Diversification, Relatedness, and Performance

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Frithjof Pils Diversification, Relatedness, and Performance

GABLER EDITION WISSENSCHAFT

Frithjof Pils

Diversification, Relatedness, and Performance With a Foreword by Prof. Dr. Andreas Bausch

GABLER EDITION WISSENSCHAFT

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.

Dissertation Jacobs University Bremen, 2008

1st Edition 2009 All rights reserved © Gabler | GWV Fachverlage GmbH, Wiesbaden 2009 Editorial Office: Frauke Schindler / Anita Wilke Gabler is part of the specialist publishing group 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: Regine Zimmer, Dipl.-Designerin, Frankfurt/Main Printed on acid-free paper Printed in Germany ISBN 978-3-8349-1404-0

Foreword A large body of business strategy literature examines the relationship between (product) diversification and firm performance. From a conceptual point of view, increasing levels of diversification should have positive effects on firm performance, particularly due to economies of scope and scale, market power influence, and risk reduction. At the same time, diversifiers have to cope with substantial negative effects associated with increasing complexity. Unsurprisingly, the results of extensive empirical analyses of diversification effects on performance are somewhat contradictory. Against this background, Frithjof Pils answers two central research questions: First, what overall relationship exists between diversification strategies and firm performance? Secondly, what newly developed indicators of business relatedness can add to the understanding of the performance implications of diversification strategies? Frithjof Pils chooses a topic which is of high practical relevance since the decision in which businesses a firm wants to be in is at the core of corporate strategy. His research objectives are well developed based on a sound description of the current research status. The empirical approaches applied reflect a suitable research design in order to answer the research questions at hand. The combination of narrative review, metaanalysis and primary empirical research indicates the wide range of the author‘s methodological knowledge. Overall, the author significantly contributes with this thesis to a comprehensive understanding of the drivers that influence the success of diversification strategies – and of those that are of less relevance. The different empirical studies are based on sound theoretical analyses and argumentations. Furthermore, the conclusions the author draws add perceptibly to the existing body of knowledge in the research field of corporate strategy. Thereby, the study does not only inform a broad readership about a central topic but also offers the expert new insights. The results may thus have important implications for future research and theory building.

Prof. Dr. Andreas Bausch

Preface The dissertation at hand results from the research that I have done as a doctoral student at the Department of Strategic Management and Controlling at Jacobs University Bremen. The dissertation is of cumulative nature. It comprises three consecutive papers all of which were accepted for presentation at the Annual International Conference of the Strategic Management Society (SMS) during the period 2006-2008. The 2008 paper has been nominated for the SMS Best Conference Paper Award.

I would like to use the occasion and express my gratitude to a number of people who have supported me throughout the process of completing this work.

My thesis supervisor and academic mentor, Professor Andreas Bausch, created a peerless environment for developing academic talent at Jacobs University. I appreciate the mix of guidance and empowerment that characterizes his leadership. I thank him for his personal support and how he paved the way for what is yet to come.

Also, I would like to thank Professor Adalbert FX Wilhelm and Professor Hans H. Hinterhuber for very fruitful discussions and for reviewing the dissertation. At the same time, I am indebted to my colleagues and fellow Ph.D. students at Jacobs University. Not least, I owe thanks to Professor Birgitta Wolff and Professor Rulzion Rattray for shaping my initial academic work.

Finally, special thanks go to my family who facilitated this education in the first place and to Duc Linh Van Tri for her invaluable support. I dedicate my dissertation to Gretchen and Margret.

Frithjof Pils

Table of Contents List of Tables................................................................................................................. XI List of Figures ............................................................................................................ XIII List of Abbreviations .................................................................................................. XV 1

Research Problems, Objectives, and Structure................................................ 1

2

Meta-Analysis on the Relationship between Diversification and Performance .............................................................................................. 9 2.1

The Construct “Product Diversification (Strategy)” ....................................... 9

2.2

Empirical Measures of Product Diversification (Strategy) ........................... 11

2.3

The Construct “Performance” ....................................................................... 20

2.4

Theory on Product Diversification as a Cause of Performance .................... 22

2.5

Evidence on Product Diversification as a Cause of Performance................. 25

2.6

Overall Meta-Analytic Proceeding................................................................ 37 2.6.1

Meta-Analytic Methods.................................................................... 38

2.6.2

Testing Contingency Variables: Hedges/Olkin versus Hunter/Schmidt................................................................................. 38

2.6.3

Technical Integration Model ............................................................ 40

2.6.4

Sampling........................................................................................... 42

2.6.5

Coding .............................................................................................. 46

2.7

Meta-Analytic Results ................................................................................... 47

2.8

Discussion...................................................................................................... 57

2.9

Intermediate Conclusion................................................................................ 60

X

Table of Contents

3

Types of Operational Relatedness, Core Business Industry, and Performance................................................................................................... 63 3.1

Theory and Hypotheses ................................................................................. 65

3.2

Methods ......................................................................................................... 71 3.2.1

Sample .............................................................................................. 72

3.2.2

Variables ........................................................................................... 73

3.3

Regression and T-Test Results....................................................................... 81

3.4

Discussion...................................................................................................... 97

3.5

Intermediate Conclusion.............................................................................. 105

4

A Two-Factor Model of Operational Relatedness and Strategic Relatedness ................................................................................... 107

5

4.1

Theory and Hypotheses ............................................................................... 109

4.2

Methods ....................................................................................................... 118 4.2.1

Structural Equation Modeling ........................................................ 118

4.2.2

Variables ......................................................................................... 119

4.3

Structural Equations Results........................................................................ 124

4.4

Discussion.................................................................................................... 136

4.5

Intermediate Conclusion.............................................................................. 143 Summary...................................................................................................... 145

Limitations and Some ex post Remarks on Meta-Analysis........................................ 157 Bibliography ............................................................................................................... 161 Appendices.................................................................................................................. 193

List of Tables Table 2.1

Studies included in the Meta-Analyses…………………………...

Table 2.2

Hedges/Olkin-based Weighted Regression of Accounting-based Correlations onto Contingency-Variables…….

Table 2.3

50

Hedges/Olkin-based Weighted Regression of Market-based Correlations onto Contingency-Variables……………………...…

Table 2.4

45

51

Hunter/Schmidt-based Weighted Integration of DiversificationPerformance Correlations and Hierarchical Breakdown………….

55

Table 3.1

Descriptive Statistics and Correlation Matrix: Pooled Sample…...

84

Table 3.2

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Pooled Sample…………………………

Table 3.3

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Food Industry………………………….

Table 3.4

88

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Pharmaceuticals Industry………………

Table 3.6

87

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Chemicals Industry…………………….

Table 3.5

86

89

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Personal Care Industry………………...

90

XII

Table 3.7

List of Tables

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Computers Industry……………………

Table 3.8

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Electronics Industry……………………

Table 3.9

91

92

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Automotive Industry…………………..

93

Table 3.10

Absolute Excess Values in Seven Core Business Industries……...

96

Table 3.11

Empirical Results in Comparison: Linkage between Types of Relatedness and between Types of Relatedness and Performance..

99

Table 4.1

Descriptive Statistics and Correlations of Structural Equations…..

126

Table 4.2

Factor Loadings: Measurement Model and Final Model…………

127

Table 4.3

Model Statistics…………………………………………………...

131

Table 4.4

Testing Sequence and Model Difference Tests…………………...

132

Table 4.5

Structural Modeling Results Comparing Hypotheses Tests for the Theoretical and Final Model……………………………………...

135

List of Figures Figure 2.1

Diversification Strategies defined in terms of the Specialization Ratio and the Related Ratio……………………………………….

Figure 2.2

15

Heuristic Logic of the Overall Goal of Diversification Strategy…........................................................................................

22

Figure 2.3

Classification of Empirical Evidence from Individual Studies…...

26

Figure 2.4

Empirical Evidence on the Linkage between Degree and Type of Diversification and Performance – Level 1 and 2………………..

Figure 2.5

Empirical Evidence on the Linkage between Degree and Type of Diversification and Performance – Level 3……………………….

Figure 2.6

31

The Inverted-U Association between Diversification and Performance……………………………………………………….

Figure 2.7

30

36

Decomposing a Curvilinear Association via Sample Range Restriction………………………………………………………...

41

Figure 4.1

Theoretical Model………………………………………………...

123

Figure 4.2

Final Model……………………………………………………….

133

List of Abbreviations BSD

Broad Spectrum Diversity

CAPX

Capital Expenditures

CFI

Comparative Fit Index

CMIN

Chi-square Statistic

cf.

confer; compare

DC

Concentric Index of Diversification

DH

Herfindahl Index of Diversification

DR

Diversity Related (Entropy)

DU

Diversity Unrelated (Entropy)

DT

Diversity Total (Entropy)

DW

Weighted Index of Diversification

ed.

editor

eds.

editors

e.g.

exempli gratia; for example

et al.

et alia; and others

et sqq.

et sequens; and the following

EV

Excess Value

GFI

Goodness of Fit Index

H

Hypothesis

i.e.

id est; that is to say

MNSD

Mean Narrow Spectrum Diversity

MSA

Measure of Sampling Adequacy

n

number of subjects, sample size

NFI

Normed Fit Index

NSD

Narrow Spectrum Diversity

OES

Organizational Employment Survey (U.S. Department of Labor)

p.

page

XVI

List of Abbreviations

PCFI

Parsimony Comparative Fit Index

Q

Tobins’s Q

R&D

Research & Development

RDX

Research & Development Expenses

RMSEA

Root Mean Square Error of Approximation

ROA

Return on Assets

ROS

Return on Sales

R2

Multiple Regression Coefficient squared

S.D.

Standard Deviation

SIC

Standard Industrial Classification

STX

Staff and related Expenses

1 Research Problems, Objectives, and Structure Product diversification strategy determines which businesses a corporation should be in. It defines the scope of the firm’s activities, is the cornerstone of corporate strategy, and, according to prevailing theory, is of paramount relevance for performance (e.g., Hofer and Schendel, 1978; Markides and Williamson, 1994; Porter, 1987). Consequentially, few topics in corporate strategy research have attracted as much attention as the relationship between product diversification strategy and performance (Bowen and Wiersema, 2005; Chatterjee and Wernerfelt, 1991; Miller, 2004).

Narrative summaries of this line of research suggest that this literature is not only characterized by the use of a variety of theoretical perspectives and methodological approaches but also by the production of most often contradictory results (e.g., Datta, Rajagopalan, and Rasheed, 1991; Denis, Denis, and Sarin, 1997; Hoskisson and Hitt, 1990; Matsusaka, 1993; Ramanujam and Varadarajan, 1989). The impression conveyed is that the most generalizable conclusion as regards the nature of the linkage between diversification strategy and performance is that there is very few to generalize, in fact.

However, in contrast to narrative reviews, a recent meta-analysis on the diversification-performance linkage – that quantitatively integrates the results of 55 empirical studies – suggests something very different. Palich, Cardinal, and Miller (2000) report evidence for an inverted-U association between diversification and performance and argue that related diversification has positive and that unrelated diversification has negative performance effects.

The rationale advanced for the performance-superiority of firms with related business portfolios is that exclusively related diversifiers may realize benefits, most notably economies of scope, from transferring and exploiting across businesses capabilities,

2

Research Problems, Objectives, and Structure

know-how, and other valuable assets (Markides and Williamson, 1996; Penrose, 1959; Rumelt, 1974; Salter and Weinhold, 1979; Teece, 1980). At the same time, unrelated diversifiers had to cope with substantial costs of organizing complex operations (Jones and Hill, 1988; Markides, 1992; Nayyar, 1992).

In the meantime, these meta-analytic findings have become standard strategic management textbook knowledge (e.g., Johnson, Scholes, and Whittington, 2006). The Palich et al. study seems to be deemed the current edge in integrative research on the diversification-performance linkage. This may be due to the fact that, in contrast to narrative reviews, effect-size meta-analysis can generate results of superior validity as regards the actual nature of relationships between variables of interest. Most importantly, meta-analysis allows correcting for effect-size variation caused by study artifacts such as sampling error (Hedges and Olkin 1985; Hunter and Schmidt, 2004).

Nonetheless, the generalizability of Palich et al.’s findings appears problematic for methodological and substantive reasons. Palich et al.’s analysis integrates the results of research published by 1998. Since then the number of empirical studies published on the diversification-performance linkage has roughly doubled, however. Thus, it is not clear whether their findings still hold if a substantially enlarged sample of empirical analyses is used that includes also most recent research. Moreover, as meta-analysis methodology is relatively new to the management discipline, we have little knowledge of the reliability of associated methods. Specifically, it is not clear whether Palich et al.’s findings can be confirmed if alternative meta-analytic techniques are applied.

Moreover, in substantive terms, Palich et al.’s findings would suggest that management practitioners, in order to make diversification succeed, are well-advised to compose portfolios of a multibusiness firm in such a way that they comprise only related or similar businesses. However, simply following the maxim that relatedness of businesses – no matter in which respect – makes diversification succeed, will most likely be misleading. This is because there is selected empirical evidence that relatedness constitutes a multidimensional construct, and that type of relatedness matters to per-


3

formance (Farjoun, 1994; Markides and Williamson, 1994; Pehrsson, 2006; Robins and Wiersema, 1995; Stimpert and Duhaime, 1997; St. John and Harrison, 1999; Tanriverdi and Venkatraman, 2005).

In fact, the dimensions along which businesses may relate have long been neglected by diversification research. For more than two decades, particularly objective measures of diversification strategy have heavily relied on the hierarchy inherent in the standard industrial classification system (SIC) and, thus, exclusively on types of tangible-, physical-, product-based relatedness. This also implies that any meta-analysis summarizing this literature is necessarily limited.

More recently, selected authors who have questioned the content validity of commonly applied indicators of diversification for research on resource-based theories of the firm, have developed alternative measures of relatedness (e.g., Markides and Williamson, 1994; Robins and Wiersema, 1995). Robins and Wiersema (1995), for instance, model business interrelationships on the basis of technology flows among industries, and Farjoun (1994; 1998) models business interrelationships on the basis of human resource profiles of industries, to name but two examples. Evidence from these studies suggests that relatedness comprises not only tangible but also intangible dimensions.

And, particularly intangible relatedness is increasingly pointed at as being conducive to superior multi-business firm performance (e.g., Farjoun, 1998; Robins and Wiersema, 1995; Szeless, Wiersema, and Müller-Stewens, 2003; Tanriverdi and Venkatraman, 2005). In this line of research, authors argue that intangible assets are both valuable and, in contrast to physical assets, difficult to copy for competitors. Therefore, intangible relatedness could nurture competitive advantages and was more important to diversification success than tangible relatedness (e.g., Barney, 1997; Robins and Wiersema, 1995).

And yet, as this line of research is getting increasingly multi-faceted, one may observe that the majority of empirical studies has emphasized relatedness and synergy at the

4


operational level (cf. Datta, Rajagopalan, and Rasheed, 1991). This has happened at the expense of synergy that may be associated with aspects of dominant logic, distinctive competence, and effective management at the corporate, strategic level.

However, effective corporate management and leveraging distinct corporate-level competences – considered to be possible in portfolios with strategically related businesses rather than with operationally related businesses – may matter as much to multibusiness firm success as economies of the scope at the operational level (Grant, 1988; Hitt and Ireland, 1985; Prahalad and Bettis, 1986). Also, strategic relatedness may interact with operational relatedness to determine the ultimate nature of the performance implications of diversification strategy (D’Aveni, Ravenscraft, and Anderson, 2004; Grant, 1988; Hill, 1994; Harrison, Hall, and Nargundkar, 1993).

Moreover, ever since, there has been anecdotal evidence suggesting that effective corporate management may be the factor that reasons the success of specific types of multibusiness firms – namely those occasionally described as premium conglomerates (e.g., Shulman, 1999). Adding to this discussion, Michael Goold and colleagues have repeatedly used case-based research to emphasize that corporate parents require having “sufficient feel” for the critical success factors of single businesses if a portfolio is to be managed successfully (e.g., Goold, Campbell, and Alexander, 1994).

Accordingly, theory suggests that research on the diversification strategy and performance linkage may be informed by approaches that account for both aspects of economies of scope at the operational level as well as aspects of dominant management logic and effective corporate management at the strategic level. And yet, empirical research available on the dimensionality of the relatedness construct and respective associations with performance has not followed any explicit distinction of operational relatedness from strategic relatedness. Accordingly, it has not been established yet how precisely operational relatedness and strategic relatedness interact, and how they matter to diversification success.


5

In fact, to date, the diversification-performance literature as a whole offers sporadic empirical evidence only on how diversification strategy in terms of different types of relatedness impacts firm financial performance. As a consequence, there is still a considerable need for management advice on what business relatedness to strive for in order to make diversification not only succeed but also to maximize corporate performance (cf. Pehrsson, 2006).

It is against this background, that the overall purpose of this study is to examine a) what the current body of empirical research truly suggests about the linkage between diversification strategy and performance, and, b) if and what newly developed indicators of business relatedness can add to our understanding of the performance implications of diversification strategy. In order to answer these and a number of related questions of detail, I proceed as follows.

In the first part of the study, I elaborate on why Palich et al.’s results cannot put an end to the diversification-performance debate. I replicate Palich et al.’s meta-analysis and subsequently use a substantially extended sample of correlational estimates empirically observed between diversification and performance that includes also most recent research. This means that I offer a quantitative integration of the relevant empirical literature of unprecedented scope. I accumulate empirical findings from studies published between 1971 and 2005. Altogether, these studies draw on 52,116 empirical observations on the nature of the diversification-performance relationship that were made between 1940 and 2000.

In addition, I employ both the techniques suggested by Hedges and Olkin (1985) and the techniques suggested by Hunter and Schmidt (2004) for meta-analytically testing the impact of contingency variables on the diversification-performance association. This proceeding allows me to examine the stability of findings across meta-analytic methods. Ultimately, I use this part of the study to tease out the nature of the linkages between product diversification strategies (as indicated by traditional measurement

6


schemes) and accounting-, market-, and growth-based performance that is truly suggested by the body of empirical research.

In the second part of this study, I pay particular attention to the performance effects of different types of business relatedness at the operational level as indicated by recently developed indices – an issue that has to be neglected in the meta-analysis due to the lack of respective empirical research. For this purpose, I study a total of 350 large multibusiness firms active in seven manufacturing industries over the period 2004-2006.

Specifically, I test whether physical-, product-based relatedness (Palepu, 1985), resource-based, technological relatedness (Robins and Wiersema, 1995), and skill-based relatedness (Farjoun, 1998) relate differently to performance. Amongst others, this allows me to verify if indeed intangible relatedness matters more to diversification success than tangible relatedness.

Moreover, next to measures of performance conventionally used in strategy research, I employ the excess value measure methodology suggested by Berger and Ofek (1995). The excess value measure originates from the finance literature and can be used to examine whether diversified firms trade at a discount or premium relative to imputed values of portfolios of stand-alone firms. Thus, it promises high content validity in terms of capturing whether corporate wholes may indeed add up to more than the sum of their parts, i.e. if net synergies are realized. If I am not mistaken, this also means that this study is the first to test the associations between a variety of indicators of different types of relatedness, i.e. synergy potentials, and the excess value measure as the best possible indicator of actual synergy realization.

In addition, I explore in this context if the nature of the performance impact of specific types of relatedness is a function of industry. This is important as industry is variously pointed at as a key contingency variable in diversification-performance research. Furthermore, as rules of competition, success factors, and the value of assets vary across industries (e.g., Markides and Williamson, 1996; Porter, 1985), it is possible that also


7

the nature of the performance impact of specific types of relatedness and types of scope economies is a function of industry.

Finally, I examine in this part of the study the association between operational relatedness and absolute values of the excess value measure (1995). This is because, to date, empirical research has not provided evidence in fact that operational relatedness can be exploited by multibusiness firms in ways that allow them to turn the frequently reported diversification discount (e.g., Berger and Ofek, 1995; Comment and Jarrell, 1995; Lamont and Polk, 2002; Lang and Stulz, 1994; Mackey and Barney, 2006) into a premium. However, if relatedness is considered a proxy for potential economies of scope (e.g., Robins and Wiersema, 2003), and if, according to resource-based theory, synergies by means of scope economies is the major justification for multibusiness firms to exist (e.g., Kanter, 1989; Mahoney and Pandian, 1992; Penrose, 1959; Porter, 1985), then some at least of the diversified firms that show the highest levels of business relatedness should not trade at a diversification discount but at a premium.

In the third part of this study, I use structural equation modeling in order to be able to first test the validity of Grant’s (1988) two-factor conceptualization of the relatedness construct that distinguishes relatedness at the operational level from relatedness at the strategic level. In response to Prahalad and Bettis’ (1986) award-winning paper on the dominant general management logic, Grant was the first to suggest that there are merely two but fundamental dimensions of relatedness – namely operational and strategic relatedness. Operational relatedness referred to similarities at the process-level and, amongst others, to product-based and technological similarities. Contrarily, strategic relatedness referred to similarities at the corporate-level and described if businesses were similar to manage in terms of functions such as resource allocation (Grant, 1988).

In the third part of the analysis, I also test associations between factors of strategic relatedness, operational relatedness, and performance. Thus, this analysis is the first in the field to examine the performance effects of strategic relatedness vis-à-vis opera-

8


tional relatedness in a single, integrative model using a multitude of objective indicators for each factor. Also, the sample used is considerably larger than the samples used in prior research in this domain.

The overall structure of the study is as follows. Chapter 2 presents narrative review elements as well as setup and results of the quantitative meta-analyses on the diversification-performance linkage. Chapter 3 comprises cross-industry and industry-specific regressions and t-tests that I use to examine the linkage between objective indicators of operational relatedness and multibusiness firm performance. Chapter 4 presents the structural equation modeling examining the two-factor structure of the relatedness construct as suggested by Grant and the performance implications of strategic relatedness vis-à-vis the effects of operational relatedness. I offer a discussion of respective findings in each of chapters 2-4 and summarize results and implications of the three analyses in chapter 5. Ultimately, in the limitations section I close with some remarks on the use of meta-analytic methods in strategic management research.

2 Meta-Analysis on the Relationship between Diversification and Performance

2.1 The Construct “Product Diversification (Strategy)” Product diversification strategy determines which businesses a corporation should be in. As it defines the scope of a firm’s activities, it is considered a cornerstone of corporate strategy (e.g., Hofer and Schendel, 1978; Markides and Williamson, 1994; Porter, 1987). While there is a great deal of variation in the way product diversification is defined in the literature (Datta, Rajagopalan, and Rasheed, 1991; Ramanujam and Varadarajan, 1989), traditional understandings of the term have a common denominator. To enumerate but some examples, Berry (1971, p. 380) defines product diversification as an increase in the number of industries in which firms are active. Pitts and Hopkins (1982, p. 620) consider corporations product-diversified if they simultaneously operate multiple different businesses. And, Ramanujam and Varadarajan (1989, p. 525) define diversification as the entry of a firm into new lines of activity.

These and the majority of other definitions draw on a notion of diversification that has somewhat been inspired by the early work of Ansoff (1957) in which he used the term “diversification” to describe a growth strategy that involves entering new markets with new product lines (p. 114). Following Ansoff, a product line is described by the physical and functional characteristics of individual products and a market is described in terms of specific customer needs (p. 113). It is this logic that underlies the traditional understanding of diversification as product-market diversification (Salter and Weinhold, 1979, p. 5). If definitions draw on terms such as industries, businesses, (strategic) business units, or lines of activity to describe corporate diversification, they essentially refer to the idea of product-markets.

10

Meta-Analysis on the Relationship between Diversification and Performance

Moreover, the term diversification has at its root the word “diverse” which literally means “different, unlike, distinct, and separate” (Pitts and Hopkins, 1982, p. 620). Accordingly, product-diversified firms are understood to be active in multiple, distinct product-markets. This is the traditional understanding of product diversification, a general perspective that I adopt also in this research: Product diversification refers to the spreading of firm activities across a number of distinct product-markets.

In practice, however, firms use different logics to compose portfolios; they are said to pursue different product diversification strategies. Rumelt, for instance, defines a firm‘s product diversification strategy as its “[…] commitment to diversity per se, together with the strengths and skills or purposes that span this diversity, shown by the way in which business activities are related one to another“ (1974, p. 29). In line with this definition, scholars in this field of research usually consider product diversification strategies and the inherent logic of business linkage being characterized by type of business relatedness. Accordingly, I understand the term product diversification strategy as follows: Product diversification strategy refers to the spreading of firm activities across a number of distinct product-markets that are more or less related on to be specified dimensions.

Please note that – in line with the vast majority of diversification-performance research – I do not distinguish the terms diversity (a state) and diversification (a process) in the analyses to come. In addition, I abstract from mode of diversification in terms of internal development versus acquisitions.

The Construct “Product Diversification (Strategy)”

11

In order to understand the central construct of this work in-depth, I provide in the following a brief overview of traditional empirical measures of product diversification (strategy) and how they developed over time. 2.2 Empirical Measures of Product Diversification (Strategy) The most popular empirical measures of product diversification (strategy) are (a) the simple business count approach, (b) weighted business count approaches, (c) Caves et al.’s concentric index and weighted index of diversification, (d) the Rumelt classification scheme, (e) the entropy measure of diversification strategy, (f) the broad and mean narrow spectrum diversity measure, and, more recently, (g) various relatedness indices. (a) Simple Business Counts This approach measures diversification by numerically counting the number of businesses, i.e. most commonly the number of four-digit standard industrial classification (SIC) industries (Bowen and Wiersema, 2005; Ciscel and Evans, 1984; Carter, 1977). While the business count method is one of the earliest measures of diversification (Keats, 1990) and enjoys limited popularity in strategic management research nowadays, it is still frequently applied in the finance literature. Here, researchers measure diversification by means of comparing single- to multiple four-digit SIC segment firms (Clarke, 2004; Singh, Mathur, and Gleason, 2004; Mansi and Reeb, 2002). However, this simple business count measure is insensitive to the size distribution and importance of businesses, a weakness that is overcome by weighted business count methods. (b) Weighted Business Counts Weighted business count measures take into account the number of businesses as well as the share Pi of each business within the firm in terms of sales, assets, employees, or income. Most popular are the inverse of the Herfindahl index and the Entropy index. The Herfindahl index of diversification DH takes the functional form:

12

DH

Meta-Analysis on the Relationship between Diversification and Performance n

1 ¦ Pi * Pi i 1

where Pi is the share of the ith business within the firm which is weighted by itself, and n is the number of businesses (e.g., Berry, 1975). The Entropy index DT is similar; however, each business’ share is weighted by the logarithm of 1/Pi and the sum of the weighted shares is not deducted from unity (Jacquemin and Berry, 1979):

DT

n

¦ P * 1 / P i

i

i 1

Both simple and weighted business count methods originate from measures of industrial structure (Jacquemin and Berry, 1979). Distinguishing firms merely on the basis of the number of businesses and the degree of activity distribution, these measures fail to account for the logic, i.e. the strategies, underlying diversification, however (Keats, 1990). (c) Concentric and Weighted Index of Diversification Another continuous measure of diversification initially developed for research on industrial organization is the Caves et al. (1980) concentric index of diversification. Next to the number of businesses and the extent of activity distribution, it factors in the nature of relations between businesses in a portfolio (Caves et al., 1980, p. 199). The concentric index of diversification DC takes the form:

DC

¦ p *¦ p j

j

i

* d ij

i

where pj is the proportion of a company’s total employment in four-digit SIC business j, and dij is a weight whose value is a function of the relations between four-digit

Empirical Measures of Product Diversification (Strategy)

13

businesses i and j in the SIC-system. d takes a value of zero if i and j are four-digit businesses within the same three-digit industry, one if they are in different three-digit industries but the same two-digit industry group, and a value of two if they are in different two-digit industries (Caves et al., 1980, p. 199).

In addition to the concentric index, Caves et al. have also suggested the weighted index of diversification. While the concentric index accounts for the relations between all secondary businesses in a portfolio, the weighted index assigns particular significance to a firm’s primary business and diversification away from the base activity (Caves et al., 1980, p. 200). The weighted index of diversification DW takes the functional form:

DW

¦p

i

* d ih

i

where pi is the proportion of a company’s total employment in four-digit SIC business i, and dih is a weight whose value equals zero if the four-digit business i is included within the three-digit base (i.e. largest) industry, one if it is in a different three-digit industry within the same two-digit industry group, and two if it lies within a different two-digit industry group (Caves et al., 1980, p. 200).

In corporate strategy research, both indices have been applied with sales-based instead of employment-based weights. The concentric index has been used by Robins and Wiersema (1995) and Montgomery and Hariharan (1991), for instance. And the weighted index has been employed in Wan and Hoskisson (2003) and Gedajlovic and Shapiro (2003), to name but two studies. (d) The Rumelt Classification Scheme Rumelt’s measurement scheme of diversification strategy was largely developed in response to weaknesses inherent in the SIC system (Montgomery, 1982). Following

14


Rumelt (1982, p. 360) “the varying degrees of breadth in the SIC classes and the implicit assumption of equal dissimilarity between distinct SIC classes” is the most serious shortcoming of SIC based measures. For this reason, drawing on work by Wrigley (1970), Rumelt developed an alternative, categorical measurement scheme of diversification strategy that involves subjective assessments.

Specifically, it is the specialization ratio, the related ratio, and the vertical ratio that are suggested for classifying firms into up to nine categories of diversification strategy (cf. Rumelt, 1974, p. 30). However, in empirical studies, the vast majority of researchers employing the Rumelt scheme limit their examination to measuring the specialization ratio and the related ratio and to classifying firms into four major groups of diversification strategy: single business, dominant business, related business, and unrelated business. The ratio-specific threshold levels suggested by Rumelt for classifying firms into these four classes of diversification strategy are shown in figure 2.1.

The specialization ratio is defined as the “proportion of a firm’s revenues derived from its largest single business” and the related ratio as the “proportion of a firm’s revenues derived from its largest single group of related businesses” (Rumelt, 1974, p. 31). Using Rumelt’s original terms, the specialization ratio is to reflect a firm’s commitment to diversity per se, while the related ratio is to indicate the relatedness between businesses. These ratios, in turn, are based on a discrete business being defined as one that is strategically independent of the firm’s other businesses, and businesses being by definition related when a common skill, resource, market, or purpose applies to each (Rumelt, 1974, p. 29).

It is at researchers’ discretion to identify discrete businesses and relatedness. As this introduces a considerable amount of subjectivity into the Rumelt scheme of diversification strategies, the scheme has been criticised by many for low reliability. Nonetheless, Datta, Rajagopalan, and Rasheed (1991) describe the Rumelt scheme as the most widely used diversification measure, and Bergh (2001) and Mayer and Whittington (2003) label it the “gold standard” in the field.


15

0.0

Unrelated Business 0.7 Related Ratio

Related Business

DominantUnrelated

Dominant Business

Single Business

1.0 0.7

0.95

0.0

Specialization Ratio

Figure 2.1

Diversification Strategies defined in terms of the Specialization Ratio and the Related Ratio (Source: Rumelt (1974, p.31))

In this context, it is important to note that a cursory look at the diversification literature indicates that the specialization ratio is also used as a stand-alone, continuous measure of concentration (Chu, 2004; Picard and Rimmer, 1999; Hill and Snell, 1988) and its complement as a measure of diversification (Skaggs and Droege, 2004; Hall and Lee, 1999). Moreover, the specialization ratio is often applied in conjunction with identifying a firm’s major or primary business on the basis of SIC industries. Similarly, the related ratio is used in isolation from the Rumelt scheme and as a continuous indicator of the level of business relatedness (Bethel and Liebeskind, 1993; Hill and Snell, 1988; Michel and Shaked, 1984).

16


(e) The Entropy Measure of Diversification Strategy The entropy measure of diversification strategy belongs to the group of weighted business count measures as described above. Originally proposed by Jacquemin and Berry (1979), the entropy measure has gained popularity in strategic management research with the publication by Palepu (1985). In the light of Rumelt’s categorical measurement scheme being of subjective nature and time-consuming, Palepu suggested the use of the entropy measure. It enabled researchers to combine the objectivity and ease of computation of business count approaches with “[…] the essential richness of Rumelt’s approach”, [i.e.] […] the degree of relatedness among the various product segments” (Palepu, 1985, p. 244).

In this respect, it is again the SIC system that is used to define related and unrelated businesses within a portfolio. Different four-digit SIC industries within the same twodigit industry group are treated as related businesses, and businesses from different two-digit SIC industry groups are classified as unrelated. By this means, a firm’s total diversity is decomposable into an unrelated component indexing the degree to which a firm’s activity is distributed across unrelated industry groups and a related component indexing the degree of activity distribution among related businesses within industry groups. The entropy index of unrelated diversification DU takes the functional form:

DU

m

1 ¦ P j * ln 1 / P j j 1

where Pj is the share of the jth two-digit industry group’s sales in the total sales of the firm, and m is the number of two-digit industry groups (Palepu, 1985, p. 253). In contrast, the entropy index of related diversification DR is calculated from:

DR j

¦H P i j

j

i

* ln 1 / P j i


17

where DRj is the related diversification arising out of operating in several businesses within an industry group j, and Pji is the share of the segment i of group j in the total sales of the group. As a firm may operate in several industry groups, its total related diversification DR equals:

DR

M

¦ DR

j

*Pj

j 1

where Pj is the share of the jth group’s sales in the total sales of the firm. Finally a firm’s total diversification DT is the sum of its unrelated diversification and its total related diversification:

DT

DU DT

As shown in the section on weighted business counts, a firm’s total diversification DT takes the functional form:

DT

n

1 ¦ Pi * ln 1 / Pi i 1

where Pi is the share of the ith four-digit business within the firm, and n is the number of four-digit businesses.

Palepu in his seminal paper (1985) uses the continuous entropy indices and classifies firms as “predominantly related diversifiers” and “predominantly unrelated diversifiers”. Firms with above median related diversification and below median unrelated diversification are identified as related diversifiers. And firms with above median unrelated diversification and below median related diversification are identi-fied as unrelated diversifiers. In contrast to Palepu, the vast majority of strategy researchers apply

18


the component indices of the entropy measure of diversification strategy in purely continuous fashion, however. (f) Broad and Mean Narrow Spectrum Diversity In response to Palepu (1985), Varadarajan and Ramanujam (1987) proposed the use of simpler measure of diversification strategy that does not require detailed business level data as does the entropy index and that can nonetheless capture degree and nature of diversification. Building on Wood (1971), the two dimensions broad spectrum diversity (BSD), i.e. the number of two-digit SIC industries, and mean narrow spectrum diversity (MNSD), i.e. the average number of four-digit SIC codes within two-digit SIC codes, are employed to categorize firm into groups of diversification strategy. Using sample-specific, average values of BSD and MNSD firms are classified as “firms with very low diversity”, “predominantly related diversifiers”, “predominantly unrelated diversifiers”, and “firms with very high diversity” (Varadarajan and Ramanujam, 1987, p. 383).

In the diversification literature the BSD/MNSD indices are used, on the one hand, indeed for classifying firms into diversification strategies (e.g., Palich, Carini, and Seaman, 2000; Qian, 1997) but, on the other hand, also as separate, continuous measures of related diversification (MNSD; e.g., Lubatkin et al., 1993), unrelated diversification (BSD; e.g., Sambharya, 2000), and total diversification (NSD), i.e. the total number of four-digit industries in a portfolio (e.g., Kim and Hoskisson, 2004). (g) Various Relatedness Indices Except for the Rumelt scheme, all of the above measures of diversification strategy focus exclusively on relatedness in terms of tangible, physical, product-based similarities between businesses in a portfolio. This is due to the fact that these measurement schemes build on the SIC system to assess business relatedness.


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This approach has been strongly criticized in the literature more recently. It has been argued that SIC-based measures largely lack content validity for research on resourcebased theories of the firm (e.g., Robins and Wiersema, 2003; Markides and Williamson, 1994). At least, any unidimensional understanding of the relatedness construct in terms of product-based similarities is seriously questioned and deemed falling short of grasping the complexity of potential (resource-based) linkages between businesses in a portfolio.

As a consequence, alternative, archival data-based measures of diversification have been proposed and applied that emphasize aspects of diverse types of business similarity. Robins and Wiersema (1995) model business interrelationships on the basis of technology flows among industries and Farjoun (1994; 1998) on the basis of human resource profiles of industries, to name but two examples. To date, there is very little empirical research available that uses these measures, however. Amongst others this may due to the complexity of calculating these relatedness indices for larger samples.

In addition to measures of relatedness based on archival data, relatedness is also increasingly measured on the basis of manager self-report data. It is argued that managers’ conceptualizations of relatedness may significantly differ from the relatedness that is measured from outside the corporation by drawing on objective measures of diversification strategy (Stimpert and Duhaime, 1997). For this reason, stressing the perceptual, behavioral perspective, questionnaire surveys are used in which managers are asked to signify the importance of a set of relatedness dimensions that are subsequently usually factor-analyzed to identify relatedness dimensions relevant to managerial practice (e.g., Pehrsson, 2006; Stimpert and Duhaime, 1997). However, again very few empirical research and almost none replication studies is available in this domain of research.

I will elaborate in detail on new measurement schemes of business relatedness in the course of part two and part three of this study in which I examine closer the associations between dimensions of relatedness and performance. For the meta-analyses they

20


do play a minor role as there are hardly any empirical studies available using these indices that could be integrated. 2.3 The Construct “Performance” In the subsequent analyses I follow the construct conceptualization of “financial performance” that was initially suggested by Venkatraman and Ramanujam (1986) and later meta-analytically verified by Combs, Crook, and Shook (2005).

Combs et al. use the label “organizational performance” to describe the idea of “financial performance” and in order to delimit it from “operational performance”. The authors define organizational performance as to be indicated by all “measures that depict [economic] outcomes attributable to the interaction among all value creation activities and the organization’s environment” (p. 267). Contrarily, indicators of operational performance are understood to reflect outcomes that can be tied to a specific value chain activity, such as indicators of product quality or innovation. Combs and colleagues meta-analytically derive that the construct of organizational performance comprises the three distinct dimensions accounting-based performance, (capital) market-based performance, and growth-based performance. Building on their quantitative concept validation, I limit my analyses to investigating the relationships between diversification and these three dimensions of organizational performance.

With a view to the empirical studies located and deemed relevant for the meta-analyses to follow this means that return on assets, return on equity, return on investment, return on sales, net income, and cash flow per sales or per assets are classified as accountingbased measures of performance. Tobin’s q, market-to-book ratios, the Sharpe ratio, the Treynor index, Jensen’s alpha, and stock returns are classified as market-based measures of performance. Eventually, sales growth, market share growth, asset growth, and earnings per share growth are considered measures of growth-based performance.

The Construct “Performance”

21

In addition, I limit my analyses to organizational performance at the corporate level. This is because I am particularly interested in the economic justification of multibusiness firms and diversification per se. Accordingly, I seek to answer the overall question whether diversified firms can make the corporate whole add up to more than the sum of its standalone parts (Collis and Montgomery, 2004; Goold and Luchs, 1993; Porter, 1987) rather than understanding the performance of individual business units.

Following this logic, I adopt a perspective in this research that is based on a key proposition in strategic management research: In order to make the corporate whole add up to more than the sum of its parts, multibusiness firms must achieve (positive net) synergy (cf. Kanter, 1989; Porter, 1985; Tanriverdi and Venkatraman, 2005; Teece, 1982). The word synergy is derived from the Greek word synergos and literally means “working together”. “In business usage, synergy refers to the ability of two or more units or companies to generate greater value working together than they could working apart” (Goold and Campbell, 1999, p. 133). More precisely, diversification and institutionalizing a multibusiness firm is considered economically reasonable only if synergistic effects between formerly distinct businesses exceed the costs of multibusiness organization. As a consequence, the idea of achieving and maximizing (positive net) synergy as the overall objective of diversification strategies, i.e. the goal to make 1 + 1 = 3 or better 4, will guide my work and be recurring throughout the analyses.

In the literature, a multibusiness firm that excels at realizing positive net synergy is sometimes considered to have a “corporate advantage” or “parenting advantage” over alternative parents (e.g., Collis and Montgomery, 2004). Theoretically, a multibusiness firm maximizes its value if it is active only in those businesses in which it can draw on a corporate advantage to add value. This general heuristic logic that guides my work is illustrated in figure 2.2.

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Value Corporate Advantage

Benefits/ Gross Synergies Standalone

Net Synergies

Net Synergies

Costs

Standalone

Sum of the Parts

Sum of the Parts

(Businesses A, B, and C)

(Businesses A, B, and C)

Multibusiness Firm A

Multibusiness Firm B

Standalone

Single Business A

Figure 2.2

Single Business B

Single Business C



Heuristic Logic of the Overall Goal of Diversification Strategy (Source: Adapted from Hungenberg (2003, p. 10)

2.4 Theory on Product Diversification as a Cause of Performance The ultimate impact that product diversification strategy will have on performance results as a function of benefits associated with diversification net of costs associated with diversification. In particular in terms of benefits, different fields of research suggest alternative foci.

Industrial organization theory has traditionally emphasized potential benefits of diversification arising from market power. Diversification may lead to increased market power that is exercisable through cross-subsidization, predatory pricing, reciprocity in buying and selling, and the creation of entry barriers (Caves, 1981; Markham, 1973; Saloner, 1987).

Theory on Product Diversification as a Cause of Performance

23

Scholars from institutional and financial economics have highlighted benefits from internal factor market efficiencies. Internal markets for capital, human-, and other resources may be more efficient in terms of allocation than external markets if they build on superior information flows and controls (Lang and Stulz, 1994; Servaes, 1996; Williamson, 1975).

The finance literature has also variously pointed at benefits associated with riskreduction. By combining businesses that are not perfectly correlated in terms of cash streams, stability of earnings may be achieved (Higgins and Schall, 1975; Lewellen, 1971). This coinsurance effect would facilitate greater debt capacity and create value through an increased tax shield and the smoothing out of gains and losses (Singh, Mathur, and Gleason, 2004).

Moreover, studies from strategic management have emphasized the potential for synergies. In this literature, the term synergy has often been used synonymously with the term economies of scope (Tanriverdi and Venkatraman, 2005). Economies of scope may accrue from transferring, sharing, and leveraging valuable resources (Barney, 1997; Markides and Williamson, 1994; Penrose, 1959; Rumelt, 1982; Salter and Weinhold, 1979; Teece, 1982). Economies of scope refer to the sub-additivity of production costs (Baumol et al., 1982) and arise when an imperfectly divisible asset with excess capacity can be used to produce several outputs (Hill, 1994). If two or more businesses share such factors of production their joint production costs may be less than the sum of their stand-alone production costs. This presupposes that the structure of the diversified firm is used to economize on transaction costs as otherwise the usage of excess capacity could be sold to other companies (Teece, 1982). If this applies, scope economies are achieved that can improve the performance of each business in a portfolio and the performance of the corporation as a whole (D’Aveni, Ravenscraft, and Anderson, 2004) – provided the corporation and relatedness is properly managed (Hill, 1994; Nayyar, 1992).

24


Synergy, however, may not only be attained by means of scope economies or subadditivities of production costs. Instead, if businesses in a portfolio can jointly draw on resources that are complementary, synergies in the form of super-additivities may arise (Milgrom and Roberts, 1995). A recent example from empirical research is that types of knowledge can be complementary for instance (Tanriverdi and Venkatraman, 2005).

Finally, the work by Prahalad and Bettis (1986) on “dominant management logic” suggests that effective corporate management in terms of fulfilling functions such as coordination, monitoring, and control can become a distinct corporate skill that adds value to the multibusiness firm. Effective corporate management is suggested to be feasible if businesses share strategic similarities and can be managed with a common paradigm, similar systems, and on the basis of routines developed though past experiences in a dominant business (Grant, 1988; Prahalad and Bettis, 1986). In other words, value may be added if a corporate parent identifies a parenting opportunity to improve the performance of a stand-alone business and draws on its own strengths, such as top management skills and management processes, to realize this opportunity (Goold, Campbell, Alexander, 1994).

Nonetheless, the option of realizing the benefits of diversification comes at costs. Diversification is variously assumed to impose substantial costs of coordination and motivation due to increased information asymmetries and interest divergences (Coase, 1937; Jones and Hill, 1988; Markides, 1992; Nayyar, 1992; Porter, 1985). Moreover, increasing portfolio diversity may entail inefficiencies due to growing strains on top management, a lack of adaptability to environmental change, and politicization of decision-making (Grant, Jammine, and Thomas, 1988; McDougall and Round, 1984). Particularly, strategic variety and conflicting management styles may create tensions and problems between headquarters and businesses and lead to wrong management decisions in the end (Goold and Luchs, 1993; Prahalad and Bettis, 1986).

Evidence on Product Diversification as a Cause of Performance

25

2.5 Evidence on Product Diversification as a Cause of Performance The question whether and how product diversification actually impacts firm performance has been researched in a vast number of individual empirical studies over the past 40 years. Qualitative summaries of this literature suggest that this line of research is characterized by the use of a variety of theoretical perspectives and methodological approaches as well as by the production of different and often contradictory results (e.g., Datta, Rajagopalan, and Rasheed, 1991; Denis, Denis, and Sarin, 1997; Hoskisson and Hitt,1990; Matsusaka, 1993; Ramanujam and Varadarajan, 1989). The impression conveyed by these narrative reviews is that the most generalizable conclusion as regards the nature of the diversification-performance linkage is that there is very few to generalize, in fact.

At the same time, these narrative reviews suggest that the multitude and variety of individual studies should be dealt with by drawing on some framework to meaningfully structure the research on the diversification-performance linkage. Accordingly, and with a view to the prime focus of this analysis, I follow Datta, Rajagopalan, and Rasheed’s (1991) suggestion and distinguish in a first step those studies that examine empirically the linkage between diversification in terms of degree and performance from those studies that examine the linkage between diversification in terms of type and performance.

Subsequently, I distinguish studies that offer overall empirical evidence for the thesis that diversification degree or type matter to performance from studies that find that diversification does not matter to performance as well as from studies that suggest that the (true) linkage between diversification (strategy) and performance emerges under contingencies only. Finally, I examine closer the studies that suggest that diversification matters to performance and highlight how diversification matters, i.e. here I focus on the nature of the association that is suggested by prior research. Figure 2.3 summarizes this logic and structure.

26


Empirical Evidence on the Diversification-Performance Linkage

Degree of Diversification and Performance

Type of Diversification and Performance

What do studies suggest as regards the linkage between diversification in terms of number of businesses and activity distribution and performance?

What do studies suggest as regards the linkage between diversification strategy in terms of relatedness and performance?

Degree matters

Degree does not matter

Positive Association

Negative Association

Figure 2.3

Contingencies Impact Linkage

Quadratic Association

Type matters

Related Diversification is Superior

Type does not matter


Unrelated Diversification is Superior

Classification of Empirical Evidence from Individual Studies

The initial distinction of degree of diversification from type of diversification draws on conceptual as well as methodological aspects. Degree of diversification refers to the number of businesses and the extent of activity distribution that characterize multibusiness firms’ portfolios. Contrarily, type of diversification refers to type of diversification strategy and involves some kind of element of assessing the relatedness or similarity between businesses in empirical measurements. In other words, degree of diversification generally refers to diversification or diversity per se, i.e. without further specifying this diversity, while type of diversification (also) refers to the logic of business linkage in portfolios. Traditionally, measures of the degree of diversification have been employed by researchers using an industrial organization perspective (e.g., Bass, Cattin, and Wittunk, 1977; Gort, 1962; Ravenscraft, 1983) and measurement schemes of the type of diversification strategy have been applied by researchers from the area


27

of strategic management (e.g., Bettis and Hall, 1982; Grant, Jammine, and Thomas, 1988; Palepu, 1985; Robins and Wiersema, 1995; Rumelt, 1974). In the literature, the degree of diversification is usually measured by drawing on continuous indicators of diversification, such as (weighted) business counts, while type of diversification strategy is usually operationalized by means of drawing on categorical measurement schemes, such as the Rumelt typology. However, while it is common in the literature to conceptually distinguish degree of diversification from type of diversification, it is to be mentioned at this stage that it is also common among empirical researchers to use continuous measures of diversification to establish categories of diversification strategy (e.g., Ciscel and Evans, 1984; Palepu, 1985; Varadarajan, 1986) and to convert categorical measurements into continuous scales indicating degree (e.g., Hoskisson et al., 1993; Keats and Hitt, 1988; Lubatkin, Merchant, and Srinivasan, 1993). As a consequence, the classification of research that I use here draws more strongly on the distinction of the concepts of degree and type than on the distinction of the methods of measuring degree on continuous scales or on categorical schemes.

Put differently, primary studies classified here as offering evidence on the diversification degree-performance association are not exclusively based on “pure” continuous measurements. Next to empirical observations using the traditional continuous scales such as simple and weighted business counts (e.g., Chen and Kim, 2004; Ciscel and Evans, 1984; Nachum, 2004) or the Caves’ indices (e.g., Gedaijlovic et al., 2003; Wan and Hoskisson, 2003), also studies that use the Rumelt typology and code it in ascending order on an ordinal scale (e.g., Lubatkin and Chatterjee, 1994; Keats and Hitt, 1988) are assigned to the group of studies examining the degree-performance linkage.

Similarly, primary studies classified as offering evidence on the diversification typeperformance association are not exclusively based on “pure” categorical measurements. Next to studies employing the Wrigley or Rumelt typology to distinguish categories of diversification strategy (e.g., Hoskisson, 1987; Mayer and Whittington, 2003; Rumelt, 1974), also studies forming categories from continuous measures by drawing

28


on the schemes of broad and mean narrow spectrum diversity (e.g., Palich, Carini, and Seaman, 2000; Ramirez-Aleson and Escuer, 2002) or the entropy measure of diversification strategy (e.g., Hall and St. Jon, 1994; Palepu, 1985), for instance, are categorized as studies testing the type-performance relationship.

Apart from this particularity, studies making use of the complement of the specialization ratio (e.g., Gort, 1962, Hall Jr. and Lee, 1999; Jones et al., 1977) are classified as studies examining the degree-performance association. Contrarily, studies employing newly developed relatedness indices (e.g., Farjoun, 1998; Robins and Wiersema, 1995; Szeless, Wiersema, and Müller-Stewens, 2003) that are explicitly designed to measure differences between types of portfolios independent of the number of businesses are assigned to the group of analyses that offer evidence on the linkage between type of diversification and performance.

Please note that for the qualitative review, I list only those studies the explicit focus of which is to illuminate the diversification (strategy)-performance association. The goal of this section is to show what major studies suggest in terms of the nature of the diversification-performance linkage. That is to say that in this section I limit the overview to studies that the uninformed reader would normally consult to understand more about the focal relationship. In the quantitative meta-analysis to follow later, I am going to include – for methodological reasons – as well studies that offer evidence on the diversification-performance linkage despite these studies’ focus being on other topics. In these studies diversification is used as a control variable, for instance. Please be also aware that a few studies are classified as such that report evidence on whether diversification matters or not and simultaneously as such that offer evidence on specific contingency factors impacting the diversification-performance linkage.

Figure 2.4 shows an overview of major studies that offer empirical evidence on the relationship between diversification in terms of degree and performance vis-à-vis major studies that offer empirical evidence on the relationship between diversification in


29

terms of type and performance. A brief glimpse suffices already to understand that both types of diversification-performance linkages have been extensively researched.

The overview also indicates that the question whether degree and type of diversification are related to performance has not been answered unequivocally. Nonetheless, in both cases (degree and type), studies that suggest that diversification matters to performance seem to exceed in number the studies that suggest that diversification is not related to performance. Moreover, in terms of both the linkage between degree of diversification and performance and between type of diversification and performance a great deal of studies suggests that the nature of the linkage largely emerges under contingencies.

In figure 2.5 I break down further those studies that suggest that there are significant effects of degree and type of diversification on performance. With a view to degree of diversification, I distinguish studies suggesting a positive association from studies that suggest a negative association and from studies proposing a quadratic relationship. With a view to type of diversification, I distinguish those studies that suggest that related diversifiers outperform unrelated diversifiers from those ones that suggest the opposite.

It is evident that among the studies arguing for significant performance effects of diversification there are again no unequivocal answers to the question how precisely degree and type of diversification relate to performance.

Figure 2.4

Type matters to Performance Barton (1988) Bass et al. (1977) Bettis (1981) Bettis and Mahajan (1985) Capon et al. (1988) Chatterjee (1986) Chatterjee and Blocher (1992) Ciscel and Evans (1984) Dubofsky and Varadarajan (1987) Farjoun (1998) Elgers and Clark (1980) Hall Jr. and St. John (1994) Holzmann et al. (1975) Hoskisson (1987) Lecraw (1984) Lubatkin and Rogers (1989) Luffman and Reed (1982) Markides and Williamson (1994) Mayer and Whittington (2003) Melicher and Rush (1974) Michel and Shaked (1984) Palepu (1985) Ravenscraft (1983) Qian (1997) Ramirez-Aleson and Escuer (2002) Robins and Wiersema (1995) Rumelt (1974) Rumelt (1982) Szeless et al. (2003) Varadarajan (1986) Varadarajan and Ramanujam (1987) Weston et al. (1972) Zhao and Luo (2002)

Contingencies Impact Linkage Bergh (1995a) Chen and Kim (2000) Gassenheimer and Keep (1995) Gedaijlovic et al. (1993) Geiger and Hoffman (1998) Geringer et al. (2000) Hall Jr. and Lee (1999) Hill et al. (1992) Jones et al. (1977) Lee et al. (2003) Lins and Servaes (2002) Nachum (2004) Narasimhan and Kim (2002) Park (2002) Servaes (1996) Tallman and Li (1996) Wan and Hoskisson (2003) Wernerfelt and Montgomery (1986)

Degree does not matter Beattie (1980) Carter (1977) Chang and Thomas (1989) Delios and Beamish (1999) Gort (1962) Grinyer et al. (1980) Li and Wong (2003) Lubatkin et al. (1993) Sharma and Kesner (1996) Singh, Mathur, and Gleason (2004)

Bettis and Hall (1982) Bishara (1980) Busija et al. (1997) Hill (1983) Hill and Snell (1988) Kaul (2003) Lim and Teck (1995) Melicher and Rush (1973) Smith and Weston (1977)

Type does not matter

Type of Diversification

Busija et al. (1997) Christensen and Montgomery (1981) Chu (2001) Ciscel and Evan (1984) Gomez-Mejia (1992) Grant and Jammine (1988) Hamilton and Shergill (1992) Hill et al. (1992) Hoskisson (1987) Kaul (2003) Lecraw (1984) Lubatkin and Chatterjee (1991) Markides and Williamson (1996) Mayer and Whittington (2003) Montgomery (1985) Nathanson and Cassano (1982) Palich et al. (2000)


Empirical Evidence on the Linkage between Degree and Type of Diversification and Performance – Level 1 and 2

Amit and Livnat (1988a, b) Bengtsson (2000) Bühner (1983) Chang and Choi (1988) Comment and Jarrell (1995) Gedaijlovic et al. (1993) Geiger and Hoffman (1998) Grant et al. (1988) Hill and Snell (1988) Hoskisson et al. (1993) Imel and Helmberger (1971) Jose et al. (1986) Keats and Hitt (1988a) Kim et al. (2004) Lang and Stulz (1994) Lins and Servaes (2002) Lubatkin and Chatterjee (1994) Markham (1974) Miller (2004) Montgomery and Wernerfelt (1988) Nachum (2004) Nguyen et al. (1990) Pandya and Rao (1998) Picard and Rimmer (1999) Qian (2002) Sambharya (2000) Servaes (1996) Skaggs and Droege (2004) Tallman and Li (1996) Wan (1998)

Degree matters to Performance

Degree of Diversification

Empirical Evidence on the Diversification-Performance Linkage


Figure 2.5

Amit and Livnat (1988b) Bühner (1983) Comment and Jarrell (1995) Gedaijlovic et al. (1993) Hill and Snell (1988) Hoskisson et al. (1993) Imel and Helmberger (1971) Kim et al. (2004) Lang and Stulz (1994) Lins and Servaes (2002) Lubatkin and Chatterjee (1994) Markham (1974) Miller (2004) Montgomery and Wernerfelt (1988) Servaes (1996) Wan (1998)

Negative Association Grant et al. (1988) Nachum (2004) Qian (2002) Tallman and Li (1996)

Quadratic Association Barton (1988) Bettis (1981) Bettis and Mahajan (1985) Capon et al. (1988) Ciscel and Evans (1984) Dubofsky and Varadarajan (1987) Farjoun (1998) Hall Jr. and St. John (1994) Hoskisson (1987) Lecraw (1984) Lubatkin and Rogers (1989) Markides and Williamson (1994) Mayer and Whittington (2003) Palepu (1985) Qian (1997) Ramirez-Aleson and Escuer (2002) Robins and Wiersema (1995) Rumelt (1974) Rumelt (1982) Szeless et al. (2003) Varadarajan (1986) Varadarajan and Ramanujam (1987) Zhao and Luo (2002)

Related Diversification is Superior

Bass et al. (1977) Chatterjee (1986) Elgers and Clark (1980) Luffman and Reed (1982) Michel and Shaked (1984) Ravenscraft (1983) Weston et al. (1972)

Unrelated Diversification is Superior

How does Type Matter?

Empirical Evidence on the Linkage between Degree and Type of Diversification and Performance – Level 3

Chang and Choi (1988) Jose et al. (1986) Keats and Hitt (1988) Pandya and Rao (1998) Picard and Rimmer (1999) Skaggs and Droege (2004)

Positive Association

How does Degree Matter?

Empirical Evidence suggesting that Diversification Matters to Performance

Evidence on Product Diversification as a Cause of Performance 31

32


With a view to the linkage between diversification in terms of degree and performance authors such as Chang and Choi (1988), Jose et al. (1986), and Keats and Hitt (1988) suggest that increasing the number of businesses entails positive performance implications. Contrarily, author such as Comment and Jarrell (1995), Hill and Snell (1988) and Lubatkin and Chatterjee (1994) suggest a negative association between degree of diversification and performance. Moreover, a third group of studies among which, for instance, Grant, Jammine, and Thomas (1988), Nachum (2004), and Tallman and Li (1996) suggest that the focal linkage is not of linear but of quadratic nature. Quadratic nature means here that these studies found evidence that increasing diversification in terms of degree pays off for firms up to a point at which the costs of diversification start to exceed the benefits and thus lead to performance declines at levels of diversification that lie beyond this point.

Nonetheless, overall, the overview of studies reporting evidence on the relationship between degree of diversification and performance suggests that the majority of studies report negative performance implications of increasing the number of businesses and the degree of activity distribution (cf. Figure 2.5).

With a view to the linkage between diversification in terms of type and performance authors such as, for instance, Barton (1988), Palepu (1985), and Rumelt (1974, 1982) suggest that related diversifiers systematically outperform unrelated diversifiers. Contrarily, the studies conducted by Chatterjee (1986), Michel and Shaked (1984), and Weston, Smith, Shrieves (1972), for instance, argue and present evidence in favour of unrelated diversification. What is striking in this context is that these and other studies that found unrelated diversification to be superior to related diversification in terms of performance were all conducted some time ago already, i.e. in the present case between 1972 and 1986. This may suggest that specific types of benefits associable with unrelated diversification, such as internal market efficiencies or market power, possibly were to a greater extent realizable in less efficient and less munificent environments than is the case nowadays.


33

However, overall, the overview of studies reporting evidence on the relationship between type of diversification and performance shows that the majority of studies seems to report evidence that related diversification is superior to unrelated diversification (cf. Figure 2.5).

Finally, a great deal of diversification-performance research suggests that the nature of the relationship between degree and type of diversification and performance is ultimately determined by contingency factors. Most of this evidence either relates to contingency factors that pertain to industry structure characteristics or to organizational structure characteristics. In addition, in selected studies also other contingency factors impacting the focal linkage were elicited.

Gassenheimer and Keep (1995), Grant and Jammine (1988), and Jones et al. (1977), for instance, find that the nature of the linkage between degree of diversification and performance is moderated by type of industry. Park (2002) and Wernerfelt and Montgomery (1986) specifically point at industry profitability and its effects on both diversification and performance. And, Christensen and Montgomery (1981), Kaul (2003), and Montgomery (1985) suggest even that it is industry profitability alone – and not diversification strategy – that explains the performance differentials of multibusiness firms. Across these studies, most often industry structure characteristics such as concentration, growth, and market entry barriers are examined in terms of their relation to industry profitability, diversification, and firm performance.

With a view to characteristics of organizational structure, Hoskisson (1987), for instance, suggests that the implementation of the M-Form versus the U-Form structure, i.e. a multidivisional form versus a unitarian form, results in performance increases for unrelated diversifiers. Hill, Hitt, and Hoskisson (1992) suggest that related diversifiers seeking to realize economies of scope perform better if they centralize and integrate certain specific activities and, thus, stress cooperation between business units. Contrarily, unrelated diversifiers striving to benefit from internal market and governance efficiencies perform better if their organizational structures are decentralized to a greater

34


extent and support competition between business units. Similar results are reported in Chu (2001), Nathanson and Cassano (1982), and Hamilton and Shergill (1992). While these studies suggest the performance implications of a so-called strategy-structure fit, there are other studies that suggest that organizational structure type does not matter to the diversification-performance relationship (e.g., Grinyer et al. 1980, Rumelt, 1974).

In terms of organizational structure, other studies find that the diversificationperformance association is influenced in nature by ownership structure (Gedaijlovic, Shapiro, and Buduru, 2003; Lins and Servaes, 2002; Chen and Kim, 2000), compensation strategy (Gomez-Mejia, 1992), and supply chain strategy (Narasimhan and Kim, 2002).

Finally, other studies report that the diversification-performance linkage is moderated by degree of international diversification (Palich, Carini, and Seaman, 2000; Tallman and Li, 1996), time period (Geringer, Tallman, and Olsen, 2000; Servaes, 1996), economic climate (Ciscel and Evans, 1984; Busija O’Neill, and Zeithaml, 1997), country, and degree of development and munificence (Lee, Hall Jr., and Rutherford, 2003; Mayer and Whittington, 2003; Nachum 2004; Wan and Hoskisson, 2003).

Ultimately, having a look at the diversification-performance literature as a whole, it becomes also apparent that the vast majority of research examines US-based multibusiness firms. Except for some very few earlier studies, this has begun to change roughly by the end of the nineties only. Since then also studies investigating the diversification-performance association for Asian and European firms have successively become available.

At least equally important is the observation that an increasing number of scholar acknowledges the necessity to investigate multiple dimensions of the performance construct. And yet, the lion’s share of empirical evidence available today is based on accounting-based performance assessments. The question if and how the multidimensionality of the performance construct contributes to the variance of the findings on the


35

diversification-performance association will be comprehensively tested and discussed in the course of the quantitative integration of the literature that is yet to follow.

Overall, the above overview of the literature reaffirms that – despite some observable tendencies – there is mixed evidence on if and how diversification precisely relates to performance. However, narrative literature reviews suffer some limitations that potentially lead to drawing wrong conclusions. These limitations, in turn, may be overcome by quantitative reviews and, in particular, by effect-size meta-analysis.

In contrast to narrative reviews and vote-counting methods, effect-size meta-analysis can generate results of superior validity as regards the true relationships between variables of interest. Most importantly, meta-analysis allows correcting for effect-size variation caused by study artifacts such as sampling error, for instance (Hedges and Olkin 1985; Hunter and Schmidt, 2004). With a view to the diversificationperformance relationship, it is Palich, Cardinal, and Miller (2000) who offer such an analysis.

For the purpose of quantitatively integrating largest possible parts of the diversification-performance literature, Palich et al. make a critical assumption that underlies their analysis: The degree of diversification (low, moderate, high) corresponds to type of diversification (single, related, unrelated).

This assumption seems warranted as Chatterjee and Blocher (1992), Hoskisson et al. (1993), and Montgomery (1982) offer empirical evidence in support of this assertion. Moreover, as alluded to in earlier sections, researchers frequently convert measures of types of diversification, such as the Rumelt scheme, into continuous measures of the level of diversification (e.g., Hoskisson et al., 1993; Keats and Hitt, 1988; Lubatkin, Merchant, and Srinivasan, 1993) and vice versa (e.g., Ciscel and Evans, 1984; Palepu, 1985; Varadarajan, 1986).

36


Following this logic, Palich, Cardinal, and Miller (2000) managed to quantitatively summarize 55 empirical studies of the diversification-performance literature.

The results of their meta-analysis suggest that the association between diversification and performance is inverted-U-shaped, in fact. Palich et al. conclude that related diversification (a moderate level of diversification) has positive and that unrelated diversification (a high level of diversification) has negative performance effects. In other words, diversification seems to pay off for firms up to a certain level. Past this point, diversification seems to cause performance declines (Fig. 2.6).

Performance

Single/ Low

Related/ Moderate

Unrelated/ High Diversification

Figure 2.6

The Inverted-U Association between Diversification and Performance

It is important to understand that, as a consequence, in contrast to the qualitative literature reviews, the meta-analysis by Palich et al. suggests something very different in terms of the generalizability of findings on the diversification-performance relationship.


37

What is most important about this is that, in the meantime, these meta-analytic findings have become standard strategic management textbook knowledge (e.g., Johnson, Scholes, and Whittington, 2006). The Palich et al. study (2000) seems to be deemed the current edge in integrative research on the diversification-performance linkage.

It is exactly against this background that I examine in this study and the meta-analyses to follow whether Palich et al.’s finding (2000) can be confirmed if a) a substantially enlarged sample of empirical analyses is used that includes also most recent research, b) particular attention is drawn to the multidimensionality of the performance construct, and c) alternative meta-analytic techniques are applied.

Accordingly, I test exactly the hypothesis for which Palich, Cardinal, and Miller (2000, p. 161) report strong support when using accounting-based measures of performance and weak evidence when using market-based measures of performance. Recall that this hypothesis rests on the argument that exclusively related diversifiers may realize economies of scope, while unrelated diversifiers, at the same time, have to cope with substantial costs of organizing complex operations. H1: Diversification exhibits an inverted-U relationship with corporate performance: diversification is positively related to performance across the low to moderate range of diversification (from single business to related diversifier) and is negatively related to performance across the moderate to high range of diversification (from related diversifier to unrelated diversifier). 2.6 Overall Meta-Analytic Proceeding The correlational meta-analysis to follow is multi-level. I will first replicate the analysis by Palich, Cardinal, and Miller (2000). Subsequently, I will use a substantially extended sample that includes most recent research. In both cases I will use the metaanalytic techniques suggested by Hedges and Olkin (1985). In addition, I apply alternative meta-analytic techniques suggested by Hunter and Schmidt (2004) to the ex-

38


tended sample. This proceeding allows me to maintain comparability to prior research and to verify the stability of findings across meta-analytic techniques. As I shall demonstrate later, the use of both types of meta-analytic methods is complementary in a number of respects. 2.6.1

Meta-Analytic Methods

Meta-analysis is an analysis of analyses (Glass, 1976). Integrating potentially conflicting findings on focal relationships from a multitude of primary studies, the goal of meta-analysis is to elicit true relationships between variables of interest. In contrast to narrative reviews and vote-counting methods, effect-size meta-analysis can generate results of greater validity. Most importantly, it allows correcting for effect-size variation caused by study artifacts such as sampling and measurement error (Hunter and Schmidt, 2004). Sampling error, for instance, causes observed correlations to vary randomly from the true score correlation (Koslowsky and Sagie, 1994).

In strategic management research the Hunter/Schmidt and the Hedges/Olkin procedures are the two most widely used methods of correlational meta-analysis. Next to the Hedges/Olkin procedures, I employ the meta-analytic techniques suggested by Hunter and Schmidt (2004) as applied in Orlitzky, Schmidt, and Rynes (2003). Main focus will be on testing the impact of the contingency variable type of diversification strategy on the nature of the diversification-performance linkage, while allowing for effects of type of diversification operationalization and type of performance operationalization. In fact, it is in the analysis of contingency variables causing effect size variation rather than in the calculation of mean effect sizes that there are the greatest procedural differences between Hedges/Olkin and Hunter/Schmidt. 2.6.2

Testing Contingency Variables: Hedges/Olkin versus Hunter/Schmidt

Hedges and Olkin’s approach (1985) involves regressing correlations onto hypothesized contingency variables. Correlations are used as dependent variables, while individual study and sample characteristics, such as type of method applied or type of firm studied, are used as predictors in the regression equation. In this context, often dummy

Overall Meta-Analytic Proceeding

39

variables are used to represent these predictors. Moreover, in the regressions, a weight of n-3 is assigned to each case to account for unequal sample sizes underlying individual studies (1985, p. 241). Finally, the Hedges and Olkin procedures involve transforming the distribution of individual correlations into a Fisher-z distribution to approximate normal distribution and to stabilize variance prior to any effect-size integration or any regression of effect-sizes onto contingency variables.

Practical applications of the Hedges/Olkin procedures on (strategic) management topics may be found, for instance, in Palich et al. (2000), in Miller and Cardinal (1994), and in Huber, Miller, and Glick (1990), further also in Kirca (2005) and in Shoham (2002).

In contrast to Hedges/Olkin, Hunter/Schmidt advocate a hierarchical breakdown of superordinate sets of correlations into subsets that represent “parameter values“ or categories of contingency variables, such as type of method applied or type of firm studied. Specific contingency variables are confirmed to cause effects size variation if true score correlations differ meaningfully across meta-analytic subsets and if, on average, a higher percentage of variance is accounted for by artifacts in subsets than in superordinate sets. If statistical artifacts account for 75% or more of the variance of observed correlations, the remaining variance is assumed to be likely due to artifacts not corrected for (Hunter and Schmidt, 2004). In this case, the population of correlations can be considered homogenous. It is unlikely that methodological or substantive contingency variables have caused variation in the correlations, and results are generalizable across studies. Hunter and Schmidt do not suggest any distributional transformation of effects sizes before aggregation.

Given that Hunter and Schmidt strongly advocate correcting effect sizes for various artifacts next to sampling error – either before integration or via artifact distributions after integration – I will correct effects sizes also for measurement error.

40


Specifically, I use an artifact distribution and an average attenuation factor for this purpose (Hunter and Schmidt, 2004; Hunter, Schmidt, and Jackson, 1982; Schmidt and Hunter, 1977). This is necessary as primary studies relevant to my hypothesis in the majority of cases do not report reliabilities that could be used to correct correlations individually and prior to the integration. Following Orlitzky, Schmidt, and Rynes (2003), I approximate reliabilities by interpreting the correlations between alternative measures of diversification and performance as (conservative) coefficients of generalizability. The inter-measure correlations used for this purpose are reported in appendices 1 and 2 in the form of sample-size weighted mean correlations. In addition, appendix 3 reports intra- and inter-group, sample size-weighted correlations validating the applicability of Combs et al.’s conceptualization of the performance construct to the diversification domain (cf. section 2.3).

Practical applications of the Hunter/Schmidt procedures on (strategic) management topics may be found, for instance, in Dalton, Daily, Ellstrand, and Johnson (1998), in Dalton, Daily, Certo, and Roengpitya (2003), in Ketchen et al. (1997), and in Tihanyi, Griffith, and Russell (2000). 2.6.3

Technical Integration Model

In the meta-analyses, attention is drawn to examining a contingency variable that corresponds to restriction of range in the samples of firms studied in the individual empirical analyses (cf. Palich, Cardinal, and Miller, 2000). This is necessary as otherwise the hypothesized curvilinear relationship between diversification and performance cannot be meaningfully tested in a meta-analytic framework. I draw on type of range restriction to capture the effects of type of diversification strategy. This allows me to decompose a curvilinear association into two linear associations (cf. figure 2.7).

Correlations empirically observed between diversification and performance on samples of firms comprising exclusively single business firms and related diversifiers are understood to represent associations between related diversification and performance. Such samples are, following the terminology used by Palich et al. (2000), denoted “re-


41

stricted away from the high end of diversification”. In theory, this sample corresponds to the portion of the curvilinear relationship where the slope of the function is positive.

Correlations empirically observed between diversification and performance on samples of firms comprising exclusively related diversifiers and unrelated diversifiers are understood to represent associations between unrelated diversification and performance. Such samples are denoted “restricted away from the low end of diversification” In theory, this sample corresponds to the portion of the curvilinear relationship where the slope of the function is negative.

Finally, correlations observed between diversification and performance on samples of firms comprising all three types of firms (single business firms, related diversifiers, and unrelated diversifiers) are denoted “unrestricted samples”. In theory, this sample includes both the positive and negative portions of the function, in turn, resulting in indicators of linear association being close to zero.

Performance Samples restricted away from the high end of diversification

Samples restricted away from the low end of diversification

Related Diversification

Unrelated Diversification

Single/ Low

Related/ Moderate

Unrelated/ High Diversification

Figure 2.7

Decomposing a Curvilinear Association via Sample Range Restriction

42


In the regression-based meta-analysis, a three category dummy variable is used to indicate restriction of range. If a sample of firms studied in individual analyses comprises only single business firms and related diversifiers it is coded as 1-0-0 to indicate restriction away from the high end of diversification. If a sample has only related and unrelated diversifiers it is coded 0-0-1 to indicate restriction away from the low end of diversification. And if a sample comprises all three types of firms, it is coded as 0-1-0. The correlations from unrestricted samples are used as the benchmark category in the Hedges/Olkin-based regressions against which correlations from restricted samples are compared (cf. Palich et al., p. 161). That is to say they will represent “the omitted category” in the dummy-regressions.

In the Hunter/Schmidt-based analysis, restriction of range will be represented by classifying samples and corresponding correlations into one of three subsets denoted “restricted away from high end of diversification”, “restricted away from low end of diversification”, and “unrestricted samples”.

To maintain greatest possible comparability, I decided to use the terminology introduced by Palich et al. also in this work despite the fact that they may not always be intuitive. Please note again therefore that “restricted away from the high end of diversification” refers to “related diversification”, while “restricted away from the low end of diversification” refers to “unrelated diversification” (cf. figure 2.7). 2.6.4

Sampling

I started by replicating the Palich et al. (2000) sample. The authors list in their paper a number of 82 studies that they found to be relevant. 55 of these studies reported usable effect size statistics and were used in their quantitative synthesis. Unfortunately, this subset of studies is not identifiable in the paper. I contacted the authors on this issue; they could not provide the relevant information, however. Thus, I necessarily browsed all 82 studies to identify the ones relevant.


43

Following Palich et al.’s conceptual integration model outlined above, I identified merely 50 usable studies (instead of 55), a number of 61 samples (instead of 71), and 78 usable correlations (instead of 96). Among the latter I found 58 correlations between diversification and accounting-based performance (instead of 71) and 20 correlations between diversification and market-based performance (instead of 25). Thus, if one considers samples the unit of analysis, I managed to replicate the Palich sample to 86%. If one considers correlations the unit of analysis, I replicated the Palich sample to 81%.

The primary studies included in the extended sample were identified in the database reported by Palich, Cardinal and Miller (2000), by means of electronic keyword searches using a combination of “diversi*”, “performance”, and “empiri*” in Business Source Premier, ABI/Inform, JSTOR, WISO I, and in the literature lists of relevant studies, including qualitative reviews of the field (e.g., Datta, Rajagopalan, and Rasheed, 1991; Denis, Denis, and Sarin, 1997; Ramanujam and Varadarajan, 1989). Overall, I screened almost 1,000 articles using my criteria for inclusion.

Studies had to report product-moment correlations (or effect sizes convertible to correlations such as d-, t-, F-, Z-, or associated p-values) between the variables of interest. Respective conversion formula may be found in Hunter and Schmidt (1990) and Rosenthal and DiMatteo (2001). The formulae used in this analysis are reported in appendix 4. In one case a complete raw data set was reported that I used to calculate Pearson correlations (Bengtsson, 2000). The analyses comprise English- and Germanlanguage literature and were limited to material published by 2005.

To be included in the analysis, it was not necessary for the diversification-performance relationship to be the main focus of the analysis; in this way I sought to reduce potential problems of availability bias. Following Hunter and Schmidt (2004), there is evidence that availability bias is relatively unimportant in meta-analyses that integrate empirical results on the nature of relationships that are not the major focus in primary studies.

44


An array of studies that analyzed samples already used in other studies was not considered for the meta-analytic integration in order to avoid dependencies (Amit and Livnat, 1989; Amit and Livnat 1988b; Amit and Livnat 1988c; Bergh and Lawless, 1998; Bettis, 1981; Bettis and Mahanjan, 1985; Dubofsky and Varadarajan, 1987; Gassenheimer and Keep, 1998; Hitt and Ireland, 1986; Keats, 1990; Varadarajan and Ramanujam, 1987). The extended sample of correlations comes from 99 studies published between 1971 and 2005 and comprises data from 122 samples. I gathered 168 diversificationperformance correlations, among which 124 between diversification and accountingbased performance (in the language of Combs et al.: Accounting-based and growthbased performance) and 44 between diversification and market-based performance. Thus, the extended sample is roughly twice as large as the sample used in Palich et al. (2000). The studies included in the meta-analyses are shown in table 2.1.

Overall Meta-Analytic Proceeding Table 2.1

45

Studies included in the Meta-Analyses

Author(s)

Year

Author(s)

Year

Amit and Livnat Barton Bass, Cattin, and Wittunk Beattie Bengtsson Bergh Bergh Bergh and Holbein Bethel and Liebeskind Bettis and Hall Bishara Boeker Bowen and Wiersema Bühner Busija, O’Neil, and Zeithaml Capon et al. Chatterjee and Blocher Chatterjee and Wernerfelt Chen and Kim Chu Ciscel and Evans Clarke, Fee, and Thomas Daellenbach et al. Dawley, Hoffman, and Brockman Delios and Beamish Farjoun Fauver, Houston, and Naranjo Gassenheimer and Keep Gedajlovic, Shapiro, and Buduru Geringer, Tallman, and Olsen Gomez-Mejia Grant, Jammine, and Thomas Hadlock, Ryngaert, and Thomas Hall Jr. and Lee Hall Jr. and St. John Hill and Snell Hill, Hitt, and Hoskisson Hitt, Hoskisson, and Kim Holzmann, Copeland, and Hayya Hoskisson Hoskisson et al. Hoskisson and Johnson Jensen and Zajac Johnson, Hoskisson, and Hitt Kaul Keats and Hitt Kim, Hoskisson, and Wan Lane, Cannella, and Lubatkin Lang and Stulz Lee and Habte-Giorgis

1988a 1988 1977 1980 2000 1995a 1995b 1997 1993 1982 1980 1997 2005 1983 1997 1988 1992 1991 2000 2004 1984 2004 1999 2003 1999 1998 2004 1995 2003 2000 1992 1988 2001 1999 1994 1988 1992 1997 1975 1987 1993 1992 2004 1993 2003 1988 2004 1998 1994 2004

Lee, Hall Jr., and Rutherford Li and Wong Lim and Teck Lins and Servaes Low and Chen Lu and Beamish Lu and Beamish Lubatkin and Chatterjee Lubatkin et al. Lubatkin and Rogers Markides and Williamson, Mayer and Whittington Melicher and Rush Melicher and Rush Michel and Shaked Miller Montgomery Mosakowski Nachum Narasimhan and Kim Palepu Palich, Carini, and Seaman Palmer, Jennings, and Zhou Pandya and Rao Park Picard and Rimmer Qian Qian Qian and Li Raju and Dhar Ramaswamy, Li, and Veliyath Ramirez-Aleson and Escuer Riahi-Belkaoui and Pavlik Robins and Wiersema Sambharya Servaes Singh, Davidson, and Suchard Singh, Mathur, and Gleason Singh, Mathur, and Gleason Skaggs and Droege Smith and Weston Szeless et al. Tallman and Li Tongli, Ping, and Chiu Varadarajan Vermeulen and Barkema Wan Wan and Hoskisson Weston and Mansinghka

2003 2003 1995 2002 2004 2001 2004 1994 1993 1989 1994 2003 1973 1974 1984 2004 1985 1997 2004 2002 1985 2000 1993 1998 2002 1999 1997 2002 2003 1999 2002 2002 1993 1995 2000 1996 2003 2001 2004 2004 1977 2003 1996 2005 1986 2001 1998 2003 1971

46

2.6.5


Coding

Whenever more than one correlation was reported between the same type of rangerestricted sample and multiple operationalizations of performance within the same performance dimension (accounting-based, market-based, growth-based), I averaged the correlations to reduce dependencies (Hunter and Schmidt, 2004). However, whenever more than one type of range-restricted sample was investigated within the same study, I treated respective correlations as if coming from different studies. This introduces some non-independence into the data, but facilitates testing the hypothesis.

For replication purposes, following Palich et al., I subsequently pooled the correlations observed on profitability and the correlations observed on growth-based performance into the group accounting-based performance. I analyzed this set of correlations separately from the set of correlations based on market-returns. In addition, in the regression-based analysis that tested the correlations between diversification and accountingbased performance, I used the dummy variable “profitability or not” to control for the effects of type of performance operationalization. Profitability-based performance was coded with the value of 1 and growth-based performance with the value of 0.

This overall integration model also led me to break up selected unrestricted samples into range-restricted samples. If means and standard deviations for performance measures were reported for all three types of range-restriction (e.g., in Lubatkin and Rogers, 1989), I could calculate a standardized mean difference (Cohen’s d) and subsequently convert it into Pearson correlations.

In addition to effect-size estimates, I collected from the studies sample sizes, operationalization of diversification, and operationalization of performance. Sample sizes were coded as the number of firms and not as the number of observations (CamisónZornosa, Lapiedra-Alcami, Segarra-Ciprés, and Boronat-Navarro, 2004). Proceeding in this manner is particularly important when pooled time-series data with multiple observations per firm is included in meta-analysis. Pooled time-series data nonetheless tends to overstate correlations; however, I included the studies of this type of analysis


47

in my meta-analysis since a later countercheck indicated that their exclusion would have changed results marginally only.

Moreover, operationalization of diversification was, following Palich, Cardinal, and Miller (2000), coded into the four dimensions Herfindahl, Entropy, Count, and Rumelt (cf. section 2.2). In the regression-based analysis, Rumelt was used as the omitted, benchmark category. That is to say it was coded with the value 0 while all other diversification operationalizations were coded with the value 1 in the dummy variables “Herfindahl or not”, “Entropy or not”, and “Count or not”.

Throughout the coding process, I used a coding form that specified the information to be extracted from primary studies in order to reduce coding error (Lipsey and Wilson, 2001). The form was developed by reading a random subset of 10 studies. 2.7 Meta-Analytic Results Tables 2.2 and 2.3 show the results of the Hedges/Olkin-based meta-analysis. Regression models 1-4 are tested for the set of correlations between diversification and accounting-based performance. Regression models 5-8 are tested for the set of correlations observed between diversification and market-based performance. Table 2.4 shows the results of the Hunter/Schmidt-based meta-analysis involving separately both accounting- and market-based measures of performance. In the Hunter/Schmidt analysis I use the extended sample only.

Results in table 2.2 indicate that model 1 in which correlations between diversification and accounting-based performance are regressed onto restriction of range for the Palich et al. sample is not significant and, thus, cannot explain variation in the empirically observed correlations. At the same time, model 2 which regresses this sample of correlations onto restriction of range as well as onto type of diversification and performance operationalization is overall significant. However, here, the regression coefficients of the range restriction dummy variables are not significant. Thus, model 1 and 2 suggest that restriction of range in the samples of firms studied in individual empiri-

48


cal analyses does not significantly influence the nature of the association observed between diversification and accounting-based performance.

Nonetheless, model 2 provides some evidence that correlations between diversification and performance in terms of profitability are more negative than correlations observed between diversification and growth-based performance. Recall that the variable “profitability or not” is a dummy variable that takes the value of 1 if a correlation is observed between diversification and profitability and the value of zero if a correlations is observed between diversification and growth-based performance. Accordingly, the negative sign of the regression coefficient means that the dependent variable, i.e. the diversification-performance correlation, takes a more negative value if the contingency variable “profitability or not” takes the value of 1 than when it takes a value of 0.

Moreover, model 3 and 4 (table 2.2) test the same models examined in model 1 and 2; here the extended sample of diversification-performance correlations is used, however. As both model 3 and 4 lack overall significance, results suggest again that neither the nature of the correlations observed for samples with firms restricted away from the high end of diversification (including single business firms and related diversifiers) nor the nature of the correlations observed for samples with firms restricted from the low end of diversification (including related diversifiers and unrelated diversifiers) do differ from the nature of the correlations observed for unrestricted samples (including single business firms, related diversifiers, and unrelated diversifiers).

Thus, in sum, regression results in table 2.2 indicate that correlations between related diversification and accounting-based performance and correlations between unrelated diversification and accounting-based performance are not significantly different from correlations observed between diversification and accounting-based performance for firms in samples that are unrestricted in terms of diversification strategy. And this applies to both the replicated and the extended sample.

Meta-Analytic Results

49

Table 2.3 reports the results of regressing the correlations observed between diversification and market-based performance onto restriction of range and choice of diversification operationalization. Model 5 and 6 are tested for the Palich et al. sample and model 7 and 8 are tested for the extended sample. Results indicate that neither models 5 and 6 nor models 7 and 8 show adequate model fit. As a consequence, similar to models 1-4, models 5-8 suggest that type of diversification strategy as indicated by restriction of range in the samples of firms studied cannot explain variation in the empirically observed diversification-performance correlations. Specifically, correlations between related diversification and market-based performance and correlations between unrelated diversification and market-based performance are not significantly different from correlations observed between diversification and market-based performance for firms in samples that are unrestricted in terms of diversification strategy.

Intermediate result is that replicating and extending the Palich et al. sample and using the Hedges/Olkin-procedures, I have no evidence in support of hypothesis 1 suggesting an inverted-U relationship between diversification and performance. Diversification is neither found to be positively related to performance across the low to moderate range of diversification nor to be negatively related to performance across the moderate to high range of diversification.

-.132

-.536***

.115

-.038

.026

-.010

.733

Count or not

Profitability or not

Constant R2 Adjusted R2 F statistic

3.442**

.207

1.189

.003

.019

-.026

.137

1.355

.017

.065

-.008

-.165*

.059

.144

.131

.203**

.032

Model 4 (n=124)

Model 3 (n=124) .031

Extended sample accounting-based performance

Extended sample accounting-based performance

Standardized regression coefficients reported; b Palich sample 81% replicated; n: number of correlation coefficients meta-analyzed; * p < 0.10, ** p < 0.05, *** p < 0.01

a

-.005

Entropy or not

.292

.068

-.184

Herfindahl or not

-.157

Restriction of range (restricted away from low and or not)

.093

Model 2 (n=58)

Model 1 (n=58) .034

Palich et al. sample b accounting-based performance

Palich et al. sample b accounting-based performance

Hedges/Olkin-based Weighted Regression of Accounting-based Correlations onto Contingency-Variablesa

Restriction of range (restricted away from high and or not)

Table 2.2


-.701**

.003

-.115

.054

-.058

.482

Count or not

Constant R2 Adjusted R2 F statistic

1.954

.209

1.502

.023

.068

-.056

.072

1.447

.049

.160

-.046

-.146

.208

.045

.054

.261*

Model 8 (n=44)

Model 7 (n=44) .253

Extended sample market-based performance

Extended sample market-based performance

Standardized regression coefficients reported; b Palich sample 81% replicated; n: number of correlation coefficients meta-analyzed; * p < 0.10, ** p < 0.05, *** p < 0.01

a

.062

Entropy or not

.411

-.164

-.212

Herfindahl or not

-.030

Restriction of range (restricted away from low and or not)

.133

Model 6 (n=20)

Model 5 (n=20) .229

Palich et al. sample b market-based performance

Palich et al. sample b market-based performance

Hedges/Olkin-based Weighted Regression of Market-based Correlations onto Contingency-Variablesa

Restriction of range (restricted away from high and or not)

Table 2.3

Meta-Analytic Results 51

52


Table 2.4 shows the results from employing the Hunter/Schmidt techniques to metaanalytically test hypothesis 1. Here, I used the extended sample of correlations only (see bold print n). Note that I start from the entire set of diversification-performance correlations (line 1 in table 2.4) and subsequently break it down hierarchically into subsets. This also means that I break down the entire set of correlations into two groups representing those correlations empirically observed between diversification and accounting-based performance (line 2 in table 2.4) and those correlations empirically observed between diversification and market-based performance (line 3 in table 2.4) before I get to testing the contingency variable of prime interest “type of diversification strategy”. Recall that type of diversification strategy is again indicated by type of range restriction of the sample of firms studied in individual empirical analyses.

Results in table 2.4 indicate that the absolute values of mean observed correlation and true score correlation between diversification and accounting-based performance observed for samples of firms restricted away from the high end of diversification (single businesses and related diversifiers) are as close to zero as they can be. This is also confirmed by the 95% confidence interval which includes zero. Exactly, the same applies to the mean observed correlation and true score correlation between diversification and accounting-based performance observed for samples of firms restricted away from the low end of diversification (related diversifiers and unrelated diversifiers). As a consequence, results in table 2.4 suggest that related diversification and unrelated diversification as indicated by range restriction in the samples of firms empirically examined are not significantly associated with accounting-based performance. This confirms the findings on associations between diversification strategy and accounting-based performance found when using the Hedges/Olkin procedures.

Thus, using the Hunter/Schmidt techniques, hypothesis 1 is rejected for the subset of correlations observed between diversification and accounting-based performance. Diversification does neither seem to be positively related to performance across the low to moderate range of diversification (from single business to related diversifier) nor to be negatively related to performance across the moderate to high range of diversifica-

Meta-Analytic Results

53

tion (from related diversifier to unrelated diversifier). The inverted-U relationship of diversification with performance seems not to prevail.

Nonetheless, it is to be pointed out that the weighted mean correlations observed between diversification and accounting-based performance for firms in unrestricted samples, which is by far the largest of the three range restriction subgroups, are significant and negative on average, though at trivial levels.

Due to very small subset sizes there is no point in breaking up further into performance dimensions the set of correlations observed for firms in samples restricted away from the high end of diversification (single businesses and related diversifiers) and accounting-based returns. Moreover, further breaking down the subset of correlations between diversification and accounting-based performance observed for firms in samples restricted away from the low end of diversification (related diversifiers and unrelated diversifiers) into the performance dimensions profitability and growth does not elicit any significant associations. In both cases the confidence intervals include zero, indicating that the true score correlations are not significantly different from zero.

Table 2.4 also shows the weighted mean correlations observed between diversification and market-based performance. Firms in samples restricted away from the high end of diversification (single business firms and related diversifiers) are found to exhibit on average a significant and positive association between diversification and marketbased performance. At the same time, firms in samples restricted away from the low end of diversification (related diversifiers and unrelated diversifiers) are found to exhibit on average a significant and negative but also trivial association between diversification and market-based performance. Interestingly, the mean observed and mean true score correlations observed between diversification and performance for firms in unrestricted samples are again significant and negative.

Thus, for the sample of correlations observed between diversification and marketbased performance, type of diversification strategy as indicated by type of sample

54


range restriction can be confirmed as a contingency variable that causes effect size variation. True score correlations differ across subsets and, on average, a higher percentage of variance is accounted for by artifacts in subsets than in superordinate sets (Hunter and Schmidt, 2004).

As a consequence, using the Hunter/Schmidt techniques, hypothesis 1 is confirmed for the subset of correlations observed between diversification and market-based performance. Diversification seems to exhibit an inverted-U relationship with performance. It is positively related to performance across the low to moderate range of diversification (from single business to related diversifier) and is negatively related to performance across the moderate to high range of diversification (from related diversifier to unrelated diversifier).

However, the findings as regards the linkage between diversification strategy and market-based performance suffer a number of limitations – the most severe of which is sample size – which I shall detail in the discussion section.

124

44

5

26

93

Diversification and accounting-based performance

Diversification and market-based performance

Restriction of range (restricted away from high end) and accounting-based performance

Restriction of range (restricted away from low end) and accounting-based performance

Restriction of range (unrestricted samples) and accounting-based performance

20

6

Restriction of range (restricted away from low end) and accounting-based performance in terms of profitability

Restriction of range (restricted away from low end) and accounting-based performance in terms of growth

…

168

n

451

4,057

30,311

4,508

656

15,028

37,088

52,116

K

.072

-.009

-.026

-.001

.001

-.036

-.025

-.027

robs

.025

.012

.012

.014

.022

.015

.012

.012

sr 2

.015

.005

.003

.006

.009

.003

.003

.003

s e2

.0015

.0000

.0002

.0000

.0000

.0004

.0002

.0002

sm 2

.671

.423

.280

.423

.423

.211

.299

.265

explained

% of sr2

.177

-.021

-.064*

-.001

.002

-.088*

-.057*

-.066*

.050

.043

.052

.050

.076

.071

.051

.057

sU 2 C.I

-.003 : .357

-.112 : .070

-.111 : -.018

-.087 : .084

-.240 : .244

-.167 : -.009

-.096 : -.017

-.102 : -.030

Hunter/Schmidt-based Weighted Integration of Diversification-Performance Correlations and Hierarchical Breakdown

Diversification and performance (entire set of correlations)

Table 2.4

Meta-Analytic Results 55

9

32

Restriction of range (restricted away from low end) and market-based performance

Restriction of range (unrestricted samples) and market-based performance 14,098

792

138

K

-.055

-.019

.252

robs

.013

.012

.015

sr 2

.002

.013

.028

s e2

.0009

.0001

.0183

sm 2

% of sr2

.256

1.000

1.000

explained

-.163*

-.046*

.624*

.057

.000

.000

sU 2

a

n: number of correlation coefficients meta-analyzed; K: total sample size; robs: sample size-weighted mean observed correlation; sr2: observed variance; se2: sampling error variance; sm2: measurement error variance; : mean true score r (corrected); sU2: true score variance; C.I.: Confidence Interval; * p < 0.05

…

3

Restriction of range (restricted away from high end) and market-based performance

…

n

-.218 : -.053

-.046 : -.046

.624 : .624

C.I


Discussion

57

2.8 Discussion The meta-analyses strongly challenge major conclusions drawn from an earlier metaanalytic synthesis of the literature.

Related diversification is not found to be positively associated with accounting-based performance. Similarly, unrelated diversification is not found to be negatively related to accounting-based performance. This finding is stable across samples and across meta-analytic techniques applied. Thus, I have strong evidence that the inverted-U curve association between diversification and accounting-based performance suggested in the meta-analysis by Palich et al. (2000) does not exist.

In addition, the Hedges/Olkin-based analysis suggests that neither related diversification nor unrelated diversification is significantly associated with market-based performance. This applies to both the replicated and the extended sample. Again, this is in contrast to what Palich, Cardinal, and Miller report.

However, the Hunter/Schmidt-based analysis suggests a positive association between related diversification and market-based performance and a negative association between unrelated diversification and market-based performance. Thus, I have mixed evidence on the existence of the inverted-U association between diversification and market-based performance reported in Palich et al. (2000).

Noteworthy is that the Hunter/Schmidt analysis points out that across methods the meta-analyses in some respects draw on very few correlations to derive results. This applies particularly to associations between related diversification and performance dimensions. Accordingly, one may question the validity of parts of the meta-analytic results (Dalton and Dalton, 2005).

The lack of pertinent, empirically observed correlations is on the one hand due to the use of a specific technical integration model in this analysis and on the other hand due

58


to the neglect of tests of specific variable linkages in empirical research. In strategic management studies, for instance, the focus has traditionally been on investigating the performance differentials of related diversifiers versus unrelated diversifiers (which corresponds to restriction away from low end of diversification in my analysis) at the expense of investigating related diversification relative to single business strategies.

Nonetheless, if one considers the Hunter/Schmidt-based findings on the association between diversification and market-based performance viable, one may argue that the performance effects of diversification strategy materialize in the long- rather than in the short term (Bergh, 1995a; Wan, 1998). Market-based measures may reflect shareholders’ longer-term expectations of future accounting-performance, while accountingbased measures reflect historical, shorter-term performance.

Many scholars consider market-based measures of performance more pertinent to diversification research than accounting-based measures. This is due to market-based performance indicators being considered forward-looking, of higher internal consistency, and less prone to managerial manipulation (Barney, 1997; Hoskisson et al., 1993; Palich, Cardinal, and Miller, 2000). Moreover, indicators of market-based performance incorporate risk aspects, while measurements of accounting-returns usually do not. If increasing diversification is paralleled by decreases in overall risk due to joining within a portfolio businesses with less than perfectly correlated financial flows (Berger and Ofek, 1995; Lewellen, 1971), which is suggested by some authors (Amit and Livnat, 1988; Michel and Shaked, 1984) and disputed by others (Bettis and Hall, 1982; Chang and Thomas, 1989; Lubatkin and Chatterjee, 1994), the use of accounting returns not adjusted for risk may mislead interpretations of the performance effects of diversification (Bettis and Mahanjan, 1985; Jahera, Oswald, and McMillan, 1993). Adding to these lines of argumentation, also my results suggest that if there is a performance dimension that is worth further study in terms of its linkage with diversification strategy it is market-based performance.

Discussion

59

In terms of performance dimensions, meta-analytic results also suggest that the use of growth-based performance measures may be misleading in diversification performance research, especially the use of measures such as sales-growth and asset-growth. I found that growth-based performance indicators are generally more positively related to diversification than profitability-based measures. This comes as no surprise, however. Sales- and asset-growth are standard indicators of absolute company size growth in the empirical management literature, and it has variously been shown that diversification is positively related to company size (e.g., Haines, 1970; Palich, Carini, and Seaman, 2000). Thus, if growth-based measures have to be examined at all, they should be distinguished from profitability-based measures at least. The finding reported in other empirical studies that company size per se is not related to accountingand market-based performance speaks against the use of such measures in diversification-performance research, however (Bausch, Pils, and Van Tri, 2007; Gooding and Wagner, 1985).

Another issue to be emphasized is that in the majority of cases in which significant associations were found between diversification strategy and performance in my analyses, effect sizes reached trivial to small strength, if at all (Cohen, 1988). This finding is in line with Palich, Cardinal, and Miller’s earlier meta-analytic results and can be considered another indication that diversification strategy per se is less important to performance than may have been imagined by many.

Methodologically, the meta-analyses suggest that choice of meta-analytic technique in research practice may influence the nature of results. This finding raises some serious questions – especially in the light of the fact that I am not aware of any meta-analytic paper in strategic management research that uses multiple methods to strengthen the validity of findings. One may have assumed that meta-analysis is a remedy to the problem that results reported in individual empirical studies are affected by choice of research method used. However, my analysis suggests that results of meta-analysis as well may be prone to be distorted by type of method used.

60


In addition, my study suggests that – in research practice – meta-analysis appears far from being “[…] the perfect vehicle for disclosure and replicability” that it is supposed to be (Dalton and Dalton, 2005, p. 49). This is certainly due to reporting practices and journal space constraints. At the same time, however, it is in some cases due to the difficulty of fully comprehending construct conceptualizations and of reproducing one-toone coding approaches correspondingly used by other authors. 2.9 Intermediate Conclusion Replicating and extending the sample used by Palich, Cardinal, and Miller (2000), I present evidence that strategies of related and unrelated diversification per se are not significantly associated with accounting-based performance. In addition, I have mixed evidence on the inverted-U association between diversification strategy and marketbased performance.

As a consequence, the meta-analysis casts some doubt on what has become standard theory in strategic management – the performance effects of product diversification strategy (Palich, Cardinal, and Miller, 2000; Rumelt, 1974). Put differently, the metaanalysis as a whole suggests that diversification strategy – as measured in the majority of cases to date – per se is far less important to performance than may have been expected by some.

Key implication for further research is that alternative and richer approaches to measuring diversification strategy seem needed. In addition, focus must be shifted towards testing the diversification strategy-performance linkage under specific contingencies rather than examining linkages at levels of aggregation that may seem superficial. Finally, market-based assessments seem most pertinent in capturing the performance effects of diversification, while the use of growth-based measures may be misleading. Methodologically, the analyses suggest that choice of meta-analytic technique may influence the nature of results. This problem seems to aggravate with the use of decreasing sample sizes in meta-analysis. As this issue has been ignored in research prac-

Intermediate Conclusion

61

tice to date, the validity of some meta-analytic findings in the domain of strategic management may be questioned.

3 Types of Operational Relatedness, Core Business Industry, and Performance In the second major analysis of this study, I pay particular attention to two issues that are important to a more comprehensive understanding of the diversificationperformance linkage. These issues had to be neglected in the meta-analyses given the lack of respective empirical material that could be used for contingency variable analysis: effects of types of relatedness and effects of industry. In addition, I add to the analysis a market-based performance measure that promises greatest possible content validity in terms of capturing synergy realization: Berger and Ofek’s excess value measure which can indicate whether a multibusiness firm trades at the so-called diversification discount.

I seek to find out whether performance differentials of multibusiness firms can better be explained if diversification strategies and the logic of business linkages are substantiated in content to a greater extent than is the case when drawing on traditional measurement schemes (as was the case in the meta-analysis). In addition, I want to examine if and how core business industry may matter to the performance implications of diversification strategy.

Recall that business relatedness generally refers to the logic of business linkages within corporate portfolios and is usually considered to proxy potential economies of scope (Robins and Wiersema, 2003). Following resource-based theory, synergies by means of scope economies provide the economic foundation for the existence of the multibusiness firm (Mahoney and Pandian, 1992; Penrose, 1959). In this context, I identify three major problems with the diversification-performance literature.

First, theory clearly suggests that the construct of relatedness is multidimensional in nature (e.g., Stimpert and Duhaime, 1997). However, only sporadic empirical evidence

64

Types of Operational Relatedness, Core Business Industry, and Performance

is available on how different types of relatedness impact firm financial performance (e.g., Farjoun, 1998; Robins and Wiersema, 1995). As a consequence, there is still a considerable need for management advice on what business relatedness to strive for in order to maximize corporate performance (e.g., Pehrsson, 2006).

Second, although it is sufficiently clear in theory that type of industry, industry structure characteristics, and industry profitability may strongly influence the nature of the diversification-performance linkage observed in empirical research (cf. section 2.5), models tested are often not sufficiently specified to account for these contingency issues (cf. Datta, Rajagopalan, and Rasheed, 1991). Also the use of (multiple) single industry samples that could prevent any confounding effects from the outset is the exception rather than the rule in this line of research. Moreover, while it is generally acknowledged that industry matters to the diversification-performance relationship through industry profitability, the idea that specific types of relatedness and specific types of scope economies may relate differently to performance in different industries remains largely undiscussed. Taking up this idea, however, appears worthwhile, given that rules of competition, success factors, and the value of assets are usually understood to vary across industries (e.g., Markides and Williamson, 1996; Porter, 1985).

And, third, to date, empirical research in strategic management has not provided evidence in fact that business interrelatedness can be exploited by multibusiness firms in ways that allow them to turn the frequently reported diversification discount (e.g., Berger and Ofek, 1995; Comment and Jarrell, 1995; Lamont and Polk, 2002; Lang and Stulz, 1994; Mackey and Barney, 2006) into a premium. However, if synergies by means of scope economies is the major justification for multibusiness firms to exist (e.g., Kanter, 1989; Porter, 1985), then some at least of the diversified firms that show the highest levels of business relatedness and, thus, the highest potential for scope economies, should not only outperform less related multibusiness firms but also trade at a diversification premium.


65

It is for these reasons that I explore for a total of 350 large manufacturing multibusiness firms with 50 firms each in seven core business industry samples the nature of the effects of three alternative types of relatedness on accounting- and market-based performance. For this purpose, I apply next to a “standard” measure of product-based, tangible relatedness, two only recently developed, objective measures of intangible types of relatedness: Robins and Wiersema’s “resource-based relatedness” that is based on patterns of technology flows among industries as indicated by patent usage data and Farjoun’s “skill-based relatedness” that is based on profiles of type and extent of human skills required in specific industries as indicated by occupational distributions. In addition, I explore whether relatedness can be used to turn the diversification discount into a premium, and whether the level of the diversification discount itself is a function of core business industry.

Please be aware that in this part of the analysis I focus the attention exclusively on types of operational relatedness. The vast majority of research in the field has defined relatedness at the operational level and, thus, suggest that it is operational economies of scope that are critical to the performance implications of diversification strategy (cf. Datta, Rajagopalan, and Rasheed, 1991, p. 532). Issues pertaining to performance consequences of relatedness at the strategic or management level as suggested by Prahalad and Bettis (1986) are going to be tested and discussed in the third major part of this work. 3.1

Theory and Hypotheses

The interest in the linkage between the logic of portfolio composition and financial performance was largely triggered by Rumelt’s (1974) landmark study on strategy/structure combinations of large firms in which he found that, independent of structure choice, firms with interrelated portfolios outperform firms with unrelated portfolios. Theory quite strongly suggests that related diversification has positive and that unrelated diversification has negative performance effects (Palich, Cardinal, and Miller, 2000; Rumelt, 1974). Recall that the rationale advanced for the performancesuperiority of firms with related business portfolios is that exclusively related diversifiers may realize benefits from transferring and exploiting across businesses capabili-

66


ties, know-how and other valuable assets (Markides and Williamson, 1996; Penrose, 1959; Rumelt, 1982; Salter and Weinhold, 1979; St. John and Harrison, 1999; Teece, 1980), while unrelated diversifiers, at the same time, have to cope with substantial costs of organizing complex operations (Jones and Hill, 1988; Markides, 1992; Nayyar, 1992).

Since Rumelt’s study, the relationship between product diversification strategy and financial performance has been extensively researched. While product diversification strategy notionally refers to the distribution of firm activities across a number of distinct businesses that are more or less related (Palepu, 1985; Pitts and Hopkins, 1982; Rumelt, 1974), the dimensions along which businesses may relate have largely been neglected by traditional diversification research, however. For more than two decades, particularly objective measures of diversification strategy have heavily relied on the hierarchy inherent in the standard industrial classification-system (SIC) and, thus, exclusively on types of tangible-, physical-, product-based relatedness. Alternative types of business linkages and similarities have not been distinguished.

More recently, however, selected authors have questioned the content validity of commonly applied indicators of diversification for research on resource-based theories of the firm, and alternative measures of diversification strategy and relatedness have been proposed (e.g., Markides and Williamson, 1994; Robins and Wiersema, 2003).

Robins and Wiersema (1995), for instance, model business interrelationships on the basis of technology flows among industries. Farjoun (1994; 1998) models business interrelationships on the basis of human resource profiles of industries. Markides and Williamson (1994) model business interrelationships on the basis of various types of intangible assets. And, St. John and Harrison (1999) develop a judgment-based system for describing “manufacturing relatedness”. Moreover, in addition to measures of relatedness based on archival data, relatedness is also increasingly measured on the basis of manager self-report data (Pehrsson, 2006; Stimpert and Duhaime, 1997).


67

The theme that emerges across this line of research is that relatedness itself is a multidimensional construct that comprises not only tangible but also intangible assetlinkage dimensions. Of similar importance is that the studies enumerated above present initial empirical evidence that specific relatedness dimensions differ in terms of their relationship with corporate financial performance. As a consequence, it is no longer sufficient to merely distinguish between related and unrelated diversification, i.e. between spreading activities into similar or into dissimilar businesses.

In other words, simply following the maxim that the potential for economies of scope is a function of the similarity of a firm’s businesses – no matter in which respect – will most likely be misleading. To make this point clear again: the more recent diversification-performance literature suggests that relatedness constitutes a multidimensional construct, and that type of relatedness matters to performance. In this respect, it is particularly intangible relatedness that is increasingly pointed at as being conducive to superior multi-business firm performance (e.g., Farjoun, 1998; Robins and Wiersema, 1995; Szeless, Wiersema, and Müller-Stewens, 2003). It is argued that intangible assets are both valuable and, in contrast to physical assets, difficult to copy for competitors. Therefore, intangible assets could nurture competitive advantages and were strategically more important than tangible assets (e.g., Barney, 1997; Robins and Wiersema, 1995).

In addition, prior research has shown that industry effects play a major role in explaining variability in the performance of multibusiness firms (cf. section 2.5). If not properly controlled for, they may seriously influence the performance effects attributed to diversification strategies (Bettis and Hall, 1982; Christensen and Montgomery 1981; Montgomery, 1985). In this context, though, it appears that the idea that specific types of relatedness may relate differently to performance in different industries has neither been discussed nor empirically tested. As there are only a very limited number of studies available that examine the performance effect of alternative types of relatedness this is not a surprise, however.

68


Nonetheless, one may argue that the combination of businesses in a portfolio should be used to gain preferential access to the types of strategic assets that support a firm’s cost or differentiation advantage in order to achieve superior performance (Markides and Williamson, 1996; St. John and Harrison, 1999). Hence, it is, from a theoretical perspective, unlikely that the same types of relatedness will pay equally across all industries. Success factors and rules of competition differ across industries and so may the benefits realizable from types of scope economies (e.g., Markides and Williamson, 1996; Porter, 1985).

Thus, in addition to the more traditional argument of the confounding effects of industry structure and industry profitability, this argument of “industry-specificity” underscores the importance of examining single industry samples and/or of verifying if effects detected in pooled samples possibly recruit themselves from effects prevailing in single industries only (cf. Bass, Cattin, and Wittunk, 1977; Bettis, 1981; Bettis and Hall, 1982).

Against this background, I seek to explore in this part of the study whether different types of operational relatedness are more or less valuable to the multibusiness firm, and whether the nature of these linkages is a function of core business industry. Accordingly, I test the following hypotheses: H2: The nature of the relationship between operational relatedness and performance is a function of type of operational relatedness H3: The nature of the relationship between (types of) operational relatedness and performance is a function of core business industry

So far, I would limit the analysis to studying the performance differentials of diversified firms. However, theoretical arguments on the benefits of related diversification do not only suggest that related diversifiers should outperform unrelated diversifiers but


69

go as far as proposing that related diversification can be superior in terms of performance to staying focused. Recall that some authors argue that business relatedness can result in tangible and intangible synergies that make the corporate whole add up to more than the sum of its parts (e.g., Porter, 1987; St. John and Harrison, 1999).

In this respect, Palich, Cardinal, and Miller (2000) report meta-analytic evidence suggesting that related diversifiers on average indeed outperform single business firms; a finding that I have shown to be questionable, however. Noteworthy is that both metaanalytic studies (have to) limit the quantitative integration of research results largely to studies from the field of strategic management and, thus, base it on measurement approaches commonly used in strategy research.

However, in terms of testing whether corporate wholes may indeed add up to more than the sum of their parts, it is in fact the finance literature that offers a measurement approach that promises greatest content validity. The excess value measure methodology can be used to examine whether diversified firms trade at a discount or premium relative to imputed values of portfolios of stand-alone firms (Berger and Ofek, 1995).

In contrast to strategy research that in the majority of cases has studied the performance differences of related vis-à-vis unrelated diversifiers, scholars in finance have traditionally questioned the value of diversification per se, i.e. in relation to staying focused. Both literatures are voluminous. Martin and Sayrak (2003) offer a review of the finance literature on the topic and point out that the prevailing wisdom among financial economists throughout much of the 1990s has been that diversified firms sell at a discount.

In terms of reasons for the existence of the diversification discount the research usually points at inefficient capital allocation and other agency problems (e.g., Denis, Denis, and Sarin, 1997; Shin and Stulz, 1998). Empirical results on whether corporate diversification actually creates or destroys shareholder value are inconsistent, however. The discount initially found (Berger and Ofek, 1995; Comment and Jarrell, 1995; La-

70


mont and Polk, 2002; Lang and Stulz, 1994), later was refuted and reported to be a premium (Campa and Kedia, 2002; Miller, 2004; Villalonga, 2004), while most recently its existence was re-confirmed (Mackey and Barney, 2006).

Please note at this stage that meta-analysis can hardly be applied to reconcile this line of research and associated conflicting results as all of these studies essentially draw on the same sample of firms, namely the entire Compustat Database. Quantitative integration of multiple results generated from the same sample of firms would violate the independency assumption that is crucial to meta-analysis.

Furthermore, unlike the majority of research on the diversification discount, it is not my ambition to test in this study whether all multibusiness firms in the Compustat Database do on average trade at a discount relative to their single segment firm counterparts or not. Instead, I am interested in expressing the excess value measure of performance used in the finance literature as a function of the level of different types of operational relatedness as indicated by more recently developed measures from strategic management research. I am guided by the assertion that if the rationale for the multibusiness firm to exist is synergies by means of scope economies, then those diversified firms that show the highest levels of business relatedness, and, thus, the highest potential for scope economies, should not trade at a discount.

In terms of content validity, the excess value measure is the most suitable approach to capture whether multibusiness firms can realize benefits from inter-relating their businesses in the way they do. For these reasons, I express the diversification discount/premium as indicated by the excess value measure as a function of operational relatedness using core business industry-specific samples. I test the hypothesis that: H4: Multibusiness firms with comparatively high levels of (types of) operational relatedness trade at a diversification premium, while firms with comparatively low levels of (types of) operational relatedness trade at a discount.

Methods

3.2

71

Methods

In order to answer hypothesis 2 and 3, the effects of portfolio characteristics on corporate accounting- and market-based performance were tested using multiple least squares regression. The analyses were conducted for a pooled sample as well as separately for 7 core business manufacturing industries over the period 2004-2006. The general model tested specifies multibusiness firm financial performance in relation to three objectively measurable types of operational relatedness (tangible and intangible) and controls for vertical relatedness, the level of product diversification, and the level of international diversification: Multibusiness Firm Financial Performance = + 1 (Product-based Relatedness) + 2 (Technological Relatedness) + 3 (Skill-based Relatedness) + 4 (Vertical Relatedness) + 5 (Level of Product Diversification) + 6 (Level of International Diversification) +

72


In addition, in the pooled, cross-industry regressions, I used (core business) industry dummy variables to control for potential effects of industry structure on performance (Dess, Ireland, and Hitt, 1990; Grant and Jammine, 1988).

In order to answer hypothesis 4, regressions were complemented by independentsamples t-tests and one-sample t-tests to compare absolute average excess values of firms with comparatively high levels of relatedness (fourth quartile of firms) to the excess values of firms with comparatively low levels of business relatedness (first quartile of firms). Conducting these analyses with industry-specific samples allowed to exploring, in addition, if the nature of the diversification discount is a function of core business industry. 3.2.1

Sample

The overall sample totals 350 firms. It consists of the 50 largest multibusiness firms in terms of total sales for the year 2005 in each of a number of 7 core business manufacturing industries as indicated by Compustat Global Vantage.

I included the food and kindred products industry (SIC 20; henceforth denoted food industry) as well as the drugs industry (SIC 283; henceforth denoted pharmaceuticals industry) as both industries played a major role in landmark studies that were used to build diversification theory (Rumelt, 1974; Bettis and Hall, 1982; Palepu, 1985). In addition, I examined the industries plastics materials and synthetic resins (SIC 282; henceforth denoted chemicals industry), soap, detergents, and cleaning preparations (SIC 284; henceforth denoted personal care and household goods industry), computer and office equipment (SIC 357; henceforth denoted computers industry), electronic and other electrical equipment (SIC 36; henceforth denoted electronics industry), and motor vehicles and equipment (SIC 371; henceforth denoted automotive industry).

The analysis, accordingly, comprises samples of firms that offer durable consumer goods, non-durable consumer goods as well as producer’s goods. Moreover, the

Methods

73

industries examined cover a large part of the activity-spectrum of the manufacturing firms in the Global Fortune 500. Thus, this sample compares well with prior research in diversification-performance domain. Next to the points just enumerated, the choice of industries studied was to some extent also driven by data availability.

Business segment data was drawn from Datastream Worldscope, and financial data was drawn from Compustat. All data was corrected for outliers (2.5 standard deviations above/below mean), and sample composition was adjusted correspondingly. An industry-specific breakdown of the firms studied is reported in appendix 5. 3.2.2

Variables

Relatedness Variables Prior research suggests multiple dimensions along which businesses may be interlinked within corporate portfolios. I use three objective measurement schemes of business interrelatedness, i.e. product-based relatedness (Palepu, 1985), resource-based/ technological relatedness (Robins and Wiersema, 1995), and skill-based relatedness (Farjoun, 1994). Most importantly, these indices identify similarity of businesses at the operational level using alternative bases. The two latter types represent types of intangible relatedness, while the former type refers to more tangible relatedness. As relatedness is to proxy potential for economies of scope, type of relatedness serves as an indicator of the potential for economies of scope in particular areas, or, put differently, for types of economies of scope.

Palepu’s (entropy) measure of physical, product-based relatedness (1985) builds on the hierarchy inherent in the SIC system that focuses on similarities in products, raw materials, facilities and physical production processes, end uses and distribution, and other more tangible business characteristics. Different four-digit SIC industry groups within the same two-digit major industry group are treated as related businesses, and businesses from different two-digit major SIC industry groups are classified as unrelated. The SIC manual and hierarchy including division-, major group-, and industry

74


group structure is available, for instance, from the U.S. Department of Labor. For illustration purposes an excerpt is provided in appendix 6.

A firm’s total diversity is decomposable into an unrelated component indexing the degree to which a firm’s activity is distributed across unrelated industry groups and a related component indexing the degree of activity distribution among related businesses within major industry groups. I deduct the index of unrelated diversity from the index of total diversity to arrive at the index of related diversity. Subsequently, I use the ratio of related-to-total diversity to capture product-based relatedness independent of the number of businesses within corporate portfolios (Farjoun, 1998). Recall that, according to resource-based theory, the relatedness among businesses within a portfolio should have an effect on financial performance that is independent of the degree of diversification (Peteraf, 1993; Robins and Wiersema, 1995; Teece, 1982).

The entropy indices of unrelated (DU) and total diversity (DT) take the functional form

DU

m

1 ¦ P j * ln 1 / P j j 1

DT

n

1 ¦ Pi * ln 1 / Pi i 1

where Pj is the share of the jth two-digit industry group sales in the total sales of the firm, m is the number of two-digit industry groups, Pi is the share of the ith four-digit business within the firm, and n is the number of four-digit businesses (Jacquemin and Berry, 1979; Palepu, 1985). Product-based relatedness is then expressed by the ratio of related-to-total diversity:

Product-based Relatedness

DT DU / DT

Robins and Wiersema’s index of “resource-based relatedness” (1995) is based on patterns of technology flows among industries that are identified by drawing on patent

Methods

75

usage data and industry input-output table data (Scherer, 1982). Specifically, two industries are considered related or similar to the extent that they import a similar mix of technology from other industries. For the purpose of delimitation from other indices in this work, I use the label “technological relatedness” henceforth. Robins and Wiersema (1995) propose their measure as indirect indicator of the transferability of intangible capabilities and know-how among industries. In other words, the index is to proxy the potential for scope economies that arise due to shared underlying technological capabilities within a portfolio (p. 283). The measure takes the form

Mk

¦R ¦r ij

ij

* ( Pki Pkj )

where Mk is an aggregate index of business interrelatedness in firm k, Rij is a salesweighted measure of interrelationship for each combination of two different industry categories i and j in the portfolio, rij is a similarity coefficient of i and j in terms of technology flows, Pi is the percentage of sales in industry category i, and Pj is the percentage of sales in industry category j. Mk is subsequently divided by the absolute value of n industries minus one to form an adjusted index ranging from -1.0 to +1.0 (Robins and Wiersema, 1995, p. 299):

Technological Relatedness

M k /( n 1)

The data necessary to calculate this index – among which the coefficients of technological similarity between each possible pair of two industries - is reported in the appendix to Robins and Wiersema (1995). Moreover, the original technology flow data between specific industries can be found in Scherer (1982, p. 232 et sqq.). I report in appendices 7 and 8 the categorization of industries that is underlying this measurement scheme as well as, for illustration purposes, an excerpt of the table of inter-category technological similarity coefficients.

76


In contrast to Robins and Wiersema, Farjoun (1998) uses profiles of type and extent of human skills required in specific industries as indicated by occupational distributions derived from the Occupational Employment Survey (OES) conducted by the U.S. Department of Labor Statistics. Industries with similar profiles, i.e. similar skill combinations (41 types of occupations from the 6 major groups: management and management support, professionals, marketing and related, administration, service, and production, cf. Farjoun, 1994, p. 190), are clustered to form skill-related industry groups. These skill-related industry groups, in turn, are used instead of SIC hierarchy-based industry groups in calculating entropy-based indices of diversification. The calculation is analogous to the measure of product-based relatedness as described above except that industry groups are not defined by 2-digit SIC codes but by the identified groups of similar skills. The data on the skill-based industry grouping is reported in the appendix to Farjoun (1994). I report the 41 types of occupations and expertise underlying this measurement scheme in appendix 9.

The entropy indices of unrelated (DU) and total diversity (DT) take the functional form

DU

m

1 ¦ P j * ln 1 / P j j 1

DT

n

1 ¦ Pi * ln 1 / Pi i 1

where Pj is the share of the jth skill-related industry group sales in the total sales of the firm, m is the number of skill-related industry groups, Pi is the share of the ith four-digit business within the firm, and n is the number of four-digit businesses. Skill-based relatedness is then expressed by the ratio of related-to-total diversity:

Skill-based Relatedness

DT DU / DT

In terms of the discriminating validity of the three major measures just detailed, Robins and Wiersema (1995) present evidence that technological relatedness is distinct

Methods

77

from product-based relatedness. They report that the two measures are insignificantly correlated (p. 289 and p. 291). Also, Szeless, Wiersema, and Müller-Stewens (2003, p. 154-156) find that the two indices are insignificantly associated except for a sample in which they pool time-series data. Moreover, Farjoun (1998, p. 624) reports that skillbased relatedness discriminates sufficiently from product-based relatedness (correlation of .167 at the 5% significance level). As regards the linkage between technological and skill-based relatedness, both of which are indicators of intangible relatedness, this work is the first that can offer evidence on its empirical relation. It is expected that both measures overlap to an extent that is, however, negligible as they identify relatedness on bases that are conceptually different. The index of technological relatedness is to capture similarities of businesses merely in terms of technological know-how and capabilities required. Contrarily, the measure of skill-based relatedness is more broadly defined and to capture similarities of businesses in terms of diverse types of know-how and expertise required (cf. appendix 9). Performance Variables Accounting for meta-analytic evidence on the multidimensionality of the financial performance construct (Combs, Crook, and Shook, 2005), both indicators of accountingand market-based performance were used in the analyses.

Accounting-based measures of corporate financial performance reflect historical performance, whereas market-based measures reflect shareholders’ expectations of future performance. Moreover, while past performance may be a good predictor of future expected performance, and diversification may indirectly – through accounting-based performance – impact market-based performance (Hoskisson et al., 1993), the market may well have a different view of the performance effects of diversification (e.g., Berger and Ofek, 1995; Lang and Stulz, 1994).

Return on assets (ROA) and return on sales (ROS) were used to reflect accountingreturns and a Tobin’s Q approximation (Chung and Pruitt, 1994) was used to indicate market-based performance. This choice of measures allows for greatest possible com-

78


parability with prior research. Growth-based performance measures were not used because of the reasons elaborated on in the meta-analysis (cf. section 2.7). The measures employed are defined as follows: ROA = Earnings before Interest and after Tax / Total Assets ROS = Earnings before Interest and after Tax / Total Sales Tobin’s Q = (Market Value of Equity + Book Value of Debt) / Total Assets

In addition, I calculated sales-based excess values. Following Berger and Ofek (1995), I compared multibusiness firms’ total value to the sum of imputed stand-alone values of the segments they are active in. The sum of the imputed values of a company’s segments estimates the value of the firm if all of its segments are operated as stand-alone entities. Excess value is the natural log of the ratio of a firm’s actual value to its imputed value and measures the gain or loss in value from diversification. Equation 1 and 2 illustrate the approach:

(1)

I (V )

n

¦ AI

i

( Indi (V AI ) mf )

i 1

(2)

EV

ln(V I (V ))

where I(V) is the imputed value of the sum of a firm’s segments as stand-alone firms, AIi is segment i’s sales, Indi(V/AI)mf is the multiple of market value of equity plus book value of debt per sales for the median single segment firm in segment i’s industry, EV is the firm’s excess value, V is the diversified firm’s (total) market value of equity plus book value of debt, and n is the total number of segments in segment i’s firm. For an illustration of the calculation of this measure please refer to appendix 11.

Methods

79

As Worldscope does not provide the revenues generated within each segment but lists them in order of importance, I used imputed segment-weights. Following Gedaijlovic and Shapiro (1998, p. 545), I imputed these weights from a geometric series. For instance, if a multibusiness firm operates in two businesses, the revenues are assumed to be distributed in a ratio of 2:1, that is to say a 2/3 weight is attributed to the first fourdigit SIC and a 1/3 weight to the second four-digit SIC code. If the firm is active in three businesses, the weights would be 4/7, 2/7 and 1/7.

Moreover, in order to prevent outliers from distorting the results and choice of beginning and ending years not to be unduly influential (Grant, Jammine, and Thomas, 1988), the accounting-based measures of performance were measured as 3-year averages around 2005, the year in which the relatedness indices were measured. The market-based measures of performance were measured concurrent to the relatedness measurements, i.e. data from the year 2005 was used. Control Variables Industry structure is an important if not the most important determinant of the variability of firm performance (Bettis and Hall, 1982; Christensen and Montgomery, 1981; Montgomery, 1985; Schmalensee, 1985). Therefore, I use (core business) industry dummy variables in the pooled analyses to control for potential effects of industry structure on performance (Dess, Ireland, and Hitt, 1990; Farjoun, 1998; Grant and Jammine, 1988).

In addition, I control for performance effects of vertical relatedness. For this purpose, I employ Fan and Lang’s (2000) measure that is based on commodity flow data from U.S. input-output tables. The authors find that vertical relatedness, on average, is associated with poor performance. In contrast to other measures of relatedness, the measure of vertical relatedness does not proxy the potential for a type of economy of scope in the sense of asset amortization and leverage. Rather, Fan and Lang’s measure refers to potential gains that firms may realize by using vertical integration to mitigate the costs of market transactions (e.g., Williamson, 1979).

80


Another reason for controlling for vertical relatedness is that SIC based measures may misleadingly classify businesses as unrelated according to the two-digit code classifications when in fact they are vertically related (Fan and Lang, 2000). Moreover, originally, vertical relatedness had been recognized as an important dimension of diversification (Rumelt, 1974); since then it has largely been neglected in diversificationperformance research, however. Also, Lemelin (1982) uses the dimension of vertical relatedness to strengthen the predictive power of overall relatedness.

Fan and Lang (2000) draw on inter-industry relatedness coefficients that equal the average of the bilateral input requirements, i.e. the mean of the dollar value of one industry’s output required to produce one dollar’s worth of another industry’s output and vice versa (cf. Fan and Lang 2000, p. 633-634). The measures of vertical relatedness V takes the form

V

¦ (w

j

* Vij )

j

where wj is a sales weight that is equal to the ratio of the jth secondary business’ sales to the total sales of all secondary businesses within a portfolio, Vij are inter-industry relatedness coefficients of the two industries from the input-output tables to which the primary business i (defined as the largest business in terms of sales) and the jth secondary businesses belong (Fan and Lang, 2000, p. 640).

Diversification-performance research also usually controls for firm size in the belief of an association with both diversification and performance because of economies of scale and market power (Grant, Jammine, and Thomas, 1988; Sheperd, 1975). There is meta-analytic evidence, however, that firm size does not cause accounting-returns and/or market-based performance (Bausch, Pils, and Van Tri, 2007; Gooding and Wagner, 1985). Therefore, I do not use firm size as a control in the analyses.

Methods

81

As the three relatedness measures employed are explicitly designed not to rise with the number of businesses within corporate portfolios, I use the entropy index of the total level of product diversification (Jacquemin and Berry, 1979; Palepu, 1985) to control for the degree of sales distribution. Excessive sales spread may entail substantial costs of organization that impact financial performance (Jones and Hill, 1988; Markides, 1992; Nayyar, 1992).

In addition, I use the foreign- to total sales ratio to control for the level of international diversification (Bowen and Wiersema, 2007; Denis, Denis, and Yost, 2002). Prior research suggests that firms may derive benefits from international diversification such as scale and scope economies (e.g., Caves, 1971), learning effects (e.g., Barkema and Vermeulen, 2001), market power (e.g., Kogut, 1985), and operational risk reduction (e.g., Kim, Hwang, and Burgers 1993). At the same time, higher levels of international diversification may, similar to higher levels of product diversification, result in complexity and coordination requirements that entail substantial costs of organization (e.g., Hymer, 1976; Tallman and Li, 1996).

In the light of this research’s focus and in order not to unnecessarily reduce the number of degrees of freedom in the analyses, I do not add squared terms of the level of product- and international diversification and assess potential curvilinearity in the relationships with performance (e.g., Palich, Cardinal, and Miller, 2000; Gomes and Ramaswamy, 1999). For the same reason, I do not use interaction terms of the level of product- and international diversification and test any moderating effects (e.g., Geringer, Tallman, and Olsen, 2000). 3.3 Regression and T-Test Results Table 3.1 presents means, standard deviations, and Pearson correlations for all variables used in the analysis.

82


Most importantly, correlations indicate that the three major independent variables are strongly associated with each other. In sharp contrast to prior research, the measures of product-, technological-, and skill-based relatedness appear to have limited discriminating validity. This pattern applies to the pooled sample as well as to each of the seven core business industry samples.

For this reason, I conducted multiple regressions of the three explanatory variables onto each other and examined variance inflation factors in the performance models. As both analyses pointed at severe problems of multicollinearity, a factor analysis was conducted for the three relatedness indices. The Kaiser-Meyer-Olkin measure of sampling adequacy (MSA) was .72 for the pooled sample and suggested middling adequacy of the correlation matrix for factor analysis (Kaiser and Rice, 1974). By way of principal component analysis and using the Eigenvalue criterion a single factor was identified that explained 74% of the variance in the three relatedness measures. All three measures loaded high on this factor with loadings > 0.85.

This means that hypothesis 2, suggesting that type of operational relatedness (as measured in this part of the study) matters to the nature of the relatedness-performance linkage, is to be rejected. As the three indices of product-, technological-, and skill-based relatedness do not discriminate sufficiently, they cannot have associations with performance that are different in meaningful ways.

This also means that the empirical, statistical reality does not allow using the three indicators of relatedness separately in the regressions. Thus, I run all models using the factor and corresponding component values generated from factor analysis in order not to have multicollinearity distorting regression estimation results. In consideration of the content of the factor identified, I assigned it the label operational relatedness.

Regression and T-Test Results

83

Apart from this point, inspection of scatterplots and residual statistics did not indicate any problems of violating regression model assumptions in terms of normality, nonlinearity, or heteroscedasticity.

-.09

10. Excess Value .64

1.14

.07

.05

29.0

.26

.04

.32

.34

.33

S.D.

.031

-.066

.003

-.024

.048

.084

.213***

.602***

.647***

1.000

1

.037

-.011

.016

.051

.044

-.095*

.282***

.597***

1.000

2

.048

-.041

-.028

-.025

.166**

.062

.116**

1.000

3

4

-.132**

.067

.067

.053

-.005

-.358***

1.000

n = 350, * p < .10, ** p < .05, *** p < .01; for Clarity the Industry Dummy Variables are not shown.

1.86

.07

8. Return on Sales

9. Tonbin’s Q

.07

7. Return on Assets

40.4

.02

4. Vertical Relatedness

6. Level of International Diversification

.59

3. Skill-based Relatedness

1.12

.34

2. Technological Relatedness

5. Level of Product Diversification

.47

Mean

Descriptive Statistics and Correlation Matrix: Pooled Sample

1. Product-based Relatedness

Table 3.1

-.075

-.111**

-.138**

-.120**

-.006

1.000

5

.062

.022

.117**

.084

1.000

6

.355***

.690***

.774***

1.000

7

.471***

.566***

1.000

8

.479***

1.000

9

1.000

10

84 Types of Operational Relatedness, Core Business Industry, and Performance


85

Pooled Sample Table 3.2 shows the results of regressing multibusiness firm ROA, ROS, Tobin’s Q, and excess value on portfolio characteristics using the pooled sample. All models are highly significant explaining between 13% and 28% of the variance in the performance measures (adjusted R2 between .10 and .26).

Operational relatedness is not significantly related to any of the performance measures used. Vertical relatedness is significantly and negatively related to the excess value measure. Moreover, the level of international diversification is positively associated with return on assets, return on sales, and excess value.

However, the variance in performance explained by the models is to a large part due to the use of the industry-dummies. Relative to the omitted dummy variable (electronics, SIC 36), measures of multibusiness firm accounting- and market-based performance are higher for firms with primary activities in the food-, the pharmaceuticals-, and the personal care and household goods-industry. Contrarily, firms with core businesses in the automotive industry suffer negative effects of industry structure on accountingand market-based performance. Finally, firms in the chemicals industry score weaker on Tobin’s Q and higher on excess value than the omitted industry.

.157

Constant

R2

5.721***

11.895***

.256

.280

.064

-.161**

-.073

.101

.435***

.008

.112*

.178***

-.065

-.015

-.029

2 Return on Sales

5.886***

.136

.164

1.809

-.174**

.016

.163**

.214**

-.155**

-.046

.046

-.018

.067

.035

3 Tobin’s Q

n = 350. Note: Standardized beta-weights reported. * p < .10, ** p < .05, *** p < .01; Omitted Industry Dummy: SIC 36 (Electronics)

F statistic

Adjusted R

.130

.060

Prime SIC 371 (Automotive)

2

.032 -.125*

Prime SIC 357 (Computers)

.245***

Prime SIC 284 (Personal Care)

.017

Prime SIC 282 (Chemicals) .254***

.132*

Prime SIC 20 (Food)

Prime SIC 283 (Pharma)

-.073 .150**

Level of International Diversification

.029

Vertical Relatedness

Level of Product Diversification

-.007

1 Return on Assets

4.542***

.103

.132

-.211

-.104

-.096

.220**

.047

.159**

.148**

.128**

-.018

-.125**

.046

4 Excess Value

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Pooled Sample

Operational Relatedness (Factor of three Relatedness Indices)

Table 3.2



87

The Food Industry Table 3.3 shows the results of regressing multibusiness firm ROA, ROS, Tobin’s Q, and excess value on portfolio characteristics for the food industry sample (SIC 20). The models using return on assets, return on sales, and excess value as dependent variables are significant and explain between 22% and 38% of the variance in the performance measures (adjusted R2 between .14 and .32).

The slope coefficient of operational relatedness is positive and significant in the model using the excess value measure. Vertical relatedness is not significantly associated with any measure of performance. In terms of other controls, the level of product diversification is negatively related to return on assets, and the level of international diversification is positively related to return on sales.

Table 3.3

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Food Industry (SIC 20) 1 Return on Assets

2 Return on Sales

3 Tobin’s Q

4 Excess Value

Operational Relatedness (Factor)

.241

.080

.309*

.383**


-.176

-.091

.001

-.113


-.306**

-.038

.016

.016


.126

.560***

.211

.246

Constant

.119

.054

1.427

-.083

R2

.219

.379

.175

.273

Adjusted R2

.143

.316

.088

.196

F statistic

2.873**

6.093***

2.017

3.559**

n = 50. Note: Standardized beta-weights reported. * p < .10, ** p < .05, *** p < .01

88


The Chemicals Industry Table 3.4 shows the results of regressing multibusiness firm ROA, ROS, Tobin’s Q, and excess value on portfolio characteristics for the chemicals industry sample (SIC 282). None of the models is significant in explaining performance differentials of the multibusiness firms studied.

Table 3.4

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Chemicals Industry (SIC 282)

1 Return on Assets

2 Return on Sales

3 Tobin’s Q

4 Excess Value


.050

.044

-.032

.098


-.111

-.216

-.198

-.187


-.051

.074

.107

.232


.309**

.282*

.227

.213

Constant

.052

.039

1.036

-.427

R

.108

.129

.104

.154

Adjusted R2

.021

.044

.017

.072

F statistic

1.238

1.523

1.189

1.869

2


The Pharmaceuticals Industry Table 3.5 shows the results of regressing multibusiness firm ROA, ROS, Tobin’s Q, and excess value on portfolio characteristics for the pharmaceuticals industry sample (SIC 283). None of the models is significant in explaining performance differentials of the multibusiness firms studied.

Regression and T-Test Results Table 3.5

89

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Pharmaceuticals Industry (SIC 283)

1 Return on Assets

2 Return on Sales

3 Tobin’s Q

4 Excess Value


.076

.026

.122

.403


-.113

.059

-.018

-.404


-.303

-.105

-.312

-.100


.143

-.031

.021

-.088

Constant

.150

.174

4.327

.568

R2

.072

.022

.094

.067

Adjusted R2

-.021

-.076

-.002

-.031

F statistic

.774

.227

.983

.683


The Personal Care and Household Goods Industry Table 3.6 shows the results of regressing multibusiness firm ROA, ROS, Tobin’s Q, and excess value on portfolio characteristics for the personal care and household goods industry sample (SIC 284). None of the models is significant in explaining performance differentials of the multibusiness firms studied.

90 Table 3.6

Types of Operational Relatedness, Core Business Industry, and Performance Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Personal Care and Household Goods Industry (SIC 284)

1 Return on Assets

2 Return on Sales

3 Tobin’s Q

4 Excess Value


-.208

-.276*

-.066

-.321**


.200

-.056

.245

-.164


.114

-.041

.370

-.140


.211

.288*

-.013

.221

Constant

.032

.073

-.291

.447

R2

.133

.156

.078

.151

Adjusted R2

.037

.063

-.024

.056

F statistic

1.386

1.668

.765

1.597


The Computers Industry Table 3.7 shows the results of regressing multibusiness firm ROA, ROS, Tobin’s Q, and excess value on portfolio characteristics for the computers industry sample (SIC 357). None of the predictors is significant in explaining performance differentials of the multibusiness firms studied.


91

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Computers Industry (SIC 357)

1 Return on Assets

2 Return on Sales

3 Tobin’s Q

4 Excess Value


.069

.073

-.078

.093


.266

-.061

.332*

-.063


-.215

-.377**

-.038

-.231


.120

.057

-.077

-.036

Constant

.093

.138

1.896

.483

R2

.215

.123

.127

.044

Adjusted R2

.140

.039

.048

-.043

F statistic

2.878**

1.470

1.607

.503


The Electronics Industry Table 3.8 shows the results of regressing multibusiness firm ROA, ROS, Tobin’s Q, and excess value on portfolio characteristics for the electronics industry sample (SIC 36). None of the models is significant in explaining performance differentials of the multibusiness firms studied.

92 Table 3.8

Types of Operational Relatedness, Core Business Industry, and Performance Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Electronics Industry (SIC 36)

1 Return on Assets

2 Return on Sales

3 Tobin’s Q

4 Excess Value


.028

-.058

-.263

-.277*


-.053

-.005

-.199

.136


-.189

-.239

-.125

.032


.189

.238

.185

.184

Constant

.119

.180

2.530

-.602

R2

.071

.137

.156

.116

Adjusted R2

-.024

.049

.067

.023

F statistic

.743

1.553

1.757

1.245


The Automotive Industry Table 3.9 shows the results of regressing multibusiness firm ROA, ROS, Tobin’s Q, and excess value on portfolio characteristics for the automotive industry sample (SIC 371). All four performance models are significant and explain between 19% and 26% of the variance in the performance measures (adjusted R2 between .11 and .19).

Operational relatedness is found to be positively related to excess value, and vertical relatedness to be negatively related to excess value. In terms of other controls, the level of product diversification is positively related to accounting-based measures of performance. In addition, the level of international diversification is found to be negatively related to Tobin’s Q and to be positively related to the excess value measure.


93

Regression of Multibusiness Firm Financial Performance on Portfolio Characteristics: Automotive Industry (SIC 371)

1 Return on Assets

2 Return on Sales

3 Tobin’s Q

4 Excess Value


-.211

-.255

.154

.378**


.280

.165

-.284

-.381**


.509**

.451**

.096

.022


-.195

-.030

-.371**

.351**

Constant

-.074

-.046

1.408

-.597

R2

.220

.188

.204

.260

Adjusted R2

.149

.114

.129

.186

F statistic

3.095**

2.539*

2.699**

3.516**

N = 50. Note: Standardized beta-weights reported. * p < .10, ** p < .05, *** p < .01

Thus, looking at the seven industry samples as a whole, there is mixed evidence as regards hypothesis 3. Results suggest that the nature of the association between operational relatedness and the excess value measure is a function of core business industry. Contrarily, with a view to the association between the factor of operational relatedness and performance in terms of ROA, ROS, and Tobin’s Q, results are stable across the seven industries studied: there are no significant associations. T-Test Results In order to assess whether firms in specific industries trade at a diversification discount or not and whether operational relatedness may allow turning the discount into a premium, absolute values of the excess value measure are examined. Recall that excess value is the natural log of the ratio of a firm’s actual value to its imputed value and measures the gain or loss in value from diversification (Berger and Ofek, 1995).

94


Table 3.10 shows the absolute average excess values of the firms in the pooled sample and in the seven core business industry samples. In addition, the absolute average excess values of the first and fourth quartile in terms of operational relatedness are reported for each sample.

Figures indicate that the 350 multibusiness firms examined in the analysis on average do trade at a statistically significant diversification discount. This finding is in line with prior research reporting a diversification discount for firms in cross-industry samples (e.g., Berger and Ofek, 1995; Comment and Jarrell, 1995; Lamont and Polk, 2002; Lang and Stulz, 1994; Mackey and Barney, 2006).

However, the industry breakdown indicates that this discount does not necessarily apply to all types of firms. A significant discount prevails merely for firms active in specific core business industries – namely firms in the computers- and automotive industry. Contrarily, it is found that firms with their primary activity in the personal care and household goods industry do trade at a premium, in fact. Finally, neither a significant discount nor premium is evidenced for firms having diversified out of the food-, the pharmaceuticals-, the electronics-, and the chemicals industry.

Moreover, comparing the excess values of firms scoring comparatively high and firms scoring comparatively low on operational relatedness indicates that firms in the food industry apparently realize scope economies that allow them to achieve a diversification premium. Largely in line with regression results, no significant difference emerges when comparing the excess values of firms with low and high levels of operational relatedness with the remaining industries. For the automotive industry, however, findings suggest that although operational relatedness is positively related to the excess value measure, firms do not manage to avoid the diversification discount.

Thus, overall, hypothesis 4, suggesting that multibusiness firms with comparatively high levels of specific types of relatedness trade at a diversification premium, while firms with comparatively low levels of specific types of relatedness trade at a discount,


95

is to be rejected. Only in the food industry, high levels of operational relatedness are associated with a premium. However, while firms in the food industry with low levels of operational relatedness suffer from inferior performance they do not trade at a significant diversification discount.

-.311 12 -.363 12 -.282 12 -.083 12 -.476** 12 -.116

350 .089 50 -.170 50 -.258** 50 -.123 50 -.365*** 50 .039 50 .183** 50

n

Food (SIC 20)

n

Pharmaceuticals (SIC 283)

n

Computers (SIC 357)

n

Electronics (SIC 371)

n

Automotive (SIC 371)

n

Chemicals (SIC 282)

n

Personal Care and Household Goods (SIC 284)

n

.539

.383

.416

.602

.717

.893

.591

87

.637

-.095**

Pooled Sample

12

.191

12

-.223**

Excess Value Standard Deviation

Excess Value 1st Quartile Operational Relatedness (Low)

Absolute Excess Values in Seven Core Business Industries

Excess Value Mean

Table 3.10

13

.075

13

-.015

13

-.385**

13

-.167

13

-.349*

13

-.118

13

.346*

88

-.138**

Excess Value 4th Quartile Operational Relatedness (High)

-.498

.675

.486

-.280

-.219

.498

2.556**

.771

Mean Difference t-value


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3.4 Discussion A number of important findings emerge from the empirical analysis above. First, measures of product-, technological-, and skill-based types of relatedness are found to be highly correlated suggesting that relatedness at the operational level should be represented by a multidimensional factor rather than by separate indices of tangible and intangible types of operational relatedness. Second, a factor of operational relatedness may explain performance differentials of multibusiness firms in the minority of industries studied (here food and automotive) and only if specific measures of performance are used (here the excess value measure). Third, while diversified firms on average trade at a diversification discount, the prevalence of a discount/premium is a function of core business industry. And, fourth, in six of the seven industries examined, firms do not achieve a diversification premium by means of high levels of operational relatedness. Similarly, firms do not suffer from a discount that is due to low levels of operational relatedness. These findings have strong implications for both past and future research on the diversification-performance linkage as well as for business practice.

The finding that the objective measures of product-, technological-, and skill-based relatedness lack discriminating power is surprising in the light of recent research into corporate strategy and portfolio composition proposing the opposite (Farjoun, 1998; Robins and Wiersema, 1995; Szeless, Wiersema, Müller-Stewens, 2003; see also table 3.11). Nonetheless, the results of this study – especially given that I used a substantially larger sample size than prior studies – suggest that either the three measures are weak in capturing the information they are supposed to capture, or, that there is no necessity to distinguish tangible and intangible types of operational relatedness. If we assume that the three relatedness measures indicate the underlying constructs adequately, the high correlation between them suggests that, in empirical reality, higher levels of product-based relatedness are usually associated with higher levels of technological as well as skill-based relatedness and vice versa. In other words, in the majority of cases, if businesses in a portfolio are more similar in tangible respects at the operational level, they are also more similar in intangible respects at the operational level.

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Specifically, this means that businesses that sell similar products and draw on similar raw materials, physical processes, and facilities are usually also similar in terms of the technologies they import from other industries as well as the type of human skills and expertise they require. Accordingly, the added-value of distinguishing types of tangible and intangible relatedness at the operational level has to be questioned – also in light of the fact that these measures are not easy to calculate.

Moreover, these findings suggest that some of the results of recent corporate strategy research may be in need of reevaluation. And this applies in particular to those studies that suggest that intangible relatedness (e.g., technological or skill-based relatedness) is distinct from tangible, product-based relatedness, and that intangible relatedness exclusively is conducive to superior multibusiness firm performance (cf. table 3.11). In this context, it is to be pointed out that, in line with Farjoun (1998), I measured all three indices independent of the number of businesses in corporate portfolios in order to stay close to resource-based theory (e.g., Peteraf, 1993 and Teece, 1982). This is because, according to resource-based theory, the relatedness among businesses within a portfolio should have an effect on financial performance that is independent of the degree of diversification (Peteraf, 1993; Robins and Wiersema, 1995; Teece, 1982). Robins and Wiersema (1995) and Szeless, Wiersema, and Müller-Stewens (2003), however, measure product-based relatedness by drawing on the standard definition of the related component of the entropy measure of diversification strategy (Palepu, 1985). This is surprising as Robins and Wiersema’s measure of technological relatedness has explicitly been formulated not to account for the degree of activity distribution. Nonetheless, this may explain in part the deviation of my results from the results reported in these two studies as regards measure inter-correlation.

Moreover, it is to be mentioned that the finding of limited unique information being captured by the three indices applies to the seven core business industries examined in this study and to the patterns in which incumbent firms diversify. However, it is up to

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further research to show whether the discriminating power of the relatedness indices is higher for other industries and corresponding diversification patterns. Table 3.11

Empirical Results in Comparison: Linkage between Types of Relatedness and between Types of Relatedness and Performance

Study

Sampl Size

Results: Linkage between Relatedness Indicators

Results: Linkage between Relatedness and Performance

Robins and Wiersema (1995)

n = 84

Correlation between physical- and technological relatedness at -.059 (not significant)

Technological relatedness positively related and physical relatedness negatively related to ROA

Szeless et al. (2003)

n = 33

Correlation between physical- and technological relatedness between .174 and .245 (not significant), and at .215 (5% significance) for pooled sample

Technological relatedness largely positively related to ROCE, ROS, Sharpe, and Treynor; physical relatedness not related to performance

Farjoun (1998)

n = 158

Correlation between physical- and skill-based relatedness at .167 (5% significance)

Physical- and skill-based relatedness alone not related to performance; when joined they are positively related to ROA and Market-to-Book Ratio

This study (2008)

n = 350

Correlation between physical- and technological relatedness at .647, between physical- and skill-based relatedness at .602, and between technologicaland skill-based relatedness at .597 (all 1% significance)

Factor of operational relatedness not significantly related to ROA, ROS, and Tobin’s Q; and significantly positively related to Excess Value in specific core business industries only

With a view to associations between relatedness and performance examined in the regressions, it was found that – in all seven industries studied – a factor of operational relatedness is not significantly associated with ROA, ROS, or Tobin’s Q. Moreover, it was found that operational relatedness is positively associated with the excess value measure in the food and automotive industry, while it is not associated with the excess value measure in the chemicals-, pharmaceuticals-, personal care and household goods-, computers-, and electronics industry.

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This means that, in the majority of core business industries studied, diversification strategy in terms of the level of operational business relatedness per se appears not to have any performance effects.

At the same time, these findings suggest that indeed it seems to be the excess value measure that is most pertinent to capturing the performance effects of diversification strategy. Moreover, as the excess value measure is a market-based measure of performance that is influenced by investors’ expectations of the future, this may be an indication that, if at all, the performance effects of diversification strategy materialize in the longer term only. This suggests that more studies in strategic management research should draw on the methodology suggested by Berger and Ofek (1995) to capture synergy effects.

And, finally, this implies that core business industry can matter to the performance effects of diversification strategy. Specifically, multibusiness firms with their core business in food or automotive seem to systematically generate benefits from transferring, sharing, and leveraging resources at the operational level. However, this does not apply to firms in the remaining five industries examined.

It is difficult to argue why exactly the performance effects of operational relatedness materialize systematically in the food and automotive industries only. Theoretically, in these industries, the benefits of operational relatedness must systematically exceed the costs of diversifying into businesses that are similar at the operational level. Both industries are consumer goods industries. And yet, there are other industries, such as personal care and pharma, which are as well consumer goods industries and are not characterized by systematic performance effects of operational relatedness.

Nonetheless, it is an important finding that positive net effects of operational relatedness, i.e. operational synergies due to sharing and leveraging the similarity of products, processes, end-customers, technologies and related capabilities, and of types of production- and other functional skills, seem realizable to a greater extent in some indus-

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tries than in others. Either the value of operational scope economies must be systematically higher and/or the costs of running a multi-business firm with operationally similar businesses must be systematically lower for these firms than for firms diversifying out of other industries. At least, this is what capital market and investors expect. Further studies should take up this issue and detail those industry characteristics that may cause these effects.

In terms of control variables, vertical relatedness is found to exhibit a negative association with the excess value measure in the pooled analysis. These findings are in line with Fan and Lang (2000) who report a negative association between vertical relatedness and excess value for a large cross-industry sample. However, industry-specific analyses indicate that a significant negative association prevails in the automotive industry only. Contrarily, in the remaining industries, though the majority of slope coefficients are negative, I find no significant association of vertical relatedness with financial performance. Thus, also the nature of the relationships between vertical relatedness and measures of performance seems to be a function of core business industry. Generally, I have no evidence that strategies of vertical integration are a suitable means to elevate multibusiness firm financial performance. Consequentially, benefits associated with vertical relatedness should not be used as economic justification for the diversification decision.

With a view to other control variables, using the pooled sample, I find that the level of product diversification is not associated with performance and that the level of international diversification is positively associated with return on assets, return on sales, and excess value. In the food-industry, the level of product diversification is negatively associated with return on assets, and the level of international diversification is positively associated with return on sales. In the automotive industry, the level of product diversification is positively related to return on assets and return on sales, and the level of international diversification is negatively related to Tobin’s Q and positively related to excess value. No significant association is found between levels of product- and international diversification and performance in other core business industries. Thus, results suggest that next to the nature of the relationships between operational related-

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ness and performance and vertical relatedness and performance, also the nature of the associations between levels of product- and international diversification and performance seem to be a function of core business industry.

Employing the excess value methodology and examining absolute values of this measure allowed shifting in focus from comparing the performance of multibusiness firms towards comparing multibusiness firm performance to single business firm performance, i.e. towards assessing the performance effects of diversification itself – in relation to the theoretical case of staying a single-business firm.

In this respect, results are in line with prior research in terms of finding an average diversification discount for multibusiness firms in cross-industry samples (e.g., Berger and Ofek, 1995; Comment and Jarrell, 1995; Lamont and Polk, 2002; Lang and Stulz, 1994; Mackey and Barney, 2006).

However, the industry-specific breakdown indicates that the discount does not necessarily prevail. The discount is found to be a function of core business industry. In fact, this finding suggests that nature and extent of net benefits – of whatever type – realizable from diversification are a function of core business industry. This finding somewhat parallels the observation made in terms of the food and automotive industry in the regressions.

Again, either the value of specific benefits of diversification must be systematically higher and/or the costs of running a multi-business firm must be systematically lower for these firms than for firms diversifying out of other industries. If I am not mistaken, this idea has not been taken up in any prior study on the diversification-performance linkage.

Findings further indicate that multibusiness firms in the food industry with comparatively high levels of business interrelatedness trade at a premium, while those firms

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that score comparatively low on operational relatedness do not. This suggests that firms in the food industry manage to achieve a diversification premium when pursuing operational scope economy strategies. This lends some support, at least, to resourcebased theory pointing at scope economies as the economic reason for being of multibusiness firms (Mahoney and Pandian, 1992; Penrose, 1959; Peteraf, 1993; Teece, 1982). In the food industry, firms pursuing operational scope economy strategies seem to manage to make the corporate whole add up to more than the sum of its parts (Porter, 1987; St. John and Harrison, 1999).

With a view to the automotive industry, regression results suggest that firms are in a position to realize operational scope economies and to increase the excess value measure by this means. However, t-test comparisons of absolute excess values suggest that these firms do not manage to turn the discount into a premium. In general, in the automotive industry, focused firms systematically outperform diversified firms, and diversification generally destroys shareholder value. In addition, the finding that the control variable level of product diversification is positively related to return on assets and return on sales of multibusiness firms suggests that multibusiness firm that have diversified away from the core business industry outperform those multibusiness firms that have not – at least in the short term.

While next to the automotive industry an average diversification discount is also evidenced for the sample of firms in the computers-industry, no average discount prevails in the food-, chemicals, pharmaceuticals- and electronics industries. Moreover, firms in the personal care and household goods industry are found to trade at a diversification premium even. In this core business industry, diversified firms apparently systematically outperform single business firms in the long run. However, this seems not being caused by the exploitation of synergies in terms of operational scope economies.

Taken together, these findings suggest that diversification by firms with their core business in the automotive- or computers-industry – as opposed to staying focused – is on average detrimental in the long term and destroys shareholder value. Contrarily, if

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one interprets the absence of an average discount as the results of favorable and detrimental diversification endeavors cancelling out each other, diversification can be undertaken in ways that create value by firms from the food-, chemicals-, pharmaceuticals-, and electronics industries.

While firms from the food industry are well-advised to pursue operational scope economy strategies and, thus should inter-relate their businesses physically, technologically, and along skill-based lines, firms from the chemicals-, pharmaceuticals-, personal care and household goods- and electronics industries may well succeed without inter-relating their businesses operationally.

Apparently, types of benefits other than operational scope economies are realizable from multibusiness organization that compensate for the costs of diversification. Another possibility is of course that types of operational scope economies not measured in this analysis are realizable by multibusiness firms and can economically justify diversification, or, that the true performance effects of operational relatedness emerge only after controlling for contingency factors such as characteristics of organizational structure.

Finally, in six of the seven industries examined, the mean excess value of multibusiness firms that score high on operational relatedness relative to their industry peers does not significantly differ from the mean excess value of firms that score comparatively low on operational relatedness. In other words, except for the food industry, firms do not achieve a diversification premium by means of high levels of operational relatedness. Similarly, firms do not suffer from a discount that is due to low levels of operational relatedness.

If we assume proper measurement, this finding casts some doubt on the strength, at least, of a key proposition in strategic management research: the economic rationale for multibusiness firms to exist is economies of scope (e.g., Kanter, 1989; Porter, 1985). Recall that economies of scope are understood to be realizable in situations in

Discussion

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which businesses in a portfolio are related (e.g., Robins and Wiersema, 1995). Despite making use of newly developed indices that were designed to overcome the limitations inherent in traditional measurement approaches, I am in the majority of industries not able to show that scoring high on these indices leads to making the corporate whole add up to more than the sum of its parts. 3.5 Intermediate Conclusion The study in this section is the first that examines multiple measure of accounting- and market-based performance, most notably the excess value measure, as a function of multiple, objectively measurable types of operational business relatedness in separate intra-industry samples. In this way, it links the state-of-the-art in empirically measuring operational business relatedness and the state-of-the-art in empirically measuring synergy realization.

Using a larger sample than prior research, I find that measures of product-, technological-, and skill-based relatedness do not sufficiently discriminate from each other to be used as separate predictors of multibusiness firm performance. These measures all load very high on a single factor suggesting that relatedness at the operational level should be represented by a multidimensional factor rather than by separate indices of tangible and intangible types of operational relatedness.

Moreover, this factor of operational relatedness is found to explain performance differentials of multibusiness firms active in specific core business industries only (here food and automotive) and only when specific measures of performance are used (here the excess value measure). However, in the majority of core business industries studied, diversification strategy in terms of lower or higher levels of operational business relatedness is found not to have any accounting-based or market-based performance effects.

Furthermore, the finding that an average diversification discount prevails for multibusiness firms in cross-industry samples suggests that diversification is in many

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cases detrimental to firm performance and often difficult to be accomplished successfully. Even more important, however, is that a discount does not necessarily prevail. It is found to be a function of core business industry suggesting that the extent of net benefits realizable from diversification systematically varies across industries. This issue and the reasons underlying should be taken up by future research.

Finally, in six of the seven industries examined, the mean excess value of multibusiness firms that score high on operational relatedness relative to their industry peers does not significantly differ from the mean excess value of firms that score comparatively low on operational relatedness. In other words, except for the food industry, firms do not achieve a diversification premium by means of high levels of operational relatedness. Similarly, firms do not suffer from a discount due to low levels of operational relatedness.

Overall, this empirical analysis indicates that diversification strategy defined in terms of levels of operational business relatedness and indicated by state-of-the-art, archival measures is per se not related to corporate performance in the vast majority of cases. In this respect, results from the empirical analysis seem to corroborate findings from the meta-analysis in the first part of the study that called into question already any over-arching, generalizable cause-and-effect relationship between diversification strategy and performance.

4 A Two-Factor Model of Operational Relatedness and Strategic Relatedness Recall that current research on the performance implications of diversification strategy is characterized by an increasing interest to conceptualize the relatedness construct. New measurement schemes are proposed that indicate relatedness on alternative bases and along multiple dimensions in order to capture a broadest possible range of potentials for synergy (Farjoun, 1994; Markides and Williamson, 1994; Pehrsson, 2006; Robins and Wiersema, 1995; Stimpert and Duhaime, 1997; St. John and Harrison, 1999; Tanriverdi and Venkatraman, 2005). As this line of research is getting increasingly multi-faceted, one may observe that the majority of empirical studies emphasize synergy in terms of economies of scope at the operational level. This happens at the expense of synergy that may be associated with aspects of dominant logic, distinctive competence, and effective management at the corporate, strategic level (Grant, 1988; Prahalad and Bettis, 1986).

However, effective corporate management and leveraging distinct corporate-level competences – considered to be possible in portfolios with strategically similar businesses – may matter as much to multibusiness firm success as economies of the scope at the operational level. Also, it may interact with operational relatedness to determine the ultimate nature of the performance implications of diversification strategy (D’Aveni, Ravenscraft, and Anderson, 2004; Grant, 1988; Hill, 1994; Hitt and Ireland, 1985; Harrison, Hall, and Nargundkar, 1993).

Moreover, ever since, there has been anecdotal evidence suggesting that effective corporate management may be the factor that reasons the success of specific types of multibusiness firms – namely those occasionally described as premium conglomerates (e.g., Shulman, 1999). Adding to this discussion, Michael Goold and colleagues have repeatedly used case-based research to emphasize that corporate parents require having

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A Two-Factor Model of Operational Relatedness and Strategic Relatedness

“sufficient feel” for the critical success factors of single businesses if a portfolio is to be managed successfully (Goold, Campbell, and Alexander, 1994).

Accordingly, it is the strategic relatedness of businesses that deserves further study. I propose that research on the diversification strategy and performance linkage may be informed by approaches that take a holistic view of aspects of (operational) economies of scope, dominant management logic, and synergy. I suggest that Grant’s (1988) logic of a two-factor model of the relatedness construct distinguishing operational relatedness from strategic relatedness, may provide both some degree of structure to the literature and additional insight in terms of the performance differentials of multibusiness firms.

As I shall demonstrate in the course of the analysis to follow, this assertion follows directly from the results in the preceding analyses. First, the meta-analyses suggested that traditional measurements of diversification strategy can hardly evidence any performance effects. Second, the empirical analyses showed that different types of operational relatedness seem to load on a single factor. And, third, this analysis also suggested that core business industry, i.e. the dominant business of a firm, matters – in to be defined respects – to the performance implications of diversification strategy.

In response to Prahalad and Bettis’ (1986) award-winning paper on the dominant general management logic, Grant suggested that there are two fundamental dimensions of relatedness. Strategic relatedness referred to similarities at the corporate-level and described if businesses were similar to manage in terms of functions such as resource allocation. It allowed for effective management of a portfolio as “[…] strategically similar businesses can be managed using a single dominant general management logic” (Prahalad and Bettis, 1986, p. 490). Contrarily, operational relatedness referred to similarities rather at the process-level, among which are product-based and technological similarities, for instance (Grant, 1988).


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Empirical research available on the dimensionality of the relatedness construct and associations with performance as outlined above has not followed the explicit distinction of strategic and operational relatedness. As a consequence, it has not been established how precisely strategic and operational relatedness interact and matter to diversification success. However, at the same time, the research in this domain appears to present – often implicitly – initial evidence that next to aspects of operational relatedness it is aspects of strategic relatedness that deserve further empirical study.

In this part of the study, therefore, I use structural equation modeling to validate Grant’s (1988) two-factor conceptualization of the relatedness construct and to subsequently test associations between strategic/management relatedness, operational relatedness, and performance. In this way, this study becomes the first to examine the performance effects of strategic relatedness vis-à-vis operational relatedness in a single, integrative model using a multitude of objective indicators.

Please note that, in the following section, I occasionally reiterate some of the arguments made in earlier sections in order to be able to make the full case for why indicators of strategic relatedness should be used to complement the traditional study of the linkage between diversification strategy in terms of operational relatedness and performance. 4.1 Theory and Hypotheses Diversification implies joining under one corporate roof two or more distinct businesses. This comes at costs. Thus, diversification makes sense economically only if costs incurred are at least recouped by synergies. If synergies exceed the costs of corporate organization, the value of the corporate whole should add up to more than the sum of its standalone parts (Collis and Montgomery, 2004; Goold and Luchs, 1993; Porter, 1987).

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In terms of synergy, the literature in strategic management suggests that economies of scope are the fundamental success factor of diversification (Mahoney and Pandian, 1992; Peteraf, 1993; Teece, 1982). Economies of scope, by definition, refer to the subadditivity of production costs (Baumol et al., 1982) and can improve the performance of each business in a portfolio and, thus, the performance of the corporation as a whole (D’Aveni, Ravenscraft, and Anderson, 2004). Synergy, however, may not only be attained by means of sub-additivities of production costs. If businesses can jointly draw on resources that are complementary, synergies in the form of super-additivities may arise (Milgrom and Roberts, 1995). In other words, businesses being simultaneously related along multiple dimensions may then be a desirable scenario (Farjoun, 1998; Harrison et al., 2001; Nayyar, 1993; Tanriverdi and Venkatraman, 2005). Thus, making the corporate whole add up to more than the sum of its parts may be achieved by means of net sub-additivities and/or super-additivities.

Irrespective of type of additivity, necessary condition to benefit from some common pool of assets is that businesses in a portfolio have something in common, i.e. that they are somewhat interdependent or related. Therefore, the relatedness or similarity between businesses is usually considered a necessary condition for synergy realization (Robins and Wiersema, 2003; Tanriverdi and Venkatraman, 2005). Problematic is that the construct of relatedness is multidimensional in nature (e.g., Farjoun, 1998; Pehrsson, 2006; Robins and Wiersema, 1995; St. John and Harrison, 1999; Stimpert and Duhaime, 1997). Businesses related on one dimension may well be unrelated on another. And, although this stream of research is growing, there is little evidence available on how different types of relatedness relate to each other and how they – independently or jointly – impact financial performance. I have made this point clear earlier.

This literature is characterized by two major methodological approaches in terms of type of data used. The first group of studies relies on using manager self-report data and assesses relatedness dimensions from within a corporation. This measurement approach uses subjective assessments as a consequence. The second group of indicators relies on archival data and measures relatedness dimensions from outside the corpora-


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tion. In general, it is assumed that managers’ conceptualizations of relatedness may significantly differ from the relatedness that is measured objectively from outside the firm (Pehrsson, 2006; Stimpert and Duhaime, 1997).

In terms of managerial assessments of relatedness, Stimpert and Duhaime (1997) find support for a two-dimensional conceptualization of the relatedness construct. Managers in their sample of firms think of relatedness in terms of product-market similarities (businesses share customers, require similar raw materials, share manufacturing processes, and share distribution networks) and in terms of differentiation similarities (businesses characterized by a common emphasis on product design, brand name products, research and development, and the development of new products). Stimpert and Duhaime do not look at associations of these dimensions with performance.

Also asking managers, Pehrsson (2006) extracts a five-factor conceptualization of relatedness. Managers in his sample assess relatedness along dimensions labeled product technology, general management skills, end customers, brand recognition, and supply channel types. Clustering firms Pehrsson finds that firms characterized by high levels of product technology relatedness enjoy superior financial performance.

Also, Tanriverdi and Venkatraman (2005) study managerial perceptions of relatedness. Investigating types of perceived knowledge relatedness, the authors find that product-, customer-, and management knowledge resource relatedness on their own do not improve performance. A second-order factor capturing complementarities between the three types of knowledge relatedness is found to be positively related to financial performance, however.

In terms of archival assessments of relatedness, research has heavily relied on the hierarchy inherent in the SIC system. Measures such as the entropy- and concentric index (Jacquemin and Berry, 1975; Palepu, 1985; Caves et al., 1981) have been used to capture product-based similarities of businesses emphasizing a physical, tangible type of relatedness. These measures have been criticized, however, for their neglect of reflect-

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ing linkages between intangible assets and for a loose connection to resource-based theory (Markides and Williamson, 1994; Robins and Wiersema, 2003).

As a response, alternative approaches to objectively measuring relatedness have been proposed. Robins and Wiersema (1995) model business interrelationships on the basis of technology flows among industries and find that multibusiness firms with higher levels of resource-based relatedness outperform firms with less interrelated businesses. Moreover, Farjoun (1994; 1998) models business interrelationships on the basis of human resource profiles of industries and finds that the skill-based measure of relatedness and an SIC based measure of product-based relatedness alone do not impact performance. However, when combined, a positive effect on indicators of multibusiness firm performance is found.

Also St. John and Harrison (1999) suggest an approach to measure relatedness from outside the corporation. Seeking to capture “manufacturing relatedness”, they use a judgment-based system and focus on identifying similarities of businesses in terms of common raw materials, common science, and common processing technologies. The authors find that, on average, organizations involved in manufacturing-related businesses are not realizing financial benefits from sharing resources in manufacturing. They also find, however, that explicit commitment to coordination and specific administrative mechanisms allowed some firms to reap fruits from manufacturingrelatedness.

Yet another approach of measuring relatedness that combines using archival- and survey data may be found in Markides and Williamson (1994) who model business interrelationships on the basis of various indicators of intangible assets and stress the strategic importance of these assets across businesses. They develop their measure of “strategic relatedness” and oppose it to “market relatedness” with the latter being captured by the Rumelt classification (1974). Markides and Williamson find that indicators of strategic relatedness explain a large part of the performance differential even of a sample of firms classified as related diversifiers according to the Rumelt-scheme.


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Thus, indicators of whether firms serve markets that are similar in terms of service requirement, number of customers, needed intermediaries, product customization, and required level of labor skill appear to be superior to market-based assessments of relatedness in predicting multibusiness firm performance.

As empirical research into the dimensionality of relatedness and its associations with performance is evolving, I propose that distinguishing types of relatedness according to a logic suggested by Grant already in 1988 may provide some degree of structure and clarity to this literature. In particular, this approach promotes the importance of businesses being strategically similar.

Grant suggested that there are two fundamental dimensions of relatedness – namely strategic and operational relatedness. Strategic relatedness refers to similarities at the corporate-level and describes if businesses are similar to manage in terms of functions such as resource allocation, strategy formulation, and targeting, monitoring, and control of business unit performance (p. 641). Contrarily, operational relatedness refers to similarities rather at the process-level stressing product-market relatedness, technological relatedness, and vertical relatedness (Grant, 1988).

The basic case made by Grant is that operational relatedness, i.e. economies of scope at the operational level, are not a sufficient condition for successful diversification, and that strategic relatedness is at least as important to diversification success. Using a logic from transaction-cost economics (Teece, 1982; Williamson, 1979), Grant stresses the importance of the role of the corporate center and effective corporate management for successful diversification in terms of coordinating, monitoring and controlling business units. Effective corporate management, in turn, was most likely feasible in situations in which business units shared “strategic characteristics” (Grant, 1988, p. 640). This was due to the fact that “strategically similar businesses can be managed using a single dominant general management logic” (Prahalad and Bettis, 1986, p. 490).

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Business are considered strategically similar, for instance, if there are similar sizes and time spans of investment projects, similar sources of risk, similar critical success factors, similar stages of the industry life cycle, similar competitive positions occupied by the businesses, and/or similar performance variables used to define goals (Grant, 1988, p. 641; Prahalad and Bettis, 1986). And, a dominant general management logic is defined as […] the way in which managers conceptualize the business and make critical resource allocation decisions – be it in technology, product development, distribution, advertising, or human resource management” (Prahalad and Bettis, 1986, p. 490). Dominant logic is also reflected in the administrative tools to accomplish goals and to make decisions (Prahalad and Bettis, 1986, p. 491).

If strategically similarity prevails, a corporate center may apply similar types of corporate knowledge, systems, and processes to the different businesses within the portfolio. This also means that experience and routines that have developed over time in the core business can become a strength rather than an obstacle. Moreover, if dominant logics do not differ greatly across businesses, top managers are more likely to respond appropriately and quickly enough to critical situations, i.e. top management is likely to make better decisions (Prahalad and Bettis, 1986). In sum, strategic relatedness of businesses is considered facilitating effective corporate management in terms of coordination, monitoring, and control (Grant, 1998).

Using different terminology, issues centering on this idea have repeatedly been examined also by Michael Goold and colleagues. The authors report that for a corporate parent to add value to individual businesses there must – next to a genuine parenting opportunity – be not only suitable skills and management processes available but also a “sufficient feel” for the critical success factors in individual businesses in order to avoid destroying value through inappropriate influences (Goold, Campbell, and Alexander, 1994). Goold and colleagues also report that in the majority of cases corporate parents would still do more harm than good. “Real sources of value destruction typically lie much more in mistakes that the parent causes through its influence on the businesses than in the corporate overhead as such” (1994, p. 34).


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In fact, Goold and colleagues argue that their research over more than 10 years, as a whole, suggests that “[…] the most important role of the corporate parent lies in influencing the performance of businesses as stand-alone entities, not in the realization of [operational] synergies between business units […]” (1994, p. 33). Following this argumentation, it is issues of corporate management and corporate management processes in terms of appointing key executives, budgetary control, strategy reviews, and capital investment decision-making that get critical. As Goold et al. emphasize the parent could make more timely and better decisions than the standalone firm on these issues only if there is an in-depth understanding of the individual business.

Against this background, it becomes more and more obvious that issues of strategic similarity between business – facilitating effective corporate management – may play a major role in the performance implications of diversification strategy.

Unfortunately, empirical research available on the dimensionality of the relatedness construct and associations with performance has not followed the explicit distinction of strategic and operational relatedness (see above). Also, it appears as if most attention is paid to theories of operational economies of scope at the expense of theories of effective management and of leveraging a dominant logic. At the same time, however, this research – partially implicitly – presents initial evidence that next to aspects of operational relatedness it is aspects of strategic relatedness that deserve further study. In fact, Stimpert and Duhaime’s (1997) two-factor model of product-market relatedness and differentiation relatedness seems to be comparable to a distinction of operational and strategic relatedness. Noteworthy is also that Pehrsson (2005) defines management knowledge relatedness in terms of knowledge facets such as investment-, risk-, alliance-management as one of three important sources of cross-business knowledge synergy. Management knowledge relatedness alone was not found to impact performance, however. Also, Markides and Williamson’s approach (1994) involved measuring opportunities to share knowledge and systems at the corporate rather than at

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the operational level. To name but one example, examining whether few or many customers are served describes in a particular way if businesses are similar to manage. In terms of studies that use archival data, I could locate two studies that explicitly sought to grasp from outside the corporation aspects of strategic relatedness (Harrison, Hall, and Nargundkar, 1993; D’Aveni, Ravenscraft, and Anderson, 2004). Both studies use resource allocation profiles, i.e. a number of expenditure intensity ratios, to gauge the strategic similarity of businesses and build on theory of effective management and dominant logic to explain performance differentials of multibusiness firms. Specifically, D’Aveni, Ravenscraft, and Anderson (2004) examine what they call resource congruence. They take each individual business in a firm’s portfolio as a focal point and test the degree to which profiles of advertising intensity, R&D intensity, selling intensity, and capital intensity, resemble or differ from the profiles of other businesses in the portfolio. The authors examine business-level performance and find support for their arguments that businesses are most efficient and profitable when their resource allocation patterns are highly similar to those of the parent’s other businesses.

Similarly, Harrison, Hall, and Nargundkar (1993) argue that similarities in resource allocations across businesses may indicate corporate strategic consistency that may lead to superior performance. In support of this argument, the variance in R&D intensity across businesses of diversified firm is found to be inversely related to industryadjusted return on assets. No support for a similar conclusion with regard to capital intensity is found, however.

Overall, these findings suggest that next to aspects of operational relatedness it is strategic relatedness in terms of Grant’s logic that matters to multibusiness firm performance. Strategic relatedness may allow for effective corporate management and, thus, help in making operational relatedness work. At the same time, strategic relatedness itself may allow to generate net synergies and make diversification succeed. Strategic relatedness may offer opportunities for both effective management and economies of


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scope through resource sharing. As a consequence, our knowledge of the dimensionality of the relatedness construct and of the diversification strategy-performance linkage may be substantially informed by investigating issues of economies of scope, dominant logic, and synergy in integrative frameworks.

For these reasons, I test in this part of the study with a set of hypotheses Grant’s logic of a two-dimensional conceptualization of the relatedness construct and respective associations with performance. First of all I examine whether Grant’s logic applies in that objective indicators of different types of relatedness can be represented by a twofactor model that distinguishes strategic relatedness from operational relatedness (H5).

Stimpert and Duhaime (1997) found a correlation of r = .19 between the factors product-market relatedness and differentiation relatedness. It appears plausible that businesses that are strategically similar, i.e. similar to manage, do also offer some opportunities for sharing resources at the operational level. I conjecture in this context that the constructs operational and strategic relatedness are positively associated (H6).

In the light of recent evidence that new measures of relatedness have in the majority of cases shown positive associations of operational relatedness with accounting- and market-based returns (e.g., Farjoun, 1998; Robins and Wiersema, 1995; Szeless, Wiersema, and Müller-Stewens, 2003), I again examine whether operational relatedness is positively associated with accounting-based performance (H7), and whether operational relatedness is directly positively associated with market-based performance (H8). I do so despite the fact that no such effects were detected in the crossindustry analysis in part two of this study. In this way I can verify if results from part two of this study hold if stronger statistical techniques are used and if strategic relatedness is accounted for in the model.

In terms of strategic relatedness, I test the proposition that it is positively associated with accounting-based performance (H9), and that it is directly positively associated with market-based performance (H10). Finally, I assume that past performance in

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terms of profitability may be used as a predictor of future expected performance (H11). Accordingly, diversification may also indirectly – through accounting-based performance – impact market-based performance (Hoskisson et al., 1993). 4.2 Methods 4.2.1

Structural Equation Modeling

I employ structural equation modeling and use the AMOS 6.0 program to test the set of hypotheses (Byrne, 2001). By definition, structural equation modeling is a hybrid of factor analysis and path analysis (Hoskisson, Johnson, and Moesel, 1994). As such it is particularly suitable for my analysis as it allows assessing issues in terms of construct measurement and validity as well as causal linkages between latent variables. Further strengths of structural equation modeling pertinent to my analysis are the ability to estimate coefficients of linkages between multiple interdependent constructs simultaneously, to accommodate measurement errors in exogenous and endogenous variables, and to measure direct and indirect effects of exogenous on endogenous variables (Hopkins and Hopkins, 1982; Jöreskog and Sörbom, 1989).

My approach to estimating structural equations follows the two-step approach recommended by Anderson and Gerbing (1988) to minimize the potential of interpretational confounding. I estimate the measurement model prior to estimating the full latent variable model, i.e. prior to estimating measurement model and structural model simultaneously.

The measurement model is estimated in confirmatory factor analysis to test whether or not the variables selected to measure each construct exhibit sufficient convergent and discriminant validity. Subsequently, nomological/criterion-related validity is tested in the full model using a recursive causal sub-model.

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119

In order to identify the final model that corresponds best to the data, I use a set of sequential chi-square difference tests and the decision-tree framework suggested by Anderson and Gerbing (1988). In addition, I use comparisons of changes in various goodness-of-fit indices to assess model fit. The maximum likelihood method is used to derive parameter estimates for the initial and modified models. The sample of firms and sources of data used corresponds to the first empirical analysis and are described in section 3.2.1. 4.2.2

Variables

My central measure of performance is Berger and Ofek’s excess value measure as it is most adequate in indicating synergistic effects (cf. sections 3.1 and 3.2.2). Recall that following Berger and Ofek (1995), I compare multibusiness firms’ total value to the sum of imputed stand-alone values of the segments they are active in. The sum of the imputed values of a company’s segments estimates the value of the firm if all of its segments are operated as stand-alone entities. Excess value is the natural log of the ratio of a firm’s actual value to its imputed value and measures the gain or loss in value from diversification (see also appendix 11). I measure excess values concurrent to relatedness measurements.

In addition, I again use three-year averages of Return on Assets (ROA) and Return on Sales (ROS) at the corporate level to reflect accounting-returns. However, this time I calculate ROA and ROS premiums over industry-averages to account for industryeffects on performance. This means that I deduct from a specific multibusiness firm’s ROA (ROS) the average ROA (ROS) of the 50 multibusiness firms in the respective industry. I do not include Tobin’s Q as a second measure of market-based performance in this analysis for reasons of limited discrimination from accounting-based returns (cf. section 3.3).

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Furthermore, I use the three archival data-based measurement schemes detailed and used in the preceding empirical analysis to indicate aspects of operational business relatedness, i.e. Palepu’s measure of product-based relatedness, Robins and Wiersema’s index of technological relatedness, and Farjoun’s measure of skill-based relatedness (cf. section 3.2.2).

In order to grasp strategic relatedness, I use three asset-intensity similarity measures that I calculate as a three-year average (2004-2006). Specifically, I use in this study the ratio of capital expenditures to sales (capital intensity), the ratio of R&D expenditures to sales (R&D intensity), and the ratio of staff and related expenditures to sales (labor intensity).

In this respect, I build on prior empirical work that suggests that expenditure intensities associated with specific businesses can convey important information on the logic of business linkage. Harrison, Hall, and Nargundkar (1993) suggest that financial resource allocation profiles can be used to assess “corporate strategic consistency”, “similarity in business strategies”, and “fit of dominant logic”. Similarly, D’Aveni, Ravenscraft, and Anderson (2004) argue that asset-intensities can be employed to capture “strategic congruence”, “congruence in terms of approaches to competing required in businesses”, and ”businesses’ alignment with the parent’s dominant logic”. Recall also, that in their seminal paper on the dominant management logic, Prahalad and Bettis (1986) define a general management logic as the way in which managers conceptualize the business and make critical resource allocation decisions in technology, product development, distribution, advertising, or human resource management (Prahalad and Bettis, 1986, p. 490).

Unlike prior studies, however, I measure the absolute average difference between secondary four-digit business expenditure profiles and core business expenditure profiles. In this way, I seek to account for the dominant logic of a multibusiness firm. Prahalad and Bettis (1986) argue that the dominant logic is strongly determined through the managers’ experiences which, in turn, are influenced by the core business of a firm:

Methods

121

“The characteristics of the core business, often the source of top managers in diversified firms, tend to cause managers to define problems in certain ways and develop familiarity with and facility in the use of those administrative tools that are particularly useful in accomplishing the critical tasks of the core business” (Prahalad and Bettis, 1986, p. 491).

Furthermore, I use average expenditures of all single business firms active in a fourdigit industry (corrected for outliers) to arrive at the expenditure profile of an industry rather than drawing on a multibusiness firm’s actual expenditures in a business segment (cf. D’Aveni, Ravenscraft, and Anderson, 2004). This proceeding is required as the overall goal is to assess characteristics of an industry in terms of management requirements rather than assessing multibusiness firms’ specific investment policies.

I weight the absolute differences between each core business asset intensity and each secondary business asset intensity by the share the respective secondary business has in a multibusiness firm’s total sales. Finally, I deduct the resulting figure from unity to capture intensity similarity. The measure takes the functional form:

j n

Asset Intensity Similarityki

1 ¦ I k CB I k SB j * w j j 1

where k is the type of asset intensity, i is the multibusiness firm studied, j is the number of secondary businesses within firm i’s portfolio, Ik is intensity k, CB is the core business of firm i, SBj is the jth secondary business in firm i, and wj is the share of sales that secondary business j has in the total sales of firm i.

The data on the asset-intensities for all four-digit industries relevant for the sample of multibusiness firms studied in this analysis is reported in appendix 12. In those 4-digit industries in which there was no data available on single business firms, I used the as-

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set intensity of the superordinate SIC industry group, in very few cases up to the SIC division level. Moreover, I again applied imputed segment-weights (cf. section 3.2.2).

Finally, I use the foreign sales to total sales ratio to control for the level of international diversification. Prior research suggests that firms may derive benefits from international diversification such as scale and scope economies, learning effects, market power, and operational risk reduction. At the same time, higher levels of international diversification may, similar to higher levels of product diversification, result in complexity and coordination requirements that entail substantial costs of organization (e.g., Hymer, 1976; Tallman and Li, 1996). Thus, operational relatedness may allow for international diversification, and international diversification may require operational relatedness.

Please note at this stage that I do not use the additional controls level of product diversification and vertical relatedness in the structural equations for reasons of model parsimony. Initial analyses showed that the model could not be fitted with operational relatedness being indicated by four items (including vertical relatedness) instead of three items (excluding vertical relatedness). In addition, as a negative error variance of the variable ROS was produced as a consequence of the very high correlation between ROS and ROA, I tested the model with accounting-based performance being indicated by a single item only (Byrne, 2001). Subsequently, I ran all models with ROS instead of ROA. No significant difference in results was detected.

A simplified version of the expected theoretical model including indicator variables tested in my analyses is shown in figure 4.1. Straight paths refer to my hypotheses. Dashed paths refer to the linkages between control variables used and endogenous and exogenous variables.

Foreign Sales/ Total sales

Operational Relatedness

(+)

Strategic Relatedness

Figure 4.1 Theoretical Model

Skill-based Similarity

Technological Similarity

Product-based Similarity

Labor Intensity Similarity

Capital Intensity Similarity

R&D Intensity Similarity

International Diversification

(+)

(+)

(+)

(+)

Market-based Performance

(+)

Accounting-based Performance

Excess Value

Return on Assets

Methods 123

124


4.3 Structural Equations Results Table 4.1 reports means, standard deviations, and correlations of all observed variables used in the analysis. Next to product-, technological-, and skill-based relatedness being strongly positively associated, also indicators of strategic relatedness are significantly and positively related, though at smaller effect sizes. Tables 4.2 to 4.5 and figure 4.2 summarize the results of the structural equation modeling.

Table 4.2 indicates the results derived from confirmatory factor analysis on the measurement model. In the measurement model, I specify the relations of the observed variables to the underlying constructs and allow the constructs to intercorrelate freely (Anderson and Gerbing, 1988). For comparative purposes also measurement information from the final model (best fitting model) is provided. Given acceptable unidimensional measurement, the pattern coefficients from the measurement model should change only trivially, if at all, when the measurement submodel and alternate structural submodels are simultaneously estimated (Anderson and Gerbing, 1988).

Each variable is identified as an indicator of an underlying factor, and factor loadings are reported where applicable. All of the factor loadings for the measurement model in table 4.2 are significant at the .001 level and higher in effect size than .52. The factor loadings of the indicators of strategic relatedness do not meet the threshold level of .70 suggested by Carmines and Zeller (1979). However, factor loadings at the .40 level and above are routinely used in the social sciences (Hitt et al., 1996; Ford, MacCallum, and Tait, 1986).

In addition, the shared variance between the two constructs was less than the average variance extracted from each of the constructs. This provides some evidence for discriminant validity (Capron, 1999; Fornell and Larcker, 1981; Steensma and Corley, 2000).

Structural Equations Results

125

Furthermore, none of the confidence intervals for the correlation between two constructs contained 1.0, which is another indication of discriminant validity (Anderson and Gerbing, 1988). Thus, all factors appeared adequate for use in subsequent model estimation steps.

.33 .32 .02

.34 .59 .97 .95 .91

2. Technological Similarity

3. Skill-based Similarity

4. CAPX Similarity

5. RDX Similarity

6. STX Similarity

-.09

9. Excess Value

n = 350, * p < .10, ** p < .05, *** p < .01

.05

.00

8. ROA Premium .62

22.23

.07

40.47

7. Foreign / Total Sales

.33

.47

1. Product-base Similarity

.04

S.D.

.03

-.05

.05

.19***

.05

.19***

.60***

.65***

1.00

1

.04

.06

.04

.31***

.17***

.17***

.60***

1.00

2

3

.14**

.02

.17***

.15***

.21***

.16***

1.00

Descriptive Statistics and Correlations of Structural Equations Mean

Table 4.1

.14**

-.02

-.05

.27***

.32***

1.00

4

.11**

.03

-.03

.32***

1.00

5

.02

-.03

.03

1.00

6

.06

.14***

1.00

7

.32***

1.00

8

1.00

9

126 A Two-Factor Model of Operational Relatedness and Strategic Relatedness

.527*** .797*** .801*** .752*** 1.000 1.000 1.000

Strategic Relatedness Operational Relatedness Operational Relatedness Operational Relatedness International Diversification Accounting-based Performance Market-based Performance

Labor Intensity Similarity Product-based Similarity Technological Similarity Skill-based Similarity Foreign Sales / Total Sales Industry-adjusted ROA Excess Value *** p < .001

.554***



Loading .571***

Factor

Measurement Model


Variable

Factor Loadings: Measurement Model and Final Model


Table 4.2

1.000

1.000

1.000

.752***

.801***

.797***

.531***

.551***

.572***

Loading

Final Model

Structural Equations Results 127

128


Table 4.3 shows chi-square statistics and the values of goodness-of-fit indices of all models estimated in the course of the modeling procedures. The pseudo chi-square constructed from the chi-square value for the saturated model (the smallest value possible for any structural model) and the degrees of freedom of the null model (independence model; the largest number of degrees of freedom for any structural model) is insignificant suggesting that a structural model with acceptable fit exists and that additional structural models be estimated (Anderson and Gerbing, 1988).

Overall, the chi-square statistics and goodness-of-fit indices for the measurement model (model 1) reported in table 4.3 suggest a strong measurement model. Thus, I have support for hypothesis 5 that the state-of-the-art in objective measures of relatedness can be represented by a two-factor model that distinguishes the dimension operational relatedness from the dimension strategic relatedness.

In terms of chi-square, a p-value exceeding .05 is usually assumed to indicate that the model is correctly specified (Hopkins and Hopkins, 1982). Elsewhere, the rule of thumb has been suggested that a chi-square value that is less than five times the degrees of freedom indicates a correctly specified model (Wheaton et al., 1977). Contrarily, Carmines and McIver (1981) suggest that a ratio of chi-square to degrees of freedom with values less than 3.0 indicate a good fit. While the latter two criteria are satisfied by the measurement model, the first is not.

Following Byrne (2001), the use of chi-square provides little guidance in determining the extent to which a model does not fit, however. This is due to the sensitivity of the chi-square statistic to sample size (Bentler and Bonett, 1980). Accordingly, it is common to rely on other indices of fit.

In this respect, I followed Hair et al. (1998) and assessed three aspects of model fit, i.e. absolute (GFI, RMSEA), incremental (NFI, CFI), and parsimonious fit (PCFI). Goodness-of-fit measures of .90 and above are generally considered desirable in strategic management research (e.g., Combs, Crook, and Shook, 2005; Hitt et al., 1996). Al-

Structural Equations Results

129

though a value > .90 was originally considered representative of a well-fitting model, a revised cut-off value close to .95 has recently been advised for research in other areas of science (Hu and Bentler, 1999). Values of .95 and above can be considered strong evidence of practical significance (Bentler, 1989). In terms of the root mean square residual (RMSEA) Browne and Cudeck (1993) suggest that a good fit is achieved if RMSEA < .05 and that values as high as .08 represent reasonable errors of approximation in the population. Finally, values > .50 on the parsimony comparative fit index (PCFI) are generally considered an indication for a parsimonious model although there is no commonly agreed-upon cut-off value. The improvement in parsimony in the model testing sequence is clearly visible, however. Table 4.4 shows the results of the hierarchical modeling procedure involving chisquare difference tests as suggested by Anderson and Gerbing (1988). This procedure was used to compare alternative theoretical models and to identify the final best model (model 4).

The theoretical model (model 2 and figure 4.1) suggests the removal of the paths from strategic relatedness to the control international diversification and from international diversification to market-based performance. In table 4.4, the comparison of the fully saturated measurement model (model 1) to the theoretical model (model 2) is reflected in the first row “model 2 vs. 1”. The change in chi-square statistic is insignificant. Improvement in goodness-of-fit indices and in model parsimony suggest that model 2 is to be preferred over model 1, however.

The next step involves comparing the theoretical model (model 2) to the next best constrained model (model 3) in which paths specified in the theoretical model are eliminated. In the next-best constrained model I dropped the paths from strategic relatedness to accounting-based performance and from operational relatedness to marketbased performance. This was done to test the proposition that operational relatedness and economies of scope have short-term rather than long-term performance effects, and that the true performance effects of strategic relatedness materialize in the long-

130


term (Markides and Williamson, 1994; Prahalad and Bettis, 1986). Comparing model 3 to model 2 results in an insignificant change in chi-square suggesting the preference of the next-best constrained over the theoretical model.

a

Next-best constrained

Final

3

4

43.702

43.700

43.548

25

24

22

20

30

Df

.012

.008

.004

.002

.056

P

.974

.974

.974

.974

GFI

.046

.049

.053

.058

RMSEA

.926

.926

.927

.927

NFI

.966

.965

.961

.958

CFI

.671

.643

.587

.532

PCFI

1.748

1.821

1.979

2.160

1.440

CMIN / df

CMIN = chi-square, GFI = goodness of fit index, RMSEA = root mean square error of approximation, NFI = normed fit index, CFI = comparative fit index, PCFI = parsimony comparative fit index

Theoretical

2

43.185

Pseudo-Chi Saturated and Null Model

Measurement

43.185

Description

Model

1

CMIN

Model Statisticsa

Table 4.3


132


Subsequently, I tested the next-best constrained model (model 3) against the fully saturated model (model 1). As this comparison suggested a non-significant change in chisquare as well, there was no need to estimate the next-best unconstrained model (Anderson and Gerbing, 1988). Moreover, model 3 is preferred over model 1.

Finally, I removed all insignificant paths from the next-best unconstrained model to arrive at the final model (model 4). The chi-square difference test with model 3 was insignificant, suggesting the adoption of model 4 as the final best model. The final best model is the most parsimonious structural model, and it provides adequate explanation of the estimated construct covariances (Anderson and Gerbing, 1988). It is illustrated in figure 4.2.

Table 4.4

Testing Sequence and Model Difference Tests

Comparison

Delta Chi-square

Delta df

p

Model Preference

Model 2 vs. 1 Model 3 vs. 2 Model 3 vs. 1 Model 4 vs. 3

0.363 0.152 0.515 0.002

2 2 4 1

>.10 >.10 >.10 >.10

2 3 3 4a

a Anderson and Gerbing’s decision-tree framework suggests that the next-best unconstrained model need not be estimated and that model 4 is to be accepted as the final best model.

Figure 4.2

Final Model

Skill-based Similarity

Technological Similarity

Product-based Similarity

Labor Intensity Similarity



Foreign Sales/ Total sales

Operational Relatedness

.38***


International Diversification

.11**

.16***

.14***


.32***


Excess Value

Return on Assets


134


Table 4.5 shows a summary of results of the structural equations modeling procedures comparing the path coefficients identified in the theoretical model and the final best model to the hypothesized nature of the variable linkages.

I find that three of the six hypothesized relationships are significant in the hypothesized direction. These results remain stable for the theoretical as well as the final model. I find support for hypothesis 6 suggesting that strategic and operational relatedness are positively interrelated (.382, p < .01). Hypotheses 7 and 8 suggesting that operational relatedness is positively associated with accounting- and market-based performance are rejected, however. Also, hypothesis 9 is rejected. Strategic relatedness is not positively related to profitability. Contrarily, strategic relatedness is positively correlated with market-based performance (.161, p < .01). Thus, hypothesis 10 is confirmed. Finally, I also find support for hypothesis 11 proposing that higher levels of profitability are associated with higher levels of market-based assessments of performance (.322, p < .01).

In terms of controls, I find that operational relatedness is positively associated with international diversification (.105, p < .1), and that international diversification is positively associated with accounting-based performance (.140, p < .01).

+ + + +

Strategic Relatedness Æ Market-based Performance

Accounting-based Performance Æ Market-based Performance

Operational Relatedness Æ International Diversification

International DiversificationÆ Accounting Returns

10

11

Control

Control

*** p < .01

.140***

+

Strategic Relatedness Æ Accounting Returns

9

* p < .10 ** p < .05

.105*

+

Operational Relatedness Æ Market-based Performance

8

.323***

.175***

-.008

-.024

.001

+

Operational Relatedness Æ Accounting Returns

7

.385***

Path Coefficient

+

Hypothesized Direction

Strategic Relatedness Æ Operational Relatedness

Path Description

Structural Modeling Results Comparing Hypothesis Tests for the Theoretical and Final Model

6

Hypothesis

Table 4.5

.140***

.105*

.322***

.161***

Path Coefficient


136


4.4 Discussion The central finding of the third major analysis of this study is that different types of business relatedness as indicated by the state-of-the-art in objective measurement schemes can be represented by a two-factor structure that distinguishes strategic relatedness from operational relatedness. Strategic and operational relatedness emerge as two related and yet distinct dimensions of the relatedness construct out of which merely strategic relatedness is found to be significantly and directly positively associated with the excess value measure. The analysis implies that perspectives that integrate aspects of operational relatedness and economies of scope, dominant logic, and synergy may substantially inform our knowledge of the diversification strategyperformance linkage.

My results have important implications for both strategic management theory and practice. In terms of the performance effects of diversification strategy, structural equation results suggest that operational relatedness, and, thus, a potential for economies of scope at the operational level, is not a sufficient condition for successful diversification (e.g., Grant, 1988; D’Aveni, Ravenscraft, and Anderson, 2004; Nayyar, 1992). The third part of this study does not offer any evidence for operational relatedness being directly linked to accounting-based performance or to market-based performance. Accordingly, there is also no evidence that operational relatedness is indirectly linked to market-based performance via accounting-based performance.

This finding is similar to St. John and Harrison (1999) who found their measure of manufacturing-relatedness not to be per se related to performance. At the same time, this finding contrasts with prior research that elicited positive associations between indicators of operational relatedness (used in this study) and performance (Farjoun, 1998; Robins and Wiersema, 1995; Szeless, Wiersema, and Müller-Stewens, 2003).

In addition, I find support for the proposition that relatedness at the strategic level is a determinant of the success of diversification (Grant, 1988; Prahalad and Bettis, 1986).

Discussion

137

Higher levels of strategic relatedness are found to be directly positively associated with higher values of the excess value measure. This suggests that multibusiness firms that can apply the dominant logic prevailing in the core business to secondary businesses in the portfolio are able to add more value to the corporate whole than corporations that have to manage strategically unrelated businesses.

Prahalad and Bettis (1986) argue that strategic dissimilarity adds to the complexity of the top management process. Top managers are less likely to respond correctly and quickly enough to problems that arise in businesses that are unfamiliar, i.e. in businesses in which internalized mental maps are not applicable (Prahalad and Bettis, 1986). In this context, Grant (1988) suggests that the effectiveness with which corporate management performs functions such as allocating resources between businesses, formulating and coordinating business unit strategies, and setting and monitoring performance targets for business units is determined in part by top management’s ability to apply similar knowledge and systems to the different businesses within a firm. And, also Goold and colleagues argue that parents must have a “sufficient feel” for the critical success factors in individual businesses in order to avoid destroying value through inappropriate influences (Goold, Campbell, and Alexander, 1994).

My findings support these lines of argumentation. My results suggest that strategically similar businesses allow the corporate center to more effectively and efficiently manage a multibusiness firm. Strategic relatedness appears to allow for exploiting suband/or super-additivities at the corporate level. At the same time, it seems to make it easier for corporate parents to add value by influencing the performance of individual businesses as stand-alone entities.

However, strategic relatedness is not found to be associated with higher levels of profitability. The absence of any such effect in my model may be due to the limitations of measures of profitability to capture synergistic effects, especially when compared to the excess value measure. At the same time, strategic relatedness may not be associated with profitability because accounting-based measures of performance merely in-

138


dicate short-term performance. Contrarily, market-based performance captures investors’ expectations of future returns and, thus, indicates (expected) long-term performance. Accordingly, the positive effects of being able to build on the dominant logic prevailing in the core business and associated management systems also in secondary businesses may materialize in the long- rather than in the short-term.

Moreover, the positive correlation found between strategic and operational relatedness suggests that joining under one corporate roof businesses that are strategically similar does usually entail some potential to realize economies of scope also at the operational level. As operational relatedness is not found to be directly related to performance, while strategic relatedness is, strategic relatedness may act as a moderator here. Strategic relatedness suggests that corporate managers have a comprehensive understanding of the businesses their corporations are active in, and that they are well-aware of the requirements to successfully compete in these businesses. In this situation, it may be easier for corporate managers to efficiently manage operational relatedness and to realize net benefits from scope economies at the operational level. In other words, if businesses are strategically similar, corporate management is – in the strive for organizing for economies of scope at the operational level – less likely to detrimentally intervening in individual businesses’ affairs (D’Aveni, Ravenscraft and Anderson, 2004; Nayyar, 1992).

This argumentation has found some support also in St. John and Harrison (1999) who found that successful manufacturing-related firms were those that simultaneously built on specific types of organization capital in terms of planning, controlling, and coordinating businesses. St. John and Harrison found that high-performers in their sample exhibited a dominant manufacturing logic with their core resources and core capabilities being in manufacturing and technology management.

In principle, positive interaction effects may next to moderator-effects also point at complementarities, i.e. super-additivities, between types of relatedness (Farjoun, 1998; Tanriverdi and Venkatraman, 2005). Economic theory of complementarities defines a

Discussion

139

set of activities as complementary when doing more of any one of them increases the returns to doing more of the others (cf. Tanriverdi and Venkatraman, 2005, p. 100; Milgrom and Roberts, 1995).

As merely strategic relatedness was found to generate direct positive returns, there can, by definition, be no complementarities between strategic relatedness and operational relatedness. Thus, testing a second-order factor model of complementarities (Tanriverdi and Venkatraman, 2005) in the structural equation modeling was discarded for theoretical reasons. Anyway, technically it had not been feasible because of only two first-order factors available (Byrne, 2001).

However, in order to test for possible moderation effects, I estimated supplementary models in which I used an interaction term between the latent variables strategic relatedness and operational relatedness to predict excess value (Williams, Gavin, and Hartman, 2004). Here, I found a significant positive sign for the predictor strategic relatedness, a non-significant effect of operational relatedness, and a negative sign for the interaction term.

Thus, although strategic relatedness and operational relatedness are to an extent correlated, joining high levels of strategic relatedness with very high levels of operational relatedness appears to be detrimental to synergy realization. As such, these additional results suggest that there are no positive interaction effects between strategic and operational relatedness, and that the positive performance effects of strategic similarity found in this study likely arise because of sub- or super-additivities at the strategic level.

In fact, Grant suggested that strategic relatedness may conflict with operational relatedness. Operational relatedness creates interdependence between businesses and an administrative burden which entails substantial coordination costs (Jones and Hill, 1988; Lorsch and Allen, 1973; Nayyar, 1992). Benefits of strategic relatedness, however, are explicitly associated with efficient corporate-wide management and applica-

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tion of control systems which are nurtured by the separation of corporate from operational management and divisional autonomy (Grant, 1988, p. 641-642).

Thus, I can conclude that while higher levels of strategic relatedness are associated with higher levels of operational relatedness, corporations should avoid striving simultaneously for excessive exploitation of strategic and operational relatedness. Strategic relatedness emerges as the business linkage of choice in my cross-industry study and organizational structures ought to be aligned with this strategy and not be compromised for by-product opportunities of economies of scope at the operational level.

This conclusion, however, poses some questions with a view to the findings of the industry-specific analyses (cf. section 3.4). Multibusiness firms with their core business in the food and automotive business were found to exhibit a positive association between higher levels of an operational relatedness and the excess value measure. This is contradictory to the logic that strategic relatedness is the desirable type of business linkage in portfolios, and that strategic relatedness conflicts with operational relatedness.

In this context, one must first understand that the regressions relied on factor scores generated from factor analysis to indicate operational relatedness. Using factor analysis, i.e. data reduction techniques, always entails a loss of information as only a fraction of the variance of multiple original variables can be captured by a factor. Accordingly, results from the regressions in this analysis by design have to be considered cautiously, and, what is more important, have generally to be considered inferior to results from structural equations modeling in terms of validity. The structural equations allowed making use of all original indicator variables of relatedness, the correlations between them, as well as the correlations between all indicators of operational and strategic relatedness. Thus, the structural equations analysis could capture much richer information than the regressions.

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Nonetheless, I ran supplementary regression analyses on the core business industry samples in which I used scores from factor analysis for the factor strategic relatedness in addition to the variables used in the original regressions. I discarded the option of testing structural equations for industry-specific samples for reasons of small sample size. Results indicated that a factor of strategic relatedness could only capture 53% of the original variance of the indicators suggesting that these add-on analyses must be considered tentative and need verification in subsequent research. Please note at this stage that it is for this reason also that I discarded relating absolute discounts/premiums to levels of strategic relatedness.

In the add-on regressions, I found that the factors operational relatedness and strategic relatedness are higher correlated for the food industry than for any other of the seven industries studied. This may suggest that the positive association found between operational relatedness and excess value in the food industry is spurious, i.e. caused by the third variable strategic relatedness. No such evidence was found with a view to the automotive industry, however. To fully answer the question of how precisely operational relatedness and strategic relatedness interact in specific industries, future research is required that uses sufficiently large industry-specific samples to test structural equation models such as the one proposed in this study.

With a view to the results of this study as a whole, the structural equations analysis must be considered superior to the regression analyses. Nonetheless, although this study suggests as a consequence that strategic relatedness may pay off when diversifying out of all seven industries studied in this analysis, its relationship with operational relatedness and the potential performance effects of operational relatedness deserve particular attention in the study of firms from the food and automotive industries.

Overall, the results of this research have implications for the interpretation of past research and the design of future studies. The support I find for Grant’s distinction of strategic relatedness versus operational relatedness involves the finding that recently suggested measures of resource-based/technological relatedness (Robins and

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Wiersema, 1995) and skill-based relatedness (Farjoun, 1994; Farjoun, 1998) not only are highly correlated with each other but are also strongly associated with productbased relatedness (Palepu, 1985). Accordingly, I find very high loadings of these measures on a single factor (operational relatedness) in my comparatively large sample. This suggests that these measures despite capturing partially unique information cannot be considered to be much better indicators of (resource-based) relatedness and much better predictors of performance than the heavily criticized indicators of product-based relatedness, at least if the latter are measured independent of the number of businesses within corporate portfolios (cf. section 3.2.2).

Future diversification-performance research should avoid using these indices in isolation from other indicators of operational relatedness. As a consequence, related empirical findings on associations of these specific types of relatedness with multibusiness firm performance must be treated cautiously. Put differently, this type of research may only produce more of mixed evidence on the performance effects of diversification strategy and promises no leaps forward.

In fact, the support found in this study for a factor of strategic relatedness that is distinct and yet positively associated with the factor operational relatedness whilst at the same time related to market-based performance points at a first possible reason why there is mixed empirical evidence on the linkage between indicators of operational relatedness and corporate performance.

Another reason is that some of the positive performance effects attributed to operational relatedness by prior research may have been due to positive performance effects of international diversification, in fact. In this study, I find operational relatedness to be positively related to the control international diversification, and international diversification to be directly positively related to profitability and indirectly positively related to market-based performance via profitability.

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4.5 Intermediate Conclusion In conclusion, this part of the study has added to the literature on the linkage between diversification strategy and corporate financial performance as it offers an analysis that is the first to test in a single, integrative model the performance effects of strategic relatedness vis-à-vis operational relatedness using a multitude of objective indicators of relatedness.

I validated Grant’s (1988) two-factor conceptualization of the relatedness construct and found evidence for the meaning of strategic relatedness to diversification success. At the same time, results relegate the meaning of operational business relatedness and, thus, of potentials for economies of scope at the operational level, to multibusiness firm performance. Finally, it was found that strategic relatedness most likely conflicts with operational relatedness and that corporations should avoid seeking to excessively exploit both types of relatedness simultaneously.

5 Summary This thesis comprises three major consecutive analyses that contribute to the literature on the diversification strategy and performance linkage.

In the first analysis, I meta-analytically integrated empirical data from 99 individual empirical studies published between 1971 and 2005. I tested the popular proposition that diversification strategy impacts financial performance by replicating and extending an earlier meta-analysis by Palich, Cardinal, and Miller (2000). For this purpose, I employed both the techniques suggested by Hedges and Olkin (1985) and the techniques suggested by Hunter and Schmidt (2004) for testing effects of contingency factors in meta-analytic frameworks. This allowed me to tease out the nature of the linkage between product diversification strategies and accounting-based performance, market-based performance, and growth-based performance that is truly suggested by the body of empirical research.

In the second analysis, I explored for a number of 350 large multibusiness firms the nature of the relationships between three objectively measured types of operational business relatedness and between a multidimensional factor of operational relatedness and accounting- and market-based performance, most notably the excess value measure. In addition, I examined the meaning of core business industry to diversification success. Thus, the second analysis linked the state-of-the-art in empirically, objectively measuring operational relatedness, and thus, potentials for economies of scope at the operational level, and the state-of-the-art in empirically measuring synergy realization.

Finally, in the third analysis, I used structural equation modeling to initially validate Grant’s (1988) two-factor conceptualization of the business relatedness construct. Subsequently, I tested the associations between strategic relatedness, operational relatedness, and accounting-based and market-based performance for the same sample of 350

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large multibusiness firms. Accordingly, the third part of my study offers an analysis that is the first to test in a single, integrative model the performance effects of strategic relatedness vis-à-vis operational relatedness with both dimensions of the relatedness construct being represented by a multitude of objective indicators.

Overall, the results of the three analyses challenge contemporary theory on the performance effects of diversification strategy and business relatedness in a number of ways. They have important implications for further research and theory development on corporate strategy. In addition, my results entail concrete, performance-relevant advice for top executives deciding on how to best compose business portfolios.

Drawing on a total sample size of 52,116 empirical observations of the diversificationperformance relationship made between 1940 and 2000, the initial meta-analysis casts some serious doubt on what has become standard theory in strategic management – the performance effects of product diversification strategy (Palich, Cardinal, and Miller, 2000; Rumelt, 1974).

I present evidence that strategies of related and unrelated diversification (as indicated by traditional measurement schemes) per se are not significantly associated with accounting-based performance. This finding is stable across samples and across metaanalytic techniques applied. Thus, I have strong evidence that the inverted-U curve association between diversification and accounting-based performance suggested in the meta-analysis by Palich et al. (2000) does not exist.

In addition, the Hedges/Olkin-based analysis suggests that neither related diversification nor unrelated diversification is significantly associated with market-based performance. This applies to both the replicated and the extended sample. Again, this is in contrast to what Palich, Cardinal, and Miller report. However, the Hunter/ Schmidtbased analysis suggests a positive association between related diversification and market-based performance and a negative association between unrelated diversification and market-based performance. Thus, I have mixed evidence on the existence of the

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inverted-U association between diversification and market-based performance that is suggested by Palich and colleagues (2000).

If one considers the Hunter/Schmidt-based findings on the association between diversification and market-based performance viable, performance effects of diversification strategy likely materialize in the long- rather than in the short term (Bergh, 1995a; Wan, 1998). Thus, my results suggest that if there is a performance dimension that is worth further study in terms of its linkage with diversification strategy it is marketbased performance.

In terms of performance dimensions, meta-analytic results also suggest that the use of growth-based performance measures may be misleading in diversification performance research, especially the use of measures such as sales-growth and asset-growth. I found that growth-based performance indicators are generally more positively related to diversification than profitability-based measures. This is, however, due to the fact that these measures indicate company size growth, and company size has variously been shown to be positively correlated with diversification.

Another issue to be emphasized is that in the majority of (the few) cases in which significant associations were found between diversification strategy and performance in the meta-analysis, effect sizes reached trivial to small strength (Cohen, 1988). This finding is in line with Palich, Cardinal, and Miller’s earlier meta-analytic results. To make that point clear, the meta-analysis as a whole suggests that diversification strategy per se – as indicated by traditional measurement schemes –, if it matters at all, is far less important to performance than may have been expected by some.

Methodologically, the meta-analysis shows that choice of meta-analytic technique may influence the nature of results in research practice. This finding raises some serious questions with regard to meta-analytic findings in strategic management research in general, a domain in which it is not common to use multiple methods to strengthen the validity of meta-analytic findings.

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In addition, the process of conducting the meta-analysis indicated that – in actual research practice – meta-analysis may be far from being the perfect vehicle for disclosure and replicability that it is supposed to be. While this is due to reporting practices and journal space constraints, it is also due to the difficulty of fully reproducing conceptualization- and coding approaches used by other authors.

And yet, with a view to empirical diversification-performance research, the metaanalyses in this study indicate that further studies are needed that make use of alternative approaches to measuring diversification strategy that are richer in content than traditional approaches. Also, the focal linkage ought to be tested under specific contingencies rather than being examined at superficial levels of aggregation. Finally, market-based assessments seem most pertinent in capturing the performance effects of diversification. Contrarily, the use of growth-based measures may be misleading and should be refrained from. These issues, in turn, were directly reflected in the design of subsequent empirical analyses.

In the second major analysis of this study, I concretized the content of diversification strategies by specifying in greater detail the business relatedness construct, i.e. the logic of business linkages in portfolios. In this analysis, I focused the attention exclusively on types of operational relatedness given that the vast majority of research in the field suggests that it is economies of scope at the operational level that are critical to diversification success.

Using a sample that is considerably larger than samples investigated in prior research, I found that measures of product-based relatedness, technological relatedness, and skill-based relatedness appear not to sufficiently discriminate from each other to be used as separate predictors of multibusiness firm performance. This is in sharp contrast to prior findings in this line of research. The three indices of relatedness at the operational level were found to all load very high on a single factor. This suggests that relatedness at the operational level should be represented by a single multidimensional fac-

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tor rather than by separate indices of tangible and intangible types of operational relatedness.

In other words, the high correlation between the three indicators suggests that if businesses in a portfolio are more similar in tangible respects at the operational level, they are also more similar in intangible respects at the operational level. Specifically, this means that businesses that sell similar products and draw on similar raw materials, physical processes, and facilities are usually also more similar in terms of the technologies they import from other industries as well as the type of human skills and expertise they require. Accordingly, the added-value of distinguishing types of tangible and intangible relatedness at the operational level may have to be questioned.

Moreover, these findings suggest that some of the results of recent corporate strategy research may be in need of reevaluation. And this applies in particular to those studies that suggest that intangible relatedness (e.g., technological or skill-based relatedness) is distinct from tangible, product-based relatedness, and that exclusively intangible relatedness is conducive to superior multibusiness firm performance.

In terms of the performance effects of operational relatedness, it was found in the regressions that the identified factor explains performance differentials of multibusiness firms active in specific core business industries only (here food and automotive) and only when specific measures of performance are used (here the excess value measure). However, in the majority of core business industries studied, diversification strategy in terms of lower or higher levels of operational business relatedness was found not to have any accounting-based or market-based performance effects.

This implies that core business industry can matter to the performance effects of diversification strategy. Specifically, multibusiness firms with their core business in food or automotive seem to systematically generate benefits from transferring, sharing, and leveraging resources at the operational level. However, this does not apply to firms in the remaining five industries examined.

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Thus, positive net effects of operational relatedness, i.e. operational synergies due to sharing and leveraging the similarity of products, processes, end-customers, technologies and related capabilities, and of types of production- and other functional skills, seem realizable to a greater extent in some industries than in others. Either the value of operational scope economies must be systematically higher and/or the costs of running a multi-business firm with operationally similar businesses must be systematically lower for these firms than for firms diversifying out of other industries. Further studies should take up this issue and detail those industry characteristics that may cause these effects.

At the same time, these findings suggest that indeed it seems to be the excess value measure that is most pertinent to capturing the performance effects of diversification strategy. Also, as it is a market-based measure of performance, the performance effects of diversification strategy seem to materialize in the longer term rather than in the short term. This suggests that more studies in strategic management research should draw on the methodology suggested by Berger and Ofek (1995) to capture synergy effects.

Subsequent examination of absolute values of the excess value measure allowed shifting in focus from comparing the performance of multibusiness firms towards comparing multibusiness firm performance to single business firm performance, i.e. towards assessing the performance effects of diversification itself – in relation to the theoretical case of staying a single-business firm.

It was found that an average diversification discount prevails for multibusiness firms in the cross-industry sample of 350 firms studied. This finding parallels findings from the meta-analysis in which an overall negative association between diversification and performance was identified. This suggests that diversification is in many cases detrimental to firm performance and often difficult to be accomplished successfully.

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However, results also indicate that a discount does not necessarily prevail. It was found to be a function of core business industry. In fact, this finding suggests that nature and extent of net benefits – of whatever type – realizable from diversification are a function of core business industry. This finding somewhat parallels the observation made in terms of the food and automotive industry in the regressions. Again, either the value of specific benefits of diversification must be systematically higher and/or the costs of running a multi-business firm must be systematically lower for these firms than for firms diversifying out of other industries. At least, this is what the capital market and investors seem to expect. If I am not mistaken, this idea has not been taken up in any prior study on the diversification-performance linkage. This issue and the reasons underlying should be investigated in future research.

Finally, in six of the seven industries examined, the mean excess value of multibusiness firms that score high on operational relatedness relative to their industry peers does not significantly differ from the mean excess value of firms that score comparatively low on operational relatedness. In other words, except for the food industry, firms do not achieve a diversification premium by means of high levels of operational relatedness. Similarly, firms do not suffer from a discount due to low levels of operational relatedness. Despite making use of newly developed indices that were designed to overcome the limitations inherent in traditional measurement approaches, I was in the majority of industries not able to show that scoring high on these indices leads to making the corporate whole add up to more than the sum of its parts.

Overall, the second analysis of this study indicates that diversification strategy defined in terms of levels of operational business relatedness and indicated by state-of-the-art, archival measures is per se not related to corporate performance in the vast majority of cases. In this respect, results from this analysis corroborate findings from the metaanalysis in the first part of this study that called into question already any overarching, generalizable cause-and-effect relationship between diversification strategy and performance.

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Up to this stage, I examined diversification strategy and its performance implications merely in terms of relatedness at the operational level. However, some authors suggest that synergy may not only be associated with economies of scope at the operational level but also with aspects of leveraging a dominant logic and distinctive competence as well as with effective management at the corporate, strategic level. Authors such as Grant (1988) and Prahalad and Bettis (1986) argue that effective corporate management and leveraging distinct corporate-level competences is possible in portfolios that are characterized by businesses that are similar at the strategic level.

For this reason, I devoted the third part of this study to examining the diversificationperformance linkage from a perspective that integrates aspects of operational economies of scope, dominant management logic, and performance. Specifically, I used structural equations modeling to first validate Grant’s (1988) two-factor conceptualization of the relatedness construct and to second test the performance implications of operational relatedness vis-à-vis the effects of strategic relatedness.

I found that different types of business relatedness as indicated by objective measurement schemes can indeed be represented by a two-factor structure that distinguishes strategic relatedness from operational relatedness. Strategic and operational relatedness emerged as two related and yet distinct dimensions of the relatedness construct.

Merely strategic relatedness was found to be significantly and directly positively associated with the excess value measure, however. At the same time, results again relegated the meaning of operational business relatedness and, thus, of economies of scope at the operational level, to multibusiness firm performance. Finally, strategic relatedness was found to most likely conflict with operational relatedness. This means that corporations should avoid seeking to excessively exploit both types of relatedness simultaneously.

Overall, my results suggest that across industries issues of effective management, dominant logic, and corporate distinctive competence are more important to diversifi-

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cation success than issues of operational relatedness and realizing economies of scope at the operational level.

Multibusiness firms that can apply the dominant management logic and corporatelevel competences prevailing in the core business to secondary businesses in the portfolio are able to add more value to the corporate whole than corporations that have to manage strategically unrelated businesses. This is because strategically similar businesses allow the corporate center to more effectively and efficiently manage a multibusiness firm. If mental maps internalized at the core business, similar knowledge, and similar management systems can be applied also in secondary businesses, the effectiveness with which corporate management performs functions such as appointing key executives, allocating resources between businesses, formulating and coordinating business unit strategies, and setting and monitoring performance targets rises. By means of concentrating a portfolio on strategically similar businesses, the complexity of top management tasks and processes is limited. Moreover, having a sufficient feel for the critical success factors in individual businesses makes top managers more likely to respond correctly and quickly enough to problems that arise. In sum, strategic relatedness paves the way for exploiting sub- and/or super-additivities at the corporate level, and it becomes easier for the corporate parent to influence the performance of individual businesses to the favour of the corporation as a whole.

As a consequence, this study seems to empirically corroborate what has long been a supposition based on largely anecdotal evidence. Strategic relatedness matters to the success of multibusiness firms. Also, strategic relatedness may in part explain why multibusiness firms that are frequently described as premium conglomerates are premium in fact, and why they outperform their peers.

At the same time, my findings may add some substance to what Goold and colleagues concluded after more than ten years of their largely case study-based research: “[…] The most important role of the corporate parent lies in influencing the performance of

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businesses as stand-alone entities, not in the realization of [operational] synergies between business units […]” (1994, p. 33).

Moreover, if diversification is comparatively difficult to be accomplished successfully in practice and in many cases does more harm than good, then corporate executives seem well-advised to first make sure that value is not destroyed by exerting inappropriate influence on individual businesses. The exercise of inappropriate influence, however, most likely occurs in situations in which businesses in a portfolio are strategically dissimilar. My work suggests that understanding this logic and appreciating its implications for practice appears in the vast majority of cases to be more conducive to the long-term success of diversification than continuing to do what still seems more popular in practice – striving for implementing comprehensive operational synergy initiatives.

All of the above means that this research has strong implications both for further research on the diversification strategy and performance linkage as well as for corporate top-management deciding on the composition of business portfolios. It is hoped that the findings from this research are used to advance future research designs in strategic management and to enrich decision-making processes in management practice that involve answering questions of corporate strategy and the scope of the firm.

Nonetheless, at the same time, it is to be acknowledged that this work could demonstrate merely small effects of strategic relatedness on multibusiness firm performance. Therefore, it is up to further research to show whether strategic relatedness in combination with additional contingency variables may lead to greater performance effects. In this context, also alternative indicators of strategic relatedness should be developed and used in order to more comprehensively capture the construct.

Overall, I hope that a two-factor distinction of strategic- and operational relatedness and a corresponding reflection of both aspects of dominant management logic and aspects of operational economies of scope is used to provide some structure to further

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work in an area of research that appears getting increasingly diverse. Future research should also pay careful attention to the meaning of the core business of a firm to diversifying patterns and to the performance implications of diversification strategy and strategic relatedness. Accordingly, core business industry-specific analyses appear most promising.

Given the difficulty of measuring strategic relatedness from outside corporations, particularly studies using manager self-report data may be used to cross-validate and build on the conceptualization of strategic relatedness versus operational relatedness. Possible interaction effects of strategic and operational relatedness deserve further study as well as the role of actual organizational structures in place in moderating the linkage between strategic relatedness, operational relatedness, and performance.

Limitations and some ex post Remarks on Meta-Analysis The meta-analysis is limited in that I was unable to test the effects of the full set of contingency variables on the diversification-performance linkage in some situations. This becomes obvious in the Hunter/Schmidt-based analysis within which the hierarchical breakdown, i.e. the stepwise combination of contingency variables, causes rapidly decreasing subset sizes. Moreover, in all those cases in which the hierarchical breakdown suggests population heterogeneity, contingency variables other than the ones tested in this analysis are contributing to the variance of the nature of the focal subset relationship.

In addition, the use of small sample sizes in specific correlation subsets may be criticized. This applies particularly to associations between related diversification and performance dimensions. Accordingly, one may question the validity of parts of the metaanalytic results. Small subsets simply point at the fact, however, that there is few empirical research available that is testing specific types of relationships. Though small subset size may limit the generalizability of parts of this study’s results, it is less a shortcoming of this research than it is one of prior empirical research.

Subsequent empirical analyses are limited in that I measure relatedness objectively with archival data. Managerial perceptions of relatedness may differ from objective assessments of relatedness, however.

Moreover, it is to be mentioned that the finding of limited unique information being captured by the three indices of operational relatedness applies to the seven core business industries examined in this study and to the patterns in which incumbent firms diversify. However, it is up to further research to show whether the discriminating power of the relatedness indices is higher for firms in other industries and corresponding diversification patterns.

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Also, though substantively warranted, I discarded for statistical reasons, i.e. for reasons of small sample size, the option of testing the structural equation models for specific industries. Future research should take up this issue and test whether the effects of strategic relatedness detected in the cross-industry sample in this analysis indeed apply across industries or apply to specific industries only.

Ultimately, I would like to close this work with some critical remarks on meta-analysis – an approach to research synthesis that is getting increasingly popular in strategic management research.

In the course of the analyses I observed that the Hedges/Olkin approach by design is somewhat less transparent than is the Hunter/Schmidt approach. In principle, the Hedges/Olkin technique does not require the reporting of any absolute values of (weighted mean) correlations observed between the variables examined. At the same time, it does seem to “seduce” the researcher not to report the sample sizes truly underlying the meta-analysis of contingency variables. Contrarily, the Hunter/Schmidtbased analysis points out that across methods the meta-analyses in some respects draw on very few correlations to derive results. This applies particularly to associations between related diversification (samples with firms restricted from the high end of diversification) and performance dimensions. This information would have been lost completely if only the regressions of correlations onto contingency variables had been used.

In addition, as Hedges/Olkin-based regressions require continuous indicators, dummy variables must usually be used to assess the effects of contingency factors on correlations. However, these effects can as a consequence merely be assessed and expressed in relative terms, i.e. relative to an omitted benchmark category. Thus, without any subsequent estimations from derived regression equations, the actual direction of specific variable linkages and its strength in absolute terms cannot be elicited.


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In general, both types of meta-analyses initially require abstracting entirely from any context and contingency variables. All correlations between the variables of interest located – no matter where, when and how observed – are pooled in a set that is only subsequently analyzed for specific contingency variables. While this proceeding seems necessary for meta-analysis, the Hunter/Schmidt approach appears somewhat more problematic in this respect. Here, the researcher is required to report the weighted mean correlations between the variables of interest at the highest possible level of aggregation and to only subsequently break it down into subsets. These absolute values may be strongly misleading, however, if interpreted in isolation. This is because these averages arise simply as a consequence of some underlying distribution of measurement characteristics of individual correlations that are neglected at this stage, if not ignored.

In the present study, these characteristics comprise, for instance, type of diversification operationalization, type of performance operationalization, temporal sequence of variable measurement and so forth. As a consequence, mean correlations are always and necessarily abstracting from additional information and should only be used to argue for a generalizable nature of a variable linkage at sufficiently detailed levels.

Across methods, a general problem of meta-analysis is that it somewhat “requires us to abstract though we know better”. Moreover, even if we planned to test for various contingency factors at later stages of the meta-analysis, we simply lack the number of studies in strategic management research that are necessary to conduct this examination under valid statistical premises. To some extent this also applied to the study reported in this paper (recall the small sample sizes of the correlation subsets that prevented analysis of further contingency effects).

What is also important about the Hunter/Schmidt approach is that it suggests correcting correlations for various statistical artifacts. However, the statistics and measurement information required to actually do so, such as reliabilities, for instance, are usually not reported in strategic management studies. Moreover, in this context, I found

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that the use of attenuation factors in order to more or less globally correct for a number of these artifacts (other than sampling error) can be used to strongly increase and also to manipulate absolute values of correlation coefficients. Readers of Hunter/Schmidtbased meta-analyses should be aware of this substantial lever and authors should be very explicit about it.

Finally, it is important to understand that, by design, only linear associations can be tested in meta-analytic frameworks except for the case in which some “circumventionstrategy” is found. This study provides a concrete example: I have to draw on rangerestricted samples to be able to decompose a single linear association into two linear associations representing a curvilinear association. If curvilinearity is ignored in metaanalysis, mean correlations may arise simply as a consequence of positive and negative effect size statistics (from individual empirical studies) cancelling out each other in the integration. In this case, mean correlations have virtually no meaning.

Nonetheless, despite the problems just enumerated, what I also learned in the course of conducting this study, is that meta-analysis forces the researcher to really look into the matter, i.e. into the very details of sampling, methods, construct operationalization, and quantitative results of individual empirical analyses. In other words, “academic rigour required” appears an in-built feature of meta-analysis. Premature overall judgement of individual study results is less likely. For this reason, I am convinced that metaanalysis has the potential to become an indispensable complement to narrative literature reviews if we are able to establish transparent methodological standards. Possibly it is a combination of quantitative and qualitative integration elements that will characterize the future standard in literature reviews in strategic management research.

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.94* (2/989)

-

-

4 NSD/Four-digit Count

5 Specialization Ratio

6 Resource-based

-

-

8 Skill-based Relatedness

9 Product-based &

.58* (2/730) -

.59* (2/730)

-

-

16 Amit / Livnat 15

17 Concentric Index

18 Rumelt Categories coded

a

-

-

-

-

-

-

-

.75 (1/205)

.74 (1/205)

-

.15 (1/205)

-

-

-

-

-

-

-

1

3

Number of correlations meta-analyzed and total sample size in brackets; * p < .05

coded in ascending order

20 Varadarajan Categories

coded

19 Entropy Categories

-

.26 (1/54)

.28 (1/54)

15 BSD / Two-digit Count

-

.27* (2/433)

.64 (1/205)

14 Herfindahl Two-digit

i n ascending order

.64* (2/259)

.56* (4/547)

.16 (1/54)

.40* (4/989)

.34* (2/186)

-

-

-

-

.82* (2/186)

.87*

.86 (1/205)

1

2

13 DU Entropy

.16 (1/54)

11 DR Entropy

12 MNSD

-

.41* (6/1,277)

10 Related Ratio


-

7 DR / DT

Relatedness

.83* (1/205)

3 Herfindahl Three-digit

1

.95* (5/1,938)

2 Herfindahl Four-digit

1 DT Entropy

1

-

-

.5 (1/229)

-

-

.56* (3/630)

-

-

.43 (1/229)

-

-

-

-

-

-

-

1

4

-

-

-

-

-

.51 (1/94)

-

-

-

-

.40* (2/187)

-

-

-

-

1

5

-

-

-

.06 (1/80)

-

-

-

-

-

-.03 (2/113)

-

-

-

-

1

6

-

-

-

-

-

-

-

-

-

-

-

.76 (1/114)

.17 (1/119)

1

7

-

-

-

-

-

-

-

-

-

-

-

.61 (1/114)

1

8

-

-

-

-

-

-

-

-

-

-

-

1

9

-

-

-

-

-

.52 (1/94)

-

-

-

-

1

10

Appendices

Appendix 1: Diversification – Sample Size-weighted Inter-measure Correlationsa

1

a

-

-


coded in ascending order

20 Varadarajan Categories

in ascending order

19 Entropy Categories coded

in ascending order -

-

.26 (1/229)

-

-

-

-

.93 (1/80)

17 Concentric Index

18 Rumelt Categories coded

-

-

.25* (2/730)

16 Amit / Livnat 15

-

-

-

.31 (1/54)

-.25* (3/499)

-.7 (1/54)

15 BSD / Two-digit Count

1

1 .97(1/205)

-

-.39 (1/205)

14 Herfindahl Two-digit

1

14

-.11 (1/54)

13

-.31* (5/685)

.33 (1/54)

12

13 DU Entropy

12 MNSD

11 DR Entropy

10 Related Ratio


9 Product-based &

8 Skill-based Relatedness

7 DR / DT

Relatedness

6 Resource-based

5 Specialization Ratio

4 NSD/Four-digit Count

3 Herfindahl Three-digit

2 Herfindahl Four-digit

1 DT Entropy

11

-

-

.49 (1/229)

-

-

1

15

-

-

-

-

1

16

-

-

-

1

17

.40 (1/160)

.82 (1/160)

1

18

.37 (1/160)

1

19

1

20

194 Appendices

continued

.08 (3/881) -

.04 (9/2,982) .04 (2/146)

15 Sales Growth

a

1

3

-

-

-

-

-

-

-

.37 (1/33)

-.02 (2/143)

-

-

-

-

.62* (1/33)


17 Asset Growth

16 Market Share Growth

-

-

11 Tobin’s Q

14 Stock Returns

.28* (1/160)

10 Treynor Index

.07 (2/600)

.17 (1/160)

.10 (2/270)

9 Sharpe Ratio

.22* (4/898)

.03 (2/270)

.27* (3/399)

8 Jensen’s Alpha

13 Market-to-Book Assets

.18* (1/169)

-

7 Cashflow / Assets

-

-

-

6 Cashflow / Sales

.34* (2/565)

-

-

5 Net income

.73 (1/147)

-

.63* (8/1,785)

4 ROS

.31* (7/4114)

.05 (4/1165)

.87* (2/216)

1 ROI

12 Market-to-Book Equity

1 .87* (2/178)

1 .36* (7/2,220)

2 ROE

2

1 ROA

1

.10 (1/108)

-

.15* (3/908)

-

.42 (1/70)

.66 (1/147)

-

.24* (2/193)

.27* (2/193)

.19* (2/307)

-

-

-

1

4

-

-

.48 (1/22)

1

5

-

-

-

-

-

-.03 (1/232)

-

-

-

-

-

1

6

.54* (2/730)

.58* (2/730)

-

-

-

-

1

7

-

-

-

-

-

.06 (1/147)

-

.87 (1/160)

.90 (1/160)

1

8

-

-

-

-

-

-

.71 (1/110)

.94 (1/33)

1

9

Appendices 195

Appendix 2: Corporate Financial Performance – Sample Size-weighted Inter-measure Correlationsa

-

-

13 Market-to-Book Assets

14 Stock Returns

15 Sales Growth

16 Market Share Growth

17 Asset Growth

1

12

-

-

-

-

.95* (2/730)


-

-

12 Market-to-Book Equity

a

1

1

11 Tobin’s Q

11

10 Treynor Index

9 Sharpe Ratio

8 Jensen’s Alpha

7 Cashflow / Assets

6 Cashflow / Sales

5 Net income

4 ROS

1 ROI

2 ROE

1 ROA

10

-

-

-

-

1

13

-

-

-

1

14

.11 (1/108)

.59* (2/623)

1

15

-

1

16

1

17

196 Appendices

continued

Appendices

197

Appendix 3: Financial Performance – Intra- and Inter-Dimension Sample Size-weighted Mean Correlationsa




.51* (28/6,828)


.29* (42/10,176)

.82* (8/1,500)

Growth-based Performance

.08 (19/5,147)

-

a

Number of correlations cumulated and total sample size in brackets; * p < .05

Growth-based Performance

.52* (3/731)

198

Appendices

Appendix 4: Effect Size Statistics Conversion Formulae

d

Mean1 Mean2

V pooled

n1 1 V 12 n2 1 V 2 2 n1 n2 2

V pooled 2

r

d2 d 4

r

t2 t df

r

F2 F df

r

z n

2

2

2

d: a standardized mean difference, V : standard deviation, r : Pearson correlation coefficient, t: t-statistic, F: F-statistic, df : degrees of freedom, z : z-statistic, ni : (group) sample size

Source: Rosenthal and DiMatteo (2001)

Appendices

199

Appendix 5: Sample of Firms studied per Industry (SIC) Primary SIC Code

20xx Food and Kindred Products

282x Chemicals/Plastics Materials and Synthetic Resins

1

AJINOMOTO CO INC

ACHILLES CORP

2

ANHEUSER-BUSCH COS INC

BURELLE SA

3

ARCHER-DANIELS-MIDLAND CO

CHEMTURA CORPORATION

4

ASAHI BREWERIES LTD

CHENG SHIN RUBBER INDUSTRY

5

ASSOCIATED BRITISH FOOD PLC

CI KASEI CO LTD

6

CADBURY SCHWEPPES PLC

DAICEL CHEMICAL IND

7

CAMPBELL SOUP CO

DOW CHEMICAL

8

CARLSBERG A/S

DU PONT (E I) DE NEMOURS

9

CIA DE BEBIDAS DAS AMERICAS

EASTMAN CHEMICAL CO

10

CJ CORP

EMS-CHEMIE HOLDING AG

11

COCA-COLA CO

ETERNAL CHEMICAL CO LTD

12

CONAGRA FOOD INC

HANWHA CHEMICAL CORP

13

DANONE (GROUPE)

HEXCEL CORP

14

DEAN FOOD CO

HONAM PETROCHEMICAL CORP

15

DIAGEO PLC

HUNTSMAN POLYMERS CORP

16

FOMENTO ECONOMICO MEXICANO

INDIAN PETROCHEMICALS CORP

17

GENERAL MILLS INC

JSP CORP

18

GRUPO BIMBO SA DE CV

JSR CORP

19

HEINEKEN HOLDING NV

KANEKA CORP

20

HEINZ (H J) CO

KONINKLIJKE DSM NV

21

HERSHEY CO

KOREA KUMHO PETROCHEMICAL

22

HORMEL FOOD CORP

KURARAY CO LTD

23

INBEV SA

KUREHA CORP

24

ITOHAM FOOD INC

LENZING AG

25

KELLOGG CO

MITSUBISHI RAYON CO LTD

26

KERRY GROUP PLC

MITSUI CHEMICALS INC

27

KIRIN BREWERY CO LTD

NUPLEX INDUSTRIES LTD

28

KRAFT FOOD INC

OKURA INDUSTRIAL CO LTD

29

MAPLE LEAF FOOD INC

OMNOVA SOLUTIONS INC

30

MEIJI DAIRIES CORP

PETKIM PETROKIMYA HLDG AS

31

MOLSON COORS BREWING CO

PLASTIC OMNIUM SA

32

MORINAGA MILK INDUSTRY CORP

POLYONE CORP

33

NESTLE SA/AG

ROHM AND HAAS CO

34

NIPPON MEAT PACKERS INC

SANYO CHEMICAL INDS LTD

35

NIPPON SUISAN KAISHA LTD

SCHULMAN (A.) INC

36

ORKLA ASA

SHANGHAI WORLDBEST CO LTD

37

PEPSICO INC

SHIN-ETSU CHEMICAL CO LTD

38

PERNOD RICARD SA

SHIN-ETSU POLYMER CO LTD

39

PILGRIMS PRIDE CORP

SHINKONG SYNTHETIC FIBERS

40

SABMILLER PLC

SINOPEC YIZHENG CHEM FIBRE

41

SARA LEE CORP

SK CHEMICALS

42

SCOTTISH & NEWCASTLE PLC

SOLUTIA INC

200

Appendices

continued Primary SIC Code

20xx

282x

Food and Kindred Products

Chemicals/Plastics Materials and Synthetic Resins

43

SMITHFIELD FOOD INC

SUMITOMO BAKELITE CO LTD

44

SUEDZUCKER AG

TAEKWANG INDUSTRIAL CO LTD

45

TATE & LYLE PLC

TEIJIN LTD

46

THAI BEVERAGE PCL

TSRC CORP

47

TYSON FOOD INC

UBE INDUSTRIES LTD

48

UNILEVER GROUP (GBP)

USI CORP

49

UNI-PRESIDENT ENTERPRISE CO

WELLMAN INC

50

YAMAZAKI BAKING CO LTD

ZEON CORP

Primary SIC Code

283x

284x

Pharmaceutical/Drugs

Personal Care/Soap and Detergents

1

ABBOTT LABORATORIES

2

ALLERGAN INC

ALES GROUPE ALLIANCE BOOTS PLC

3

ALTANA AG

AVON PRODUCTS

4

AMGEN INC

BEIERSDORF AG

5

ASTELLAS PHARMA INC

BOMBRIL SA

6

ASTRAZENECA PLC

CCL INDUSTRIES

7

BARR PHARMACEUTICALS INC

CHURCH & DWIGHT INC

8

BAUSCH & LOMB INC

CLARINS SA

9

BIOGEN IDEC INC

COLGATE-PALMOLIVE CO

10

BRISTOL-MYERS SQUIBB CO

DABUR INDIA LTD

11

CHUGAI PHARMACEUTICAL CO LTD

DAI-ICHI KOGYO SEIYAKU CO

12

DAIICHI SANKYO COMPANY LTD

DR.CI:LABO CO LTD

13

DAINIPPON SUMITOMO PHARMA CO

ECOLAB INC

14

EISAI CO LTD

FANCL CORP

15

FOREST LABORATORIES

HENKEL KGAA

16

GENENTECH INC

HINDUSTAN LEVER LTD

17

GENZYME CORP

INTER PARFUMS SA

18

GILEAD SCIENCES INC

JACQUES BOGART

19

GLAXOSMITHKLINE PLC

KAO CORP

20

H LUNDBECK A/S

KATY INDUSTRIES INC

21

HOSPIRA INC

KOSE CORP

22

JOHNSON & JOHNSON

LAUDER ESTEE COS INC

23

KING PHARMACEUTICALS INC

LG HOUSEHOLD & HEALTHCARE

24

KOBAYASHI PHARMACEUTICAL CO

LION CORP

25

KYOWA HAKKO KOGYO CO LTD

L'OREAL SA

26

LILLY (ELI) & CO

MANDOM CORP

27

MERCK & CO

MARICO LTD

28

MERCK KGAA

MATSUMOTO YUSHI SEIYAKU CO

29

MIRACA HOLDINGS INC

MCBRIDE PLC

30

MYLAN LABORATORIES INC

MEXICHEM SA DE CV

Appendices

201


283x

284x

Pharmaceutical/Drugs

Personal Care/Soap and Detergents

31

NBTY INC

32

NOVARTIS AG

MIRATO SPA

33

NOVO NORDISK A/S

NEOCHIMIKI L.V. LAVRENTIADIS

34

ONO PHARMACEUTICAL CO LTD

NICCA CHEMICAL CO LTD

35

ORION CORP (FINLAND)

NIRMA LTD

36

PERRIGO CO

NOEVIR CO LTD

37

PFIZER INC

NOTOS COM.HOLDINGS SA

38

ROCHE HOLDING AG

ORIFLAME COSMETICS SA

39

SANOFI-AVENTIS

PIGEON CORP

40

SCHERING-PLOUGH

PROCTER & GAMBLE CO

41

SHANGHAI PHARMACEUTICAL CO

PZ CUSSONS PLC

42

SHIONOGI & CO LTD

RECKITT BENCKISER PLC

43

SIGMA-ALDRICH CORP

REVLON INC

44

STADA ARZNEIMITTEL AG

SHISEIDO CO LTD

45

TAISHO PHARMACEUTICAL CO LTD

SOFT99 CORP

46

TAKEDA PHARMACEUTICAL CO

STEPAN CO

47

TANABE SEIYAKU CO LTD

SUNSTAR INC

48

TEVA PHARMACEUTICALS

T HASEGAWA CO LTD

49

WATSON PHARMACEUTICALS INC

TOHO CHEM IND CO LTD

50

WYETH

WELLA AG

Primary SIC Code

MILBON CO LTD

357x

360x

371x

Computer and Office Equipment

Electronic and Other Electrical Equipment

Motor Vehicles and Equipment

1

ACER INC

ABB LTD

AISIN SEIKI CO LTD

2

APPLE COMPUTER INC

ADVANCED MICRO DEVICES

ARVINMERITOR INC

3

ASUSTEK COMPUTER INC

ALCATEL-LUCENT

AUDI AG

4

BENQ CORP

ALPS ELECTRIC CO LTD

AUTOLIV INC

5

BROTHER INDUSTRIES LTD

AU OPTRONICS CORP

BMW-BAYER MOTOREN WERKE AG

6

CAL-COMP ELECTRONICS PCL

CELESTICA INC

BORGWARNER INC

7

CANON INC

ELECTROLUX AB

COLLINS & AIKMAN CORP

8

CASIO COMPUTER CO LTD

EMERSON ELECTRIC CO

DAIHATSU MOTOR CO LTD

9

CISCO SYSTEMS INC

ERICSSON (LM) TELEFON

DAIMLERCHRYSLER AG

10

COMPAL ELECTRONIC INC

FLEXTRONICS INTERNATIONAL

DANA CORP

11

CREATIVE TECHNOLOGY LTD

FOXCONN INTL HOLDINGS LTD

DELPHI CORP

12

DE LA RUE PLC

FREESCALE SEMICONDUCTOR INC

DENSO CORP

13

DELL INC

FUJI ELECTRIC HLDGS CO LTD

EATON CORP

14

DIEBOLD INC

INFINEON TECHNOLOGIES AG

FEDERAL-MOGUL CORP

202

Appendices


357x

360x

371x

Computer and Office Equipment

Electronic and Other Electrical Equipment

Motor Vehicles and Equipment

15

EMC CORP/MA

INTEL CORP

FIAT SPA

16

FOXCONN TECHNOLOGY CO LTD

JABIL CIRCUIT INC

FORD MOTOR CO

17

GATEWAY INC

KYOCERA CORP

FUJI HEAVY INDUSTRIES LTD

18

GEMPLUS INTERNATIONAL SA

L-3 COMMUNICATIONS HLDGS INC

GENERAL MOTORS CORP

19

HEWLETT-PACKARD CO

LG CORP

GKN PLC

20

HIGH TECH COMPUTER CORP

LG PHILIPS LCD CO LTD

HINO MOTORS LTD

21

HITACHI LTD

MATSUSHITA ELECTRIC INDL CO

HONDA MOTOR CO LTD

22

HON HAI PRECISION IND CO LTD

MATSUSHITA ELECTRIC WORKS

HYUNDAI MOBIS

23

INVENTEC CO LTD

MITSUBISHI ELECTRIC CORP

HYUNDAI MOTOR CO LTD

24

JUNIPER NETWORKS INC

MOTOROLA INC

ISUZU MOTORS LTD

25

JURONG TECHNOLOGIE INDL CORP

NEC ELECTRONICS CORP

KANTO AUTO WORKS LTD

26

KINPO ELECTRONICS INC

NIKON CORP

KIA MOTORS CORP

27

LENOVO GROUP LTD

NOKIA (AB) OY

MAGNA INTERNATIONAL

28

LEXMARK INTL INC

NORTEL NETWORKS CORP

MAN AG

29

LG ELECTRONICS INC

OKI ELECTRIC INDUSTRY CO LTD

MAZDA MOTOR CORP

30

LITE-ON IT CORP

ONEX CORP

MITSUBISHI MOTORS CORP

31

LOGITECH INTERNATIONAL SA

PHILIPS ELECTRONICS (KON) NV

NAVISTAR INTERNATIONAL CORP

32

MEDION AG

PIONEER CORP

NISSAN MOTOR CO LTD

33

MICRO-STAR INTERNATIONAL CO

QUALCOMM INC

OSHKOSH TRUCK CORP

34

MITAC INTERNATIONAL CORP

SAMSUNG ELECTRONICS CO LTD

PACCAR INC

35

MITSUMI ELECTRIC CO LTD

SAMSUNG SDI CO LTD

PEUGEOT SA

36

NCR CORP

SANMINA-SCI CORP

PORSCHE AG

37

NEC CORP

SHARP CORP

RENAULT SA

38

NETWORK APPLIANCE INC

SOLECTRON CORP

SCANIA AB

39

PALM INC

SONY CORP

SSANGYONG MOTOR CO LTD

40

PITNEY BOWES INC

STMICROELECTRONICS NV

SUMITOMO WIRING SYSTEMS LTD

41

PROVIEW INTL HLDGS LTD

TAIWAN SEMICONDUCTOR MFG CO

SUZUKI MOTOR CO LTD

42

QUANTA COMPUTER INC

TATUNG CO

TATA MOTORS LTD

43

SANDISK CORP

TCL CORP

TENNECO INC

44

SANYO ELECTRIC CO LTD

TDK CORP

TOMKINS PLC

45

SEIKO EPSON CORP

TEXAS INSTRUMENTS INC

TOYODA GOSEI CO LTD

46

SUN MICROSYSTEMS INC

THOMSON

TOYOTA MOTOR CORP

47

SYMBOL TECHNOLOGIES

TOSHIBA CORP

VALEO SA

48

TOSHIBA TEC CORP

TSA TP 83

VISTEON CORP

49

WESTERN DIGITAL CORP

VICTOR CO OF JAPAN LTD

VOLKSWAGEN AG

50

XEROX CORP

WHIRLPOOL CORP

VOLVO AB

Appendices

203

Appendix 6: The Standard Industrial Classification System (SIC) underlying the Measure of Product-based Relatedness (Excerpt) Division A-J Division D: Manufacturing Major Group 20: Food And Kindred Products Industry Group 201: Meat Products Industry Group 202: Dairy Products 2021 Creamery Butter 2022 Natural, Processed, and Imitation Cheese 2023 Dry, Condensed, and Evaporated Dairy Products Baby formula: fresh, processed, and bottled Buttermilk: concentrated, condensed, dried, evaporated, and powdered Casein, dry and wet Cream substitutes Cream: dried, powdered, and canned Dietary supplements, dairy and non-dairy base Dry milk products: whole milk, nonfat milk, buttermilk, whey, and Eggnog, canned: nonalcoholic Ice cream mix, unfrozen: liquid or dry Ice milk mix, unfrozen: liquid or dry Lactose, edible Malted milk Milk, whole: canned Milk: concentrated, condensed, dried, evaporated, and powdered Milkshake mix Skim milk: concentrated, dried, and powdered Sugar of milk Whey: concentrated, condensed, dried, evaporated, and powdered Whipped topping, dry mix Yogurt mix 2024 Ice Cream and Frozen Desserts 2026 Fluid Milk Industry Group 203: Canned, Frozen, And Preserved Fruits, Vegetables, and Food Specialties Industry Group 204: Grain Mill Products Industry Group 205: Bakery Products Industry Group 206: Sugar And Confectionery Products Industry Group 207: Fats And Oils Industry Group 208: Beverages Industry Group 209: Miscellaneous Food Preparations And Kindred

204

Appendices … Major Group 21: Tobacco Products Major Group 22: Textile Mill Products Major Group 23: Apparel And Other Finished Products Made From Fabrics And Similar Materials Major Group 24: Lumber And Wood Products, Except Furniture Major Group 25: Furniture And Fixtures Major Group 26: Paper And Allied Products Major Group 27: Printing, Publishing, And Allied Industries Major Group 28: Chemicals And Allied Products Major Group 29: Petroleum Refining And Related Industries Major Group 30: Rubber And Miscellaneous Plastics Products Major Group 31: Leather And Leather Products Major Group 32: Stone, Clay, Glass, And Concrete Products Major Group 33: Primary Metal Industries Major Group 34: Fabricated Metal Products, Except Machinery And Transportation Equipment Major Group 35: Industrial And Commercial Machinery And Computer Equipment Major Group 36: Electronic And Other Electrical Equipment And Components, Except Computer Equipment Major Group 37: Transportation Equipment Major Group 38: Measuring, Analyzing, And Controlling Instruments; Photographic, Medical And Optical Goods; Watches And Clocks Major Group 39: Miscellaneous Manufacturing Industries

Appendices

205

Appendix 7: Robins and Wiersema's Measure of Resource-based/Technological Relatedness – Categories of Industries used Category

SIC Codes included

Category

SIC Codes included

1

0090-0900

21

3500-3519

2

0999-1500

22

3520-3529

3

2000-2199

23

3530-3539

4

2209-2299

24

3540-3549

5

2309-2399

25

3550-3568

3110-3119

3572-3593

3130-3179 3190-3199 6

3598-3599 26

3569-3571

27

3610-3629

2410-2459 2490-2499

2572-2574

7

2500-2599

3640-3649

8

2609-2669

3690-3699

9

2709-2799

28

10

2809-2819

29

3630-3639 3650-3679

2870-2899

30

3710-3719 3720-3729

11

2820-2829

31

12

2830-2839

32

3760-3769

13

2859-2870

33

3730-3759 3810-3879

14

2840-2859

15

2900-2999

34

3009-3049

35

16

3790-3799

3060-3079

3910-3919 3930-3969

17

3210-3299

18

3310-3329

36

5009-5999

19

3330-3335

37

4000-4999

3338-3369 3390-3399 20

3410-3499

Source: Robins and Wiersema (1995, p. 295).

3990-3999

206

Appendices

Appendix 8: Robins and Wiersema's Measure of Resource-based/Technological Relatedness – Inter-Industry Category Coefficients of Technological Similarity (Excerpt) Category

1

…

10

11

12

13

14

…

37

1

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

20

…

…

-0.05

-0.10

-0.07

-0.07

0.10

…

…

21

…

…

-0.04

-0.11

-0.04

-0.07

0.06

…

…

22

…

…

-0.04

-0.06

0.39

-0.04

0.94

…

…

23

…

…

-0.05

-0.12

-0.08

-0.08

-0.08

…

…

24

…

…

-0.10

-0.10

-0.06

-0.16

0.18

…

…

25

…

…

0.00

-0.09

-0.09

-0.01

0.10

…

…

26

…

…

0.03

-0.09

-0.08

-0.05

-0.03

…

…

27

…

…

-0.05

-0.10

-0.07

-0.08

0.01

…

…

28

…

…

-0.06

-0.08

-0.07

-0.08

-0.04

…

…

29

…

…

-0.06

-0.08

-0.06

-0.05

0.01

…

…

30

…

…

-0.05

-0.09

-0.01

-0.08

0.18

…

…

…

…

…

…

…

…

…

…

…

…

37

…

…

…

…

…

…

…

…

…

Source: Robins and Wiersema (1995, p. 296 et sqq.).

Appendices

207

Appendix 9: Farjoun’s Measure of Skill-based Relatedness – Types of Expertise/Occupations used to identify skill-related Industry Groups A. Mgmt. & Mgmt. Support:

E. Service:

1. Top Management

26. Cleaning and Building services

2. Financial Managers

27. Food Prep. and Service

3. Marketing, Adv. Pr. Managers

28. Health Service

4. Human Resource Managers

29. Personal Service

5. Purchasing Managers

30. Protective Services

6. All other Managers

31. All Other Service

7. Management Support Occupations F. Production (Including Agriculture): B. Professionals:

32. Supervisory-Blue Collar

8. Engineers

33. Construction and Extraction

9. Social Sciences Scien. and Work.

34. Mechanics, Installers, Repairers

10. Health Diagnosing and Treating

35. Precision Production

11. Cultural Occupations

36. Machine Setting and Operators

12. Health Technicians

37. Assembling and Hand Workers

13. Engineering and Science Technic.

38. Plant and System

14. Other technicians

39. Transportation Material Moving

15. Other professionals

40. Helpers and Laborers 41. Agri. Forest. Fish.

C. Marketing & Related: 16. Ins. Secur. Real. Bus. Sales 17. Salespersons, Retail 18. Cash. Counter. Travel 19. All Other Sales Occupations

D. Administration: 20. Adjusters, Invest. Collectors 21. Comm. and D. P. Operators 22. Financial and Info Processing 23. In. Out. Process Clerks 24. Record Process. Secret. 25. Other Clerical

Source: Farjoun (1994, p. 190)

208

Appendices

Appendix 10: Multiples of Market Value of Equity plus Book Value of Debt per Sales [Indi(V/AI)mf ] for the Median Single Segment Firm in 360 four-digit Industries in 2005* No.

4-digit SIC

Indi(V/AI)mf

No.

4-digit SIC

Indi(V/AI)mf

No.

4-digit SIC

Indi(V/AI)mf

No.

4-digit SIC

Indi(V/AI)mf

1

0241

1.34

51

2091

0.70

101

2844

1.25

151

3524

1.24

2

0253

1.34

52

2092

0.70

102

2851

1.14

152

3531

1.05

3

0254

1.34

53

2095

1.05

103

2861

1.15

153

3537

1.09

4

0721

0.86

54

2096

1.05

104

2865

1.15

154

3541

1.85

5

0912

2.98

55

2099

1.05

105

2869

1.59

155

3542

1.85

6

0919

2.98

56

2100

2.00

106

2873

1.59

156

3546

1.85

7

1311

7.49

57

2211

0.80

107

2879

1.24

157

3549

1.85

8

1521

1.27

58

2231

0.87

108

2891

1.24

158

3552

1.78

9

1522

1.27

59

2241

0.87

109

2892

1.26

159

3555

1.55

10

1531

1.30

60

2260

0.87

110

2895

1.26

160

3556

1.78

11

1541

2.38

61

2273

1.05

111

2899

1.26

161

3559

1.87

12

2000

2.09

62

2281

0.87

112

2911

1.95

162

3562

3.65

13

2010

0.83

63

2282

0.87

113

3011

1.10

163

3564

1.58

14

2011

0.68

64

2295

0.87

114

3021

0.88

164

3568

1.25

15

2013

1.32

65

2298

0.87

115

3052

1.36

165

3569

1.25

16

2015

0.67

66

2299

0.87

116

3061

1.67

166

3570

1.39

17

2020

1.05

67

2300

1.15

117

3069

1.67

167

3571

1.80

18

2022

1.01

68

2331

0.85

118

3081

0.80

168

3572

1.20

19

2023

1.03

69

2335

0.85

119

3082

1.22

169

3575

1.19

20

2024

0.70

70

2341

0.85

120

3084

1.22

170

3577

1.49

21

2026

1.08

71

2389

0.96

121

3085

1.22

171

3578

1.71

22

2030

0.93

72

2396

0.85

122

3086

0.97

172

3579

0.87

23

2032

1.40

73

2399

0.85

123

3087

1.22

173

3581

1.04

24

2033

1.40

74

2435

0.86

124

3089

1.15

174

3585

0.92

25

2034

1.14

75

2611

1.68

125

3221

1.79

175

3589

1.04

26

2035

1.14

76

2621

1.56

126

3229

3.19

176

3599

1.41

27

2038

1.14

77

2671

1.71

127

3241

2.78

177

3610

1.27

28

2041

1.02

78

2672

1.71

128

3269

1.43

178

3612

1.21

29

2043

1.04

79

2676

1.10

129

3291

2.54

179

3613

1.31

30

2045

1.06

80

2679

1.10

130

3295

2.54

180

3621

1.64

31

2046

1.09

81

2711

1.89

131

3299

2.54

181

3624

1.57

32

2047

1.10

82

2721

2.05

132

3312

0.97

182

3625

1.57

33

2048

1.05

83

2741

3.21

133

3315

1.03

183

3629

1.57

34

2051

1.08

84

2759

1.26

134

3316

1.03

184

3631

2.09

35

2052

0.86

85

2812

1.55

135

3325

1.23

185

3632

2.09

36

2053

1.08

86

2816

1.55

136

3357

1.04

186

3633

1.86

37

2061

1.65

87

2819

1.43

137

3411

1.00

187

3634

1.86

38

2062

1.65

88

2820

1.43

138

3421

1.36

188

3635

2.09

39

2063

1.65

89

2821

1.02

139

3433

1.70

189

3639

2.09

40

2064

1.65

90

2822

1.28

140

3442

0.91

190

3640

1.36

41

2066

1.65

91

2823

1.28

141

3443

2.37

191

3641

1.36

42

2074

2.03

92

2824

1.28

142

3448

5.75

192

3645

1.36

43

2075

2.10

93

2830

2.94

143

3465

0.97

193

3646

1.36

44

2077

2.05

94

2833

2.94

144

3479

1.48

194

3647

1.36

Appendices

209

45

2079

2.04

95

2834

3.80

145

3483

1.48

195

3651

0.98

46

2082

2.07

96

2835

4.99

146

3491

1.19

196

3652

1.18

47

2084

2.58

97

2836

9.77

147

3494

1.19

197

3660

2.10

48

2085

2.39

98

2841

1.39

148

3499

1.19

198

3661

2.10

49

2086

1.53

99

2842

1.39

149

3511

2.91

199

3663

1.63

50

2087

2.16

100

2843

1.25

150

3519

2.91

200

3669

1.44

continued No.

4-digit SIC

Indi(V/AI)mf

No.

4-digit SIC

Indi(V/AI)mf

No.

4-digit SIC

Indi(V/AI)mf

No.

4-digit SIC

Indi(V/AI)mf

201

3670

1.84

251

4961

3.17

301

5734

0.86

351

8711

1.34

202

3671

1.59

252

5010

0.92

302

5812

1.17

352

8731

7.63

203

3672

1.59

253

5012

0.59

303

5813

1.17

353

8733

2.90

204

3674

2.63

254

5013

0.59

304

5912

0.86

354

8734

2.50

205

3675

1.87

255

5015

0.88

305

5921

0.87

355

8741

1.36

206

3677

1.70

256

5031

0.86

306

5961

2.75

356

8742

1.50

207

3678

1.17

257

5039

0.88

307

5962

2.15

357

8748

1.50

208

3679

1.70

258

5043

1.01

308

5995

1.30

358

8999

1.50

209

3691

1.54

259

5045

1.04

309

5999

1.30

359

9311

3.98

210

3694

1.71

260

5047

2.56

310

6141

6.84

360

9999

3.98

211

3695

1.71

261

5051

0.80

311

6153

7.28

212

3699

1.54

262

5052

0.80

312

6159

7.44

213

3711

1.33

263

5063

0.84

313

6311

5.67

214

3713

0.93

264

5064

1.71

314

6331

3.51

215

3714

1.14

265

5065

0.78

315

6411

4.83

216

3720

2.16

266

5078

1.02

316

6512

5.78

217

3724

1.04

267

5083

0.91

317

6519

5.88

218

3728

1.43

268

5084

0.91

318

6531

4.97

219

3743

1.29

269

5087

0.99

319

6552

3.89

220

3751

1.01

270

5088

0.99

320

6719

6.67

221

3799

1.82

271

5099

0.68

321

6794

4.41

222

3812

2.32

272

5113

0.74

322

6799

6.84

223

3823

1.92

273

5122

0.90

323

7323

4.39

224

3825

2.50

274

5136

0.87

324

7359

4.97

225

3826

2.03

275

5137

0.87

325

7363

0.78

226

3827

2.59

276

5142

0.84

326

7370

2.38

227

3829

2.27

277

5143

0.84

327

7371

2.05

228

3841

2.85

278

5145

0.84

328

7372

2.80

229

3842

2.27

279

5147

0.84

329

7373

1.66

230

3843

3.56

280

5149

0.84

330

7374

2.62

231

3845

3.82

281

5153

0.62

331

7375

2.38

232

3851

1.27

282

5162

0.71

332

7377

2.66

233

3861

1.27

283

5169

0.71

333

7378

2.38

234

3873

1.87

284

5180

0.88

334

7379

2.38

235

3944

1.05

285

5181

0.84

335

7382

1.58

236

3949

1.55

286

5182

0.88

336

7389

1.87

237

3961

1.25

287

5191

0.78

337

7513

2.65

238

3991

1.92

288

5199

0.78

338

7514

2.65

239

3999

1.92

289

5311

1.25

339

7515

2.65

210

Appendices

240

4213

1.08

290

5399

1.15

340

7532

241

4214

1.50

291

5441

1.15

341

7812

2.65 1.94

242

4222

3.27

292

5461

1.15

342

7832

2.86

243

4225

3.27

293

5499

1.15

343

7948

5.76

244

4226

3.27

294

5511

1.61

344

7996

3.01

245

4783

2.28

295

5521

1.61

345

7999

2.40

246

4812

2.28

296

5599

1.61

346

8062

2.11

247

4813

2.64

297

5621

1.61

347

8071

3.44

248

4822

2.62

298

5651

0.61

348

8099

2.40

249

4899

2.65

299

5699

1.61

349

8351

8.48

250

4911

3.35

300

5731

0.33

350

8412

8.49

* This table comprises only the 4-digit businesses in which the 350 multibusiness firms studied operate

Data Source: Compustat Global

Appendices

211

Appendix 11: Illustration of the Calculation of the Excess Value Measure Nestlé SA (Food, SIC 20)

(1) Market Value of Equity plus Book Value of Debt 2005: € 132.585.014.000

Calculation of Imputed Value of the Firm [‘000] Segments

Absolute Sales

Nestlé

Nestlé

Median Single Segment Firm Multiple

Absolute Sales Nestlé * Median Single Segment Firm Multiple

SIC 1

2086

29522874

1.53

45169997

SIC 2

2023

14761437

1.03

15204280

SIC 3

2099

7380718

1.05

7749754

SIC 4

5441

3690359

1.15

4243913

SIC 5

2048

1845180

1.05

1937439

SIC 6

2834

922590

3.80

3505841

SIC 7

2095

461295

1.05

484360

SIC 8

2026

230647

1.08

Sum

(2) Imputed Value 2005: € 78.544.683.000 Excess Value = LN ((1)/(2)): 0.52

58815100

249099 78544683

212

Appendices

CISCO Systems Inc (Computers, SIC 357)



Absolute Sales

CISCO

CISCO


Absolute Sales CISCO * Median Single Segment Firm Multiple

SIC 1

3577

13326365

1.49

19856283

SIC 2

7373

6663182

1.66

11060883

SIC 3

7379

3331591

2.38

Sum

(2) Imputed Value 2005: € 38.846.353.000 Excess Value = LN ((1)/(2)): 0.95

23321138

7929187 38846353

Appendices

213

OMRON Corp (Electronics, SIC 36)



Absolute Sales

OMRON

OMRON


Absolute Sales OMRON * Median Single Segment Firm Multiple

SIC 1

3625

2428290

1.57

3812416

SIC 2

3669

1214145

1.44

1748369

SIC 3

3578

607073

1.71

1038094

SIC 4

3841

303536

2.85

Sum

4553044

(2) Imputed Value 2005: € 7.463.957.000 Excess Value = LN ((1)/(2)): -0.11

Data Source: Datastream Worldscope and Compustat Global

865078 7463957

214

Appendices

Appendix 12: Asset-Intensity Profiles of 4-digit SIC Industries Single Business Firms

SIC Code

Single Business Firms

Average

Average

Average

Capital Intensity

R&D Intensity

Labor Intensity

2004-2006

2004-2006

2004-2006

SIC Code

Average

Average

Average

Capital Intensity

R&D Intensity

Labor Intensity

2004-2006

2004-2006

2004-2006

…

2253

0.0527

0.0167

0.1985

0241

2273

0.0438

0.0091

0.2226

0253

2300

0.0711

0.0185

0.1805

0254

2320

0.0493

0.0045

0.1607

0721

2330

0.0420

0.0130

0.2062

0912

2340

0.0285

0.0043

0.2231

0919

2390

0.0701

0.0247

0.2300

1311

0.4554

0.0164

0.1339

2400

0.1231

0.0075

0.1246

1381

0.2450

0.0210

0.1475

2421

0.1044

0.0073

0.1671

1382

0.4671

0.0229

0.2032

2430

0.0713

0.0032

0.1985

1389

0.1645

0.0205

0.2004

2611

0.1330

0.0071

0.1709

1400

0.1113

0.0112

0.2201

2621

0.0870

0.0134

0.1748

1500

0.0617

0.0203

0.2487

2631

0.1684

0.0048

0.1880

1520

0.0353

0.0053

0.1889

2650

0.0943

0.0032

0.2024

1531

0.0359

0.0042

0.1165

2670

0.0632

0.0199

0.2095

1540

0.0273

0.0077

0.1722

2673

0.0759

0.0122

0.2362

2000

0.0761

0.0124

0.1540

2700

0.0940

0.0063

0.2964

2011

0.0358

0.0047

0.1507

2711

0.0522

0.0108

0.3237

2013

0.0519

0.0071

0.1647

2721

0.0749

0.0240

0.3016

2015

0.0532

0.0059

0.1266

2731

0.0509

0.0109

0.2259

2020

0.0434

0.0077

0.1182

2732

0.0579

0.0375

0.3318

2024

0.0627

0.0066

0.1367

2741

0.0531

0.0131

0.3445

2030

0.0608

0.0123

0.2008

2750

0.0667

0.0179

0.2510

2033

0.1195

0.0037

0.0898

2761

0.0274

0.0108

2040

0.0569

0.0117

0.0930

2771

0.0524

2050

0.0545

0.0061

0.2425

2780

0.0236

2052

0.0664

0.0099

0.2044

2790

0.0493

2060

0.1020

0.0136

0.1164

2800

0.1316

0.0329

0.1071

2070

0.1057

0.0098

0.0453

2810

0.0874

0.0251

0.1298

2080

0.0632

0.0124

0.1589

2820

0.0964

0.0210

0.1628

2082

0.0854

0.0043

0.2037

2821

0.0568

0.0267

0.1658

2084

0.1006

0.0133

0.1704

2833

0.1196

0.0740

0.1795

2085

0.0301

0.0130

0.1334

2834

0.0911

0.1738

0.1877

2086

0.0668

0.0762

0.1965

2835

0.0883

0.1850

2090

0.0656

0.0139

0.2073

2836

0.2171

0.3757

0.4952

2092

0.0861

0.0045

0.1339

2840

0.0517

0.0270

0.0904

2100

0.0359

0.0084

0.0763

2842

0.0602

0.0350

0.3770

2111

0.0420

0.0100

0.1225

2844

0.0513

0.0383

0.1637

2200

0.1001

0.0142

0.2274

2851

0.0314

0.0214

0.1590

2221

0.0651

0.0191

0.1978

2860

0.0703

0.0337

0.0271

2250

0.0637

0.0244

0.3263

2870

0.0575

0.0360

0.0904

0.0103 0.3480

Appendices

215 Single Business Firms

SIC Code


Average

Average

Average

Average

Average

Average

Capital Intensity

R&D Intensity

Labor Intensity

SIC Code

Capital Intensity

R&D Intensity

Labor Intensity

2004-2006

2004-2006

2004-2006

2004-2006

2004-2006

2004-2006

2890

0.0877

0.0292

0.1644

3430

0.0409

0.0213

0.2970

2891

0.0402

0.0199

0.1952

3433

0.0291

0.0174

0.1702

2911

0.0711

0.0026

0.0352

3440

0.0438

0.0123

0.2125

2950

0.0619

0.0179

3442

0.0632

0.0131

0.3245

2990

0.0584

0.0347

0.0076

3443

0.1038

0.0124

3011

0.0999

0.0197

0.1061

3444

0.0400

3021

0.0540

0.0230

3448

0.0489

0.0071

3050

0.0660

0.0235

0.1811

3451

0.0659

0.0296

3060

0.0872

0.0242

0.1704

3452

0.0647

0.0120

0.2701

3080

0.1004

0.0185

0.2069

3460

0.0816

0.0253

0.2227

3081

0.0738

0.0154

0.0284

3470

0.0829

0.0198

0.2107

3086

0.0585

0.0215

0.3027

3480

0.0408

0.0361

0.1085 0.2214 0.2345

3089

0.0901

0.0170

0.2043

3490

0.0492

0.0181

0.2040

3100

0.0485

0.0202

0.2782

3500

0.0922

0.0420

0.0691

3140

0.0296

0.0166

0.1614

3510

0.1065

0.0700

0.1881

3211

0.1886

0.0208

0.1514

3523

0.0278

0.0207

0.3114

3220

0.2265

0.0313

0.2203

3524

0.0416

0.0185

0.1358

3221

0.1055

0.0068

0.2203

3530

0.0669

0.0132

0.2609

3231

0.2155

0.0086

0.0714

3531

0.0461

0.0197

0.3193

3241

0.1326

0.0063

0.1110

3532

0.0366

0.0162

3250

0.0881

0.0150

0.2453

3533

0.0979

0.0236

3260

0.0651

0.0225

0.3661

3537

0.0343

0.0195

0.2198

3270

0.0874

0.0042

0.1617

3540

0.0613

0.0277

0.3039

3272

0.0762

0.0091

0.1605

3541

0.0520

0.0287

0.3275

3281

0.1265

0.0271

0.3465

3550

0.0460

0.0323

0.2880

3290

0.1617

0.0211

0.2169

3555

0.0421

0.0508

0.3460

3300

0.0605

0.0084

0.0350

3559

0.0744

0.0855

0.2691

3310

0.0880

0.0049

0.0786

3560

0.0732

0.0313

0.2482

3312

0.0678

0.0055

0.1234

3561

0.0430

0.0176

0.2916

3317

0.0490

0.0055

0.1713

3562

0.0643

0.0175

0.0897

3320

0.0756

0.0141

0.0653

3564

0.0582

0.0256

0.2268

3330

0.0812

0.0206

0.0788

3567

0.0347

0.0260

0.2408

3334

0.0947

0.0104

0.0593

3569

0.1119

0.0328

0.3475

3341

0.0458

0.0579

0.1825

3570

0.0630

0.0681

0.2178

3350

0.0625

0.0063

0.2109

3571

0.0318

0.0513

0.1215

3357

0.0575

0.0338

0.1908

3572

0.0736

0.0836

0.1563

3360

0.0644

0.0142

0.2952

3575

0.0930

0.0335

0.5056

3390

0.0905

0.0171

0.2509

3576

0.0629

0.1833

3411

0.0764

0.0087

3412

0.1935

3420

0.0356

0.0132

0.1629

3577

0.0603

0.0759

0.2183

0.0486

3578

0.0564

0.0459

0.3456

0.3276

3579

0.0421

0.0478

0.3934

216

Appendices Single Business Firms

SIC Code


Average

Average

Average

Average

Average

Average

Capital Intensity

R&D Intensity

Labor Intensity

SIC Code

Capital Intensity

R&D Intensity

Labor Intensity 2004-2006

2004-2006

2004-2006

2004-2006

2004-2006

2004-2006

3580

0.0688

0.0224

0.2470

3822

0.1028

0.0465

3585

0.0342

0.0166

0.1212

3823

0.0537

0.0614

3590

0.0610

0.0263

0.2630

3824

0.0307

0.0202

3600

0.0720

0.0418

0.2525

3825

0.0504

0.1374

0.2102

3612

0.0417

0.0268

0.1187

3826

0.0614

0.1206

0.3482

3613

0.0512

0.0435

0.3617

3827

0.1198

0.0804

0.3334

3620

0.0633

0.0499

0.1127

3829

0.0704

0.1090

0.3461

3621

0.0657

0.0390

0.2654

3841

0.0887

0.0926

0.2942

3630

0.0702

0.0230

0.1972

3842

0.0762

0.0850

0.2678

3634

0.0997

0.0125

0.1041

3843

0.0429

0.0477

0.6754

3640

0.0744

0.0235

0.2436

3844

0.0546

0.0924

0.1530

3845

0.0780

0.1318

0.2950

3851

0.0687

0.0503

0.3206 0.3753

0.2624

3651

0.0666

0.0546

3652

0.1071

0.0425

3661

0.0334

0.1161

0.2659

3861

0.0644

0.0629

3663

0.0577

0.1036

0.2211

3873

0.0919

0.0221

3669

0.1042

0.0878

0.2683

3910

0.0231

3670

0.1189

0.0642

0.2290

3911

0.0464

0.0122

0.3866

3672

0.1214

0.0353

0.2422

3931

0.0247

0.0446

0.2785

3674

0.1416

0.1283

0.2177

3942

0.0338

0.0336

0.2864

3677

0.0572

0.0869

0.2311

3944

0.0518

0.0929

0.1198

3678

0.1343

0.0517

3949

0.0380

0.0269

0.1996

3679

0.0934

0.0701

0.2352

3950

0.0430

0.0249

0.2570

3690

0.0697

0.0963

0.2882

3960

0.0819

0.0103

0.2228

0.1232

3695

0.1701

0.0601

3700

0.0789

0.0258

3711

0.0794

0.0353

3713

0.0439

3714

0.0810

0.0299

3715

0.0278

3716

0.0102

3720

0.2051 0.1680

3990

0.0893

0.0754

0.2230

4011

0.1949

0.0044

0.3479

0.1534

4013

0.0251

0.0330

0.1904

4100

0.1418

0.0052

0.2210

4200

0.1151

0.3021

0.0026

4210

0.0685

0.2860

0.0081

4213

0.0794

0.3476

0.0987

0.0285

4220

0.2640

0.2122

3721

0.0383

0.0691

0.3030

4231

0.3356

3724

0.0339

0.0307

0.1776

4400

0.1900

0.0260

0.2691

3728

0.0363

0.0591

0.3668

4412

0.2893

0.0018

0.1539

3730

0.0934

0.0109

0.1668

4512

0.1465

0.0017

0.2303

3743

0.0597

0.0136

0.2581

4513

0.1465

3751

0.0377

0.0251

0.1008

4522

0.1237

3760

0.0331

0.0262

0.1156

4581

0.2295

0.0115

0.3107

3790

0.0612

0.0236

0.1410

4610

0.2537

3812

0.0352

0.0495

0.4208

4700

0.2037

3821

0.0443

0.0451

4731

0.0947

0.0579

0.3821

0.2024 0.2169

Appendices


SIC Code


Average

Average

Average

Average

Average

Average

Capital Intensity

R&D Intensity

Labor Intensity

Capital Intensity

R&D Intensity

Labor Intensity 2004-2006

SIC Code

2004-2006

2004-2006

2004-2006

2004-2006

2004-2006

4810

0.1988

0.0259

0.2294

5093

0.0985

0.0024

4812

0.1753

0.0219

0.1457

5094

0.0400

4813

0.1689

0.0185

0.2081

5099

0.0157

0.0472

0.0853

4822

0.0391

0.1315

5110

0.0390

0.0003

0.1292

4832

0.0885

0.1188

5122

0.0151

0.0205

0.0438

4833

0.0750

0.0014

0.1730

5130

0.0233

0.0008

4841

0.1251

0.0425

0.1756

5141

0.0140

4899

0.1481

0.0957

0.2810

5150

0.0368

0.0007

4900

0.0900

0.0021

0.1570

5160

0.0414

0.0148

4911

0.1803

0.0079

0.1245

5171

0.0616

4922

0.1674

0.0006

0.0396

5172

0.0393

4923

0.1962

0.0181

0.0836

5180

0.0953

4924

0.0988

0.0018

0.0886

5190

0.0276

0.0015

4931

0.1353

0.0015

0.1976

5200

0.0384

0.0095

4932

0.1232

5211

0.0517

4941

0.3118

0.0034

0.2409

5311

0.0390

4950

0.2302

0.0229

0.1483

5331

0.0391

4953

0.1226

0.2206

5399

0.0461

4955

0.1553

0.2762

5400

0.0471

4961

0.3332

0.1712

5411

0.0419

4991

0.2172

0.2276

0.1196

5412

0.0432

5000

0.0514

0.0008

0.1363

5500

0.0413

0.0014

5010

0.0761

0.0050

0.0809

5531

0.0420

0.0140

5013

0.0243

0.0069

0.0822

5600

0.0437

0.1935

5020

0.0878

5621

0.0419

0.1863

5030

0.0357

0.0089

0.1219

5651

0.0361

0.1694

5031

0.0615

0.0829

5661

0.0288

0.2315

5040

0.0312

0.0498

0.2525

5700

0.0418

0.1133

5045

0.0329

0.0307

0.1247

5712

0.0498

5047

0.0430

0.0415

0.2639

5731

0.0252

0.0108

5050

0.0450

0.1238

5734

0.0373

0.0110

5051

0.0329

0.0553

5735

0.0194

5063

0.0258

0.0094

0.1298

5810

0.1123

5064

0.0612

0.0181

0.2105

5812

0.0849

0.0153

0.3432

5065

0.0340

0.0401

0.0810

5900

0.0709

0.0023

0.1755

5070

0.0162

0.0165

0.1006

5912

0.0261

0.0294

0.2332

5072

0.0221

0.0054

0.1101

5940

0.0391

0.0197

5080

0.0387

0.0154

0.1049

5944

0.0332

5082

0.0377

0.0063

5945

0.0378

5084

0.0285

0.0084

0.1501

5960

0.0634

0.0248

0.2093

5090

0.0229

0.0093

0.2414

5961

0.0318

0.0532

0.1481

0.0214

0.1197

0.1648 0.0385 0.0668 0.0583 0.1795

0.0009 0.1032 0.1028 0.1546 0.2114 0.0003

0.1526 0.1515 0.0897

0.0028

0.1633 0.1392 0.1497 0.0578 0.2223

0.1438 0.0824 0.2772

0.1241 0.0670

218

Appendices Single Business Firms


Average

Average

Average

Average

Average

Average

Capital Intensity

R&D Intensity

Labor Intensity

SIC Code

Capital Intensity

R&D Intensity

Labor Intensity

2004-2006

2004-2006

2004-2006

2004-2006

2004-2006

2004-2006

0.0523

0.0045

0.2103

7340

0.1043

0.0044

0.6942

6020

0.1824

7350

0.2730

0.0441

0.2435

6029

0.1180

7359

0.2094

0.0064

0.2382

6035

0.1583

7361

0.0347

0.0389

0.8161

6036

0.1301

7363

0.0120

0.0066

0.5519

7370

0.0827

0.1273

0.4245

0.0730

7371

0.0672

0.1258

0.5766

0.1303

7372

0.0724

0.1826

0.4805

6153

0.1417

7373

0.0677

0.1116

0.4815

6159

0.1664

7374

0.0619

0.0734

0.3466

0.1258

7377

0.3268

7380

0.0591

0.0179

0.5304 0.7246

SIC Code

5990

6099

0.0308

6111 6141

6162

0.0179

0.1066

6163

0.7178

6172

0.1842

7381

0.0426

0.0596

6199

0.2003

7384

0.0899

0.1707

0.2834

7385

0.0622

0.0585

0.2902

6200

0.0314

6211

0.0270

0.3219

7389

0.0682

6282

0.1627

0.2945

7500

0.1580

0.0508

7510

0.3363

0.0922

7600

0.0241

7812

0.1598

0.1701

0.2154

0.1004

7819

0.1618

0.0974

0.6104

0.3092

7822

0.0516

6300 6311

0.0123

6321 6331

0.0080

6361 0.0405

0.3382 0.0690

0.3568 0.0964 0.3684

6411

0.0197

0.1577

7829

0.0350

0.1205

6510

0.1721

0.0870

7830

0.1693

0.1892

6513

0.3697

0.0920

7841

0.1364

0.0284

6519

0.2297

0.1923

7900

0.2084

0.0101

6531

0.1693

0.2491

7941

0.0826

6532

0.0412

0.1152

7948

0.1931

6552

0.1301

0.0803

7990

0.1549

6722

0.0428

0.2534

7996

0.1279

0.1326

6726 6792

0.1301

6794

0.0478

0.3039

0.5706 0.2477

0.0413

0.3018 0.4070

7997

0.1614

0.4278

8000

0.0683

0.0127

8011

0.1072

0.3643

8050

0.1097

0.4964

0.1231

8051

0.0921

0.5228

0.0328

0.1991

8060

0.0943

0.4503

0.0528

0.0284

0.3275

8062

0.0948

7311

0.0674

0.0002

0.3511

8071

0.0686

7320

0.0335

0.0356

0.4772

8082

0.1168

7330

0.0501

0.0082

0.2682

8090

0.0971

7331

0.0606

8093

0.0457

6795 6798

0.0415

6799

0.0941

7310

0.4517 0.0847

0.2690 0.3810

0.0293

0.2246 0.5795

Appendices



Average

Average

Average

Average

Average

Average

Capital Intensity

R&D Intensity

Labor Intensity

Capital Intensity

R&D Intensity

Labor Intensity

2004-2006

2004-2006

2004-2006

2004-2006

2004-2006

2004-2006

8200

0.0928

0.0126

0.4046

8300

0.1319

8351

0.1497

0.4063

8400

0.1329

0.1942

SIC Code

8111

8600 8700

0.0705

0.0573

0.3282

8711

0.0574

0.0117

0.3806

8721

0.0215

0.0192

8731

0.1512

0.2779

0.4758

8734

0.1327

0.0379

0.4029

8741

0.1024

8742

0.0290

0.0132

0.4138

8744

0.0251

0.0095

0.2563

8900

0.0380

0.0870

0.5632

9995

0.0561

0.2809

0.0944

9997

0.1152

0.0116

0.1969

9998

0.0701

0.2349

0.1438

0.5430

SIC Code

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Performance

Psychoanalysis and Performance

Aircraft Performance and Design

Performance and place

Internationalization and Firm Performance

Learning and Performance Matter

Performativity and Performance

Performance and Fault Management

People Management and Performance

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Performance and Cultural Politics

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Polymer Degradation and Performance

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Shakespeare, Theory and Performance

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Performance Modeling and Engineering

Performance, Exile and 'America' (Studies in International Performance)

Assessing Performance: Designing, Scoring, and Validating Performance Tasks

Diversification, Relatedness, and Performance

Purposive Diversification and Economic Performance

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Funds of Hedge Funds: Performance, Assessment, Diversification, and Statistical Properties (Quantitative Finance)

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Ghosts of Memory: Essays on Remembrance and Relatedness

Attractivenes and relatedness: Notes on Turkish language contacts

Portfolio Selection: Efficient Diversification of Investments

Finfish Aquaculture: Species Selection for Diversification

Performance

Psychoanalysis and Performance

Aircraft Performance and Design

Performance and place

Internationalization and Firm Performance

Learning and Performance Matter

Performativity and Performance

Performance and Fault Management

People Management and Performance

Diversification in Modern Language Teaching: Choice and the National Curriculum

Biofuels Refining and Performance

Performance and Cultural Politics

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