./Barr, P. S. (2000), pp. 4-20; Ungson, G. R./Braunstein, D. N./Hall, P. D. (1981), p. 121. 7

Problem statement

3

Both normative and descriptive SDM theories cannot universally explain why the decisions of the two examples were effective. However, such explanations are important for managers who are concerned with improving their managerial work and company performance14 given the specific circumstances and their idiosyncratic ways of decision making. The following four considerations are to be made in order to better understand effective SDM. Firstly, comprehensive decision making means that decision makers seek to consider all decision relevant factors, to identify all alternative courses of actions and to thoroughly evaluate these alternatives against a given set of criteria.15 Although SDM may substantially depart from a prescriptive approach, there is broad empirical evidence that comprehensive examination of a decision problem and a thorough development and evaluation of strategic alternatives prior to choice – in whatever way this is accomplished – improves the effectiveness of decision making.16 Secondly, the organizational and individual activities involved in SDM are the means for accomplishing comprehensive decision making. Among these activities, information gathering, analyzing and evaluating form a key process dimension of SDM.17 From a normative view, the more information use, the better for the effectiveness of SDM.18 However, empirical evidence is mixed and this assertion is not unambiguously supported.19 Political behavior is another key process dimension and refers to the social processes of organizational participants to pursue their personal interests.20 It is most often proposed to inhibit decision effectiveness which is supported in many empirical studies.21 Finally, some more process dimensions such as

14

Cf. Rajagopalan, N. et al. (1998), p. 245. Cf. Fredrickson, J. W./Mitchell, T. R. (1984), p. 402. 16 Cf. Papadakis, V. M. (1998), p. 125; Atuahene-Gima, K./Haiyang, L. (2004), p. 590; Elbanna, S./Child, J. (2007), p. 443-445; Lipshitz, R./Bar-Ilan, O. (1996), p. 57. 17 Cf. Ketchen Jr, D. J./Thomas, J. B./McDaniel Jr, R. R. (1996), p. 237. 18 Cf. Dean, J. W./Sharfman, M. P. (1996), p. 373. 19 Cf. Elbanna, S. (2006), p. 4 and Forbes, D. P. (2007), p. 363-366 for a review of empirical studies. 20 Cf. Eisenhardt, K. M./Zbaracki, M. J. (1992), pp. 22-27; Elbanna, S. (2006), pp. 7-8. 21 Cf. Elbanna, S. (2006), p. 7. 15

4

Introduction

intuitive synthesis22 have been proposed, whereas their empirical relevance appears limited.23 Thirdly, in SDM research organizations are considered to interact with the environment in two ways. On the one hand, strategic decisions are a response to environmental changes.24 Decisions serve for defining a company’s interactions with its environment,25 e.g. by changing products or markets or by performing other entrepreneurial actions.26 On the other hand, SDM and its effectiveness may be influenced by environmental conditions such as environmental turbulence, hostility or uncertainty.27 The effectiveness of SDM has been related to environmental characteristics from such a contingency perspective, whereas empirical findings show mixed and partly opposing effects. Some studies support a positive effect of comprehensiveness on company performance in a stable and a negative effect in an unstable environment.28 Other findings suggest a reversed moderating effect of environmental conditions.29 Fourthly, SDM is a combination of individual level and organizational level processes.30 The individual decision makers are faced with a ubiquitous amount of information from a large variety of sources from the external and organizational environment. Due to their cognitive limitations, they can only use a subset of the information they receive.31 Hence, they have to select some and discard some other information which they are to use for SDM.32 This imposes a danger, because not all 22

Cf. Khatri, N./Ng, H. A. (2000), pp. 76-77. Other dimensions include disruption, impedance or speed-up and other dynamic factors, formalization and standardization, or centralization. Cf. e.g. Papadakis, V. M. (2006), pp. 370-371 and the authors mentioned there. However, one may question whether these characteristics are process dimensions or not rather structural or outcome characteristics. 23 Cf. Elbanna, S./Child, J. (2007), pp. 443-445. One may furthermore question whether intuitive synthesis is not a specific form of information processing instead of a separate process dimension. Cf. Sadler-Smith, E./Sparrow, P. (2008), pp. 306-307. 24 Cf. Dent, J. F. (1990), p. 5; Huber, G. P./Glick, W. H. (1993), p. 7. 25 Cf. Hofer, C. W./Schendel, D. E. (1978), p. 25; Porter, M. E. (1980); Dent, J. F. (1990), p. 17. 26 Cf. Chaffee, E. E. (1985), p. 90; Mueller, G. C. et al. (2007), p. 853. 27 Cf. Miller, D./Friesen, P. H. (1983), p. 231; Forbes, D. P. (2007), p. 363. 28 Cf. Fredrickson, J. W. (1984), p. 455; Fredrickson, J. W./Mitchell, T. R. (1984), pp. 416-418; Fredrickson, J. W./Iaquinto, A. (1989), p. 532; Smith, K. G./Gannon, M. J./Grimm, C. et al. (1988), p. 228. 29 Bourgeois Iii, L. J./Eisenhardt, K. M. (1988), pp. 827-828; Goll, I./Rasheed, A. M. A. (1997), pp. 588-589; Priem, R. L./Rasheed, A. M. A./Kotulic, A. G. (1995), p. 924; Elbanna, S./Child, J. (2007), pp. 446-448; Eisenhardt, K. M. (1989), pp. 556-559 and 562-567. 30 Cf. Corner, P. D./Kinicki, A. J./Keats, B. W. (1994), p. 294. 31 Cf. Simon, H. A. (1978), p.13; Hambrick, D. C. (1982), p. 160. 32 Cf. Garg, V. K./Walters, B. A./Priem, R. L. (2003), p. 725.

Research objective and questions

5

information is equally effective. The use of a specific set of information may lead to choice of one alternative which is inferior compared to other possible alternatives.33 Last but not least, individual information use is a cognitive process34 and subject to individual idiosyncracies.35 Therefore, organizational SDM may be influenced by these idiosyncracies. To summarize, although SDM refers to organizational processes, ultimately individuals make strategic decisions. These individuals are subject to constraints imposed by the organizational and environmental context as well as their own cognitive limitations. Despite these limitations, decision making appears to be more effective, if comprehensiveness is large. Furthermore, information use is generally said to be the means for achieving comprehensiveness in decision making. However, SDM research falls short of empirical support that any information use is equally effective. Given the abundance of information decision makers are faced with, a main challenge is to select and use the information that is particular beneficial for making good decisions.36 This challenge may be further complicated by specific environmental conditions and idiosyncracies of the individual decision maker. Therefore, a main problem of SDM research is how environmental, organizational and individual level factors of SDM can be integrated into one unifying model. 1.2 Research objective and questions The research objective of this study is the investigation of effective information use for SDM by individual decision makers. For this purpose this study seeks to investigate how individual level processes of information use interact with the provision of information from inside and outside organizational channels. This study also seeks to take into account the social context within the organization, characteristics of the organizational environment and individual characteristics of a decision maker, because factors from all three domains influence the effectiveness of SDM. Based on prior research, the following discussion develops the specific research questions for this study. Thereafter the expected contribution to research and practice are explained.

33

Cf. Glazer, R./Steckel, J. H./Winer, R. S. (1992), p. 223. Cf. Daft, R. L./Weick, K. E. (1984), p. 285; Hambrick, D. C./Mason, P. A. (1984), pp. 194-195; Huff, A. S. et al. (2000), p. 14; Schwenk, C. R. (1988), pp. 476-478. 35 Cf. Hambrick, D. C./Mason, P. A. (1984), p. 196; Hitt, M. A./Beverly, B. T. (1991), pp. 345-346. 36 Cf. Saunders, C./Jones, J. W. (1990), p. 32; Finkelstein, S./Hambrick, D. C. (1996), p. 41. 34

6

Introduction

At first, organization level SDM studies may be based on misleading assumptions about information use of individuals as indicated before. Generally speaking, organization level studies assume that information which is available within organizations is used for SDM when needed.37 Therefore, some studies narrowly focus on decision making activities and do even not consider any information use aspect at all.38 Other conceptions of organizational level studies do model information use, but in a very general way.39 However, there is empirical evidence that availability of information within an organization must not imply that individual decision makers actually use it effectively.40 One reason is that availability of information causes decision makers to perceive situations as more controllable and manageable.41 As a consequence, high availability of information must not necessarily translate into individual information use. Another reason is that individual decision makers employ a variety of ways of gathering and using information both from inside and outside the formal processes specified by organizations.42 Consequently, investigating information use should allow for grasping the variety of ways how managers obtain and use information instead of limiting it to a formal organizational perspective.43 However, few SDM studies examine specific facets of information use. For example Eisenhardt (1989) proposes the use of internal experienced counselors and real-time information from management information systems and companies’ financial executives are effective means of information use in highly unstable environments.44 In contrast to that, another study shows that external information sources are particularly effective.45 Thomas et al. (1993) results go even further by suggesting that external sources are more effective than internal sources.46 Furthermore, Fredrickson and colleagues comprehensiveness construct includes internal and external meetings for information gathering. While internal meetings always show positive effects on 37

Cf. Elbanna, S. (2006), pp. 6-7; Forbes, D. P. (2007), p. 370. Cf. the studies of Judge, W. Q./Miller, A. (1991); Khatri, N./Ng, H. A. (2000); Atuahene-Gima, K./Haiyang, L. (2004). 39 Cf. studies of Bourgeois Iii, L. J./Eisenhardt, K. M. (1988); Priem, R. L. et al. (1995); Goll, I./Rasheed, A. M. A. (1997); Dean, J. W./Sharfman, M. P. (1996); Elbanna, S./Child, J. (2007). 40 Cf. Thomas, J. B./Clark, S. M./Gioia, D. A. (1993), pp. 254 and 256; Hough, J. R./White, M. A. (2003), p. 487. 41 Cf. White, J. C./Varadarajan, P. R./Dacin, P. A. (2003), p. 72; Kuvaas, B. (2002), p. 989. 42 Cf. Daft, R. L./Sormunen, J./Parks, D. (1988), p. 125; De Alwis, G./Majid, S./Sattar Chaudhry, A. (2006), p. 363. 43 Cf. Corner, P. D. et al. (1994), p. 295. 44 Cf. Eisenhardt, K. M. (1989), pp. 549 and 559. 45 Cf. Dollinger, M. J. (1984), p. 364. 46 Cf. Thomas, J. B. et al. (1993), p. 259. 38

Research objective and questions

7

company performance, external meetings had positive and negative effects dependent on whether the organizations operated in stable or unstable environments.47 Finally, some findings suggest that only information from personal sources is used effectively,48 while other findings show that financial reporting activities, i.e. formal information sources, increase decision making effectiveness.49 In brief, organizational level studies appear to make a too simplistic assumption on the use of information by individual decision makers in organizations. In other words, they are “theoretically under-specified” with respect to managerial actions and cognitions.50 The mentioned and fragmented empirical evidence suggests a closer examination of information use from different information sources. This leads to research question 1:

Is information from different sources used with different effectiveness for SDM?

In addition to that, the environmental factors such as uncertainty, hostility or munificence have an important influence on SDM effectiveness, whereas environmental uncertainty is considered as key moderating variable. 51 However, there is contradictory empirical evidence and controversial debate about the value of information use under differing environmental conditions.52 This issue is related to two contrasting theoretical approaches, namely information processing theory and behavioral decision theory. Both theories have the common assumption that individuals possess limited information processing capacity and cannot be aware of all environmental factors.53 Furthermore, environmental conditions have an effect on the analyzability of a decision problem and thus on the value of comprehensive decision making and information use.54 However, the two theories differ in their views of how environmental conditions and information use are related to each other. On the one hand, information processing theory defines uncertainty as the difference between information required for performing a task and information available. According to this view, it is just a matter of matching information processing capacity to the demand 47

Cf. Fredrickson, J. W. (1984), p. 456; Fredrickson, J. W./Mitchell, T. R. (1984), p. 417 and 418. Cf. Cramme, C./Lindstädt, H./Wolff, M. (2009), pp. 52-53. 49 Cf. Papadakis, V. M. (1998), p. 125. 50 Rajagopalan, N. et al. (1998), p. 239. Cf. also Ungson, G. R. et al. (1981), p. 26; , O'Reilly, I. I. I. C. A. (1983), p. 105; Saunders, C./Jones, J. W. (1990), p. 31f. 51 Cf. Elbanna, S. (2006), p. 6; Forbes, D. P. (2007), p. 363. 52 Cf. Priem, R. L. et al. (1995), p. 913; Hough, J. R./White, M. A. (2003), p. 481. 53 Cf. Kuvaas, B. (2002), p. 978. 54 Cf. Forbes, D. P. (2007), p. 369. 48

8

Introduction

for information processing imposed by environmental conditions.55 On the other hand, behavioral decision theory posits that environmental factors can be distinguished into uncertain and ambiguous factors.56 Uncertain factors are analyzable, whereas ambiguous factors are not. According to this view, it is not only a matter of matching information processing capacity to the requirements imposed by the environment, but there may be situations where information processing is not effective at all.57 Another perspective proposes that different modes of information processing possess differing capabilities for resolving uncertainty.58 This contention is furthermore supported by the empirical results cited before. Consequently, the effectiveness of information use from different sources may be contingent on the environmental conditions SDM takes place in. This leads to research question 2:

Does environmental uncertainty moderate the effectiveness of information use from different sources for SDM?

Next, a focus on individual information level processes suggests the inclusion of cognitive characteristics of strategic decision makers.59 Among cognitive characteristics three basic categories can be distinguished, namely cognitive content, cognitive structures and cognitive styles.60 Cognitive content refers to knowledge about basic facts or cause-effect relationships, whereas cognitive structure refers to more comprehensive representations of general business environments. Cognitive styles refer to preferred ways of information gathering and processing. 61 From a procedural perspective, cognitive style is closely related to information use for SDM and may influence how effectively different information received can subsequently be used for SDM.62 Moreover, strategic decision processes in organizations are not necessarily tailored to these individual information processing preferences.63 Instead, decision makers receive large amounts of information which they have to cope with. However, “[t]here is no sense providing information to a decision maker whose 55

Cf. O'Reilly Iii, C. A. (1980), p. 614; Kuvaas, B. (2002), p. 978; Forbes, D. P. (2007), p. 367. Cf. Daft, R. L./Lengel, R. H. (1986), p. 556f.; Atuahene-Gima, K./Haiyang, L. (2004), pp. 585-586; Forbes, D. P. (2007), p. 367. 57 Cf. Atuahene-Gima, K./Haiyang, L. (2004), pp. 590-591; Forbes, D. P. (2007), pp. 370-371. 58 Cf. Daft, R. L./Lengel, R. H. (1986), p. 560. 59 Cf. Hitt, M. A./Beverly, B. T. (1991), p. 335; Hodgkinson, G. P. (2003), pp. 3-5. 60 Cf. Finkelstein, S./Hambrick, D. C./Cannella, A. A. (2009), pp. 60-66. 61 Cf. Hayes, J./Allinson, C. W. (1994), p. 53; Hodgkinson, G. P./Healey, M. P. (2008), p. 402. 62 Cf. Blaylock, B. K./Rees, L. P. (1984), p. 87; Hunt, R. G./Krzystofiak, F. J./Meindl, J. R. et al. (1989), p. 437; Hodgkinson, G. P./Clarke, I. (2007), pp. 248-249; Hodgkinson, G. P./Healey, M. P. (2008), p. 402. 63 Cf. Jennings, D./Disney, J. J. (2006), p. 609. 56

Research objective and questions

9

cognitive make-up is such that he or she will ignore it.”64 Consequently, there may be patterns of effective information use depending on a match between information received and an individual’s cognitive style.65 This leads to research question 3:

Does cognitive style moderate the effectiveness of information use from different sources for SDM?

Finally, there are three different views on what governs human information processing in general.66 Firstly, a deterministic view proposes that any outcome of individuals’ information processing is determined by their environment. Secondly, the view of choice proposes that individuals determine themselves their information processing and any outcome would accordingly be a function of his or her cognitive processes and characteristics only. Thirdly, actual information processing is situation specific and both theories are said to hold, because individuals are inseparable from their environments.67 These considerations are relevant for the present research objective, because cognitive style and environmental uncertainty may interact with respect to information use for SDM.68 This leads to research question 4:

How do environmental uncertainty and cognitive style interact with respect to their effects on information use in SDM?

This research study contributes in several ways to SDM research and management practice. Firstly, the basic objective of this research study is to investigate individuals’ information use for SDM while at the same time taking specific factors of environmental context and cognitive characteristics into account. This objective addresses several calls of SDM researchers to move from a mere description of SDM attributes to conceptualizations that account for the interactions between the environment, as well as organizational and individual level processes.69 Information

64

Blaylock, B. K./Rees, L. P. (1984), p. 88. Cf. Hodgkinson, G. P./Clarke, I. (2007), pp. 244-246.; Finkelstein, S. et al. (2009), p. 68. Cf. Neisser, U. (1976), pp. 184-185. 67 Cf. Starbuck, W. H./Milliken, F. J. (1988), p. 42. 68 Cf. Sadler-Smith, E. (1998), p. 199; Hough, J. R./Ogilvie, D. (2005), p. 443. 69 Cf. Elsbach, K. D./Barr, P. S./Hargadon, A. B. (2005), p. 432; Hitt, M. A./Beverly, B. T. (1991), p. 347; Hough, J. R./White, M. A. (2003), p. 488; Hambrick, D. C. (2007), p. 337; Hough, J. 65 66

10

Introduction

use for SDM is considered as a key individual level action to be examined from a psychological and social perspective.70 More specifically one researcher raises the question “[w]hat is the relationship between top management characteristics, which may affect their perceptual and evaluational processes and the [SDM process]?”71 At the same time another researcher emphasizes “this black box research has not been done because it is exceedingly difficult.”72 Secondly, this research study addresses a central debate in organizations research concerning the role of the environment vs. individual actors within the organization. From an environmental determinism perspective, organizational behavior is only a function of environmental characteristics. According to this view, the role of organizational participants is merely to facilitate the adoption of the courses of action prescribed by the environment.73 From a strategic choice perspective, organizational behavior is also a function of the individuals within organizations. They may – at least partly – influence the courses of organizational action and thus be a cause of variation in firm outcomes.74 This study will contribute to this debate. Thirdly, several authors argue that the effectiveness of information use is a function of environmental characteristics and mode of information use. They furthermore argue that specific modes of information use are particularly suitable for specific environmental conditions.75 By examining the moderating effects of environmental uncertainty on the effectiveness of information use from different sources this study will also contribute to this debate. Fourthly, potential effects of cognitive characteristics on organizational behavior and SDM have been proposed. However, only little has been tested in organizational research settings. Therefore, “beyond describing cognitive models, investigators need

R./Ogilvie, D. (2005), p. 443; Hutzschenreuter, T./Kleindienst, I. (2006), p. 706; Rajagopalan, N. et al. (1998), p. 243. Cf. Choo, C. W. (1996), p. 36; Rajagopalan, N. et al. (1998), p. 231; Hough, J. R./White, M. A. (2003), p. 488. 71 Elbanna, S. (2006), p. 14. 72 Hambrick, D. C. (2007), p. 337. 73 Cf. Hannan, M. T./Freeman, J. H. (1977); Lieberson, S./O'Connor, J. F. (1972); Papadakis, V. M. (2006), p. 371. 74 Cf. Child, J. (1972); Finkelstein, S. et al. (2009), pp. 23-26. 75 Cf. Daft, R. L./Bettenhausen, K. R./Tyler, B. B. (1993), pp. 140-141; Eisenhardt, K. M. (1989), pp. 549 and 559. 70

Research approach

11

to establish connections to choices, behaviors, and organizational performance.” 76 By examining the moderating effect of cognitive style, this study addresses long-standing propositions concerning the role of cognitive style for organizational information processing and SDM.77 Finally, managers can draw conclusions for their managerial work. Based on the findings of this study, managers can tailor their information actions to the environmental conditions and their individual information processing preferences and thus improve the effectiveness of SDM. Most notably, managers are not even necessarily aware of their habitual information processing preferences and their effects during work. Consequently, this study provides managers with a basis for reflecting and improving their work for such an important activity as SDM. 1.3 Research approach The following section provides details on the research approach chosen for this study. It starts with a brief explanation of empirical research principles and the concept of methodological fit. Both aspects should briefly be discussed in order to determine the specific research approach for this study thereafter. The current research study belongs to the class of empirical management research and should be distinguished from research that is concerned with purely deductive theorizing and mathematical modeling or so-called morphological research.78 In order to derive an appropriate research design for this study the basic principles of empirical management research and the concept of methodological fit are discussed. Empirical social science and business research follows three basic research principles.79 Firstly, the principle of scientific realism postulates that reality and thought are independent and research is concerned with actual phenomena and causal relationships. Therefore, the aim of social sciences is to most accurately describe reality through theories. This is why research should be inter-subjectively provable and latent measurement of unobservable variables ideally corresponds with reality not with subjective observations. Secondly, the principle of naturalism postulates that social 76

Finkelstein, S. et al. (2009), p. 70. Cf. O'Reilly, I. I. I. C. A. (1983), p. 126; Gardner, W. L./Martinko, M. J. (1996), p. 65; Finkelstein, S. et al. (2009), p. 68; Sadler-Smith, E. (1998), p. 193. 78 Cf. Homburg, Ch. (2007), p. 29. According to Homburg morphological research also bases on empirical observations, whereas it focuses on definitions and classifications of phenomena. 79 Cf. Homburg, Ch. (2007), pp. 34-35. 77

12

Introduction

sciences can apply principles and methods of natural sciences. This provides the basis for acknowledging causal mechanisms in social and organizational behavior and allows for applying quantitative research methods. Thirdly, although empirical research bases on theoretical considerations, it is problem oriented. This means, the explanation of actual phenomena and problems is based on existing theories supported with empirical findings.80 Finally, any empirical research study should assure methodological fit. Methodological fit refers to the internal consistency of four major elements of an empirical management research study.81 These elements are research questions, state of prior research, research design and contribution to the literature. The literature review revealed that the state of SDM research can be considered as relatively mature with respect to the criteria outlined by Edmondson, A. C./McManus, S. E. (2005).82 A large number of variables describing SDM, its antecedents and consequences along with control variables can be identified. A current focus of SDM research is the reconciliation of interrelated theories and concepts such as bounded rationality, incremental or cognitive theories of SDM. Furthermore, research concerning information use, environmental context and cognitive style is not at its nascent stage either. Human information processing has intensively been studied since the 1950s and its relevance for strategic decision making was established from the 1970s on.83 Environmental context has seen its introduction into management research starting with contingency approaches in the 1960s and the link to strategic decision processes was established in 1983.84 Furthermore, although there is debate about what dimensions cognitive style define exactly, the body of knowledge, measurement instruments and evaluative studies is large.85 Also, the theoretical and empirical relevance of cognitive style for management and SDM research has been established in a number of conceptual86 and experimental studies.87 Although these mentioned

80

Cf. Homburg, C. (2007), pp. 35-39. Cf. Edmondson, A. C./McManus, S. E. (2005), p. 2 and 41. Cf. Edmondson, A. C./McManus, S. E. (2005), pp. 8-19. 83 Cf. here and for the following Ungson, G. R. et al. (1981), p. 26. 84 Cf. the study of Miller, D./Friesen, P. H. (1983). 85 Cf. the reviews on cognitive style of Miller, A. (1987); Riding, R./Cheema, I. (1991); Kozhevnikov, M. (2007); Evans, J. S. B. T. (2008). Furthermore, Leonard, N. H./Scholl, R. W./Kowalski, K. B. (1999) carry out an evaluative empirical study of various cognitive style constructs. 86 Cf. the reviews and conceptual papers of Hayes, J./Allinson, C. W. (1994); Leonard, N. H./Beauvais, L. L./Scholl, R. W. (2005); Hodgkinson, G. P./Clarke, I. (2007); Finkelstein, S. et al. (2009), pp. 66-69. 81 82

Research approach

13

research fields are still developing, the existing theories, concepts and tested measurement instruments provide a large body of knowledge for the current research undertaking. Given this, the state of prior work appears in a mature state. As a result specific research questions are formulated in section 1.2. These questions are concerned with a refinement and connection of interrelated SDM theories, which is another aspect of performing mature theory research.88 Answering these questions seeks a number of contributions to the existing literature. Firstly, research question one challenges the assumptions on information use of behavioral SDM studies and thus seeks to clarify whether SDM research should expand its boundaries to managerial cognition or not. By doing so a connection between the interrelated behavioral and cognitive theories of SDM is established. Secondly, this study seeks to add to the debate of information processing and behavioral decision theory views on the moderating effect of environmental uncertainty. Thirdly, the moderating role of cognitive style has not been examined in field research yet and the insights generated might lead to refinement of SDM theories. Given these considerations, research questions and expected contribution fit to the mature state of prior literature.89 The research design has to take account for the preceding considerations in order to achieve methodological fit. Four main decisions concerning the research design have to be made. These are decisions on type of data to be collected, on data collection tools and procedures, on analysis procedures and on sample characteristics.90 Firstly, this study seeks to collect quantitative data in field settings, because it focuses on specific concepts each with a large body of knowledge behind them. Secondly, the data collection can be performed through a questionnaire field survey, because all areas of research provide reliable and tested measurement instruments for questionnaire design. It should be noted, that early cognitive style measures based on brain measurement, physical observation or comprehensive self-description inventories such as the MyersBriggs Type Indicator.91 These were not suitable for questionnaire design but had to be

87

Cf. the experimental studies of McKenney, J. L./Keen, P. G. W. (1974); Henderson, J. C./Nutt, P. C. (1980); Blaylock, B. K./Rees, L. P. (1984); Hunt, R. G. et al. (1989); Nutt, P. C. (1990); Hough, J. R./Ogilvie, D. (2005); Gallén, T. (2006). 88 Cf. Edmondson, A. C./McManus, S. E. (2005), p. 11. 89 Cf. Edmondson, A. C./McManus, S. E. (2005), p. 42. 90 Cf. Edmondson, A. C./McManus, S. E. (2005), pp. 41-46; Diekmann, A. (2006), pp. 102-103 and pp. 166-170. 91 Cf. Robey, D./Taggart, W. (1981), pp. 375-380.

14

Introduction

used during personal observations and interviews thereby limiting sample size.92 Therefore, cognitive style measures suitable for questionnaire design in field research settings have only recently been developed and tested.93 As a result, a questionnaire survey design appears a feasible measurement approach. Thirdly, the data is analyzed with statistical hypotheses testing. A number of statistical methods are available, whereas this study will use second-generation modeling because of the complexity of the research topic and the use of latent variables.94 Fourthly, the current research is concerned with general firm behavior and by employing a questionnaire survey it is possible to conduct a large scale study in a number of industries. To conclude, the large scale, quantitative field research design of this study contributes in two more ways to SDM research. Firstly, most cognitive decision making studies use quasi-experimental or experimental research designs. These studies apply artificial research settings such as given decision scenarios or simulation games in order to control for external factors. However, such settings fail to capture the context of organizational decision making, because they determine clear goals upfront or employ pre-defined information sets. Last but not least, the participants do not have a long-term stake in the decisions made.95 In contrast to that, these aspects are considerably different in organziational settings and such experimental or quasi-experimental studies have limitations with respect to their external validity. In contrast to that, this study will use a field survey providing for high external validity of this study. Consequently, the findings will allow for assessing the empirical and theoretical relevance of managerial cognition and cognitive style in organizational settings. Secondly, sample characteristics of behavioral survey studies vary considerably.96 Most studies focus on one or few industries exhibiting specific, rather homogeneous environmental conditions. Additionally, sample size is most often limited and only few studies exist with more than 100 data sets. This limits the generalizability of research findings and further large scale studies covering multiple industries should be

92

Cf. Allinson, C. W./Hayes, J. (1996), p. 119. Cf. e.g. Vance, C. M./Groves, K. S./Paik, Y. et al. (2007); Cools, E./van den Broeck, H. (2007). 94 Cf. section 5.3 for details on the data analysis methodology. 95 Cf. O'Reilly, I. I. I. C. A. (1983), p. 104. 96 Cf. Elbanna, S. (2006), p. 6. 93

Outline of this document

15

conducted.97 In contrast to that, this study will provide for generalizable results across a broad range of industries. 1.4 Outline of this document After a description of the research problem in section 1.1, research objective and questions were developed and their potential contribution to research and practice was explained in section 1.2. Then the research approach and methodology for this study was determined in section 1.3. The first chapter closes with a description of the outline of this research document in this section. The outline of this document and the logical flow between the chapters is depicted in Figure 1. Chapter 2 reviews the relevant research literature, establishes the basic terminology and defines the research variables of this study. First of all, section 2.1 establishes an empirically grounded definition of strategic decisions and SDM as the basic research phenomenon of interest. Then the discussion turns to individual information behavior and processing in order to establish the basic terminology for use throughout the study. Overall, this literature review provides the basis for developing the specific definitions of the research variables in section 2.2. The chapter is summarized in section 2.3. Chapter 3 aims at the selection of an appropriate theoretical framework. For this purpose three alternative theoretical perspectives on SDM are explained in section 3.1. Then theoretical requirements are developed and a critical evaluation of selected SDM theories against these requirements follows in section 3.2. It will turn out that one theoretical framework provides the main basis for this study. However, it needs some amendment from a complementary theoretical basis which has implications for the theory development in the subsequent chapter. Next, a detailed explanation of the theoretical framework follows in section 3.3. Finally, the chapter is summarized in section 3.4. Chapter 1 aims at developing hypotheses about the relationships between the variables of interest. Since the theoretical basis draws from two complementary perspectives, the theoretical premises are explicitly stated in section 1.1. Then, the hypotheses are developed in section 4.2. The chapter closes with a summary in section 4.3.

97

Cf. Forbes, D. P. (2007), p. 374.

16

Introduction

Chapter 5 elaborates on the research design of this study. First of all the unit of analysis is defined on theoretical grounds in section 5.1. Then the operationalization of the measurement instrument is developed in section 5.2. Finally, some basic methodological considerations with respect to the data analysis are made and a specific data analysis method is chosen and described in section 5.3. Chapter 6 describes the data collection and evaluation of the data basis of this study. In section 6.1, the data collection procedure and how the data collection turned out in practice are described. In section 6.2, the sample characteristics are described and the representativeness of the sample is evaluated. In section 6.3, the data basis in terms of missing values and biases is evaluated and the data preparation for the analysis explained. Chapter 7 provides the results of the data analysis. Section 7.1 presents the statistical results of the direct effects model. Next, the statistical results of the two moderating effects models in section 7.2. Thereafter in section 7.3, financial performance measures are included in the research model in order to provide an additional validation of the empirical data. Then, the research model is tested for control variable effects in section 7.4. Finaly, the evaluation of hypotheses given the empirical results is follows in section 7.5. The last chapter 8 turns to the discussion of results and implications for research and practice. Section 8.1 discusses the empirical results with respect to the research questions and additional insights that can be drawn from this study. Section 8.2 provides the theoretical implications and discusses limitations and avenues of further research. Next, section 8.3 develops implications and recommendations for managerial practices. Finally, section 8.4 provides an overall summary of this study and its main findings.

Outline of this document

17

1 Introduction 1.1 Problem statement 1.2 Research objective and questions 1.3 Research approach 1.4 Outline of document

2 Conceptual basis 2.1 Literature review and basic terminology 2.2 Definition of research variables 2.3 Summary

3 Selection of a theoretical framework 3.1 Alternative theoretical perspectives and theories 3.2 Evaluation and selection of theoretical basis 3.3 The Upper Echelon View 3.4 Summary

5 Research design 5.1 Unit of analysis 5.2 Operationalization of variables 5.3 Data analysis methodology

4 Theory and hypotheses development 4.1 Theoretical premises 4.2 Hypotheses development 4.3 Summary

6 Data collection and evaluation 6.1 Data collection 6.2 Sample characteristics 6.3 Evaluation and preparation of data basis

7 Results 7.1 Results of direct effects model 7.2 Results of moderating effects models 7.3 Triangulation with financial performance variables 7.4.Test for control variable effects 7.5 Evaluation of hypotheses

8 Discussion and implications

8.1 Discussion of results 8.2 Implications for research 8.3 Implications for practice

8.4 Summary

Figure 1: Outline of this document (Source: own compilation)

18

Conceptual basis

2 Conceptual basis The main aim of this chapter is to establish the conceptual basis for this study. For that purpose section 2.1 reviews relevant literature and provides basic definitions of SDM and information behavior as core phenomena of interest. Then, section 2.2 defines the specific research variables for this study. Finally, section 2.3 summarizes this chapter. 2.1 Literature review and basic terminology This literature review focuses on strategic decision making (sub-section 2.1.1) and individual information behavior (sub-section 2.1.2) as the core phenomena of interest of this study. Furthermore, the basic terminology for use throughout this study will be established. 2.1.1 Strategic decision making A decision refers to choice among alternative actions and a specific commitment to action.98 Hence, decision making is “simply put, the act of choosing among alternatives”.99 A formal decision model can be described as decision problem space which decision makers are to solve for making a choice.100 This problem space comprises x Perception of a decision problem x Set of evaluative criteria x Set of decision relevant factors and cause-effect relationships x Alternative actions and outcomes Empirical research describes decision making from a behavioral and sociopsychological view.101 This means, the formal model of decision making is expanded by model immanent, behavioral and cognitive constraints.102 Model immanent constraints comprise goal ambiguity, complexity, dynamism and interdependence of decision related factors and decision making rules. These constraints are typical of managerial decision problems. Behavioral and cognitive constraints take furthermore account for decision making as individual or collective behavior. This view adds 98

Cf. Mintzberg, H. et al. (1976), p. 246. O'Reilly Iii, C./Chatman, J. A./Anderson, J. C. (1987), p. 105. Cf. also Saunders, C./Jones, J. W. (1990), p. 29; Ketchen Jr, D. J. et al. (1996), p. 237 100 Cf. O'Reilly, I. I. I. C. A. (1983), p. 105; O'Reilly Iii, C. et al. (1987), p. 105; Rehkugler, H./Schindel, V. (1989), p. 198; Kahle, E. (1997), p. 39. 101 Cf. Fahey, L. (1981), p. 44. 102 Cf. Kahle, E. (1997), pp. 83-87. 99

W. Gänswein, Effectiveness of Information Use for Strategic Decision Making, DOI 10.1007/978-3-8349-6849-4_2, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

Literature review and basic terminology

19

individual cognitive limitations and processes, individual motivations, as well as social interactions to a description of decision making.103 Since management decisions are an empirical phenomenon it is not possible to define them independently from the organizational settings and circumstances under which they are made.104 Therefore, empirically grounded definitions of strategic decision and SDM are established in the following sub-sections. 2.1.1.1 Characteristics of strategic decisions Empirical studies show that the understanding of what is a strategic decision varies widely between organizations.105 What one organization considers as a strategic decision another organization may not consider as such. Therefore, strategic decisions are defined according to some constituting characteristics as follows. Strategic decisions are important for organizations’ health and survival,106 because of substantial commitment of resources and precedents set for subsequent organizational action.107 They can be described as ill-structured and unfamiliar. Ill-structured refers to problems with ambiguous or conflicting decision criteria, incompletely specified tasks and information gaps for solving the problem.108 Strategic decisions are highly ambiguous and „nothing is given or easily determined“.109 Disagreements by decision participants about goals and cause-effect relationships add to the ambiguity of strategic decision situations.110 Furthermore, strategic decisions are marked by lack of knowledge about available alternatives, outcomes, cause-effect relationships and probabilities associated with them.111 This is closely related to the high complexity of strategic decisions, because the number of factors and their interdependence is usually large. Furthermore, strategic decisions are highly instable and marked by openendedness.112 As a result the solution of strategic decision problems is not programmable, i.e. it cannot be defined in terms of an endless number of

103

Cf. von Nitzsch, R. (2007), columns 375-378. Cf. Beach, L. R./Mitchell, T. R. (1978), p. 444. Cf. Mintzberg, H. et al. (1976), p. 250; Bhimani, A./Langfield-Smith, K. (2007), p. 24. 106 Cf. Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 17; Dean, James W./Sharfman, Mark P. (1993), p. 591. 107 Cf. Mintzberg, H. et al. (1976), p. 246; Wally, S./Baum, J. R. (1994), p. 933. 108 Cf. Ungson, G. R. et al. (1981), p. 120; Kahle, E. (1997), p. 19-21. 109 Mintzberg, H. et al. (1976), p. 251. 110 Cf. Dean, James W./Sharfman, Mark P. (1993), p. 592. 111 Cf. Dean, James W./Sharfman, Mark P. (1993), p. 592. 112 Cf. Mintzberg, H. et al. (1976), p. 250. 104 105

20

Conceptual basis

computational steps.113 Simply put strategic decisions are complex and made of far more phenomena than a decision maker can comprehend.114 Hence, decision makers begin “with little understanding of the decision situation or the route to its solution.”115 2.1.1.2 Process view of strategic decision making As already stated, SDM refers to “the processes involved in choosing a firm’s strategy”.116 The empirical examination of decision processes received considerable research interest. Such empirical approaches have their roots in descriptive models of individual problem solving behavior, which can be adapted to organizational decision process models.117 These models are also called phase models of problem solving or decision making or simply phase theorems.118 Although details of these models are not a central concern of this study, they serve as basis for establishing a definition of SDM as the core phenomenon of interest as follows. An early phase model of individual problem solving was proposed by Dewey in 1933.119 This model comprised of five phases of problem solving assuming that a problem was already given.120 Other phase models expand this narrow view by proposing that individuals have to become aware of a problematic situation before they can turn to its solution. These activities are called problem recognition, identification or perception of stimuli pointing to problematic situations.121 Similarly, other models integrate later stages of problem solving such as the implementation of choice through action or the monitoring of decision implementation in order to evaluate actions taken or to make adjustments.122 Similarly, a number of organizational decision making phase models are proposed in the literature. Here, Simon’s trichotomy of intelligencedesign-choice can be regarded as the most well-known organizational decision phase

113

Cf. Wally, S./Baum, J. R. (1994), p. 933; Simon, H. (1977); Kahle, E. (1997), p. 21. Cf. Hambrick, D. C./Mason, P. A. (1984), p. 195. 115 Mintzberg, H. et al. (1976), p. 250. 116 Fahey, L. (1981), p. 43. 117 Cf. Mintzberg, H. et al. (1976), pp. 246-247; Gerwin, D./Tuggle, F. D. (1978), p. 764. 118 Decision making and problem-solving are similar concepts, whereas the term problem refers to a situation where only one solution is possible. As such the aim of problem-solving is to arrive at the appropriate solution. Decision making per definition means choice among alternative solutions. As such decision making can be Cf.n as a special case of problem-solving, because arriving at one preferred alternative is the ultimate aim of decision making. 119 Cf. Mintzberg, H. et al. (1976), p. 251; Lipshitz, R./Bar-Ilan, O. (1996), p. 48. 120 Cf. Mintzberg, H. et al. (1976), pp. 251-252. 121 Cf. Lipshitz, R./Bar-Ilan, O. (1996), p. 50 and the models of Thomae, H. (1960); Bransford, J. D./Stein, B. S. (1984); Rehkugler, H./Schindel, V. (1989). 122 Cf. Pólya, G. (1957); Bransford, J. D./Stein, B. S. (1984); Rehkugler, H./Schindel, V. (1989). 114

Literature review and basic terminology

21

model.123 It suggests that all decision related information gathering in the organization is carried out in the intelligence phase. Only thereafter, the organization turns to the processing of this information for designing a decision problem and making a choice. In addition to that, other organizational phase models amend problem recognition and / or implementation phases.124 A common characteristic of all phase models is a problem-oriented description of behavior or cognitive processes, because all activities within a decision model are described and evaluated with respect to their role for dealing with problems. However, these models differ in the following three main aspects: 1) Scope of decision process: The proposed phase models differ in terms of scope of the activities included from when decision making starts to when it ends.125 2) Process dimensions: They take different perspectives on how decisions are made or, in other words, what process dimensions of decision making are to be considered. 3) Level of analysis: Finally, the phase models are concerned with different levels of analysis, namely individual vs. organizational level. These three aspects are to be discussed in more detail for establishing a definition of SDM for this study. First of all, the aforementioned phase models of decision making differ in their scope of decision activities included. Basically, there are different points of view when decision making begins and ends. Therefore, a definition of the scope of the decision process for this study needs to be established. Two considerations are made for accomplishing this: 1) Do the proposed phases describe decision making behavior? 2) Do these phases have relevance for the effectiveness of decision making? Basically, much research has been devoted to these two aspects in order to establish the so-called descriptive (question 1) and prescriptive (question 2) validity of the phase

123

Cf. Mintzberg, H. et al. (1976), p. 252. Cf. the models of Brim, O. G./Glass, D. C./Lavin, D. E. et al. (1962); Irle, M. (1971); Kast, F. E./Rosenzweig, J. E. (1979); Huber, G. P. (1980). 125 Cf. Table 1 on p. Fehler! Textmarke nicht definiert.. 124

22

Conceptual basis

theorem.126 Descriptive validity refers to whether actual decision processes follow the proposed phases in a strictly sequential manner or not. Most phase models assume a strict sequence of these phases, i.e. all activities of one phase are carried out completely before another phase follows. However, such a strict description of the phase theorem is not supported empirically. Empirical findings show that decision making processes are marked by parallel activities and that decision makers cycle through the various phases of decision making.127 Nonetheless, other findings show decision making is not completely random but a sequential view is valid in a probabilistic sense.128 This means that decision making activities are most likely to occur within an expected phase of decision making, and consecutive pairs of decision phases most likely follow the proposed sequence.129 Prescriptive validity refers to whether decision processes are superior effective, if they follow the proposed sequences. The prescriptive validity of the phase theorem is confirmed empirically from neither a strictly sequential nor a probabilistic point of view.130 However, empirical findings show two prescriptive facets of successful decision making, namely the importance of problem diagnosis and the compatibility of the problem solution activities based on the diagnosed problem.131 So although some authors propose problem diagnosis and solution are conceptually different,132 these results suggest they should not be treated separately, because ideally the problem solution takes account for problem diagnosis in order to produce superior outcomes. Two main conclusions can be drawn from these empirical findings. Firstly, decision processes are sequential in the sense that one phase is more likely to precede a subsequent phase than any other phase while still much of the different activities happen in parallel. Secondly, although different activities such as problem definition, generation and evaluation of alternatives are identifiable, a disctinction into sub-phases does not appear to be useful because they do not posses any prescriptive validity. This means it does not matter in terms of decision effectiveness whether the various

126

Cf. Lipshitz, R./Bar-Ilan, O. (1996), p. 48. Cf. Witte, E. (1968), p. 632; Mintzberg, H. et al. (1976), p. 265. Cf. Lipshitz, R./Bar-Ilan, O. (1996), p. 57. 129 Cf. Lipshitz, R./Bar-Ilan, O. (1996), pp. 54-55. 130 Cf. Lipshitz, R./Bar-Ilan, O. (1996), p. 56. 131 Cf. Lipshitz, R./Bar-Ilan, O. (1996), p. 57; Lipshitz, R./Levy, D. L./Orchen, K. (2006), p. 424. 132 Cf. Lyles, M. A. (1981), p. 62. 127 128

Literature review and basic terminology

23

decision making activities occur in any proposed sequence or not.133 The findings rather show that once a problem is recognized, diagnosis and solution of this problem are complementary activities in order to effectively make decisions. Consequently, these phases do not have to be separated when one considers the effectiveness of overall decision processes. Overall, the following three stages of decision making can conceptually be distinguished:134 1) Recognition of a problematic situation 2) Diagnosis and solution of a decision problem including final choice 3) Implementation of a decision Therefore, this study follows earlier definitions and refers to SDM as those processes involved from when a strategic decision problem is recognized to a specific commitment to action.135 This scope of a decision process entails the activities of problem diagnosis, search and evaluation of alternatives as well as making a final choice. Table 1 provides an overview of the aforementioned phase models of problem solving and decision making. Their respective phases are assigned to the above mentioned stages of problem recognition, problem diagnosis and solution as well as implementation.

133

Cf. Lipshitz, R./Bar-Ilan, O. (1996), p. 57; Lipshitz, R. et al. (2006), p. 424. At least this applies to the stages 1) problem recognition and 2) problem definition and solution. Decision implementation has usually not been empirically examined as being part of the phase theorem and only recently research interest shifts to strategy implementation aspects at all. Cf. Hutzschenreuter, T./Kleindienst, I. (2006), p. 701. 134 Cf. Hodkginson / Healey (2008), p. 400. 135 Cf. Mintzberg, H. et al. (1976), p. 246.

Context Individual problem solving

Mathematical problem solving

Psychological model of individual decision making

Organizational decision making

Individual and social model of decision making

Organizational decision making

Strategic decision making from operations research view

Organizational decision making

Managerial decision making

Individual problem solving

Individual decision making

Author(s) Dewey, J. (1933)

Pólya, G. (1957)

Thomae, H. (1960)

Simon, H. A. (1960)

Brim, O. G. et al. (1962)

Irle, M. (1971)

Sagasti / Mitroff (1973), Lyles (1981)

Kast, F. E./Rosenzweig, J. E. (1979)

Huber, G. P. (1980)

Bransford, J. D./Stein, B. S. (1984)

Rehkugler, H./Schindel, V. (1989)

Perception of stimuli

Identify problem

Sense the problem

Problem sensing

Problem

Identify the problem

Stimulation

Problem recognition

Problem diagnosis and solution Suggestion Intellectualization Development of hypotheses Reasoning, mental elaboration Testing of hypotheses Understand the problem Devise a plan for solution Carry out plan for solution Look back (evaluate) Disorientation Orientation Alienation Choice Intelligence Design Choice Diagnose causes Generate solutions Evaluate solutions Choose a solution Search Alternatives Comparison Choice Conceptualization Development of scientific model Use of scientific model Choice Define the problem Generate solutions Evaluate solutions Choose a solution Intelligence Design Choice Define problem Evaluate solution Act Definition of situation Cognitive search and evaluation Choice Search after choice

Implementation Monitoring

Plan implementation Implement

Implementation Feedback

Initiation Implementation Control

Implement and revise the selected solution

Implementation

24 Conceptual basis

Table 1: Selected phase models of problem solving and decision making (Sources: Own compilation based on Lipshitz, R./Bar-Ilan, O. (1996), p. 49; Kahle, E. (1997), p. 43)

Literature review and basic terminology

25

Second of all, strategic decision making activities can be described by two key process dimensions, which have been established on theoretical and empirical grounds:136 x The first key process dimension is information use.137 The reason why information use is a key process dimension is attributable to two circumstances. On the one hand, SDM requires knowledge about decision relevant factors and assumptions about future events, as well as about alternative actions and consequences attached to these alternatives.138 On the other hand, decision making is marked by a limited knowledge base. Information use is basically the means for increasing this knowledge base. x The second key process dimension is political behavior which refers to those observable actions by which people seek to influence SDM.139 The reason for political behavior in SDM roots in a view of organizations as political systems, “i.e. collectives of people with at least partially conflicting goals.”140 In order to pursue their interests individuals engage in a range of political tactics. Finally, the various decision models reside on two separate levels of analysis namely either the individual or the organizational level.141 From an organizational level perspective SDM processes are “patterns of behavior that develop in organizations” which are relatively independent from whether the people within organizations change or not.142 From an individual level perspective decision making are those behaviors and cognitive processes directly associated with information processing.143 However, a single level focus is claimed to be an incomplete representation of SDM and rather a multilevel perspective should be taken. A multilevel perspective may enhance the understanding of both organizational level data collection and processing as well as the role of individual idiosyncracies in the overall SDM process.144 This assertion has led to the research objective of this study to comprehensively examine individual information use within the settings of an organization. Therefore, a multilevel perspective of SDM is taken for this study. 136

Cf. for literature reviews Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 35 and Schwenk, C. R. (1995), p. 475 and for empirical support of this two-dimensional view Elbanna, S./Child, J. (2007), p. 445. 137 Cf. Forbes, D. P. (2007), p. 362; Ketchen Jr, D. J. et al. (1996), p. 237 138 Cf. March, J. G./Simon, H. A. (1958); Hambrick, D. C./Mason, P. A. (1984), p. 195. 139 Cf. Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 26. 140 Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 23. 141 Cf. Schwenk, C. R. (1995), pp. 478-481. 142 Fredrickson, J. W./Mitchell, T. R. (1984), p. 400, emphasis added by original author. 143 Cf. Corner, P. D. et al. (1994), p. 294. 144 Cf. Corner, P. D. et al. (1994), p. 294.

26

Conceptual basis

2.1.2 Individual information behavior In the preceding section, a multilevel view of SDM has been favored over a purely individual or organizational level perspective. At the same time it is a fact that information use for SDM occurs on the individual level.145 Consequently, the information behavior of individuals within organizations is of particular interest of this study.146 At first, sub-section 2.1.2.1 discusses the basic elements of information behavior and disentangles related terms from individual and organizational level research. Based on these considerations a definition of information is established in sub-section 2.1.2.2. Next, the term information use is defined for this study in subsection 2.1.2.3. Thereafter, the discussion elaborates on two core elements of information use in more detail. These elements are information acquisition (subsection 2.1.2.4) and information processing (sub-section 2.1.2.5). 2.1.2.1 Basic elements of information behavior Information behavior can be defined as “those activities a person may engage in when identifying his or her own needs for information, searching for such information in any way, and using or transferring that information.“147 A similar definition takes this a step further by including any activities directed towards information that are actually performed and omitted.148 Managerial cognition is a term with a similar meaning as information behavior. Although the term suggests a focus on cognitive activities, it is generally defined with reference to a mix of behavioral and cognitive activities as the two following definitions exemplify. “In the managerial cognition perspective, managers […] spend their time absorbing, processing and disseminating information”149 Another author defines managerial cognition as “the way a person acquires, stores, and uses

145

Cf. Simon, H. A. (1976), pp. 40-41. A unifying theory of information processing in organizations does not exist, whereas individual information behavior is one of the following six areas of investigation of organizational information processing: 1) Information processing requirements of organizations, 2) acquisition of information by organizations, 3) information behaviors of organizational participants, 4) nature of information in organizations, 5) use of information in organizations, and 6) role of information technology in organizational information processing. Cf. a more detailed description of these six areas Choo, C. W. (1996), pp. 22-40. 147 Wilson, T. D. (1999), p. 249. 148 Cf. Witte, E. (1984), column 1916. 149 Walsh, J. P. (1995), p. 280. 146

Literature review and basic terminology

27

knowledge.”150 Therefore, information behavior and managerial cognition are used interchangeably throughout this document. Overall two conclusions can be drawn from these definitions. Firstly, information behavior is an overarching term comprising behaviors such as interpersonal activities and man-machine interactions as well as cognitive activities within the individual.151 Secondly, information behavior refers to behavioral and cognitive activities which can be arranged in a process perspective according to their main functions. Given the preceding definitions, a simplified model of information behavior can be summarized as comprising the following four functional elements: 1) Information need: Individual information behavior is initiated by information needs which root in physical, cognitive or social motives and can be defined as “a cognitive representation of a future goal that is desired“.152 As such, information needs originate from problematic situations.153 For problem solving individuals require knowledge which they do not completely possess and thus they engage in information behavior. Furthermore, organizational problems translate into individual problems, because individual managers have to carry out decision making.154 As a consequence, organizational decision making translates into individual information needs and behaviors for addressing these needs. 2) Information acquisition: Information acquisition refers to the various activities by which individuals discover and acquire information.155 A number of terms for information acquisition have been used throughout the literature. The most established term for information acquisition in the strategic management literature is scanning, established by Aguilar’s study Scanning the business environment in 1967. Besides the term scanning, a number of loosely used and interrelated terms

150

Hayes, J./Allinson, C. W. (1994), p. 53. Cf. Gemünden, H. G. (1992), column 1010. 152 Kagan, J. (1972), p. 54 cited by Wilson, T. D. (1997), p. 552. 153 Cf. Wilson, T. D. (1999), pp 265-266; Spink, A./Cole, C. (2006), p. 26. 154 Cf. Simon, H. A. (1976), pp. 40-41. 155 Cf. Wilson, T. D. (1999), p. 263. 151

28

Conceptual basis such as monitoring, intelligence gathering or surveillance are used in the management literature.156 They all refer to information acquisition in some way.

3) Information processing (including internal storage): The term information processing has received a number of ambiguous definitions. On the one hand, some authors define information processing as activities of 1) information acquisition, 2) processing, 3) storage, and 4) dissemination.157 Another definition of information processing refers to 1) information scanning, 2) decision making, 3) information storage, and 4) information dissemination.158 However, for two reasons these definitions are not suitable for the present research study. Firstly, they are similar to the definition of individual information behavior established before and comprise behaviors and cognitive processes alike. This is due to their origin in organizational level research. From this perspective organizational information processing comprises all individual information behaviors taking place within the organization. Secondly, the first definition of information processing is ambiguous in itself. It defines information processing by reference to four functional stages of which information processing itself is one stage. In order to distinguish information behavior from information processing the present study adheres to a psychological, i.e. individual level perspective. From this perspective information processing refers to the “cognitive (thinking) processes” of individuals. 159 The cognitive storage of knowledge is one of these processes.160 4) Information dissemination (including external storage): Finally, another outcome of information processing is observable behavior.161 As indicated by its 156

Cf. El Sawy, O. A. (1985), p. 57. Cf. Kramer, R. (1965), pp. 82-84; Witte, E. (1984), columns 1918-1919; Gemünden, H. G. (1992), column 1011. 158 Cf. Mintzberg, H. (1980), p. 138. 159 Ungson, G. R. et al. (1981), p. 117. 160 Cf. Miller, A. (1987), p. 252. 161 Cf. Massaro, D. W./Cowan, N. (1993), p. 384. 157

Literature review and basic terminology

29

terminology, information dissemination is the most important observable outcome of information processing in organizational settings, because action in organizational settings is a collective phenomenon.162 Thus, information behavior is inevitably linked to interaction with and communication to other members of the organization. A large number of such information dissemination behaviors such as information sharing, interpretation, teaching, mediation, conflict resolution, or persuasion can be identified.163 In addition to that, information dissemination can also serve for external storage of knowledge.164 Figure 2 provides a depiction of this simplified model of human information behavior.

Information need

Triggers

Information acquisition

Information processing (including internal storage)

Information dissemination (including external storage)

Behavioral activities Cognitive activities Figure 2: A simplified model of human information behavior (Source: own compilation)

162 163 164

Cf. Corner, P. D. et al. (1994), p. 301; Weick, K. E./Sutcliffe, K. M./Obstfeld, D. (2005), p. 413. Cf. Robertson, E. (1998), p. 13. Cf. Gemünden, H. G. (1992), column 1012. The reason for distinguishing into internal and external knowledge is as follows. The organizational level definitions of information processing refer to information storage as one stage of organizational information processing. However, this happens on two levels. On the individual level, it is a cognitive activity knowledge creation or learning. On the organizational level, it is a behavioral activity of making knowledge externally available.

30

Conceptual basis

2.1.2.2 Information from an individual level perspective After establishing definitions for the basic elements of human information behavior, a definition of the term information is established in the following. The first notable scientific discussion of the term information was undertaken by Shannon and Weaver (1949).165 They used mathematical models to develop a communication theory of technical data transmission. Although this theory was not concerned with any content of information,166 it provides the basis for a number of interdisciplinary connections such as to human information processing. The reason is that human information processing can be seen as particularly comprehensive communication. It can thus be subsumed under Shannon and Weaver’s communication theory.167 Reference to their work helps to disentangle various interrelated terms such as signals, data, information and knowledge.168 Signals are physical representations of messages, such as electrical, magnetic, acoustic or optical signals. Data are signs and arrangements of signs, that base on implicitly or explicitly agreed upon meanings. However, data exists independently from a recipient and can be stored, copied, reproduced and transferred.169 In contrast to data, information results from interpretation of data in a cognitive system with reference to context and knowledge. Therefore, information is inherently linked to a cognitive system. Knowledge itself is accumulated information in a cognitive system based on facts, experience, beliefs, concepts, expectations and other information. It persists in the cognitive system and may be altered or modified by cognitive processes and the arrival of new information. The preceding considerations are useful for a discussion of selected definitions of information in the management and human information processing literature as shown in Table 2.

165

Cf. Ungson, G. R. et al. (1981), p. 117. Cf. Favre-Bulle, B. (2001), p. 12. 167 Cf. Wilson, T. D. (1999), pp. 263-264. 168 Cf. here and in the following Favre-Bulle, B. (2001), pp. 34-37. 169 Cf. Massaro, D. W./Cowan, N. (1993), p. 386. 166

Literature review and basic terminology

31

Source

Definition

Wittmann (1959)

Information is knowledge used for the purpose of preparing action.170

Mag (1977)

Information is decision-oriented knowledge.171

Szyperski (1980)

Information comprises statements which improve the knowledgebase of a subject about an object in a given situation in fulfillment of a particular task.172

Ungson et al. (1981)

Information refers to “stimuli (or cues) capable of altering an individual’s expectations and evaluation in problem solving or decision making”.173

Drucker, P.F. (1988)

”Information is data endowed with relevance and purpose“.174

Massaro / Cowan (1993)

Information ”refers to representations derived by a person from environmental stimulation or from processing that influences selections among alternative choices for belief or action“.175

Weber / Schäffer (2006)

Information is data and messages which contain management relevant knowledge and may potentially increase a recipients knowledge base.176

Table 2: Selected definitions of information in management and human information processing literature (Source: own compilation)

These definitions have a common reference to purpose, while these purposes are differently specified. Wittmann’s and Drucker’s definitions refer to purpose in an abstract manner thus leaving room for interpretation of what the purpose is.177 Other definitions specify the purpose of information as task fulfillment, problem solving, decision making or increasing ones knowledge base. The last purpose is particularly relevant, because decision making requires knowledge as stated before. Wittmann and Mag define information as a subset of knowledge. 178 An explanation for this may be their level of analysis, because both authors are concerned with organizations. In their view organizational knowledge comprises individuals’ cognitive states and knowledge stored in the organization.179 Consequently, these definitions neglect the fact that knowledge needs to be created and are basically limited to knowledge that is already present within an organization. Drucker and 170

Cf. Wittmann, W. (1959), p. 14; Wittmann, W. (1980), column 894. Cf. Mag, W. (1977), p. 4. 172 Cf. Gemünden, H. G. (1993), column 1725 referring to Szyperski’s definition. 173 Ungson, G. R. et al. (1981), p. 117. 174 Drucker, P.F. (1988), p. 46. 175 Massaro, D. W./Cowan, N. (1993), p. 384. 176 Cf. Weber, J./Schäffer, U. (2006), p. 72. 177 Cf. Kirsch (1971), p. 79. 178 Cf. Güldenberg, S. (2003), p. 163. 179 Cf. Gemünden, H. G. (1992), columns 1011-1012. 171

32

Conceptual basis

Weber and Schäffer (2006) refer to information as data and messages, which have relevance and purpose or the potential of increasing ones knowledgebase respectively. These definitions have one important limitation. As stated before data and messages are conceptually different from information, because data is independent from a cognitive system whereas information can only exist through interpretation of data with reference to context and prior knowledge. Thus, from a psychological perspective it is more consistent to refer to information as external data or messages that enter cognitive processes as reflected in the subsequent definitions. Massaro and Cowan define information as representations derived from environmental stimuli. This definition implies that stimuli are already (at least partly) processed in a cognitive system. Otherwise the formation of representations is not possible. Consequently it excludes stimuli that are received but not further processed. In contrast to that, Ungson et al. define information as “stimuli (or cues) capable of altering an individual’s expectations and evaluation in problem solving or decision making.” This broader definition of information encompasses stimuli that are processed but also stimuli that may not be further processed in a cognitive system. However, this definition is also limited because it only refers to expectations and evaluation, thereby neglecting other knowledge required for decision making. Therefore, the present study combines elements of the aforementioned psychological definitions and defines information as stimuli capable of altering ones management relevant knowledge base. This definition provides important connections to individual decision making for two reasons. Firstly, it encompasses the purpose of information as potentially increasing ones knowledge base which is required for decision making. Secondly, it defines information at the interface between individual information acquisition and processing and thus takes account for the multilevel nature of information use in organizations.180

180

Cf. Jensen, P. E. (2005), p. 54f.

Literature review and basic terminology

33

2.1.2.3 Information use from an individual level perspective So far, information use has been identified as a key process dimension of SDM but has not been defined. Therefore, a definition of information use is established in the following. From a problem oriented perspective, information behavior occurs in pursuit of some purposes and information is used for these purposes.181 This is reflected in the SDM literature which defines information use as “the amount of available data that organizations process in addressing strategic decisions”.182 However, once again this definition refers to information use on the organizational level and is thus not suitable for this study. In contrast to that, this study takes an individual level perspective which should be accounted for. From such a perspective, decision making is ultimately a thinking process within the individual. However, given the fact that decision makers have to gather information before they can process it, information use refers to individual decision makers’ acquisition and processing of information in addressing strategic decisions.183,184

181

Cf. Vandenbosch, B. (1999), p. 77. Ketchen Jr, D. J. et al. (1996), p. 237. Cf. also Dean, J. W./Sharfman, M. P. (1996), p. 373; Frishammar, J. (2003), p. 318 for similar definitions. 183 Cf. O'Reilly, I. I. I. C. A. (1983), p. 117; Gemünden, H. G. (1992), column 1012; Forbes, D. P. (2007), p. 362. 184 It should be noted that information use in organizational level research has received alternative definitions. According to them, information use recurs on the purposes which are pursued when using information. Different purposes of information use can be distinguished. On the one hand, information use follows rational purposes. Rational purposes refer to information use which directly serves for problem-solving or decision making (instrumental information use) or the acquisition of knowledge (learning or conceptual information use). On the other hand, information use follows symbolic or political purposes. This means information use, although concerned with decision making, must not only be used for instrumental and conceptual purposes. It can also be used for subsequent rationalizing, defending or legitimizing of decisions previously made, for persuasion, or for negotiating and conflict resolution. However, empirical findings show that decision makers mainly use information for making decisions in an instrumental sense and not for political purposes. Nonetheless, it is important to be aware of that individual decision makers may be faced with information that serves other purposes than decision making in a rational sense. Cf. Maltz, E./Menon, A./Wilcox, J. (2006), p. 149; Burchell, S./Clubb, C./Hopwood, A. et al. (1980), pp. 15-19; Langley, A. (1989), p. 607; Menon, A./Varadarajan, P. R. (1992), p. 61; Hirst, M. K./Baxter, J. A. (1993), p. 192; Vandenbosch, B. (1999), p. 87; Auster, E./Choo, C. W. (1994), p. 613; Mueller, G. C. et al. (2007), p. 868; Feldman, M. S./March, J. G. (1981), pp. 177-180. 182

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Conceptual basis

2.1.2.4 Facets of information acquisition Three domains of information acquisition can be identified. Firstly, the kinds of information sources used for acquiring information, secondly the mode of information acquisition, and thirdly the characteristics of information acquired.185 The following discussion elaborates on these domains of information acquisition. 2.1.2.4.1 Information sources Concerning information sources two distinct aspects can be identified. The first aspect pertains to what kinds of information sources are used for information acquisition. The second aspect pertains to characteristics for describing information sources. Different kinds of information sources can be described along three dimensions which are then used for a classification of information sources. Firstly, the location or origin of information refers to whether an information source is inside or outside the boundaries of an organization, i.e. internal and external respectively.186 Secondly, directional specifity refers to whether information is specifically communicated to a person or more formally and broadly available. Generally it is distinguished into personal and impersonal sources.187 Thirdly, medium of transmission is another dimension for classifying information sources and closely connected to how communication occurs. Here a more or less fine-grained approach can be identified. One classification distinguishes into face-to-face, telephone, electronic or documentary media.188 In a more fine-grained approach 14 media types can be identified which can be grouped into face-to-face, group, written, traditional communication technologies such as facsimile or telephone, and modern communication technologies such as video conferencing or electronic mail.189 Furthermore, information sources can be characterized according to three facets. Firstly, accessibility of sources relates to the effort or problems associated with using an information source190 such as ease of use, habit or perceived credibility.191 The accessibility of sources varies and shows no particular pattern: subscriptions to 185

Cf. El Sawy, O. A. (1985), p. 54; Wilson, T. D. (1997), pp. 561-565. Cf. El Sawy, O. A. (1985), p. 54; Daft, R. L. et al. (1988), p. 126. Cf. El Sawy, O. A. (1985), p. 54; Daft, R. L. et al. (1988), p. 126. 188 Cf. Saunders, C./Jones, J. W. (1990), p. 35. 189 Cf. Zmud, R. W./Lind, M. R./Young, F. W. (1990), p. 444; Dennis, A. R./Fuller, R. M./Valacich, J. S. (2008), p. 589. 190 Cf. Culnan, M. J. (1983), p. 195. 191 Cf. O'Reilly Iii, C. A. (1982), pp. 758 and 767. 186 187

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newspapers and magazines have the highest accessibility, followed by internal personal, internal impersonal and external sources.192 Secondly, quality of information sources relates to the information they provide. Quality characteristics are relevance, specifity, accuracy, reliability and timeliness.193 Information source quality is not objectively given but a perceived characteristic.194 Empirical findings suggest that CEOs rank personal information from subordinate managers, staff, customers, the management board and business associates among the highest quality in terms of relevance and reliability.195 Impersonal internal reports, studies and memos are also high in quality, whereas external impersonal sources such as newspapers, periodicals, government publications or electronic information services are ranked lower in quality than aforementioned sources.196 Thirdly, information richness refers to “the ability of information to change understanding within a given time interval.”197 Information sources can be ranked according to their information richness, where personal sources generally provide information with higher richness than impersonal sources.198 2.1.2.4.2 Modes of information acquisition Four modes of information acquisition can generally be distinguished.199 Firstly, undirected viewing refers to the unintentional collection of information. Secondly, conditioned viewing means search of information from a particular field of interest. Thirdly, informal search is a relatively limited, unstructured attempt to gather information for a particular purpose. Fourthly, formal search is intentional, planned search for specific information. Basically two modes of information acquisition are of particular importance to management research, which are scanning defined as reactive modes of information acquisition and focused search defined as intentional modes of information acquisition.200 Empirical findings in the strategic management literature suggest that these two modes of information acquisition can be mapped onto the decision making

192

Cf. Culnan, M. J. (1983), p. 198. Cf. Zmud, R. W. (1978), p. 191; O'Reilly Iii, C. A. (1982), p. 757. 194 Cf. O'Reilly Iii, C. A. (1982), p. 758. 195 Cf. Auster, E./Choo, C. W. (1993), p. 198. 196 Cf. Auster, E./Choo, C. W. (1993), p. 200. 197 Daft, R. L./Lengel, R. H. (1986), p. 560. 198 Cf. Daft, R. L./Lengel, R. H./Trevino, L. K. (1987), p. 358. 199 Cf. Choudhury, V./Sampler, J. L. (1997), p. 27; Wilson, T. D. (1997), p. 563. 200 Cf. El Sawy, O. A. (1985), p. 53; Gemünden, H. G. (1992), column 1012; Vandenbosch, B./Higgins, C. (1996), p. 202. 193

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Conceptual basis

phases identified in the preceding section. While scanning prevails for problem recognition, focused search prevails for defining and solving a decision problem.201 2.1.2.4.3 Characteristics describing information Three basic areas for describing information characteristics can be identified. These are amount or quantity of information, value of information and content of information. Firstly, the amount or quantity of information is a relevant characteristic because decision making bases on knowledge while at the same time individuals possess only limited knowledge.202 This limited knowledge base is one reason why individuals perceive a need for acquiring information. This information need determines the amount of information a subject perceives as necessary for solving a problem in such a way, that the solution meets ones aspiration levels.203 Secondly, information can be characterized according to its value. The value of information is relevant, because knowledge is a scarce resource and any information behavior requires effort, i.e. information use incurs cost on side of a decision maker.204 The concept of information value itself is derived empirically and can be defined by its contribution to choice and to improving ones’ understanding of real-world relationships.205 Furthermore, several specific information characteristics determining its value can be derived empirically. These characteristics comprise quality of information, relevancy, quantity of format and quality of meaning.206 Thirdly, information can further be characterized by its content.207 There are basically two ways of defining information content which are closely related to the aforementioned acquisition modes of scanning and focused search respectively. On the one hand, strategic scanning literature differentiates the content of information by environmental sectors. This orientation has its roots in the contingency orientation of strategic management research and the conceptualization of the environment as 201

Cf. El Sawy, O. A. (1985), p. 58; Frishammar, J. (2003), p. 321. Cf. Gemünden, H. G. (1993), column 1725. Cf. Szyperski, N. (1980), column 905; Gemünden, H. G. (1993), column 1726. 204 Cf. De Alwis, G. et al. (2006), p. 366. 205 Cf. Mock, T. J. (1971), p. 765. 206 Cf. Zmud, R. W. (1978), p. 191. 207 Cf. O'Reilly, I. I. I. C. A. (1983), p. 112. 202 203

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consisting of distinct sectors.208 Early strategic scanning studies define the environment by sectors external to the organization such as market, technology, investment activities, government, and other sectors.209 Later, the external environmental sectors were aligned with the environmental uncertainty literature and distinguish into competition, customers, technology, regulatory, economic and sociocultural sectors.210 Only recently, the strategic scanning literature has expanded its focus from external to internal sectors related to firm capabilities and resources. 211 In the following this approach will be referred to as scope of scanning. On the other hand, information systems and strategic decision making research differentiates information content based on the requirements imposed by the management tasks to be fulfilled, i.e. the diagnosis and solution of a decision problem.212 According to this approach, strategic decision making is concerned with external alignment and future-oriented long-term activities of an organization.213 These task characteristics can be transformed into three distinct continua of information characteristics required for fulfilling the task.214 These characteristics are, firstly, focus which refers to information about the internal or external environment. Secondly, quantification which refers to information expressed in monetary or non-monetary measures. Thirdly, time horizon which refers to information concerned with a historic or future orientation. In the following this approach is referred to as scope of information. 2.1.2.5 Facets of information processing Information processing in this study refers to cognitive processes of individuals. Information processing models serve to describe what these processes constitute and are most often portrayed as a sequence of steps of information processing. 215 Such information processing models have their roots in cognitive psychology, which logically deducts theoretical process models of information processing and subsequently tests these in experiments.216 This model building approach bases on five 208

Cf. Kefalas, A./Schoderbeck, P. P. (1973), pp. 65-66. Cf. Kefalas, A./Schoderbeck, P. P. (1973), pp. 65-66; Hambrick, D. C. (1981b), p. 301. 210 Cf. Daft, R. L. et al. (1988), p. 129; Auster, E./Choo, C. W. (1993), p. 196; Elenkov, D. S. (1997), p. 287; May, R. C./Stewart Jr, W. H./Sweo, R. (2000), p. 405. 211 Cf. Garg, V. K. et al. (2003), p. 726. 212 Cf. Gorry, G. A./Scott Morton, M. S. (1971), pp. 57-61; Larcker, D. F. (1981), pp. 520-521. 213 Cf. Gorry, G. A./Scott Morton, M. S. (1971), p. 58. 214 Cf. Larcker, D. F. (1981), p. 520. 215 Cf. Massaro, D. W./Cowan, N. (1993), p. 384. 216 Cf. Massaro, D. W./Cowan, N. (1993), p. 386. 209

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Conceptual basis

common principles. Firstly, the environment and mental processes can be described in terms of information. Secondly, information processing can be broken down into stages and sub-stages. Thirdly, information progresses forward through these stages of processing. Fourthly, each processing stage requires some time. Fifthly, information processing occurs in a physical system, where information is embedded in states of the system - called representations - and operations are performed to transform these representations - called processes.217 As stated, different stages or function of human information processing can be distinguished. These functions can be portrayed in a basic human information processing model as follows. Information processing is initiated by arriving stimuli and results in responses. In between stimulus and response are a number of cognitive processes which are generally conceptualized according to their functions namely perception, thought and memory,218 where problem solving and decision making are specific activities of thought. Memory can furthermore be distinguished into storage and retrieval of knowledge.219 See Figure 3 for such a human information processing model.

Stimuli

Perception

Thought (e.g. problem solving, decision making)

Response

Memory (storage and retrieval of knowledge)

Figure 3: A human information processing model (Source: Miller, A. (1987), p. 252) 217

Cf. Massaro, D. W./Cowan, N. (1993), pp. 384-385. The article discusses a metatheoretical approach to information processing psychology, the requirements for inquiry and applications within the field of cognitive psychology. 218 Cf. Miller, A. (1987), p. 252; Massaro, D. W./Cowan, N. (1993), p. 384. 219 Cf. Miller, A. (1987), p. 252; Hodgkinson, G. P. (2003), p. 4; Hodgkinson, G. P./Healey, M. P. (2008), p. 389.

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It has already been stated that information processing occurs within a physical system. However, there is much debate around what constitutes the human cognitive system and how cognitive processes are governed. Despite a comprehensive theoretical debate the most recent view is that human information processing can be seen from a dualprocessing view.220 This dual-processing view is discussed in more detail in the following. Thereafter, a discussion of the three cognitive stages perception, thought and memory follows. 2.1.2.5.1 Dual information processing Research on two modes of information processing can be traced back as far as to Pavlov’s behavioral theory of conditioned and unconditioned reflexes.221 As a result of intense cognitive psychology research, a large number of terms exist that either refer to one or the other mode of information processing. 222 The following list of terms exemplifies the variety of conceptions that describe these two modes of information processing: x One mode is described as thinking, conceptual, logical, analytical, rational, deliberative, effortful, intentional, systematic, explicit, extensional, verbal, logos, second-signal system, higher cognition, controlled, rule based, conscious, reflective, higher order, slow, low capacity, inhibitory, abstract, sequential, egalitarian. x The other mode is described as intuitive, natural, automatic, heuristic, schematic, prototypical, narrative, implicit, imagistic-nonverbal, experiential, mythos, first-signal system, input modules, tacit, associative, holistic, adaptive unconscious, reflexive, stimulus bound, impulsive, preconscious, rapid, perceptual, contextualized, pragmatic, parallel, stereotypical, high capacity. One distinguishing characteristic is the consciousness of cognitive processes. Whereas, the question “what is consciousness?” is of philosophical concern, a functional perspective provides insight on how cognitive processes happen and what provides the

220

Cf. Taggart, W./Robey, D. (1981), p. 187; Hodgkinson, G. P./Clarke, I. (2007), p. 244; Evans, J. S. B. T. (2008), p. 271 221 Cf. Epstein, S./Pacini, R./Denes-Raj, V. et al. (1996), p. 390. 222 Cf. for a summary of different conceptions Epstein, S. et al. (1996), p. 390 and Evans, J. S. B. T. (2008), p. 257.

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Conceptual basis

basis for these processes.223 Conscious thought is intentional, slow, sequential and analytical in nature. It uses human working memory and is thus limited in capacity. Unconscious thought is automatic, fast, parallel and not necessarily analytical. Some authors argue that conscious processing can become unconscious over time and by experience. Furthermore, conscious thought is marked by abstraction, decontextualization and rule based reasoning.224 Only this provides the basis for deduction, hypothesizing and the building of mental models. In contrast to that, unconscious processes are concrete, contextualized and domain-specific. Finally, conscious processing is linked to general intelligence through working memory, whereas unconscious processing is not. As a result, some authors argue that only conscious processing is linked to ability and experience, while other authors argue that unconscious processing develops in accordance with conscious processing. Despite cognitive theories acknowledge two different modes of information processing there has been a large debate whether cognitive processes are governed by one unique or two independent cognitive systems in human brain.225 The former approaches are called unitary and the latter approaches are called dual-processing theories.226 Again a number of theories exist for explaining unitary or dual-processing of human cognition. Epstein et al. (1996) identify twelve theories of unitary and dual-processing cognition.227 Later, Evans (2008) identifies twelve dual-processing theories alone.228 While this differentiation is more relevant to human information processing than management research, it is important to note, that all theories of cognitive processes acknowledge two modes of processing.229 The overall conclusion is that two fundamentally different information processing modes can be identified. Firstly, some cognitive processes are described as fast, automatic and unconscious. Secondly, other cognitive processes are described as slow, deliberate and conscious.230 Where relevant the subsequent discussion of cognitive functions takes account for the dual processing nature of human cognition.

223

Cf. here and in the following Evans, J. S. B. T. (2008), p. 258. Cf. here and in the following Evans, J. S. B. T. (2008), pp. 258-259. 225 Cf. Hodgkinson, G. P./Healey, M. P. (2008), p 402. 226 Cf. Dane, E./Pratt, M. G. (2007), pp. 35-36; Evans, J. S. B. T. (2008), p. 256. 227 Cf. Epstein, S. et al. (1996), p. 390. 228 Cf. Evans, J. S. B. T. (2008), pp. 257-258. 229 Cf. Evans, J. S. B. T. (2008), p. 263. 230 Cf. Taggart, W./Robey, D. (1981), p. 187; Hodgkinson, G. P./Clarke, I. (2007), p. 244; Evans, J. S. B. T. (2008), p. 271. 224

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2.1.2.5.2 Cognitive functions: Perception, thought, knowledge learning and retrieval As already stated three functional stages of human information processing can be distinguished. These functions are 1) perception, 2) thought comprising problem solving and decision making, and 3) knowledge learning and retrieval. Cognitive research identifies a number of cognitive activities within each of these functions. First of all, perception starts with the registry of stimuli over ones individual senses, followed by a mental preparation of information for subsequent cognitive processes.231 Perception basically comprises attention to and interpretation of information, whereas a number of synonymous and associated terms can be identified in the literature.232 Attention means the concentration of mental activity on some incoming stimuli.233 Attention bases on two governing mechanisms.234 Conscious or selective attention is under direct control of cognition and provides for attention focus on specific information,235 whereas automatic attention is a pre-conscious mechanism. Selective attention is limited to approximately 7 items of information at one point in time, and is therefore also called field of vision or selective perception.236 As a result of limited attention, any information received is filtered and only a portion of it enters the subsequent cognitive process.237 Interpretation means the comparison of stimuli with knowledge held in memory for arriving at meaning.238 Other terms associated with this process stage are pattern recognition, encoding or understanding.239 Interpretation is governed by two mechanisms. Firstly, featured analysis refers to an analytical approach to pattern recognition, where elementary features and relationships of the incoming stimuli are recognized. Secondly, prototype matching involves interpretation based on comparison with existing knowledge in the memory held.240 In brief, interpretation is the result of either conscious logical reasoning or of unconscious comparisons based on prior knowledge held in memory. 231

Cf. Miller, A. (1987), p. 253. Cf. Starbuck, W. H./Milliken, F. J. (1988), p. 45. 233 Cf. Miller, A. (1987), p. 254. 234 Cf. Miller, A. (1987), p. 255; Corner, P. D. et al. (1994), p. 296. 235 Cf. Corner, P. D. et al. (1994), p. 296. 236 Cf. Hambrick, D. C./Mason, P. A. (1984), p. 195. 237 Cf. Starbuck, W. H./Milliken, F. J. (1988), pp. 40-41;Corner, P. D. et al. (1994), p. 296. 238 Cf. Miller, A. (1987), p. 254. 239 Cf. Daft, R. L./Weick, K. E. (1984), p. 286; Corner, P. D. et al. (1994), p. 297. 240 Cf. Miller, A. (1987), p. 254. 232

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Conceptual basis

Second of all, thought or reasoning means transformation of internal knowledge and external information in pursuit of some goals. Furthermore, reasoning involves going beyond information which is given by making inferences or drawing implications.241 Again two modes of reasoning can be distinguished depending on whether it is based on explicit decision rules or on implicit tacit knowledge.242 The former is called logical, analytical or analogical reasoning. The latter is called judgment or intuition. Problem solving or decision making are specific processes of thought. These processes occur within task environments which are mentally represented as problem spaces.243 Problem solving refers to cognitive processes of selecting, combining and altering knowledge and information in pursuit of solving a problem or making a decision.244 Besides the cognitive processes of reasoning, information processing has two more purposes within a given problem space. Firstly, it provides information from the internal knowledge base, i.e. the memory of the decision maker.245 Secondly, information processing provides external knowledge because of the limited knowledge base of a decision maker and thereof resulting gaps in the problem space definition. As a result, “[d]ecisions, wherever taken, are a function of information received.”246 As such, information processing for decision making comprises information processing related to the original problem and to the problem of knowledge and information retrieval itself.247 The information retrieval from the internal knowledge base is discussed in the subsequent section, whereas external information retrieval relates back to information acquisition behavior and the stage of information perception. Third of all, before understanding learning and knowledge retrieval it is necessary to understand what knowledge actually is. Knowledge is based on two components. The first component comprises the mental representations of information. The second component comprises the organization of these representations in mind. Concerning the former, there is some debate about how representations are stored in human memory.248 One view supports a coding as verbal or visual concepts, whereas another view supports a multifaceted coding of information. Concerning the latter aspect, 241

Cf. Miller, A. (1987), p. 260. Cf. Ungson, G. R. et al. (1981), p. 117; Miller, A. (1987), pp. 261-262. Cf. Bahn, D. (1997), p. 100. 244 Cf. Ungson, G. R. et al. (1981), p. 117. 245 Cf. Bahn, D. (1997), p. 100. 246 Arrow, K. J. (1973), p.48. 247 Cf. Miller, A. (1987), p. 260. 248 Cf. Miller, A. (1987), p. 256. 242 243

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managerial cognition literature follows the semantic view of information organization. This view proposes that knowledge is stored in a network of nodes and ties. Nodes represent the coded knowledge, whereas ties provide for relationships between the various nodes.249 Furthermore, this network is organized in hierarchies of superior and subordinate nodes. Finally, this network can be described according to its degree of differentiation and integration. Differentiation refers to the extent to which one field of vision is broken down into a number of parts, whereas integration refers to the extent of relationships between these different parts.250 The managerial cognition literature refers to such internal knowledge as knowledge structures,251 cognitive content or structures,252 cognitive maps, cognitive schemata or mental models.253 Two important processes related to internal knowledge, namely storage and retrieval of knowledge, deserve further explanation. Firstly, processes of information storage can be considered as individual learning. A first important step for learning is classification, which refers to how individuals categorize concepts and their characteristics. Then, individuals relate these concepts to each other by hypothesis building and testing.254 From a cognitive learning perspective information can enter knowledge structures of an individual in two distinct modes.255 On the one hand, if information fits into existing mental models it confirms existing knowledge and is absorbed into the mental model. On the other hand, if information does not fit into existing mental models it changes mental models by building new concepts, relationships or discarding existing representations. Secondly, processes of information retrieval from the internal knowledge comprise two elements. Firstly, an activating mechanism and secondly, search through the internal knowledge base. The activating mechanism has its foundations in motives of the decision maker and the perceptions of a problem situation as outlined earlier. The latter is achieved through mental search within the knowledge network. Thereby, the

249

Cf. Miller, A. (1987), p. 257; Corner, P. D. et al. (1994), p. 300. Cf. Miller, A. (1987), p. 257. 251 Cf. Walsh, J. P. (1995), p. 281; Hodgkinson, G. P./Healey, M. P. (2008), p. 389. 252 Cf. Finkelstein, S. et al. (2009), pp. 59-67. 253 Cf. Hodgkinson, G. P. (2003), p. 4. 254 Cf. Miller, A. (1987), p. 260. 255 Cf. Vandenbosch, B./Higgins, C. (1996), p. 201. The authors base these two modes of individual learning on three established individual learning conceptualizations in the literature, namely those of Maier, N. R. F. (1945), Piaget, J. (1954) and Norman, D. A. (1982). 250

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Conceptual basis

search is conducted from one node to another by following the established relationships in the knowledge network.256 2.2 Definition of research variables In section 2.1 SDM and individual information behavior were discussed and the basic terminology for this study was established. Now the discussion turns to the selection and definition of the specific research variables of this study. At first, SDM effectiveness is defined in sub-section 2.2.1. Furthermore, information use and political behavior are the two key process dimensions of SDM as shown before. These process dimensions are defined in sub-section 2.2.2. Finally, perceived environmental uncertainty and cognitive style are the two contextual factors considered in this study. They are defined in sub-section 2.2.3. Figure 4 provides an overview of these elements of the conceptual basis of this study.

Perceived environmental uncertainty

Information use

Effectiveness of SDM

Political behavior

Cognitive style

Figure 4: Elements of conceptual basis of this study (Source: own compilation)

256

Cf. Miller, A. (1987), p. 259; Corner, P. D. et al. (1994), p. 300.

Definition of research variables

45

2.2.1 Strategic decision making effectiveness The effectiveness of strategic decision making is a central concern of this study and of SDM research in general. It can be evaluated in three distinct ways.257 Firstly, decision processes can be evaluated based on their outcomes. Many SDM process studies are concerned with the effects of strategic decisions on firm performance.258 However, this is criticized because firm performance is influenced by a number of other factors than only SDM and the causal order of process and outcome is ambiguous. Furthermore, decision outcomes can be more closely related to the decision itself. Such outcomes include commitment to or quality of a decision made.259 Secondly, decision processes can be evaluated based on their solution. This refers to an evaluation of the solution based on some substantive knowledge regarding its potential effectiveness with respect to the goals established at the time a decision is made .260 This conception of decision making effectiveness has some appeal, because managerial objectives may alter after decisions are made and the evaluation of decision outcomes may depend on changes subsequent to choice.261 Thirdly, decision processes can be evaluated based on process characteristics themselves when information about solutions or outcomes is absent.262 One process characteristic is decision speed. The interest in decision speed bases on the assumption that decision speed translates into superior company performance in particular in highly dynamic environments.263 Other process characteristics may be number and validity of assumptions during decision making264 or clarity of decision making.265 This study measures strategic decision effectiveness in terms of strategic decision outcomes for the following reasons. Firstly, decision outcome measures take a positive view by assuming that company behavior is ultimately intended to improve performance. This is not only common to SDM process research but also of high 257

Cf. Dean, J. W./Sharfman, M. P. (1996), pp. 370-372; Lipshitz, R. et al. (2006), p. 413. Cf. Dean, J. W./Sharfman, M. P. (1996), pp. 370. 259 Cf. Rajagopalan, N. et al. (1998), p. 238. 260 Cf. Dean, J. W./Sharfman, M. P. (1996), pp. 372; Lipshitz, R. et al. (2006), p. 414. 261 Cf. Dean, J. W./Sharfman, M. P. (1996), pp. 372. 262 Cf. Lipshitz, R. et al. (2006), p. 414. 263 Cf. Eisenhardt, K. M. (1989), p. 565-567. 264 Cf. Schweiger, D. M./Sandberg, W. R./Ragan, J. W. (1986), p. 60. 265 Cf. Schwenk, C. R. (1990), p. 443. 258

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Conceptual basis

managerial relevance.266 In contrast to that, decision solution or process characteristic based measures have some disadvantages. Firstly, solution based measures require a person, i.e. researcher, to evaluate the solution with respect to its effects on firm performance. This basically requires the evaluator to have more knowledge about the decision and its effects than the managers involved in the decision themselves. From a practical perspective this would rather be possible in experimental or quasiexperimental studies. However, in field studies such a superior knowledge is hardly attainable. Secondly, solution and process characteristic based measures require that these measures translate into a desired outcome. As a result, research would either be normative in nature or require additional effort for establishing the relationship between process characteristic and desired outcome. Effectively, research would only be shifted to surrogate measures of decision effectiveness. In brief, from a positivist perspective such as taken in this research study decision outcome measures are an appropriate and practical way of measuring decision effectiveness. More specifically, this study measures decision outcomes in a two-step way. The first step is formed by strategic decision quality as direct outcome measure for the following reasons. “[T]he concept of decision quality can be understood to correspond to the extent to which firms’ decisions reflect accurate understandings of the causal relationships that link strategic choices with strategic outcomes“. 267 Given the information processing perspective of this study, the characteristic understanding of causal relationships closely relates to the constituting characteristics of a decision problem space as outlined before. Therefore, decision quality is a sensible outcome measure from the information processing perspective such as taken in this study. Furthermore, decision quality is a broad concept encompassing dimensions pertaining to how a decision solution fits to the overall strategy or the financial position of a firm as well as how the outcomes of a decision contribute to overall achievement of goals and economic performance.268 These aspects clearly establish the managerial relevance of decision quality which is one objective of this study. The second step of outcome measurement is formed by organizational performance as rather indirect outcome measure. As already discussed, strategic decision quality has been attributed a mediating role between SDM process and organizational 266

Cf. Rajagopalan, N. et al. (1998), p. 243. Forbes, D. P. (2007), p. 363. 268 Cf. Schwenk, C. R. (1990), p. 443; Amason, A. C. (1996), p. 134; Dooley, R. S./Fryxell, G. E. (1999), p. 395. 267

Definition of research variables

47

performance. However, this mediating role has most often been assumed but not empirically tested in SDM studies so far.269 In order to address this short-coming of preceding research, organizational performance is included as a second effectiveness measure in this study. 2.2.2 Strategic decision making process dimensions 2.2.2.1 Information use Information use was defined as an individual decision maker’s acquisition and processing of information in addressing strategic decisions. Two considerations are made for specifying the nature of information use for this study. Firstly, managerial information use draws from a broad range of sources in organizational settings. Therefore, a conception of information use should allow “for both routinized information collection as specified by collective context and specialized information gathering done by individuals”.270 This is accounted for by adopting earlier conceptions of information sources.271 Four classes of information sources can be distinguished. Firstly, external personal sources refer to personal contacts to customers, competitors, business associates, officials or contacts on business trips or conferences. Secondly, external impersonal sources refer to newspapers and broadcast media, internet services, periodicals or trade-magazines, government publications, and market reports. Thirdly, internal personal sources refer to board members, managers and staff within the company. Fourthly, internal impersonal sources refer to the management accounting system and written documents that are made available on a systematic basis such as internal reports or special studies. The following Table 3 summarizes this conception of information sources for this study.

269 270 271

Cf. Forbes, D. P. (2007), p. 361; Rajagopalan, N. et al. (1998), p. 239. Corner, P. D. et al. (1994), p. 295. Cf. Daft, R. L. et al. (1988), p. 126.

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Conceptual basis

External personal sources Customers Competitors Business associates Officials

External impersonal sources Newspapers, broadcast media Internet services Periodicals, trade-magazines Government publications

Business trips, conferences

Market reports

Internal personal sources Board members

Internal impersonal sources Management accounting system

Managers Staff

Internal reports, special studies

Table 3: Information sources used for strategic decision making (Source: Adapted from Auster, E./Choo, C. W. (1993), p. 196)

Secondly, SDM was defined as the process from problem diagnosis to making a final choice. Therefore, the conception of information use should pertain to those aspects relevant for problem diagnosis and solving as opposed to problem recognition or decision implementation. This consideration has particular relevance for defining the content of information use. Two concepts describing the content of information have been identified earlier. Firstly, scope of scanning, which refers to information about different environmental sectors. Secondly, scope of information, which refers to the three information dimensions focus, quantification or time-horizon. As discussed in the preceding section, scope of scanning has its origin in the environmental scanning literature which is concerned with recognition of strategic problems.272 In contrast to that, scope of information has its origin in information systems and decision making research which takes a problem solving oriented perspective.273 Therefore, scope of information is a more suitable concept for describing the content of information use for this study. In contrast to that, the subsequent facets of information behavior shall not be included in the conceptualization of information use for reasons discussed respectively. At first, accessibility and quality characteristics of information sources are important antecedents of information use, whereas empirical findings provide mixed evidence of

272

273

Cf. Aguilar, F. (1967); Hambrick, D. C. (1982), p.159. In the German literature this concept is referred to as “strategische Frühaufklärung”, “strategische Frühwarnung” or “strategische Früherkennung”, Cf. Kirschkamp, A. (2008), p. 7. Cf. Larcker, D. F. (1981), p. 523; Frishammar, J. (2003), pp. 319-320; Bhimani, A./LangfieldSmith, K. (2007), p. 24.

Definition of research variables

49

their effects.274 In addition to that, decision makers in the field most probably cannot control for source characteristics such as information quality or accessibility275 but have to cope with what is available to them. Similarly to quality characteristics of information sources, information characteristics such as value, quality or relevance of information are not included in this conceptualization of information use. Again, the reason is that a decision maker in organizational settings will receive large amounts of information which he or she simply has to cope with.276 Then, medium of information transmission certainly varies for most of these sources. However, it is difficult for respondents to discern in questionnaire surveys 277 and shall thus not further be included in the conceptualization of information use. Furthermore, the mode of information acquisition appears of little relevance to this study. Two distinct modes can be identified, namely scanning and focused search. Empirical findings show, that the acquisition mode of scanning dominates for problem recognition,278 whereas focused search dominates for problem solving.279 Provided the dominance of focused search for problem solving this study does not further distinguish into different modes of information acquisition. Finally, the characteristic of information richness is inherently tied to the information sources used. Personal sources are information rich, because they provide multiple cues via body or verbal language and allow for rapid feedback.280 This holds for direct personal contact and for indirect contact through media alike.281 In contrast to that, impersonal sources are lower in information richness than personal sources. They do not provide multiple cues and feedback because they are most often written and prepared based on rules, forms, procedures or databases. 282 As such, impersonal sources are particularly useful for condensing data on the one hand, but do not provide the richness of personal information sources on the other hand.283 This already indicates information richness is expected to influence the performance of 274

Cf. O'Reilly Iii, C. A. (1982), p. 764 and 767; Auster, E./Choo, C. W. (1993), p. 201. Cf. O'Reilly Iii, C. A. (1982), pp. 756-757. 276 Cf. Hambrick, D. C. (1982), p. 277 Cf. El Sawy, O. A. (1985), p. 54. 278 Cf. El Sawy, O. A. (1985), p. 58. 279 Cf. Frishammar, J. (2003), p. 321. 280 Cf. Daft, R. L./Lengel, R. H. (1986), p. 560; Daft, R. L. et al. (1988), p. 126. 281 Cf. Marginson, D. (2006), p. 190. 282 Cf. Daft, R. L./Lengel, R. H. (1986), p. 560; Daft, R. L. et al. (1988), p. 126. 283 Cf. Daft, R. L. et al. (1988), p. 126. 275

50

Conceptual basis

accomplishing a task with certain types of information.284 Therefore it is relevant for the present research study. However, information richness is inherently tied to information sources themselves. Therefore, it is not separately conceptualized but will be considered during hypotheses development. 2.2.2.2 Political behavior Organizational decision making is also a social process, because organizations are social systems, which make decisions for collaborative actions. As a result, action, interaction and counteraction are inherent to SDM.285 Undoubtedly, many social processes are present in SDM whereas political behavior is of particular relevance and a key process SDM process dimension.286 Political behavior is a result of diverging interests among people in organizations. Although people may share some objectives such as general organizational welfare, they may also have conflicting interests.287 On the one hand, they may have diverging interests concerning specific organizational objectives. Some people may aim at organizational growth, others may aim at profitability. On the other hand, people may also have personal interests diverging from organizational goals. Overall, these diverging interests result in conflicts over scarce resources. For resolving goal conflicts and putting their own position through, individuals may engage in political behavior for changing the power structure within an organization. Political behavior most basically means, getting other people to do what they might not elect to do so.288 A number of political tactics can be identified such as coalition formation, agenda control or timing, the use of outside consultants, negotiating and bargaining, or the use of power.289 Furthermore, decision participants may manipulate critical information and control the information flow to top decision makers.290 However, not only political actors may be a cause of interrupted information flows. Also a top decision maker himself may be subject to social barriers of information acquisition in particular in case of stable behavior patterns in organizations.291 284

Cf. Dennis, A. R./Kinney, S. T. (1998), p. 257. Cf. Hickson, D. J./Butler, R. J./Cray, D. et al. (1986), p. 54. 286 Cf. Dean, J.W./Sharfman, Mark P. (1993), p. 1078; Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 27; Elbanna, S./Child, J. (2007), pp. 445-446. 287 Cf. here and in the following Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 23. 288 Cf. Elbanna, S. (2006), p. 7. 289 Cf. Elbanna, S. (2006), p. 8. 290 Cf. Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 23 291 Cf. Wilson, T. D. (1997), p. 560. 285

Definition of research variables

51

2.2.3 Contextual factors 2.2.3.1 Perceived environmental uncertainty From a contingency perpective, organizations are influenced by their environments.292 According to this view, organizational characteristics such as a firm’s strategy or structure and SDM processes are affected by the environment.293 A cognitive perspective furthermore suggests that managers perceive the environment and drive firm actions based on these individual perceptions.294 The external environment of organizations can be described in a number of ways.295 Environmental dynamism refers to the rate of change and innovation in an industry and uncertainty and unpredictability of actions of competitors and customers.296 A similar concept is industry velocity which describes rate and predictability of changes in the environment.297 While these characteristics refer to characteristics over time, other characteristics take a static view of the environment. Environmental heterogeneity refers to variations among a firm’s markets which result in diversity of production and marketing.298 Similarly, environmental complexity refers to the number and interdependence of factors outside an organization.299 Finally, the environment can be described according to their hostility vs. munificence. Environmental hostility refers to the degree of threats a firm is exposed to by competition and industry volatility. 300 Environmental munificence refers to the capacity of an environment to support organizational growth.301 From a decision making perspective the environment is considered as those physical and social factors that are taken into consideration for decision making.302 Two relevant environmental dimensions can be identified for describing these factors. Firstly, complexity refers to the number and similarity of environmental factors and their interdependence. Secondly, dynamism refers to the extent and frequency of

292

Cf. Lawrence, P./Lorsch, J. (1969), pp. 23-27. Cf. Miller, D./Friesen, P. H. (1983), p. 230. Cf. Schwenk, C. R. (1988), p. 41; Nadkarni, S./Barr, P. S. (2008), p. 1395. 295 Cf. Goll, I./Rasheed, A. M. A. (1997), pp. 584-585; Elbanna, S./Child, J. (2007), pp. 436-437. 296 Cf. Garg, V. K. et al. (2003), p. 727. 297 Cf. Eisenhardt, K. M. (1989), p. 544. 298 Cf. Miller, D./Friesen, P. H. (1983), p. 222. 299 Cf. Duncan, R. (1972), pp. 315-316. 300 Cf. Miller, D./Friesen, P. H. (1983), p. 222. 301 Cf. Wiersema, M. F./Bantel, K. A. (1993), p. 487. 302 Cf. here and in the following Duncan, R. (1972), pp. 314-316. 293 294

52

Conceptual basis

change of relevant decision factors over time. Both factors compose environmental uncertainty, which is a perceived characteristic of the environment. This is why it is called perceived environmental uncertainty (PEU). However, the concept of environmental uncertainty also received some criticism. Critics argue there may be environmental states for which it is not possible to perfectly determine the required information as proposed by theories related to environmental uncertainty.303 Such environmental states are referred to as equivocal or ambiguous states. Under such conditions a decision maker would not be capable to discern what information is required or appropriate for making a decision because of contradictory evidence. As a result, the required information for resolving this ambiguity could not clearly be defined. Albeit these arguments are conceptually reasonable, the distinction into uncertainty and ambiguity in real-life settings is difficult.304 This assertion is supported by empirical findings which show a high correlation between strategic variability (i.e. dynamism) and ambiguity.305 So although it may be conceptually reasonable to discern uncertainty and ambiguity, its effects are blended in empirical settings. To conclude, this study conceptualizes the environmental context as PEU consisting of perceived complexity and dynamism, because these dimensions describe a decision problem space.306 Finally, environmental uncertainty is used in several SDM studies and a core moderating variable in SDM research.307 2.2.3.2 Cognitive style Individual differences in human cognition have been researched in many domains of organizational cognition research.308 Therefore, individual differences are a crosssectional topic of interest in organizational cognition research and a number of individual differences concepts relate to cognition in organizations.309 In recent organizational cognition research, self-efficacy and cognitive styles are most prominent.310 Self-efficacy and related concepts such as locus of control, self esteem, 303

Cf. here and in the following Daft, R. L./Lengel, R. H. (1986), pp. 556-557. Cf. Forbes, D. P. (2007), p. 369. 305 Cf. Boyd, B./FuIk, J. (1996), p. 10. 306 Cf. Duncan, R. (1972), p. 314. 307 Cf. Elbanna, S./Child, J. (2007), p. 436.; Forbes, D. P. (2007), p. 363. 308 Cf. Hodgkinson, G. P./Healey, M. P. (2008), pp. 391-402. The authors identify nine areas of organizational cognition research. 309 Cf. Hodgkinson, G. P./Healey, M. P. (2008), p. 401. 310 Cf. Hodgkinson, G. P./Healey, M. P. (2008), p 402. 304

Definition of research variables

53

or emotional stability are specific facets of individuals’ evaluations of themselves and their relationship with the environment.311 These characteristics impact cognition from a social perspective. They determine how individuals approach tasks, learning, organizational change, decision making or choice in social interactions.312 Cognitive styles refer to individual differences in information processing.313 Thus, cognitive styles are specifically relevant from the procedural perspective of this study. Cognitive styles were first identified in the 1940s and 1950s during experimental studies on simple cognitive tasks such as perception or categorization.314 In the 1970s, Messick established a general definition of cognitive style as consistent individual differences in information processing.315 Other authors such as Witkin, Moore, Goodenough, and Cox (1977) similarly define cognitive styles as individual differences in the way “people perceive, think, solve problems and relate to others.”316 These individual differences in information processing can be traced back to the dualprocessing nature of human cognition.317 As outlined earlier, there are two modes of human information processing namely a conscious, logical mode on the one hand, and an unconscious, automatic mode on the other hand. Cognitive styles basically describe individual preferences to one or the other mode of information processing. Since then a large number of different cognitive style theories, concepts and terms have been proposed. The field is furthermore expanded by examining individual differences with respect to learning or decision making, such that there are specific learning and decision making styles in the literature. In a review of style literature Rayner, S./Riding, R. (1997) identified 17 cognitive style approaches and twelve learning style approaches.318 Another review identifies even more, namely 23 cognitive style approaches and 28 learning style approaches. 319 Furthermore, there are a nearly equally large number of decision making style approaches.320 Last but not 311

Cf. Hiller, N. J./Hambrick, D. C. (2005), pp. 299-300. Cf. Hodgkinson, G. P./Healey, M. P. (2008), p 401; Hiller, N. J./Hambrick, D. C. (2005), pp. 303306. 313 Cf. Hodgkinson, G. P./Healey, M. P. (2008), p 402. 314 Cf. Kozhevnikov, M. (2007), p. 465. 315 Messick, S. (1976) as cited in Hayes, J./Allinson, C. W. (1994), p. 54 and Kozhevnikov, M. (2007), p. 464. 316 Witkin, H. A./Moore, C. A./Goodenough, D. R. et al. (1977) as cited in Hayes, J./Allinson, C. W. (1994), p. 53. 317 Cf. Miller, A. (1987), p. 253; Hodgkinson, G. P./Clarke, I. (2007), p. 244. 318 Cf. Rayner, S./Riding, R. (1997), pp. 8-9 and pp. 14-15. 319 Cf. Desmedt, E./Valcke, M. (2004), p. 451. 320 Cf. Kozhevnikov, M. (2007), pp. 468-469. 312

54

Conceptual basis

least, personality style approaches, most notably the Myers-Briggs Type Indicator (MBTI), contain cognitive elements.321 Due to a large number of different conceptualizations there have been attempts for unifying cognitive style concepts.322 These concepts basically differ in two main dimensions of cognitive style. The first dimension can be seen in the underlying assumptions on the cognitive system. Some studies assume a unitary cognitive system governing conscious and unconscious processes alike. Other studies assume a dualprocessing nature of cognition and cognitive style. The implication is that unitary cognitive styles are measured by one bipolar scale, whereas dual-processing styles are measured by two unipolar, independent scales.323 The second dimension can be seen in how many information processing functions such as perception, problem solving, decision making, or learning are conceptualized. One attempt for unification is made with the so called Cognitive Style Index (CSI). The CSI proposes a unitary cognitive system which governs all information processing functions alike. According to the CSI, conscious and unconscious processing resides on one bipolar continuum.324 Other unifying attempts account for the dual-processing nature of cognition by conceptualizing two independent, orthogonal dimensions of cognitive style.325 These attempts do not further distinguish into different information processing functions. Another attempt is made by comparing different existing cognitive style measures and by deriving common dimensions of information processing empirically.326 The authors identify three common dimensions: 1) the extent of use and development of theoretical concepts for decision making, 2) the use of an overarching attitude mechanism, namely cognitive or affective orientation, 3) the preference for information gathering from the external world or from an internal, reflective process. While the first and third dimensions directly relate to information processing functions, namely perception and decision making, the second dimension is rather an overarching mechanism or a so-called meta-style.327 Another empirical

321

Cf. Kozhevnikov, M. (2007), p. 469. Cf. Kozhevnikov, M. (2007), pp. 472-475. Cf. Hodgkinson, G. P./Healey, M. P. (2008), p. 402. 324 Cf. Allinson, C. W./Hayes, J. (1996), pp. 121-123. 325 Cf. Riding, R./Cheema, I. (1991), p. 211; Epstein, S. et al. (1996), p. 399. 326 Cf. Leonard, N. H. et al. (1999), p. 411. 327 Cf. Kozhevnikov, M. (2007), p. 473. 322 323

Definition of research variables

55

validity study reaches similar results.328 Despite these approaches identify various functional dimensions they base their empirical studies on concepts which assume one cognitive system. In contrast to that, other attempts of unification assume dualprocessing of cognition and functional differentiation. This implies each functional dimension of cognitive style is measured with two unipolar scales.329 Figure 5 visualizes how these approaches can be categorized.

Conceptualization of cognitive functions

Dualprocessing system

Assumption on cognitive system

Unconscious thinking

Universal / uni-dimensional

Distinguished / multi-dimensional

high

low

• Epstein et al. 1996 • Riding / Cheema 1991

• Sadler-Smith 2004

• Allinson / Hayes 1996

• Leonard et al. 1999

high

low Conscious thinking

intuitive

rational

Unitary System

Figure 5: Overview and selected authors of conceptualization approaches to cognitive styles (Source: own compilation)

Overall findings of the unification attempts show that cognitive styles are multifaceted in terms of functions and underlying systems, whereas the two general dimensions information perception and thinking can generally be distinguished.330 Given the large number of psychological concepts and measurement instruments, this study draws on existing research for its conceptualization of cognitive style. A key requirement is that not only a conception but also an operationalization suitable for questionnaire surveys exists. A number of cognitive style concepts and instruments can be identified. Very popular in management and managerial cognition research is the Myers-Briggs Type-Indicator,

328

Cf. Bokoros, M. A./Goldstein, M. B./Sweeney, M. M. (1992), pp. 99-101; Kozhevnikov, M. (2007), p. 473. 329 Cf. e.g. for such an approach Sadler-Smith, E. (2004), pp. 162-163. 330 Cf. McKenney, J. L./Keen, P. G. W. (1974), pp. 80-81; Henderson, J. C./Nutt, P. C. (1980), pp. 372-373; Leonard, N. H. et al. (1999), p. 411; Sadler-Smith, E. (2004), pp. 162-163.

56

Conceptual basis

although it is a psychological type and not a cognitive style measure.331 Despite this, two of four psychological dimensions covered by the MBTI pertain to information processing preferences.332 Furthermore, other cognitive style instruments, most notably the Cognitive Style Index (CSI)333 and the Rational-Experiential Inventory (REI), are available.334 Although all of these instruments are based on self-reporting questionnaires they are still comprehensive in terms of their number of items335 and thus practically not suitable for conducting large scale research. Given these practical limitations, cognitive style measurement instruments suitable for survey research were developed. One is the Cognitive Style Indicator (CoSI),336 and another is the Linear-Nonlinear Thinking Style Profile (LNTSP).337 Both instruments have incorporated the considerations of the unification attempt and identified only few cognitive style dimensions based on a comprehensive review of the existing literature. Then both instruments have been tested and re-tested with several large scale empirical samples. Last but not least, both instruments were cross-validated with existing cognitive style measures, and both instruments demonstrated their discriminant and content validity. This study uses the LNTSP for the following reasons: 1) Firstly, the LNTSP develops a two-dimensional construct resembling perceptual and thinking preferences.338 In contrast to that, the CoSI develops a three-dimensional measure with a planning, creating and knowing style respectively.339 While the LNTSP closely resembles the human information processing model, the CoSI takes a more task-oriented perspective and thus appears less suitable for the purposes of this study.

331

Cf. Gardner, W. L./Martinko, M. J. (1996), p. 46; Sample, J. (2004), p. 67. Cf. Gardner, W. L./Martinko, M. J. (1996), p. 46; 333 Cf. Allinson, C. W./Hayes, J. (1996), p. 123. 334 Cf. Epstein, S. et al. (1996), p. 394. 335 The MBTI provides a number of different forms, whereas in management research most often the so-called from G is used; Cf. Gardner, W. L./Martinko, M. J. (1996), p. 46. The self-scorable form G form contains 94 items; Cf. in the internet Anonymous author (2009). Similarly, the CSI contains 38 items; Allinson, C. W./Hayes, J. (1996), p. 124. Finally, the REI containts 31 items; Cf. Epstein, S. et al. (1996), p. 394. 336 Cf. Cools, E./van den Broeck, H. (2007), pp. 364-366. 337 Cf. Vance, C. M. et al. (2007), pp. 167-170. 338 Cf. Vance, C. M. et al. (2007), pp. 169-170. 339 Cf. Cools, E./van den Broeck, H. (2007), p. 372. 332

Summary

57

2) Secondly, the LNTSP has been developed and tested in several empirical studies. Its discriminant validity with the MBTI information processing dimensions or the CSI is empirically supported.340 3) Thirdly, the LNTSP measurement instrument is short and thus allows for using it in questionnaire survey. In brief, the LNTSP is a suitable cognitive style concept while at the same time it provides a reliable and valid measurement instrument. According to the LNTSP two modes of thinking can be distinguished:341 1) Linear thinking can be described as analytical way of thinking, where the focus is on delineating a field of interest into single, understandable parts and relate them to each other by predictable cause-effect relationships. Linear thinking involves attention to external data and facts and processing of information through conscious logic, deduction and reasoning. As such linear thinking refers to the conscious, sequential, intentional and deductive mode of information processing as outlined before. 2) Nonlinear thinking employs holistic hunches for grasping interrelated aspects and vague cause-effect relationships of a field of interest. Nonlinear thinking involves attention to internal feelings and impressions and both conscious and unconscious processing of information for attaining understanding and drawing inferences. Furthermore, it is marked by rapid and intuitive processing of information. As such nonlinear thinking refers to the unconscious, parallel and automatic mode of information processing as outlined before. 2.3 Summary Overall this chapter can be summarized as follows. First of all, strategic decisions are defined as a company’s choice among alternative courses of actions. They are important in the sense, that they require substantial investment of resources, imply a commitment to and set precedents for organizational action, and have long-term effects on survival and performance of a company. Strategic decisions can furthermore be characterized as ill-structured, i.e. they are highly complex, ambiguous, unfamiliar and open-ended. Therefore strategic decisions can neither be programmed nor easily be solved. 340 341

Cf. Vance, C. M. et al. (2007), p . 176. Cf. Vance, C. M. et al. (2007), p. 169-170. Basically this conceptualization is similar to findings of the comparative study of cognitive style constructs by Leonard, N. H. et al. (1999), p. 411.

58

Conceptual basis

SDM refers to the processes involved in choosing a firm’s strategy. These processes start with diagnosing a problem once it is recognized and end with the final choice among alternatives. In between activities for generating and evaluating alternatives take place. Since these activities empirically happen in parallel and are marked by cycles, a futher distinction into sub-phases has not any prescriptive relevance. Therefore, a distinction into subphases is not further made for this study. Furthermore, SDM activities can be described by two key process dimensions. Firstly, information use is a key process dimension because of individuals’ cognitive limits in terms of knowledge and information processing and the resulting need to acquire and process information for SDM. Secondly, political behavior is a key process dimension because of SDM being a collective phenomenon within the organization and potential conflicts of interests and related political behaviors among decision participants. Last but not least, information use for SDM ultimately takes place on the level of the individual decision maker. However, it cannot be neglected his or her decision making takes place within the wider context of the organization. Therefore, SDM was identified as a multilevel phenomenon, where individual information behavior is the interface between the individual and his or her organizational context. Individuals’ information behavior comprises behavioral and cognitive activities directed at information. These activities can roughly be distinguished into information acquisition, (cognitive) information processing and information dissemination by an individual. All these activities are triggered by an information need arising from a problematic situation, e.g. a strategic decision problem. Information from such an individual perspective was defined as stimuli capable of altering ones management relevant knowledge base. This definition has two important facets. Firstly, it provides a link between information and SDM by reference to knowledge which is required for decision making. Secondly, it takes account for the multilevel nature of SDM by defining information as stimuli at the interface between information acquisition behaviors and the cognitive information processing activities of the individual decision maker. As such information is distinguishable from data or messages that can exist independently from the individual and knowledge which are mental representations of reality within the individual.

Summary

59

Then, information use was defined as an individual’s acquisition and processing of information in addressing strategic decisions. This definition differs from organizational level definitions, because the latter refer to organizational routines and systems of information processing but not to the the individual level processes. Furthermore, individual information acquisition and processing can be described in a number of facets. Firstly, information acquisition comprises behavioral activities related to the kinds of information sources used, the mode of information acquisition or the characteristics of the information acquired. Secondly, information processing comprises cognitive activities which can be grouped according to three functions. These functions are perception, thought (including problem solving or decision making) and memory, i.e. storage and retrieval of knowledge. Information processing is initiated by incoming stimuli, i.e. information, and the outcome is some kind of response. Last but not least, cognitive processes are characterized by a dual-processing nature. This means information processing occurs in both a conscious, logical mode as well as an unconscious, habitual mode. After establishing the basic terminology for SDM and information behavior, the conceptual variables were specifically defined for this study. These definitions draw from existing literature as detailed beforehand and can be summarized as follows: x The dependent variables are strategic decision quality and organizational performance, which are both measures of strategic decision effectivenesss. This two-step evaluation of effectiveness addresses most often implicitly assumed but not tested relationships between decision processes and outcomes. x One group of independent variables is defined as an individual’s amount and scope of information use from personal and impersonal as well as internal and external information sources. These variables capture information use as the first key process dimension of SDM. x Another independent variable is political behavior defined as individuals’ behaviors in pursuit of their personal interests during SDM. This variable captures the social context of SDM and has widely been used in SDM research. x The first contextual factor is defined as perceived environmental uncertainty comprised of environmental complexity and dynamism. x The second contextual factor is defined as cognitive style from a dualprocessing perspective on human cognitive processes. More specifically cognitive style distinguishes into linear and nonlinear thinking drawing from the LNTSP as a newly developed concept and measure of cognitive style.

60

Conceptual basis

These variables appear suitable for answering the research questions and result in the conceptual basis as depicted in Figure 6.

Perceived environmental uncertainty (complexity and dynamism)

Scope of information use from • internal and external • personal and impersonal information sources

Effectiveness • Strategic decision quality • Organizational performance

Political behavior

Cognitive style (linear vs. nonlinear thinking) Figure 6: Conceptual basis of this study (Source: own compilation)

Alternative theoretical perspectives and theories

61

3 Selection of a theoretical framework The preceding chapter established the conceptual basis of this study and introduced individual information use and political behavior as the key process dimensions of SDM. Furthermore, PEU and cognitive style were defined as the two contextual factors of interest. Overall this provides a conceptual basis that seeks to describe the interaction between environment, organizational and individual level processes with respect to the effectiveness of SDM. Now a theoretical framework is required that allows for an explanation of the relationships between these variables and ultimately serves for developing testable hypotheses. Broadly speaking, three perspectives for studying SDM exist. These are economic, behavioral and cognitive perspectives,342 with a number of varied theories existing under each of these perspectives. Given the fact, that a number of SDM theories are available, this chapter aims at selecting a theoretical framework which is suitable for developing testable hypotheses in the next chapter. For that purpose, the three SDM perspectives and relevant SDM theories under each of these perspectives are discussed in section 3.1. Thereafter a theoretical framework is selected on the basis of a critical evaluation of theories against some requirements established in section 3.2. Next, the theoretical basis is described in more detail in section 3.3. Finally, section 3.4 summarizes this chapter. 3.1 Alternative theoretical perspectives and theories The purpose of this section is a delineation of theoretical perspectives and relevant SDM theories. These theoretical perspectives differ with respect to their central research problems and underlying assumptions on individuals, organizations and the environment which are explained at first. Then, the discussion turns to a brief explanation of specific SDM theories under each of these perspectives. Finally, a general evaluation of these perspectives is made. 3.1.1 Economic perspective and theories Economic theories are concerned with what constitutes optimal firm behavior in a market environment under equilibrium conditions. Under neoclassical theory the firm behavior is understood as profit maximizing allocation of resources for given preferences and technologies.343 Starting from such a static perspective the evolutionary perspective of firm behavior acknowledges that environmental change 342 343

Cf. Huff, A. S. et al. (2000), pp. 5-19. Cf. Pettigrew, A. M. (1992), p. 11; Huff, A. S. et al. (2000), p. 5.

W. Gänswein, Effectiveness of Information Use for Strategic Decision Making, DOI 10.1007/978-3-8349-6849-4_3, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

62

Selection of a theoretical framework

and technological innovation matter.344 Here, the question is how innovations translate into profit maximizing attributes of the firm.345 Economic theories may also increase the level of complexity. While neoclassical theory characterizes organizations in terms of a production function, agency theory characterizes organizations in terms of their governance structure.346 However, the central assumptions of economic theory about individuals, organizations and the environment are generally speaking simple and can be summarized as follows:347 1) Individuals are seen as rational actors with clear goals, unlimited knowledge and cognitive capacity in order to make strategic decisions and carry out the strategies formulated.348 Although some economic theories such as PrincipalAgent Theory, Transaction Cost Economics or Resource-Based and Dynamic Capabilities Views increase the levels of complexity, these central assumptions about individuals still hold.349 2) According to economic theories organizations are endowed with certain attributes such as the strategy followed, internal resources, capabilities, structures and processes. However, the analysis is restricted to firm attributes and internal behaviors of organizational participants are not of further interest to economic theories. Therefore, one could say organizations are treated as “black boxes” in economic theories.350 3) Finally, the environment is assumed to be deterministic or at least objectively determinable.351 Environmental conditions may differ and change, however economic theories assume that firm attributes align with environmental conditions. Economic theories accommodate strategy process research in two ways. Firstly, in the strategic planning school strategy refers to the determination of long-term goals of the organization and the adoption of courses of action and allocation of resources in 344

Cf. Nelson, R. R. (1991), p. 66. Cf. Nelson, R. R. (1991), p. 67. Cf. Huff, A. S. et al. (2000), p. 6. 347 Cf. Teece, D. J. (1990), p. 52. 348 Cf. Chaffee, E. E. (1985), p. 90. 349 Cf. Huff, A. S. et al. (2000), p. 5. 350 Huff, A. S. et al. (2000), p. 5. 351 Cf. Huff, A. S. et al. (2000), p. 5. 345 346

Alternative theoretical perspectives and theories

63

accordance with these objectives.352 The strategy process aims at a delineation of longterm objectives into an integrated set of decisions and plans upon subsequent action follows.353 This process represents a set of sequential and analytic activities of analyzing the environment and deciding on how firm strategy and structure has to be aligned with the environment.354 This planning school is basically a normative theory and provides many recommendations on how strategy processes ought to look like.355 Secondly, in economic theories organizations can be described through specific firm attributes such as organizational structure, firm resources or capabilities influencing company performance.356 From such a perspective, strategy making processes can be seen as a firm attribute which then has an effect on competitive advantage or performance of an organization.357 Additionally, such firm attribute theories may take a contingency perspective which claims that organizational characteristics align with environmental characteristics.358 For several reasons, economic theories of strategy processes are criticized by SDM researchers. Firstly, economic theories assume completely rational actors, i.e. actors with clear preferences and universal capabilities.359 Although some theories integrate specific constraints such as information asymmetries in agency theory, economic theories assume a perfect, utility maximizing behavior is followed within these constraints. Secondly, apart from describing firms in terms of strategy or structure economic theories are not concerned with intra-firm behaviors by organizational participants,360 because they assume that individuals within a firm are fully capable of performing what is prescribed in terms of processes and actions to be carried out. They treat the organization as a unitary actor. Finally, some economic theories integrate the environment, whereas the assumption is simply that firms adapt to the conditions of the environment to arrive at optimal, i.e. profit maximizing firm behavior.361

352

Two basic works of this school are Ansoff, H. I. (1965) and Andrews, K. R. (1980). Cf. Chaffee, E. E. (1985), pp. 90-91. 354 Cf. Hutzschenreuter, T./Kleindienst, I. (2006), p. 702. 355 Cf. Hitt, M. A./Beverly, B. T. (1991), p. 327. 356 Cf. Amit, R./Schoemaker, P. J. H. (1993), p. 38. 357 Cf. Hart, S./Banbury, C. (1994), p. 252. 358 Cf. Miller, D./Friesen, P. H. (1983), pp. 221-222. 359 Cf. Chaffee, E. E. (1985), p. 90; Schoemaker, P. J. H. (1993), p. 109. 360 Cf. Huff, A. S. et al. (2000), p. 5. 361 Cf. Nelson, R. R. (1991), p. 64. 353

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Selection of a theoretical framework

3.1.2 Behavioral perspective and theories As stated, economic theories are concerned with what are optimal firm attributes in a profit maximizing sense given simple assumptions on individual behavior of organizational participants. In contrast to that, behavioral theories are concerned with how organizations can direct individuals with human limits for achieving effective firm behavior.362 Hence, behavioral theories address some of the short-comings of economic theories. They propose that organizations are formed by individuals which in reality are not completely rational and do not engage in uniform behavior. Instead, behavioral theories emphasize that organizations are coalitions of individuals,363 whose characteristics and behaviors impose natural constraints on SDM. According to behavioral theories organizations are still rational, whereas organizational participants have to be controlled for by providing organizational structures that enable effective firm behavior.364 Again the underlying assumptions on individuals, organizations and the environment are important for understanding behavioral perspectives. 1) Individuals are portrayed in a more complex manner than in economic theories. Firstly, individuals may possess self-interests that are different from organizational goals and from the self-interests of other individuals.365 Secondly, individuals may possess cognitive limits in a variety of ways such as limited knowledge or limited information processing capacity. These cognitive assumptions base on empirical findings of cognitive research. For example, the satisficing assumption of the bounded rationality approach can be considered as one specific assumption based on cognitive research findings.366 Moreover, behavioral decision theory comprises cognitive research on individual axioms of the economic man paradigm.367 These findings enter behavioral theories of SDM as assumptions. 2) Organizations are portrayed as coalitions of individuals. The aim of organizing is to stabilize and direct internal behaviors towards organizational goals by

362

Cf. Huff, A. S. et al. (2000), p. 14. Cf. Carter, E. E. (1971), p. 413. 364 Cf. Choo, C. W. (1996), p. 21; Huff, A. S. et al. (2000), p. 7. 365 Cf. Huff, A. S. et al. (2000), p. 7. 366 Cf. Hodgkinson, G. P. (2003), p. 6. 367 Cf. Slovic, P./Fischhoff, B./Lichtenstein, S. (1977), p. 1; McFadden, D. (1999), p. 79. 363

Alternative theoretical perspectives and theories

65

distributing benefits among organizational members and by providing mechanisms to overcome cognitive limits.368 3) The environment is of less concern to behavioral theories, because of their orientation towards internal phenomena. Still, the relationship between firms and their environments can be integrated into behavioral perspectives,369 while then the environment is assumed to be objectively determinable. A number of behavioral theories of SDM have been proposed in the literature. Although they have some commonalities as described before, they also differ. Distinguishing characteristics are their specific assumptions on individual and collective behavior and the resulting portrayal of SDM processes. To illustrate this, four main behavioral theories of SDM are discussed in the following. First of all, the bounded rationality theory assumes cognitive limitations, such as limited attention and computational capabilities.370 This implies cost of information use and decision making and imposes limits in terms of limited knowledge and computational abilities. As a result decision makers do not search for the optimal but a satisfying solution for decision-problems.371 Finally, in the bounded rationality view individuals collectively seek to reach the best possible solution for the organization. Then, the politics and power theory assumes that individuals are not cognitively bounded, i.e. they are rational on the individual level, but they possess diverging selfinterests.372 The politics and power perspective portrays decision making as process of handling and resolving diverging interests of various decision participants in organizations.373 According to this view, the final choice represents the interest of the most powerful actor/s in an organization. Thus, the SDM process is shaped by coalition building or cooptation and information is used for political purposes instead of rational analysis.374

368

Cf. Huff, A. S. et al. (2000), p. 7. Cf. Huff, A. S. et al. (2000), p. 13. Cf. Simon, H. A. (1978), p. 13; Elbanna, S. (2006), pp. 3-4. 371 Cf. Simon, H. A. (1978), p. 10; Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 18; Elbanna, S. (2006), p. 4. 372 Cf. Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 23. 373 Cf. Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 22-23; Das, T. K./Teng, B.-S. (1999), p. 758. 374 Cf. Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 23. 369 370

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Selection of a theoretical framework

In addition to these two theories which focus either on cognitive limites (i.e. the bounded rationality view) or on diverging interests (i.e. the political behavior view) other behavioral SDM theories provide mixed perspectives by relaxing various assumptions on individual behaviour. Incremental or intuitive decision making views provide an alternative to the bounded rationality view.375 These views seek to describe actual decision processes more realistically and further relax assumptions of the rational decision making model. They portray SDM as a step-by-step approach towards a final choice.376 A unique goal is not given at the beginning but evolves as the process continues. The decision problem is not formulated upfront and then solved, but separated into less complex pieces that provide a more or less accurate representation of the whole decision problem.377 Decision makers employ heuristics and intuitive judgments for decision making due to cognitive limits, incomplete information and dynamic decision situations. 378 Finally, the garbage can model portrays organizations and decision processes as organized anarchies, where goals become clear through action, cause-effect relationships are not understood, actual choices are sometimes made upfront without analysis and the group of decision participants changes over time.379 According to this view, organizational decision making is a random process and information use rather appears in form of local activities than as a coherent decision process. To summarize, behavioral theories are an important step to a more realistic view of SDM processes in organizations. Their theoretical contribution can be seen in a relaxation of assumptions on individual actors and the emphasis on internal phenomena of the organization. However, behavioral theories also have some short-comings. At first, apart from making assumptions on cognitive limits and self-interests behavioral theories treat individuals still as a black box.380 Furthermore, behavioral theories take an

375

Cf. Fredrickson, J. W./Mitchell, T. R. (1984); p. 401f.; Isenberg, D. J. (1986); Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 22; Elbanna, S. (2006), p. 2. Cf. Fredrickson, J. W./Mitchell, T. R. (1984), p. 402. 377 Cf. Fredrickson, J. W./Mitchell, T. R. (1984), p. 402; Das, T. K./Teng, B.-S. (1999); p. 758. 378 Cf. Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 22. 379 Cf. Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 27. 380 Cf. here and in the following Huff, A. S. et al. (2000), pp. 13-14. 376

Alternative theoretical perspectives and theories

67

environmental determinism view of organizational behavior, which means that organizations are merely responding to environmental conditions. 3.1.3 Interpretive perspective and theories Interpretive theories of SDM acknowledge that not any firm beheavior can be explained by environmental and organizational level variables. Their main concern is whether the individuals within a firm are a source of variation in firm behavior. This research concern has its roots in the strategic choice paradigm, which posits that organizational behavior is not solely a function of environmental and behavioral constraints but also of managerial discretion and cognition.381 The major concern of interpretive theories is how individuals and collectives interpret complex and changing environments and how this knowledge informs collaborative action. 382 As a consequence interpretive theories do not only assume cognitive limits but are specifically focused on the cognitive processes of perception and interpretation which they consider as a source for variation.383 As result reality is not objectively given but constructed from individual and collective interpretation. Accordingly, organizational behavior is not independent of those individuals acting within the organizations.384 Central assumptions of the interpretive perspective are as follows. 1) Individuals are not only subject to cognitive limits, but their cognitive processes shape organizational action. Interpretive perspectives follow the information processing paradigm, which bases on three common assumptions. 385 Firstly, individuals possess cognitive limitations and are not able to process all stimuli of the external world. Secondly, they use a variety of information processing strategies to reduce the burden of information processing. Thirdly, cognitive characteristics and prior experience determine filtering and processing of stimuli and the subsequent response. Therefore, the subjective processes of perception and interpretation are of particular relevance to interpretive theories. The understanding of one’s own situation and the situation of the organization is the basis for action.386

381

Cf. Walsh, J. P. (1995), p. 280. Cf. Choo, C. W. (1996), p. 21; Huff, A. S. et al. (2000), p. 14; Hodgkinson, G. P. (2003), p. 3; Hodgkinson, G. P./Healey, M. P. (2008), p. 391. 383 Cf. Huff, A. S. et al. (2000), p. 15. 384 Cf. Staehle, W. H. (1999), p. 67. 385 Cf. Huff, A. S. et al. (2000), p. 15; Hodgkinson, G. P. (2003), p. 3; Bower, G. H. (2008), p. 2. 386 Cf. here and in the following Huff, A. S. et al. (2000), p. 30. 382

68

Selection of a theoretical framework 2) Organizations in the interpretive perspective provide a frame for individual and collective interpretation. Thus, organizations are considered as social systems in which individuals interact.387 Furthermore, organizations are not only a platform for interaction but may even possess own cognitive structures and thus organizational cognition serves as a meta-level cognitive system that governs individual interpretations. Finally, organizational and managerial goals must not necessarily follow the profit maximizing principle of economic and behavioral theories. In interpretive theories goal formation itself is a collective process of interpretation marked by individual idiosyncracies of the people involved. As a result profit maximization is only one of many potential goals organizations may strive for. 3) According to interpretive theories the environment is not objectively given or perceived, but enacted through individual and collective interpretation.388 As a result, the interpretation of environmental change as opportunity or threat depends on individual cognitive processes, characteristics or mental models of the perceiver.389 Furthermore, cognitive theories acknowledge that the environment is not only marked by complexity and dynamism, but that there may be ambiguous situations. Ambiguity means that possible outcomes, causeeffect relationships and thus probabilities are totally unclear and that a situation allows for multiple, conflicting interpretations.390

Again there are a number of interpretive theories of organizational behavior, whereas the three most relevant for SDM are discussed in the following. The Strategic Sensemaking View acknowledges that organizational decision makers have to become aware of a problematic situation and thus turn to the cognitive processes of perceiving, interpreting and incorporating stimuli that may hint to a problematic situation prompting for action.391 The basic premise of cognitive processing can further be elaborated to a theory of organizations as interpretation systems as proposed by Daft and Weick (1984). Their interpretation view of organizational behavior assumes organizations are open systems that gather and 387

Cf. Huff, A. S. et al. (2000), p. 30. Cf. Hodgkinson, G. P. (2003), p. 7. 389 Cf. Dutton, J./Jackson, S. (1987), p. 86; Huff, A. S. et al. (2000), p. 22. 390 Cf. March, J. G. (1988), p. 399; Forbes, D. P. (2007), p. 367. 391 Cf. Kiesler, S./Sproull, L. (1982), pp. 554-555. 388

Alternative theoretical perspectives and theories

69

interpret information about the environment before taking any strategic action.392 Furthermore, individual and collective interpretation processes are influenced by the organizational and environmental context in which they take place.393 On the one hand, organizations and their environments set antecedent factors such as environmental uncertainty, organizational strategy, complexity, or structure.394 On the other hand, organizations do provide information for the individual cognitive processes.395 The main interest of the Strategic Sensemaking View is how collective understanding is formed and how collaborative action results in organizational change.396 Consequences in terms of economic performance are of less interest to sensemaking researchers,397 although they may also be incorporated in research models following a strategic sensemaking view.398 The Attention Based View roots in the basic behavioral assumptions of limited attention.399 Its central research problem is how organizations direct the attention of decision makers to environmental issues, i.e. opportunities and threats to organization, and the available repertoire of alternative actions.400 It proposes that organizational moves depend on how organizations direct individual attention through procedural and communication channels.401 These channels are basically formal and informal activities of interaction and collaborative enactment imposed by the organization.402 The theory proposes a number of mechanisms how these activities can be designed by the organization, whereas the final goal of these mechanisms is individual and collective enactment of the environment. As such, it recurs on the cognitive processes of perception and interpretation that are at the centre of interpretive views. In contrast to the sensemaking view it not only inquires how the environment is enacted but proposes organizational mechanisms to channel the cognitive processes in a way which is beneficial to the organization.403 However, eventually the Attention Based View falls short of explaining how these processes translate into benefits of the 392

Cf. Daft, R. L./Weick, K. E. (1984), p. 285. Cf. Sutcliffe, K. M./Huber, G. P. (1998), p. 793; Kuvaas, B./Kaufmann, G. (2004), p. 247. 394 Cf. Sutcliffe, K. M. (1994), p. 1365. 395 Cf. Sutcliffe, K. M. (1994), p. 1364. 396 Cf. Gioia, D. A./Chittipeddi, K. (1991), p. 444; Gioia, D. A./Thomas, J. B. (1996), p. 390; Weick, K. E. et al. (2005), p. 414. 397 Cf. Huff, A. S. et al. (2000), pp. 16 and 30. 398 Cf. Thomas, J. B. et al. (1993), p. 256. 399 Cf. Ocasio, W. (1997), p. 188. 400 Cf. Ocasio, W. (1997), p. 189. 401 Cf. Ocasio, W. (1997), p. 192. 402 Cf. Ocasio, W. (1997), p. 194. 403 Cf. Ocasio, W. (1997), p. 204. 393

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organization. Thus it does not provide any connection to organizational outcomes such as competitive advantage or organizational performance.404 Finally, the Upper Echelon View proposes organizational behavior is a reflection of the top managers of organizations.405 This proposition bases on the politics and power perspective, according to which the most powerful coalition dominates organizational behavior. The Strategic Sensemaking View acknowledges “when one speaks of organizational interpretation, one really means interpretation of a relatively small group at the top of the organizational hierarchy.”406 The Upper Echelon View takes this argument a step further by proposing that this small group of people is represented by the top management and thus organizational action is ultimately informed by the subjective interpretations of top management.407 As a result, top management characteristics such as values, cognitive basis, experiences or demographic factors are main influential factors for strategic choice, organizational design and performance.408 Overall, the Upper Echelon View is considered as an integration of interpretive and behavioral perspectives of organizational behavior.409 Interpretive theories contribute to our understanding of organizational behavior in a number of ways. On the one hand, cognitive theories provide a basis for explaining how individuals arrive at subjective representations of the environment. On the other hand, behavioral constraints in terms of diverging interests and cognitive limits can be addressed.410 This moves behavioral theories focussing on mechanic, uniform individual behaviors. Overall, cognitive theories seek to provide a more comprehensive explanation of discretionary firm differences under similar environmental conditions. Interpretive theories are also criticized. First of all, they are criticized for their high level of complexity. “[E]conomists tend to see firms as players in a multi actor economic game, and their interest is in the game and its outcomes”.411 Basically, economists are only interested in how industry structure influences firm actions but not 404

Cf. Ocasio, W. (1997), pp. 204-205. Cf. Hambrick, D. C./Mason, P. A. (1984), p. 194. 406 Daft, R. L./Weick, K. E. (1984), p. 285. 407 Cf. Hambrick, D. C./Mason, P. A. (1984), p. 195. 408 Cf. Hambrick, D. C./Mason, P. A. (1984), p. 198. 409 Cf. Hutzschenreuter, T./Kleindienst, I. (2006), p. 702; Hodgkinson, G. P./Starbuck, W. H. (2008), p. 13. 410 Cf. Huff, A. S. et al. (2000), p. 30. 411 Nelson, R. R. (1991), p. 61. 405

Alternative theoretical perspectives and theories

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in how internal behaviors or cognition of individuals actually inform firm behavior.412 In contrast to that, interpretive theorists argue that individual level processes do matter, because they inform organizational action. These different points of view can be made clear on the basis of Boulding’s framework for describing complexity of systems.413 This framework distinguishes into nine different levels of complexity which are hierarchically organized and can be grouped into three groups. The first group is summarized under the metaphor of machine. Machine-like systems are described as mechanisms where one control mechanism regulates behavior in accordance with a prescribed criterion.414 The second group can be summarized under the term biological systems. Biological systems possess receptors that inform organizational behavior in accordance with its environment but without any self-conscience.415 The third group can be summarized under the term cultural systems. Cultural systems are marked by symbol processing mechanisms and self-conscious behavior. Organizations belong certainly to systems of highest complexity,416 whereas theories of organizations reside on different levels. While economic theories reside on the machine complexity level, interpretive views reside on the highest biological or even on cultural levels.417 Secondly, they are criticized for not taking a positivist approach. This means interpretive theories are rather concerned with how decisions happen rather than with what constitutes optimal firm behavior.418 The latter is a major concern of economic theories which take profit maximizing behavior of firms as given and seek to identify what firm attributes drive performance. Also behavioral theories are concerned with which organizational mechanisms align individual behavior of organizational participants with organizational goals. In contrast to that interpretive theories investigate why firms act as they do not focusing explicitly on what is optimal firm behavior. Nonetheless, interpretive theories can take a positivist approach such as in the Upper Echelon View and thus enrich our understanding of how individual cognition contributes to organizational performance. Table 4 provides a comparison of the aforementioned perspectives and highlights those theories relevant to the present research context. 412

Cf. Nadkarni, S./Barr, P. S. (2008), p. 1395; Nelson, R. R. (1991), p. 61. Cf. Boulding, K. E. (1956). Cf. Chaffee, E. E. (1985), p. 95. 415 Cf. Chaffee, E. E. (1985), p. 95. 416 Cf. Daft, R. L./Weick, K. E. (1984), p. 284. 417 Cf. Daft, R. L./Weick, K. E. (1984), p. 285; Chaffee, E. E. (1985), p. 95. 418 Cf. here and in the following Huff, A. S. et al. (2000), p. 29. 413 414

Selected SDM theories

Central assumptions

Conceptualization of the firm Conceptualization of SDM processes

Central research problem

Behavioral perspective How can organizations effectively guide SDM given cognitive and social limits? Firm as a coalition of individuals and social behavior Strategy making as individual and social behavior

Interpretive perspective How do individuals and collectives arrive at collaborative action?

Table 4: Overview of theoretical perspectives and relevant strategic decision making theories (Source: own compilation)

x Bounded rationality view x Politics and power view

x Strategic Planning School x Resource-based and Dynamic Capabilities theories x Contingency view

x Incremental views x Garbage Can view

x Environment is objectively perceived and influences behavior

x Environment poses conditions to adapt to

x Upper Echelon view

x Environment is subjectively perceived and interpreted x Strategic Sensemaking view x Attention Based view

x Rationality is subjective.

Firm as a network of individual and collective activities Strategy making as individual and collective act of understanding and arriving at collaborative action x Firm behavior governed by firm x Firm behavior is result of social x Firm behavior is result of characteristics behavior within the firm enacting the environment and internal interaction. x Organizational goals are given x Organizational goals are given x Goals emerge from enactment but conflated with individual interests x Individuals and firms are x Cognitive limits and / or x Cognitive limits result in selective completely rational diverging interests inhibit rationality perception and filtering

Economic perspective What are firm characteristics (e.g. strategy, structure) under market equilibrium conditions? Firm as unitary actor with certain characteristics Strategy making as structural characteristics of the organization

72 Selection of a theoretical framework

Evaluation and selection of theoretical basis

73

3.2 Evaluation and selection of theoretical basis The aim of this section is a critical evaluation of SDM theories with respect to their applicability for the present research study. For this purpose, requirements which are to be met by a theoretical framework are established. Then, the theories from the preceding discussion in section 3.1 are evaluated against these requirements. Four requirements with respect to the research questions and the preceding development of theoretical foundations can be established: 1) The theory needs to accommodate behavioral and cognitive processes, because SDM is a multilevel process in two important aspects. On the one hand, it is an individual process of information use for SDM which comprises individual information behaviors and cognitive processes as discussed before. On the other hand, individuals obtain information from the environment and organization and their information use is embedded in the social context of the organization. 2) Although SDM is a multilevel process, the theory should center on the individual and not on the organization, because strategic decision are ultimately made by individuals within the organization and not the organization per se. 3) The theory needs to accommodate for the inclusion of environmental effects on SDM processes. 4) The theory needs to provide a basis for linking SDM processes with strategic decision making effectiveness in terms of contribution to organizational goals or organizational performance. Only if these requirements are met, answers to the research questions can be expected. At first, the applicability of economic and behavioral theories is doubtful when considering the first requirement, accommodation of behavioral and cognitive processes. On the one hand, economic theories are not concerned with individual behavior at all and make simple assumptions on individuals as rational actors. On the other hand, although behavioral theories raise the importance of cognitive characteristics by making certain cognitive assumptions, they still treat actual cognitive processes as black box. Only interpretive perspectives are concerned with cognitive processes of the individual. As a result only the interpretive theories, namely Strategic Sensemaking, Attention Based and Upper Echelon Views remain. In the following these theories are evaluated against the four requirements.

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Selection of a theoretical framework

Firstly, the evaluation of the Strategic Sensemaking View against these four requirements follows. 1) Accommodation of behavioral and cognitive processes: As explained before the Strategic Sensemaking View takes cognitive processes of perception and interpretation as a basic assumption. Provided such individual level cognitive processes, it proposes sensemaking is a social activity of interpreting the world and arriving at collaborative action.419 Therefore, requirement one appears to be met by the Strategic Sensemaking View. 2) Centering on the individual in organizations: Although the Strategic Sensemaking View acknowledges that individuals are central to organizational sensemaking it is foremost a theory centering on the organization as research object.420 Therefore, the second requirement is only partly met. 3) Inclusion of environmental factors: Sensemaking theory is often portrayed with a focus on processes of sensemaking themselves.421 However, there is a link between the environment and sensemaking, because sensemaking is about perceiving and interpreting the environment. As a result a number of studies proposed specific relationships between environmental factors such as turbulence and sensemaking processes.422 Therefore, requirement three is met. 4) Link between SDM processes and organizational outcomes: The main concern of the Strategic Sensemaking View is how organizations perceive and interpret the environment in order to inform their collective action.423 Although some studies have related sensemaking activities to organizational performance,424 the effectiveness of SDM is not a central concern of the Strategic Sensemaking View.425 Therefore, requirement four is basically not met.

419

Cf. Weick, K. E. et al. (2005), p. 409. Cf. Daft, R. L./Weick, K. E. (1984), p. 285. Cf. e.g. Maitlis, S. (2005) or Weick, K. E. et al. (2005). 422 Cf. e.g. Bogner, W. C./Barr, P. S. (2000); Nadkarni, S./Barr, P. S. (2008). 423 Cf. Weick, K. E. et al. (2005), p. 410. 424 Cf. e.g. the study of Thomas, J. B. et al. (1993). 425 Cf. Huff, A. S. et al. (2000), p. 29. 420 421

Evaluation and selection of theoretical basis

75

Secondly, the evaluation of the Attention Based View against the four requirements follows. 1) Accommodation of behavioral and cognitive processes: The Attention Based View takes limited attention as a central premise for developing a theory of organizational attention. Within this theory a number of mechanisms and relationships are proposed which also include behavioral and cognitive processes without being to explicit which processes are actually meant.426 Therefore, requirement one is met by the attention based view. 2) Focus on the individual in organizations: The Attention Based View acknowledges the relevance of individuals, their actions and characteristics for organizational behavior.427 However, the theory centers on how organizations can direct limited attention to environmental issues relevant for organizational goals.428 Basically, organizations are the research object of the Attention Based View and therefore requirement two is not met. 3) Inclusion of environmental factors: The attention based view establishes two basic links to the environment. Firstly, it proposes organizations “are embedded in, and shaped by, the firm’s economic, social, and institutional environment”.429 Secondly, it adopts elements of the sensemaking view, most notably the fact that organizations and their participants are exposed to environmental stimuli which need to be perceived and interpreted.430 Therefore, requirement three is met by the attention based view. 4) Link between SDM processes and organizational outcomes: The attention based view provides a compelling theory of organizational design given environmental influences, as well as behavioral and cognitive processes. However it falls short of providing a connection to organizational outcomes, e.g. “[a]n attention based view cannot explain, by itself, the sources of the firm’s competitive advantage”.431 Therefore, requirement four is not met.

426

Cf. Ocasio, W. (1997), pp. 194-196 and 199-201. Cf. Ocasio, W. (1997), p. 199. 428 Cf. Ocasio, W. (1997), p. 203. 429 Ocasio, W. (1997), p. 194. 430 Cf. Ocasio, W. (1997), pp. 200-201. 431 Ocasio, W. (1997), p. 204. 427

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Selection of a theoretical framework

Thirdly, the evaluation of the Upper Echelon View against the requirements follows. 1) Accommodation of behavioral and cognitive processes: The Upper Echelon View assumes cognitive processes of perception and interpretation as main antecedent to choice. Furthermore, it draws on the politics and power perspective for establishing the basic proposition that top executives matter for organizational performance. Thus it is considered as an integration of behavioral and cognitive perspectives432 and provides a basis for including cognitive processes and political behaviors in a study of SDM.433 However, the Upper Echelon View does not provide any statements on how these processes interact. Therefore, requirement one is partly met by the Upper Echelon View. 2) Focus on the individual in organizations: A basic proposition of the Upper Echelon View is that individual managers do matter for organizational outcomes. Provided this basic proposition requirement two is clearly met. 3) Inclusion of environmental factors: One main reason why top executives matter for organizational outcomes is their boundary spanning role between the environment and the organization. Upper Echelons are continuously exposed to environmental and organizational stimuli informing their actions. They furthermore have a key role in analyzing external threats and opportunities upon which firm action is devised. As such environmental context is clearly accommodated by the Upper Echelon View. 4) Link between SDM processes and organizational outcomes: The Upper Echelon View proposes a connection between individual level characteristics and processes to organizational performance. Therefore, requirement four is met. Table 5 provides a summary of the preceding evaluation of interpretive theories with respect to the four theoretical requirements for the present research study.

432

433

Cf. Hodgkinson, G. P./Starbuck, W. H. (2008), p. 13; Hutzschenreuter, T./Kleindienst, I. (2006), p. 702. Cf. e.g. Hambrick, D. C. (1981a); Sutcliffe, K. M. (1994).

Evaluation and selection of theoretical basis

Criterion … is … 1) Accommodation of behavioral and cognitive processes

Strategic Sensemaking View

77

Attention Based View

Upper Echelon View

met

met

partly met

partly met

not met

met

3) Inclusion of environmental factors

met

met

met

4) Link between SDM processes and organizational outcomes

not met

not met

met

2) Focus on the individual in organizations

Table 5: Summary evaluation of interpretive theories (Source: own Compilation)

First of all, the Attention Based View only meets two of the four requirements, whereas two requirements are not fulfilled. Thus, it does not appear suitable for the present research study. Then, the Strategic Sensemaking View also meets two of the four requirements, whereas another requirement is partly met. However, it does not provide the necessary link between SDM processes and organizational performance. Finally, the Upper Echelon View most closely meets the requirements outlined before. All but the first requirement are fulfilled and also the first requirement is partly met by the Upper Echelon View. This appears contradictory, because the Upper Echelon View is considered as an integration of behavioral and cognitive perspectives. However, it only provides a general framework of how individual characteristics and cognitive processes inform organizational action and performance. It falls short of explaining how these two dimensions interact. This is particularly important for this study, because its conceptual basis comprises two important SDM process dimensions, namely information use of the individual decision maker and political behaviors which this individual is faced with. This short-coming can be addressed by the Strategic Sensemaking View which proposes SDM does not only comprise processes of information use for making a choice, but also processes of forming understanding in a social context. To conclude, the Upper Echelon View forms the main theoretical basis of this study. Following recent SDM Research studies, it will be complemented by the Strategic Sensemaking View in order to account for the social processes associated with SDM.434 434

Cf. for examples of such studies Cho, T. S./Hambrick, D. C. (2006), pp. 453-455; Thomas, J. B./McDaniel, R. R. j. (1990), p. 289; Gioia, D. A./Chittipeddi, K. (1991), p. 443; Wiersema, M.

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3.3 The Upper Echelon View 3.3.1 Introduction Theories centering on the particular role of top executives are not new and date back to theorists such as Barnard, C. I. (1938), Selznick, P. (1957) and Chandler, A. D. (1962).435 Already Chester Barnard emphasizes the nature of organizations as social systems.436 This view takes behavioral perspectives, which restrict their analysis to formal organizational mechanisms, a step further and incorporates informal behaviors and cultural values in a theory of internal organizational behavior. Furthermore, Chester Barnard and other authors emphasize the particular role of executives in shaping organizations as social systems. Subsequently, the interest of strategy theorists shifted to organizational design and its influencing context factors, most notably environment, technology, and size. Analytic models of devising strategies, such as product life cycle theory, portfolio theory or the Profit Impact of Market Strategies (PIMS) framework followed.437 Only since the 1980s a renewed interest in top executives can be seen and traced back as a two stage process.438 Firstly, Child’s (1972) study on strategic choice contrasted very much with the then current deterministic views of organizational behavior. He proposes that variations in firm behavior can be attributed to strategic decisions of the dominant coalition in the organization. Therefore, a study of organizations must integrate a study of those dominant decision makers and the individual and social processes they are involved in.439 However, this proposition leaves room for interpretation of who actually constitutes this dominant coalition.440 Secondly, exactly this issue was addressed by works of Kotter, J. P. (1982), Hambrick, D. C./Mason, P. A. (1984) and other authors proposing that top management forms the dominant coalition which Child actually refers to.441 It should be noted, that these authors are

F./Bantel, K. A. (1992), p. 93; Thomas, J. B. et al. (1993), pp. 246-249; Kuvaas, B. (2002), p. 980; Nadkarni, S./Barr, P. S. (2008), pp. 1396-1398. Cf. Finkelstein, S. et al. (2009), p. 6. 436 Cf. Koontz, H. (1961), p. 179. 437 Cf. Finkelstein, S. et al. (2009), p. 7. 438 Cf. Finkelstein, S. et al. (2009), pp. 7-8. 439 Cf. Child, J. (1972), p. 16. 440 Cf. Finkelstein, S. et al. (2009), p. 8. 441 The works of Miller, D./Kets de Vries, M. F. R./Toulouse, J.-M. (1982); Donaldson, G./Lorsch, J. W. (1983); W.Gary, W./Pfeffer, J./O'Reilly Iii, C. (1984); Meindl, J. R./Ehrlich, S. B./Dukerich, J. M. (1985) added to the momentum of Kotter’s study of top executives. 435

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not alone with this proposition. A number of other organization and strategic management theorists contend that the top management has a central role for setting strategic agendas and directions in organizations.442 Even authors of analytical approaches such as the well-known PIMS study contend that “[n]either the PIMS study or any other empirical research can lead to a ‘formula’ of these strategic choices [about pursuing a building, holding or harvesting strategy; annotation of the author].”443 According to them, strategic choice depends on management’s assessment of future market developments.444 Consequently, focusing on top management is a viable approach to studying organizational behavior and SDM in organizations.445 The basic proposition of the Upper Echelon View is that top managers do matter for organizational behavior and performance.446 This point of view is not obvious and contrasts with the environmental determinism view,447 which posits that organizations mechanically adapt to environmental opportunities, threats and constraints imposed on organizational behavior. Here, the role of management is solely to facilitate this adaptation process.448 Furthermore, the Upper Echelon View has a theoretical, predictive and explanatory focus. It seeks to answer one main question. “How can executive characteristics and behaviors be used to explain variance in organizational outcomes?”449 Based on these observations prescriptive recommendations may be possible, whereas these are not the main concern of the Upper Echelon View. Starting with this basic proposition a number of research directions can be identified. Firstly, a number of specific propositions relating managerial characteristics to strategic choice or organizational performance can be derived450 and tested. For that purpose the specific kinds and nature of relevant individual characteristics such as cognition, experiences or self-concept of top decision makers need to be identified.451 Then, these top decision makers’ characteristics and cognitions are related with strategic orientations and performances of organizations. In addition to that, some 442

Cf. Mintzberg, H. et al. (1976), p. 260; Daft, R. L./Weick, K. E. (1984), p. 284. Buzzell, R. D./Gale, B. T./Sultan, R. G. M. (1975), p. 106. Cf. Buzzell, R. D. et al. (1975), p. 105. 445 Cf. Hutzschenreuter, T./Kleindienst, I. (2006), p. 702. 446 Cf. Finkelstein, S. et al. (2009), p. 16. 447 Cf. Papadakis, V. M./Lioukas, S./Chambers, D. (1998), p. 116. 448 Cf. Hannan, M. T./Freeman, J. H. (1977); Aldrich, H. E. (1979); Papadakis, V. M. et al. (1998), p. 118. 449 Finkelstein, S. et al. (2009), p. 12. 450 Cf. Hambrick, D. C./Mason, P. A. (1984), pp. 198-203. 451 Cf. Finkelstein, S. et al. (2009), pp. 43-49 and pp. 83-85; Hiller, N. J./Hambrick, D. C. (2005), p. 300. 443 444

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studies seek to integrate top executives’ characteristics and contextual factors into more comprehensive models of organizational choice and performance.452 Secondly, Upper Echelon research is not only concerned with individual top executives, most notably the Chief Executive Officer (CEO), but also with top management teams (TMT) or management boards including their team-internal processes and ultimately their effects on strategy and organizational outcomes.453 Thirdly, Upper Echelon research identified a number of important moderating factors. One set of factors such as managerial discretion and executive job demands closely relates to individuals’ immediate task environment.454 Closely related is the study of a match of executive characteristics and job demands455 and whether change among top executives alters firm strategies and outcomes.456 Another set of moderating factors relates to the environmental context and how they impact the Upper Echelon effects on their organizations.457 However, the Upper Echelon View also received some criticisms. Firstly, supporters of the environmental determinism perspective doubt the influence of individuals in organizations at all. For example one empirical study seeking to isolate the effect of top executives reaches the conclusion that “performance variables are affected by forces beyond a leader’s immediate control”.458 A similar conclusion suggest the results of Bertrand, M./Schoa, A. (2003) where only five percent of the explained variance of return on assets is attributable to the inclusion of individual managerial characteristics.459 Secondly, some behavioral theorists argue that internal constraints such as fixed investments, restricted information flows, political behavior, and external constraints such as legal or fiscal barriers, restricted access to external information or legitimacy constraints inhibit an influential role of individuals in organizations.460 452

Cf. e.g. Norburn, D. (1989), p. 4; Hitt, M. A./Beverly, B. T. (1991), p. 328; Lewin, A. Y./Stephens, C. U. (1994), p. 186; Rajagopalan, N./Datfa, D. K. (1996), p. 210; Henderson, A. D./Miller, D./Hambrick, D. C. (2006), p. 457. And cf. Carpenter, M. A./Geletkanycz, M. A./Sanders, W. G. (2004), pp. 754-758 for a review of other empirical studies during the period from 1996 to 2003. 453 Cf. Amason, A. C. (1996), p. 670; Iaquinto, A. L./Fredrickson, J. W. (1997), p. 72; Simons, T./Pelled, L. H./Smith, K. A. (1999), p. 128. 454 Cf. Hambrick, D. C. (2007), p. 335. 455 Cf. Hambrick, D. C. (1987), p. 54. 456 Cf. Lant, T. K./Milliken, F. J. (1992), p. 589. 457 Cf. e.g. Haleblian, J./Finkelstein, S. (1993), pp. 844-847; Sutcliffe, K. M. (1994), p. 1373. 458 Lieberson, S./O'Connor, J. F. (1972), p. 121. 459 Cf. Bertrand, M./Schoa, A. (2003), p. 1185. 460 Cf. Hannan, M. T./Freeman, J. H. (1977); DiMaggio, P. J./Powell, W. W. (1983).

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Thirdly, another criticism refers to a potential lack of diversity among top executives such as gender, ethnic and socio-demographic background.461 Despite these criticisms, the Upper Echelon View provides a robust theoretical framework on organizational behavior and received empirical support in a number of management research studies. 3.3.2 Main elements and theoretical statements Strategic choice of top executives is at the center of the Upper Echelon View. Building on the politics and power perspective the Upper Echelon View proposes top executives are the most influential decision makers in organizations due to their power base. For making strategic choices, top executives require knowledge which is limited and continuously updated by an ongoing stream of external and organizational stimuli. A major assumption of the Upper Echelon View is that stimuli are subject to the individual cognitive processes of top decision makers. These processes are portrayed as a sequence from perception of stimuli to strategic choice.462 In between occurs filtering and interpretation of these stimuli which are influenced by the cognitive base, values and personalities of the individual decision maker.463 Through these interactions top executives arrive at a subjective managerial perception of the situation which informs strategic choice of the organization. As a result, strategic choice of the organization is subject to idiosyncratic givens of the individual top executive.464 The impact of top executives on organizations is three-fold according to the Upper Echelon View. Firstly, top executives influence the overall SDM processes,465 which also include information behaviors such as information acquisition and use for SDM.466 Secondly, top executives directly influence what kind of strategic choices are made. Here, the term strategic choice is comprehensive and comprises formally and informally made decisions, indecisions as well as major administrative choices.467 Finally, organizational performance is the outcome of these processes of strategy making and the strategic choices made. Thus variation in organizational performance may in part be attributable to individual characteristics of top decision makers.

461

Cf. Finkelstein, S. et al. (2009), p. 21. Cf. Hambrick, D. C./Mason, P. A. (1984), p. 195. Cf. Hambrick, D. C. (2007), p. 334. 464 Cf. Hambrick, D. C./Mason, P. A. (1984), p. 195. 465 Cf. here and in the following Finkelstein, S. et al. (2009), pp. 19 and 152-158. 466 Cf. Finkelstein, S. et al. (2009), pp. 19-20. 467 Cf. Hambrick, D. C./Mason, P. A. (1984), pp. 194-195. 462 463

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Furthermore, the Upper Echelon View identifies four main units of analysis. Firstly, much research is devoted to those individuals who have the primary responsibility for an organization, namely CEOs or Business Unit Heads.468 Apart from their responsibility for the organization, they are generally assumed to have a great influence in SDM processes and setting strategic directions. This individually focused view can be enhanced by a focus on TMT. This acknowledges that top management is actually a shared activity, in particular in large organizations. Finally, the Upper Echelon view also considers the board of directors and their interactions with the executives of an organization. This is not only due to board responsibilities of nominating or dismissing executives and authorizing major decisions, but also due to board involvement in the SDM processes.469 This basic framework allows for an integration of individual and team-related characteristics. Two basic questions need to be addressed for that purpose. Firstly, which relevant individual or team-related characteristics can be identified? Secondly how do these characteristics relate to SDM processes, strategic choice and organizational performance? On the individual level executive characteristics can be distinguished into observable and unobservable characteristics.470 Early Upper Echelon research is concerned with observable characteristics such as age, functional track, experiences, education or socio-economic background.471 This early focus is partly due to ease of measurement and high a priori interest.472 As a result Upper Echelon research took a behavioral perspective and did not further inquire into the actual cognitive processes of choice. Only recently, the interrelation of managerial characteristics, actions and cognitive processes is emphasized.473 Therefore, unobservable characteristics such as top executives’ personality and cognitive characteristics receive increasing research interest.474 The integration of individual characteristics into a theory of organizational behavior is made in two ways. Firstly, individual characteristics influence the cognitive process of 468

Cf. here and in the following Finkelstein, S. et al. (2009), pp. 9-11. Cf. Rindova, V. P. (1999), p. 958; Finkelstein, S. et al. (2009), p. 11. Cf. Hambrick, D. C./Mason, P. A. (1984), p. 198. 471 Cf. Hambrick, D. C./Mason, P. A. (1984), pp. 198-203. 472 Cf. Hambrick, D. C./Mason, P. A. (1984), p. 196. 473 Cf. Carpenter, M. A. et al. (2004), p. 770; Hambrick, D. C. (2007), p. 337. 474 Cf. Hiller, N. J./Hambrick, D. C. (2005), p. 300. 469 470

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perceiving, filtering and interpreting the large amounts of stimuli.475 The assumption of limited attention proposes that not all stimuli can be processed by individuals. As a result, individuals overcome the burden of information processing by limiting their field of vision and selectively perceiving stimuli. Moreover, those stimuli selected for further processing undergo a subjective interpretation process. This process results in a subjective construction of the objective situation given and informs executive behaviors and strategic choice. During all these stages executive characteristics such as values, personality, cognitive characteristics, experiences and socio-demographic factors impact the subjective perception and interpretation of stimuli.476 For example, executives with knowledge about technologies most probably direct their attention to information about technological innovation. Furthermore, their knowledge enables them to comprehend and interpret this information in another way as compared to executives without technological knowledge.477 Secondly, individual characteristics directly affect which strategic choices are preferred over others. For example, an executive’s familiarity with a certain technology may directly influence the choice among alternative investments in technology.478 A focus on TMTs adds more dimensions to the research inquiry. TMT characteristics and team internal processes influence SDM processes and how they translate into strategic choice and organizational outcomes.479 Main TMT characteristics are team heterogeneity and team structural aspects such as role interdependence or simply team size.480 Team heterogeneity can be seen as instrumental variable for a number of cognitively related characteristics such as problem solving abilities, creativity or diversity of information sources. Furthermore, these characteristics also influence team internal processes such as social integration or consensus within the TMT. Overall team composition and internal processes affect SDM processes, strategic choice and the implementation of strategies alike and as a result TMT partly explain organizational performance. 481

475

Cf. Finkelstein, S. et al. (2009), p. 44. Cf. Finkelstein, S. et al. (2009), pp. 41-48. for a detailed review of individual characteristics and propositions how they influence the cognitive processes of top executives. 477 Cf. Finkelstein, S. et al. (2009), pp. 60-61. 478 Cf. Finkelstein, S. et al. (2009), p. 61. 479 Cf. Finkelstein, S. et al. (2009), p. 125. 480 Cf. here and in the following Finkelstein, S. et al. (2009), pp. 131-138. 481 Cf. Finkelstein, S. et al. (2009), pp. 154-158. 476

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Management boards can be another unit of analysis of the Upper Echelon View. Due to a separation of ownership and management or regulatory conditions top management may have to justify their actions against external parties. These parties are represented in the management boards of organizations which thus may also be a unit of analysis in the Upper Echelon View. Consequently, board characteristics and their role for monitoring and disciplining top executives as well as for strategy formation can be added to a model of organizational behavior.482 In addition to this initial focus on internal processes and individual and team characteristics, the Upper Echelon View accommodates the impact of the environment and contextual factors. Firstly, top executives operate at the boundary between the organization and the external environment and gather information about environmental changes and inform internal actions.483 As such, the environment provides an objective situation to adapt to.484 By aligning current and expected environmental conditions with organizational strengths and weaknesses strategic choice and organizational action are informed.485 Secondly, these adaptation processes are ultimately informed by the subjective perception and interpretation of environmental and organizational stimuli by top executives. The basic proposition of the Upper Echelon View is exactly that individual idiosyncracies have an influence on strategic choice. Secondly, besides this boundary spanning role of top executives, the Upper Echelon View can be amended by a contingency perspective of organizational behavior. Then, the basic concern is how environmental and organizational contingency factors impact the main relationships between top executives and organizational behavior and outcomes.486 These contingent effects can be of different nature though. Firstly, a basic proposition is the higher environmental uncertainty the more top executive characteristics manifest themselves in strategic action and organization outcomes.487 Secondly, contextual factors may be determinants of top executive characteristics and processes.488 Thirdly, contextual factors affect the executive job demands which 482

Cf. Finkelstein, S. et al. (2009), p. 228. Cf. Finkelstein, S. et al. (2009), p. 19 Cf. Hambrick, D. C./Mason, P. A. (1984), p. 198. 485 Cf. Finkelstein, S. et al. (2009), p. 153. 486 Cf. Keck, S. L. (1997), pp. 145-147. and 152-153; Carpenter, M. A. et al. (2004), pp. 764-766. 487 Cf. Carpenter, M. A./Fredrickson, J. W. (2001), pp. 536 and 542. 488 Cf. Iaquinto, A. L./Fredrickson, J. W. (1997), p. 72. 483 484

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include the task challenges executives face.489 Finally, environmental factors may have an effect on individual cognitive processes of strategy formulation.490 Finally, a major assertion of the Upper Echelon View is that managers have some true possibilities for choice.491 This is addressed by the concept of managerial discretion, which refers to the latitude of managerial action available to top executives.492 Low discretion implies that top executives account for little variation in organizational behavior and outcomes, whereas high discretion means that top executives can shape the organization which in turn is reflected in organizations outcomes. As such managerial discretion can be considered as an overarching contingency factor. Managerial discretion derives from three main sources. Firstly, environmental sources such as product differentiability, demand instability, capital intensity or government regulation are important determinants of the alternative space in SDM.493 Secondly, organizational sources such as inertial forces, resource availability and authority in setting organizational objectives influence the extent of managerial latitude. Inertial forces derive from factors such as organizational size, age or culture and refer to the degree to which organizations are flexible in following executives’ choices.494 Resource availability refers to the amount of slack resources that can potentially be dedicated for pursuing a proposed strategy.495 Top executives’ authority in setting organizational objectives may be inhibited by a separation of management and ownership and respective governance structures. Thirdly, individual sources of discretion derive from executives’ abilities to generate multiple courses of action. This is closely related to their personal characteristics and behaviors. 3.4 Summary Three main theoretical perspectives towards studying SDM in organizations exist. These perspectives differ with respect to their central research problems and key assumptions on individuals, organizations and the environment.

489

Cf. Hambrick, D. C./Finkelstein, S./Mooney, A. C. (2005), p. 476. Cf. Cho, T. S./Hambrick, D. C. (2006), p. 465; Nadkarni, S./Barr, P. S. (2008), p. 1416. Cf. Finkelstein, S. et al. (2009), p. 26. 492 Cf. Finkelstein, S./Hambrick, D. C. (1990), p. 484. 493 Cf. Finkelstein, S./Hambrick, D. C. (1990), p. 489; Finkelstein, S. et al. (2009), pp. 27-28. 494 Cf. Finkelstein, S. et al. (2009), p. 31. 495 Cf. Finkelstein, S./Hambrick, D. C. (1990), p. 489. 490 491

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Firstly, economic perspectives to SDM are concerned with optimal firm behavior in a given market environment. According to this perspective individuals act fully rational, i.e. utility maximizing, and do not have any self-interests. As a result SDM is studied from a normative perspective and a main proposition is that SDM is a means of translating economic goals of an organization into a strategic plan which is further broken down into tactical and operational measures. Furthermore, organizations can be described by SDM process characteristics which align with the environmental conditions an organization is faced with. Secondly, behavioral theories are descriptive theories of SDM relaxing the assumptions on the individual. They acknowledge that SDM is subject to cognitive limits and diverging interests of organizational members. The overarching research problem is how organizations are designed to overcome individual limits and direct individual behavior towards organizational goals. The various behavioral theories differ with respect to their specific assumptions. The bounded rationality view focuses on cognitive limits and how individuals overcome these limits. The politics and power view focuses on limits from diverging interests and political behavior which is intended to pursue individuals’ interest within an organization. Incremental and garbage can models further relax assumptions from both domains cognitive limits and individual interests and seek to more realistically describe SDM in organizations. Thirdly, interpretive theories further increase the level of complexity and do not only acknowledge cognitive limits but seek to investigate how managers’ cognitions influence organizational outcomes. They basically propose that individual and collective cognitive processes inform strategic action. From this perspective, SDM is highly subjective and depends on individual characteristics and cognitions of the individuals acting and interacting in organizations. The critical evaluation of these perspectives and theories showed that economic and behavioral perspectives do not allow for an integration of individual information behaviors and cognitions as well as cognitive characteristics (i.e. cognitive styles). Consequently, interpretive theories remain. A more detailed evaluation showed that the Upper Echelon View is the most suitable theoretical approach, because it is an integration of behavioral and interpretive perspectives, it focuses on the individual as research object, it allows for an inclusion of environmental context, and it provides a connection to organizational outcomes and performance.

Summary

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The Upper Echelon View proposes that individual top executives do matter for the strategic actions and performance of an organization. This proposition builds on the politics and power perspective and argues that top executives form the dominant coalition in an organization and thus have the main influence on organizational actions. It furthermore assumes that strategic action is informed by the idiosyncratic cognitive processes of these individuals. This assumption opens up the inclusion of individual characteristics in a theory of firm behavior. Last but not least, the Upper Echelon View proposes a link with environmental context, because top executives have a boundary spanning role and thus base organizational action on an analysis of both environmental and organizational context. In addition to merely chosing the Upper Echelon View as a theoretical framework, this study also contributes to the development of the Upper Echelon View as follows. Originally, Upper Echelon research focused on the effect of observable managerial characteristics such as age or education on strategic choices.496 These characteristics are used as proxy variables for individual traits such as personal values or cognitive characteristics.497 Such observable characteristics are then related to strategic choices and firm outcomes. However, some authors raise the question whether organizational research does not need to move beyond its focus on demographic variables.498 The reason is that causal relationships between observed demographic and outcome variables are not unambiguous. Instead the investigated relationships rest on partly complex theoretical assumptions on underlying causal relationships such as the perceptual process in the Upper Echelon View.499 However, these assumptions are most often far from proven and there may be alternative theoretical considerations for the assumed causal relationships. Therefore, “the use of demographic indicators leaves us at a loss as to the real psychological and social processes that are driving executive behavior”.500 This research study addresses this short-coming in two ways. Firstly, this study models use and effectiveness of different information inputs. So far, the perceptual process of top managers was a central assumption of the Upper Echelon View but has rarely been tested.501 Secondly, it relates these information inputs to a cognitive characteristic (i.e. cognitive style) instead of demographic characteristics and 496

See Hambrick, D. C./Mason, P. A. (1984), pp. 198-202. See Carpenter, M. A. et al. (2004), p. 770. See Lawrence, B. S. (1997), p. 4; Carpenter, M. A. et al. (2004), p. 770; Hambrick, D. C. (2007), p. 337. 499 See Lawrence, B. S. (1997), p. 4. 500 Hambrick, D. C. (2007), p. 335. 501 See Hambrick, D. C. (2007), p. 337. 497 498

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further inquires into information use processes. Instead of merely predicting organizational outcomes with observable individual characteristics such as in most former Upper Echelon studies, this study seeks to explain the underlying psychological and social processes of SDM and their relationships with executive psychological characteristics. Finally, the Upper Echelon View itself has one short-coming with respect to the present research study. Although it is considered as an integration of behavioral and interpretive perspectives it does not provide statements on how individual cognitions of top executives interact with the social context of the organization. However, the social context most notably the political dimension of SDM has important effects in SDM. This gap is filled with the help of the Strategic Sensemaking View. It provides a theoretical basis of how individual and collective understanding is formed within a social context. Thus it provides a complementary theory to the Upper Echelon View.

Theory and hypotheses development

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4 Theory and hypotheses development The Upper Echelon View has been identified as the main theoretical basis, because it is an integration of interpretive and behavioral perspectives on SDM and allows for an inclusion of the research variables of this study. However, the Upper Echelon View in its basic form requires theoretical amendment, because it does not entail the social context within which SDM takes place. This short-coming can be addressed with the help of the Strategic Sensemaking View, which provides a theory of collective cognition and action in organizations. Thus, Upper Echelon and Strategic Sensemaking Views are complementary theoretical perspectives on SDM which both serve for theory and hypotheses development for this study. This integration is valid, because both views build on similar assumptions with respect to individual cognition, diverging interests and power as an organizational force. Last but not least, the Upper Echelon and Strategic Sensemaking Views have only recently been intertwined in SDM research and provided new insights into SDM.502 In general, three requirements for developing a robust and consistent theoretical basis for management research are to be met:503 x Firstly, the phenomenon of interest needs to be identified. x Secondly, key premises and assumptions need to be stated. x Thirdly, the relationships among the elements of the theoretical framework need to be described and testable hypotheses need to be stated. Concerning the first requirement, SDM is the basic phenomenon of interest and has been described and defined in section 2.1. Therefore, the following theory development continues with establishing the key theoretical premises on the basis of the Upper Echelon and Strategic Sensemaking Views in section 1.1. Thereafter, the relationships and specific hypotheses between the variables of interest are developed in section 4.2.

502 503

Cf. e.g. Nadkarni, S./Barr, P. S. (2008). Cf. Crossan, M. M./Lane, H. W./White, R. E. (1999), p. 523.

W. Gänswein, Effectiveness of Information Use for Strategic Decision Making, DOI 10.1007/978-3-8349-6849-4_4, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

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4.1 Theoretical premises The objective of this section is to establish key premises upon which hypotheses among the variables of interest can be derived. Six theoretical premises (P) are established based on the Upper Echelon and Strategic Sensemaking Views as summarized in Figure 7.

P1

Organizations are open social systems and SDM is a means to adapt to environmental changes.

P2

SDM is subject to cognitive limits (limited knowledge and information processing capability) and behavioral limits (conflicts of interest and political behavior)

P3

SDM is ultimately an individual activity of key decision makers in the organization.

P4

On the individual level SDM is a cognitive process of perception, knowledge creation and problem solving where information needs to be perceived before it can effectively be used.

P5

These individual level cognitive process are influenced by the social context within which they take place. Cognition affects action and vice versa.

P6

Information use can be evaluated from a knowledge transfer and a sensemaking perspective and different information sources possess different capabilities for facilitating the associated processes.

Figure 7: Overview of theoretical premises (Source: own compilation)

In the following these premises are further detailed. A common insight of strategy research is the inseparability of organizations and their environment. As stated in premise 1, strategy is intended to adapt organizations to environmental change504 by formulating strategic directions upon which firm action is devised. Furthermore, organizations are open social systems and they process information from the environment in order to base their action on this information.505 SDM is a special case of information processing in order to adapt the organization to changes, i.e. opportunities and threats, arising in the environment.506 Furthermore, the environment contains uncertainty, which in case of SDM directly pertains to the shape 504 505 506

Cf. Chaffee, E. E. (1985), p. 89. Cf. Daft, R. L./Weick, K. E. (1984), p. 285. Cf. Huber, G. P./Glick, W. H. (1993), pp. 3-5.

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of a decision problem because uncertainty is a measure for the number, interdependence and variability of decision relevant factors.507 Premise 2 states, SDM is subject to cognitive and behavioral limits and has a long tradition in behavioral and cognitive SDM theory. 508 Decision makers are subject to cognitive limits in terms of limited knowledge, conflicting goals, limited attention or computational limits.509 All these limitations prevent them from following the Economic Man paradigm. Instead, they follow a satisficing instead of optimizing approach and engage in problemistic, not fully comprehensive search for information in order to arrive at a final choice.510 In addition to that, behavioral limits arise from conflicts between organizational and individual goals of organizational participants. As a result decision making may be subject to behaviors of decision participants directed at pursuing their own instead of organizational goals.511 Premise 3 states SDM is the activity of individual key decision makers in the organization and can be established on the basis of the Upper Echelon View. 512 As already pointed out the SDM process is one of three distinct facets of organizational behavior addressed by the Upper Echelon View.513 According to this perspective individual top executives matter for strategic choice and organizational outcomes because they bring together and interpret information for the firm as a whole. 514 Data gathering and analysis may be performed throughout the whole organization, while the point where information converges and translates into organizational action is at very few individuals at the top. According to the Upper Echelon View, these key decision makers derive their central role from their special power base in the organization. Thus, key decision makers are most often the top executives because they generally form the dominant coalition in the organization.515 Typically, the CEO is the key decision maker within this coalition because he or she has the primary responsibility

507

Cf. Duncan, R. (1972), p. 314; Frishammar, J. (2003), p. 320. Cf. Hodgkinson, G. P./Starbuck, W. H. (2008), pp. 5-11. Cf. Eisenhardt, K. M./Zbaracki, M. J. (1992), pp. 18; Hambrick, D. C./Mason, P. A. (1984), pp. 194-195; O'Reilly, I. I. I. C. A. (1983), p. 106; Simon, H. A. (1978), pp. 10-13. 510 Cf. Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 22. 511 Cf. Cyert, R. M./March, J. G. (1963); Eisenhardt, K. M./Zbaracki, M. J. (1992), p. 23. 512 Cf. Hambrick, D. C./Mason, P. A. (1984), pp. 194-195; Hambrick, D. C. (2007), p. 334. 513 Cf. Finkelstein, S. et al. (2009), p. 152. 514 Cf. Daft, R. L./Weick, K. E. (1984), p. 285; Hambrick, D. C./Mason, P. A. (1984), p. 195; Nadkarni, S./Barr, P. S. (2008), p. 1396. 515 Cf. Hambrick, D. C./Mason, P. A. (1984), p. 193. 508 509

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for setting strategic directions and plans for the organization.516 However, there may be cases where top executives other than the CEO make strategic decisions517 largely depending on whether they have the decision making authority or not. For example, a top executive may make a decision for which the commitment of resources is below any authorization threshold while the decision itself still fulfills the general characteristics of a strategic decision.518 Therefore, the basic premise is not to be seen to restrictive by focusing on the CEO only, but takes a broader view by focusing on any top executive making a strategic decision. According to premise 4, SDM is an individual level cognitive process of perception, knowledge creation (i.e. learning) and problem solving.519 For making strategic decisions individuals draw from knowledge which is cognitively stored in simplified representations of the real world, so-called mental models or cognitive maps.520 In other words, mental models contain knowledge about characteristics of decision relevant factors and the relationships among these factors. Due to a limited knowledge base, decision making requires additional knowledge about decision relevant factors and their interdependence as well as knowledge about strategic alternatives and their potential consequences.521 Based on this conception SDM can be seen from a learning perspective where information use is directed at creating knowledge for SDM which is then furthermore used in problem solving activities.522 Information use has two effects on the knowledge contained in mental models. Firstly, information use can add or update knowledge about characteristics of decision relevant factors that are already present in the mental model. Secondly, information use can structure the mental representations by adding, confirming or disconfirming decision relevant factors and their relationships.523 Therefore, in any choice process information assimilation and use is a key component.524

516

Cf. Gioia, D. A./Chittipeddi, K. (1991), p. 433. Cf. Mintzberg, H. et al. (1976), pp. 259-260; Hitt, M. A./Tyler, B. B. (1991), p. 333. Cf. Mintzberg, H. et al. (1976), pp. 260. 519 Cf. Miller, A. (1987), p. 252. The author uses different terms (perception, thought, storage) while referring basically to the above mentioned cognitive functions perception, problem solving (thought) and storage (knowledge creation / learning). 520 Cf. Ungson, G. R. et al. (1981), p. 123; Kiesler, S./Sproull, L. (1982), p. 556; Hodgkinson, G. P. (2003), pp. 4-5. 521 Cf. Hambrick, D. C./Mason, P. A. (1984), p. 195. 522 Cf. Cross, R./Sproull, L. (2004), p. 446 523 Cf. Vandenbosch, B./Higgins, C. (1996), p. 201. 524 Cf. O'Reilly, I. I. I. C. A. (1983), p. 106. 517 518

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However, any information assimilation and use is preceded by perception of stimuli. Decision makers are “exposed to an ongoing stream of stimuli both within and outside the organization” continuously updating their knowledge base.525 These stimuli need to be perceived before they can be used for knowledge creation or problem solving. Two distinct activities during the perceptual process can furthermore be distinguished.526 The first step has several names in the literature and is called scanning,527 noticing528 or attention.529 All these terms basically mean distinguishing signals from noise.530 Noticing of stimuli is a prerequisite to whether stimuli enter subsequent cognitive processes. The second step is interpretation which means stimuli are given meaning, which happens through categorizing of information and placing or adding these categories in an existing knowledge framework.531 After stimuli have passed these perceptual processes, decision makers arrive at their individual mental representation of a decision problem space which informs the subsequent processes of SDM.532 Therefore, perceptual processes are considered as the most fundamental act of cognition533 and do precede any other cognitive activities of knowledge creation or problem solving. In any case, perceptual processes are subject to cognitive limits and all incoming stimuli cannot effectively be processed. Firstly cognitive limits result in selective attention or selective noticing because only a fraction of stimuli can effectively be noticed.534 Secondly, cognitive limits result in distortion of interpretation because stimuli are interpreted highly dependent on the cognitive base of a decision maker.535 Furthermore, perceptual processes are individually different. “The stimuli one executive receives may be exactly the same stimuli that another executive filters out”.536 Executives may also use different strategies for interpreting stimuli and thus arrive at differing interpretations of one and the same set of stimuli.537 These differences occur because the individual cognitive model has an influence on “whether

525

Hambrick, D. C./Mason, P. A. (1984), p. 195. Cf. Daft, R. L./Weick, K. E. (1984), p. 286; Hambrick, D. C./Mason, P. A. (1984), p. 195; Starbuck, W. H./Milliken, F. J. (1988), p. 43. 527 Cf. Daft, R. L./Weick, K. E. (1984), p. 286. 528 Cf. Starbuck, W. H./Milliken, F. J. (1988), p. 45. 529 Cf. Miller, A. (1987), p. 253. 530 Cf. Starbuck, W. H./Milliken, F. J. (1988), p. 45. 531 Cf. Daft, R. L./Weick, K. E. (1984), p. 286; Corner, P. D. et al. (1994), p. 297; Starbuck, W. H./Milliken, F. J. (1988), p. 51. 532 Cf. Daft, R. L./Weick, K. E. (1984), p. 286; Hambrick, D. C./Mason, P. A. (1984), p. 195. 533 Cf. Neisser, U. (1976), p. 4. 534 Cf. Starbuck, W. H./Milliken, F. J. (1988), pp. 51-58. 535 Cf. Hambrick, D. C./Mason, P. A. (1984), p. 195. 536 Starbuck, W. H./Milliken, F. J. (1988), p. 45. 537 Cf. Hambrick, D. C./Mason, P. A. (1984), p. 195. 526

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and how new stimuli will be noticed, encoded and acted upon.”538 In general, a cognitive model comprises three main elements. These are 1) one’s cognitive content, i.e. knowledge, 2) one’s cognitive structure, i.e. how this knowledge is arranged in the mind, and 3) one’s cognitive style, i.e. information processing preferences.539 However, perception does not only occur within a perceiver, because perceivers are inseparable from their environments. Therefore, perception is a result of individual characteristics on the one hand and the context of the individual on the other hand.540 Premise 5 states individual cognitive processes are influenced by the social context within which they take place. Simply speaking, cognition affects action and vice versa.541 This premise builds on the Strategic Sensemaking View, which considers SDM as a process of collectively creating and maintaining interpretations of the world.542 Furthermore, according to the Strategic Sensemaking View “[c]onduct is contingent on the conduct of others, whether those others are imagined or physically present.”543 Overall this implies SDM is not a static series of decision making episodes but a situated social activity544 mutually shaped by cognition and actions of people involved in the SDM process. Consequently the influence of social context is two-fold. Firstly, social context directly informs strategic choice which would then be reflected in strategic decision quality. The reasons are that SDM is informed by collectively sharing context and individual cognitive processing.545 Neutrally speaking, people exchange knowledge and collectively create meaning. When adding agency into the equation people seek to influence the actual or perceived decision situation a key decision maker is faced with.546 Secondly, social context informs the SDM process in the following way. “Perception and behavior are controlled interactively; their course depends on the individual as well as the environment. […] Any consideration of this problem must begin with the

538

Finkelstein, S. et al. (2009), p. 59. Cf. Finkelstein, S. et al. (2009), p. 59. 540 Cf. Starbuck, W. H./Milliken, F. J. (1988), p. 42. 541 Cf. Weick, K. E. (1979); Neisser, U. (1976), pp. 184-185; Crossan, M. M. et al. (1999), p. 524. 542 Cf. Balogun, J./Pye, A./Hodgkinson, G. P. (2008), p. 235-236; Corner, P. D. et al. (1994), pp. 295296; Gioia, D. A./Chittipeddi, K. (1991), p. 434; 543 Weick, K. E. (1995), p. 39. 544 Cf. Balogun, J. et al. (2008), p. 244f.; Weick, K. E. (1995), p. 39. 545 Cf. Corner, P. D. et al. (1994), p. 295. 546 Cf. Balogun, J. et al. (2008), p. 236. 539

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fact that we do predict one another’s behavior, with consistency and success.”547 Insofar, social context influences the individual level actions associated with SDM, i.e. information use throughout the SDM process. Premise 6 states, information use can be seen from two different perspectives.548 From a knowledge transfer perspective information use “focuses on knowledge transfer abstracted from a particular setting”.549 From a sensemaking perspective, information use focuses on how individuals construct meaning in a social and environmental context. Therefore, the effectiveness of information use can be evaluated according to two basic questions. Firstly, does information use provide knowledge which is relevant for solving a strategic decision problem? Secondly, does information use provide stimuli which are effectively noticed and interpreted given the interplay of stimuli, context and cognitive style of a key decision maker? The evaluation of these two basic questions will differ depending on the information source considered, because different information sources possess different capabilities with respect to knowledge transfer and sensemaking.550 From a knowledge transfer perspective, information sources can be evaluated to the extent to which they provide decision relevant information. From this perspective, information use is effective as long as it has the general capability of providing relevant knowledge for SDM.551 On the other hand, from a sensemaking perspective information sources can be evaluated to the extent to which they are capable of creating mutual understanding. Although there is some debate around which dimensions most validly describe this capability, 552 two key dimensions can be identified that clearly differ between personal and impersonal information sources.553 The first key dimension is the possibility for feedback. Feedback generally speeds up information exchange and allows for immediate clarification of specific issues. Impersonal sources are characterized by one way communication and do not provide for feedback. In contrast to that, personal sources generally provide the possibility for feedback where the immediacy of 547

Neisser, U. (1976), p. 186. Cf. Cross, R./Sproull, L. (2004), p. 446. 549 Cross, R./Sproull, L. (2004), p. 446. 550 Cf. e.g. Daft, R. L./Lengel, R. H. (1986), pp. 559-561; Dennis, A. R. et al. (2008), pp. 580 and 583585; Rice, R. E. (1992), pp. 476-480. 551 Cf. Byström, K./Järvelin, K. (1995), p. 192; O'Reilly, I. I. I. C. A. (1983), p. 106; Tushman, M. L./Nadler, D. A. (1978), p. 614. 552 Cf. Dennis, A. R. et al. (2008), pp. 577-579 for a review of different media theories. 553 Cf. here and in the following Daft, R. L. et al. (1987), p. 358; Dennis, A. R./Kinney, S. T. (1998), pp. 259-261; Kahai, S. S./Cooper, R. B. (2003), p. 265. 548

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feedback is highest for face-to-face communication. Nonetheless, any personal communication allows for feedback such that there is a clear difference compared with impersonal sources. The second key dimension is multiplicity of information cues, which refers to the number of ways information can be communicated. These ways of communication include text or words, verbal cues and non-verbal cues.554 The presence of multiple cues can have specific effects on creating mutual understanding. Verbal and nonverbal cues include information beyond the mere words transmitted. They are used to emphasize important points or to express uncertainty, doubt or acceptance. These Sensemaking capabilities of different information sources provide the theoretical basis for deriving hypotheses about the moderating effects of PEU and cognitive style, because as already stated effective perception is a function of characteristics of the individual perceiver and his or her social and environmental context of perception. 4.2 Hypotheses development Several variables for investigating the effectiveness of SDM have been identified in section 2.2. Furthermore, a combination of the Upper Echelon and Strategic Sensemaking Views provides the theoretical basis for explaining how environmental, organizational and individual level variables interact with respect to the effectiveness of SDM. As a consequence of this combination, it is now possible to explain SDM as a multilevel process in which individual information use is embedded in the social context of the organization and its broader environment. Based on the theoretical premises established beforehand, specific hypotheses on the relationships between the research variables are developed in the following. Section 4.2.1 develops hypotheses on the direct effects between the independent and dependent variables of interest. Thereafter hypotheses on the moderating effects of PEU are developed in section 4.2.2. Then hypotheses on the moderating effects of cognitive style are developed in section 4.2.3. Finally, a hypothesis on the interaction of PEU and cognitive style is developed. 4.2.1 Direct effects The following section establishes the direct effects among information use, political behavior, strategic decision quality, and company performance. The section is organized as follows. Firstly, hypotheses on the effectiveness of information use are 554

Cf. here and in the following Dennis, A. R./Kinney, S. T. (1998), p. 260.

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developed, which basically means the effects of information use on strategic decision quality as main dependent variable. For that purpose information use is furthermore distinguished into information use from personal and impersonal sources as stated in premise 6. Then, hypotheses on the effects of political behavior are developed. Finally, a hypothesis on the effect of strategic decision quality on organizational performance is developed. Information use – strategic decision quality As stated in the premise 4, information use provides knowledge for understanding characteristics of and cause-effect relationships between decision relevant factors and for drawing inferences in order to make decision.555 From a knowledge transfer perspective, the effectiveness of information use depends on whether it provides SDMrelevant knowledge or not.556 The scope of information concept employed in this study addresses this task-oriented perspective. Firstly, information use provides knowledge about the characteristics of decision relevant factors in the environment e.g. competitors, customers or suppliers as well as in the organization, e.g. strengths and weaknesses.557 Hence, information from both inside and outside the organization is required for effective SDM.558 Furthermore, the ill-structuredness of strategic decisions implies that not all relevant factors and their relationships can be grasped in terms of quantitative information. This is why both quantitative and qualitative information are required for SDM.559 Finally, historic information may serve as basis for building mental models about causal relationships, whereas future-oriented information may serve as basis for creating, confirming or disconfirming own beliefs about future consequences.560 In addition to that, four different classes of information sources are distinguished in this study, because information acquisition is accomplished in both routinized information collection as specified by collective context and specialized information gathering by individuals.561 It is important to establish the basic effects of information 555

Fiske, S. T./Taylor, S. E. (1984) call this phenomenon causal logics. Cf. Nadkarni, S./Barr, P. S. (2008), p. 1398. 556 Cf. Larcker, D. F./Parker Lessig, V. (1980), p. 123. 557 Cf. Gorry, G. A./Scott Morton, M. S. (1971), p. 58; Bhimani, A./Langfield-Smith, K. (2007), p. 8. 558 Cf. Larcker, D. F. (1981), p. 532 559 Cf. Gorry, G. A./Scott Morton, M. S. (1971), p. 58; Larcker, D. F. (1981), p. 532; Frishammar, J. (2003), p. 321; Bhimani, A./Langfield-Smith, K. (2007), p. 12. 560 Cf. Kiesler, S./Sproull, L. (1982), p. 557; Langley, A. (1989), pp. 604-605; Wilson, T. D. (1997), p. 553. 561 Cf. Corner, P. D. et al. (1994), p. 295.

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from these four classes of information sources on strategic decision quality because of two reasons. Firstly, the basic relationships are a foundation for developing hypotheses about moderating effects later on. Secondly and more importantly, a distinction into information use from different sources has not explicitly been made in hypothesestesting SDM research so far. For example some researchers argue that only personal sources are effective means of decision making,562 while other findings also support a positive effect of impersonal sources. 563 Therefore, the effects of information use from different sources are not as clear cut as one might assume up-front. Instead, it seems warranted to elaborate on the extent to which specific characteristics of information use from the four classes of information sources facilitate effective decision making. Different characteristics may have different effects. This is exactly the approach of the following hypotheses development. Internal impersonal sources comprise two main categories namely management information systems and special reports. Although the primary purpose of internal impersonal sources is to provide operational support564 they may also provide information which is relevant for SDM. The management information system mainly provides quantitative data about financial performance or information about existing and new customers.565 Additionally, special reports such as competitor analysis, SWOT-analysis,566 benchmarking reports, scenario analyses or early warning systems provide specific qualitative or forward-looking information.567 As such information use from internal impersonal sources may serve for analyzing strengths and weaknesses of the organization, for identifying slack resources available or strategic alternatives and for assessing future consequences of strategic actions taken.568 External impersonal sources comprise a broad range of sources such as newspapers, periodicals, information services, trade-journals and statistics, government sources or market reports. They are frequently used to obtain information about the external environment.569 Thereby, these sources are used for keeping abreast in relevant external areas for the organization. While newspapers, periodicals and government 562

Cf. Cramme, C. et al. (2009), p. 52. Cf. section 1.2 for a more detailed review of literature. Cf. Bruns, J. W. J./McKinnon, S. M. (1993), p. 105. 565 Cf. Bruns, J. W. J./McKinnon, S. M. (1993), p. 105. 566 Abbreviation for Strengths-Weaknesses Opportunities-Threats analysis. 567 Cf. Berens, W./Püthe, T./Siemes, A. (2005), p. 191. 568 Cf. Bhimani, A./Langfield-Smith, K. (2007), p. 9. 569 Cf. El Sawy, O. A. (1985), p. 55. 563 564

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sources are mainly used for learning about the technological and regulatory

environment, trade-journals, official statistics and market reports are used for providing knowledge about customers and competitors.570 In general, external impersonal sources are used for identifying upcoming decision relevant factors in the general environment. As such they are particularly relevant for assessing long-term consequences of alternatives that may be impacted by these general trends. To summarize, impersonal sources are particularly useful for identifying decision relevant factors and updating decision makers’ knowledge about characteristics of factors they already know. Moreover, information from impersonal sources can generally be used for evaluating consequences of strategic alternatives both from an internal and external perspective. This leads to Hypothesis 1.1a: Information use from impersonal sources has a positive effect on strategic decision quality. Similarly to that, a hypothesis on the effect of information use from personal sources on strategic decision quality can be developed. Personal sources from inside the organization comprise other managers, direct subordinates and other staff within an organization. Internal personal sources are heavily used, because they provide detailed information about internal factors not included in financial or other management reports. However, not only qualitative information but also quantitative information is exchanged during personal conversations.571 As such internal personal sources do not only serve for acquiring new knowledge about the organization, but also for confirming or disconfirming beliefs held about decision relevant factors and causeeffect relationships.572 In addition to that, internal sources do not only provide information about the organization but also about the external environment.573 This information is exchanged in order to learn about external conditions and trends in the absence of the decision maker himself.574 Furthermore, internal sources are consulted in order to tap expert knowledge, e.g. about technological developments which a top decision maker does not fully understand, or to grasp the opinions and assessments of

570

Cf. here and in the following Auster, E./Choo, C. W. (1994), p. 617. Cf. Bruns, J. W. J./McKinnon, S. M. (1993), p. 104. 572 Cf. Bruns, J. W. J./McKinnon, S. M. (1993), p. 104. 573 Cf. El Sawy, O. A. (1985), p. 55. 574 Cf. Keegan, W. J. (1974), p. 414. 571

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organizational participants.575 Overall, the content and nature of information used from internal personal sources is comprehensive and may be related to any domain of decision making, be it the identification of decision relevant factors, personal assessments about their relationships or future events. External personal sources are personal contacts to customers, competitors or business associates. Information use from external personal sources allows for identifying new external factors in particular new competitors or regulatory initiatives.576 As such it provides an important basis for strategic problem recognition577 rather than for actually solving decision problems. Nonetheless, external personal sources may also provide important information for the problem solution phase. Customer contacts are a frequently used in order to learn about their needs and customer trends.578 Third party sources, in particular business associates, are frequently used for getting to know about competitive developments.579 As such information use from external personal sources provides knowledge about relevant customer trends or competitive actions which allows for generating strategic alternatives or assessing future consequences of strategic company actions.580 Finally, external personal sources are generally useful for defining and solving decision problems or validating potential strategic alternatives.581 Basically information use from personal sources provides relevant knowledge for any aspect of SDM be it getting to know about decision relevant factors such as competitors or customers, generating strategic alternatives or assessing their consequences. Therefore, Hypothesis 1.1b: Information use from personal sources has a positive effect on strategic decision quality. According to premise 5 the effect of social context of SDM is two-fold. Firstly, it has an effect on the choice of a strategic decision maker, i.e. strategic decision quality. Secondly, it also has an effect on his or her behaviors during the SDM process, i.e. information use by the key decision maker. Political behavior has been identified as a 575

Cf. Langley, A. (1989), pp. 604-605. Cf. Auster, E./Choo, C. W. (1994), p. 617. Cf. Keegan, W. J. (1974), p. 413; El Sawy, O. A. (1985), pp. 55-56. 578 Cf. Frishammar, J. (2003), p. 322. 579 Cf. Auster, E./Choo, C. W. (1994), p. 617. 580 Cf. Auster, E./Choo, C. W. (1994), p. 615. 581 Cf. Cross, R./Sproull, L. (2004), p. 449. 576 577

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key dimension of social context of SDM and the effects on strategic decision quality and information use can be established as follows. Political behavior - information use The evaluation of the effect of political behavior on information use takes account for the dynamic nature of SDM in a two-step way. Firstly, political behavior may impact information use from personal sources. Secondly, this effect on information use from personal sources very likely impacts information use from impersonal sources, because their use is most often triggered by personal interaction. The following discussion provides further elaborates on this rationale. The interpersonal nature of managerial work results in interaction patterns for information acquisition.582 The extent, to which a top executive consults personal information sources, depends on the experience with the responsiveness of a particular information source. Political actors involved in decision making may restrict information flows in order to promote their favored alternatives or to avoid that other alternatives are presented to the key decision maker.583 This basically means the responsiveness of a personal source which is a political actor may be more restricted than in situations without political behavior. If this personal source does not respond after a number of requests the decision maker would generally turn to other, more responsive information sources.584 However, personal sources are not necessarily interchangeable, because some people know more than others in a specific domain of interest.585 This is particularly detrimental for SDM because direct subordinates or other internal sources are in general the most frequently used among personal sources.586 At the same time they may be particularly involved in political behaviors as their personal interests may be in direct conflict with the strategic decision to be taken. Furthermore, there may only be limited alternatives for acquiring information because a personal source may possess functional expertise which is particularly relevant for making a strategic decision. As a result the overall information exchange among these key decision participants most likely decreases once political behavior is present.

582

Cf. Saunders, C./Jones, J. W. (1990), p. 35; Wilson, T. D. (1997), p. 560. Cf. Pettigrew, A. M. (1973); Narayanan, V. K./Fahey, L. (1982), p. 30; Eisenhardt, K. M./Bourgeois Iii, L. J. (1988), p. 763. 584 Cf. Saunders, C./Jones, J. W. (1990), p. 35. 585 Cf. Cross, R./Sproull, L. (2004), p. 447. 586 Cf. Saunders, C./Jones, J. W. (1990), p. 34; Frishammar, J. (2003), p. 322. 583

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In addition to that political behavior may also result in a loss in trust between key decision maker and information source. The reason is that common goals and shared values have a positive effect on trust and trustworthiness between organizational members.587 In contrast to that, diverging goals and opportunistic behavior – both defining characteristics of political behavior - create an atmosphere of distrust.588 Although a number of factors influence information source utilization such as expertise, trust has shown to be an equally or even more important antecedent of knowledge sharing between two parties.589 Consequently political behavior will most likely have a negative effect on information use from personal sources. Finally, decreasing information use from personal sources may have an effect on information use from impersonal sources. This effect is supported if one considers SDM as a dynamic and not a static process as stated in premise 5. This means information use at one point in time during the SDM process triggers further information use before a final choice is made.590 There is evidence why decreasing information use from personal sources triggers decreasing information use from impersonal sources. Firstly, information use from personal sources has a dominating role at the beginning of a SDM process, i.e. when the decision situation is defined.591 This information provided by personal sources does not only help in defining a decision situation but also serves as basis for defining which specific information should be sought from management information systems or reports. The information from such impersonal sources is then used for substantiating specific issues raised during the personal conversations beforehand.592 Secondly, in order to speed up information processing information use from personal sources includes referrals to documentary sources. Such a referral by a personal source serves as relevancy filter to assure that the information contained in the documentary source provides information which is useful for SDM.593 Taken these two dynamic aspects of information use together it appears very likely that a decrease in information use from personal sources entails a decrease of information use from impersonal sources.

587

Cf. O'Reilly, I. I. I. C. A. (1983), p. 119; Tsai, W./Ghoshal, S. (1998), p. 466. Cf. Pillemer, F. G./Racioppo, S. G. (2003), pp. 149-151. Cf. Chowdhury, S. (2005), p. 321; O'Reilly, I. I. I. C. A. (1983), p. 119; Szulanski, G./Cappetta, R./Jensen, R. J. (2004), p. 601; Tsai, W./Ghoshal, S. (1998), p. 473. 590 Cf. Feldman, M. S./March, J. G. (1981), p. 180. 591 Cf. Frishammar, J. (2003), p. 321. 592 Cf. Heidmann, M./Schaffer, U./Strahringer, S. (2008), p. 253. 593 Cf. Cross, R./Sproull, L. (2004), p. 450. 588 589

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To summarize, much of managerial and SDM work is interpersonal. Once political behavior is present information use from personal sources diminishes for two reasons. On the one hand, political actors may restrict information flows. On the other hand, a key decision maker may not use a political actor as information source because of a loss of trustworthiness. In addition to that, information use for SDM is dynamic and generally starts off with interpersonal information exchange which is then underpinned by further information use from impersonal sources. Insofar, information use from personal sources has a key role for triggering subsequent information use from impersonal sources. Therefore Hypothesis 1.2: Political behavior has a negative effect on information use from personal and impersonal sources. Political behavior - strategic decision quality Besides the effect on information use during a SDM process, political behavior may also have direct effects on strategic decision quality which are not necessarily related to information use. There are three aspects to be discussed in the following. Firstly, political behavior is a result of goal conflicts between organizational and individual interests of organizational members. These conflicts may impose further constraints on decision making, because the consequences of a choice have not only to account for the organizational goals but also the interests of individual political actors.594 Although this study builds on the premise that key decision makers have the power base for making a strategic decision, they may be reluctant to change the power structure or replace political actors because of two reasons. Firstly, these organizational participants may derive power from particularly important positions in the organization.595 Secondly, top executives may be dependent on these organizational members because of their particular contribution to performance and their task related expertise and skills.596 Even if key decision makers are generally the most powerful in an organization, they may accept individual interests as constraints to decision making.

594 595 596

Cf. Dean, J. W./Sharfman, M. P. (1996), p. 375. Cf. Brass, D. J. (1984), p. 535. Cf. Bartol, K. M./Martin, D. C. (1988), pp. 366-369.

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Secondly, political behavior diminishes the comprehensiveness of searching for strategic alternatives in several aspects. Firstly, the generation and provision of alternatives by organizational members may be hampered because they anticipate that politics involved in SDM prevents the alternatives for coming into consideration at all.597 Secondly, political actors may promote their favored alternatives and seek to down-play other viable or even better alternatives during the SDM process. Neutrally speaking, this process is called strategic issue selling or framing.598 However, it might also be used for influencing a SDM process towards ones personal goals.599 The reason why this tactic may be fruitful for the political actor is the fact that decision makers act locally rational, which means they base decisions on a consistent set of information even if the final choice has a minor effect on organizational performance.600 Thirdly, political behavior distorts information use in several ways. As already stated, political behavior distorts free exchange of information. Decision participants who engage in political behavior will control or restrict information flows to the top decision maker.601 There is not only a direct effect on information use, but also on the outcome of decision making, because decisions base on less, irrelevant or misleading information.602 Furthermore, political behavior demands scarce attention while leaving less attention for information use for the analytical aspects of decision making.603 Attention and time is drawn from critical decision relevant factors to political factors such as resolution of goal conflicts or simply dealing with politics. As a result the solution of a decision problem must again be based on less decision-relevant information.604 Taken altogether, these three reasons lead to Hypothesis 1.3: Political behavior has a negative effect on strategic decision quality. Strategic decision quality – company performance Strategic decision quality is defined as the contribution of a strategic decision to organizational objectives. Therefore, it appears intuitively obvious that strategic decision quality has a positive effect on organizational performance. However, there 597

Cf. Fahey, L. (1981), p. 61; Walter, J./Lechner, C./Kellermanns, F. W. (2008), p. 539. Cf. Dutton, J./Ashford, S. J./O'Neill, R. M. et al. (2001), p. 716. 599 Cf. Balogun, J. et al. (2008), p. 236. 600 Cf. Glazer, R. et al. (1992), p. 223. 601 Cf. Pettigrew, A. M. (1973); Eisenhardt, K. M./Bourgeois Iii, L. J. (1988), p. 763 602 Cf. Dean, J. W./Sharfman, M. P. (1996), p. 375; Feldman, M. S./March, J. G. (1981), p. 177. 603 Cf. Narayanan, V. K./Fahey, L. (1982), p. 30; Walter, J. et al. (2008), p. 534. 604 Cf. Dean, J. W./Sharfman, M. P. (1996), p. 375; Eisenhardt, K. M./Bourgeois Iii, L. J. (1988), p. 761. 598

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are reasons for testing the mediating role of strategic decision quality.605 Firstly, a positive effect of strategic decision processes on organizational performance must not necessarily be attributable to the decision itself. Strategic decision processes may influence other internal processes such psychological states of managers or increase the perception of legitimacy to external stakeholders which provide necessary resources to an organization.606 Furthermore, external factors may moderate the link between SDM processes and organizational behavior, such that the positive effect of a high quality decision may be deteriorated by these external factors.607 Despite these reasons for testing the relationship, the general rationale results in Hypothesis 1.4: Strategic decision quality has a positive effect on organizational performance. 4.2.2 Moderating effects of perceived environmental uncertainty As stated in premise 1, organizations are open systems and SDM is a means of processing information about the environment in order to inform strategic action.608 One environmental characteristic closely related to decision making is PEU. PEU is defined as the number and interdependence (complexity) and the rate of change (dynamism) of decision relevant factors. As a result, PEU influences the ability of decision makers to form a representation of the decision problem and to assess the consequences of alternative actions.609 In low PEU environments the number of decision relevant factors is relatively small and their variability relatively little. Such environments are analyzable because “events and processes are hard, measurable and determinant”.610 High PEU environments are rather perceived as unanalyzable, because of a relatively large number of decision relevant factors, ambiguous interdependencies as well as high variability of these factors and relationships. 611 Therefore, information use has different emphases depending on the environmental conditions.612 In low PEU environments the emphasis is on creating and maintaining accurate interpretations in order to arrive at a seemingly rational choice. In high PEU

605

Cf. Dean, J. W./Sharfman, M. P. (1996), pp. 369-370; Forbes, D. P. (2007), p. 363. Cf. Forbes, D. P. (2007), p. 366. Cf. Dean, J. W./Sharfman, M. P. (1996), p. 369. 608 Cf. Huber, G. P./Daft, R. L. (1987); Daft, R. L. et al. (1988), p. 125. 609 Cf. Forbes, D. P. (2007), p. 367. 610 Daft, R. L./Weick, K. E. (1984), p. 287. 611 Cf. Boyd, B./FuIk, J. (1996), p. 10. 612 Cf. Daft, R. L./Weick, K. E. (1984), p. 287. 606 607

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Theory and hypotheses development

environments it is more important to have an individual and mutually agreed collective understanding in order to arrive at an actionable state. Moderating effect on information use – strategic decision quality As stated in premise 6, information sources possess different capabilities in terms of what kind of information they provide from a knowledge transfer perspective and in terms of how their characteristics allow to form individual and collective understanding from a sensemaking perspective. These differing capabilities are the basis for establishing the moderating effects of PEU on the information use – strategic decision quality relationship provided differing emphases of information processing in different environments as shown above. Internal impersonal sources such as information systems, MIS reports or special studies are prepared according to rules prescribed by financial accounting standards or management accounting techniques. From a knowledge transfer perspective the information from these sources pertains to better understood parts of business, i.e. it captures decision relevant factors that are already known.613 Therefore, they are useful for substantiating specific issues614 or for maintaining mental representations about the internal environment.615 In contrast to that such reports might not capture a large portion of decision relevant factors in highly variable or complex environments, because any preparation of reports requires resources and technical capabilities. These requirements increase with increasing need for timely and broad scope information. Information technology has certainly reduced resource requirements for building management information systems while at the same time increasing the information processing capabilities.616 Nonetheless, empirical evidence shows that management information systems still do not fulfill managers’ requirements for timely and broad scope information.617 Furthermore, satisfaction with information systems decreases as environmental uncertainty increases618 indicating that these short-comings of internal impersonal sources are becoming more prevalent as PEU increases.

613

Cf. Daft, R. L./Lengel, R. H. (1986), p. 562. Cf. Heidmann, M. et al. (2008), p. 253. Cf. Vandenbosch, B./Higgins, C. (1996), p. 209. 616 Cf. Martinsons, M. G./Davison, R. M. (2007), p. 287; Griffith, T. L./Northcraft, G. B./Fuller, M. A. (2008), p. 110. 617 Cf. Pierce, B./O'Shea, T. (2003), pp. 274-276. 618 Cf. Karimi, J./Somers, T. M./Gupta, Y. P. (2004), p. 189. 614 615

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Information use from external impersonal sources bases on a broad range of sources such as official statistics, market reports, annual reports, newspapers, trade magazines and other media publications. Many of these sources e.g. statistics or annual reports are prepared based on predefined standards and on a regular basis. Therefore, they capture decision relevant factors that are already known and appear particularly useful for maintaining mental representations about the external environment. Other external impersonal sources such as newspapers or trade magazines that are not prepared such rule-driven are most often used for keeping abreast of developments in the technological or regulatory environment.619 Information from these sources indicates environmental changes that are of a longer-term nature. Thus information from these sources is rather relevant for long-term planning when consequences of these changes are understood. Therefore, the value of such information most likely diminishes when the environment is highly variable and consequently less predictable. Taken together, information use from external impersonal sources appears to provide knowledge which is particularly relevant when decision relevant factors are already identified and the predictability of environmental changes is relatively high. Finally, impersonal sources do not have the possibility for feedback and multiple cues. Their information is seemingly objective and most often there are minor disagreements about the interpretation of data from such sources.620 Furthermore, in order to utilize this information for SDM the cause-effect relationship between decision relevant factors should be clearly established, because then predictions about future outcomes are possible. This is specifically true for low PEU environments with a relatively little number and variability of decision relevant factors.621 To summarize, impersonal sources appear to be particularly effective under conditions of low uncertainty, because they describe decision relevant factors that are already known and their information is less likely to become obsolete as compared to high uncertainty conditions. The way of communication associated with impersonal sources furthermore implies relative accuracy in measuring specific issues of interest and most often there is minor disagreement in interpretation. This accurate knowledge is then particularly useful for predicting outcomes in low PEU environments where causeeffect relationships are established and relatively stable.

619 620 621

Cf. Auster, E./Choo, C. W. (1994), p. 617. Cf. Daft, R. L./Lengel, R. H. (1986), p. 562. Cf. Daft, R. L./Weick, K. E. (1984), p. 287.

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Hypothesis 2.1a: Information use from impersonal sources has a positive and stronger effect on strategic decision quality for low than high perceived environmental uncertainty (PEU). From a knowledge transfer perspective, information use from personal sources provides information relating to many aspects of SDM be it for identifying decision relevant factors, assessing cause-effect relationships or future consequences of strategic alternatives. One distinct characteristic of information use from personal sources is the provision of information which may not be available from impersonal sources.622 For example, personal sources are used for obtaining information about competitive or customer developments.623 That is particularly beneficial in high dynamic environments where SDM has to account for changing customer tastes and competitive actions in order to provide for a competitive advantage.624 Personal sources are also suitable for providing real-time information which is defined as information with only little delay between occurrence of an event and the provision of information about this event.625 Such information appears particularly useful in highly dynamic environments in order to base strategic actions on the most current information as possible. In addition to that, information use from personal sources can be directed at tapping expertise.626 By doing so, a key decision maker can reduce the information processing burden for oneself and comprehend a wider array of decision relevant factors in a specific area of interest.627 This appears particularly useful for highly complex areas of interest. In addition to that, personal sources provide multiple cues and feedback.628 These characteristics allow for clarification of issues, emphasis on specific points or expression of doubt, acceptance or other opinions. Therefore, from a sensemaking perspective information use from personal sources is effective for arriving at a common understanding. This may be a reason why information use from personal sources prevails at the beginning of a SDM process when the focus is on arriving at an interpretation of a decision situation.629 This information processing step is crucial, 622

Cf. Auster, E./Choo, C. W. (1994), p. 617; Bruns, J. W. J./McKinnon, S. M. (1993), p. 104. Cf. Auster, E./Choo, C. W. (1994), p. 617, Frishammar, J. (2003), p. 322. Cf. Garg, V. K. et al. (2003), p. 728. 625 Cf. Eisenhardt, K. M. (1989), p. 549. 626 Cf. Frishammar, J. (2003), p. 322. 627 Cf. Lord, R. G./Maher, K. J. (1990), pp. 13-14. 628 Cf. Daft, R. L./Lengel, R. H. (1986), p. 561. 629 Cf. Frishammar, J. (2003), p. 322. 623 624

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because much of information use during a SDM process is guided by the definition of a strategic decision problem space and subsequent information processing throughout the organization occurs based on this definition. Then this information is fed back to a key decision maker. Finally, this interpretative aspect is likely more important for high than for low PEU environments, because the number of decision relevant factors is larger and cause-effect relationships less clear. The emphasis under such conditions is more on reaching an actionable state through a common understanding than on reaching a seemingly perfect accuracy in decision making.630 Therefore Hypothesis 2.1b: Information use from personal sources has a positive and stronger effect on strategic decision quality for high than low PEU. Moderating effect on political behavior - information use There is no indication why there should be different effects of political behavior on the use of information in situations of low and high uncertainty. Therefore: Hypothesis 2.2: There is no significant difference between low and high PEU concerning the negative effect of political behavior on information use from personal and impersonal sources.631 Moderating effect on political behavior – strategic decision quality With increasing uncertainty the amount of required knowledge generally increases. This is paralleled by a disproportionately larger need for information use in order to pick the relevant pieces of information. Therefore, the distorting effects of political behavior are particularly detrimental under conditions of high uncertainty, because attention and time of decision makers is taken away from analytical tasks for decision making.632 Furthermore, political behavior slows down decision processes. This is also more detrimental under conditions of high uncertainty than under conditions of low uncertainty, because conditions of high uncertainty require more immediate action.633 Therefore

630

Cf. Daft, R. L./Lengel, R. H. (1986), p. 566. Stating a neutral moderating effect may question whether to formulate a hypothesis or not. However, when theoretically plausible it is justifiable to hypothesize and test for a neutral relationship. Cf. e.g. Venkatesh, V./Morris, M. G./Davis, G. B. et al. (2003), pp. 455-456. 632 Cf. Mueller, G. C. et al. (2007), p. 859. 633 Cf. Judge, W. Q./Miller, A. (1991), p. 461; Mueller, G. C. et al. (2007), p. 860. 631

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Theory and hypotheses development

Hypothesis 2.3: Political behavior has a negative and stronger effect on strategic decision quality for high than low PEU. Moderating effect on strategic decision quality – company performance Concerning the dynamism dimension of PEU the effect on the strategic decision quality – company performance relationship can be two-fold. On the one hand, upcoming events and changes in the environment may be disadvantageous, e.g. if new competitors emerge, existing competitors take unexpected actions or customer demands change.634 On the other hand, environmental dynamism may also be advantageous, e.g. if the strategic action taken by a company emerges as a pioneering move in the market place.635 Concerning the complexity dimension of PEU the effect may rather be disadvantageous. High complexity refers to the presence of a large number of decision relevant factors. In such an environment the use of information may be misleading if it does not cover the most important decision relevant factors. As has been shown, a decision made on the basis of “misleading” information appears locally rational although the performance effect is inferior compared to a decision based on a superior set of information.636 Nonetheless, this decision may be the basis for subsequent action and only later it turns out, that important factors have been missed out during the SDM process. Therefore Hypothesis 2.4: Strategic decision quality has a positive, but weaker effect on organizational performance for high than low PEU. 4.2.3 Moderating effects of cognitive style For evaluating the moderating effects of cognitive style two steps are made. First of all, the theoretical basis for a moderating effect of cognitive style is established. Then the specific hypotheses for the research model are developed. A number of propositions relating cognitive styles to managerial information use have been formulated in the literature. O'Reilly, I. I. I. C. A. (1983) proposes “Given the same information set, different decision makers will use different parts in different ways”637 Gardner, W. L./Martinko, M. J. (1996) propose on the basis of the MyersBriggs Type-Indicator that “Thinking managers use objective information to decide, 634

Cf. Dean, J. W./Sharfman, M. P. (1996), p. 377. Cf. Eisenhardt, K. M. (1989), p. 570. 636 Cf. Glazer, R. et al. (1992), p. 223. 637 O'Reilly, I. I. I. C. A. (1983), p. 126. 635

Hypotheses development

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feeling managers prefer subjective information.”638 Similarly Finkelstein, S. et al. (2009) develop three propositions how information gathering preferences have an effect on the cognitive processes of attention, selective perception and interpretation of information.639 Starting with these propositions two effects of cognitive style on information use can be identified. Firstly, cognitive style may have behavioral effects on how and what kind of information individuals seek for making decisions.640 However, the behavioral effects of cognitive style are neither theoretically nor empirically very well established. Some experimental studies find relationships between cognitive style and amount, scope and sources of information use.641 In contrast to that, other empirical findings do not support any interaction of cognitive styles and information behaviors.642 Secondly, cognitive style may have effects on how effectively individuals handle different information inputs from a cognitive perspective. At first, this proposition seems intuitive, because cognitive styles are preferences in information processing. Nonetheless, the sole existence of preferences must not necessarily have an effect on differential effectiveness of information processing. The latter argument is further supported by the fact that individuals can engage in coping behavior for handling information and information processing tasks that do not meet their preferences.643 Coping behavior intervenes between stable, preferred cognitive styles and the required behavior in order to achieve the objectives of a specific information processing task at hand.644 However, coping behavior is effortful and results in dissatisfaction of the individual. Therefore, individuals will ultimately fall back to their preferred behaviors in particular under conditions of time constraints and stress.645 Such conditions are typical for top executives’ jobs, because of a large number and variety of tasks and tight time schedules. Therefore, it is plausible that in organizational settings cognitive style governs executives’ cognitive processes. Otherwise behavioral effects of

638

Gardner, W. L./Martinko, M. J. (1996), p. 65. Cf. Finkelstein, S. et al. (2009), p. 68. 640 Cf. Sadler-Smith, E. (1998), p. 193. 641 Cf. Driver, M. J./Mock, T. J. (1975), p. 506; Vasarhelyi, M. A. (1977), p. 147; Benbasat, I./Dexter, A. S. (1979), p. 745; Blaylock, B. K./Rees, L. P. (1984), p. 87; , pp. 110-112; Ford, N./Wilson, T. D./Foster, A. et al. (2002), p. 733; Anderson, M. H. (2008), p. 64. 642 Cf. Henderson, J. C./Nutt, P. C. (1980), p. 384. 643 Cf. Kirton, M. J. (1994); Hayes, J./Allinson, C. W. (1994), p. 54. 644 Cf. Kirton, M. J. (1994); Hayes, J./Allinson, C. W. (1994), p. 54. 645 Cf. here and in the following Haley, U. C. V./Stumpf, S. A. (1989), pp. 481-482. 639

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Theory and hypotheses development

cognitive style would also be more significantly confirmed in empirical studies, which is not the case as stated above. For assessing the influence of cognitive style it is reasonable to recall some basic definitions of cognitive style. According to the linear-nonlinear concept, linear thinkers prefer to attend to external data and facts, whereas nonlinear thinkers prefer to attend to internal feelings, impressions, intuitions and sensations. Linear thinkers can furthermore be described as attending to detailed, concrete, practical and reality-based information, whereas nonlinear thinkers rather attend to meanings, associations, possibilities, hunches, or subjective information.646 Taking this as basis, a moderating effect of cognitive style on perceptual processes can be established.647 In general, attention is limited which means attention is focused on a subset of information received.648 Some stimuli one executive effectively notices may exactly be the same another filters out.649 Exactly this phenomenon is governed by preferences for information perception as detailed above. Basically these effects of cognitive style can be considered as input biases, because some data is effectively noticed and processed while other is not.650 Taken these effects together cognitive style has a moderating effect on information use, because only a fraction of stimuli is effectively perceived and subsequently processed for decision making. Moderating effect on information use – strategic decision quality Information use from impersonal sources particularly meets the preferences of linear thinkers because of the following reasons. Firstly impersonal sources provide facts, figures, and most often quantifiable data. The information obtained pertains to measurable areas of the business or environment and is characterized by low variety of language and limited tolerance for interpretation.651 This information meets linear thinkers’ preference for data, facts and reality-based information. Secondly, impersonal sources focus on important aspects of the organization and provide meansends knowledge, because they relate to better understood parts of the business.652 Many impersonal sources are also prepared according to rules and on a regular basis 646

Cf. Gardner, W. L./Martinko, M. J. (1996), p. 47. Cf. Miller, A. (1987), pp. 253-263; Kozhevnikov, M. (2007), p. 475. Cf. Miller, A. (1987), p. 253. 649 Cf. Starbuck, W. H./Milliken, F. J. (1988), p. 45. 650 Cf. Haley, U. C. V./Stumpf, S. A. (1989), p. 481. 651 Cf. Daft, R. L./Lengel, R. H. (1986), p. 562; Roberts (1991); Marginson, D. (2006), p. 189. 652 Cf. Daft, R. L./Lengel, R. H. (1986), p. 562; Marginson, D. (2006), p. 189. 647 648

Hypotheses development

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such as MIS reports, but also official statistics or market reports follow a certain structure and regularity.653 Therefore, the information obtained can easily be related to what is already known which in turn increases its practical applicability. Again this meets the preferences of linear thinkers. Hypothesis 3.1a: For linear thinkers information use from impersonal sources has a positive and stronger effect on strategic decision quality than for nonlinear thinkers. In contrast to that, information use from personal sources particularly meets the preferences of nonlinear thinkers because of the following reasons. Firstly, personal sources do not only provide facts and figures but also qualitative data, personal assessments and opinions.654 Therefore, information use from personal sources meets nonlinear thinkers’ preferences for speculations, possibilities or hunches. Secondly, personal sources provide the opportunity for immediate feedback and exchange of information.655 In light of nonlinear thinkers’ preference for speculations and possibilities these characteristics are particularly suitable for drilling down on topics of interest, once they have been raised during conversations. Thirdly, personal sources provide nonverbal cues in form of facial expressions, body language or tone-of-voice even if communication takes place through telephone or computerized media such as email.656 Nonverbal cues pertain to the socio-emotional aspects of communication. They convey information about emotional states of the sender657 but also about environmental objects658 and can be used to emphasize important issues, express doubt or uncertainty, or to show acceptance.659 This additional information again meets nonlinear thinkers’ preferences for meanings, associations and subjective information. Fourthly, nonverbal cues do not only convey information about emotions but also evoke complementary or reciprocal emotions on part of the receiver.660 Furthermore, emotions that are relevant for a judgmental task are considered as informative and have an effect on the cognitive processes of judgment.661 This certainly holds for any 653

Cf. Daft, R. L./Lengel, R. H. (1986), p. 562; Marginson, D. (2006), p. 189. Cf. Bruns, J. W. J./McKinnon, S. M. (1993), p. 104. Cf. Daft, R. L./Lengel, R. H. (1986), p. 561. 656 Cf. Carlson, P. J./Davis, G. B. (1998), p. 338. 657 Cf. van Dijk, E./van Kleef, G. A./Steinel, W. et al. (2008), p. 600. 658 Cf. Keltner, D./Haidt, J. (1999), p. 511. 659 Cf. Dennis, A. R./Kinney, S. T. (1998), p. 260. 660 Cf. Hatfield, E./Cacioppo, J./Rapson, R. L. (1994); Keltner, D./Haidt, J. (1999), p. 511; Hess, U./Philippot, P./Blairy, S. (1998), p. 510; Barsade, S. G. (2002), pp. 646-648; van Dijk, E. et al. (2008), p. 601. 661 Cf. Lerner, J. S./Keltner, D. (2000), p. 475. 654

655

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Theory and hypotheses development

decision maker, whereas due to nonlinear thinkers’ preference for internal impressions, feelings and sensations this emotion evoking effect is particularly relevant for them.662 Hypothesis 3.1b: For nonlinear thinkers information use from personal sources has a positive and stronger effect on strategic decision quality than for linear thinkers. Moderating effect on political behavior – information use H1.3 states that political behavior has a negative effect on information use from any source. This effect has partly been attributed to a loss of trustworthiness of political actors in the view of the key decision maker. Loss of trustworthiness then results in decreasing information exchange between them and subsequent decreases of information use from impersonal sources. Trustworthiness is actually a perceived characteristic and is positively influenced by the extent to which a “trustee is believed to want to do good to the trustor.”663 Basically predictions about another’s behavior are a central input for forming these believes. These predictions of individual behavior are generally based on emotional and physiognomic perceptions of other people’s hostile or friendly intent based on certain emotional expressions (e.g. angriness, frightened, cheerfulness).664 Furthermore, these emotional and physiognomic perceptions are even more idiosyncratic and individual than the development of object or event perception.665 This directly pertains to nonlinear thinkers’ preference for sensations, feelings and internal hunches as information inputs which is such a form of idiosyncrasy.666 Given these preferences it appears very likely that nonlinear thinkers are more receptive to perceptions indicating political behavior (i.e. opportunistic behavior or negative intent) than linear thinkers. Therefore Hypothesis 3.2: For nonlinear thinkers political behavior has a negative and stronger effect on the use of information than for linear thinkers. Moderating effect on political behavior – strategic decision quality Concerning the moderating effect of cognitive style on the political behavior – strategic decision quality relationship two main effects can be identified. 662

It should be noted that the role of emotions are a much more complex area of investigation and theorizing and the analysis here is restricted to those emotional aspects feeding into the relevant cognitive processes for decision making. 663 Mayer, R. C./Davis, J. H./Schoorman, F. D. (1995), p. 718. 664 Cf. Neisser, U. (1976), p. 189. 665 Cf. Neisser, U. (1976), p. 191. 666 Cf. also the preceding hypothesis development.

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115

Firstly, as hypothesized before political behavior most likely has a negative on information use from any source and this effect is expected to be stronger for nonlinear than for linear thinkers. Consequently, SDM bases on less decision relevant information for nonlinear than for linear thinkers if political behavior is present. Secondly, political actors provide information which is conflated from serving personal instead of organizational goals in two ways. On the one hand, political actors rather promote inferior decision alternatives which do not serve organizational, but personal goals. On the other hand, their evaluation of superior alternatives may be less favorable and they would seek to divert attention from these alternatives to their favored alternative.667 However this effect can be alleviated in two ways. Firstly, information use is a mix of blending several information inputs into a coherent picture of a strategic decision situation.668 Secondly, once information is received from distrusted personal sources, other information can be used for validating the veracity of the information received.669 Information use from impersonal sources may play a particular role for these purposes for two reasons. On the one hand, personal sources are not simply interchangeable and a decision maker may not be able to turn to another personal source in order to verify the information received. On the other hand, information use from impersonal sources generally serves for substantiating specific issues with measurable facts.670 Therefore, information use from impersonal sources may point to inconsistencies in the inferior alternatives and safe-guard against political uses of information. Despite these counter-measures against political uses of information, nonlinear thinkers may not effectively benefit from them, because they perceive information from impersonal sources less effectively than linear thinkers. As a result they likely have more blind-spots concerning the alternatives presented by political actors. Therefore Hypothesis 3.3: For nonlinear thinkers political behavior has a negative and stronger effect on strategic decision quality than for linear thinkers. Moderating effect on strategic decision quality – company performance There is no indication why there should be different effects of strategic decision quality on organizational performance for linear or nonlinear thinkers. Therefore: 667

Cf. hypothesis development for H1.3 in sub-section 4.2.1. Cf. Auster, E./Choo, C. W. (1994), p. 616. 669 Cf. McEvily, B./Perrone, V./Zaheer, A. (2003), p. 97. 670 Cf. Heidmann, M. et al. (2008), p. 253. 668

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Theory and hypotheses development

Hypothesis 3.4: There is no significant difference between linear and nonlinear thinkers concerning the positive effect of strategic decision quality on company performance.

4.2.4 Interaction effects of perceived environmental uncertainty and cognitive style As stated before perceptual processes occur within perceivers whereas perceivers are inseparable from their environments. Therefore, it appears likely that PEU and cognitive style interact with respect to the above stated moderating effects.671 However, there are two opposing points of view on the nature of this interaction. The first point of view builds on a model of switching between habitual versus conscious modes of cognitive processing. According to this model individuals are able to switch between habitual or automatic and deliberate or conscious modes of processing.672 Although most often these two modes are associated with experience based schematic processing of information, the ability to switch between two modes is “applicable whether one adopts an information-processing, script-processing, schemabased, or other models of cognitive functioning.”673 Therefore, this point of view is also applicable to the effects of cognitive styles, which are defined as habitual information processing preferences. Therefore, their effect on cognitive functioning can be considered as the habitual or automatic mode proposed by the switching model. In contrast to that conscious cognitive processing is characterized by awareness, attention, reflection, and by noticing oneself.674 The basic model of switching between the two modes of cognitive processing suggests that in general individuals are in the automatic mode of cognitive processing. However, they may sense situational conditions which require switching to a conscious mode of processing.675 The relevant conditions and effects on switching can generally be characterized by three kinds of situations.676 Firstly, conscious processing is provoked by novel situations, when something is unusual or different from prior experience. Secondly, conscious processing is provoked by discrepancy, which is defined as a significant difference between expectations and reality. Thirdly, 671

Cf. Hough, J. R./Ogilvie, D. (2005), p. 443. Cf. Louis, M. R./Sutton, R. I. (1991), pp. 56-57. 673 Louis, M. R./Sutton, R. I. (1991), p. 57. 674 Cf. Louis, M. R./Sutton, R. I. (1991), p. 58. 675 Cf. Louis, M. R./Sutton, R. I. (1991), p. 57. 676 Cf. here and in the following Louis, M. R./Sutton, R. I. (1991), p. 60. 672

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individuals can take deliberate initiative for conscious processing which is the response to an internal or external request for conscious attention. All of these three situational facets are not only a function of the situation but also of predispositions and experience of the individual which serve as reference for recognizing conditions as novel, discrepant or requiring conscious attention. Although SDM is characterized by its novelty and initiated by a problematic situation, i.e. a discrepancy between actual and desired state, there may also be strategic decision situations where automatic processing is more prevalent than in other situations.677 One key characteristic directly relating to the situational triggers of conscious vs. automatic processing is environmental stability which is similar to the environmental variability dimension of uncertainty.678 Generally speaking, under conditions of low environmental variability the environment is familiar and provides only few unexpected events. Therefore, habitual cognitive processing guides managerial action. Conversely, under conditions of high environmental variability the environment is highly unfamiliar and expectations are not met. As a result the extent of conscious cognitive processing increases under conditions of high variability. 679 Given these considerations the moderating effects of cognitive style would be more prevalent for conditions of low than high PEU. The second point of view builds on two arguments pertaining to information processing. Firstly, time pressure associated with the information processing context is an important determinant of whether individuals use automatic or conscious cognitive processes. With increasing time pressure the use of automatic information processing increases.680 Similarly, coping behaviors that are said to overcome cognitive style suffer from time pressure, which means with increasing time pressure people engage in less coping behavior and revert to their cognitive style.681 Now, one might expect increasing PEU implied increasing time pressure for making a decision because of increased urgency to adjust to the environment. However, although fast decision making is more effective in highly dynamic environments, dynamism in the 677

Cf. Starbuck, W. H. (1982), p. 16. Cf. Reger, R. K./Palmer, T. B. (1996), p. 24. 679 Cf. Dutton, J. E. (1993), p. 343; Reger, R. K./Palmer, T. B. (1996), p. 26. 680 Cf. Dutton, J. E. (1993), p. 346. 681 Cf. Hayes, J./Allinson, C. W. (1994), p. 54. 678

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environment is not an antecedent to decision making speed.682 Therefore, an effect of PEU on the time pressure in SDM cannot be supported and this would be no reason for differences in automatic vs. conscious processing. Secondly, information load requires increasing cognitive capacity and individuals seek cognitive shortcuts such as provided by their cognitive styles for handling these requirements. Furthermore, PEU is generally expected to have an effect on information load which would then influence the use of cognitive short-cuts. From an information processing perspective PEU is positively and linearly related to information processing by an individual.683 This may lead to automatic cognitive processing when PEU and consequently information load is high. However, from a socio-cognitive perspective uncertainty and information processing are not linearly related and information processing does not linearly increase with PEU.684 So basically, there are two opposing points of view how PEU translates into information load and would thus have an effect on automatic vs. conscious information processing. To conclude, these arguments opposing the conclusion of the cognitive switching model discussed before are neither theoretically nor empirically conclusive. Therefore, Hypothesis 4: Moderating effects of cognitive style are more prevalent in low than in high PEU environments.

682

Cf. Eisenhardt, K. M. (1989), p. 571; Judge, W. Q./Miller, A. (1991), pp. 459-460. Cf. e.g. Daft, R. L. et al. (1988), p. 132 or Boyd, B./FuIk, J. (1996), p. 13 for empirical support. 684 Cf. Hough, J. R./White, M. A. (2004), pp. 782-783; Kuvaas, B. (2002), p. 978 and Hough, J. R./White, M. A. (2004), p. 788 as well as Kuvaas, B. (2002), p. 987 for empirical support. 683

Summary

119

4.3 Summary Figure 8 depicts the research model for this study as a result of the previously developed relationships among the variables of interest.

PEU

Political behavior

Information use from impersonal sources

Strategic decision quality

Organizational performance

Information use from personal sources

Cognitive style

Figure 8: Overview of basic relationships (Source: own compilation)

Finally, Table 6 summarizes the hypotheses (H) to be tested with empirical data.

120

Theory and hypotheses development Basic Relationships Endogenous Variable

Hypothesis

Exogenous Variable

H1.1a

Information use from impersonal sources

Strategic decision quality

Positive

H1.1b

Information use from personal sources

Strategic decision quality

Positive

H1.2

Political behavior

Information use from any source

Effect

Negative

H1.3

Political behavior

Strategic decision quality

Negative

H1.4

Strategic decision quality

Company performance

Positive

Hypothesis

Moderated Relationship

Effect

H2.1a

Information use from impersonal sources Æ strategic decision quality

Stronger for low than high PEU

H2.1b

Information use from personal sources

Stronger for high

Æ strategic decision quality

than low PEU

H2.2

Political behavior Æ information use from any source

No significant difference

H2.3

Political behavior

Stronger for high

Æ strategic decision quality

than low PEU

H2.4

Strategic decision quality Æ company performance

Stronger for low than high PEU

Hypothesis

Moderated Relationship

Effect

H3.1a

Information use from impersonal sources

Stronger for linear

Æ strategic decision quality

than nonlinear thinkers

Information use from personal sources

Stronger for nonlinear

Æ strategic decision quality

than linear thinkers

H3.2

Political behavior Æ information use from any source

Stronger for nonlinear

H3.3

Political behavior Æ strategic decision quality

Stronger for nonlinear than linear thinkers

H3.4

Strategic decision quality Æ company performance

No significant difference

Hypothesis

Effect

H4

Moderating effects of cognitive style are more prevalent in low than in high PEU environments

Moderating Effects of PEU

Moderating Effects of Cognitive Style

H3.1b

than linear thinkers

Interaction of PEU and Cognitive Style

Table 6: Overview of hypothesized relationships (Source: own compilation)

Unit of analysis

121

5 Research design Empirical management research aims at describing real-life phenomena and explaining causal relationships between them.685 It bases on the assumption that thinking and reality are independent. Therefore, it is necessary to test theoretical descriptions of real-life phenomena and their relationships with empirical data. Thus, a key characteristic of empirical research is to gather, prepare and analyze external data in order to test the theoretically established relationships. For doing so the theoretical variables and their relationships need to be made measurable and analyzable similarly to research methods employed in natural sciences. This is the aim of this chapter. At first, section 5.1 defines the unit of analysis. Next, section 5.2 develops a measurement instrument which is to be used for data gathering. Finally, section 5.3 discusses methodologies for analyzing empirical data, selects a specific data analysis method for this study and describes this method in more detail. 5.1 Unit of analysis The following section aims at defining the unit of analysis in accordance with the conceptual basis and theoretical considerations outlined before. Single strategic decision There are two options for defining the unit of analysis from a procedural perspective. On the one hand, SDM can be investigated with respect to general organizational processes of decision making. On the other hand, SDM can be investigated with respect to a single strategic decision. The focus of analysis of this study is a single strategic decision because of two reasons.686 Firstly, this study is concerned with the effectiveness of decision processes in terms of a strategic decision’s contribution to organizational performance. However, organizational outcomes are influenced by a range of factors other than SDM. Therefore, relating decision processes to decision outcomes and then to organizational performance is more consistent with this purpose than directly relating them to organizational outcomes. Secondly, asking for process and outcome of a single decision avoids ambiguity in causal ordering and thus the effect of a single strategic decision on organizational outcomes can be separated in a more robust way. 685 686

Cf. here and in the following Homburg, C. (2007), pp. 27-35. Cf. here and in the following Dean, J. W./Sharfman, M. P. (1996), p. 371; Forbes, D. P. (2007), p. 366.

W. Gänswein, Effectiveness of Information Use for Strategic Decision Making, DOI 10.1007/978-3-8349-6849-4_5, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

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Research design

The latter reason requires specific attention to the temporal ordering of decision processes and outcomes investigated empirically. Longitudinal study designs have some advantages in this respect, because a researcher has control over the time period between the various measurement incidences.687 In contrast to that, cross-sectional studies such as used in this study are more limited in this respect. Imposing a narrow time window for measuring effectiveness may be too restrictive with respect to sample size. Therefore, the survey of this study will inquire the specific period of decision making. Then, the data will undergo a post-hoc evaluation with respect to this temporal ordering issue and data sets can be excluded in case it appears to be necessary. Individual key decision makers / CEOs In the preceding section SDM has been identified as an individual level activity of a key decision maker. According to the Upper Echelon View, top executives have a central role for SDM because of their power base and because organizational information processing converges at very few individuals at the top of an organization. Furthermore, the CEO is typically considered to be the most important decision maker because he or she is made responsible for the strategic directions and plans of a company.688 At least, this view has guided most of Upper Echelon research studies because of convenience in sampling approaches. However, there may be cases in which other individuals also make strategic decisions because they have a sufficient authority and power base. Consequently, another approach is to identify those key informants actually constituting the dominant coalition with respect to a specific research inquiry.689 Taken this together, the CEO but also other individual decision makers are the focal point of this analysis as long as they have a central role in SDM. Medium-sized companies For deciding on which companies should be included in the present research study two considerations are made. Firstly, the choice of the unit of analysis in Upper Echelon research “rests on an implicit assumption about the distribution of power among top managers. For example, in an organization in which the CEO wields dominant power, studying only the CEO may provide sufficient information with which to test propositions.”690 As a result, the sample of companies should allow for a dominant 687

Cf. Dean, J. W./Sharfman, M. P. (1996), p. 379 for such a study design. Cf. Gioia, D. A./Chittipeddi, K. (1991), p. 433. 689 Cf. Carpenter, M. A. et al. (2004), p. 753 and 759. 690 Finkelstein, S. (1992), p. 505. 688

Operationalization of variables

123

role of the CEO or another key decision maker which can generally be assumed in small and medium size companies.691 Therefore, large companies are excluded from the present research study. Secondly, for drawing statistical inferences the variables examined need to show sufficient variation and the population of a research study should be defined accordingly.692 The following considerations are made for controlling for sufficient variation in the variables of interest. Variation in information behavior, which is tied to the information sources available, should be ascertained. Small companies have only rudimentary bookkeeping, accounting and management information systems available and informal information exchange prevails.693 In contrast to that, medium-sized companies at least have some kind of formal systems such as bookkeeping and accounting and a management information system, as well as the resources for accessing external documentary sources, such as memberships of trade-associations or market reports.694 Therefore, medium-sized companies provide a variable mix of formal and informal information sources. The availability of such a mix presumably translates into greater variation in information behavior than for small companies. To summarize, the unit of analysis is a single strategic decision made by CEOs or other key decision makers in medium-sized companies. 5.2 Operationalization of variables 5.2.1 Measurement basics and guiding principles The measurement of managerial phenomena is difficult to accomplish, because they are most often not directly observable and cannot be measured with one single indicator.695 Due to this unobservable nature, the theoretical phenomena describing real-life phenomena are called latent variables.696 For making them measurable, empirical management research adopted methods of the social sciences and psychology. These methods accomplish latent variable measurement by transferring theoretical variables into language describing the underlying real-life phenomena.697

691

Cf. Miller, D./Dröge, C. (1986), p. 554. Cf. Bortz, J./Döring, N. (2006), p. 23. Cf. Welsh, J. A./White, J. F. (1981), p. 18; McGee, J. E./Sawyerr, O. O. (2003), p. 389. 694 Cf. Berens, W. et al. (2005), pp. 189-190. 695 Cf. Homburg, C. (2007), p. 39. 696 Cf. Homburg, C./Giering, A. (1996), p. 6. 697 Cf. Prim, R./Tillmann, H. (2000), pp. 25-27. 692 693

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Research design

This language is represented in indicators or so-called manifest variables. Furthermore, latent variables are most often measured by a number of indicators instead of only one. The set of indicators measuring one latent variable is called construct.698 This implies there are two levels of analysis, a theoretical level and an operational level,699 which are both connected through language.700 The process of connecting these two levels is called operationalization which means selecting those manifest variables that are to measure a latent variable.701 This selection should meet the correspondence rule. This rule states that theoretical and operational variables need to correspond in their meanings.702 The correspondence of latent variable and constructs is critical for how accurately the theoretical phenomenon of interest is actually measured.703 As a result of correspondence concerns, a number of post-hoc statistical measures have been developed for assessing how accurately a construct measures the latent variable of interest.704 There are three main areas of concern with respect to the development of a measurement instrument.705 The first area of concern pertains to accurately measuring the theoretical phenomenon of interest. There are basically two key criteria for developing and using a measurement instrument in this respect.706 Firstly, the operational construct has to correspond to the theoretical meaning of a latent variable and a difference in observed scores reflects true differences in the latent variable. This characteristic is referred to as validity.707 Secondly, the operational construct should measure a latent variable free of random error. This characteristic is referred to as reliability. Given these two criteria, a researcher has two possibilities for constructing a measurement instrument.708 Firstly, a researcher may use existing constructs from the literature. When choosing an existing construct for measurement, the requirements for validity and reliability in measurement are ideally demonstrated in empirical research studies 698

Cf. Homburg, C./Giering, A. (1996), p. 6. Cf. Runkel, P./McGrath, J. (1972); Chenhall, R. H./Moers, F. (2007), pp. 175-176. Cf. Prim, R./Tillmann, H. (2000), pp. 25-27. 701 Cf. Little, T. D./LIndenberger, U./Nesselroade, J. R. (1999), p. 192. 702 Cf. Fornell, C. (1989), p. 160; Bortz, J./Döring, N. (2006), p. 19. 703 Cf. Homburg, C. (2007), p. 41. 704 Cf. Little, T. D. et al. (1999), p. 194. 705 Cf. Müller, T. (2008), p. 111. 706 Cf. Homburg, C./Giering, A. (1996), p. 6; Fassot, G./Eggert, A. (2005), p. 33. 707 Cf. Homburg, C./Giering, A. (1996), p. 6; Homburg, C./Klarmann, M. (2006), p. 732. 708 Cf. Homburg, C./Klarmann, M. (2006), pp. 731-732. 699 700

Operationalization of variables

125

already. Secondly, if a suitable construct is not available in the literature, the researcher must develop a construct. This development bases on the theoretical variables of interest and should follow a rigor development methodology such as the ones presented by Churchill, G. A. (1979)709 or Diamantopoulos, A./Winklhofer, H. M. (2001).710 Which of the two possibilities is actually followed largely depends on the availability of constructs in the literature. However, several researchers suggest using existing measures711 in order to allow comparing different research studies712 and building a sound set of measurement models for empirical management research in general.713 However, when using existing measures there is some potential for using mis-specified constructs in terms of their validity.714 Therefore, researchers should pay particular attention to conceptualizing latent variables and to choosing the appropriate indicators and constructs for measuring these.715 The second area of concern pertains to the measurement philosophy adopted. The measurement philosophy is reflected in the specification of constructs. The term specification refers to the causal relationship between a latent variable and its indicators.716 There are two reasons why specification is important. Firstly, specification has implications for the applicability and choice of statistical methods.717 Secondly, there is some potential for mis-specifying constructs which may have important consequences for the validity of constructs and the whole research model.718 This potential threat is not only theoretical in nature, but it is a matter of fact that a considerable portion of constructs in empirical management research is mis-specified. Several research reviews identify that between 29% and 32% of constructs are misspecified in research studies published in leading Anglo-American marketing journals during the period from 1977 to 2000719 and in Germany’s marketing journal Marketing Zeitschrift für Forschung und Praxis until the year 2002 respectively.720

709

Cf. Homburg, C./Giering, A. (1996), pp. 6-11. Cf. Fassot, G./Eggert, A. (2005), p. 40. 711 Cf. Churchill, G. A. (1979), p. 67; Homburg, C./Klarmann, M. (2006), p. 732. 712 Cf. Homburg, C./Klarmann, M. (2003), p. 77. 713 Cf. Homburg, C./Klarmann, M. (2006), p. 732. 714 Cf. Homburg, C./Klarmann, M. (2006), p. 732. 715 Cf. Little, T. D. et al. (1999), pp. 193-195; Diamantopoulos, A./Winklhofer, H. M. (2001), pp. 271274; Bollen, K. A./Lennox, R. (1991), p. 308; Homburg, C./Klarmann, M. (2006), p. 732. 716 Cf. Homburg, C./Klarmann, M. (2006), p. 730. 717 Cf. Chin, W. W. (1998a), p. ix; Eberl, M. (2006), p. 654f. 718 Cf. Diamantopoulos, A./Winklhofer, H. M. (2001), p. 271; Eberl, M. (2006), pp. 654-655. 719 Cf. Jarvis, C. B./Mackenzie, S. B./Podsakoff, P. M. et al. (2003), p. 206. 720 Cf. Fassot, G./Eggert, A. (2005), pp. 41 and 45. 710

126

Research design

Basically, there are two types of specifications, namely reflective and formative specifications. These specifications manifest themselves in four constituting characteristics.721 The first characteristic is the direction of causality between construct and latent variable. The second characteristic concerns the interchangeability of indicators. The third characteristic pertains to covariation between the latent variable indicators. The fourth characteristic concerns the nomological net of a construct’s indicators. These four characteristics are particularly important for assessing the specification of existing constructs in the literature in order to avoid the potential and associated negative consequences for validity of model mis-specification in structural equation modeling (SEM).722 Reflective specification refers to the causal relationships from a latent variable to its indicators. This implies covariation among indicators is caused by, and therefore reflects, variation in the latent variable.723 Because of this, indicators in one construct are interchangeable and elimination of one indicator does not change the meaning of the latent variable.724 Furthermore, all reflective indicators are expected to covary with each other and a change in one indicator is expected to change the other indicators.725 Finally, the nomological net of all indicators is required to be the same and all indicators have the same antecedents and consequences.726 Formative specification refers to the causal relationships between a construct’s indicators and the latent variable. The construct is thus a function of its indicators.727 This implies, variation in the latent variable is a result of variation in its composing formative indicators and their combination.728 In general, formative indicators are not interchangeable, because they compose the meaning of the latent variable and an exchange of indicators may alter the meaning of the latent variable.729 Furthermore, formative indicators are not expected to covary although the existence of covariation is

721

Cf. Jarvis, C. B. et al. (2003), p. 203; Müller, T. (2008), p. 117. Cf. Jarvis, C. B. et al. (2003), p. 216; Homburg, C./Klarmann, M. (2006), p. 731. 723 Cf. Fornell, C./Bookstein, F. L. (1982), p. 442; Homburg, C./Giering, A. (1996), p. 6; Chin, W. W. (1998b), p. 305; Jarvis, C. B. et al. (2003), p. 200. 724 Cf. Jarvis, C. B. et al. (2003), p. 203. 725 Cf. Jarvis, C. B. et al. (2003), p. 203; Götz, O./Liehr-Gobbers, K. (2004), p. 718. 726 Cf. Jarvis, C. B. et al. (2003), p. 203. 727 Cf. Homburg, C./Giering, A. (1996), p. 6. 728 Cf. Chin, W. W. (1998b), p. 307; Jarvis, C. B. et al. (2003), p. 201. 729 Cf. Jarvis, C. B. et al. (2003), p. 203; Homburg, C./Klarmann, M. (2006), p. 732. 722

Operationalization of variables

127

not a problem to formative constructs pe se.730 Consequently, a change in one indicator does not necessarily result in a change of a construct’s other indicators.731 Finally, the indicators of formative constructs are not required to have the same nomological net and thus do not necessarily have the same antecendents and consequences.732 The choice of latent variable specification mainly depends on the theoretical nature of the latent variable to be measured.733 However, the following considerations should be made with respect to construct specification. At first, formative measures are criticized on theoretical grounds because they are constructed by the researcher. As such formative constructs rather reflect a constructivist research paradigm which questions the assumption of independence of research and reality as stated by the scientific realism paradigm.734 Therefore, measurement instruments should be reflective. Secondly, the specification of constructs should clearly be stated in order to assure appropriate choice of the statistical SEM method and validity of measurement and thus the whole research model. For assessing the specification of constructs the four main criteria outlined above can be used. Some authors furthermore propose the so-called Tetrad test as statistical measure for identifying a construct’s specification.735 However, this test is not completely reliable736 and therefore, the specification assessment in this study follows the qualitative, theoretically grounded approach.737 The third area of concern pertains to reliability of measurement. Each construct for a latent variable should comprise several indicators. This allows for attaining a more accurate description of the latent variable and for evaluating measurement error.738 However, there may also be exceptions to this principle because SEM does not only require multiple-item constructs but also allows for including single-item constructs.739 If single-item constructs are to be used, they need to fulfill two requirements. 740 Firstly, the object of the construct must be “easily and uniformly imagined.”741 730

Cf. Chin, W. W. (1998b), p. 306; Diamantopoulos, A./Winklhofer, H. M. (2001), p. 271; Jarvis, C. B. et al. (2003), p. 203. 731 Cf. Götz, O./Liehr-Gobbers, K. (2004), p. 718. 732 Cf. Jarvis, C. B. et al. (2003), p. 203. 733 Cf. Homburg, C./Klarmann, M. (2006), p. 730. 734 Cf. Homburg, C. (2007), p. 35. 735 Cf. Bollen, K. A./Ting, K. (2000), pp. 5-8. 736 Cf. Eberl, M. (2006), pp. 657-659. 737 Cf. Homburg, C./Klarmann, M. (2006), p. 731; Müller, T. (2008), p. 117. 738 Cf. Little, T. D. et al. (1999), p. 194; Bagozzi, R. P./Youjae, Y./Phillips, L. W. (1991), p. 421. 739 Cf. Backhaus, K./Erichson, B./Plinke, W. (2006b), p. 11. 740 Cf. Rossiter, J. R. (2002); Bergkvist, L./Rossiter, J. R. (2007), p. 176. 741 Bergkvist, L./Rossiter, J. R. (2007), p. 176.

128

Research design

Secondly, the attribute of interest again must be “easily and uniformly imagined.”742 Although these requirements pertain to single-item latent variable measurement, it is analogically possible to include manifest variables such as company sales in a SEM research model.743 These considerations lead to the following principles for constructing the measurement instrument of this research study: ƒ Assure construct validity, i.e. correspondence between latent variable and indicators ƒ Assure construct reliability, i.e. measurement free of random error ƒ Use existing constructs where possible ƒ Assess and state specification of constructs ƒ Prefer reflective over formative measures ƒ Use constructs with several indicators The ultimate choice for measurement constructs should furthermore be supported by expert interviews and a pretest with the potential key informants in the research population.744 This pretest has two main aims.745 Firstly, the wording and comprehensibility of indicators shall be evaluated. Secondly, the content validity of indicators should be assessed with the support of academic experts and practitioners. The construction of the measurement instrument accounted for this and a number of interviews were conducted prior to data collection. Six interviews with academic experts in related research areas and ten interviews with entrepreneurs and executive managers were conducted. 5.2.2 Measurement instrument 5.2.2.1 Information use The conceptual basis for information use comprises the dimensions amount and scope of information use from four classes of information sources.746 This results in four variables of information use:747

742

Bergkvist, L./Rossiter, J. R. (2007), p. 176. Cf. Backhaus, K. et al. (2006b), p. 11. 744 Cf. Bohrnstedt, G. W. (1970), p. 92; Brettel, M./Heinemann, F./Hiddemann, T. (2006), p. 12. 745 Cf. here and in the following Homburg, C./Giering, A. (1996), pp. 11-12. 746 Cf. sub-section 2.2.2.1 for the conceptual basis of information use. 747 Cf. Daft, R. L. et al. (1988), p. 129; Auster, E./Choo, C. W. (1993), p. 196. 743

Operationalization of variables 1)

Information use from internal, impersonal sources.

2) 3) 4)

Information use from internal, personal sources. Information use from external, impersonal sources. Information use from external, personal sources.

129

Furthermore, scope of information is defined along three dimensions: 1) quantitative and qualitative, 2) historic and forward-looking, as well as 3) internal or external information.748 One established measurement instrument for scope of information was developed by Chenhall, R. H./Morris, D. (1986), whereas it originally was intended for application in management accounting and information systems research. 749 However, an organization’s information system is neither restricted to one single system nor to computerized information sources only. “The term information system should not be interpreted to mean a single, integrated system. Most information systems consist not only of formal, organized, tangible records […] but also of informal, intangible bits of data such as memos, special studies, and managers’ impressions and opinions.”750 This leads to the conclusion that the scope of information construct of management accounting research can be adapted to the other classes of information sources of the present research study. This adaptation was accomplished by providing a general description of the information scope dimension accompanied with some specific examples for the underlying information source. A similar approach was already employed in other related empirical management research studies.751 Then, these indicators were constructed as seven-point Likert scales asking to what extent the informant used the information described in the respective items for the purpose of SDM.752 Finally, the specific adaptations in this study were validated through several interviews with academic experts and top executives. The assessment of the constructs’ specification is as follows. Firstly, the cause for using information is the individual decision maker with the purpose of using information for SDM. Therefore, the causality is from the latent variable to the indicators. Secondly, the indicators are expected to covary, because the bits and pieces of information received will very likely not pertain to only one dimension but rather be a mix of information dimensions. Thirdly, the antecedents and consequences of the 748

Cf. Larcker, D. F. (1981), pp. 521-522. Cf. Chenhall, R. H./Morris, D. (1986), p. 32 750 Zimmerman, J. L. (2006), p. 2, emphasis from the original author. 751 Cf. Daft, R. L. et al. (1988), p. 129. 752 Cf. e.g. Mia, L. (1993), p. 274. 749

130

Research design

individual items are very likely the same for all indicators. The main antecedent factor is a decision maker’s need to make a strategic decision. The consequence is the use of information in SDM and the resulting effectiveness. Fourthly, the items are not mutually exclusive, e.g. financial information may be internal or external. Thus they are interchangeable. Overall, this assessment leads to the conclusion that information use is specified as reflective. The measurement instrument for information use from internal, impersonal sources is presented in Table 7. Construct

Information use from internal, impersonal sources (InfoIntImp)

Specification

Reflective

Source(s)

Adapted from Daft, R. L. et al. (1988), Chenhall, R. H./Morris, D. (1986), Hambrick, D. C. (1982)

Variable No.

Indicator text

Scale

How intensely did you use the following information in addressing the aforementioned example of a strategic decision? InfoIntImp1

Financial historic information, e.g. annual statements, financial / cost accounting reports, budget deviation analyses, financial key performance indicators

InfoIntImp2

Forward looking information / probabilities, e.g. plans, budgets, forecasts, sensitivity analyses, scenario analyses, cost-benefit-analyses

InfoIntImp3

Non-financial information relevant for production, e.g. production and productivity reports / analyses, capacity utilization analyses, learning curve or value chain analyses, balanced scorecard figures, production benchmarking reports

InfoIntImp4

Non-financial information relevant for marketing, e.g. portfolio-analysis, life-cycle-analysis, balanced scorecard figures, benchmarking reports

InfoIntImp5

Non-economic information, e.g. strengths-weaknesses, opportunities-threats analysis, employee surveys

InfoIntImp6

Information about intermediate external factors, e.g. formal risk assessments, early warning systems

Table 7: Measurement instrument for information use from internal, impersonal sources (Source: own compilation)

7-Likert

Operationalization of variables

131

The measurement instrument for information use from internal, personal sources is presented in Table 8. Construct

Information use from internal, personal sources (InfoIntPers)

Specification

Reflective

Source(s)

Adapted from Daft, R. L. et al. (1988), Chenhall, R. H./Morris, D. (1986), Hambrick, D. C. (1982)

Variable No.

Indicator text

Scale

How intensely did you use the following information in addressing the aforementioned example of a strategic decision? InfoIntPers1

Financial historic information, e.g. meetings / conversations with managers, staff or board members about the economic, financial and profit situation of your own or other companies, and about monetary market value and price developments

InfoIntPers2

Forward looking information / probabilities, e.g. assessments made by board members, managers or staff about potential consequences of external trends or internal measures on your company’s development

InfoIntPers3

Non-financial information relevant for production, e.g. conversations with board members, production managers or staff about production related issues

InfoIntPers4

Non-financial information relevant for marketing, e.g. conversations with board members, marketing / sales managers or staff about market volumes and growth as well as about general market and customer trends

InfoIntPers5

Non-economic information, e.g. conversations with board members, managers or staff about general customer behavior, competitor actions, employee satisfaction

InfoIntPers6

Information about intermediate external factors, e.g. conversations with board members, managers or staff about technological trends, social, political, legal or economic developments

Table 8: Measurement instrument for information use from internal, personal sources (Source: own compilation)

7-Likert

132

Research design

The measurement instrument for information use from external, impersonal sources is presented in Table 9. Construct

Information use from external, impersonal sources (InfoExtImp)

Specification

Reflective

Source(s)

Adapted from Daft, R. L. et al. (1988), Chenhall, R. H./Morris, D. (1986), Hambrick, D. C. (1982)

Variable No.

Indicator text

Scale

How intensely did you use the following information in addressing the aforementioned example of a strategic decision? InfoExtImp1

Financial historic information, e.g. statistics / reports about financial market or price developments, financial reports of other companies

InfoExtImp2

Forward looking information / probabilities, e.g. market trend reports and forecasts, upcoming legal initiatives, economic outlook expert panels from business press

InfoExtImp 3

Non-financial information relevant for production, e.g. market research or trade association reports, production relevant key performance indicators in competitor profiles, or production related information in general press / media or special magazine articles about suppliers, competitors, customers

InfoExtImp4

Non-financial information relevant for marketing, e.g. market research or trade association reports, marketing relevant information in general press / media or special magazine articles about suppliers, competitors, customers, product tests, or internet articles

InfoExtImp5

Non-economic information, e.g. from information services from trade associations or IHK, official statistics, press / media articles about legal, customer or competitor trends

InfoExtImp6

Information about intermediate external factors, e.g. from information services, special magazine / press / media articles about general social, legal, economic or technological trends

Table 9: Measurement instrument for information use from external, impersonal sources (Source: own compilation)

7-Likert

Operationalization of variables

133

The measurement instrument for information use from external sources is presented in Table 10. Construct

Information use from external, personal sources (InfoExtPers)

Specification

Reflective

Source(s)

Adapted from Daft, R. L. et al. (1988), Chenhall, R. H./Morris, D. (1986), Hambrick, D. C. (1982)

Variable No.

Indicator text

Scale

How intensely did you use the following information in addressing the aforementioned example of a strategic decision? InfoExtPers1

Financial historic information, e.g. from conversations with customers, suppliers, competitors, consultants about financial performance of competitors or customers, as well as about monetary market or price developments

InfoExtPers2

Forward looking information / probabilities, e.g. assessments from external contacts about future purchasing or sales trends, competitive developments

InfoExtPers3

Non-financial information relevant for production, e.g. conversations with external contacts about capacity or quality standards of customers, competitors, suppliers or production benchmarks from external consultants / industry experts

InfoExtPers4

Non-financial information relevant for marketing, e.g. conversations about new products, brand image, customer purchasing behavior, quality or service demands by customers, competitors or marketing benchmarks from external consultants / industry experts

InfoExtPers5

Non-economic information, e.g. conversations with external contacts about the situation or developments concerning competitors, customers, suppliers in general

InfoExtPers6

Information about intermediate external factors, e.g. conversations with trade-association representatives, researchers, consultants or on business or conference trips about general social, economic and technological trends

Table 10: Measurement instrument for information use from external, personal sources (Source: own compilation)

7-Likert

134

Research design

5.2.2.2 Political behavior For measuring political behavior, the construct from Dean and Sharfman (1993, 1996) is used.753 This construct reflects the conception of political behavior as describing decision participants following their self-interests and engaging in political behaviors associated with following these interests.754 Again, the construct is specified as reflective because of the following reasons. Firstly, the causal direction is from latent variable to indicator – the extent to which people are interested in own goals results in behavior pursuing these goals.755 Secondly, the items are interchangeable, because complementary aspects of political behavior are incorporated in the measurement instrument. Thirdly, the items are expected to covary because of their complementary nature. Fourthly, the nomological net appears to be the same for all indicators. However, the original construct consists of four items, whereas two items had to be dropped during the expert interviews. A large part of the experts consulted explicitly stated that they did not comprehend the two items which were ultimately dropped. Construct

Political behavior (PolBeh)

Specification

Reflective

Source(s)

Dean, J.W./Sharfman, Mark P. (1993); Dean, J. W./Sharfman, M. P. (1996)

Variable No.

Indicator text

PolBeh1

Decision participants were only concerned about the goals of the company.

PolBeh2

Personal goals and interests of decision participants were dealt with completely open during the decision process.

Scale

7-Likert

Table 11: Measurement instrument for political behavior (Source: own compilation)

753

Cf. Dean, J.W./Sharfman, Mark P. (1993), p. 1082; Dean, J. W./Sharfman, M. P. (1996), p. 395; Cf. sub-section 2.2.2.2 for the conception of political behavior. 755 Cf. here and in the following Allen, R. W./Madison, D. L./Porter, L. W. et al. (1979), p. 77; Dean, J.W./Sharfman, Mark P. (1993), p. 1072. 754

Operationalization of variables

135

5.2.2.3 Perceived environmental uncertainty The measurement instrument for PEU bases on the conception of Duncan, R. (1972) which distinguishes into complexity and dynamics in different environmental sectors.756 A basic measurement issue is which environmental sectors are to be included and several approaches ranging from four to eleven environmental sectors can be identified.757 In general, these distinct environmental sectors can be assigned to two environmental layers, the immediate task environment and the more intermediate general environment of an organization. The task environment includes sectors of particular relevance for the competitive situation of a company,758 whereas the general environment includes sectors of more general and long-term relevance.759 The operationalization of PEU focuses on the task environment because medium-sized companies, which form the population of this study, are not only small versions of large corporations but have particular limits concerning their resources.760 As a result they are more constraint with respect to the strategies they can actually choose. Therefore, niche strategies focusing on narrowly defined market segments are the dominant strategic approach of medium-sized companies.761 For strategy development they build on their operational strengths pertaining to product development, process technology and their selling capability.762 Therefore, their strategic alternative space is most importantly influenced by the immediate task environment and it is sensible to restrict the analysis of PEU to the immediate task environment.763 To conclude, this research study measures PEU in the four immediate task environment sectors customers, competitors, technology as well as policy and regulation.764 The specification of the construct is formative because the direction of causality is from the indicators to the latent variable. Furthermore, the indicators are not interchangeable, because the four environmental sectors form distinct environmental domains. Next, the indicators are also not expected to covary, because the extent of 756

Cf. Duncan, R. (1972), pp. 318-321. Cf. Duncan, R. (1972), p. 315; Hambrick, D. C. (1981a), p. 257; Culnan, M. J. (1983), p. 198; Farh, J./Hoffman, R./Hegarty, W. (1984), p. 214; Daft, R. L. et al. (1988), p. 129; Elenkov, D. S. (1997), p. 295; May, R. C. et al. (2000), p. 410; Brettel, M. et al. (2006), p. 15. 758 Cf. Sawyerr, O. O./McGee, J./Peterson, M. (2003), p. 275. 759 Cf. Daft, R. L. et al. (1988), p. 124. 760 Cf. Lee, K. S./Lim, G. H./Tan, S. J. et al. (2001), p. 146. 761 Cf. Lee, K. S. et al. (2001), p. 146. 762 Cf. Rangone, A. (1999), p. 235. 763 Cf. Sawyerr, O. O. et al. (2003), p. 275. 764 Cf. Daft, R. L. et al. (1988), p. 129. 757

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PEU can be very different in these environmental sectors.765 Finally, the nomological net is very different for these various environmental sectors. Following preceding SDM research studies, PEU is measured by two Likert subscales, namely perceived environmental complexity and dynamism.766 This requires a combination of these scales for statistical analysis. This combination is accomplished by taking the average of the complexity and dynamism items for each data set and by using the resulting values as the ultimate indicators in the structural model.767 Table 12 presents the measurement instrument for PEU. Construct

PEU, consisting of perceived environmental complexity (PEC) and perceived environmental dynamism (PED)

Specification

Formative

Source(s)

Daft, R. L. et al. (1988)

Variable No.

Indicator text

Scale

Please assign your perception of the complexity (i.e. number / differences in events) in the following environmental sectors. PEC1

Competitive environment (e.g. competitors in general, competitive products, substitute products, strategic actions of competitors)

PEC2

Customers and market (e.g. individuals or firms who buy your products, customer trends, demand and purchasing behavior)

PEC3

Technology (e.g. production technologies, innovations at supply side, technological trends)

PEC4

Policy and regulation (e.g. international, national or regional legislation and legal instructions, political trends)

7-Likert

Please assign your perception of the rate of change (i.e. frequency / speed of events) in the following environmental sectors. PED1

Competitive environment

PED2

Customers and market

PED3

Technology

PED4

Policy and regulation

Table 12: Measurement instrument for perceived environmental uncertainty (Source: own compilation)

765 766 767

Cf. Brettel, M. et al. (2006), p. 15. Cf. Daft, R. L. et al. (1988), p. 129. Cf. Daft, R. L. et al. (1988), p. 130.

7-Likert

Operationalization of variables

137

5.2.2.4 Cognitive style The LNTSP has been identified as the cognitive style instrument for this research study, because of a number of reasons. Firstly, its conception comprises an information perception and information processing dimension.768 This is particularly relevant because the subjective perception of information is a central assumption of the Upper Echelon View. Therefore, the study uses the perceptual dimension of the LNTSP. Secondly, it was developed during four consecutive research studies which demonstrated its measurement reliability, internal consistency and cross-validity with the MBTI and the CSI.769 Finally, it was already employed in a follow-up study, demonstrating its nomological validity with respect to entrepreneurial orientation.770 Its specification is reflective because the causal relationship is from latent variable to its indicators, and the indicators are expected to covary. Table 13 presents the measurement instrument for cognitive style. Construct

Cognitive style (Cogn)

Specification

Reflective

Source(s)

Vance, C. M. et al. (2007)

Variable No.

Indicator text

Scale

The following words or phrases describe alternative decision making input. Please mark to which extent you generally rely on the respective input for decision making. Cogn1

Concepts

…. instincts

Cogn2

Rationality / thinking

…. empathy

Cogn3

Reason

…. felt sense

Cogn4

Logic

…. inner knowing

Cogn5

Facts

…. feelings

Cogn6

Proof

…. heartfelt

Cogn7

Data

…. hunch

Cogn8

Deduction

…. intuition

Table 13: Measurement instrument for cognitive style (Source: own compilation)

768 769 770

Cf. Vance, C. M. et al. (2007), p. 170. Cf. Vance, C. M. et al. (2007), pp. 171-178. Cf. Groves, K./PĂUNESCU, C. (2008), p. 13.

6-Likert, bipolar

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5.2.2.5 Strategic decision quality The measurement of strategic decision quality bases on Amason, A. C. (1996).771 It is specified as reflective because there are causal relationships from latent variable to its indicators. Furthermore, indicators appear to be interchangeable and are expected to covary. Finally, the antecedents and consequences are expected to be the same for all indicators, namely the actual decision making process and its effect on organizational performance respectively. The indicators are measured at a 7-Likert scale. Table 14 presents the measurement instrument for strategic decision quality. Construct

Strategic decision quality (DecQual)

Specification

Reflective

Source(s)

Amason, A. C. (1996)

Variable No.

Indicator text

DecQual1

I am satisfied with the overall quality of the strategic decision made.

DecQual2

The strategic decision made contributes to the achievement of the company’s goals.

DecQual3

The strategic decision made contributes to the financial performance of our company.

Scale

7-Likert

Table 14: Measurement instrument for strategic decision quality (Source: own compilation)

5.2.2.6 Company performance At first, the measurement of organizational performance draws on Brettel, M./Heinemann, F./Kessell, A. (2005), which was further amended by indicators from Homburg, C./Pflesser, C. (2000).772 The instrument builds on subjective company performance because of a general lack of consistent objective data and informants’ reluctance to answer objective performance data in small and medium-sized companies.773 Subjective company performance is specified as reflective because of the following reasons. Firstly, the causal relationships are from latent variable to its composing indicators. Secondly, the nomological net is the same for all indicators. With respect to 771 772 773

Cf. Amason, A. C. (1996), pp. 133-134. Cf. Homburg, C./Pflesser, C. (2000), p. 460; Brettel, M. et al. (2005), p. 11. Cf. Dess, G. G./Robinson, R. B. (1984), p. 266; Sawyerr, O. O. et al. (2003), p. 276.

Operationalization of variables

139

the two other assessment criteria, the theoretical considerations are not so clear as the two following arguments show. On the one hand, indicators such as historic and forecast financial performance are conceptually not interchangeable. For the same reason, the measures must not necessarily covary. On the other hand, these indicators are very likely not fully independent. For example, one would expect that product innovation success goes along with financial success. Despite the preceding conceptual considerations empirical data also supports a reflective specification of subjective organizational performance.774 Therefore, the specification of subjective company performance can be considered as reflective. Table 15 presents the measurement instrument. Construct

Subjective company performance (Perf)

Specification

Reflective

Source(s)

Brettel, M. et al. (2005); Homburg, C./Pflesser, C. (2000)

Variable No.

Indicator text

Perf1

The economic performance of our company is satisfactory relative to our most important competitors.

Perf2

The growth of our company is satisfactory relative to our most important competitors.

Perf3

The profit forecast of our company is satisfactory relative to our most important competitors.

Perf4

The success of our products is satisfactory relative to our most important competitors.

Perf5

The number of acquired customers of our company is satisfactory relative to our most important competitors.

Perf6

The customer loyalty of our company is satisfactory relative to our most important competitors.

Perf7

The market share of our company is satisfactory relative to our most important competitors.

Scale

7-Likert

Table 15: Measurement instrument for subjective company performance (Source: own compilation)

In order to increase the validity of the company performance measure used in this study, the conceptual model will be amended by financial performance measures.775 For this purpose items on change in sales revenues and change in return on sales are

774 775

Cf. Müller, T. (2008), p. 128. Cf. Lehmann, D. R. (2004), pp. 73-75.

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incorporated into the questionnaire. Including these items into the questionnaire is necessary, because German medium-sized companies rarely publish financial data. Furthermore, using change in return on sales instead of return on sales is an outcome of questionnaire pretests with management experts. One of these management experts argued for the adjustment because the sample would cover a broad range of industries. Accordingly, the return on sales values would differ between companies of different industries, although each company might actually perform at a similar level in relation to their respective industry profitability. To avoid such a bias from industry effects, the change in return on sales appears to be a more generalizable measure. Following the recommendations of the management expert, the adjustment was made. Each of the two financial performance indicators was measured for two time periods in order to keep some degree of flexibility once the empirical data is available. The first period refers to the year-over year change. The second period refers to five year changes. Furthermore, these indicators will be used as single item variables. Hence, identifying a reflective or formative specification is not applicable. The following Table 16 provides the measurement of financial performance in this study. Variable

Objective company performance (ObjPerf)

Specification

Not applicable / single indicator variables

Source(s)

Adapted from Homburg, C./Schilke, O. (2009)

Variable No.

Indicator text

ObjPerf1

Please indicate the change in sales revenues compared with previous year’s sales.

ObjPerf2

Please indicate the average change in sales revenues over the last five years.

ObjPerf3

Please indicate the change in return on sales compared with previous year’s sales.

ObjPerf4

Please indicate the average change in return on sales over the last five years.

Scale 7 clusters: ≤ -20% ≤ -10% ≤ 0% ≤ 5% ≤ 10% ≤ 20% > 20%

Table 16: Measures of objective company performance (Source: own compilation)

In order to increase the validity of these financial performance measures, the data will be triangulated with annual report data for those companies for which financial data are publicly available.776

776

Cf. Homburg, C./Schilke, O. (2009), p. 178.

Operationalization of variables

141

5.2.2.7 Control variables Control variables may have an influence on the dependent variable of a research model, while they do not belong to the research model itself.777 If control variables can be identified a priori, they should be included in a research model in order to test for a distorting effect. For the present study, a number of variables with a potentially distorting effect can be identified in the literature. These variables can be grouped according to their analysis level into organizational and individual level variables. The following discussion describes these variables and explains their potential influence on the dependent variables of interest. The first group of control variables comprises seven variables on organizational level. Firstly, company size may have an effect on strategic decision processes and their effectiveness.778 Larger companies may adopt more formal decision processes, while small companies tend to make their strategies based on rather informal, intuitive processes.779 Furthermore, increasing organizational size increases the impersonal information input to top executives. Top executives in larger organizations receive considerably more formal reports and documentary sources than their counterparts in smaller organizations.780 Company size is measured in terms of full-time employees.781 Secondly, a company’s industry environment may be an important determinant of organizational performance782 and strategic decision process characteristics.783 Furthermore, industry characteristics may have effects on organizational technologies and other aspects of their operations.784 Finally, empirical studies show that industry characteristics are an important determinant of managerial discretion.785 To summarize, industry characteristics may interfere with strategic decision processes, the importance of managerial actions and cognitions and general organizational outcomes.

777

Cf. here and in the following Bortz, J./Döring, N. (2006), p. 544. Cf. Fredrickson, J. W. (1984), p. 453; Wiersema, M. F./Bantel, K. A. (1992), p. 106. Cf. Brouthers, K. D./Andriessen, F./Nicoales, I. (1998), p. 137; Khatri, N./Ng, H. A. (2000), p. 69; Elbanna, S./Child, J. (2007), p. 438. 780 Cf. Jones, J. W./Saunders, C./McLeod Jr, R. (1988), p. 88; McGee, J. E./Sawyerr, O. O. (2003), p. 389. 781 Cf. e.g. Fredrickson, J. W. (1984), p. 453. 782 Cf. Dess, G. G./Ireland, R. D./Hitt, M. A. (1990), p. 8. 783 Cf. Judge, W. Q./Miller, A. (1991), p. 461. 784 Cf. Fredrickson, J. W./Mitchell, T. R. (1984), p. 406; Dess, G. G. et al. (1990), p. 9. 785 Cf. Finkelstein, S. et al. (2009), pp. 30 and 34. 778 779

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Research design

Therefore, it should be included as control variable. Industry is measured with a dichotomous scale distinguishing into services and manufacturing companies.786 Thirdly, company ownership, measured as minority vs. majority shareholdings of the top management team,787 may have an effect on strategic decision quality and organizational performance. A company’s shareholder structure has legal implications for the degree of external control influences, e.g. from board of directors. 788 This has an impact on managements’ decision making authority and managerial discretion. Generally speaking, majority shareholdings of top management increase the authority of internal top executives.789As a result, strategic decision processes and outcomes may be influenced by the share of managements’ shareholdings.790 Fourthly, the TMT size is incorporated as control variable because individual level processes may be influenced by other top executives in a similar position as the main decision maker.791 Fifthly, company age may have an influence on strategic decision quality and organizational outcomes. At first, company age has shown to shift organizations’ information processing emphasis from external to internal information sources and from personal to impersonal information sources792 as well as to formal and informal methods of information gathering.793 Therefore, the hypothesized information use relationships may be influenced. Furthermore, old companies are more entrenched to corporate cultures than young companies which results in organizational inertia inhibiting strategic change.794 This may lead to relatively inferior, i.e. low quality outcomes of strategic decision processes. In addition to that, company age has shown to have significant direct effects on organizational performance.795 Sixthly, type of decision has an influence on which information characteristics are particularly suitable for making a strategic decision. While for example a competitive 786

Cf. Müller, T. (2008), p. 130. Cf. Güttler, K. (2008), p. 145. 788 Cf. Hambrick, D. C./Finkelstein, S. (1995), p. 176. 789 Cf. Feltham, T. S./Feltham, G./Barnett, J. J. (2005), p. 13. 790 Cf. Hambrick, D. C./Finkelstein, S. (1995), p. 177. 791 Cf. e.g. Volkema, R. J./Gorman, R. H. (1998). 792 Cf. McGee, J. E./Sawyerr, O. O. (2003), p. 396. 793 Cf. Mohan-Neill, S. I. (1995), p. 18. 794 Cf. Finkelstein, S. et al. (2009), pp. 31 and 34. 795 Cf. Brüderl, J./Schüssler, R. (1990), pp. 530-533. 787

Operationalization of variables

143

positioning decision is heavily based on qualitative market and competitor information,796 a strategic investment decision is rather based on financial information.797 This may influence the information use pattern and relationships hypothesized in this study.798 Therefore, type of decision is included in the research model. Its measurement draws from practical, normative literature in order to make decision types recognizable for the informants.799 Finally, decision speed has effects on SDM800 and organizational performance.801 At the same time different decision making strategies may cause variation in decision speed.802 It is measured as decision duration in months from the beginning of first deliberate action of decision making until the final choice.803 Table 17 provides an overview of the organizational level control variables. Variable

Description

Measure

Scale

Source(s)

OrgSize

Organizational size

No. of employees

Interval

Fredrickson, J. W. (1984)

OrgIndustry

Industry of the organization

Manufacturing Services

Nominal

Müller, T. (2008)

OrgOwner

Management shareholdings

Minority / majority shareholding

Interval

Güttler, K. (2008)

OrgTMTsize Size of TMT

Number of TMT members

Ordinal

n.a.

OrgAge

Organizational age

No. of years

Metric

McGee, J. E./Sawyerr, O. O. (2003)

DecType

Type of decision

Competitive positioning, other types

Nominal

Preißner, A. (2001)

DecDur

Decision duration

No. of months

Metric

Nutt, P. C. (1993)

Table 17: Measurement instruments for control variables – organizational level characteristics (Source: own compilation)

796

Cf. Preißner, A. (2001), pp. 31-35. Cf. Papadakis, V. M. (1998), p. 116. 798 Cf. Mintzberg, H. et al. (1976), pp. 268-273; Elbanna, S./Child, J. (2007), pp. 446-447. 799 Cf. Preißner, A. (2001), p. 11. 800 Cf. Mintzberg, H. et al. (1976), p. 265. 801 Cf. Judge, W. Q./Miller, A. (1991), p. 460. 802 Cf. Eisenhardt, K. M. (1989), p. 571; Judge, W. Q./Miller, A. (1991), pp. 459-461; Papadakis, V. M. (1998), p. 119. 803 Cf. Nutt, P. C. (1993), p. 234; Papadakis, V. M. (1998), p. 123. 797

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Research design

The second set of control variables comprises individual level variables. At first, Upper Echelon Theory proposes that individual characteristics of top executive exert a major influence on managers’ perceptual processes and thus strategic choice and organizational outcomes.804 Therefore, this study controls for a number of demographic variables that have been found to be influential in the present research context.805 These individual level demographic variables and their measurement are summarized in Table 18. Variable

Description

Measure

Scale

Source(s)

IndAge

Age of decision maker

No. of years

Metric

Wiersema, M. F./Bantel, K. A. (1992)

IndTenure

Company tenure No. of years

Interval

Wiersema, M. F./Bantel, K. A. (1992)

IndExpDep

Depth of functional experience

Interval

Govindarajan, V. (1989)

No. of years in below

x General management x Marketing x Production x Research & Development x Administrative Calculated from functional Metric experience depth measures

IndExpDiv

Diversity of functional experience

IndExpInd

Industry experience

No. of years

IndEdu

Highest education level

IndFunc

Functional responsibility

x x x x x x x x x

Interval

Ordinal School Apprenticeship First university degree Advanced degree Nominal General management Marketing Production Research & Development Administrative

Bunderson, J. S./Sutcliffe, K. M. (2002) Govindarajan, V. (1989) Wiersema, M. F./Bantel, K. A. (1992)

Govindarajan, V. (1989)

Table 18: Measurement instruments for control variables – individual demographic characteristics (Source: own compilation)

804 805

Cf. Hambrick, D. C./Mason, P. A. (1984), p. 198. Cf. e.g. Bunderson, J. S./Sutcliffe, K. M. (2002), p. 885; Govindarajan, V. (1989), pp. 257-259; Wiersema, M. F./Bantel, K. A. (1992), pp. 97-99 and 110; Hough, J. R./White, M. A. (2004), p. 788; Papadakis, V. M. (2006), p. 384.

Operationalization of variables

145

In addition to that, motivation is an influential factor for the use of information.806 Most importantly, motivation has an effect on information use patterns807 and may distort the patterns of information use as hypothesized in this study. Therefore, motivation is included in the research model. Furthermore, motivation is a latent variable and needs to be measured by another construct. This study adapts an existing reflective measurement instrument, which has been used in information systems research, to the present research context.808 This instrument is presented in Table 19. Construct

Motivation to engage in information use for SDM (Mot)

Specification

Reflective

Source(s)

Davis, F. D. et al. (1992); Venkatesh, V. et al. (2003)

Variable No.

Indicator text

Mot1

I am very much interested in dealing with our company’s strategy and in acquiring and using information in order to do so.

Mot2

Gathering and using information decreases the likelihood of making errors for making a strategic decision considerably.

Mot3

Gathering and using information improves reaching my personal and organizational goals.

Mot4

I think strategic issues as well as gathering and using information for dealing with them are exciting.

Scale

7-Likert

Table 19: Measurement instrument for control variable – individual motivation (Source: own compilation)

806

Cf. O'Reilly Iii, C. A. (1982), p. 759. Cf. O'Reilly Iii, C. A. (1982), p. 764. 808 Cf. Davis, F. D./Bagozzi, R. P./Warshaw, P. R. (1992); Venkatesh, V. et al. (2003), pp. 448 and 456. 807

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5.3 Data analysis methodology 5.3.1 Basic methodological considerations 5.3.1.1 Statistical hypothesis testing Once operationalizations of latent variables are established and the data is collected, the process of theory testing is performed by means of mathematical modeling and statistical inference.809 Statistical inference means that theoretically derived hypotheses are transferred into a quantitative form which can then be tested with the help of statistical hypotheses.810 However, empirical data most often describes only a sample and not the whole population. Furthermore, the data observed follows probabilistic and not deterministic patterns.811 Due to the probabilistic nature of statistical hypotheses they can neither be falsified nor verified with certainty. Therefore, any theoretical hypothesis is transformed into a pair of statistical hypotheses, where the basic hypothesis (H0) reflects the statement of the theoretical hypothesis and the alternative hypothesis (H1) means exactly the opposite.812 The statistical hypotheses are then supported or rejected on basis of a probabilistic criterion, the significance level, which is determined by statistical significance test.813 Furthermore, it can be distinguished between explanatory, independent or exogenous variables and explained, dependent or endogenous variables.814 The distinction is made by the direction of the causal relationship. Causality means that probabilistic variation in the independent variable causes probabilistic variation in the dependent variable.815 Then, the aim of hypotheses testing is to evaluate whether such a relationship can be supported with the empirical data at a given probability level or not. Statistical significance testing requires that a statistical method is able to accommodate the measured variables and to model the causal relationships hypothesized.816 Empirical management research most often identifies a number of latent variables and complex relationships. Therefore, the statistical method needs to accommodate for 809

Cf. Chenhall, R. H./Moers, F. (2007), pp. 175-176. Cf. Bortz, J./Döring, N. (2006), pp. 9 and 22. 811 Cf. Bortz, J./Döring, N. (2006), pp. 9. 812 Cf. Bortz, J./Döring, N. (2006), pp. 24. 813 Cf. Bortz, J./Döring, N. (2006), pp. 9. 814 Cf. Homburg, C./Baumgartner, H. (1995b), p. 1092. 815 Cf. Backhaus, K. et al. (2006b), p. 344. 816 Cf. Bortz, J./Döring, N. (2006), pp. 25. 810

Data analysis methodology

147

modeling latent variables and complex relationships between them. These requirements are met by second generation multivariate statistical methods or more specifically SEM.817 Compared to other multivariate statistical methods, such as multivariate regression analysis,818 SEM has some unique advantages.819 Firstly, SEM allows for modeling complex relationships among multiple independent and dependent variables, e.g. causal chains between more than two variables. 820 Secondly, SEM allows to model latent variables. Thirdly, SEM allows for modeling measurement error in the observed variables. Fourthly, SEM allows for testing a priori theoretical and measurement assumptions against empirical data. As a result, SEM is a method of confirmatory analysis for hypotheses testing and has received increasing presence in empirical management research.821 For these reasons, SEM is used in this study. Finally, different statistical SEM methods are available and possess very different characteristics.822 Therefore, it is an important task to select the method which is most appropriate for the specific research context. This is accomplished in the following sub-sections. 5.3.1.2 Structural equation modeling SEM is the statistical approach for hypotheses testing in this study. A key characteristic of SEM is the distinction between measurement and structural model.823 The following discussion elaborates on the various elements of these two models and how they are generally depicted in empirical management research studies. The measurement model represents the manifest, observable variables which serve as indicators for the latent variables of interest. It consists of the relationships between constructs representing the latent variable and their indicators. Indicators are generally denoted as squares with Arabic letters (e.g. x). The relationship between construct and indicators is represented as arrows reflecting the specification of the construct. For formative indicators the relationships are from indicators to construct (e.g. [). The extent to which formative indicators contribute to composing the construct is 817

Cf. Fornell, C. (1987), p. 411; Homburg, C./Baumgartner, H. (1995b), p. 1092. Cf. Backhaus, K. et al. (2006b), pp. 7-12. Cf. here and in the following Fornell, C. (1987), p. 420; Homburg, C./Baumgartner, H. (1995b), p. 1092; Chin, W. W. (1998a), p. vii; Backhaus, K. et al. (2006b), p. 11. 820 Cf. Homburg, C./Baumgartner, H. (1995b), p. 1092. 821 Cf. Homburg, C./Baumgartner, H. (1995a), p. 162. 822 Cf. Chin, W. W. (1998a), p. vii; Homburg, C./Klarmann, M. (2006), p. 734. 823 Cf. Backhaus, K. et al. (2006b), p. 340. 818 819

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Research design

expressed in the so-called factor weights (Ox1 and Ox2).824 Furthermore, as the construct is a dependent variable in the measurement model it also possesses a residual variance (V). In contrast to that, for reflective indicators the relationships are from construct to indicators (e.g. K). The extent to which indicators reflect the underlying construct is expressed in their so-called factor loadings (Sy1 and Sy2).825 Furthermore, as indicators are the dependent variables in a reflective construct, they possess residual variance (Hx1 and Hx2). The structural model represents the theoretically derived causal relationships between these latent variables.826 It is generally depicted as consisting of circles and arrows, where the circles represent latent variables and the arrows the hypothesized relationship. Furthermore, the latent variables are denoted with Greek letters (e.g. [ and K) such as in the example in Figure 9. Two more components of a structural model are important. Firstly, the direction and value of the causal relationship between [ and K. This relationship is referred to as path coefficient (e.g. 8). Furthermore, due to the probabilistic nature of empirical data only a portion of the variance of the dependent variable is explained by [. This residual variance is denoted with e.g. 9.827 Finally, measurement and structural model are combined to form the structural equation model, which can then be estimated with the help of statistical SEM methods. See Figure 9 for an example of structural model and the aforementioned components.

824

Cf. Götz, O./Liehr-Gobbers, K. (2004), p. 718. Cf. Chin, W. W. (1998b), p. 299. 826 Cf. Backhaus, K. et al. (2006b), p. 340. 827 Cf. Chin, W. W. (1998b), p. 299. 825

Data analysis methodology

149

Structural model

Structural equation model ξ

x1

x2 λ x2

λx1

γ

ξ

σ

η

ς

γ

Measurement model ξ

σ λ x1 x1

λ x2 x2

πy1

η

ς

η πy2

y1

y2

εy1

εy2

πy1

πy2

y1

y2

εy1

εy2

Figure 9: Components of a structural equation model (Source: Chin, W. W. (1998b), p. 298-300, Müller, T. (2008), p. 104)

The estimation of structural equation models has basically two functions. The first function is the estimation and evaluation of the relationships between indicators and latent variables (factor loadings or weights). The second function is the estimation and evaluation of the relationships between independent and dependent latent variables (path coefficients).828 These functions are the same for any SEM method. Furthermore, SEM methods provide a number of parameters about the validity and reliability of measurement, as well as about the quality of the structural model. 5.3.1.3 Selection of partial least squares Two basic statistical methods for estimating structural equation models do exist. These two are covariance and variance based methods.829 They fundamentally differ in their parameter optimization methods.830 Covariance based methods minimize the difference between empirical and theoretical covariance matrices, which implies they optimize the overall measurement model.831 Variance based methods minimize the residual variance of reflective indicators and the dependent variables.832 828

Cf. Homburg, C./Baumgartner, H. (1995b), p. 1092; Chin, W. W. (1998b), p. 307; Backhaus, K. et al. (2006b), p. 341. Cf. Fornell, C./Bookstein, F. L. (1982), p. 440; Herrmann, A./Huber, F./Kressmann, F. (2006), p. 35; Müller, T. (2008), p. 105. 830 Cf. Homburg, C./Klarmann, M. (2006), p. 734. 831 Cf. Chin, W. W./Newsted, P. R. (1999), p. 309; Homburg, C./Klarmann, M. (2006), p. 734. 832 Cf. Fornell, C./Bookstein, F. L. (1982), p. 450; Homburg, C./Klarmann, M. (2006), p. 736. 829

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The selection of one of these two SEM methods is to be guided by consistency between theoretical objectives and objectives of the estimation approach and statistical assumptions on the data base.833 More specifically, the selection of the SEM approach should consider consistency between the following four areas:834 ƒ Aim of analysis and state of theoretical base ƒ Model specification in terms of number and specification of constructs as well as complexity of relationships ƒ Data base in terms of sample size, multicollinearity among independent variables and distribution of indicator data ƒ Requirements concerning estimation accuracy and quality criteria The following discussion elaborates about each of these areas and evaluates the two SEM approaches against each of these areas in light of the present research context. The discussion concludes with a selection of the appropriate optimization approach. First of all, covariance and variance based methods differ considerably in their aims of statistical analysis and requirements concerning the theoretical base for model specification. Covariance based methods optimize the overall measurement model and their path coefficient represent consistent parameter estimates. This implies covariance based methods are particularly suitable for confirmatory tests of theories. 835 Closely related is the requirement of having a well developed theoretical basis for specifying both measurement and structural model. In comparison to that, variance based methods optimize the explained variance of the dependent variables and are thus particularly suitable for prediction.836 This implies, they are far less restrictive in terms of the theoretical requirements for measurement and structural model specification.837 Although the aim of this study is a confirmatory test of hypotheses, the theoretical base can be considered as relatively novel and limited for the following reasons. The literature review and conceptual basis sections made clear, a number of perspectives and concepts for describing information use and SDM exist. A unifying theory of organizational information processing for SDM does not exist.838 As a result of this 833

Cf. Fornell, C./Bookstein, F. L. (1982), p. 451. Cf. Chin, W. W./Newsted, P. R. (1999), p. 336; Homburg, C./Klarmann, M. (2006), p. 735. Cf. Fornell, C./Bookstein, F. L. (1982), p. 451; Chin, W. W. (1998b), p. 299. 836 Cf. Fornell, C./Bookstein, F. L. (1982), p. 451; Chin, W. W. (1998b), p. 299; Götz, O./LiehrGobbers, K. (2004), p. 721. 837 Cf. Chin, W. W./Newsted, P. R. (1999), p. 336. 838 Choo, C. W. (1996), p. 22. 834 835

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fragmented conceptual and theoretical base, this study restricts its analysis to a certain cutout of information use and SDM which is particularly relevant for answering the research questions. Furthermore, the theoretical basis of this study can be considered as relatively new and has specifically been developed based on the Upper Echelon and Strategic Sensemaking Views. Last but not least, the measurement model of information use has to adapt existing measurement constructs to very different research contexts. To conclude, variance based methods provide more appropriate optimization approaches given the novelty of the theoretical base and the adaptation of existing measurement instruments of this research study. This conclusion is underpinned by the fact that some authors explicitly recommend to investigate information use and SDM in organizational settings with the help of variance based methods.839 Second of all, two facets of model specification are relevant for choosing the appropriate SEM method. Firstly, the specification of constructs might play a role. A number of authors propose the particular suitability of variance based methods for including formative constructs.840 While this argument may have held in early SEM research, in the meantime covariance based methods are able to accommodate formative measures.841 Secondly, the number of variables and the complexity of relationships of a research model might haven an influence on the choice among optimization approaches. Variance based methods have an advantage over covariance based methods when it comes to complex models with many variables and small sample sizes.842 The complexity of relationships, with four latent variables in a causal chain and a number of interrelationships, can be considered as relatively high while this still does not exclude the use of covariance based methods. These considerations lead to the conclusion from a model specification perspective both methods appear suitable. Third of all, two aspects concerning the underlying data base may be relevant for choosing the appropriate SEM method. Firstly, some authors argue sample size is one

839

Cf. Hulland, J. S. (1999), p. 202; Smith, D./Langfield-Smith, K. (2004), pp. 75-76. Cf. Chin, W. W./Newsted, P. R. (1999), p. 336; Götz, O./Liehr-Gobbers, K. (2004), p. 721. 841 Cf. Herrmann, A. et al. (2006), p. 43. 842 Cf. Fornell, C./Bookstein, F. L. (1982), p. 450; Garthwaite, P. H. (1994), p. 122. 840

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criterion for selecting one or the other SEM method.843 Covariance based methods generally need large sample sizes because the use of small sample sizes can lead to poor parameter estimates.844 In extreme cases the covariance matrix is unidentified and cannot be estimated at all.845 Therefore, covariance based estimations require at least 200 data sets.846 In contrast to that variance based methods can be used for sample sizes as small as 30 data sets.847 However, sample size may not be a decisive criterion because the sample size requirements should be accounted for in the research design. Secondly, statistical characteristics of the data set are more important than sample size for chosing the appropriate SEM method. In this respect, variance based methods have another advantage against covariance based methods. They are specifically designed for optimizing explanatory power when multicollinearity among independent variables is high.848 In case of high multicollinearity, variance based methods have the advantage of optimizing single relationships between independent and dependent variables. The focus is more on isolating the impact of each independent variable on the dependent variable. This is particularly relevant for this study, because high multicollinearity among information use variables frequently occurs in related research areas. Previous empirical studies examining SDM and information use facets face relatively high latent variable correlations and cross-loadings on item level. Consequently, they employ variance based methods for hypotheses testing.849 To conclude, as one can expect high multicollinearity among the independent variables in the present research context, variance based methods are more appropriate than covariance based methods for the present research study. Finally, covariance based methods have specific advantages over variance based methods with respect to accuracy and quality aspects. Covariance based methods 843

Cf. Chin, W. W. (1998b), p. 299. Cf. Chin, W. W./Newsted, P. R. (1999), p. 309. 845 Cf. Homburg, C./Baumgartner, H. (1995b), p. 1093. 846 Cf. Chin, W. W./Newsted, P. R. (1999), p. 309. 847 Cf. Chin, W. W./Newsted, P. R. (1999), p. 314. However, variance based methods have a minimum requirement of data sets which is determined by the maximum number of endogenous relationships from formative indicators to constructs or from independent to dependent latent variables. This maximum number multiplied by ten is equal to the minimum number of cases required for using variance based methods. Cf. Chin, W. W./Newsted, P. R. (1999), pp. 326-327; Gopal, A./Bostrom, R. P./Chin, W. W. (1992), p. 57. 848 Cf. Garthwaite, P. H. (1994), p. 127. 849 Cf. May, R. C. et al. (2000), p. 413; Jänkälä, S. (2005), p. 44. Subramanian, R./Gopalakrishna, P. (2001), p. 7; Karimi, J. et al. (2004), p. 185. 844

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provide consistent and thus more accurate parameter estimates than variance based methods.850 Since covariance based methods optimize the overall measurement model, they furthermore provide quality criteria for the overall measurement model fit. In contrast to that, variance based methods do not allow for global measurement model evaluation.851 Furthermore, parameter estimates are inconsistent because the measurement model is optimized with respect to the explained variance of the dependent variables and not with respect to the overall covariance of indicators.852 However, this inconsistency problem can be alleviated by increasing the sample size when using variance based methods (consistency-at-large).853 From this accuracy and quality criteria perspective, generally covariance based methods are more appropriate for data analysis. The following Table 20 summarizes the advantages of one over the other SEM method in the present research context. Variance based methods Aim of analysis and state of theoretical basis

Covariance based methods

+

-

Model specification

o

o

Data base characteristics

+

-

Estimation accuracy and quality criteria

-

+

+ : Method has advantages in the present research context - : Method has disadvantages in the present research context o : Method has neither advantages nor disadvantages in the present research context Table 20: Summary evaluation of structural equation modeling methods (Source: own Compilation)

As the table shows, variance based methods have advantages with respect to the aim of analysis and state of theoretical base as well as data base characteristics. As the discussion before has shown, high expected multicollinearity among independent variables and the nascent stage of the theoretical base of this study make variance based methods more suitable than covariance based methods for the present research study. 850

Cf. Chin, W. W./Newsted, P. R. (1999), p. 314. Cf. Homburg, C./Klarmann, M. (2006), p. 734. 852 Cf. Chin, W. W./Newsted, P. R. (1999), p. 314; Homburg, C./Klarmann, M. (2006), p. 734. 853 Cf. Chin, W. W./Newsted, P. R. (1999), pp. 328-329. 851

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5.3.2 Partial least squares 5.3.2.1 Basic functionality As outlined in the preceding section, partial least squares (PLS) is the appropriate SEM method for conducting the data analysis in this study. A particular contribution to the development of PLS provides the work of WOLD between the 1960s and 1980s.854 The estimation procedure of PLS aims at minimizing the residual variance of dependent variables whether they are observed indicators or latent variables.855 It starts with an initial solution where each latent variable is derived as non-trivial, linear combination of its manifest indicators.856 Then the estimation procedure follows four basic, iterative steps in order to attain a converged solution of the structural model estimate. These four steps are as follows: Step 1: Estimation of path coefficients by means of partial least squares for minimizing residual variance of all dependent latent variables and by means of multivariate regression to attain the weights of independent latent variables Step 2: Estimation of dependent latent variable scores on basis of the independent latent variable scores and weighted path coefficients (so-called inside approximation) Step 3: Estimation of weights or loadings of latent variables by means of a univariate or multivariate regression for reflective and formative constructs respectively Step 4: Aggregation of indicator data as linear combination of the indicators’ weights or loadings to obtain a second set of latent variable scores (so-called outside approximation) These four steps are repeated as long as no essential changes in weights or loadings occur any more. Once the iterative procedure stopped, the overall structural model is estimated with the help of multivariate regression on the basis of the weights or loadings attained from the iterative procedure.857

854

Cf. Fornell, C./Bookstein, F. L. (1982), p. 440; WOLD, H. (1985). Cf. Chin, W. W. (1998b), p. 301. 856 Cf. here and in the following Götz, O./Liehr-Gobbers, K. (2004), p. 723. 857 Cf. Götz, O./Liehr-Gobbers, K. (2004), p. 724. 855

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The structural equation model specification follows a priori theoretical considerations concerning latent variable specifications and structural model relationships.858 After the parameter estimation procedure is finalized, the estimation results are evaluated in the following two basic aspects: 1) The measurement model evaluation aims at assessing validity and reliability of latent variable measurement. 2) The structural model evaluation aims at assessing how the overall structural model fits the empirical data. The following discussion elaborates on how measurement model and structural model evaluation are accomplished. 5.3.2.2 Measurement model evaluation The measurement model evaluation aims at assessing whether a construct measures what it intends to measure. It comprises a reliability and validity assessment of the indicators and constructs used for SEM.859 Assessments are generally made with the help of a number of statistical measures which pursue different purposes and are different for reflective and formative constructs respectively.860 For some assessments statistical measures are not available and qualitative assessments are made instead. Reflective constructs Content validity describes the extent to which the semantic description of indicators captures a latent variable’s meaning.861 There are no statistical measures for assessing content validity. Therefore, 16 expert interviews about the meaning of the latent variable and the selection of indicators had been conducted prior to data collection. In addition to that, a test for unidimensionality of the proposed factor structure is advisable.862 The test investigates whether all indicators of one theoretically hypothesized construct load on one factor and indicates, whether the content validity of the proposed factor structure is reflected in the indicator data.863 Furthermore, the

858

Cf. Götz, O./Liehr-Gobbers, K. (2004), p. 716. Cf. Homburg, C./Giering, A. (1996), pp. 6-11. Cf. Chin, W. W. (1998b), pp. 316-318; Götz, O./Liehr-Gobbers, K. (2004), pp. 727-731; Krafft, M./Götz, O./Liehr-Gobbers, K. (2005), pp. 71-82; Huber, F./Herrmann, A./Meyer, F. et al. (2007), pp. 34-36. 861 Cf. here and in the following Bohrnstedt, G. W. (1970), pp. 91-92. 862 Cf. Anderson, J. C./Gerbing, D. W. (1988), p. 415. 863 Cf. Krafft, M. et al. (2005), p. 75. 859 860

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test includes only reflective constructs, because their indicators are expected to correlate. In contrast to that, formative indicators do not have to correlate and must not be eliminated, because this altered the meaning of the construct. The test is performed with the help of an exploratory factor analysis including all reflective indicators of the measurement model.864 If the factor structure is confirmed, the indicator can be used for subsequent SEM. If the factor structure is not confirmed, there are the two following possibilities. Firstly, one item does not load onto the expected factor. Then the item is to be eliminated because it does not measure the latent variable.865 The elimination of items bases on the following criteria concerning the value of factor loadings of one indicator. The indicator loading of a construct’s indicators should be considerably larger than 0.4 while the loadings on other factors should be smaller than 0.4. Furthermore, the loading differences should be larger than 0.2 to assure sufficient discriminance. 866 Secondly, there may be composites of indicators although they do not reflect the expected factor structure. A major cause for this is correlated measurement error. 867 In a post-hoc SEM analysis this implies inferior fit of the measurement model which is then alleviated by eliminating indicators of one construct or adding a correlated error term.868 However, “[i]n actual data analysis correlated error terms within a factor can mask an alternate, more meaningful structure“.869 Instead of eliminating indicators, a measurement model may be respecified if the expected first-order factors do not correspond to the constructs hypothesized but a number of unexpected composite factors evolve from a set of items.870 Then, these composites can be interpreted within a theoretical frame of reference871 and may be modeled as a higher-order factor.872 After testing unidimensionality, the reliability of measurement is evaluated. A number of criteria are used for that purpose. Firstly, indicator reliability refers to the degree to 864

Cf. DeVellis, R. F. (2003); Brettel, M./Engelen, A./Heinemann, F. et al. (2008), p. 96. The exploratory factor analysis is performed as principal components analysis with varimax rotation. Cf. Huber, F. et al. (2007), p. 93. 865 Cf. Huber, F. et al. (2007), p. 93. 866 Cf. Nunnally, J. C./Bernstein, I. H. (1994); Homburg, C./Giering, A. (1996), p. 13; Huber, F. et al. (2007), p. 93. 867 Cf. Gerbing, D. W./Anderson, J. C. (1984), p. 574. 868 Cf. Gerbing, D. W./Anderson, J. C. (1984), pp. 574 and 578-579. 869 Gerbing, D. W./Anderson, J. C. (1984), p. 576. 870 Cf. Anderson, J. C./Gerbing, D. W. (1988), p. 415. 871 Cf. e.g. Hughes, M. A./Price, R. L./Marrs, D. W. (1986), pp. 133-135. 872 Cf. Gerbing, D. W./Anderson, J. C. (1984), p. 579; Anderson, J. C./Gerbing, D. W. (1988), p. 415.

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which an indicator’s variance is explained by the underlying latent variable.873 Indicator reliability should be larger than 0.5, which implies that the indicator loading in the structural equation model is larger than 0.707.874 For newly developed scales the threshold may be lower and loadings larger than 0.5 or 0.6 may be acceptable.875 Indicators below these thresholds need to be eliminated in order to proceed with the SEM evaluation. Secondly, construct reliability refers to how well all indicators of one construct measure the underlying latent variable.876 It can be considered as the more important criterion for assessing measurement reliability. 877 There are three construct reliability measures for reflective constructs. Cronbach’s alpha, composite reliability (pc) and average variance explained (AVE). Cronbach’s alpha is the unweighted average of all indicator correlations of one construct and its value should exceed 0.7.878 pc is similar to Cronbach’s alpha, whereas it uses the weighted average of indicator correlations. 879 Finally, AVE measures a construct’s variance explained by the indicators and not by measurement error.880 Its value should exceed 0.5.881 In addition to reliability evaluations, discriminant validity evaluations are made. Discriminant validity evaluation seeks to identify whether reflective constructs within a measurement model can clearly be distinguished from each other.882 It is performed on indicator and on construct level. Discriminant validity on indicator level requires that each indicator loads strongest on its respective construct.883 It is assessed by the cross-loadings matrix of the measurement model.884 Discrimant validity on construct level requires that constructs are clearly distinguishable from each other because they are intended to measure distinct phenomena.885 For assessing discriminant validity on construct level the Fornell-Larcker criterion is used.886 According to this criterion, a 873

Cf. Krafft, M. et al. (2005), p. 73. Cf. Götz, O./Liehr-Gobbers, K. (2004), p. 727; Krafft, M. et al. (2005), p. 73. 875 Cf. Chin, W. W. (1998b), p. 325. 876 Cf. Götz, O./Liehr-Gobbers, K. (2004), p. 727; Krafft, M. et al. (2005), p. 74. 877 Cf. Homburg, C./Giering, A. (1996), p. 10. 878 Cf. Hulland, J. S. (1999), p. 198; Götz, O./Liehr-Gobbers, K. (2004), p. 728. 879 Cf. Chin, W. W. (1998b), p. 326; Götz, O./Liehr-Gobbers, K. (2004), p. 728. 880 Cf. Fornell, C./Larcker, D. F. (1981), pp. 45-46. 881 Cf. Fornell, C./Larcker, D. F. (1981), p. 46. 882 Cf. Götz, O./Liehr-Gobbers, K. (2004), p. 728. 883 Cf. Chin, W. W. (1998b), p. 321. 884 Cf. this matrix is part of the PLS estimation output. 885 Cf. Bohrnstedt, G. W. (1970), p. 96; Homburg, C./Klarmann, M. (2006), p. 729. 886 Cf. Fornell, C./Larcker, D. F. (1981), p. 46; Brettel, M. et al. (2008), p. 100. 874

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construct shows sufficient discriminant validity if its square-root of AVE is larger than correlations with all other constructs.887 Finally, the nomological validity of a construct is to be evaluated. Nomological validity refers to whether a construct has predictive value in a larger theoretical context.888 This is basically the aim of empirical management research using SEM. Therefore, the evaluation of the nomological validity will be judged as part of the overall structural model evaluation. Formative constructs Concerning content validity the same holds for formative as for reflective constructs. After theoretically deriving the latent variable meaning and developing measuring indicators on a semantic basis, the formative constructs need to be pre-tested with expert interviews.889 The reliability evaluation of formative constructs is more limited than for reflective constructs because formative indicators compose the construct and are not expected to correlate.890 As a result, a number of reliability measures recurring on indicator correlations such as for reflective measures cannot be used for formative measures. Moreover, formative indicators cannot be eliminated because this may alter the meaning of the underlying construct.891 Indicator weights are a first measure of reliability of formative constructs.892 Indicator weights allow for a subjective assessment of how important are individual indicators for explaining the latent variable of interest.893 The larger the weight the more important is an indicator’s contribution to a construct’s meaning.894 Multicollinearity among indicators is a second measure of reliability of formative constructs. If multicollinearity is high, indicators can be represented as linear

887

Cf. Chin, W. W. (1998b), p. 321; Homburg, C./Giering, A. (1996), p. 11; Götz, O./Liehr-Gobbers, K. (2004), p. 728. 888 Cf. Homburg, C./Giering, A. (1996), p. 7. 889 Cf. Götz, O./Liehr-Gobbers, K. (2004), p. 728. 890 Cf. Götz, O./Liehr-Gobbers, K. (2004), p. 728. 891 Cf. Jarvis, C. B. et al. (2003), p. 203; Eberl, M. (2006), p. 652. 892 Cf. Chin, W. W. (1998b), p. 307; Götz, O./Liehr-Gobbers, K. (2004), p. 728. 893 Cf. Götz, O./Liehr-Gobbers, K. (2004), p. 728. 894 Cf. Krafft, M. et al. (2005), p. 78.

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combinations of each other. This implies the indicators are interchangeable.895 Furthermore, multicollinearity may impose a problem on the SEM estimation procedure and with perfect multicollinearity the latent variable score cannot be estimated anymore.896 Two statistical measures can be used for evaluating multicollinearity. The Variance Inflation Factor (VIF) measures to which extent multicollinearity has an effect on the regression coefficient’s variance.897 The VIF should not exceed a value of 10.898 However, this measure alone does not certainly indicate that multicollinearity is not present.899 Therefore, the condition index (CI) is used as a second measure.900 The CI “represents the collinearity of variables”.901 It is the higher the more collinearity among variables is present.902 The CI should not exceed a value of 30.903 The validity evaluation of formative indicators is limited and discriminant validity cannot be tested.904 Furthermore, similarly to reflective indicators the nomological validity evaluation takes place through the structural model evaluation. 5.3.2.3 Structural model evaluation After performing the measurement model evaluation, the evaluation of the structural model follows. This evaluation bases on three criteria, namely 1) explained variance (R2), 2) the path coefficients and 3) the Stone-Geisser-Criterion (Q2).905 First of all, the R2 of a PLS model estimate has the equivalent meaning as in multiple regression analysis.906 Therefore, R2 allows for evaluating how well the structural model fits the empirical data.907 R2 takes values between 0 and 1. A value of 1 implies the structural model completely explains the empirical variance. A value of 0 implies that none of the empirical variance is explained.908 In empirical management research R2 values of less than 19% can be assessed as “weak”, values of less than 33% as 895

Cf. Fassot, G./Eggert, A. (2005), p. 40. Cf. Diamantopoulos, A./Winklhofer, H. M. (2001), p. 272; Backhaus, K. et al. (2006b), p. 89. 897 Cf. Backhaus, K. et al. (2006b), p. 91. 898 Cf. Gujarati, D. N. (1995), p. 339; Mason, C. H./Perreault, W. D. (1991), p. 270. 899 Cf. Belsley, D. A./Kuh, E./Welsch, R. E. (1980), p. 93. 900 Cf. Belsley, D. A. et al. (1980), p. 93. 901 Hair, J. F./Anderson, R. E./Tatham, R. L. et al. (1998), p. 220. 902 Cf. Belsley, D. A. et al. (1980), pp. 104-106. 903 Cf. Gujarati, D. N. (1995), p. 338; Mason, C. H./Perreault, W. D. (1991), p. 270. 904 Cf. Bollen, K. A./Lennox, R. (1991), p. 307; Schnell, R./Hill, P. B./Esser, E. (2005), p. 162. 905 Cf. Chin, W. W. (1998b), pp. 316-318; Götz, O./Liehr-Gobbers, K. (2004), p. 730. 906 Cf. Chin, W. W. (1998b), p. 316. 907 Cf. Backhaus, K. et al. (2006b), p. 64. 908 Cf. Backhaus, K. et al. (2006b), p. 66; Götz, O./Liehr-Gobbers, K. (2004), p. 730. 896

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“moderate” and values of 67% as substantial.909 However, when making an evaluation of the explained variance, one should bear in mind whether a research model seeks to comprehensively describe a theory or whether it covers a specific area of interest.910 In that case one may refer to a partial model. The more comprehensive a theoretical model is intended to be, the higher are the expectations towards R2. Second of all, the Q2 of a PLS model indicates how well the structural model and the PLS parameters reconstruct the empirical data.911 Q2 is calculated by the so-called Blindfolding procedure.912 Blindfolding takes only a fraction of the cases and the indicators and then estimates the omitted parts with the help of the parameter estimates from the PLS model.913 This procedure is repeated until any data point has been once omitted and estimated. Q2 takes values between -1 and 1. Values larger than zero mean the structural model has predictive relevance. Values of smaller than zero imply the research model has a lack of predictive relevance. Finally, the meaning of path coefficients in a PLS model is equivalent to the meaning of beta-coefficients in a multiple regression analysis.914 The path coefficients do not allow for inference statistical tests, because they base on non-parametric assumptions. Still they can be used to evaluate whether the empirical data reflects the hypothesized relationships or not. For this purpose, the reliability of path coefficients can be evaluated with the help of t-values that are a result of the estimation procedure.915 In case the path coefficients are non-significant or in the opposed direction as hypothesized, the hypothesis is to be rejected. If a path coefficient is significant and in the expected direction, the empirical data supports the hypothesized relationship. 5.3.2.4 Testing of causal relationships and significance level As already pointed out, PLS does not allow for conducting parametric tests. Therefore, inferential hypothesis testing is strictly speaking not possible.916 Nonetheless, PLS estimates allow for evaluating whether the empirical data set supports a systematic relationship or not. For that purpose a t-test can be performed analogically to classical

909

Cf. Chin, W. W. (1998b), p. 323. Cf. Jain, D. (1994), p. 168. Cf. Fornell, C./Cha, J. (1994), p. 72; Chin, W. W. (1998b), p. 318. 912 Cf. Chin, W. W. (1998b), p. 317. 913 Cf. here and in the following Chin, W. W. (1998b), pp. 316-318 914 Cf. Chin, W. W. (1998b), p. 316. 915 Cf. here and in the following Götz, O./Liehr-Gobbers, K. (2004), p. 730. 916 Cf. here and in the following Götz, O./Liehr-Gobbers, K. (2004), p. 730. 910 911

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test theory. Such a t-test requires t-values on the one hand and critical significance levels on the other hand. Concerning the former, the PLS bootstrapping procedure provides an empirical t-statistic for indicator loadings and weights as well as for path coefficients.917 This t-statistic can then be used for a comparison with critical t-values representing a specific significance level.918 Concerning the latter, values a statistical power analysis can be performed for deriving critical t-values. The statistical power analysis builds on the interrelation of four parameters: 1) The significance level (p), 2) the statistical power (1-beta), 3) the sample size (N), and 4) the effect size. 1) p is the a priori specified, chosen risk of incorrectly rejecting a true nullhypothesis.919 Classical significance testing has sought to minimize p, whereas this implies an increasing beta-error. This means the risk of incorrectly accepting a false null-hypothesis increases when p decreases. Consequently, a trade-off decision needs to be made and the power analysis allows for doing so. 2) The statistical power (1-beta) refers to the probability that a false null hypothesis is correctly rejected.920 In other words, statistical power is the probability that a significance test provides for significant results. 3) The statistical power depends on the sample size N. The inclusion of the sample size in the calculation allows for taking into account the specific requirements of an empirical data set in order to produce generalizable results. 4) The effect size “represents the magnitude or strength of the relationship among the variables in the population”.921 Concerning the effect size a range of values in empirical research can be identified.922 Effect sizes from 0.10 to 0.25 provide plausible assumptions for empirical management research which seeks to describe phenomena with rather weak causal relationships.923 For performing the statistical power analysis any three of these four parameters are required. Vice-versa, if any three of these four parameters are given the fourth parameter can be calculated.924 Depending on which parameters are given two different approaches are identifiable. The compromise analysis allows for calculating a 917

Cf. Chin, W. W. (1998b), p. 320. Cf. Bortz, J./Döring, N. (2006), p. 25. Cf. Baroudi, J. J./Orlikowski, W. J. (1989), p. 88; Bortz, J./Döring, N. (2006), p. 26. 920 Cf. Baroudi, J. J./Orlikowski, W. J. (1989), p. 88; Cohen, J. (1992), pp. 156-157. 921 Baroudi, J. J./Orlikowski, W. J. (1989), p. 88. 922 Cf. Mazen, A. M./Graf, L. A./Kellogg, C. E. et al. (1987), pp. 371-373. 923 Cf. Cashen, L. H./Geiger, S. W. (2004), p. 156; Ferguson, T. D./Ketchen, D. J. (1999), p. 390. 924 Cf. Baroudi, J. J./Orlikowski, W. J. (1989), p. 88. 918 919

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critical significance level p on the basis of a given sample size, an assumption on the effect size and a predefined alpha-beta-error ratio.925 The post-hoc analysis allows for calculating the statistical power for a given alpha error. For performing the calculation the present study uses the software GPOWER 3.0.10926 which was developed and introduced by Erdfelder et al. (1996).927 Since the calculation depends on the sample size, it can only be performed once the empirical data is available. 5.3.2.5 Testing of moderating effects Several moderating hypotheses have been developed and need to be tested. A moderating effect can be identified, if the moderating variable influences direction and / or strength of the causal relationship between independent and dependent variable.928 There are two basic approaches for testing moderating effects in PLS.929 The moderating effect can either be tested by conducting a group comparison or by calculating product-interaction term.930 This study adopts the group comparison method, similarly to preceding PLS analysis in the literature.931 The procedure for testing moderating relationships by means of group comparison is as follows. At first, the sample is split into two groups with the help of the moderating variable scores. In case of ordinal scales, the group formation is accomplished by using the median of the latent variable scores of these variables.932 Then, the SEM is estimated for both groups and an examination of differences of each path coefficient in the model is made. A moderating hypothesis is confirmed if the difference between the two comparable path coefficients is significant and into the expected direction (i.e. the difference is either positive or negative). 933 For obtaining a t-value in order to assess the significance level of the two path coefficients’ difference, the following formula of

925

Cf. Erdfelder (1984), pp. 27-29. The software is available for free from http://www.psycho.uni-duesseldorf.de/aap/projects/gpower/ (accessed on 7th August 2008). 927 Erdfelder, E./Faul, F./Buchner, A. (1996), p. 2. 928 Cf. Baron, R. M./Kenny, D. A. (1986), pp. 1173-1174. 929 Cf. Chin, W. W./Marcolin, B. L./Newsted, P. R. (2003), p. 196. 930 Cf. Chin, W. W. et al. (2003), pp. 191-194; Avolio, B. J./Howell, J. M./Sosik, J. J. (1999), p. 222; Homburg, C./Klarmann, M. (2006), p. 730; Hiddemann, T. (2007), p. 114. 931 Cf. Avolio, B. J. et al. (1999), p. 222; Voll, L. K. (2008), p. 147. 932 Cf. Avolio, B. J. et al. (1999), p. 222; Voll, L. K. (2008), p. 146. 933 Cf. Avolio, B. J. et al. (1999), p. 224; Carte, T. A./Russell, C. J. (2003), p. 493; Dibbern, J./Chin, W. W. (2005),p. 152; Voll, L. K. (2008), p. 147. 926

Data analysis methodology

163

Chin, W. W. (2004) is used.934 In this formula m and n represent the sizes of the two groups and S.E. the standard error of the respective path coefficients. −

ℎ

= (

(

− 1) ∗ . . + − 2)

+

(

ℎ

( − 1) ∗ . . + − 2)

∗

1

+

1

In order to perform a group comparison the two samples are required to be comparable which is evaluated against two criteria.935 Firstly, the two samples should have comparable sizes. This means the larger sample should not contain more than 1.5 times the smaller samples’ number of data sets.936 Furthermore, similarity of constructs is required in order to assure that each construct has the same meaning in the two samples. The similarity of constructs is assessed with the Coefficient of Congruence proposed by Harman, H. H. (1976):937

=

∑ ∑

∗ ∗∑

CCAB represents the Coefficient of Congruence between construct A and B. pv is the matrix of factor loadings or weights of the underlying construct in group A and B. The Coefficient of Congruence takes values between zero and one. A value of one means identical measurement. A value of more than 0.9 represents a congruent level of similarity.938 Only after similarity of measurement between two groups has been established the path coefficients can be compared with respect to significance differences.

934

Cf. Brettel, M. et al. (2008), p. 96; Voll, L. K. (2008), p. 147. Cf. Chin, W. W. (2004). 936 Cf. Hiddemann, T. (2007), p. 114; Voll, L. K. (2008), p. 146. 937 Cf. Carte, T. A./Russell, C. J. (2003), p. 493; Brettel, M. et al. (2008), p. 100; Voll, L. K. (2008), p. 147. 938 Cf. Teel, C./Verran, J. A. (1991); Brettel, M. et al. (2008), p. 100. 935

164

Data collection and evaluation

6 Data collection and evaluation The measurement instrument developed in the preceding chapter allows for data collection through a questionnaire survey. Section 6.1 describes the data collection procedure of this study. Next, section 6.2 describes the sample characteristics and evaluates the sample representativeness of the resulting data base. Finally, section 6.3 tests for biases and explains how missing values are handled prior to the subsequent data analysis. 6.1 Data collection This section describes the survey design and sample generation procedure. 6.1.1 Survey design This sub-section describes the selection of informants, the choice among survey media, the procedure of generating respondents and the online questionnaire design. The data collection of this study follows the key informant method. A key informant is a person who is able to provide general statements about the phenomenon of interest as opposed to personal opinions or attitudes.939 The selection of a key informant may be made on the basis of ones special abilities or ones position in the company.940 The key informants for this study are those executive managers mainly involved in SDM, because the process and outcomes of SDM are a function of these people.941 Following the Upper Echelon View these people are formed by the top executives of a company, among who the CEO is considered to be the key decision maker. In case of medium-sized companies the CEO is most often an owner-manager.942 Ownermanager firms possess high internal discretion and medium-sized companies often depend on a single decision maker in owner-manager firms which underpins their importance as key informant.943 However, medium-sized companies also employ professional managers,944 e.g. in cases when the owner left the company or in publicly held companies. Therefore, professional top managers in medium-sized companies are also included. However, characterizations of the CEO range from a very dominant role 939

Cf. Ernst, H. (2003), p. 1250. Cf. Seidler, J. (1974), p. 817; Bagozzi, R. P. et al. (1991), p. 423. 941 Cf. Elbanna, S./Child, J. (2007), p. 439. 942 Cf. Wolter, H. J./Hauser, H. E. (2001), p. 72. 943 Cf. Feltham, T. S. et al. (2005), p. 13. 944 Cf. Smith, K. G. et al. (1988), p. 224. 940

W. Gänswein, Effectiveness of Information Use for Strategic Decision Making, DOI 10.1007/978-3-8349-6849-4_6, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

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to a more cooperative or context influencing role.945 There are cases, where important decision may be made on lower than CEO management levels. One study of a sample of 765 firms of family-owned businesses shows in 23% one additional manager but in 70% two or more additional managers. 16% of the medium-sized companies have even more than five key managers.946 Therefore, it is plausible that some key managers other than the CEO are a key decision maker in SDM. The survey is more broadly addressed to CEOs or other top executives being in charge of SDM. The survey was conducted by using an online software application that allows for creating online questionnaires, administering invitation e-mails and extracting the response data for the purposes of analysis.947 Online surveys have certain advantages over postal mail surveys.948 These mainly pertain to the cost and ease of conducting survey research. Firstly, online surveys are much more time and cost effective because they do not incur material cost or time for conducting interviews. The costs are limited to a license fee. Secondly, the response time takes about 6 days and is thus considerably quicker than for postal mail or interview surveys.949 Thirdly, apart from offline-responses the data input is made by informants themselves. This is very time efficient and decreases the risk of incurring input errors by the researcher him- or herself.950 Fourthly, the researcher can track input behavior for identifying questions where cancellation rates are high and for taking adjustments to the questionnaire design.951 Finally, online surveys have proven to fulfill the same requirements for reliability and validity as compared to other survey methods.952 In contrast to these advantages, online surveys also have some disadvantages. Firstly, there is a danger of obtaining data sets that are not representative for the population, e.g. because not all informants may have the technical equipment or internet access for 945

Cf. Gioia, D. A./Chittipeddi, K. (1991), p. 433. Cf. Feltham, T. S. et al. (2005), p. 5. 947 The survey was conducted with the internet application EFS Survey from Globalpark (Cf. http://www.unipark.info; last accessed on 15th November 2008). In addition to that the e-mails provided a hyperlink to a downloadable PDF-file containing a print-version of the survey instrument. Hence, informants could also use a paper-and-pencil instrument and send it to the researcher by mail or fax. These offline answers were then manually input into the database. Cf. appendix 1 for the print-version of the survey instrument. 948 Cf. e.g. Grether, M. (2003), pp. 212-214. 949 Cf. Granello, D. H./Wheaton, J. E. (2004), p. 388; Cobanoglu, C./Warde, B./Moreo, P. J. (2001), p. 448. 950 Cf. Granello, D. H./Wheaton, J. E. (2004), p. 388. 951 Cf. Bosnak, M./Tuten, T. L. (2001). 952 Cf. Batinic, B. (2000), p. 130. 946

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participating with online surveys.953 However, this threat is alleviated because at the time the survey was conducted the penetration of internet accesses in Germany was around 95%.954 Secondly, online applications may encounter technical problems such that the online questionnaire cannot be smoothly accessed. This may prevent from participation or result in increased cancellation rates.955 For minimizing this risk an established software application with a proven track record is used and extensive pretests of the online questionnaire are performed. Thirdly, online surveys are criticized for their low response rates compared to other survey methods. 956 Several specific measures aim at alleviating this issue.957 These measures pertain to the design and delivery of invitation e-mails and are as follows:958 ƒ Explicit statement of the research institution and the scientific, non-profit ƒ ƒ ƒ ƒ ƒ ƒ ƒ

purpose of the survey Personal form of address including names of organization and key informant Emphasis on exclusivity of participation Offer of a non-monetary incentive in form of a results report Assurance of confidentiality Avoidance of stating a deadline Personal appeal emphasizing the importance of the informants expert knowledge Successive dispatch of invitation e-mails for avoiding technical problems due to server overcharge

ƒ Dispatch of invitation e-mails during working hours In addition to the invitation e-mail two reminder e-mails are sent after consecutive periods of two to three weeks later. While some researchers do not find a significant effect on response rate,959 others find that even the larger portion of responses is

953

Cf. Couper, M. P. (2000), p. 436. A national government statistic shows that 95% of all German companies had internet access in 2007. Cf. Graumann, S./Wolf, M. (2008), p. 36. 955 Cf. Granello, D. H./Wheaton, J. E. (2004), p. 388 956 Cf. Diamantopoulos, A./Schlegelmilch, B. B. (1996), p. 505; Mitchell, V.-W./Brown, J. (1997), p. 853; Sheehan, K. B./McMillan, S. J. (1999), p. 46; Granello, D. H./Wheaton, J. E. (2004), p. 389. 957 Cf. Diamantopoulos, A./Schlegelmilch, B. B. (1996); Couper, M. P./Traugott, M. W./Lamias, M. J. (2001); Porter, S. R./Whitcomb, M. E. (2003); Newby, R./Watson, J./Woodliff, D. (2003); Dillmann, D. A./Tortora, R. D./Bowker, D. (1998); Laatz, W. (1993). 958 Cf. Müller, T. (2008), p. 135. 959 Cf. Mitchell, V.-W./Brown, J. (1997), pp. 858-860. 954

Data collection

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received after reminding the informants.960 The reminder e-mails build on the initial invitation e-mail, while exhibiting the following differences: ƒ While the invitation e-mails do not contain a deadline, the reminder e-mails may contain a deadline in order to emphasise the urgency. ƒ While the invitation mail addresses the importance of a recipients’ expert knowledge for conducting the survey, the reminder mails shift emphasis on more altruistic motives such as the support of an important research study or the contribution to the researcher’s dissertation.961 The online survey design exhibits the following features:962 ƒ It starts with a landing page which explains the research objectives, provides

ƒ ƒ ƒ ƒ

background information on the research institution and researcher(s) and how much time the survey demands. Online surveys should not last longer than 15 minutes, which should be emphasized on the landing page.963 Throughout the survey a progress display indicates how much of the survey has already been conducted. The online survey avoids compulsory questions which may distract informants from continuing a survey. It consists of several web-pages splitting the overall measurement instrument in more manageable parts for the informant. The online survey concludes with an acknowledgement of participation

Finally, both the questionnaire was split into parts to guide the informant: ƒ The first part asked for individual characteristics of the top decision maker. ƒ The second part asked for PEU. ƒ The third part started with a general definition of strategic decision and SDM. Then the informant should recall of a strategic decision made in his current position. The respondent should then answer questions about this example decision. These questions addressed type of the decision made, period of decision making, information use, political behavior and decision quality with respect to this decision. Such retrospective data collection methodologies are

960

Cf. Müller, T. (2008), p. 141; Voll, L. K. (2008), pp. 112 and 116. Cf. Müller, T. (2008), p. 137. 962 Cf. Müller, T. (2008), pp. 137-138. 963 Cf. Bosnak, M./Tuten, T. L. (2001), p. 150. 961

168

Data collection and evaluation successfully employed in strategic decision making research and appeared as a

sensible approach for this study.964 ƒ The fourth part asked for company characteristics and performance. 6.1.2 Sample generation Medium-sized companies are the unit of analysis of this study. This needs to be operationalized, in order to establish the size of the population and to generate a sample of companies to be surveyed. The operationalization of medium-sized companies can be established upon quantitative and also qualitative criteria.965 Quantitative criteria most often pertain to number of employees and revenues. These data are used by official statistics from the national bureau of statistics or the European Union Commission.966 Other possible quantitative criteria comprise total assets, equity, production figures, market shares or profit.967 Similarly, there are a number of qualitative criteria such as unity of ownership and management. It can also be employed for operationalizing medium-sized companies.968 Furthermore, an organization’s independence of group companies is a qualitative criterion for distinguishing medium-sized companies.969 However, the measurement of qualitative criteria requires large effort, because much data such as ownership shares is most often not publicly available.970 Therefore, this study follows mainly a quantitative approach for identifying medium-sized companies, which is in part a result of adhering to official reporting practices and thus not uncommon in empirical management research.971 Furthermore, quantitative criteria give some indication of the existence of typical characteristics of medium-sized companies.972 In a first step, this study defines medium-sized companies according to their number of employees and covers companies with a minimum of 50 and a maximum of 499 employees. The minimum of 50 employees is established in order to assure the company is a consequential entity,973 i.e. has some established structures, processes 964

Cf. Lipshitz, R./Bar-Ilan, O. (1996), p. 51; Elbanna, S./Child, J. (2007), p. 439. Cf. Lachnit, L./Ammann, H. (1989), pp. 16-17; Mugler, S. C. (1995), p. 18; Flacke, K. (2006), p. 11. 966 Cf. Berens, W. et al. (2005), p. 9. 967 Cf. Berens, W. et al. (2005), p. 9; Pfohl, H.-C. (2006). 968 Cf. Pfohl, H.-C. (2006); Mugler, S. C. (1995), pp. 18-23; Ghobadian, A./Gallear, D. (1996), p. 87; Wolter, H. J./Hauser, H. E. (2001), p. 30-33. 969 Cf. Wolter, H. J./Hauser, H. E. (2001), S. 33; Fueglistaller, U. (2004), p. 24. 970 Cf. Wossidlo, P. R. (1993), columns 2891f. 971 Cf. Flacke, K. (2006), p. 12; Krämer, W. (2003), p. 9. 972 Cf. Welter, F. (2003), p. 29. 973 Cf. Yasai-Ardekani, M./Nystrom, P. C. (1996), p. 193; Garg, V. K. et al. (2003), p. 731. 965

Data collection

169

and formal systems as stated in the unit of analysis section. The maximum number of 499 employees is established because large companies where the effects of individual behaviors on company outcomes are likely to be distorted were excluded for theoretical reasons.974 The definition of 499 employees as threshold value is a matter of practical considerations because it is used by official statistics and company data bases.975 Finally, only independent companies are included in the sample to assure that the SDM process is not influenced by a parent company.976 The data base Hoppenstedt Firmendatenbank was used for establishing the population size and generating the sample.977 This database covers more than 250.000 organizations with 10 or more employees. These organizations contribute 85% to value creation in the German economy.978 For establishing the size of the population following filtering steps were applied. At first, governmental and non-profit institutions were filtered out on the basis of specific industry codes. Then, the the company size was set to the range of 50 to 499 employees. After applying these filters, Hoppenstedt Firmendatenbank indicates a population size of 38,225 companies.979 Subsequently, a random sample of 5,000 data sets was drawn and then manually qualified with respect to the required data points.980 This procedure resulted in 121 data sets that were unusable either because the firm has vanished or the required data was not obtainable. After this procedure was finalized, 4,879 data sets were available for conducting the online survey.

974

Cf. Miller, D./Toulouse, J.-M. (1986); Beal, R. M. (2000), p. 31. Cf. Wolter, H. J./Hauser, H. E. (2001), p. 72. 976 Cf. Priem, R. L. et al. (1995), p. 920; Garg, V. K. et al. (2003), p. 731. 977 This data base is freely accessible for university associates through the database information system of the library. The access allows for searching for companies along a number of criteria, most notably name of the company, location, industry, number of employees and revenues. Once companies are identified, company profiles with further information can be displayed. 978 Cf. Anonymous author (2008), p. 1. 979 The population size was established in June 2008. 980 The necessary data points derive from the practical issues of administering the online survey and include the firm name, the CEOs first and last names, gender and title as well as an E-mail address of the company or the informant directly. This qualification was accomplished by either consulting Hoppenstedt or the company websites for the necessary data points. 975

170

Data collection and evaluation

6.2 Sample characteristics This section discusses response rate and usable answers of the survey procedure, the sample representativeness and some descriptive characteristics of the sample. 6.2.1 Response rate and usable answers At first, the sample of 4,879 companies initially addressed had to be corrected for 230 companies whose invititation e-mails bounced back as the respective e-mail addresses were unknown to the recipients’ servers or a server was unavailable. This further reduced the initial sample to 4,649 companies. Then, 309 online and offline answers were received from these 4,649 companies. Next, these answers were further investigated in order to arrive at the sample for analysis of this study. Firstly, the data set was cleaned for responses with input errors or a high share of missing values. Input errors occur from data not corresponding to the defined input scale. For example some informants put in their year of birth instead of their age in years. If possible input errors were corrected in order to assure analyzability of responses.981 Otherwise, the values were declared as missing. After this correction, missing values were analyzed in order to assure reliable SEM parameter estimates. There are statistical techniques with which missing values can be estimated and replaced.982 However, the replacement of missing values has certain limits with respect to reliability. Therefore, threshold values for missing data are established. A data set is considered as usable for SEM if it contains not more than 30% missing values among the latent variable indicators, otherwise it is eliminated.983 Most of data sets eliminated contained any values at all. A reason for this may be, the informant quickly clicked through the survey and then decided to leave it aside. In addition to that, the share of missing values per indicator is examined.984 In the data received, the share of missing values per indicator is 1% on average and a maximum of 3% for one indicator. This can be considered as acceptable. 985 Thus, no further data sets are eliminated.

981

Cf. Bortz, J./Döring, N. (2006), p. 85. Cf. Bortz, J./Döring, N. (2006), p. 85. 983 Cf. Roth, P. L./Switzer III, F. S. (1995), p. 1010. 984 Cf. Müller, T. (2008), p. 142. 985 Cf. Schnell, R. et al. (2005), p. 468. 982

Sample characteristics

171

These steps of data clean-up resulted in the elimination of 27 of 309 responses.986 Consequently, 282 data sets from the reachable sample of 4,649 companies can be used for analysis. The result is a response rate of 6.1%. The response rate of 6.1% appears low. However, two considerations are to be made before judging this. Firstly, SDM is a top executive activity and CEOs and other top executives are the key informants for this survey. Other people of an organization can hardly answer the questionnaire of this study. This is a main reason why response rates from CEOs in empirical management research are typically low. One study finds that a 10 to 12% response rate from CEOs of American corporations is typical. 987 Furthermore, a literature review of articles published in six leading information systems research journals from 1998 to 2002 finds even lower response rates of 7% in the Information Systems Research or 5.7% in the Management Information Systems Quarterly journals.988 Secondly, the questions on cognitive style and demographic control variables may be perceived as even more intriguing than rather general questions on the organization or its environment. This may pre-empt even more top executives to participate. Given these considerations, the response rate of this study is deemed acceptable and in line with other empirical management research surveys among CEOs and top executives. Finally, a test for non-response bias will later be conducted in order to further investigate the representativeness of the answers received. After establishing the usable responses and respective response rate, the answers were considered as to whether they meet the criteria as outlined in the unit of analysis section or not. For this purpose two criteria, company size in terms of number of employees and independence, are investigated. Some companies do not meet the size criterion but are smaller than 50 or larger than 499 employees. Furthermore, the independence of a company cannot be established from Hoppenstedt Firmendatenbank and thus the criterion was part of the survey instrument. Responses indicating their company was part of a larger group were eliminated. This filtering procedure results in the exclusion of 52 of the 282 usable answers. Consquently, the final sample for further analysis consists of 230 data sets. 986

Of the remaining data only two data sets had more than 15% missing values, which is another, more conservative missing value threshold. Cf. Voll, L. K. (2008), p. 112. 987 Cf. Hambrick, D. C./Geletkanycz, M. A./Fredrickson, J. W. (1993), p. 407. 988 Cf. Sivo, S. A./Saunders, C./Qing, C. et al. (2006), p. 356.

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Figure 10 visualizes the underyling numbers of these data preparation steps.

309 282

27

52

Responses - Online - Offline

Eliminated due to missing values

Usable responses

Criteria not met - Size - Independence

230

Sub-sample used for analysis

Response rate: 6.1%

Figure 10: Received and used responses (Source: own compilation)

Last but not least, the sample used for analysis has two important characteristics. Firstly, the sample size of 230 cases represents a comparatively large field-study sample in SDM research.989 A recent literature review of 15 key SDM studies shows only four studies with more than 200 cases. However, out of these four studies three employ experimental or quasi-experimental designs outside field-study settings. Instead respondents were students of an executive Master of Business Administration program outside their current positions. Only one of these four studies employed field research. Secondly, this sample covers a broad range of manufacturing and services industries. A focus on manufacturing companies has been identified as a key shortcoming of preceding SDM research.990 Given these characteristics, the present sample itself provides a remarkable contribution to SDM research with respect to generalizability of results. 6.2.2 Sample representativeness The final sample of this research study is the result of a survey among a randomly drawn subset of the population of medium-sized companies. In order to assure generalizability of results the sample needs to reflect the important characteristics of

989 990

Cf. Elbanna, S. (2006), p. 5. Cf. Forbes, D. P. (2007), p. 374.

Sample characteristics

173

the population.991 Therefore, the sample needs to be representative for characteristics of the population bearing in mind, that representativeness does not require a perfect match but little deviations are acceptable.992 For assessing the representativeness of a sample, comparable data about the relevant characteristics in the population and the sample are required. Three indicator data used for comparison needs to be available for both the population and the sample alike. For the following comparison, these indicators are industry sector, location and company size. Industry sector is measured with the help of the WZ93 industry classification. This is a five digit number system used in official statistics and market research data bases. Thus, the data can be obtained for both population and sample. A Chi-Square test at a 5% significance level was performed in order to assess whether the sample distribution follows the distribution among the population. The test revealed a significant difference and the largest differences are highlighted in Figure 11.993 The manufacturing (WZ93-code 1) and retail (WZ93-code 5) sectors have a disproportionately large share in the sample. In contrast to that, financial services and logistics (WZ93-code 6) and professional services (WZ93-code 7) sectors have a disproportionately small share in the sample. Potential explanations are that manufacturing and retail sectors may be more responsive to information use research as they typically have more elaborate information systems than professional services companies. Another potential explanation is that financial services companies are rather secretive. Last but not least, professional services companies among the population comprise consulting or attorney partnerships among which one dominant decision maker cannot be identified. Overall, the deviations are not large, such that the sample’s representativeness in terms of industry structure is deemed acceptable. Figure 12 shows the structure of population and sample in terms of industry structure by WZ93-code and indicates the sectors with main deviations.

991

Cf. Bortz, J./Döring, N. (2006), p. 396. Cf. Laatz, W. (1993), p. 35 and 63. 993 Cf. Bleymüller, J./Gehlert, G./Gülicher, H. (2000), pp. 127-130. A Chi-Square test with a degree of freedom of six and significance level of 5% was performed. The resulting test score was 32.34, which is beyond the threshold value of 12.59. 992

174

Data collection and evaluation 100% = 38,225

100% = 230

Deviation

7

16%

9% 5%

-

6

10% 28%

+

5

20%

4

9%

3

10%

2

29%

1

7%

12%

Population

Sample

8% 11% 27%

+

Figure 11: Industry structure of companies in population and sample (Source: own compilation)

Concerning the size of the companies, the population data allows a comparison of two clusters only namely companies with 50 to 249 employees and with 250 to 499 employees. A Chi-Square test supports the representativeness of the sample in terms of company size.994 Figure 12 shows the structure of population and sample in terms of size by number of employees. 100% = 38,225

100% = 230

250-499

13%

11%

50-249

87%

89%

Population

Sample

Figure 12: Size structure of companies in population and sample by number of employees (Source: own compilation)

994

Cf. Bleymüller, J. et al. (2000), pp. 127-130. A Chi-Square test with a degree of freedom of one and significance level of 5% was performed. The resulting test score was 0.96, which is beyond the threshold value of 3.84.

Sample characteristics

175

Finally, the representativeness in terms of location is assessed. For this purpose the first-digit of the postal zip-code is used which results in 10 different groups. Furthermore, a Chi-Square test is performed to compare distribution among the population and sample which provides significant support for similar distributions.995 Therefore, the representativeness in terms of location is deemed acceptable. Figure 13 shows the distribution of zip-codes among population and sample. 100% = 38,225

100% = 230

9

9%

13%

8

10%

7

13%

6

9%

5

12%

6% 10%

4

13%

14%

3

11%

11%

2 1 0

10% 5% 7%

14% 3% 6%

Population

Sample

10% 14%

Figure 13: Location structure of companies in population and sample by zip-code (Source: own compilation)

To conclude, the sample is representative in terms of size and location. Only, with respect to industry structure some minor differences are revealed. Given these differences are small and some deviations are tolerable, the sample representativeness in terms of industry structured is deemed acceptable. Thus, the results of this study appear generalizable for the population of medium-sized companies.

995

Cf. Bleymüller, J. et al. (2000), pp. 127-130. A Chi-Square test with a degree of freedom of nine and significance level of 5% was performed. The resulting test score was 10.74, which is below the threshold value of 16.92.

176

Data collection and evaluation

6.2.3 Sample description After the representativeness of the sample is examined, some descriptive statistics of the sample are provided. Firstly, besides a distinction by industry code, the companies can generally be assigned to manufacturing or services industries. Most often SDM research studies focus on manufacturing industries.996 In contrast to that, the sample here has a balanced mix of manufacturing and services companies. 55% of the 230 companies are in manufacturing businesses, while 45% are in services businesses. See Figure 14 for a representation of sample structure by industry sector. This underpins the generalizability of the results of this study. 100% = 230

Services

45% = 104

Manufacturing

55% = 126

Sample

Figure 14: Sample structure of companies by manufacturing vs. services sector (Source: own compilation)

Furthermore, decision characteristics have some influence on strategic decision processes.997 Therefore, the distribution by decision type is presented as an indicator of the diversity of decision characteristics covered by this study. Decisions about competitive positioning have the largest share in the sample (46%), followed by functional strategy decisions (21%), business field strategy decisions (10%), other decisions (8%), mergers & acquisitions decisions (8%) and international strategy decisions (7%). See Figure 15 for a representation of sample structure by type of

996 997

Cf. Forbes, D. P. (2007), p. 374. Cf. Elbanna, S./Child, J. (2007), p. 446.

Sample characteristics

177

decision. This again underpins the generalizability of the results of this study because a broad range of decision types is covered. 100% = 230

International strategy Mergers & acquisitions Other Business field strategy

7% = 15 8% = 19 8% = 21 10% = 22

Functional strategy

21% = 48

Competitive positioning

46% = 105

Sample Figure 15: Sample structure of decisions by type of decision (Source: own compilation)

Then, the informants’ position is of interest, because the unit of analysis is defined as executives’ decision making. However, it was pointed out that not all decisions are necessarily made by the CEO, but also another top executive may be the key decision maker in SDM. The sample structure in terms of informants’ position underpins this assertion, because 8% of the informants are indeed not CEOs of the company but on a lower-tier management level. Furthermore, it should be noted the majority of informants is formed by owner managers (71%), which is typical for medium-sized companies in Germany. Finally, 21% of the informants are professional managers. One issue deserves notice. Position of the manager might also serve as indicator for the representativeness of a sample. However, information about ownership and management structure of the population is difficult to obtain from Hoppenstedt Firmendatenbank, unless one identifies this information for each of 38,225 companies individually. However, some official statistics identified the share of owner-managed and professionally managed firms by legal form.998 Medium-sized companies are typically in the form of GmbH, GmbH & Co. KG or KG 999 and this statistic identifies that 81% of these companies are owner-managed, while 19% of these companies are 998 999

Cf. Wolter, H. J./Hauser, H. E. (2001), p. 71. GmbH = Gesellschaft mit begrenzter Haftung; GmbH & Co. KG = Gesellschaft mit beschränkter Haftung und Compagnie Kommanditgesellschaft; KG = Kommanditgesellschaft.

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Data collection and evaluation

professionally managed.1000 Certainly, these figures do not pertain to the population, because there are smaller and larger companies within this statistic. Still, it suggests the representativeness of the sample in terms of ownership and management is also acceptable. Figure 16 shows the sample structure by position of informant. 100% = 230 Other manager

8% = 18

Professional manager

21% = 48

Ownermanager

71% = 164

CEO position

Sample

Figure 16: Sample structure of respondents by position of informants (Source: own compilation)

Finally, the time elapsed since decision making is important for being able to judge the outcomes of SDM. Concerning the present study, the time period elapsed could be examined because respondents were asked to indicate when SDM started and when a final choice was made. The median period elapsed since decision making is 1.2 years in the present sample. Some SDM research argue that a period of one to two years is appropriate to allow for assessing decision effectiveness for their sample of manufacturing companies with up to 6,600 employees.1001 Compared with this, the median of 1.2 years indicates there are a number of more recent decisions in the present sample. However, the present study surveys companies with a maximum of 499 employees. Decision implementation in these companies very likely does not take as long as in the companies with several thousand employees surveyed by the researchers mentioned before. One potential reason is, decision implementation in large companies very likely involves more people and more physical resources to be handled than in medium-sized companies. The final conclusion is, respondents of this study should have been able to judge the effectiveness of SDM and the causal ordering between process and outcome is deemed as sufficiently robust for analysis. 1000 1001

Cf. Wolter, H. J./Hauser, H. E. (2001) Cf. Dean, J. W./Sharfman, M. P. (1996), p. 381.

Evaluation and preparation of data basis

179

6.3 Evaluation and preparation of data basis This section discusses test for biases and the treatment of missing values in this study. 6.3.1 Biases Empirical data of respondents may be subject to systematic biases resulting from the data collection method employed. These biases may impact the results of the SEM estimation1002 and thus question the generalizability of the results of a study.1003 A number of potential biases can be identified and several statistical measures for a post hoc assessment of the presence of biases are available. In the following, non-response bias, informant bias and common method bias are evaluated. Non-response bias may result from substantial differences between respondents and non-respondents of a survey.1004 Non-response bias may occur because managers responding may be more prone to need help in the topic of a survey, than managers not responding. As a result the responses would refer to rather inferior business practices and best practices were not included in the data.1005 One measure to overcome nonresponse bias is to simply avoid non-responses.1006 However, in survey research nonresponses are common and some key informants do generally reject to participate such as in the survey of this study. Another measure is to assess non-response bias post hoc and subsequently assess whether non-response bias impose severe constraints to the generalizability of results. For assessing non-response bias the responses are split into three parts of early, moderate and late responses.1007 Then the means of indicators of early and late responses are tested for significant mean differences. The test of nonresponse bias results in seven indicators of 57 indicators with significant mean differences for early and late respondents.1008 Given the large number of indicators with non-significant differences a non-response bias can be excluded. Informant bias refers to intersubjective differences in the description of the objective phenomena of interest.1009 These differences may be a result of differences in motifs, bounded rationality, perceptions and information asymmetries between different 1002

Cf. Bagozzi, R. P./Yi, Y. (1991), p. 426. Cf. Armstrong, J. S./Overton, T. S. (1977), p. 396. 1004 Cf. Armstrong, J. S./Overton, T. S. (1977), p. 396. 1005 Cf. Diller, H. (2006), p. 616. 1006 Cf. Armstrong, J. S./Overton, T. S. (1977), p. 396. 1007 Cf. Armstrong, J. S./Overton, T. S. (1977), pp. 397-398. 1008 In this study a Mann-Whitney-U test at a 5% significance level is performed, because all indicators show univariate non-normal distribution. Cf. Bühl, A. (2006), pp. 313-315. 1009 Cf. Bagozzi, R. P. et al. (1991), p. 423-425. 1003

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Data collection and evaluation

informants.1010 The informant bias is most often associated with functional or hierarchical position of the informants of a study.1011 The informants of this study have been defined as top executives but also other key managers mainly involved in SDM. This broader definition was also reflected in the invitation emails. As a consequence, 18 of the 230 informants are indeed not among the top executives of a company, but at a lower rank in the hierarchy. Therefore, informant bias is tested for this study, comparing the 212 top executives’ with the 18 key managers’ responses. Again the test for informant bias is made by testing for significant mean differences of all the latent variable indicators in this study. 1012 According to this test only four of 57 indicators show significant differences. Therefore, informant bias can be excluded from this study. Common method bias means the description of variables does not only reflect the perceived characteristics of the variables of interest but also characteristics and effects of the data collection method.1013 There are several sources for common method biases which can basically be grouped into common rater, common measurement context, common item context, and item characteristic effects.1014 Reasons for common rater biases may be consistency motifs, social desirability, leniency, acquiescence or affective effects.1015 These are most notably problematic if a common rater assesses independent and dependent variables.1016 The Harman’s Single-Factor Test is a post hoc method for assessing common method bias.1017 According to the test procedure all indicators are factor analyzed and the unrotated factor solution is examined. This test assumes a common method bias is present, if all indicators load on one factor or if one factor accounts for the majority of variance explained.1018 Both exploratory and confirmatory factor analysis are used for this procedure. Applied to the present sample data, the Harman’s Single Factor Test shows one factor accounts for a maximum of 21% of variance for both exploratory and confirmatory factor analyses. Thus a common method bias can be excluded from this study.

1010

Cf. Ernst, H. (2003), p. 1250. Cf. Ernst, H. (2003), pp. 1253-1255. 1012 Again a Mann-Whitney-U test at a 5% significance level is performed. 1013 Cf. Backhaus, K./Blechschmidt, B./Eisenbeiß, M. (2006a), pp. 713-714. 1014 Cf. Podsakoff, P. M./Mackenzie, S. B./Lee, J.-Y. et al. (2003), p. 885. 1015 Cf. Podsakoff, P. M. et al. (2003), pp. 881-885 for a detailed discussion of the sources of common method bias. 1016 Cf. Backhaus, K. et al. (2006a), pp. 714. 1017 Cf. Harman, H. H. (1976); Podsakoff, P. M. et al. (2003), p. 889. 1018 Cf. Podsakoff, P. M. et al. (2003), p. 889. 1011

Evaluation and preparation of data basis

181

6.3.2 Missing values The evaluation of the survey data set revealed some missing data points. In order to assure statistical analyzability these missing data cannot be ignored and several techniques are available to handle missing data.1019 In general three methods of handling missing data are available, namely elimination of data sets, imputation or parameter estimates of missing values.1020 Elimination of data sets means a data set is excluded from further analysis if values are missing. This can further be distinguished into elimination of a data set if any one value is missing or into pairwise elimination which means a data set is only eliminated if missing values are present for both independent and dependent variable.1021 Cases where missing data exceed a predefined threshold value were already eliminated, while some cases still miss data points. Two methods are available for replacing missing values in the remaining data set. Imputation of missing data means data sets with missing data are not eliminated but missing values are replaced with data point estimates based on additional data.1022 Similarly, the replacement of missing values can be based on estimations derived from other parameters in the data set. For this purpose several statistical techniques such as maximum-likelihood, Bayes algorithm or expectation-maximization (EM) techniques can be used.1023 The EM technique is a commonly used method for missing value estimation.1024 The choice of treatment of missing values is guided by the following considerations. Firstly, the elimination of data sets results in a further reduction of the sample size which is not to be favored because there are methods which allow for replacement of missing values at a systematic, reliable manner such as multiple imputation and parameter estimation techniques.1025 Multiple imputation technique is relatively demanding while the EM technique is easily applicable when using statistical software packages such as SPSS.1026 Therefore, the current study opts for replacing missing values with the EM technique.

1019

Cf. Bortz, J./Döring, N. (2006), p. 85.; Decker, R./Wagner, R./Temme, T. (2000), pp. 92-95. Cf. Hair, J. F. et al. (1998), p. 51. Cf. Bankhofer, U. (1995), p. 91. 1022 Cf. Bankhofer, U. (1995), pp. 104. 1023 Cf. Bankhofer, U. (1995), pp. 155. 1024 Cf. Decker, R. et al. (2000), p. 93. 1025 Cf. Schafer, J. L./Graham, J. (2002), p. 172. 1026 Cf. Allison, P. D. (2001), p. 22. 1020 1021

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Results

7 Results The following chapter provides the results of the empirical data analysis with PLS. For performing a PLS parameter estimation a few software packages, most notably PLSGraph, SmartPLS, PLS-GUI, and Spad-PLS are available.1027 This research study uses SmartPLS 2.0 M3 for conducting the SEM analysis.1028 This software has a graphical user interface and several features which allow for creating structural models, importing indicator data and generating outputs of parameter estimates in a comfortable manner. In section 7.1, the overall measurement model and the direct effects structural model are evaluated. In section 7.2, the moderating effects are evaluated by means of group comparison. In section 7.3, financial performance measures are included for validating the basic research model. In section 7.4, tests for control variables are discussed. Finally, the hypotheses are evaluated based on the empirical results in section 7.5. 7.1 Results of direct effects model The evaluation of the direct effects comprises the measurement model evaluation for all variables including moderator variables and the structural model evaluation excluding moderator variables. The results are presented in the following. 7.1.1 Measurement model evaluation The measurement model evaluation examines the reliability and validity of the latent variable measures. Reliability evaluation refers to identifying measurement error, while validity evaluation refers to examining whether the constructs measure their underlying concepts.1029 Here a distinction into reflective and formative constructs is made, because of methodological differences in the evaluation approach. 7.1.1.1 Reflective constructs The measurement model evaluation of reflective constructs comprises three basic aspects. Firstly, all variables are examined with respect to unidimensional measurement as a post hoc criterion of content validity. Secondly, each construct is evaluated in terms of indicator and construct reliability. Thirdly, the examination of discriminant validity seeks to identify whether the indicators form distinct variables. 1027 1028 1029

Cf. Homburg, C./Klarmann, M. (2006), p. 735. Cf. Ringle, C. M./Wende, S./Will, A. (2005). Cf. Homburg, C./Giering, A. (1996), p. 7.

W. Gänswein, Effectiveness of Information Use for Strategic Decision Making, DOI 10.1007/978-3-8349-6849-4_7, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

Results of direct effects model

183

7.1.1.1.1 Unidimensionality Following DeVellis (2003) the unidimensionality of reflective variables is tested by means of exploratory factor analysis.1030 The essential activity in this procedure is the examination of each individual indicator loading and its cross-loadings on other factors. Indicator loadings should be more than 0.4 on the expected factor while the cross-loadings on other factors should be less than 0.4. Furthermore, in order to assure sufficient discriminance the difference between indicator loading and maximum crossloading should be more than 0.2.1031 If these thresholds are not met, indicators are to be eliminated from further analysis. This guideline is followed with one exception. One indicator for internal, impersonal sources loads 0.71 on its expected factor, while it shows a cross-loading of 0.44 on another factor. Since the cross-loading is just little above the threshold value and the difference between loading and cross-loading is considerably above the 0.2 threshold the indicator is kept for further analysis. The reason for doing so is the trade-off between losses of information from eliminating the indicator versus detrimental effects to the measurement model. Here the former appears to outweigh the latter. Applying these threshold criteria to the present data results in elimination of five indicators as can be seen in Table 21. These eliminations and potential reasons are discussed in the following. Firstly, the indicator use of marketing related information from external impersonal sources (InfoExtImp4) was eliminated because of a considerable cross-loading on use of marketing related information from personal sources. A potential explanation is that external impersonal sources are talked over when they are received from personal external contacts. For example, trade associations are an important source of market related information. Typically, these associations are not anonymous entities but have work-groups or meetings which might in turn provide a platform for discussing most recent market research publications. Secondly, the indicators use of qualitative and contextual information from internal, impersonal sources (InfoIntImp5 and InfoIntImp6) are eliminated as they cross-load onto use of qualitative and contextual information from personal sources. A potential explanation is that management information systems in SME rarely cover qualitative

1030 1031

Principal component analysis with varimax rotation is performed on all reflective indicators. Cf. Nunnally, J. C./Bernstein, I. H. (1994); Homburg, C./Giering, A. (1996), p. 13; Huber, F. et al. (2007), p. 93.

184

Results

and contextual information1032 and the state of information supply may vary very much across companies. As a result it may again be possible that these qualitative and context related information items are obtained in a similar as described before. Thirdly, two indicators from use of production related information from external and internal personal sources are eliminated because one indicator does not have a discriminant loading on any factor and the second indicator has a loading of 0.61 below the threshold value of 0.7. A potential explanation is that the sample covers manufacturing and services companies to almost equal shares. The latter kind of companies might not have such a clear view and need for information on their production function as it is typically the case for manufacturing firms. In addition to that, the hypothesized factor structure should generally be confirmed by the test of unidimensionality. However, the results confirm only seven of nine factors hypothesized. Information use from internal and external personal information sources yield more than the two factors hypothesized. The items did not load on two different sources of information but on four different information dimensions, namely quantitative historic and forward looking information, production related information, marketing related information and qualitative information. A possible explanation is that informants do not distinguish between internal and external personal sources of information but rather between the various information dimensions. Decision makers may turn to different people with different intensity for acquiring different pieces of information which then results in the observed factor loadings. Following the recommendations of Anderson and Gerbing the structural model is respecified and the new first-order factors are combined to form a second-order factor in the reflective mode.1033 This second-order factor is then included in the structural model as a measure for overall information use from personal sources. Overall, the test of unidimensionality yields two conclusions. Firstly, a distinction into information use from different sources is generally supported by the empirical data although the factors are not as clear-cut as anticipated. Secondly, the results underpin some degree of multicollinearity among information use variables. This was anticipated and the reason for selecting PLS as appropriate analysis method. The empirical data in this study supports this choice of PLS. 1032 1033

Cf. Berens, W./Wüller, F. (2007), p. 398. Cf. Gerbing, D. W./Anderson, J. C. (1984), p. 579; Anderson, J. C./Gerbing, D. W. (1988), p. 415.

Component 1 2 3 Cognitive Style Cogn1 0.61 Cogn2 0.72 Cogn3 0.78 Cogn4 0.61 Cogn5 0.75 Cogn6 0.72 Cogn7 0.66 Cogn8 0.73 Motivation Mot1 0.80 Mot2 0.76 Mot3 0.77 Mot4 0.80 Scope of information use from external, impersonal sources InfoExtImp1 0.71 InfoExtImp2 0.79 InfoExtImp3 0.73 InfoExtImp4 0.72 InfoExtImp5 0.77 InfoExtImp6 0.79 Scope of information use from internal, impersonal sources InfoIntImp1 InfoIntImp2 InfoIntImp3 InfoIntImp4 InfoIntImp5 InfoIntImp6 Only loadings larger than 0.40 are shown

Table 21: Results of principal components analysis of reflective indicators (Source: own compilation)

0.77 0.78 0.71 0.70 0.55 0.63

4

5

6

0.44

0.51

7

0.61 0.46

8

9

10

11

eliminated eliminated

eliminated

Comment

Results of direct effects model 185

Component 1 2 3 4 5 6 Use of quantitative historic and forward looking information from personal sources InfoIntPers1 0.65 InfoExtPers1 0.65 InfoIntPers2 0.63 InfoExtPers2 0.55 Use of production related information from personal sources InfoIntPers3 0.42 InfoExtPers3 0.61 Use of marketing related information from personal sources InfoIntPers4 InfoExtPers4 Use of qualitative and contextual information from personal sources InfoIntPers5 InfoExtPers5 InfoIntPers6 InfoExtPers6 Political behavior PolBeh1 PolBeh2 Strategic decision Quality DecQual1 DecQual2 DecQual3 Subjective company performance Perf1 Perf2 Perf3 Perf4 Perf5 Perf6 Perf7 Only loadings larger than 0.40 are shown 0.70 0.69

7

0.75 0.66 0.70 0.63

0.49

8

0.68 0.73

9

0.77 0.83 0.76

10

0.84 0.88 0.78 0.83 0.75 0.69 0.75

11

eliminated eliminated

Comment

186 Results

Table 21 (continued): Results of principal components analysis of reflective indicators (Source: own compilation)

Results of direct effects model

187

7.1.1.1.2 Reliability The test for unidimensionality required some adjustments to the structural model and resulted in the inclusion of a second-order factor as detailed in the preceding section. In the following the reliability of the partly adjusted reflective constructs is evaluated. At first, indicator reliability is evaluated. Each construct should explain 50% of the indicator variance. Therefore, indicators with a factor loading of less than 0.707 are eliminated.1034 If indicator loadings of less than 0.707 can be identified the procedure follows several consecutive steps of elimination starting with the indicator with the smallest loading. After an indicator is eliminated the model estimate is performed again and all remaining indicators examined. This is repeated until all reflective indicators load higher than the threshold value. Furthermore, the indicator loadings need to be significant which is commonly the case when loadings are larger than 0.707. Nonetheless, the t-values for each indicator are examined with respect to the critical t-value of 3.30 which represents a significance level of 0.1%.1035 Next, the construct reliability measures of each construct are examined. These are Cronbach’s Alpha, composite reliability and AVE. The threshold value for the first two is 0.7 while AVE is to be greater than 0.5.1036 In the following the indicator and construct reliability results are shown and discussed for each reflective construct. The reliability evaluation starts with the moderator variable cognitive style. The PLS estimation resulted in three indicators with loadings below the 0.707 threshold value. These items include terms such as instincts, empathy and heartfelt. Although the instrument had been tested and re-tested, respondents in the present survey may not have associated these terms with cognitive activities as unambiguously as the terms of the other indicators. Still, a sufficient number of indicators with high indicator reliability remains after elimination. Furthermore, all construct reliability criteria are met as can be seen in Table 22. Therefore, cognitive style is deemed reliable and kept as moderator variable.

1034

Cf. sub-section 5.3.2.2 and Götz, O./Liehr-Gobbers, K. (2004), p. 728. The critical t-value is determined with a power analysis for N = 230, effect size = 0.2 and alphabeta-error ratio = 1:1. 1036 Cf. sub-section 5.3.2.2 and Fornell, C./Larcker, D. F. (1981), p. 46; Homburg, C./Giering, A. (1996), p. 8; Hulland, J. S. (1999), p. 199; Götz, O./Liehr-Gobbers, K. (2004), p. 728. 1035

188 Construct

Results Cognitive style (Cogn)

Indicator reliability Variable No.

Indicator text (abbreviated)

Cogn1

Concepts

…. instincts

Loading

eliminated

Cogn2

Rationality / thinking

…. empathy

eliminated

Cogn3

Reason

…. felt sense

0.73

6.04

Cogn4

Logic

…. inner knowing

0.74

7.36

Cogn5

Facts

…. feelings

0.74

6.10

Cogn6

Proof

…. heartfelt

Cogn7

Data

…. hunch

Cogn8

Deduction

…. intuition

Construct reliability

T-Value

eliminated 0.73

6.07

0.84

12.37

Value

Cronbach’s Alpha

0.82

Composite reliability

0.87

Average Variance Explained (AVE)

0.57

Table 22: Reliability evaluation of cognitive style (Source: own compilation)

The construct motivation to engage in information use for SDM shows high reliability on indicator and on construct level as shown in Table 23. All loadings are well above the threshold value of 0.707 and all construct reliability criteria are also met. Therefore, strategic decision quality is deemed reliable and kept for further analysis. Construct

Motivation to engage in information use for SDM (Mot)

Indicator reliability Variable No.

Indicator text (abbreviated)

Loading

T-Value

Mot1

Interest in strategic decision making

0.81

32.20

Mot2

Decrease of error rate

0.85

28.88

Mot3

Goal achievement

0.85

22.06

Mot4

Excitement

0.86

37.72

Construct reliability

Value

Cronbach’s Alpha

0.86

Composite reliability

0.91

Average Variance Explained (AVE)

0.71

Table 23: Reliability evaluation of motivation (Source: own compilation)

Results of direct effects model

189

Information use from external, impersonal sources has seen elimination of one indicator from the test for unidimensionality. The remaining indicators show high reliability and significances and all construct reliability measures are well above the threshold values. Therefore, this variable is deemed reliable and used for further analysis. See Table 24 for the results of the reliability evaluation. Construct

Information use from external, impersonal sources (InfoExtImp)

Indicator reliability Variable No.

Indicator text (abbreviated)

Loading

T-Value

InfoExtImp1

Financial historic information

0.77

15.89

InfoExtImp2

Forward looking information / probabilities

0.82

22.98

InfoExtImp3

Non-financial information for production

0.80

17.85

InfoExtImp4

Non-financial information for marketing

InfoExtImp5

Non-economic information

InfoExtImp6

Information about indirect external factors

Construct reliability

eliminated 0.85

23.97

0.86

29.00

Value

Cronbach’s Alpha

0.88

Composite reliability

0.91

Average Variance Explained (AVE)

0.68

Table 24: Reliability evaluation of information use from external, impersonal sources (Source: own compilation)

Two indicators from information use from internal, impersonal sources have already been eliminated due to the test for unidimensionality. The two indicators are the use of qualitative and contextual information. The remaining indicators are all significant and exceed the threshold value of 0.707. Furthermore, all construct reliability measures meet their criteria. Therefore, information use from internal, impersonal sources is deemed to be a reliable measure and used for further analysis. Table 25 shows the reliability measures for this variable.

190 Construct

Results Information use from internal, impersonal sources (InfoIntImp)

Indicator reliability Variable No.

Indicator text (abbreviated)

Loading

T-Value

InfoIntImp1

Financial historic information

0.78

13.24

InfoIntImp2

Forward looking information / probabilities

0.83

15.28

InfoIntImp3

Non-financial information for production

0.82

18.05

InfoIntImp4

Non-financial information for marketing

0.83

18.04

InfoIntImp5

Non-economic information

eliminated

InfoIntImp6

Information about indirect external factors

eliminated

Construct reliability

Value

Cronbach’s Alpha

0.83

Composite reliability

0.89

Average Variance Explained (AVE)

0.66

Table 25: Reliability evaluation of information use from internal, impersonal sources (Source: own compilation)

Three first-order variables for information use from personal sources are formed as a result of testing unidimensionality in the preceding section. The first factor, use of quantitative historic and forward looking information has sufficient reliabilities and significances for all its four indicators. All three construct reliability measures meet the threshold criteria as can be seen in Table 26. Therefore, this variable is deemed reliable and included in structural model. Construct

Use of quantitative historic and forward looking information from personal sources (InfoPersQuant)

Indicator reliability Variable No.

Indicator text (abbreviated)

Loading

T-Value

InfoIntPers1

Financial historic information

0.80

22.62

InfoIntPers2

Forward looking information / probabilities

0.75

16.04

InfoExtPers1

Financial historic information

0.70

10.83

InfoExtPers2

Forward looking information / probabilities

0.74

15.80

Construct reliability

Value

Cronbach’s Alpha

0.74

Composite reliability

0.84

Average Variance Explained (AVE)

0.56

Table 26: Reliability evaluation of use of quantitative information from personal sources (Source: own compilation)

Results of direct effects model

191

The second factor, use of marketing related information has sufficient reliabilities and significances for its two indicators and again all three construct reliability measures meet the threshold criteria as can be seen in Table 27. Therefore, this variable is deemed reliable and included in the further analysis. Construct

Use of marketing related information from personal sources (InfoPersMark)

Indicator reliability Variable No.

Indicator text (abbreviated)

InfoIntPers4

Non-financial information for marketing

InfoExtPers4

Non-financial information for marketing

Construct reliability

Loading

T-Value

0.92

52.15

0.94

57.94

Value

Cronbach’s Alpha

0.85

Composite reliability

0.93

Average Variance Explained (AVE)

0.87

Table 27: Reliability evaluation of use of marketing related information from personal sources (Source: own compilation)

The third factor, use of qualitative and contextual information from personal sources again shows sufficient indicator reliabilities and significances for its four indicators. Furthermore, all three construct reliability measures meet the threshold criteria as can be seen in Table 28. Therefore, this variable is deemed reliable and included in the further analysis. Construct

Use of qualitative and contextual information from personal sources (InfoPersQual)

Indicator reliability Variable No.

Indicator text (abbreviated)

Loading

T-Value

InfoIntPers5

Qualitative, non-economic information

0.86

36.55

InfoIntPers6

Information about indirect external factors

0.84

30.42

InfoExtPers5

Qualitative, non-economic information

0.84

24.04

InfoExtPers6

Information about indirect external factors

0.82

22.03

Construct reliability

Value

Cronbach’s Alpha

0.86

Composite reliability

0.91

Average Variance Explained (AVE)

0.71

Table 28: Reliability evaluation of use of qualitative information from personal sources (Source: own compilation)

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Results

Finally, these three variables are combined measures of a reflective, second-order factor for information use from personal sources. The basis for this construct specification are the same considerations as for personal information sources as outlined before. Furthermore, a decision is to be made how this second-order factor is modeled in PLS. There are basically two possibilities. Firstly, in the hierarchical components model the first order constructs are formed and then related to an additional factor onto which all indicators of the first-order constructs load.1037 The hierarchical components model works best if the first-order constructs all possess the same number of indicators.1038 Secondly, two-step modeling means that in a first step the latent variable scores of the first-order factors are estimated and then included as indicators in a second structural model.1039 The two-step procedure does not have the requirement of equal number of indicators and is therefore used for modeling the second-order factor in this study. As a result, its reliability can be evaluated analogically to first-order factors.1040 Table 29 presents the reliability measures for information use from personal sources. As can been seen, all indicator and construct reliability measures meet the threshold criteria. Consequently, this variable is deemed reliable and included in the further analysis. Construct

Information use from personal sources (InfoPers) 2nd-order construct, reflective

Indicator reliability Variable No.

Indicator text (abbreviated)

Loading

T-Value

InfoPers1

Quantitative historic and forward looking information

0.87

40.62

InfoPers2

Marketing related information

0.81

19.32

InfoPers3

Qualitative information

0.86

34.79

Construct reliability

Value

Cronbach’s Alpha

0.81

Composite reliability

0.89

Average Variance Explained (AVE)

0.72

Table 29: Reliability evaluation of information use from personal sources (second-order construct) (Source: own compilation)

1037

Cf. Lohmoller, J.-B. (1989). Cf. Chin, W. W. (1997). 1039 Cf. Agarwal, R./Karahanna, E. (2000), pp. 678-685. 1040 Cf. Agarwal, R./Karahanna, E. (2000), p. 684. 1038

Results of direct effects model

193

Next, political behavior meets the indicator reliability criteria. However, Cronbach’s Alpha is below the threshold value as shown in Table 30. However, although Cronbach’s Alpha is an established measure, it receives some criticism because its value has a positive relationship with the number of indicators.1041 Here the number of indicators is two and thus relatively low. Therefore, the other two measures are deemed to be more meaningful reliability construct reliability measures. Since their threshold values are met, the construct is deemed reliable and used for further analysis. Construct

Political behavior (PolBeh)

Indicator reliability Variable No.

Indicator text (abbreviated)

PolBeh1

Diverging goals

PolBeh2

Openness about goals

Construct reliability

Loading

T-Value

0.88

24.12

0.82

16.22

Value

Cronbach’s Alpha

0.61

Composite reliability

0.84

Average Variance Explained (AVE)

0.72

Table 30: Reliability evaluation of political behavior (Source: own compilation)

The construct strategic decision quality shows high reliability on indicator and construct level as can be seen from Table 31. All loadings are well above the threshold value of 0.707 and also the construct reliability criteria are all three met. Therefore, strategic decision quality is deemed reliable and kept for further analysis. Construct

Strategic decision quality (DecQual)

Indicator reliability Variable No. Indicator text (abbreviated) DecQual1 DecQual2 DecQual3

Satisfaction with quality Contribution to company goals Contribution to company performance

Construct reliability Cronbach’s Alpha Composite reliability Average Variance Explained (AVE) Table 31: Reliability evaluation of strategic decision quality (Source: own compilation)

1041

Cf. Homburg, C./Giering, A. (1996), p. 8.

Loading 0.87 0.88 0.86 Value 0.84 0.91 0.76

T-Value 39.12 39.81 29.82

194

Results

The construct subjective company performance also shows reliability on indicator and construct level as can be seen in Table 32. All loadings are above the threshold value of 0.707 and all construct reliability criteria are met. Therefore, subjective company performance is deemed reliable and kept for further analysis. Construct

Subjective company performance (Perf)

Indicator reliability Variable No.

Indicator text (abbreviated)

Loading

T-Value

Perf1

Relative economic performance

0.87

42.90

Perf2

Relative growth performance

0.88

40.23

Perf3

Relative profit performance

0.77

14.22

Perf4

Relative product success

0.89

49.30

Perf5

Relative customer acquisition performance

0.78

25.61

Perf6

Relative customer loyalty performance

0.75

19.57

Perf7

Relative market share performance

0.73

15.75

Construct reliability

Value

Cronbach’s Alpha

0.91

Composite reliability

0.93

Average Variance Explained (AVE)

0.66

Table 32: Reliability evaluation of subjective company performance (Source: own compilation)

Overall, the reliability criteria of reflective variables except for Cronbach’s Alpha for political behavior are met. Conse quently, the reflective variables are a reliable basis for further analysis. In addition to that, it should be noted this study employs recently developed measures such as the cognitive style construct and has developed a new information use measure based on existing operationalizations. Given the novelty of the adapted measurement instruments, the low number of eliminations appears demonstrates a strong reliability of the operationalization of this study. 7.1.1.1.3 Discriminant validity After evaluating the reliability of reflective constructs, discriminant validity is evaluated. Discriminant validity on indicator level means reflective indicator loadings are largest on their hypothesized factors. Furthermore, though formative, PEU is also included, because reflective indicators need to be discriminant from any construct. The examination of cross-loadings in Table 33 shows all indicators indeed load largest on their hypothesized constructs and discriminant validity on indicator level is given.

Results of direct effects model

Construct Cogn

Mot

Indicator

195

Info

Info

Info

Info

Info

Pol

Dec

Ext Imp

Int Imp

Pers Quant

Pers Mark

Pers Qual

Beh

Qual

Perf

PEU

Cogn3 Cogn4 Cogn5

0.73 0.74 0.74

-0.17 -0.15 -0.19

-0.09 -0.18 -0.15

-0.22 -0.16 -0.32

-0.08 -0.19 -0.01

-0.21 -0.25 -0.22

-0.06 -0.18 -0.10

0.06 0.12 0.05

-0.10 -0.16 -0.10

-0.08 -0.16 -0.12

-0.05 -0.12 0.08

Cogn7 Cogn8

0.73 0.84

-0.14 -0.22

-0.21 -0.18

-0.42 -0.32

-0.14 -0.23

-0.28 -0.27

-0.12 -0.22

0.07 0.10

-0.15 -0.21

-0.09 -0.11

0.00 -0.07

Mot1

-0.21

0.25

0.35

0.31

0.33

-0.22

0.29

0.19

0.16

-0.24 -0.23

0.85 0.81 0.85

0.32

Mot2 Mot3

0.40 0.38

0.12 0.21

0.30 0.39

0.20 0.26

0.27 0.43

-0.16 -0.22

0.25 0.29

0.12 0.17

0.22 0.25

Mot4

-0.11

0.86

0.29

0.15

0.27

0.23

0.25

-0.26

0.34

0.28

0.14

InfoExtImp1

-0.18

0.37

0.77

0.28

0.36

0.29

0.33

-0.10

0.14

0.17

0.30

InfoExtImp2

-0.23

0.32

0.82

0.25

0.39

0.41

0.36

-0.12

0.19

0.21

0.18

InfoExtImp3 InfoExtImp5

-0.22 -0.13

0.34 0.33

0.26 0.24

0.36 0.33

0.37 0.28

0.43 0.49

-0.17 -0.09

0.14 0.23

0.24 0.33

0.20 0.21

InfoExtImp6

-0.15

0.33

0.80 0.85 0.86

0.22

0.36

0.35

0.49

-0.11

0.20

0.26

0.25

InfoIntImp1 InfoIntImp2 InfoIntImp3 InfoIntImp4

-0.25 -0.36 -0.32 -0.31

0.17 0.18 0.15 0.21

0.10 0.24 0.32 0.31

0.78 0.83 0.82 0.83

0.23 0.29 0.33 0.33

0.19 0.37 0.39 0.58

0.12 0.23 0.37 0.31

-0.14 -0.10 -0.18 -0.11

0.15 0.18 0.15 0.19

0.13 0.17 0.19 0.22

0.09 0.14 0.08 0.09

InfoIntPers1

-0.13

0.30

0.23

0.36

0.80

0.37

0.47

-0.31

0.34

0.22

0.18

InfoIntPers2 InfoExtPers1

-0.17 -0.14

0.32 0.26

0.29 0.34

0.26 0.25

0.45 0.46

0.43 0.40

-0.25 -0.18

0.25 0.24

0.17 0.18

0.19 0.26

InfoExtPers2

-0.17

0.30

0.47

0.21

0.75 0.70 0.74

0.48

0.48

-0.22

0.29

0.32

0.25

InfoIntPers4

-0.33

0.28

0.32

0.46

0.55

0.56

-0.21

0.26

0.20

0.12

InfoExtPers4

-0.29

0.27

0.45

0.42

0.52

0.92 0.94

0.55

-0.15

0.29

0.24

0.22

InfoIntPers5 InfoIntPers6 InfoExtPers5 InfoExtPers6

-0.14 -0.23 -0.11 -0.19

0.34 0.23 0.39 0.29

0.36 0.35 0.48 0.55

0.29 0.28 0.23 0.29

0.47 0.54 0.49 0.51

0.47 0.56 0.47 0.51

0.86 0.84 0.84 0.82

-0.35 -0.30 -0.25 -0.21

0.29 0.24 0.26 0.23

0.31 0.29 0.28 0.33

0.17 0.17 0.16 0.15

PolBeh1

0.14

-0.26

-0.09

-0.11

-0.25

-0.18

-0.27

0.88

-0.44

-0.27

-0.14

PolBeh1

0.04

-0.18

-0.16

-0.17

-0.31

-0.14

-0.30

0.82

-0.32

-0.25

-0.09

DecQual1 DecQual2

-0.16 -0.20

0.36 0.26

0.17 0.22

0.17 0.15

0.35 0.33

0.31 0.18

0.32 0.23

-0.47 -0.40

0.27 0.33

0.22 0.26

DecQual3

-0.18

0.30

0.19

0.22

0.30

0.27

0.25

-0.31

0.87 0.88 0.86

0.43

0.31

Perf1

-0.10

0.19

0.22

0.21

0.28

0.17

0.30

-0.25

0.37

0.17

Perf2 Perf3 Perf4

-0.07 -0.12 -0.18

0.17 0.18 0.21

0.22 0.14 0.30

0.19 0.13 0.21

0.25 0.18 0.29

0.15 0.20 0.26

0.28 0.26 0.36

-0.23 -0.19 -0.31

0.32 0.26 0.40

0.87 0.88

Perf5 Perf6

-0.11 -0.17

0.22 0.18

0.28 0.32

0.13 0.17

0.27 0.28

0.14 0.25

0.26 0.34

-0.28 -0.28

Perf7

-0.11

0.17

0.21

0.22

0.11

0.20

0.21

-0.13

Table 33: Discriminant validity on indicator level (Source: own compilation)

0.77 0.89

0.12 0.05 0.17

0.31 0.32

0.78 0.75

0.17 0.22

0.22

0.73

0.13

196

Results

Furthermore, discriminant validity on construct level requires a reflective construct is distinct from all other constructs of a measurement instrument. It is tested with the Fornell-Larcker criterion which states square-root of AVE of one construct has to be larger than all correlations with other latent variables.1042 Table 34 shows this requirement is met for all reflective variables in this study. Thus the measurement model demonstrates discriminant validity on construct level. Construct

Cogn

Mot

Construct

Info Ext

Info Int

Info Pers

Info Pers

Info Pers

Imp

Imp

Quant

Mark

Qual

Pol Beh

Cogn Mot InfoIntImp InfoExtImp

0.76 -0.23 -0.22 -0.38

0.84 0.41 0.22

0.82 0.30

InfoPersQuant

-0.20

0.39

0.44

0.82 0.36

InfoPersMark InfoPersQual PolBeh DecQual

-0.33 -0.20 0.11 -0.21

0.29 0.38 -0.26 0.35

0.41 0.51 -0.14 0.22

0.47 0.32 -0.16 0.20

0.75 0.57 0.60 -0.33 0.38

0.93 0.60 -0.19 0.29

0.84 -0.33 0.31

0.85 -0.45

Perf PEU

-0.15 -0.06

0.23 0.22

0.30 0.27

0.22 0.12

0.30 0.29

0.24 0.19

0.36 0.19

-0.30 -0.13

Dec Qual

0.87 0.39 0.30

Perf

PEU

0.81 0.18

n.a.

Table 34: Discriminant validity on construct level (Source: own compilation)

Overall, the reflective measures meet the discriminant validity criteria on indicator and construct level. Since they also meet the reliability criteria, the overall conclusion is the reflective measures can be used for the structural model estimation. Finally, the nomological validity means a construct has significant relationships with other constructs. Therefore, this cannot be assessed without any causal analysis, which is the basic aim of SEM. Therefore, nomological validity can only be demonstrated through the complete SEM estimation.

1042

Cf. Fornell, C./Larcker, D. F. (1981), p. 46; Chin, W. W. (1998b), p. 321.

Results of direct effects model

197

7.1.1.2 Formative construct Only one formative construct, namely PEU, is used in this research study. For evaluating reliability and validity of formative constructs, fewer measures exist. At first, content validity is attained by a careful construction of the concept and measurement instrument. The measure for PEU has been frequently used in strategic management and other empirical management research studies.1043 Thus content and also nomological validity have already been demonstrated. Furthermore, extensive interviews with 16 experts were conducted prior to conducting the survey, which validates the use of PEU in the present research context. Other measures are not available for assuring and evaluating validity of formative constructs. Next, reliability of PEU is evaluated by investigating indicator weights and significances. Table 35 shows the respective values, indicating that all measures have significant weights at least at a 0.1 significance level. Their weights furthermore suggest a substantial contribution to the construct for each indicator. Finally, a test for multicollinearity shows whether the formative indicators are correlated. In case they are correlated the specification as a formative construct is questionable. For that purpose the VIFs for each indicator and the overall condition index are examined. The VIFs are all below 10 and the condition index is below 30, indicating that multicollinearity is not a problem for this measure. Perceived environmental uncertainty (PEU)

Construct Indicator reliability Variable No.

Indicator text (abbreviated)

Weight

T-Value

VIF

PEU1

Competitive environment

0.45

2.02

1.52

PEU2

Customers and market

0.55

2.84

1.50

PEU3

Technology

-0.36

1.68

1.39

PEU4

Politics / regulation

0.55

2.91

1.09

Condition index

13.75

Table 35: Reliability evaluation of perceived environmental uncertainty (Source: own compilation)

1043

Cf. e.g. Daft, R. L. et al. (1988); May, R. C. et al. (2000); Brettel, M. et al. (2006).

198

Results

7.1.2 Structural model evaluation The measurement model evaluation in the preceding sub-section required some model respecification for the variables information use from personal sources. The result of this respecification is a structural model as depicted in Figure 17.

Political behavior Use of internal, impersonal information

Use of external, impersonal information

Strategic decision quality

Subjective company performance

Use of personal information

Figure 17: Structural model for Partial Least Squares analysis (Source: own compilation)

Then the reliability and validity evaluation supported an overall robust measurement instrument implying the structural model estimates can be performed. Then, three parameters are used for the structural model evaluation: 1) The explained variance can be interpreted analogically to the R2 in a multiple regression model and shows how well the empirical data explains the dependent variables.1044 2) The Stone-Geisser criterion Q2 shows the predictive robustness of the model, where a Q2 larger than zero supports predictive power of the empirical data. 3) The direction and significance of path coefficients is used to support or reject hypothesized relationships.1045

1044 1045

Cf. sub-section 5.3.2.3. Unlike inference statistical techniques, PLS can only perform non-parametric tests which does not allow for inferential hypothesis testing. Cf. Götz, O./Liehr-Gobbers, K. (2004), p. 730. Still it is common practice to support or reject hypotheses with the help of the path coefficients’ estimates.

Results of direct effects model

199

Last but not least, for the evaluation of path coefficients the critical significance level (p) needs to be determined. This is accomplished with the help of the so-called power analysis as explained before.1046 For this calculation the present study assumes an effect size of 0.2 and alpha-beta-error ration of 1:1. Given these parameter values and a sample size of N = 230 the critical significance level for a one-sided t-test is determined with a critical alpha of 0.07 with a statistical power of 93%. The respective critical t-value is 1.52.1047 Thus, significance levels of 0.07 (*), 0.05 (**) and 0.01 (***) are used for evaluating the significance of the model parameters. The respective critical t-values are 1.52, 1.65 and 2.34. Figure 18 shows the results of the structural model parameter estimates as well as an indication of the hypothesized relationships. A discussion of these results follows. H(-) -0.36***

Political behavior H(-) -0.16**

Use of internal, impersonal information

H(+) +0.03 n.s.

(R2 = 3%, Q2 = 2%)

H(-) -0.14**

Use of external, impersonal information (R2

H(-) -0.34***

= 2%,

Q2

H(+) +0.04n.s.

(R2 =

= 1%)

Use of personal information

Strategic decision quality 26%,

Q2

= 20%)

H(+) +0.39***

Subjective company performance (R2 = 15%, Q2 = 10%)

H(+) +0.23***

(R2 = 12%, Q2 = 8%)

n.s. not significant; * p < 0.07; ** p < 0.05; *** p < 0.01; one-sided t-test H = hypothesized direction is positive (+) / negative (-)

Figure 18: Structural model evaluation of direct effects

(Source: own compilation)

First of all, the R2 of the main dependent variable strategic decision quality is 26%. At first sight, this seems relatively low in comparison with some other empirical management or SDM research studies in general. However, for two important reasons an R2 of 26% of strategic decision quality is indeed a robust explained variance. 1046 1047

Cf. sub-section 5.3.2.4. A one-sided t-test is used when the direction of the effect is hypothesized. A two-sided t-test is used when a direction is not hypothesized as for example in H1.4. For a two-sided t-test the critical alpha is 0.091 and the respective t-value is 1.70.

200

Results

Firstly, by taking an Upper Echelon perspective this study examines decision making on the level of the individual decision maker in order to to explain the organizational level variables strategic decision quality and company performance. Admittedly, individual level factors have a smaller effect on organizations than organizational level or environmental level factors. Therefore, it is not surprising that organizational level studies such as that of Dean, J. W./Sharfman, M. P. (1996) or Elbanna, S./Child, J. (2007) report high R2 of 55% and 41% for their direct effects models respectively.1048 In contrast to that, more directly comparable, individual level studies such as that of Amason, A. C. (1996), p. 140 or Dooley, R. S./Fryxell, G. E. (1999) report R2 between 13% and 24% for strategic decision quality.1049 This clearly underpins that the R2 of 26% of this study actually compares very well within the field of Upper Echelon SDM research. Secondly, most SDM research studies focus on manufacturing companies only.1050 This has basically the effect for controlling of external industry factors, which also contributes to a high R2. Not without reason increasing the heterogeneity of sample structures is one important area for enhancing the generalizability of SDM research.1051 The present study addresses this need for broader generalizability by covering a broad range of manufacturing and services industries. However, at the same time this implies a loss in explained variance of the dependent variable. Overall, these considerations put the R2 of strategic decision quality into a different perspective and demonstrate it is actually a robust explained variance. Furthermore, the R2 for subjective company performance is 15%. This can be explained by the fact, that a number of other factors than strategic decision quality influence organizational performance.1052 Moreover, again this value compares well with other SDM studies. For example, Zahra, S. A./Neubaum, D. O./El-Hagrassey, G. M. (2002) investigate effects of information use on firm performance. They report R2 between 10% and 22% for subjective company performance.1053 Thus not only strategic decision quality but also subjective company performance has a robust explained variance. 1048

Cf. Dean, J. W./Sharfman, M. P. (1996), p. 388; Elbanna, S./Child, J. (2007), p. 443. Cf. Amason, A. C. (1996), p. 140; Dooley, R. S./Fryxell, G. E. (1999), p. 397. 1050 Cf. Dean, J. W./Sharfman, M. P. (1996), p. 379; Elbanna, S./Child, J. (2007), p. 439. 1051 Cf. Forbes, D. P. (2007), p. 374. 1052 Cf. Dean, J. W./Sharfman, M. P. (1996), pp. 369-371; Forbes, D. P. (2007), p. 363. 1053 Cf. Zahra, S. A. et al. (2002), p. 16. 1049

Results of moderating effects models

201

Finally, the R2 for the information use variables are lower than the R2 of the two effectiveness variables. In particular, impersonal information sources show low levels of explained variance with 2% and 3%, while information use from personal sources has an R2 of 12%. Nonetheless, all three path coefficients from political behavior to information use variables are highly significant in the hypothesized direction. This basically suggests a rather small but nonetheless significant impact of political behavior on information use. However, when taking moderating factors into account the impact of political behavior on information use increases to attain a remarkable effect in SDM as will be discussed later. Next, the values of the Stone-Geisser-Criteria are evaluated. In general, Q2 can take values between -1 and 1, while a structural model has predictive power if Q2 is larger than zero.1054 The Q2 for strategic decision quality, subjective company performance and information use from personal sources are noteworthy larger than zero, while both variables for information use from impersonal sources are just above zero. As all variables show Q2 larger than zero, the predictive power of the research model is deemed acceptable. Finally, the direction and significance of path coefficients are evaluated. As can be seen in Figure 18 six out of eight hypothesized relationships have significant path coefficients in the direction as hypothesized. Only the two path coefficients from information use from internal and external impersonal sources to strategic decision quality are not significant. These results underpin that it was important to develop basic hypotheses on the direct effects of information from different sources on strategic decision quality although it might have seem trivial. The empirical results show, the effects are not granted. A more detailed evaluation of the specific hypotheses follows later in section 7.5. 7.2 Results of moderating effects models In the following, the statistical results for testing the moderating effects of PEU and cognitive style as well as their interaction effects are presented. These effects are evaluated by means of group comparison as explained in sub-section 5.3.2.5 and the statistical results are presented accordingly.

1054

Cf. Chin, W. W. (1998b), p. 318.

202

Results

7.2.1 Perceived environmental uncertainty At first, the sample is split into two sub-samples. Cases with a PEU value below the median are assigned to the low PEU group, cases with a PEU value above the median are assigned to the high PEU group.1055 After the two sub-samples are formed, the structural models are estimated. The evaluation of the measurement model for both PEU groups appears generally acceptable although five factor loadings do not meet the threshold criterion of 0.707.1056 However, for newly developed scales loadings above 0.5 are still acceptable. Furthermore, a trade-off decision between retaining a larger number of indicators vs. improving indicator reliability needs to be made.1057 Since the majority of the five items has loadings of more than 0.6 no adjustments are made. Furthermore, the Cronbach’s Alpha for political behavior is below the threshold value of 0.7 whereas composite reliability and AVE thresholds are met similarly as in the direct effects model. Consequently, both models are deemed suitable for analysis. The next step is the evaluation of similarity of measurement. The two sub-samples are equally large. Thus the first similarity criterion is met. Furthermore, all Coefficients of Congruence are equal to or larger than 0.98 as shown in Table 36. Six out of nine constructs have a Coefficient of Congruence of 1.00 indicating identical measurement. Therefore, the second criterion of similarity is met and a group comparison is valid. Low vs. high PEU groups InfoExtImp

1.00

InfoIntImp

1.00

InfoPersQuant

0.98

InfoPersMark

1.00

InfoPersQual

1.00

InfoPers (second-order)

1.00

PolBeh

0.99

DecQual

1.00

Perf

0.99

Table 36: Coefficients of Congruence – perceived environmental uncertainty group comparison (Source: own compilation)

1055

The median of the standardized latent variable scores for PEU is 0.00. To avoid confusion it should be noticed that this is just coincidently the same value as the mean. 1056 Cf. appendix 2. 1057 Cf. Chin, W. W. (1998b), p. 318; Götz, O./Liehr-Gobbers, K. (2004), p. 728.

Results of moderating effects models

203

Before comparing the two structural models, the critical significance levels need to be established because the sample sizes of each group are now half the size of the overall sample. Again effect size is assumed at 0.2 and alpha-beta-ratio at 1:1. Given these parameter values and a sub-sample size of N = 115 the critical significance level for a one-sided t-test is determined with a critical alpha of 0.14 with a statistical power of 86%. The respective critical t-value is 1.08. Overall, significance levels of 0.14 (*), 0.10 (**) and 0.05 (***) are used for evaluating the significance of the path coefficients.1058 The respective critical t-values are 1.08, 1.29 and 1.65. The explained variances for strategic decision quality and subjective company performance are 25-29% and 13-19% respectively. Again R2 for information use variables is lower, with information use from impersonal sources having one-digit explained variances. Furthermore, the Stone-Geisser criteria are larger than zero for all but one variable in one group. Use of internal, impersonal information shows a Q2 of 0% thus indicating that political behavior does not predict this variable in the group of high PEU. Finally, the group comparison of path coefficients shows only for information use from internal, impersonal sources to strategic decision quality a significant difference. Again the evaluation of hypotheses follows in section 7.5. Table 37 shows the results of the structural model estimation as well as significant differences in path coefficients.

1058

A significance level of 0.01 is omitted, because its statistical power is less than 50% which is generally considered as not reliable.

204

Results Low PEU group

High PEU group

Quality criteria

R2

Q2

R2

Q2

InfoExtImp

1%

1%

3%

2%

InfoIntImp

5%

3%

1%

0%

Infopers

14%

10%

10%

6%

DecQual

25%

18%

29%

22%

Perf

19%

13%

13%

7%

Path

Path coefficients low PEU group

PolBeh Æ InfoExtImp

-0,10*

(H-)

PolBeh Æ InfoIntImp

-0.22***

PolBeh Æ InfoPers

-0.37***

PolBeh Æ DecQual InfoExtImp Æ DecQual InfoIntImp Æ DecQual

Path coefficients high PEU group

Delta high vs. low PEU groups

-0.17***

(H-)

(H+/-)

(H-)

-0.09*

(H-)

(H+/-)

(H-)

-0.32***

(H-)

(H+/-)

-0.33***

(H-)

-0.40***

(H-)

(H-)

+0,02n.s.

(H+)

+0.05n.s.

(H+)

+0.09*

(H+)

-0.05n.s.

(H+)

InfoPers Æ DecQual

+0.20***

(H+)

+0.24**

(H+)

(H+)

DecQual Æ Perf

+0.43***

(H+)

+0.36***

(H+)

(H-)

n.s. not significant; * p<0.14; ** p<0.10; hypothesized; two-sided t-test for neutral effects

(H-) -0.14*

(H-)

*** p<0.05; one-sided t-test where direction is

H = hypothesized direction is positive (+) / negative (-) / neutral (+/-) Table 37: Structural model evaluation – moderating effects of perceived environmental uncertainty (Source: own compilation)

7.2.2 Cognitive style For evaluating the moderating effects of cognitive style the sample is again split into two sub-samples based on the moderator variable scores. Cases with a cognitive style value below the median form the group of linear thinkers (LT group), cases with a cognitive style value above the median consequently form the group of nonlinear thinkers (NLT group).1059 After the two sub-samples are formed, the structural model is estimated for each. The evaluation of the measurement model results in four item loadings not meeting the conservative threshold criterion of 0.707 while they range from 0.65 to 0.70. For the same reasons as in the PEU comparison the items are kept. Furthermore, Cronbach’s Alpha for political behavior does not meet the threshold 1059

The median of the standardized latent variable scores for PEU is -0.11 indicating that the distribution is slightly skewed to the left (linear thinkers group).

Results of moderating effects models

205

criterion while again composite reliability and AVE thresholds are met. Overall, both structural measurement models are deemed suitable for further analysis. The next step is the evaluation of similarity of measurement. As the median forms the splitting criterion, the two group sizes are nearly equally large1060 and the first criterion for comparability is thus met. Furthermore, the Coefficients of Congruence for all latent variables are are all equal to or larger than 0.99 as shown in Table 38. Seven out of nine constructs have a Coefficient of Congruence of 1.00 indicating identical measurement. This supports congruent measurement for all constructs. Overall, the similarity criteria are met and group comparisons are valid. LT vs. NLT groups InfoExtImp

1.00

InfoIntImp

1.00

InfoPersQuant

0.99

InfoPersMark

1.00

InfoPersQual

1.00

InfoPers (second-order)

1.00

PolBeh

1.00

DecQual

1.00

Perf

0.99

Table 38: Coefficients of Congruence – cognitive style group comparison (Source: own compilation)

Before comparing the two structural models, the critical significance levels need to be established because the sample sizes of each group are now half the size of the overall sample. This leads to significance levels of 0.14 (*), 0.10 (**) and 0.05 (***) to be used for evaluating the significance of the path coefficients.1061 The respective critical t-values are 1.08, 1.29 and 1.65. The explained variances for all five dependent variables show considerable difference between the two groups. While strategic decision quality is explained at 39% for the NLT group, only 15% of variance is explained for the LT group. Similarly, subjective 1060 1061

The group of LT contains 116 cases, and the group of NLT contains 114 cases. A significance level of 0.01 is omitted, because its statistical power is less than 50% which is generally considered as not reliable.

206

Results

company performance has considerable differences with R2 of 22% for the NLT group and 8% for the LT group respectively. Moreover, the difference in explained variance is relatively large for information use from personal sources. While the R2 for LT is 4%, the R2 for NLT is 21%. Finally, again the use from impersonal information sources shows low R2 and Q2 respectively. Indeed for the LT group use of internal, impersonal sources is neither explained nor predicted by political behavior, while the use of external, impersonal sources shows values of 1% only. In contrast to that, again the values of the NLT group are comparatively large. Overall, the model has a good explanatory power with respect to the main variables of interest. Finally, the comparison of path coefficients reveals a considerable number of significant differences between the two groups. Seven out of eight path coefficients are significantly different – most often at a 0.05 significance level. Only one nonsignificant path coefficient difference exists. Overall, the results indicate an important moderating effect of cognitive style on the main relationships in the research model.

Table 39 shows quality criteria, path coefficients and significance levels for both LT and NLT groups. For reasons of clarity only significant differences in path coefficients are shown.

Results of moderating effects models

207

LT group

NLT group

Quality criteria

R2

Q2

R2

Q2

InfoExtImp

1%

1%

4%

3%

InfoIntImp

0%

0%

7%

5%

InfoPers

4%

3%

21%

15%

DecQual

15%

12%

39%

30%

Perf

8%

4%

22%

15%

Path

Path coefficients LT group

Path coefficients NLT group

Delta NLT vs. LT groups

PolBeh Æ InfoExtImp

-0,11**

(H-)

-0.20***

(H-)

n.s.

(H-)

-0.27***

(H-)

-0.23***

(H-)

(H-)

-0.46***

(H-)

-0.25***

(H-)

(H-)

PolBeh Æ InfoIntImp

-0.04

PolBeh Æ InfoPers

-0.21***

PolBeh Æ DecQual

-0.20***

(H-)

-0.52***

(H-)

-0.32***

(H-)

InfoExtImp Æ DecQual

+0.15**

(H+)

-0.04n.s.

(H+)

-0.20**

(H-)

InfoIntImp Æ DecQual

+0.11**

(H+)

-0.07n.s.

(H+)

-0.19***

(H-)

InfoPers Æ DecQual

+0.13**

(H+)

+0.24***

(H+)

0.11*

(H+)

DecQual Æ Perf

+0.28***

(H+)

+0.47***

(H+)

0.19***

(H+/-)

n.s. not significant; * p<0.14; ** p<0.10; hypothesized; two-sided t-test for neutral effect

*** p<0.05; one-sided t-test where direction is

H = hypothesized direction is positive (+) / negative (-) / neutral (+/-) Table 39: Structural model evaluation – moderating effects of cognitive style (Source: own compilation)

7.2.3 Interaction of perceived environmental uncertainty and cognitive style For evaluating the interaction effects of PEU and cognitive style, four group comparisons are performed. Firstly, a comparison by PEU is made for each group of cognitive style. Secondly, a comparison by cognitive style is made for each group of PEU. For this purpose, the sample is split in two sub-samples based on the cognitive style latent variable scores as outlined above. Then these two sub-samples are further split by using the respective median values of PEU.1062 The result is four nearly equal sized sub-samples which allow for four group comparisons as depicted in Figure 19.

1062

The median value of the standardized latent variable scores of PEU in the group of LT is 0.24, in the group of NLT it is -0.19.

208

Results high LT-high PEU (N = 58)

NLT-high PEU (N = 57)

LT-low PEU (N = 58)

NLT-low PEU (N = 57)

PEU

low Linear thinkers (LT)

Nonlinear thinkers (NLT)

Cognitive style Figure 19: Overview group comparisons for testing interaction effects (Source: own compilation)

After splitting the sample in equal sub-samples the structural model is estimated for each of the four groups. Then the measurement model evaluation is performed for each group. The group LT-low PEU meets all reliability and validity criteria, even Cronbach’s Alpha of political behavior is above the threshold value of 0.7. Only group LT-high PEU does not meet all criteria. Firstly, Cronbach’s Alpha of political behavior is below the threshold value as in the overall sample. Since the other two construct reliability criteria are fulfilled, political behavior is still deemed as reliable and valid for analysis. Furthermore, the variable information use from internal, impersonal sources does not show sufficient discriminant validity on indicator level. Furthermore, the construct’s composite reliability and AVE do not meet the threshold values. Finally, both NLT groups meet the reliability and validity criteria apart from Cronbach’s Alpha for political behavior. As a consequence, reliability and validity evaluation the path coefficients to and from information use from internal, impersonal sources are omitted where the group LT-high PEU is included. Next, similarity of measurement is to be evaluated with the help of the Coefficients of Congruence. Table 50 shows the Coefficients of Congruence for the four group comparisons excluding the Cofficient of Congruence for the variable information use from internal, impersonal sources where group LT-high PEU is included. Apart from that all Coefficients of Congruence are larger than 0.98, with the majority taking values of 1.00. Overall, the measurement models are nearly equally sized and show nearly identical measurement. Thus group comparisons are valid.

Results of moderating effects models

209 Comparisons by cognitive style

Comparisons by PEU

InfoExtImp

LT-high PEU NLT-high PEU NLT-low PEU NLT-high PEU vs. NLT-low vs. LT-low vs. LT-low vs. LT-high PEU groups PEU groups PEU groups PEU groups 0.99 1.00 0.99 1.00

InfoIntImp

n.a.

1.00

1.00

n.a.

InfoPersQuant

0.98

0.97

0.98

0.98

InfoPersMark

1.00

1.00

1.00

1.00

InfoPersQual

1.00

1.00

1.00

1.00

InfoPers

1.00

1.00

1.00

0.99

PolBeh

0.99

1.00

1.00

1.00

DecQual

1.00

1.00

1.00

0.99

Perf

0.98

0.99

0.99

0.98

Table 40: Coefficients of Congruence – interaction effects group comparisons (Source: own compilation)

Before comparing the structural models, the critical significance levels need to be established, because the sample sizes of each group are now approximately quarter the size of the overall sample. Again effect size is assumed at 0.2 and alpha-beta-ratio at 1:1. Given these parameter values and a sample size of N = 58 and M = 57 respectively the critical significance level for a one-sided t-test is determined with a critical alpha of 0.22 and a statistical power of 77%. The respective critical t-value is 0.77. Overall, significance levels of 0.22 (*) and 0.10 (**) are used for evaluating the significance of the path coefficients.1063 The respective critical t-values are 0.77 and 1.30. The results of structural model estimates and significance testing are shown in Table 41. For reasons of clarity only significant path coefficient differences are shown. A discussion of these results follows thereafter.

1063

Significance levels of 0.05 and 0.01 are omitted, because their statistical power is less than 50% which is generally considered as not reliable.

Table 41: Structural model evaluation – interaction effects (Source: own compilation)

5% 12% 8%

7% 16% 14%

3% 31% 10%

n.a.

2

R 3%

2% 24% 0%

n.a.

2

Q 2%

19% 32% 17%

8%

2

Q 2%

21% 32% 24%

5%

2

R 6%

12% 24% 13%

2%

2

Q 4%

Group comparisons

Delta by PEU Delta by cognitive style NLT-low PEU NLT-high PEU LT-high NLT-high NLT-low NLT-high group group PEU vs. LT- PEU vs. PEU vs. LT- PEU vs. LT-0.19** -0.24** -0.34** -0.23** n.a. -0.27** n.a. -0.49** -0.46** +0.11* -0.22* -0.30** -0.53** -0.49** -0.31** -0.44** n.s. n.s. -0.17* -0.16* -0.01 -0.07 n.s. -0.12* -0.30** -0.06 +0.30** +0.19** +0.18* +0.49** +0.49** +0.12* +0.18*

24% 46% 24%

11%

2

R 4%

Structural model evaluations LT-high PEU NLT-low PEU NLT-high PEU group group group

Path coefficients LT-low PEU LT-high PEU group group -0.11* -0.18** n.s. n.a. -0.07 -0.27** -0.16* -0.09* -0.40** +0.16* +0.09* +0.19* n.a. +0.12* +0.17* +0.37** +0.31**

0%

0%

2

Q 0%

R 1%

2

LT-low PEU group

n.s. not significant; p<0.22; ** p<0.10; one-sided t-test where direction is hypothesized; two-sided t-test for neutral effects

*

PolBeh Æ InfoExtImp PolBeh Æ InfoIntImp PolBeh Æ InfoPers PolBeh Æ DecQual InfoExtImp Æ DecQual InfoIntImp Æ DecQual InfoPers Æ DecQual DecQual Æ Perf

Use of external, impersonal information Use of internal, impersonal information Use of personal information Strategic decision quality Subjective company performance Path

Quality criteria

210 Results

Results of moderating effects models

211

The R2 of strategic decision quality and subjective company performance are 16% and 14% respectively in the LT-low PEU group. Furthermore, the explained variance of information use from personal sources is 7%. In contrast to that, information use from impersonal sources is almost not explained by political behavior. The Q2 values are positive except for information use from internal, impersonal sources, which underpins that political behavior has no effect on information use from internal, impersonal sources in this group. The LT-high PEU group shows a high R2 for strategic decision quality (31%), while subjective company performance is much lower (10%). Furthermore, the explained variance of information use variables is, only 3%. Q2 are positive with the exception of subjective company performance. The NLT-low PEU group has considerably larger explained variances than the LT-low PEU group. R2 for strategic decision quality and subjective company performance are 46% and 24% respectively. Furthermore, political behavior explains more of the variance in any information use variables than in the LT-low PEU group. Most notably, information use from personal sources has an R2 of 24% and information use from internal, impersonal sources has an R2 of 11%. Finally, all Q2 values are positive in this group. The NLT-high PEU group has again larger explained variances than the LT-high PEU group. Strategic decision quality is explained at 32% and subjective company performance at 24%. And again political behavior explains a remarkable share of variance of the information use variables. Most notably the R2 of information use from personal sources is 21%. Finally, all Q2 values are positive underpinning the predictive capacity of the research model for this group. The differences in path coefficients are more important for the comparison by cognitive style than by PEU. Two path coefficients are significantly moderated by PEU. In contrast to that, the group comparisons by cognitive style show considerably more significant path coefficient differences. Ten out of 16 path coefficients significantly differ in the two group comparisons performed. Furthermore, seven of these differences occur under conditions of low PEU, while only three significant

212

Results

differences occur under conditions of high PEU. A more detailed discussion of these patterns follows in the subsequent chapter. 7.3 Triangulation with financial performance variables So far company performance has been measured by using subjective company performance. This measure recurs on the respondents’ subjective assessments of how their companies perform relative against their competitors with respect to seven indicators such as general economic development or growth of the company. 1064 In order to increase the validity of the performance measurement, the conceptual model is amended by financial performance measures in this sub-section.1065 For that purpose, two financial performance indicators are used. The first one is change in sales revenues. The second one is change in return on sales. The median time period elapsed from when the decision was made and the survey took place was 1.2 years. Therefore, the perceived year-over-year changes in sales revenues and return on sales are the corresponding items.1066 These data were collected through the questionnaire survey and are thus available for the whole sample. Before including these variables in the research model the respondents’ assessments of financial performance are triangulated with secondary data from those companies for which objective data are available.1067 Since in Germany, small and medium-sized enterprises rarely publish their annual reports, objective data could only be obtained for a sub-sample of the 230 companies.1068 More specifically, data on year-over-year change in sales revenues was obtained for 23 companies and data on year-over-year change in return on sales was obtained for 24 companies. These objective data were then correlated with the financial performance data reported by the respondents. The correlation for change in sales revenues is 0.62 and the correlation for change in return on sales is 0.59.1069 These results indicate the respondents’ financial performance evaluations are robust and externally valid.

1064

Cf. sub-section 5.2.2.6 for the operationalization of company performance. Cf. Lehmann, D. R. (2004), pp. 73-75. 1066 Financial performance items on the five-year development were also included in the questionnaire. However, this time frame does not correspond to the empirical data base and the items are thus omitted for further analysis. 1067 Cf. Homburg, C./Schilke, O. (2009), p. 178. 1068 For collecting these data the official filings database www.ebundesanzeiger.de was used and accessed on February, 8th 2010. 1069 Spearman correlations are used. Both correlations are significant at a level of 0.01. 1065

Triangulation with financial performance variables

213

Next, the PLS parameter estimates of the direct effects model are performed twice including the two objective performance variables. The results of these parameter estimates are shown in the following Table 42. Model 1: ObjPerf = Year-over-year change in sales revenues

Model 2: ObjPerf = Year-over-year change in return on sales

Quality criteria

R2

Q2

R2

Q2

InfoExtImp

2%

1%

2%

1%

InfoIntImp

3%

2%

3%

2%

InfoPers

12%

8%

12%

8%

DecQual

26%

20%

26%

20%

Perf

15%

9%

15%

9%

ObjPerf

15%

14%

27%

24%

Path

Path coefficients

Path coefficients

PolBeh Æ InfoExtImp

-0.14**

(H-)

-0.14**

(H-)

PolBeh Æ InfoIntImp

-0.16**

(H-)

-0.16**

(H-)

PolBeh Æ InfoPers

-0.34***

(H-)

-0.34***

(H-)

PolBeh Æ DecQual

-0.36***

(H-)

-0.36***

(H-)

InfoExtImp Æ DecQual

+0.04

n.s.

(H+)

n.s.

InfoIntImp Æ DecQual

+0.03n.s.

+0.04

(H+)

(H+)

+0.03n.s.

(H+)

InfoPers Æ DecQual DecQual Æ Perf

+0.23***

(H+)

+0.23***

(H+)

+0.39***

(H+)

+0.39***

Perf Æ ObjPerf

(H+)

+0.39***

(H+)

+0.52***

(H+)

n.s. not significant; * p < 0.07; ** p < 0.05; *** p < 0.01; one-sided t-test H = hypothesized direction is positive (+) / negative (-) Table 42: Structural model evaluation – direct effects including objective performance

(Source: own compilation)

These results show that both objective company performance measures are explained by subjective company performance. The R2 of change in return on sales is 27% and the R2 of change in sales revenues is 15%. The respective path coefficients from subjective company performance to objective performance are 0.39 for change in sales revenues and 0.52 for change in return on sales.

214

Results

A potential explanation for the differences in explained variances and path coefficients is the fact, that the sample covers a broad range of industries. These industries very likely differ in their market maturity and consequently in their market growth. Some companies might indicate a high relative sales growth performance against its competitors, while the objective sales growth may be very small. In contrast to that, change in return on sales may be a more generalizable measure for financial performance, because profitability improvements are more likely achievable in high and low growth industries. Therefore, change in return on sales appears to be a more generalizable measure of financial performance. To conclude, the results of the inclusion of financial performance measures and the triangulation with objective performance data support a robust validity of the basic research model and the empirical data. 7.4 Test for control variable effects The statistical method for testing for control variables differs by the variables’ characteristics. For discretionary variables the sample is divided into two groups. Then, indicators are tested for differences. If a considerable proportion of indicators are significantly different, the structural model is separately estimated for the two groups and a group comparison is performed.1070 This study controls for three discretionary variables, namely industry sector, type of decision and managerial function. The indicator mean comparisons are performed with a Mann-Whitney-U test at a 0.05 significance level. For industry sector (manufacturing vs. services) only three indicators are significantly different (ca. 5%). Consequently no group comparison of structural models is performed. For type of decision (competitive positioning vs. other types) six indicators (ca. 10%) exhibit significant mean differences. As this proportion is also comparatively small, no further group comparison is performed. For managerial function (general management vs. specialized function) again only three indicators (ca. 5%) show significant mean differences. Overall, the test for discretionary control variables showed only few differences on indicator level. Therefore, the impact of discretionary control variables on the results is deemed negligible.

1070

Cf. Chowdhury, S./Miles, G. (2006), pp. 123-125.

Test for control variable effects

215

For ordinal and metric variables this study follows the procedure applied by Huigang, Saraf, Qing and Yajiong (2007). According to this procedure control variables are included as independent variables in a second structural model and related to the dependent variable of interest. The path coefficients of the direct effects model are compared with respect to their direction and significance excluding and including control variables in the model.1071 If this comparison reveals a change of direction or considerable decreases in significances of the relationships the control variables are deemed to influence the results of the main effects model. For this study, the control variables are all related to strategic decision quality because it is the main dependent variable. The following table shows the results of the two structural model estimates. A discussion of these results follows thereafter. Table 43 on the next page indicates only two of twelve ordinal control variables have a significant effect on the dependent variable strategic decision quality. Firstly, organizational age has a significantly negative effect on strategic decision quality. This can be explained by the phenomenon of organizational inertia which partly attributed to company age.1072 Secondly, the motivation of the individual decision maker has a significantly positive effect on strategic decision quality. More importantly, a comparison of the main effects path coefficients shows, that none of the path coefficients changes its direction in the control model. With one exception also the significance levels remain the same. The exception is the relationship between information use from personal sources and strategic decision quality. However, the significance level only slightly decreases from 0.01 to 0.05. As a consequence the test for ordinal control variables does not severely question the results of the main effects model. To conclude, the overall tests for nominal and ordinal control variables do not severely question the results of the main effects model. Firstly, only few indicator differences can be identified for the three nominal control variables. Secondly, only two ordinal control variables have a significant effect on the dependent variable, while the direct effect relationships basically remain at the same level. Thus the above presented research results are deemed robust with respect to the effects of the control variables just examined. 1071 1072

Cf. Huigang, L./Saraf, N./Qing, H. et al. (2007), p. 72. Cf. Finkelstein, S. et al. (2009), p. 31.

216

Results

Model excl. controls

Model incl. controls

Hypothesized relationships

Path coefficients

Path coefficients

PolBeh Æ InfoExtImp

-0.14**

-0.14**

PolBeh Æ InfoIntImp

-0.16***

-0.16***

PolBeh Æ InfoPers

-0.34***

-0.34***

PolBeh Æ DecQual

-0.36***

-0.33***

InfoExtImp Æ DecQual

0.04

n.s.

0.00n.s.

InfoIntImp Æ DecQual

0.03n.s.

0.03n.s.

InfoPers Æ DecQual

0.23***

0.15**

DecQual Æ Perf

0.39***

0.39***

Control variables organizational level Æ DecQual Strategic decision duration Top management team size Ownership Number of employees Company age

Path coefficients 0.01n.s. -0.04n.s. 0.07n.s. -0.03n.s. -0.22***

Control variables individual level Path coefficients Æ DecQual Age of decision maker 0.05n.s. Company tenure -0.06n.s. Depth of functional experience -0.06n.s. Diversity of functional experience 0.02n.s. Industry experience 0.08n.s. Motivation 0.23** Highest education level 0.02n.s. n.s. not significant; * p<0.07; ** p<0.05; *** p<0.01; one-sided t-test for main effects; two-sided ttest for control variable effects Quality criteria strategic decision quality 2

R Q2

Main effects model

Model incl. controls

26% 20%

36% 26%

Table 43: Evaluation of ordinal control variable effects (Source: own compilation)

Evaluation of hypotheses

217

7.5 Evaluation of hypotheses Overall, the results are suitable for evaluating the hypotheses formulated in this study. Direction and significances of path coefficients are consulted for evaluating hypotheses about direct effects. Some hypotheses are evaluated by using one path coefficient. Some other hypotheses are evaluated by using two or three path coefficients. In the latter case a hypothesis is supported if all path coefficients are in the hypothesized direction and significant. It is partially supported if a fraction of path coefficients meets these two criteria. Similarly, for evaluating hypothesis about moderating effects a hypothesis is supported if all path coefficients are significantly different and the difference is in the expected direction. In case a fraction of path coefficients are significantly different the hypothesis receives partial support. Four of five direct effect relationships are supported by the empirical data in the overall sample.1073 The hypothesis on the positive effect of information use from impersonal sources on strategic decision quality is not supported by the overall sample data. Table 44 states the hypotheses on direct effects and provides their evaluation. Hypothesis

Overall

Comments

evaluation Hypothesis 1.1a: Information use from impersonal

Not supported

sources has a positive effect on strategic decision

use from internal and external impersonal sources are non-significant.

quality. Hypothesis 1.1b: Information use from personal

Both path coefficients for information

Supported

sources has a positive effect on strategic decision quality Hypothesis 1.2: Political behavior has a negative

Supported

effect on information use from personal and impersonal sources. Hypothesis 1.3: Political behavior has a negative

All three path coefficients from political behavior to information use variables are significantly negative.

Supported

effect on strategic decision quality. Hypothesis 1.4: Strategic decision quality has a

Supported

positive effect on organizational performance. Table 44: Evaluation of hypotheses on direct effects (Source: own compilation)

One of five hypotheses on moderating effects of PEU is supported and another is partially supported by the empirical data.1074 Interestingly, the hypothesis which receives support states a neutral effect of PEU. This means PEU moderates only one of 1073 1074

Cf. Figure 18 on p. 199. Cf. Table 37 on p. 204 for the results of the PEU group comparison.

218

Results

eight relationships in the research model. Table 45 provides the evaluation of the hypotheses on moderating effects of PEU. These findings are surprising from a contingency perspective because one would expect more significant moderating effects of PEU. As will be discussed in more detail later, the findings of this study rather support an idiosyncratic than a contingency perspective on SDM. Hypothesis

Overall evaluation

Comments

Hypothesis 2.1a: Information use from impersonal

Partially

Only information use from internal,

sources has a positive and stronger effect on strategic decision quality for low than high PEU.

supported

impersonal sources has a significantly positive and stronger effect.

Hypothesis 2.1b: Information use from personal sources has a positive and stronger effect on

Not supported

strategic decision quality for high than low PEU. Hypothesis 2.2: There is no significant difference between low and high PEU concerning the

Supported

negative effect of political behavior on information use from personal and impersonal sources. Hypothesis 2.3: Political behavior has a negative

A neutral effect is hypothesized and confirmed by non-significant path coefficient differences.

Not supported

and stronger effect on strategic decision quality for high than low PEU. Hypothesis 2.4: Strategic decision quality has a

Not supported

positive, but weaker effect on organizational performance for high than low PEU. Table 45: Evaluation of hypotheses on moderating effects of perceived environmental uncertainty (Source: own compilation)

Three of five hypotheses on the moderating effects of cognitive style are supported while one more is partially supported by the empirical data. Only one hypothesis is not supported by the empirical data. However, its support is surprising because a neutral effect of cognitive style has been hypothesized, while the results show a moderating effect for this relationship. Overall, seven out of eight relationships are moderated by cognitive style indicating cognitive style has a relatively important moderating role in the research model.1075 Table 46 states the moderating effects hypotheses for cognitive style and provides their evaluation.

1075

Cf. Table 39 on p. 207 for the results of the cognitive style group comparison.

Evaluation of hypotheses

219

Hypothesis

Overall

Comments

evaluation Hypothesis 3.1a: For linear thinkers information use

Supported

from impersonal sources has a positive and stronger effect on strategic decision quality than for nonlinear

Both

path

coefficients

significant differences expected direction.

show in

the

thinkers. Hypothesis 3.1b: For nonlinear thinkers information use from personal sources has a positive and stronger

Supported

effect on strategic decision quality than for linear thinkers. Hypothesis 3.2: For nonlinear thinkers political

Partially

Two of three path coefficients are

behavior has a negative and stronger effect on the use of information than for linear thinkers.

supported

significantly different.

Hypothesis 3.3: For nonlinear thinkers political

Supported

behavior has a negative and stronger effect on strategic decision quality than for linear thinkers. Hypothesis 3.4: There is no significant difference

Not supported

between linear and nonlinear thinkers concerning the positive effect of strategic decision quality on company

A neutral effect is hypothesized, while path coefficients significantly different.

are

performance. Table 46: Evaluation of hypotheses on moderating effects of cognitive style (Source: own compilation)

Finally, the interaction of cognitive style and PEU has been hypothesized to be more prevalent in low PEU than in high PEU environments. This is partially supported (see Table 47). Seven of eight relationships are moderated by cognitive style in low PEU environments. In constrast to that, only three of eight relationships are moderated in high PEU environments.1076 However, one variable in the LT-high PEU group could not be compared due to unreliable measurement. Therefore, the interpretation of these results should be seen with some caution. Hypothesis

Overall

Comments

evaluation Hypothesis 4: Moderating effects of

Partially

Seven of eight relationships are moderated by

cognitive style are more prevalent in low

supported

cognitive style in the low PEU groups. In

than in high PEU environments.

contrast to that, only three of eight relationships are moderated in the high PEU groups.

Table 47: Evaluation of hypothesis on interaction effects (Source: own compilation)

1076

Cf. Table 41 on p. 210 for the results of the four group comparisons.

220

Discussion and implications

8 Discussion and implications After the results of the empirical data analysis were presented and the hypotheses were evaluated in the preceding chapter, a discussion of results and their implications follows. Section 8.1 discusses the descriptive findings with respect to research questions and additional insights that can be drawn from this study. In section 8.2 the theoretical implications are discussed. Finally, section 8.3 provides recommendations for managerial practice. 8.1 Discussion of results At first, the findings of this study are discussed with respect to the four main research questions concerning the effectiveness of information use. Then the discussion turns to additional findings of this study. 8.1.1 Effectiveness of information use Research question 1: Is information from different sources used with different effectiveness for SDM? In the overall sample only information use from personal sources has a positive effect on strategic decision quality. At first sight, this finding reconciles with assertions made by other studies which claim that a manager’s job is marked by personal interaction and information use from personal sources is the most effective means for making decisions.1077 However, as discussed later information use from impersonal sources may also allow for effective decision making under specific environmental conditions and for specific types of decision makers. There are some explanations why information use from personal sources is generally an effective way for SDM. It has been shown that qualitative information is used early in the SDM process, for diagnosing the problem and generating ideas, and lately in the SDM process, for making a final choice.1078 In contrast to that quantitative, objective information is rather used in between for evaluating alternatives in a more analytical way. Furthermore, problem diagnosis has been shown to be a particular important activity for effective SDM.1079 The findings of this study support the assertion that 1077 1078 1079

Cf. e.g. Cramme, C. et al. (2009), p. 53. Cf. Frishammar, J. (2003), p. 321. Cf. Lipshitz, R./Bar-Ilan, O. (1996), p. 56.

W. Gänswein, Effectiveness of Information Use for Strategic Decision Making, DOI 10.1007/978-3-8349-6849-4_8, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

Discussion of results

221

personal sources are particularly capable to provide the necessary qualitative information for this activity. There are three potential reasons for this. Firstly, personal sources provide specific internal information which is not covered by internal reports.1080 Secondly, they provide external information which may not be obtainable from impersonal sources. For example, business associates are used for obtaining information about competitors in the market environment.1081 Thirdly, personal sources are information rich which means they allow for clarification of issues and feedback.1082 Nonetheless, it is surprising that information use from impersonal sources has no significant positive effect on strategic decision quality in the overall sample, because it is a matter of fact, that impersonal sources are used for SDM.1083 They are used for substantiating information and issues raised from personal sources.1084 Therefore, a positive effect of information use from impersonal sources has been hypothesized. However, a closer examination of the empirical results shows that the qualitative and contextual information items are eliminated from information use from internal impersonal sources. Also the qualitative information item is eliminated from information use from external impersonal sources. This suggests that impersonal sources are not capable of providing qualitative information while on the other hand this kind of information is particularly important for diagnosing strategic decision problems as outlined above. This characteristic may simply outweigh the beneficial effects of the other kinds of information provided by impersonal sources in the overall sample. However, these findings are not generalizable, because PEU and cognitive style have moderating effects on the effectiveness of information use from impersonal sources as discussed in the following. Research question 2: Does environmental uncertainty moderate the effectiveness of information use from different sources for SDM? According to the empirical data, PEU only moderates the relationship between information use from internal impersonal sources and strategic decision quality (H2.1a is partially supported). Under conditions of low uncertainty there is a significantly 1080

Cf. Bruns, J. W. J./McKinnon, S. M. (1993), p. 104. Cf. Auster, E./Choo, C. W. (1994), p. 617. 1082 Cf. Daft, R. L./Lengel, R. H. (1986), p. 560. 1083 Cf. Auster, E./Choo, C. W. (1994), p. 615; Frishammar, J. (2003), p. 321. 1084 Cf. Frishammar, J. (2003), p. 321; Heidmann, M. et al. (2008), p. 254. 1081

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positive effect, while under conditions of high PEU there is no significant effect. This result underpins the assertion that internal impersonal sources can also be used effectively for SDM. However, the findings suggest this applies to low PEU environments only. An explanation for this is, that internal impersonal sources are particularly useful when cause-effect relationships are understood.1085 Provided the understanding of a low PEU environment the data from internal impersonal sources resembles the main cause-effect relationships over a longer period of time. This is beneficial for tracking changes in decision relevant parameters which can be used for diagnosing a problem.1086 For example the analysis of customer contribution margins or profit and cost margins may quickly indicate where in the customer portfolio or a company’s value chain the problem is located. Furthermore, low PEU implies that decision relevant factors will less likely change in unpredicted ways in the future. Consequently internal reports or special studies facilitate a relatively reliable forwardlooking evaluation of strategic alternatives and their consequences. In contrast to that, external impersonal sources are neither effective under conditions of high nor of low PEU. Although some empirical findings suggest that external impersonal sources are used for decision making their main role is to keep abreast of environmental developments and indirect contextual factors.1087 This limited role of information from external impersonal sources might explain why under both environmental conditions it does not have any significant effect on strategic decision quality. However, again we will see later, that for specific decision makers even information use from external impersonal sources is effective. Finally, PEU is hypothesized to moderate the effectiveness from information use from personal sources (H2.1b). Although the difference in path coefficients between the two PEU groups is in the hypothesized direction it is not significant. Hence, the hypothesis is not supported. One potential reason is that strategic decisions are under any circumstances ill-structured, novel and complex and the advantages of information richness of personal sources do not result in differential effectiveness under differing environmental conditions. Furthermore, information richness of personal sources is not a fixed characteristic but depends on the interaction of the communication participants within a given context and the intended information richness.1088 Given the fact that 1085

Cf. Daft, R. L./Lengel, R. H. (1986), p. 562. Cf. Simons, R. (1995), p. 200. 1087 Cf. Auster, E./Choo, C. W. (1994), p. 615. 1088 Cf. Carlson, J. R./Zmud, R. (1994), pp. 281-282. 1086

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decision makers can use a repertoire of communication media to tap personal information sources they tailor the richness of information use to contextual factors such as decision characteristics.1089 Therefore, strategic decision makers may employ less information rich communication when they are faced with low PEU and information richer communication when they are faced with high PEU. This practically means under conditions of low PEU quick chats or phone calls with personal sources are prevalent, whereas under conditions of high PEU more elaborate meetings might be chosen by a decision maker. Research question 3: Does cognitive style moderate the effectiveness of information use from different sources for SDM? For linear thinkers information use from both internal and external impersonal sources has a significantly stronger positive effect on strategic decision quality than for nonlinear thinkers (H3.1a is supported). For nonlinear thinkers it is not only less effective but it has no significant effects on strategic decision quality. The latter is surprising because this basically means that a remarkable share of key decision makers does not effectively use information from impersonal sources. Obviously linear thinkers’ preference for facts and data allows them for capitalizing on the specific characteristics of impersonal information sources, while nonlinear thinkers solely use information from personal sources effectively for SDM. In addition to that, cognitive style has a moderating effect on the effectiveness of information use from personal sources (H3.1b is supported). Information use from personal sources has a significantly stronger effect on strategic decision quality for nonlinear than for linear thinkers. Overall, these findings show that cognitive style is an important factor influencing the effectiveness of information use from different information sources. Finally, the moderating effect of cognitive style is underpinned by considerable differences in explained variance of strategic decision quality. For nonlinear thinkers the R2 for strategic decision quality is 39% while for linear thinkers it is 15%. A potential explanation for this considerable difference is that information use is only one aspect of decision making. Another aspect is how decision makers think about the information received. Linear thinkers prefer to use data and facts for making a decision 1089

Cf. Watson-Manheim, M. B./Bélanger, F. (2007), pp. 268-269.

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Discussion and implications

and to use conscious logic and analytical thinking to form new knowledge and understanding.1090 In contrast to that nonlinear thinkers prefer internal feelings, sensations and hunches as information inputs and for attaining understanding. These differences in turn may result in differences to what extent a decision maker relies on the original information received for making a decision. A linear thinker uses more logical thinking and thus more likely transforms the information provided, while a nonlinear thinker may use the information provided in a more direct way. Furthermore, linear thinkers are more independent from political behavior than nonlinear thinkers because of their own reasoning as indicated by the results of this study. In contrast to that, nonlinear thinkers engage less in logical reasoning and their decision making may be more closely tied to the original information provided. As a result of this they are more directly influenced by political tactics, because they rely on the information provided. Given these considerations, nonlinear thinkers’ decision making is more comprehensively described by the independent variables in this research model. This explains their larger explained variance of strategic decision quality compared to linear thinkers. Research question 4: How do environmental uncertainty and cognitive style interact with respect to their effects on information use in SDM? Overall, the findings tentatively support that the moderating effects of cognitive style are more prevalent under conditions of low than high PEU. In low PEU environments all three relationships from information use to strategic decision quality are moderated by cognitive style. On the one hand, for linear thinkers the use of information from impersonal sources is more effective than for nonlinear thinkers. On the other hand, for nonlinear thinkers the use of information from personal sources is more effective than for linear thinkers.1091 In contrast to that, the moderating effects diminish in high PEU environments in two ways. First of all, both groups of decision makers use information from personal sources equally effective. Secondly, the effectiveness of information use from internal impersonal sources could not be evaluated because of a lack of measurement reliability in the LT-high PEU group. This indicates that internal impersonal sources might not be suitable for being used in high PEU environments anymore.

1090 1091

Cf. Vance, C. M. et al. (2007), p. 170. Cf. Table 41 on p. 210.

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An explanation for these effects is that decision situations may be more familiar in low PEU environments. This familiarity would not require decision makers to switch into a conscious mode of information processing and top decision makers use a rather automatic mode of information processing under conditions of low PEU.1092 Consequently, their cognitive style governs the SDM process under conditions of low PEU, while under conditions of high PEU the moderating effects of cognitive style are less relevant. In contrast to that, decision makers switch to a conscious mode of information processing which is most likely to be linked with information use from personal sources. As a consequence, there is no moderating effect on the effectiveness of personal sources in high PEU environments anymore – linear and nonlinear thinkers use information from personal sources equally effective. Overall, these results lead to three main conclusions concerning the effectiveness of information use: a) Information use from personal sources generally improves the effectiveness of SDM. b) In contrast to that the effectiveness of information use from impersonal sources is contingent upon cognitive style. Only for linear thinkers information use from impersonal sources is effective. For nonlinear thinkers it may even have negative effects. c) Finally, the effectiveness of information use depends on the interaction of environmental conditions and individual cognition. Decision makers tentatively switch from automatic modes of information use in low PEU environments to conscious modes of information use in high PEU environments. 8.1.2 Effects of political behavior on information use In addition to that, the research study provides several findings on the effects of political behavior on information use in SDM. First of all, the findings show that political behavior has a significantly negative effect on information use from all kinds of information sources (H1.2 is supported). However, different magnitudes of these effects can be identified when looking at the explained variances of the three information use variables. Information use from external and internal impersonal sources has explained variances of 2% and 3% 1092

Cf. Louis, M. R./Sutton, R. I. (1991), pp. 56-58; Dutton, J. E. (1993), p. 343; Reger, R. K./Palmer, T. B. (1996), p. 26.

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respectively. This suggests the negative impact of political behavior on information use from impersonal sources is negligible. In contrast to that the explained variance of information use from personal sources is 12% and thus not negligible. This underpins the fact that political behavior is ultimately a social phenomenon and results in restricted information flows between decision participants.1093 A moderating effect of PEU on the political behavior – information use relationships could not be identified, and a neutral effect (H2.2) is supported by the empirical data. However, a moderating effect of cognitive style on the political behavior – information use relationships receives partial support by the data (H3.2 is partially supported). Cognitive style significantly moderates the relationships between political behavior and information use from internal impersonal sources and personal sources. Furthermore, the explained variances of information use variables are considerably larger for nonlinear thinkers than for linear thinkers. For nonlinear thinkers political behavior explains 7% of variation in information use from internal impersonal sources and 21% of variation in information use from personal sources as opposed to 0% and 4% respectively for linear thinkers. These results show that nonlinear thinkers are more sensitive to political behavior than linear thinkers, which is directly reflected in their use of information. Moreover, this is particularly detrimental for them, because they use only information from personal sources effectively as discussed beforehand. Overall these results lead to two main conclusions concerning the effects of political behavior on information use: a) Political behavior hampers information use for SDM on the level of the individual decision maker. The effect is particular relevant for the information exchange between personal information sources and decision maker. b) Political behavior has a significantly stronger negative effect on information use for nonlinear thinkers than for linear thinkers. Nonlinear thinkers strongly withdraw from information use from personal sources when political behavior is present. 8.1.3 Effects of political behavior on strategic decision quality As hypothesized political behavior has a negative effect on strategic decision quality (H1.3 is supported). The main reasons for this effect are goal conflicts imposing 1093

Cf. Pettigrew, A. M. (1973); Dean, J. W./Sharfman, M. P. (1996), p. 375.

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additional constraints to SDM, disruptions in comprehensively generating and evaluating strategic alternatives and distortions in information flows within the organization. The last reason furthermore directly impacts a key decision maker’s information use as discussed beforehand. Furthermore, a moderating effect of PEU is not supported by the empirical data of this study (H2.3 is not supported). The hypothesis builds on the argument that political behavior requires attention of top decision makers. Furthermore, this was argued to be particularly detrimental in high PEU environments, because of the increased need for information processing. However, this effect may not be as severe as expected because SDM occurs besides a number of other activities a top decision maker engages in. Thus, a top decision maker may equally deal with political behavior and information use for SDM, while other activities such as dealing with operational issues in the organization receive less attention. A second argument for deriving the hypothesis builds on the increased time requirements for making a strategic decision which is particularly detrimental under conditions of high PEU. However, this effect may not be as important as expected for the present sample because of two reasons. Firstly, the number of key managers and decision-participants in medium-sized companies is not large. Therefore, dealing with political behavior may not require so much time to have a particularly detrimental effect under conditions of high PEU. Secondly, the urgency for action under high PEU conditions may only hold for particular industries, where competition is based on innovations that enter a market relatively fast such as computer technology or biotechnology industries.1094 In contrast to that, other industries may exhibit high PEU, e.g. where competition is highly fragmented,1095 but where urgency for action is not as important, because competition is based less on innovation. As this study covers a broad sample of industries, the variation among such characteristics may be large within the group of high PEU companies. This in turn might alleviate the importance of urgency for action. Finally, the effect of political behavior on strategic decision quality is moderated by cognitive style. The effect is significantly more negative for nonlinear thinkers than for linear thinkers (H3.3 is supported). The reasons can be summarized as nonlinear thinkers rely merely on information use from personal sources and are thus more susceptible to self-interested behaviors. This argument is supported by the following 1094 1095

Cf. Eisenhardt, K. M. (1989), p. 546; Judge, W. Q./Miller, A. (1991), p. 459. Cf. Forbes, D. P. (2007), p. 371.

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findings of this study. In low PEU environments linear thinkers are less susceptible to political behavior than in high PEU environments.1096 A potential explanation is that linear thinkers can effectively use information from impersonal sources, which is seemingly neutral and objective. This information use from impersonal sources appears to safeguard linear thinkers against political behavior in low PEU environments. Their use of information from impersonal sources allows for verifying information provided by personal sources. However, these beneficial effects of information use from impersonal sources diminish in high PEU environments. Finally, nonlinear thinkers rely on use of information from personal sources which implies they use misleading information once political behavior is present.1097 The preceding discussion leads to two main conclusions a) Political behavior generally has a negative effect on strategic decision quality. b) The negative effect is stronger for nonlinear thinkers than for linear thinkers, because they rely much more on information use from personal sources than linear thinkers. Furthermore, nonlinear thinkers cannot capitalize on the benefits from using impersonal sources for safeguarding decision making against political behavior in low PEU environments. 8.1.4 Effects of strategic decision quality on organizational performance

The results show a positive effect of strategic decision quality on organizational performance (H1.4 is supported). Furthermore, the explained variance of organizational performance is 15% and lower as for strategic decision quality (26% explained variance) which can be explained by other influential factors on organizational performance than SDM.1098 Furthermore, a moderating effect of PEU on the strategic decision quality – organizational performance relationship was empirically not supported by the data of this study (H2.4 is not supported). The hypothesis mainly builds on the argument that high PEU results in a larger number of decision relevant factors and an increased likelihood of overlooking important factors. This argument basically means that top 1096

Cf. Table 41 on p. 210. For linear thinkers, the negative effect of political behavior on strategic decision quality is significantly weaker in low than in high PEU environments. A similar conclusion reach Walter, J. et al. (2008), p. 550, whose results show a moderating effect of political behavior on the information use – decision effectiveness relationship. However, this study goes beyond this by showing that the effect is significantly different between decision makers depending on their individual cognitive style. 1098 Cf. Dean, J. W./Sharfman, M. P. (1996), p. 369; Forbes, D. P. (2007), pp. 363 and 365 1097

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decision makers have not understood the most important factors of their business environment. Given the empirical results, this is apparently not a reason for a moderating effect. Finally, cognitive style moderates the relationship between strategic decision quality and organizational performance. The path coefficient is significantly more positive for nonlinear thinkers than for linear thinkers and the explained variance of organizational performance is 22% for nonlinear thinkers as opposed to 8% for linear thinkers. This is surprising, because there is no obvious reason why decisions of similar quality should have different effects on organizational performance. A potential explanation is that SDM processes do not only have an effect on decision outcomes but may also influence internal organizational processes and psychological states that pave the way for strategic decision implementation.1099 Based on this assertion, the way how nonlinear thinkers approach SDM may have indirect effects on decision implementation success for the following reasons. Empirical findings show that nonlinear thinkers prefer decentralized authority in goal setting in SDM processes, while linear thinkers prefer rather centralized authority for goal setting in SDM.1100 This implies, nonlinear thinkers do not only rely more on personal information sources than linear thinkers, but also the interaction during SDM is larger for them.1101 In turn, an increase in interaction has positive effects on the implementation success of strategic decisions. One reason for that is increased consistency between the overarching strategic decision and the subsequent implementation decisions made by other managers in the organization.1102 In brief, nonlinear thinkers have a more open SDM approach which provides for more consistent and successful implementation. The preceding discussion leads to two main conclusions a) Strategic decision quality has a positive effect on organizational performance. b) Unexpectedly, the positive effect is stronger for nonlinear thinkers than for linear thinkers. This indicates rather indirect effects of SDM on organizational performance. One explanation is that a more interactive mode of SDM employed by nonlinear thinkers provides for superior implementation success once a decision is made. 1099

Cf. Forbes, D. P. (2007), p. 366. Cf. Jennings, D./Disney, J. J. (2006), p. 607. 1101 Cf. Naranjo-Gil, D./Hartmann, F. (2006), p. 46. Interaction is defined as the degree to which a SDM process is open to negotiation of goals, encouragement of new goals and strategic priorities. 1102 Cf. Naranjo-Gil, D./Hartmann, F. (2006), p. 37; Bourgeois Iii, L. J./Eisenhardt, K. M. (1988), p. 830. 1100

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8.2 Implications for research 8.2.1 Theoretical implications Beyond answering the research questions, the findings of this study also provide some important contributions to the literature. At first, the objective of this research study was to investigate information use for SDM in a comprehensive way. For that purpose this study drew on the Upper Echelon and Strategic Sensemaking Views, which provides an integration of behavioral and interpretive perspectives on SDM. In addition to that, it developed these theories further by including environmental context, managerial actions and cognitions into a comprehensive model of information use for SDM. By doing so, this study followed recent calls of SDM researchers to move from a mere description of SDM attributes to conceptualizations that account for the interactions between environmental factors, as well as organizational and individual level processes.1103 The specific conceptualization of this study is certainly one of possibly many ways for accomplishing this. Nonetheless, two overarching implications from the integrative approach of this study can be identified: 1) Behavioral SDM theories build on the assumption that any information processing within organizations effectively enters into SDM.1104 Although several arguments questioning this assumption have been raised,1105 there is limited systematic evidence of how actual decision makers then approach the task of information use for SDM.1106 By drawing from the Upper Echelon View this study shows that information availability and processing within organizations does not per se imply strategic decision effectiveness. Ultimately, information use by key decision makers is an important factor in SDM. Furthermore, the findings show that not any information use is universally effective. The findings support the conclusion that effective information use is a result of interactions between modes of information use, environmental factors and individual characteristics. Overall, these finding represent an important departure from the behavioral assumptions on information use in organizations. 1103

Cf. Elsbach, K. D. et al. (2005), p. 432; Hitt, M. A./Beverly, B. T. (1991), p. 347; Hough, J. R./White, M. A. (2003), p. 488; Hambrick, D. C. (2007), p. 337; Hough, J. R./Ogilvie, D. (2005), p. 443; Rajagopalan, N. et al. (1998), p. 243. 1104 Cf. Dean, J. W./Sharfman, M. P. (1996); Elbanna, S./Child, J. (2007). 1105 Cf. Corner, P. D. et al. (1994), p. 295; Kuvaas, B. (2002), p. 989. 1106 Cf. Hambrick, D. C. (2007), p. 337.

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2) So far SDM has been considered as a static process with the two main dimensions information use and political behavior and the dynamic of social interaction has most often been ignored.1107 For example, one behavioral study claims that political behavior and information use are two distinct and independent dimensions of SDM on organizational level.1108 In contrast to that, the findings of this study show significantly negative effects of political behavior on individual information use. Furthermore, the findings suggest that these effects are highly idiosyncratic. While for linear thinkers the negative effect on information use is rather minor, for nonlinear thinkers the effect appears to be severe in particular for information use from personal sources. The results furthermore suggest that under conditions of low PEU linear thinkers can compensate for political behavior by using impersonal information sources such that political behavior does not have such a strong negative effect on strategic decision quality. These findings altogether demonstrate that SDM is a dynamic process where information use and social processes interact on the level of the individual decision maker. Secondly, there is a long-standing debate whether organizational outcomes are completely environmentally determined or whether individual managers have an influence on choice which basically means psychological determinism is added into the equation.1109 The empirical results of this study specifically show that different information inputs are differently effective and their effectiveness interacts with cognitive style. Such psychological processes and interactions are central but rarely tested assumptions of the Upper Echelon View.1110 As such, the conception of this study provides a specific explanation of the underlying mechanism for selective perception. In addition to that, the study shows that managerial actions throughout the SDM process can be a source of managerial discretion. By chosing one over another information source, managers can improve their decision making effectiveness. This also contributes to the Upper Echelon View, because managerial discretion has most often been associated with external and organizational factors, but not with managerial

1107

Cf. Balogun, J. et al. (2008), p. 236. Cf. Dean, J.W./Sharfman, Mark P. (1993), p. 1076; Dean, J. W./Sharfman, M. P. (1996), p. 387. Cf. Finkelstein, S. et al. (2009), pp. 20-25; Papadakis, V. M. (2006), p. 371 for more detailed discussions on these two opposing viewpoints. 1110 See Hambrick, D. C. (2007), p. 337. 1108 1109

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actions.1111 Overall, the specific design of this study moves from merely predicting organizational outcomes by individual, observable characteristics to a rich description and empirical test of psychological and social processes of SDM.1112 Thirdly, one area of interest is the moderating role of PEU with respect to effectiveness of information use. There is theoretical debate around whether comprehensive decision making and information use generally provides for superior outcomes or not.1113 Basically there are two opposing views which are both supported with empirical studies. On the one hand, some authors argue that comprehensiveness is more beneficial in little dynamic environments, because then the likelihood of identifying critical variables and their relationships is larger than in highly dynamic environments. On the other hand, some authors argue that comprehensiveness is more beneficial in highly dynamic environments, because then information use would provide for greater understanding of the environment. However, this discussion is limited to sole knowledge transfer and choice, not taking into account that information use may be more than just processing information for computing the most beneficial choice.1114 From a sensemaking perspective information use means arriving at collective understanding which informs action. Basically, the findings of this study argue for both perspectives hold. For example, the findings suggest that linear thinkers in low PEU environments operate most closely as proposed by the bounded rationality model because of two reasons. Firstly, linear thinkers use information from all sources effectively, i.e. formalized processes within the organization are effective steering mechanisms for them.1115 Secondly, political behavior has a significantly less negative effect on strategic decision quality in low than in high PEU environments. These findings reconcile with the empirical findings of studies from the first camp described before.1116 In contrast to that, in high PEU environments personal sources provide the only effective mode of information use for both linear and nonlinear thinkers.1117 These findings suggest a sensemaking 1111

See Finkelstein, S. et al. (2009), pp. 30-35. Cf. Hambrick, D. C. (2007), p. 337 and Lawrence, B. S. (1997), who both emphasize the need for such explanations. 1113 Cf. for a literature review and analysis of this debate Forbes, D. P. (2007), pp. 363-365 and Elbanna, S. (2006), pp. 4-6. 1114 Cf. Weick, K. E. et al. (2005), p. 409. 1115 Cf. Table 41 on p. 210. 1116 Cf. Fredrickson, J. W./Mitchell, T. R. (1984), pp. 417-418; Fredrickson, J. W. (1984), p. 456; Smith, K. G. et al. (1988), pp. 226-228; Papadakis, V. M. (1998), p. 125. 1117 Cf. Table 41 on p.a 210. 1112

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perspective of SDM in high PEU environments. This again reconciles with previous findings most notably the study of Eisenhardt (1989) which suggest that personal, interactive modes of information use are the most effective in highly dynamic environments.1118 Overall, the findings of this study show the interaction between mode of information use, environment and individual characteristics is important for effective SDM and there is not a universal way for SDM. This conclusion adds to an emerging stream of SDM research, the so-called strategy-as-practice perspective.1119 Their proponents argue that conceptualizations of strategy making should integrate rational, incremental and interpretive perspectives on SDM into a more realistic strategy-as-practice concept.1120 The findings of this study go beyond this debate by suggesting that the effectiveness of different strategy practices depends on the interaction of environmental and individual factors alike. This implies conceptualizations of strategy-as-practice should allow for accommodating various SDM practices and relate them to environmental and individual factors in order to produce more consistent results. Fourthly, this study contributes to our understanding of the effect of cognitive style in SDM in several ways: 1) First of all, the findings show the mode of information use has a significant interaction with cognitive style in organizational settings. Linear thinkers use information from personal and impersonal sources effectively, while nonlinear thinkers only use information from personal sources effectively. These findings address a long-standing proposition in organizational decision making research,1121 which until now has only been supported by experimental studies. 2) Then, this study supports a moderating effect of cognitive style on the effectiveness of information use as proposed on conceptual1122 and empirical grounds.1123 This contrasts with some researchers who argue that cognitive style is an antecedent to SDM whereas their empirical evidence is limited.1124 1118

Cf. Eisenhardt, K. M. (1989), pp. 549 and 559. Cf. e.g. Hendry, J. (2000). Cf. Hendry, J. (2000), p. 971; Whittington, R. (2006), pp. 620-623. 1121 Cf. O'Reilly, I. I. I. C. A. (1983), p. 126; Gardner, W. L./Martinko, M. J. (1996), p. 65; Finkelstein, S. et al. (2009), p. 68; Sadler-Smith, E. (1998), p. 193. 1122 Cf. Hayes, J./Allinson, C. W. (1994), p. 1123 Cf. Volkema, R. J./Gorman, R. H. (1998), p. 1124 Cf. Jennings, D./Disney, J. J. (2006), p. 607. 1119 1120

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3) Furthermore, cognitive style moderates the negative effects of political behavior and nonlinear thinkers are generally more susceptible to political behavior with respect to both information use and decision making effectiveness. 4) Finally, the findings of this study show some indirect effects of intuitive decision making on organizational performance. The relationship between strategic decision quality and performance is significantly stronger for nonlinear thinkers than for linear thinkers. One explanation for this was that nonlinear thinkers generally take a more open, interactive approach to SDM which paves the way for superior implementation success.1125 These considerations add to a theoretical debate about the role of intuition in organizational behavior. Conceptual research on intuition in decision making suggests that decision approaches are particularly effective when a decision situation involves many judgmental tasks which are then handled by intuitive judgment instead of rational analysis.1126 According to this conceptual reasoning, the use of an intuitive decision approach implies superior decision outcomes and therefore superior organizational performance.1127 However, although some empirical evidence indicates intuitive decision making increases organizational performance,1128 a superior effect on decision effectiveness is empirically not supported.1129 Therefore, recent studies conclude “an intuitive style is associated positively with performance, but in a causally ambiguous way.”1130 Given the aforementioned effect of intuitive decision makers on implementation success, the present study provides an empirically supported alternate explanation for these superior effects of intuitive decision making on organizational performance. 8.2.2 Limitations and avenues of further research Firstly, the study examined only a specific facet of SDM, namely information use from different information sources during the overall SDM process. It was beyond the scope of this study to consider how this information is transformed when decision makers engage in causal logics or which decision making tactics are employed during different

1125

Cf. sub-section 8.1 for a more detailed discussion of this explanation. Cf. Dane, E./Pratt, M. G. (2007), p. 46. Cf. Khatri, N./Ng, H. A. (2000), p. 78; Dane, E./Pratt, M. G. (2007), p. 46. 1128 Cf. Khatri, N./Ng, H. A. (2000), p. 78; Sadler-Smith, E. (2004), p. 174. 1129 Cf. Elbanna, S./Child, J. (2007), p. 445. 1130 Sadler-Smith, E. (2004), p. 174. A similar conclusion reach Dane, E./Pratt, M. G. (2007) in their literature review and conceptual article on intuition in organizations. Cf. Dane, E./Pratt, M. G. (2007), p. 46. 1126 1127

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decision making phases and which kind of information use is particularly effective for these tactics. However, the findings of this study show the important effect of the interplay between modes of information use, PEU and cognitive style. Therefore, future research could investigate how problem definition and problem solution can effectively be accomplished by investigating a) decision tactics for each phase, b) relating different modes of information use to these tactics and c) mapping these tactics onto different situations depending on PEU and cognitive style. Such a research attempt would very well fit into an emerging research stream called strategy-aspractice, which takes a micro perspective on strategy and strategizing.1131 Another research inquiry could investigate how different modes of information use help to develop and maintain cognitive maps or mental models while controlling for environmental uncertainty. This basically means how strategic learning occurs and has also received some recent research interest.1132 Such a research study can most likely not be accomplished with survey research but should rather be case study based or use a mix of qualitative and quantitative techniques such as provided by the triangulation method. Secondly, the unit of analysis of this study is limited to medium-sized companies. This unit of analysis was chosen, because it presumably provides for sufficient variation in information use and cognitive style variables. In small companies one would expect much more informal SDM processes and information sources, while in large corporations the formalization of SDM processes and the use of impersonal, formal information sources is very likely much larger. Given these assertions the findings of this study suggest some preliminary conclusions for small and large organizations. On the one hand, in small organizations information sources prevail because of a lack of size, formal processes and sophisticated information systems. Therefore, one would expect the presence of both types of decision makers in small companies. However, one might furthermore expect that the overall information system in small organization to some extent follows these preferences, because individual executives do still have some impact on small organization designs.1133 On the other hand, large organizations are highly formalized and sophisticated information and reporting systems are generally in place. As a result, the top executives of large organizations are faced with large amounts of internal reports and other impersonal information sources. Due to the

1131 1132 1133

Cf. Johnson, G./Melin, L./Whittington, R. (2003), pp. 14-17. Cf. the exploratory study of Thomas, J. B./Sussman, S. W./Henderson, J. C. (2001). Cf. Miller, D./Toulouse, J.-M. (1986), p. 554.

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Discussion and implications

size of the organization one would furthermore expect that the organizational structure does not significantly change according to the preferences of the top executives. Last but not least, empirical findings show that individuals tend to leave a company when the information processing requirements of an organization do not meet their cognitive style.1134 As a result, one might expect a high proportion of linear thinkers in top executive positions of large organizations, because of the information processing requirements imposed by the organization. This raises an important question from a human resource perspective. Do the formal processes imposed by an organization imply specific human resource requirements from a human information processing perspective? Thirdly, the study is limited to characteristics and behavior of individual top decision makers in medium-sized companies. As the results demonstrate strong support for a significant role of individual cognitive style under these conditions, further research should explore the role of group cognitive style in top management teams’ information and social processes.1135 Fourthly, the study employed an information use measurement instrument based on existing operationalizations of information behavior. However, several items had to be eliminated from this measurement instrument and the reliability of measurement diminished in the sub-samples formed for the group comparisons. Despite this limitation, the study produced some interesting empirical results and thus encourages the development of other measurement instruments for information use in organizations. Such an attempt fits very well into the mentioned strategy-as-practice perspective and allows for investigating the dynamic nature of SDM from such a micro perspective. Fifthly, the data collection of this study used a survey addressed to a single informant. Therefore, this study is subject to the common limitations of single-respondent surveys, which are most notably informant and common method bias. One the one hand, the survey used the key informant method and the invitation mails of this study were explicitly addressed to the key strategic decision maker in the organization. Most often the respondent was the CEO. Only in some cases the survey was completed by another informant. However, the responses of these other managers appear to fulfill 1134 1135

Cf. Brigham, K. H./De Castro, J. O./Shepherd, D. A. (2007), p. 43. Cf. Leonard, N. H. et al. (2005), p. 134.

Implications for practice

237

the key informant requirements because of two reasons. Firstly, there is no informant bias between CEOs and other managers. Secondly, a closer examination of the nonCEO responses shows that they made any type of strategic decisions but mergers and acquisitions decisions. So overall, the informant was in fact a key decision maker within the organization. On the other hand, the threat of common method bias appears to be less of a problem in this study than it might be in other studies. Due to the complexity of the hypotheses and the configurational approach undertaken in this study, it is unlikely that respondents purposely matched their answers to a theoretically meaningful or desired outcome.1136 If they did so they had to be aware of their cognitive style and then match the information use items to their cognitive style. None of the sources of common method bias, such as social desirability or aiming at consistency, appear to be relevant given this specific research model. 8.3 Implications for practice This study has implications for strategic decision makers in medium-sized companies and people such as management assistants or consultants supporting these decision makers with SDM. At first, a top executive and the people supporting him or her with SDM should reflect about the PEU the company operates in and the self-interests and political behavior of decision participants. While these aspects of reflection are probably common to top executives’ decision making, they should furthermore reflect on their individual cognitive style. This is a rather subtile aspect of decision making and top executives are probably not aware of the role of their information processing preferences in the overall SDM process. Based upon this reflection the following recommendations depending upon top executives’ cognitive style can be made. Linear thinkers can effectively use information from both impersonal and personal sources. In general, they should use and be provided with all sorts of information from a broad range of sources. However, as the effectiveness of information use from impersonal sources tentatively decreases in high PEU environments, linear thinkers should carefully select those pieces of information they use in high PEU environments. A crucial dimension with this respect is timeliness of information, which refers to the degree to which information represents the current situation.1137 While the timeliness 1136 1137

Cf. Garg, V. K. et al. (2003), p. 742. Cf. Heidmann, M. et al. (2008), p. 247.

238

Discussion and implications

of information use from personal sources is generally high, linear thinkers should carefully evaluate whether the timeliness of information from impersonal sources meets the environmental dynamism component of PEU.1138 In high PEU environments linear thinkers should focus on real-time information, i.e. information about a company’s operations and environments with no or little time lag between occurrence and reporting.1139 There are two options if the evaluation reveals that their internal reporting and external impersonal sources are not high in timeliness. On the one hand, linear thinkers can invest in their internal reporting systems in order to provide more timely information or they can hire market research companies to collect and prepare more timely market information. On the other hand, they may compensate the use of information from impersonal sources by use of information from personal sources. In addition to these recommendations on information use, linear thinkers should also bear in mind the negative effects of political behavior are closely connected to their information use. For linear thinkers, the negative effect of political behavior on strategic decision quality is significantly weaker in low than in high PEU environments. This is due to their effective use of information from impersonal sources safeguarding against political interests. Linear thinkers do not have to be very much concerned with the negative impact of political behavior in low PEU environments as long as they use information from impersonal sources. However, as PEU increases and the effectiveness of information use from impersonal sources decreases, linear thinkers become susceptible to political behavior. Therefore, linear thinkers should not only assure timeliness of information from impersonal sources, but should should also devise measures to avoid or handle political behavior in high PEU environments. Finally, linear thinkers need to be aware of the indirect effects of SDM on the implementation success of strategic decisions and thus organizational performance. Linear thinkers may not be as able as nonlinear thinkers to provide for implementation success, because they employ a much more centralized decision making approach. This may hamper an interactive SDM process where strategic goals and ideas are exchanged and increased consistency between strategic decision and implementation decisions can be achieved. Therefore, linear thinkers should consider how to increase the interactivity in their SDM process. There are two possible scenarios for doing so. 1138 1139

Cf. Eisenhardt, K. M. (1989), p. 549. Cf. here and in the following Eisenhardt, K. M. (1989), p. 549.

Implications for practice

239

On the one hand, linear thinkers may use more information from personal sources and may simply be more open to other views on goal setting and strategic alternatives. However by relying more on personal information sources, there is a danger of becoming more susceptible to political behavior in particular under conditions of low PEU. On the other hand, linear thinkers may engage another person to do the interactive job during the SDM process. This person ideally fulfills two characteristics. Firstly, the person is more sensitive to other points of view than the top decision maker oneself. Secondly, this person is free from self-interests and condenses the information from personal sources in a neutral manner. This information could then be provided to the top executive in an objective manner who is then able to grasp different views on the strategic decision at hand. Finally, once such differing views are considered in top executives’ SDM, feedback to other decision participants should be provided. This makes sure other managers in the organization realize their views are considered which in turn provides for the positive effects of interactiveness on decision implementation. The

recommendations

for

nonlinear

thinkers

differ

compared

with

these

recommendations for linear thinkers,. First of all, nonlinear thinkers use only information from personal sources effectively. This implies they should not spend much time on studying MIS reports and market studies but rather delegate this task. Then they may discuss facts and figures from reports along with all sorts of other information with people who prepared or read these reports. In particular, in low PEU environments a nonlinear thinker may benefit from involving another person for analyzing impersonal information, because then information use from impersonal sources can be effective. However, this should be done in addition to his own information use from personal sources in the organization. Furthermore, nonlinear thinkers are particularly susceptible to political behavior because of their sole reliance on personal information sources. Therefore, a key task for them is the identification of self-interests and political behavior in strategic decision situations. For dealing with this, a nonlinear thinker might involve a linear thinker who deals with the political behavior during SDM and then provides a neutral view to the key decision maker. Finally, the indirect effects of SDM on the strategic decision quality – organizational performance are particular important for nonlinear thinkers. Due to their more

240

Discussion and implications

interactive SDM behavior, they effectively pave the way for strategic decision implementation. Therefore, nonlinear thinkers should capitalize on these effects by following their information processing preferences. These recommendations are summarized in Table 48. Cognitive style of top executive Recommendations for top executives

Linear thinker

Nonlinear thinker

x Use information from all kinds of sources

x Focus on information use from personal sources

x Profit from use of information from

x Delegate processing of impersonal

impersonal sources and independence of political behavior in low PEU environments x Evaluate timeliness of information from impersonal sources and be wary about political behavior in high PEU environments x Engage other people to realize the benefits of interactive SDM on implementation success Recommendations for people supporting top executives

x Focus on mediation between top executive and organization x Absorb strategic goals and ideas from personal sources x Provide this as “objective” information input to top executive x Provide feedback to organizational members in order to realize benefits for implementation success

information to realize benefits of independent view in low PEU environments x Be wary of political behavior under any environmental conditions x Engage other people for realizing benefits of impersonal information use in low PEU environments and to absorb political behavior x Capitalize on benefits of interactive SDM for implementation success x Focus on policy handling and information processing support x Absorb information from personal sources when political behavior is highly present and provide neutral view to top executive x Process information in low PEU environments and provide key insights to top executive in a personal manner

Table 48: Overview implications for strategic decision making practice (Source: own compilation)

8.4 Summary Much normative management research and practice literature proposes a linear, topdown approach towards SDM. According to this literature, decision making should be based on a thorough analysis of external opportunities and threats as well as internal strengths and weaknesses. Ultimately, such analysis shall translate organizational goals into a profit-maximizing choice among strategic alternatives. Behavioral research questions this perspective and proposes decision making in organizational settings is not linear because of political and individual cognitive limits

Summary

241

of organizational participants. Instead, SDM can be described by two main process dimensions which are information use and political behavior. While a negative effect of political behavior is generally confirmed in empirical studies, the proposed positive effect of information use received ambiguous empirical support. Then, two separate lines of inquiry have further sought to explain the effectiveness of information use in SDM. On the one hand, contingency perspectives propose the effectiveness of information use is influenced by environmental uncertainty. However, the empirical findings again were contradictory. On the other hand, interpretive perspectives suggest that individual cognitive processes and characteristics have an important role for explaining organizational decision making. However, this perspective is mainly based on conceptual or experimental research and has rarely been supported with empirical findings from large scale SDM field studies. This study addresses these separate lines of inquiry by investigating information use of individual decision makers within organizational settings. Furthermore, PEU and cognitive style are examined as two moderating factors. This specific conceptualization moves beyond merely describing SDM attributes to investigating managerial actions and cognitions taking into account environmental factors on the one hand and idiosyncracies of individual decision makers on the other hand. This approach yields a rich understanding of SDM by individuals in organizational settings and provides several important contributions to the literature. Firstly, the study’s findings question a central assumption of behavioral conceptions of SDM. This is the assumption that any information available within the organization is effectively used in the same way by any individual carrying out decision making. The results show that not any information use is equally effective. Instead, there are systematic differences in the effectiveness of information use depending on the interaction of mode of information use, environmental and individual characteristics alike. Secondly, the findings support a strategic choice perspective on organizational behavior. According to this perspective, organizations do not only adjust to environmental conditions but also to actions and cognitions of the individuals forming the organization. This also forms the central proposition of the Upper Echelon View. However, propositions of the Upper Echelon View have most often been investigated with respect to observable characteristics such as experience, education or age of a

242

Discussion and implications

decision maker. This focus on observable characteristics has been criticized for making too simplistic assumptions on the underlying social and psychological processes. Recently, Upper Echelon researchers have called for a move from a mere prediction focus of the characteristics-outcome relationships towards an explanation focus of how psychological and social processes have an influence on these relationships. This call of research has been addressed by the present study. It provides a rich theoretical explanation and empirical test of how cognitive and social processes of SDM are influenced by the cognitive style of decision makers and how this relates to organizational outcomes. Thirdly, the findings suggest a universal perspective should not be adopted when investigating SDM. Instead, different SDM perspectives, such as bounded rationality, incremental and interpretive perspectives hold, depending on the environmental conditions a company operates in and the individual characteristics of the decision makers engaged in SDM. This study exemplifies that a contextualized investigation of SDM can be theoretically grounded and move beyond the exploratory nature of experimental or qualitative research designs employed so far. Fourthly, the study addressed long-standing propositions about the effects of cognitive style on information use in organizational decision making. So far these propositions have been established on conceptual grounds derived from experimental evidence. In contrast to that, this study accomplished the examination of these effects in a large scale, hypotheses driven study. By doing so, this study established the relevance of cognitive style on information use in organizational settings. In addition to that, the findings of this study contribute in two further aspects to our understanding of the effects of cognitive style. Firstly, the findings show that cognitive style results in significant differences of the effects of political behavior. Secondly, cognitive style appears to have rather indirect effects on organizational performance. As such, the findings provide an alternate explanation why intuitive decision making results in superior organizational performance. Overall, this study produced important empirical evidence on the effects of cognitive style on organizational behavior in field settings. Fifthly, a distinguishing characteristic of this study is a relatively large sample covering a broad range of industries. So far, most SDM field studies had been marked by small sample sizes and a focus on manufacturing companies. This has been considered as a major limitation to the generalizability of results. By having such a

Summary

243

large and diverse sample as in this study, the results appear to be generalizable and the comprehensive investigation of information use and contextual factors could be accomplished at all. Finally, this study adds to an emerging stream of SDM research. Strategy-as-practice research seeks to integrate different conceptualizations of decision making, namely bounded rationality, incremental and interpretive perspectives. Their approach centers on micro practices of strategizing whereas it still needs to be developed towards a robust theory of SDM. The results of this study provide important insights for this theory development, because they clearly indicate that future conceptualizations of SDM should account for the interaction of mode of information use, political behavior, environmental and individual characteristics in order to produce consistent results.

Appendix 1: Questionnaire

Appendix Appendix 1: Questionnaire

W. Gänswein, Effectiveness of Information Use for Strategic Decision Making, DOI 10.1007/978-3-8349-6849-4, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

245

246

Appendix

Appendix 1: Questionnaire

247

248

Appendix

Appendix 1: Questionnaire

249

250

Appendix

Appendix 1: Questionnaire

251

252

Appendix

Appendix 1: Questionnaire

253

254

Appendix

Appendix 2: Partial models – perceived environmental uncertainty

x x

Quality criteria of constructs Discriminant validity on item and construct level

Appendix 2: Partial models – perceived environmental uncertainty

255

Information use from external, impersonal sources (InfoExtImp) Construct Indicator reliability Low PEU High PEU Variable No. Loading Loading InfoExtImp1 0.65 0.80 InfoExtImp2 0.81 0.84 InfoExtImp3 0.79 0.80 InfoExtImp4 eliminated eliminated InfoExtImp5 0.86 0.86 InfoExtImp6 0.85 0.86 Construct reliability Low PEU High PEU 0.86 0.89 Cronbach’s Alpha 0.89 0.92 Composite reliability 0.63 0.69 Average Variance Explained (AVE) 1.00 Coefficient of congruence Table 49: Quality criteria of construct information use from external, impersonal sources (low and high perceived environmental uncertainty groups) (Source: own compilation)

Information use from internal, impersonal sources (InfoIntImp) Construct Indicator reliability Low PEU High PEU Variable No. Loading Loading InfoIntImp1 0.81 0.74 InfoIntImp2 0.87 0.72 InfoIntImp3 0.85 0.82 InfoIntImp4 0.85 0.80 InfoIntImp5 eliminated eliminated InfoIntImp6 eliminated eliminated Construct reliability Low PEU High PEU 0.87 0.78 Cronbach’s Alpha 0.91 0.85 Composite reliability 0.72 0.60 Average Variance Explained (AVE) 1.00 Coefficient of congruence Table 50: Quality criteria of construct information use from internal, impersonal sources (low and high perceived environmental uncertainty groups) (Source: own compilation)

Use of quantitative historic and forward looking information from personal sources (InfoPersQuant) Indicator reliability Low PEU High PEU Variable No. Loading Loading InfoIntPers1 0.81 0.79 InfoIntPers2 0.69 0.79 InfoExtPers1 0.80 0.53 InfoExtPers2 0.69 0.79 Construct reliability Low PEU High PEU 0.75 0.71 Cronbach’s Alpha 0.84 0.82 Composite reliability 0.57 0.53 Average Variance Explained (AVE) 0.98 Coefficient of congruence Construct

Table 51: Quality criteria of construct use of quantitative information from personal sources (low and high perceived environmental uncertainty groups) (Source: own compilation)

256

Appendix

Use of marketing related information from personal sources (InfoPersMark) Construct Indicator reliability Low PEU High PEU Variable No. Loading Loading InfoIntPers4 0.92 0.92 InfoExtPers4 0.92 0.96 Construct reliability Low PEU High PEU 0.82 0.87 Cronbach’s Alpha 0.92 0.94 Composite reliability 0.85 0.88 Average Variance Explained (AVE) 1.00 Coefficient of congruence Table 52: Quality criteria of construct use of marketing related information from personal sources (low and high perceived environmental uncertainty groups) (Source: own compilation)

Use of qualitative and contextual information from personal sources (InfoPersQual) Indicator reliability Low PEU High PEU Variable No. Loading Loading InfoIntPers5 0.88 0.84 InfoIntPers6 0.84 0.82 InfoExtPers5 0.83 0.85 InfoExtPers6 0.78 0.86 Construct reliability Low PEU High PEU 0.85 0.87 Cronbach’s Alpha 0.90 0.91 Composite reliability 0.69 0.71 Average Variance Explained (AVE) 1.00 Coefficient of congruence Construct

Table 53: Quality criteria of construct use of qualitative information from personal sources (low and high perceived environmental uncertainty groups) (Source: own compilation)

Information use from personal sources (InfoPers), Second-order factor Indicator reliability Low PEU Variable No. Loading InfoPersQuant 0.87 InfoPersMark 0.86 InfoPersQual 0.89 Construct reliability Low PEU 0.84 Cronbach’s Alpha 0.90 Composite reliability 0.76 Average Variance Explained (AVE) 1.00 Coefficient of congruence Construct

High PEU Loading 0.88 0.74 0.81 High PEU 0.76 0.85 0.66

Table 54: Quality criteria of second-order construct information use from personal sources (low and high perceived environmental uncertainty groups) (Source: own compilation)

Appendix 2: Partial models – perceived environmental uncertainty Political behavior (PolBeh) Construct Indicator reliability Low PEU Variable No. Loading PolBeh1 0.83 PolBeh2 0.89 Construct reliability Low PEU 0.67 Cronbach’s Alpha 0.86 Composite reliability 0.75 Average Variance Explained (AVE) Coefficient of congruence

257

High PEU Loading 0.89 0.76 High PEU 0.55 0.81 0.69 0.99

Table 55: Quality criteria of construct political behavior (low and high perceived environmental uncertainty groups) (Source: own compilation)

Strategic decision quality (DecQual) Construct Indicator reliability Low PEU Variable No. Loading DecQual1 0.87 DecQual2 0.85 DecQual3 0.87 Construct reliability Low PEU 0.83 Cronbach’s Alpha 0.90 Composite reliability 0.74 Average Variance Explained (AVE) Coefficient of congruence

High PEU Loading 0.87 0.92 0.84 High PEU 0.85 0.91 0.77 1.00

Table 56: Quality criteria of construct strategic decision quality (low and high perceived environmental uncertainty groups) (Source: own compilation)

Subjective company performance (Perf) Construct Indicator reliability Low PEU Variable No. Loading Perf1 0.91 Perf2 0.88 Perf3 0.88 Perf4 0.93 Perf5 0.74 Perf6 0.78 Perf7 0.71 Construct reliability Low PEU 0.93 Cronbach’s Alpha 0.94 Composite reliability 0.70 Average Variance Explained (AVE) Coefficient of congruence

High PEU Loading 0.81 0.87 0.58 0.82 0.83 0.73 0.73 High PEU 0.89 0.91 0.60 0.99

Table 57: Quality criteria of construct subjective company performance (low and high perceived environmental uncertainty groups) (Source: own compilation)

258

Appendix Construct

Indicator

Info Ext Imp

Info Int Imp

Info Info Pers Pers Quant Mark

Info Pers Qual

Pol Beh

Dec Qual

Perf

InfoExtImp1 InfoExtImp2 InfoExtImp3 InfoExtImp5 InfoExtImp6

0.65 0.81 0.78 0.87 0.85

0.25 0.21 0.26 0.22 0.19

0.36 0.34 0.37 0.32 0.38

0.20 0.33 0.40 0.24 0.29

0.22 0.23 0.33 0.38 0.35

-0.05 -0.11 -0.13 -0.05 -0.03

0.06 0.10 0.07 0.23 0.14

0.13 0.23 0.24 0.30 0.22

InfoIntImp1 InfoIntImp2 InfoIntImp3 InfoIntImp4

0.09 0.25 0.30 0.26

0.81 0.87 0.85 0.85

0.25 0.32 0.38 0.26

0.27 0.35 0.42 0.55

0.15 0.30 0.35 0.32

-0.16 -0.15 -0.23 -0.17

0.20 0.25 0.17 0.25

0.21 0.16 0.21 0.23

InfoIntPers1 InfoIntPers2 InfoExtPers1 InfoExtPers2

0.23 0.23 0.35 0.54

0.34 0.22 0.27 0.22

0.81 0.69 0.80 0.71

0.42 0.41 0.54 0.53

0.53 0.47 0.46 0.51

-0.30 -0.35 -0.16 -0.16

0.30 0.17 0.29 0.17

0.18 0.13 0.16 0.30

InfoIntPers4 InfoExtPers4

0.22 0.45

0.46 0.42

0.55 0.59

0.92 0.92

0.59 0.59

-0.30 -0.17

0.33 0.31

0.30 0.27

InfoIntPers5 InfoIntPers6 InfoExtPers5 InfoExtPers6

0.22 0.24 0.45 0.48

0.31 0.28 0.22 0.31

0.50 0.55 0.58 0.56

0.55 0.54 0.53 0.54

0.88 0.84 0.83 0.78

-0.36 -0.34 -0.24 -0.26

0.34 0.25 0.26 0.18

0.30 0.27 0.24 0.32

PolBeh1 PolBeh1

0.00 -0.15

-0.13 -0.23

-0.20 -0.35

-0.16 -0.28

-0.28 -0.35

0.85 0.88

-0.39 -0.36

-0.26 -0.27

DecQual1 DecQual2 DecQual3

0.15 0.12 0.17

0.24 0.15 0.26

0.37 0.19 0.25

0.38 0.15 0.32

0.39 0.17 0.24

-0.51 -0.34 -0.25

0.87 0.84 0.87

0.32 0.30 0.49

Perf1 Perf2 Perf3 Perf4 Perf5 Perf6 Perf7

0.20 0.21 0.28 0.28 0.32 0.26 0.25

0.22 0.21 0.17 0.21 0.24 0.18 0.20

0.17 0.17 0.22 0.28 0.25 0.17 0.11

0.27 0.19 0.27 0.37 0.21 0.27 0.15

0.31 0.27 0.36 0.32 0.20 0.26 0.12

-0.27 -0.20 -0.30 -0.34 -0.23 -0.24 -0.09

0.45 0.30 0.41 0.48 0.27 0.27 0.15

0.91 0.88 0.88 0.93 0.74 0.78 0.71

Table 58: Cross-loadings on indicator level (low perceived environmental uncertainty group) (Source: own compilation)

Appendix 2: Partial models – perceived environmental uncertainty Construct Indicator

Info Ext Imp

Info Int Imp

Info Info Pers Pers Quant Mark

Info Pers Qual

Pol Beh

Dec Qual

Perf

InfoExtImp1 InfoExtImp2 InfoExtImp3 InfoExtImp5 InfoExtImp6

0.80 0.84 0.80 0.86 0.86

0.27 0.26 0.23 0.25 0.21

0.28 0.42 0.30 0.29 0.27

0.32 0.47 0.31 0.27 0.36

0.39 0.47 0.49 0.58 0.59

-0.11 -0.13 -0.19 -0.12 -0.16

0.16 0.26 0.17 0.19 0.21

0.16 0.19 0.21 0.34 0.28

InfoIntImp1 InfoIntImp2 InfoIntImp3 InfoIntImp4

0.06 0.20 0.31 0.32

0.74 0.72 0.82 0.81

0.18 0.21 0.22 0.37

0.09 0.36 0.33 0.59

0.07 0.13 0.36 0.28

-0.09 -0.02 -0.11 -0.04

0.07 0.05 0.08 0.11

0.01 0.14 0.13 0.17

InfoIntPers1 InfoIntPers2 InfoExtPers1 InfoExtPers2

0.18 0.28 0.28 0.38

0.37 0.24 0.20 0.14

0.79 0.79 0.53 0.78

0.27 0.46 0.34 0.39

0.37 0.36 0.31 0.41

-0.30 -0.13 -0.19 -0.27

0.35 0.29 0.14 0.38

0.24 0.17 0.15 0.30

InfoIntPers4 InfoExtPers4

0.38 0.41

0.43 0.38

0.53 0.40

0.92 0.96

0.50 0.48

-0.13 -0.11

0.16 0.21

0.08 0.17

InfoIntPers5 InfoIntPers6 InfoExtPers5 InfoExtPers6

0.47 0.43 0.52 0.60

0.27 0.25 0.24 0.24

0.41 0.49 0.37 0.44

0.36 0.56 0.38 0.47

0.84 0.82 0.85 0.86

-0.32 -0.25 -0.24 -0.14

0.21 0.18 0.24 0.25

0.33 0.29 0.33 0.34

PolBeh1 PolBeh1

-0.13 -0.16

-0.06 -0.11

-0.26 -0.27

-0.18 -0.01

-0.22 -0.24

0.90 0.75

-0.49 -0.28

-0.29 -0.23

DecQual1 DecQual2 DecQual3

0.16 0.29 0.17

0.04 0.10 0.11

0.31 0.46 0.32

0.21 0.18 0.14

0.21 0.27 0.21

-0.44 -0.47 -0.37

0.87 0.92 0.84

0.22 0.39 0.32

Perf1 Perf2 Perf3 Perf4 Perf5 Perf6 Perf7

0.19 0.19 0.01 0.30 0.21 0.33 0.13

0.16 0.11 0.05 0.15 -0.03 0.12 0.18

0.34 0.29 0.11 0.23 0.22 0.33 0.05

0.03 0.07 0.11 0.11 0.04 0.19 0.21

0.26 0.26 0.13 0.38 0.29 0.39 0.26

-0.21 -0.25 -0.04 -0.24 -0.33 -0.32 -0.18

0.23 0.31 0.07 0.27 0.32 0.35 0.25

0.81 0.87 0.59 0.82 0.83 0.73 0.73

Table 59: Cross-loadings on indicator level (high perceived environmental uncertainty group) (Source: own compilation)

259

260

Appendix

Construct Construct InfoIntImp InfoExtImp InfoPersQuant InfoPersMark InfoPersQual PolBeh DecQual Perf

Info Ext Imp

Info Int Imp

0.79 0.27 0.43 0.36 0.39 -0.09 0.17 0.30

0.85 0.36 0.48 0.34 -0.21 0.26 0.24

Info Info Pers Pers Quant Mark

0.75 0.62 0.64 -0.32 0.33 0.24

0.92 0.64 -0.26 0.35 0.31

Info Pers Qual

0.83 -0.37 0.33 0.33

Pol Beh

0.87 -0.43 -0.30

Dec Qual

0.86 0.43

Perf

0.84

Table 60: Square roots of Average Variance Explained (diagonal) and latent variable correlations (low perceived environmental uncertainty groups) (Source: own compilation)

Construct Construct InfoIntImp InfoExtImp InfoPersQuant InfoPersMark InfoPersQual PolBeh DecQual Perf

Info Ext Imp

Info Int Imp

0.79 0.29 0.38 0.42 0.61 -0.17 0.24 0.28

0.85 0.32 0.43 0.29 -0.09 0.10 0.14

Info Info Pers Pers Quant Mark

0.75 0.49 0.50 -0.31 0.42 0.31

0.92 0.52 -0.13 0.20 0.14

Info Pers Qual

0.83 -0.27 0.26 0.39

Pol Beh

0.87 -0.49 -0.32

Dec Qual

0.86 0.36

Perf

0.84

Table 61: Square roots of Average Variance Explained (diagonal) and latent variable correlations (high perceived environmental uncertainty groups) (Source: own compilation)

Appendix 3: Partial models – cognitive style Appendix 3: Partial models – cognitive style

x x

Quality criteria of constructs Discriminant validity on item and construct level

261

262

Appendix

Information use from external, impersonal sources (InfoExtImp) Construct Indicator reliability LT NLT Variable No. Loading Loading InfoExtImp1 0.74 0.78 InfoExtImp2 0.84 0.78 InfoExtImp3 0.82 0.78 InfoExtImp4 eliminated eliminated InfoExtImp5 0.79 0.90 InfoExtImp6 0.81 0.90 Construct reliability LT NLT 0.86 0.89 Cronbach’s Alpha 0.90 0.92 Composite reliability 0.64 0.69 Average Variance Explained (AVE) 1.00 Coefficient of congruence Table 62: Quality criteria of construct information use from external, impersonal sources (cognitive style groups) (Source: own compilation)

Information use from internal, impersonal sources (InfoIntImp) Construct Indicator reliability LT NLT Variable No. Loading Loading InfoIntImp1 0.75 0.78 InfoIntImp2 0.75 0.88 InfoIntImp3 0.80 0.83 InfoIntImp4 0.83 0.80 InfoIntImp5 eliminated eliminated InfoIntImp6 eliminated eliminated Construct reliability LT NLT 0.80 0.89 Cronbach’s Alpha 0.87 0.89 Composite reliability 0.62 0.68 Average Variance Explained (AVE) 1.00 Coefficient of congruence Table 63: Quality criteria of construct information use from internal, impersonal sources (cognitive style groups) (Source: own compilation)

Use of quantitative historic and forward looking information from personal sources (InfoPersQuant) Indicator reliability LT NLT Variable No. Loading Loading InfoIntPers1 0.75 0.82 InfoIntPers2 0.70 0.79 InfoExtPers1 0.79 0.62 InfoExtPers2 0.70 0.79 Construct reliability LT NLT 0.73 0.74 Cronbach’s Alpha 0.83 0.83 Composite reliability 0.55 0.56 Average Variance Explained (AVE) 0.99 Coefficient of congruence Construct

Table 64: Quality criteria of use of quantitative information from personal sources (cognitive style groups) (Source: own compilation)

Construct

Use of marketing related information from personal sources (InfoPersMark)

Appendix 3: Partial models – cognitive style Indicator reliability Variable No. InfoIntPers4 InfoExtPers4 Construct reliability Cronbach’s Alpha Composite reliability Average Variance Explained (AVE) Coefficient of congruence

263

LT Loading 0.87 0.94 LT 0.79 0.90 0.82

NLT Loading 0.95 0.94 NLT 0.88 0.94 0.89 1.00

Table 65: Quality criteria of construct use of marketing related information from personal sources (cognitive style groups) (Source: own compilation)

Use of qualitative and contextual information from personal sources (InfoPersQual) Indicator reliability LT NLT Variable No. Loading Loading InfoIntPers5 0.87 0.87 InfoIntPers6 0.84 0.81 InfoExtPers5 0.86 0.84 InfoExtPers6 0.80 0.82 Construct reliability LT NLT 0.87 0.85 Cronbach’s Alpha 0.91 0.85 Composite reliability 0.71 0.70 Average Variance Explained (AVE) 1.00 Coefficient of congruence Construct

Table 66: Quality criteria of construct use of qualitative information from personal sources (cognitive style groups) (Source: own compilation)

Information use from personal sources (InfoPers), Second-order factor Indicator reliability LT Variable No. Loading InfoPersQuant 0.86 InfoPersMark 0.84 InfoPersQual 0.81 Construct reliability LT 0.78 Cronbach’s Alpha 0.87 Composite reliability 0.70 Average Variance Explained (AVE) 1.00 Coefficient of congruence Construct

NLT Loading 0.88 0.79 0.89 NLT 0.82 0.89 0.73

Table 67: Quality criteria of second-order construct information use from personal sources (cognitive style groups) (Source: own compilation)

264 Political behavior (PolBeh) Construct Indicator reliability LT Variable No. Loading PolBeh1 0.91 PolBeh2 0.81 Construct reliability LT 0.85 Cronbach’s Alpha 0.66 Composite reliability 0.74 Average Variance Explained (AVE) Coefficient of congruence

Appendix NLT Loading 0.81 0.86 NLT 0.82 0.57 0.70 1.00

Table 68: Quality criteria of construct political behavior (cognitive style groups) (Source: own compilation)

Strategic decision quality (DecQual) Construct Indicator reliability LT Variable No. Loading DecQual1 0.86 DecQual2 0.90 DecQual3 0.85 Construct reliability LT 0.84 Cronbach’s Alpha 0.90 Composite reliability 0.76 Average Variance Explained (AVE) Coefficient of congruence

NLT Loading 0.89 0.87 0.86 NLT 0.84 0.91 0.76 1.00

Table 69: Quality criteria of construct strategic decision quality (cognitive style groups) (Source: own compilation)

Subjective company performance (Perf) Construct Indicator reliability LT Variable No. Loading Perf1 0.87 Perf2 0.88 Perf3 0.70 Perf4 0.88 Perf5 0.72 Perf6 0.77 Perf7 0.84 Construct reliability LT 0.91 Cronbach’s Alpha 0.93 Composite reliability 0.66 Average Variance Explained (AVE) Coefficient of congruence

NLT Loading 0,87 0.89 0.80 0.90 0.80 0.75 0.65 NLT 0.91 0.93 0.66 0.99

Table 70: Quality criteria of construct subjective company performance (cognitive style groups) (Source: own compilation)

Appendix 3: Partial models – cognitive style Construct Indicator

Info Ext Imp

Info Int Imp

Info Info Pers Pers Quant Mark

Info Pers Qual

265 Pol Beh

Dec Qual

Perf

InfoExtImp1 InfoExtImp2 InfoExtImp3 InfoExtImp5 InfoExtImp6

0.74 0.84 0.82 0.80 0.81

0.30 0.15 0.18 0.31 0.18

0.36 0.35 0.36 0.28 0.32

0.34 0.39 0.36 0.26 0.36

0.23 0.28 0.38 0.44 0.40

-0.03 -0.10 -0.19 -0.02 -0.02

0.19 0.23 0.26 0.20 0.18

0.15 0.17 0.22 0.30 0.20

InfoIntImp1 InfoIntImp2 InfoIntImp3 InfoIntImp4

0.02 0.19 0.27 0.35

0.76 0.75 0.80 0.83

0.21 0.22 0.34 0.32

0.01 0.22 0.33 0.56

0.08 0.14 0.34 0.32

-0.04 0.05 -0.02 -0.06

0.18 0.13 0.12 0.21

0.10 0.21 0.20 0.22

InfoIntPers1 InfoIntPers2 InfoExtPers1 InfoExtPers2

0.17 0.25 0.34 0.43

0.30 0.21 0.30 0.23

0.75 0.70 0.79 0.72

0.30 0.40 0.55 0.42

0.41 0.34 0.35 0.46

-0.21 -0.21 -0.01 -0.20

0.17 0.18 0.26 0.22

0.09 0.18 0.06 0.21

InfoIntPers4 InfoExtPers4

0.34 0.43

0.34 0.35

0.55 0.52

0.87 0.94

0.51 0.52

-0.13 -0.05

0.20 0.30

0.11 0.10

InfoIntPers5 InfoIntPers6 InfoExtPers5 InfoExtPers6

0.34 0.29 0.39 0.45

0.21 0.19 0.28 0.28

0.40 0.46 0.49 0.41

0.43 0.50 0.51 0.49

0.87 0.84 0.86 0.80

-0.25 -0.20 -0.13 -0.08

0.19 0.13 0.18 0.11

0.28 0.19 0.15 0.23

PolBeh1 PolBeh1

-0.11 -0.08

-0.01 -0.06

-0.15 -0.19

-0.16 0.03

-0.21 -0.14

0.89 0.83

-0.23 -0.18

-0.15 -0.24

DecQual1 DecQual2 DecQual3

0.25 0.27 0.19

0.18 0.15 0.23

0.26 0.22 0.26

0.32 0.17 0.24

0.26 0.11 0.11

-0.28 -0.21 -0.15

0.85 0.90 0.86

0.18 0.23 0.32

Perf1 Perf2 Perf3 Perf4 Perf5 Perf6 Perf7

0.21 0.17 0.03 0.19 0.18 0.30 0.27

0.27 0.20 0.07 0.17 0.07 0.20 0.18

0.17 0.12 0.00 0.16 0.11 0.22 0.13

0.07 0.05 -0.02 0.10 -0.05 0.20 0.11

0.25 0.20 0.09 0.22 0.14 0.26 0.17

-0.18 -0.22 -0.11 -0.22 -0.15 -0.18 -0.13

0.21 0.23 0.07 0.31 0.08 0.25 0.26

0.87 0.88 0.70 0.88 0.72 0.77 0.84

Table 71: Cross-loadings on indicator level (linear thinkers group) (Source: own compilation)

266

Appendix Construct

Indicator

Info Ext Imp

Info Int Imp

Info Info Pers Pers Quant Mark

Info Pers Qual

Pol Beh

Dec Qual

Perf

InfoExtImp1 InfoExtImp2 InfoExtImp3 InfoExtImp5 InfoExtImp6

0.78 0.78 0.77 0.90 0.91

0.22 0.28 0.29 0.16 0.23

0.34 0.42 0.35 0.37 0.39

0.20 0.40 0.35 0.26 0.32

0.44 0.40 0.44 0.53 0.56

-0.17 -0.12 -0.12 -0.15 -0.19

0.06 0.11 0,00 0.23 0.19

0.19 0.24 0.26 0.35 0.31

InfoIntImp1 InfoIntImp2 InfoIntImp3 InfoIntImp4

0.09 0.22 0.31 0.20

0.78 0.88 0.83 0.80

0.22 0.33 0.29 0.31

0.27 0.46 0.38 0.57

0.12 0.28 0.37 0.27

-0.20 -0.21 -0.29 -0.13

0.10 0.17 0.11 0.14

0.12 0.11 0.15 0.21

InfoIntPers1 InfoIntPers2 InfoExtPers1 InfoExtPers2

0.28 0.29 0.32 0.48

0.38 0.29 0.18 0.12

0.82 0.79 0.62 0.74

0.38 0.49 0.36 0.48

0.52 0.52 0.44 0.47

-0.39 -0.30 -0.32 -0.22

0.43 0.29 0.20 0.30

0.31 0.17 0.25 0.38

InfoIntPers4 InfoExtPers4

0.25 0.41

0.51 0.43

0.53 0.52

0.95 0.94

0.58 0.56

-0.27 -0.21

0.26 0.24

0.26 0.34

InfoIntPers5 InfoIntPers6 InfoExtPers5 InfoExtPers6

0.37 0.39 0.55 0.66

0.36 0.31 0.15 0.25

0.52 0.57 0.48 0.58

0.50 0.58 0.43 0.52

0.87 0.81 0.84 0.82

-0.46 -0.37 -0.36 -0.33

0.36 0.28 0.30 0.29

0.34 0.34 0.38 0.42

PolBeh1 PolBeh1

-0.05 -0.27

-0.16 -0.29

-0.29 -0.40

-0.15 -0.29

-0.31 -0.47

0.84 0.83

-0.58 -0.43

-0.35 -0.25

DecQual1 DecQual2 DecQual3

0.11 0.17 0.18

0.14 0.09 0.19

0.40 0.39 0.32

0.30 0.15 0.25

0.35 0.29 0.34

-0.63 -0.52 -0.42

0.89 0.88 0.86

0.35 0.39 0.51

Perf1 Perf2 Perf3 Perf4 Perf5 Perf6 Perf7

0.20 0.24 0.21 0.37 0.35 0.31 0.15

0.12 0.16 0.12 0.19 0.13 0.10 0.22

0.33 0.34 0.31 0.34 0.35 0.27 0.07

0.20 0.21 0.33 0.34 0.25 0.24 0.25

0.34 0.34 0.40 0.46 0.34 0.38 0.22

-0.30 -0.22 -0.23 -0.37 -0.39 -0.35 -0.12

0.45 0.37 0.38 0.44 0.43 0.34 0.18

0.87 0.89 0.80 0.90 0.80 0.75 0.65

Table 72: Cross-loadings on indicator level (nonlinear thinkers group) (Source: own compilation)

Appendix 3: Partial models – cognitive style

Construct Construct InfoIntImp InfoExtImp InfoPersQuant InfoPersMark InfoPersQual PolBeh DecQual Perf

Info Ext Imp

Info Int Imp

0.80 0.27 0.42 0.43 0.43 -0.11 0.27 0.26

0.78 0.35 0.38 0.28 -0.04 0.22 0.23

Info Info Pers Pers Quant Mark

0.74 0.58 0.52 -0.19 0.29 0.18

0.91 0.56 -0.09 0.28 0.11

267

Info Pers Qual

0.84 -0.20 0.19 0.25

Pol Beh

0.86 -0.24 -0.22

Dec Qual

0.87 0.29

Perf

0.81

Table 73: Square roots of Average Variance Explained (diagonal) and latent variable correlations (linear thinkers group) (Source: own compilation)

Construct Construct InfoIntImp InfoExtImp InfoPersQuant InfoPersMark InfoPersQual PolBeh DecQual Perf

Info Ext Imp

Info Int Imp

0.80 0.26 0.44 0.35 0.58 -0.19 0.17 0.33

0.78 0.35 0.50 0.33 -0.26 0.16 0.17

Info Info Pers Pers Quant Mark

0.74 0.56 0.64 -0.41 0.43 0.37

0.91 0.60 -0.26 0.27 0.32

Info Pers Qual

0.84 -0.46 0.37 0.44

Pol Beh

0.86 -0.60 -0.36

Dec Qual

0.87 0.47

Perf

0.81

Table 74: Square roots of Average Variance Explained (diagonal) and latent variable correlations (nonlinear thinkers group) (Source: own compilation)

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