Rivalry Within and Between Strategic Networks: An ... · 2.3.1 Models of Rivalry and Competitive...

Rivalry Within and Between Strategic Networks: An Investigation of the United States Automotive Industry.

Jennifer Davies

This thesis is presented for the Degree of Doctor of Philosophy

at Queensland University of Technology

December 2008

II

Declaration

This thesis contains no material which has been accepted for the award of any other degree or

diploma in any university. To the best of my knowledge and belief this thesis contains no material

previously published by any other person except where due acknowledgement has been made.

Signature: Date:

III

This work is dedicated to three exceptional women I have had the honour to share

my life with:

My Grandmother, Dorothy Eldridge

My Mother,

Wendy Davies

and

My Sister, Helen Davies

For your kindness, inspiration, strength, sacrifice, patience, understanding,

encouragement, unconditional love and support,

Thank You.

IV

ACKNOWLEDGEMENTS

The quest to show appreciation and acknowledgment to all those people who have contributed

toward the completion of this doctoral thesis is a daunting task. It would be possible to write an

entirely separate chapter to include all those who have provided practical and moral support,

however I am limited by space and convention to include only those that have had the greatest

influence upon myself and the production of this work.

Initially, I extend my gratitude to the School of Management, Faculty of Business, Queensland

University of Technology for provision of financial and physical resources necessary to undertake

a project of this magnitude. Within this School, there are many individuals who have guided this

research effort. Notable among these includes Professor Neal Ryan, Professor Mark Griffin,

Professor Boris Kabanoff, Professor Waldersee, Dr Kerry Donohue, Dr Stephane Tywoniak and

Professor Lisa Bradley. At an administrative level, Ms Jan Nixon and Ms Trina Robbie have

provided much appreciated guidance, assistance and facilitation of all matters relating to study.

This thanks extends to the Office of Research, Queensland University of Technology and the

Australian Government, who have administered and allocated scholarship funding for the

purposes of this research.

I would also like to extend my appreciation to Professor David Merrett and Professor Anne-Wil

Harzing at the University of Melbourne who provided support and untold kindness during my

employment with the Department of Management. I would especially like to thank Dr Prakash

Singh who without reservation offered his expertise in overcoming some of the more difficult

methodological problems that delayed the completion of this work.

I would also like to make special acknowledgment of the work performed by Sue Collins at

Queensland University of Technology Library, and Betty and Suvi at the Queensland State

Library. In addition, I would like to extend my appreciation to The Baker Library, Harvard

Business School, for assisting in the substantial activity of data collection.

To my fellow doctoral cohorts – please accept my thanks for your constructive criticisms,

methodological debates and for providing an invaluable source of moral support and friendship.

V

Within this group, I would specifically like to acknowledge the friendship of two talented and

motivated individuals – Alannah Rafferty and Lyn (‘LJ’) Clark.

Other individuals have also been instrumental in providing necessary moral support,

encouragement, motivation and friendship. I therefore extend my sincere thanks to Alyson Leech,

Roland Simons, Simone Tutecki, Shaney Balcombe and Ginny Bratton for sharing these things

with me willingly, and without compromise.

The completion of this doctoral thesis would not have been possible without the support of my

family – a family I feel blessed to be a part of.

At a supervisory level, Professor Neal Ryan has provided guidance, support, encouragement and

his valuable time to me without hesitation throughout my PhD tenure. For this I am most grateful.

Finally, I would like to pay special acknowledgment and sincere thanks to Professor Peter Galvin

who has traveled by my side in the capacity of supervisor since the beginning of my Honours year

through to the completion of this thesis. Professor Galvin has inspired me with his enthusiasm for

the field of strategic management and this research endeavour, and has always been available to

assist and guide me, despite living on the other side of the country. More importantly, Dr Galvin

has demonstrated to me what it is to be an exceptional teacher and supervisor. He has been a

true friend, source of inspiration, and a constant champion of my cause. Without his dedication,

this thesis would not have been possible.

VI

ABSTRACT

Davies, Jennifer (2008). Rivalry Within and Between Strategic Networks: An Investigation of the United States Automotive Industry.

Supervisor: Professor Peter Galvin.

As a consequence of the increased incidence of collaborative arrangements between firms, the

competitive environment characterising many industries has undergone profound change. It is

suggested that rivalry is not necessarily enacted by individual firms according to the traditional

mechanisms of direct confrontation in factor and product markets, but rather as collaborative

orchestration between a number of participants or network members.

Strategic networks are recognised as sets of firms within an industry that exhibit denser strategic

linkages among themselves than other firms within the same industry. Based on this, strategic

networks are determined according to evidence of strategic alliances between firms comprising

the industry. As a result, a single strategic network represents a group of firms closely linked

according to collaborative ties. Arguably, the collective outcome of these strategic relationships

engineered between firms suggest that the collaborative benefits attributed to interorganisational

relationships require closer examination in respect to their propensity to influence rivalry in

intraindustry environments.

Derived in large from the social sciences, network theory allows for the micro and macro

examination of the opportunities and constraints inherent in the structure of relationships in

strategic networks, establishing a relational approach upon which the conduct and performance

of firms can be more fully understood.

Research to date has yet to empirically investigate the relationship between strategic networks

and rivalry. The limited research that has been completed utilising a network rationale to

investigate competitive patterns in contemporary industry environments has been characterised

by a failure to directly measure rivalry. Further, this prior research has typically embedded

investigation in industry settings dominated by technological or regulatory imperatives, such as

the microprocessor and airline industries. These industries, due to the presence of such

imperatives, are arguably more inclined to support the realisation of network rivalry, through

subscription to prescribed technological standards (eg., microprocessor industry) or by being

VII

bound by regulatory constraints dictating operation within particular market segments (airline

industry).

In order to counter these weaknesses, the proposition guiding research - Are patterns of rivalry

predicted by strategic network membership? – is embedded in the United States Light Vehicles

Industry, an industry not dominated by technological or regulatory imperatives. Further, rivalry is

directly measured and utilised in research, thus distinguishing this investigation from prior

research efforts. The timeframe of investigation is 1993 – 1999, with all research data derived

from secondary sources.

Strategic networks were defined within the United States Light Vehicles Industry based on

evidence of horizontal strategic relationships between firms comprising the industry. The measure

of rivalry used to directly ascertain the competitive patterns of industry participants was derived

from the traditional Herfindahl Index, modified to account for patterns of rivalry observed at the

market segment level. Statistical analyses of the strategic network and rivalry constructs found

little evidence to support the contention of network rivalry; indeed, greater levels of rivalry were

observed between firms comprising the same strategic network than between firms participating

in opposing network structures. Based on these results, patterns of rivalry evidenced in the

United States Light Vehicle Industry over the period 1993 – 1999 were not found to be predicted

by strategic network membership.

The findings generated by this research are in contrast to current theorising in the strategic

network – rivalry realm. In this respect, these findings are surprising. The relevance of industry

type, in conjunction with prevailing network methodology, provides the basis upon which these

findings are contemplated. Overall, this study raises some important questions in relation to the

relevancy of the network rivalry rationale, establishing a fruitful avenue for further research.

Keywords: strategic networks, rivalry, network rivalry, collective rivalry, organizations, competition

VIII

TABLE OF CONTENTS List of Tables …………………………………………………………………………………………

XII

List of Figures …………………………………………………………………………………………

XIII

CHAPTER 1: INTRODUCTION ……………………………………………………………………… 1.0 Introduction …………………………………………………………………………….

1.1 Context for Research ………………………………………………………………… 1.2 Intraindustry Rivalry ………………………………………………………………….. 1.3 Strategic Networks & Rivalry ……………………………………………………….. 1.4 The Research Agenda ………………………………………………………………… 1.5 Realising the Research Objective ………………………………………………….. 1.6 Research Outcomes ………………………………………………………………….. 1.7 Contributions to New Knowledge & General Discussion of Findings ……… 1.8 Directions for Future Research …………………………………………………….. 1.9 Dissertation Structure ………………………………………………………………...

1

2 3 4 6 6 7 8 8

10 11

CHAPTER 2: LITERATURE REVIEW ……………………………………………………………… 2.0 Introduction ……………………………………………………………………………..

2.1 Competitive Advantage from a Historical Perspective …………………………. 2.1.1 Early Development 2.1.2 Industrial Organisation Economics 2.1.3 Organisational Economics 2.1.4 The Resource-Based View 2.1.5 Discussion 2.2 Competitive Advantage ………………………………………………………………. 2.2.1 Industrial Organisation

2.2.1.1 Classic Industrial Organisation 2.2.1.2 The New IO 2.2.1.3 Industrial Organisation and Competitive Advantage 2.2.2 The Resource-Based View of the Firm 2.2.2.1 Firm Heterogeneity 2.2.2.2 Resources 2.2.2.3 Organisational Capabilities 2.2.2.4 Discussion 2.2.2.5 The RBV and Competitive Advantage 2.2.3 Discussion 2.3 Competition and Rivalry ………………………………………………………………. 2.3.1 Models of Rivalry and Competitive Dynamics 2.3.1.1 Oligopoly Theory 2.3.1.2 Game Theory 2.3.1.3 Scenarios, Simulations and System Dynamic Modelling 2.3.1.4 Warfare Models 2.3.1.5 Limitations 2.3.2 Frameworks of Rivalry and Competitive Dynamics 2.3.2.1 Porter’s Five Forces of Competitive Rivalry 2.3.2.2 Limitations 2.3.3 Conceptualisations of Rivalry and Competitive Dynamics 2.3.3.1 Competence-Based Competition 2.3.3.2 Limitations 2.3.4 Discussion

12

13 17 18 19 20 21 23 24 24 25 26 28 31 32 32 33 33 33 35 38 39 39 40 40 41 41 42 42 44 45 45 46 47

IX

2.4 Strategic Group Theory and The Study of Intraindustry Rivalry ………………. 2.4.1 The Psychological Interpretation 2.4.1.1 Limitations of the Psychological Interpretations 2.4.2 The Economic Interpretation 2.4.2.1 Defining Strategic Groups and Group Membership 2.4.2.2 Discussion 2.4.3 Strategic Groups and Rivalry 2.4.3.1 The Case For and Against the Caves-Porter Hypothesis 2.4.3.2 Empirical Studies of the Strategic Group – Rivalry Relationship 2.4.4 Discussion 2.5 Strategic Networks ……………………………………………………………………… 2.5.1 Origins of the Strategic Network Concept 2.5.1.1 The Social Perspective 2.5.1.2 The Political Perspective 2.5.1.3 The Economic Perspective 2.5.2 Strategic Linkages 2.5.2.1 Linkage Forms 2.5.3 Networks of Strategic Linkages 2.5.3.1 Governance 2.5.3.2 Structure and Evolution of Strategic Networks 2.5.4 Strategic Networks and Rivalry 2.5.4.1 Network Research as Distinct from Block Research 2.5.4.2.1 Associated Research Utilising the ‘Block’ Methodology 2.5.4.2 Studies of the Strategic Network – Rivalry Relationship 2.5.5 Discussion 2.6 Summary and Propositions of Research ……………………………………………

49 50 51 52 53 55 55 56 57 58 60 62 63 63 65 66 66 67 68 70 71 73 76 77 78 79

CHAPTER 3: METHODS OF RESEARCH ………………………………………………………….. 3.0 Introduction ……………………………………………………………………………….

3.1 Thesis Overview …………………………………………………………………………. 3.2 Methodology ……………………………………………………………………………… 3.2.1 Data Collection 3.2.2 Timeframe of Research 3.2.2.1 Industry Context as a Moderating Consideration 3.2.2.2 Years of Analysis 3.2.3 Population and Sample 3.2.3.1 Exclusions 3.3 Research Design …………………………………………………………………………. 3.3.1 Study 1: The Rivalry Measure 3.3.1.1 The Herfindahl Index 3.3.1.1.1 Limitations of the Herfindahl Index 3.3.1.2 The Modified Herfindahl Index Utilised in this Research 3.3.1.3 Product Market Segmentation 3.3.2 Study 2: Network Configuration Determination 3.3.2.1 Defining the Network 3.3.2.1.1 Classifying Network Data 3.3.2.1.2 Data Classification 3.3.2.1.3 Data Entry 3.3.2.2 Commentary on Analytical Approaches 3.3.2.3 Network Data Analysis Methods 3.3.2.3.1 Clustering as the Method of Analysis of Network Data Employed 3.3.2.3.2 Limitations 3.3.3 Study 3: Testing for Within and Between Network Rivalry 3.3.3.1 Testing for Within and Between Network Rivalry 3.4 Conclusion …………………………………………………………………………………

81

82 82 84 84 85 86 87 87 88 91 91 91 92 94 95 97 97 98 99

100 100 101 104 105 105 106 107

X

CHAPTER 4: RESULTS ………………………………………………………………………………… 4.0 Introduction ………………………………………………………………………………...

4.1 Chapter Overview …………………………………………………………………………. 4.2 Study 1: The Rivalry Measure – Results ……………………………………………… 4.3 Study 2: Strategic Network Determination – Results ………………………………. 4.3.1 1993 Strategic Network Configurations 4.3.2 1995 Strategic Network Configurations 4.3.3 1997 Strategic Network Configurations 4.3.4 1999 Strategic Network Configurations

4.4 Study 3: Testing for Within and Between Network Rivalry – Results …………… 4.4.1 Network Membership and Rivalry Results 4.4.2 MANOVA Results 4.4.2.1 MANOVA Results for ‘Firm’ as Controlling Factor 4.4.2.2 MANOVA Results for ‘Year’ as Controlling Factor 4.4.2.3 MANOVA Results for ‘Segment’ as Controlling Factor 4.4.2.4 MANOVA Results for ‘Network’ as Controlling Factor 4.4.3 Summary of MANOVA Results 4.5 Summary and Conclusions ………………………………………………………………

108

109 109 110 115 116 116 116 117 126 131 132 132 133 134 135 138 138

CHAPTER 5: DISCUSSION …………………………………………………………………………….. 5.0 Introduction ……………………………………………………………………………….. 5.1 Assessing the Results of Research …………………………………………………... 5.1.1 The Proposition Guiding Research 5.1.3 Key Findings 5.2 Study Findings …………………………………………………………………………… 5.2.1 Study 1 Findings: Rivalry 5.2.2 Study 2 Findings: Strategic Network Membership 5.2.2.1 Strategic Network Structure and Evolution 5.2.3 Study Findings: The Relationship Between Strategic Network Membership and Rivalry 5.2.4 Research Outcome: Answering the Central Question of Research 5.3 Research Findings in Light of Prior Research ……………………………………… 5.4 Theoretical Contribution of Research ………………………………………………... 5.5 Practical Relevance of Research Findings ………………………………………….. 5.5.1 The Significance of the Industry Context 5.6 Discussion ………………………………………………………………………………… 5.7 Limitations of Research ……………………………………………………………….. 5.7.1 The Rivalry Measure 5.7.2 Strategic Network Formation 5.7.3 Sample Size 5.8 Directions for Future Research ……………………………………………………….. 5.8.1 Industry Context 5.8.2 Measure of Rivalry 5.8.3 Strategic Network Determination 5.8.4 Research Agendas 5.9 Conclusion …………………………………………………………………………………

139

140 140 141 142 142 142 143 144 145 146 148 149 152 152 154 155 155 155 156 156 157 158 159 160 161

CHAPTER 6: CONCLUSION …………………………………………………………………………….

162

REFERENCES ……………………………………………………………………………………………..

169

XI

APPENDIX A: Types of Strategic Relationships and Generic Definitions …………………….. 181 APPENDIX B: Approaches to Network Analysis and Associated Limitations ………………..

183

APPENDIX C: Supporting Clustering Outcome Data ………………………………………………

186

XII

LIST OF TABLES

Table 2.1 Overview of the Benefits and Limitations of Rivalry Models, Frameworks and

Conceptualisations ……………………………………………………………………………..

47

Table 3.1 Firms Comprising Sample ………………………………………………………………………. 86

Table 3.2 Population – Total Number of Producers Available for Analysis …………………………... 87

Table 3.3 Sample – Number of Subjects in Analysis per Period of Study…………………………….. 87

Table 3.4 Firms Excluded from Analysis & Their Percentage Input into Sales ……………………... 88

Table 3.5 Rating Criteria for the Strength of Strategic Linkages ……………………………………... 98

Table 4.1 1993 Market Segment Herfindahl Scores and Producer Rivalry Scores ………………… 110




Table 4.5 1993 Strategic Network Configurations ……………………………………………………… 115




Table 4.9 1993 Market Segment Network Within and Between Herfindahl Scores and Rivalry Scores 125




Table 4.13 Market Segment Count Cross-Tabulation by Year …………………………………………… 129

Table 4.14 Within and Between Network Rivalry Indices with Respect to Firms ……………………….. 129

Table 4.15 Within and Between Network Rivalry Indices with Respect to Years ………………………. 132

Table 4.16 Within and Between Network Rivalry Indices with Respect to Market Segments ………… 133

Table 4.17 Within and Between Network Rivalry Indices with Respect to Networks …………………... 134

Table 4.18 Summary of MANOVA Results …………………………………………………………………. 135

XIII

LIST OF FIGURES Figure 2.1 The Four Stages of Strategy …………………………………………………………… 22

Figure 2.2 The Economic Tradition ………………………………………………………………… 24

Figure 2.3 The Traditional Mason-Bain Structure-Conduct-Performance Paradigm …………. 26

Figure 2.4 An Updated Version of the Industrial Organisation Paradigm ……………………… 27

Figure 2.5 Conceptual Differences in Perspectives and Sources of Competitive Advantage … 36

Figure 2.6 The Rivalry Matrix ………………………………………………………………………. 38

Figure 2.7 Forces Driving Industry Competition ………………………………………………….. 41

Figure 2.8 Competence Based Competition ……………………………………………………… 44

Figure 4.1 1993 Network Data Output Dendogram ……………………………………………… 116

Figure 4.2 1993 Netdraw Simulation of Strategic Relationships ……………………………… 117







7

CHAPTER 1CHAPTER 1

I N T R O D U C T I O NI N T R O D U C T I O N

8

1.0 INTRODUCTION

Depending on the article you read, you could be mistaken for presuming that

network rivalry is indeed a reality (Rowley, Baum, Shipilov, Greve & Rao, 2004). The

common presumption underlying discussion of strategic networks has been the

capacity of these structures to engage in collective rivalry, in that all firms associated

with each other through strategic relationships endorse the same competitive targets

when enacting rivalry in their industry domain (Gomes-Casseres, 1994; Rowley,

Baum, Shipilov, Greve & Rao, 2004). However, adopting a broad perspective, it is

possible to identify potential faults in this logic. How is it that all members of a single

strategic network – a collection of firms tied more closely and densely with each

other through strategic alliances in comparison with other firms in the industry – are

aware of their prescription to this network, particularly if the network comprises

multiple members? How is it that such network structures are able to coordinate and

govern their network system to ensure such unity in competitive intent in their

relative product markets? What empirical evidence clearly finds in favour of network

rivalry? On what basis is it reasonable to promote the concept of strategic networks

as the next champion of intraindustry rivalry analysis?

In reality, little evidence exists to support many of the generalised assumptions that

have developed in the strategic network literature. Indeed, despite the apparent

acceptance of many of the above contentions as true by academics in the strategic

management field, little empirical evidence can be identified that supports without

reservation these conclusions. Close examination of the strategic network rationale

elicits problems associated with network methodology, industry context and the role

of mediating variables, and a tendency for research results to be misinterpreted.

This has led, in many regards, to the concept of strategic networks assuming a

popularity in strategic management literature that should be countered with caution.

This thesis seeks to tackle some of the general assumptions associated with

strategic network theory, particularly in relation to the strategic network – rivalry

relationship. In order to develop a sound basis upon which this thesis is to progress

further, this chapter will provide background to the strategic network – rivalry debate,

illustrating the relevance of strategic network theory to intraindustry rivalry

investigation, and upon what basis this research finds significance in strategic

management research and literature.

9

1.1 CONTEXT FOR RESEARCH

It is not uncommon for organizations to suffer significant economic loss should their

ability to interpret their competitive environment flounder. The underlying foundation

of successful strategy development is based on maximising profits in light of the

prevailing rivalry from other firms in the industry intent on achieving the same

outcome. Simple economic reasoning tells us that not all firms will succeed in this

endeavour; given a finite number of consumers, it is not feasible that all firms will

achieve their profit maximising potential.

The erosion of clearly defined market boundaries through the advent of improved

technologies facilitates the introduction of new competitors into markets previously

constrained by geographical and technological impediments. This movement away

from the traditional business environment has seen the influx of new competitors,

coupled with increased business uncertainty. Consequently the importance of a

firm’s competitive strategy attains greater significance in the firm’s efforts to not only

maximise profit opportunities, but simply to retain ongoing economic viability.

One way in which firms have sought to counter increased competition and

environmental uncertainty has been through the pursuit of strategic alliances. These

alliances are designed to capture value for partner firms via a range of scenarios:

joint product development, access to new markets, collaborative marketing ventures,

technology sharing, and the like. A common presumption of such alliances is that

these relationships are competitive in nature, designed, in essence, to improve the

competitive position of all partners to the relationship via a process of mutual value

creation.

The relative influence of these alliances in advancing the fortunes of the parties to

the relationship has been the subject of countless investigations exploring both the

benefits and failures of these collaborations. Despite the varied evidence these

studies have generated, strategic alliances have become a common feature of

contemporary industry environments, prompting this phenomena to be referred to as

‘alliance capitalism’ (Dunning, 1995). Clearly the collaborative element of these

strategic alliances are embraced in an organisation’s competitive strategy given the

creation of the original relationship, however much debate exists as to whether this

10

relationship influences how a firm develops that aspect of its competitive strategy

dealing with rivalry.

These alliance relationships further cloud what has already been a contentious issue

in strategic management literature. ‘Over the past 20 years one basic question

which has occupied the attention of both strategy researchers and practitioners is

‘with whom and how do firms compete?’’ (Thomas & Pollock, 1999, p.127). The

significance of understanding the dynamics of rivalry and the reality that some firms

compete more aggressively with select firms over other organisations within an

industry is offset by the knowledge that neither organizations or industries are

homogeneous in nature, prompting industries to demonstrate differences in market

segments and therefore products. This results in firms having at their disposal

different resources and capabilities upon which to engage in competition.

1.2 INTRAINDUSTRY RIVALRY

The realisation that industries were heterogeneous in nature was supported by the

theoretical arrival of strategic group theory in the 1970s by Hunt (1972) who

investigated the whitegoods industry. He found that, in contrast to the general

prescriptions of industrial economics, firms, and indeed industries were strongly

characterised by resource and capability differentials that had an observable impact

on the products and services offered within an industry, and as a consequence

influenced the very nature of competitive interchange between firms. According to

this argument, it was possible to clearly distinguish firms within an industry based

upon the differences demonstrated in their strategy and based on their ownership of

resources and capabilities. Those firms displaying similarities in these attributes

could be grouped together, with each group observed within the industry

characterised by significant differences according to their identified strategy,

resource and capability portfolio, and in terms of their geographical scope. These

collections of firms became subsequently known as ‘strategic groups’.

Strategic group theory became perhaps one of the most prominent mechanism by

which differences in firm performance could be investigated (for instance, see Cool

& Schendel, 1987, 1988; McGee & Thomas, 1986). However, it was not long before

the research agenda moved to studying intraindustry rivalry, especially given the

implicit assumption that the single most significant influence of performance

11

differences between firms was based on rivalry – both in terms of supply and

product markets. The development of the strategic group construct further supported

the contention that firms do not engage in homogeneous competition, but rather are

engaged in more aggressive rivalry with some firms in the industry.

Caves and Porter (1977) proposed that based on the strategic group rationale, it

was more likely that greater rivalry would be observed between strategic groups as

opposed to the level of rivalry observed within a strategic group. The rationale

supporting this contention was based on the logic that firms would be inclined to

target those firms in other strategic groups as greater collective economic gains

would be made by all firms in the strategic group through the adoption of this

approach. Cognitive theorists would argue against this proposition by Caves and

Porter, instead contending that firms occupying the same strategic space are more

inclined to translate this shared familiarity as the basis for which these firms are

more likely to perceive of themselves as competitors. Research into the strategic

group – rivalry relationship failed to generate a conclusive outcome as to the rivalry

debate, due in large part to the criticisms levelled at strategic group methodology.

Despite the limitations attributed to strategic group methodology, this concept

enabled the role of rivalry research to progress in terms of recognising that not all

firms engage in aggressive competition with all firms in an industry, but rather tend

to focus their competitive intent on only a single or limited selection of firms.

The advent of alliance capitalism has now led to collaboration adopting a more

substantial role in a firm’s competitive armoury. Firms engage in strategic

relationships in order to achieve competitive benefit. As a result, firms traditionally

understood to be in contention with each other has been replaced by collaborative

enterprise between firms, challenging established and accepted principles of

competition. Further, these single alliances represent only a part of what has

become recognised as strategic networks representing webs of relationships that

effectively tie all firms in an industry directly and indirectly into broader systems of

exchange.

Given the rapid proliferation of strategic alliances between firms, which in turn has

prompted the advent of strategic networks in some industries, traditional conceptual

12

methods of assessing patterns of intraindustry rivalry, such as the strategic group

approach, are challenged. As a consequence the strategic network rationale has

assumed increased importance as a means by which intraindustry rivalry can be

investigated.

1.3 STRATEGIC NETWORKS AND RIVALRY

The relevance of strategic networks, now characterising many industries, and their

relationship to competitive outcomes have generated diverse conclusions. Most

overtly observed in the airline and technology-intensive industries, these strategic

networks are said to facilitate what is termed collective rivalry or network rivalry,

whereby the actors of one network, in an attempt to further their shared competitive

interest, channel their rivalry away from partner firms and towards those firms

engaged in other strategic networks (Gomes-Casseres, 1994, 1996). This rationale

is similar to that found within strategic group literature and research as proposed by

Caves and Porter (1977). Anecdotal evidence to date suggests that in those

industries demonstrating regulatory or technological imperatives (such as the airline

and the microprocessor industries), this hypothesis of collective rivalry, achieved via

the strategic network construct, may be true.(Gomes-Casseres, 1994, 1996; Boyd,

2004).

The airline industry, the subject of numerous rivalry-related research investigations,

has provided the backdrop to Gomes-Casseres (1994, 1996), who argues for the

recognition of network rivalry. Empirical research suggests that firms in this industry

are engaged in strategic relationships designed to overcome regulatory impediments

that constrain their opportunities to improve their market share. Boyd (2004), in

proposition of the integrated use of the strategic group and strategic network

constructs to the study of intraindustry rivalry, sought to engage in empirical study of

the airline industry. In order to define strategic networks in this work, the author

relied on the overt network structures evident in the industry, based on firm

subscription to dominant alliance collectives. This author thereby surpassed the

need to engage in network analysis to define the strategic networks characterising

the industry during the period of study. Return on Sales (ROS) was used to infer

rivalry for the purposes of this research, with strategic networks found to have a

predictive ability to account for some performance differences between firms.

13

1.4 THE RESEARCH AGENDA

In common the research undertaken to date in the strategic network – rivalry realm

has investigated those industries that are dominated by technological and regulatory

imperatives (Vanhaverbeke & Noorderhaven 2001 completed a study on the RISC

mircroprocessor industry, however the methodology employed for this research

utilised the ‘strategic block’ concept). Collectively, these studies (Gomes-Casseres,

1994, 1996; Boyd, 2004) elicit weaknesses in that rivalry has not been directily

assessed, either excluding the direct measurement from analysis, or alternatively

employing an inferred measure. The purpose of the research completed in this

thesis was to improve upon the measure of rivalry employed in prior research, whilst

also investigating an industry not overtly dominated by regulatory or technological

imperatives. As a result, the central research question characterising this thesis

investigation was developed: Are patterns of rivalry predicted by strategic network

membership? To further capitalise on the potential for industry type to confound the

study of horizontal strategic networks, the setting for empirical investigation is the

United States Light Vehicles Industry, over the period 1993 to 1999 – an industry not

overtly influenced by technological or regulatory imperatives.

1.5 REALISING THE RESEARCH OBJECTIVE

In order to engage in a retrospective study of the United States Light Vehicles

Industry, it was necessary to rely on secondary data derived from an authoritative

source – complimentary publications Ward’s Automotive Yearbook and How the

World’s Automakers are Related. The data provided in these publications

constituted the primary data source, used in conjunction with a range of alternative

publications in order to ensure the reliability and validity of the data used in

research. This secondary data provided the basis upon which it was possible to

determine strategic networks operational in the industry, calculated on a biannual

basis throughout the timeframe of analysis, via the use of the social networking

software package UCInet (Borgatti, Everett & Freeman, 2002). In addition, the data

obtained through this data collection process allowed for additional data relating to

rivalry to be acquired. The automotive industry is characterised by multiple

producers participating across distinct product market segments, allowing for rivalry

to be captured at the product market level to obtain a more accurate determination

of rivalry patterns evident throughout the industry. Rivalry was directly measured by

14

the use of a modified Herfindahl Index (Cool & Dierickx, 1993), whereby the level of

rivalry firms faced from other firms participating in the same product market segment

of the industry could be defined.

Network analysis via UCInet (Borgatti, Everett & Freeman, 2002) revealed that

strategic networks – collections of firms more closely tied to each other through

strategic relationships than other firms within the industry – were present in the

United States Light Vehicles Industry over the timeframe 1993-1999. The onus

within this research was to identify horizontal networks with members therefore

encompassing actors that operated in the same value chain component of the

industry, and whose input and outputs were similar. In this respect, auto producers

represented the population under investigation, with those firms active in the United

States industry defining the sample. Strategic networks were identified in each time

period – 1993, 1995, 1997 and 1999 – however the number of networks, and the

actors tied to each network, were observed to change over each period of analysis.

In order to test the central proposition of research – whether strategic network

membership can account for patterns of rivalry observed in the industry – it was

necessary to investigate rivalry from the complimentary perspectives of between

network rivalry, and within network rivalry. Between network rivalry was concerned

with assessing the level of rivalry between defined network structures, whereas

within network rivalry was focused on determining the level of rivalry observed

between firms comprising the same network. If indeed the argument for collective

rivalry / network rivalry is true, greater rivalry should be observed as occurring at the

between network level.

1.6 RESEARCH OUTCOMES

The results of analysis revealed that it is not possible to predict patterns of rivalry in

the United States Light Vehicles Industry over the timeframe 1993 – 1999 based on

strategic network membership. Further, levels of within network rivalry (0.310) were

found to exceed the levels of rivalry observed at the between network level (0.238).

These results suggest that firms engaged in strategic relationships with each other,

and who form part of the same strategic network, are inclined to compete more

aggressively with each other than with other firms in the industry to whom they have

no strategic affiliation. These results find against current theoretical conjecture in the

15

strategic network – rivalry field which contend that strategic networks act to focus

the competitive orientation of members away from co-members and toward firms in

the industry that do not share membership in the same strategic network (Gomes-

Casseras, 1994). The results of this research are in contrast with the findings of prior

investigations which posit that strategic networks are conduits for collective

competitive action (Gomes-Casseras, 1994).

1.7 CONTRIBUTIONS TO NEW KNOWLEDGE & GENERAL DISCUSSION OF

FINDINGS

A number of reasons exist as to why the findings in the research completed here

and prior propositions and related research outcomes may find little common

ground. Initially, this project directly measured rivalry, whereas other studies have

largely inferred rivalry. Secondly, the industry types investigated vary significantly.

Prior research has focussed on industry types that have demonstrated strong

subscription to either technological or regulatory imperatives, such as the

microprocessor or airline industries. The research completed here broke from this

traditional research setting to investigate the United States Light Vehicles Industry –

an industry that is not overtly dominated by either technological or regulatory

imperatives which may act to accentuate network activities.

The findings that this research has generated are significant in that the results act as

a counterpoint to some of the current theorising and conjecture in the strategic

management field that contend that competitive intent may be crystalised by

participation in a strategic network (Gomes-Casseras, 1994; Lazzarini, 2007). This

conjecture was built upon the presumption that firms engaged in a strategic alliance

would be less inclined to competitively target partner firms in the product market.

The results of this research suggest otherwise. In many ways the idea that

collaborative partnerships would lead to heightened rivalry between partner firms is

counter-intuitive to commonsense reasoning.

It is possible to infer a number of plausible scenarios to explain why the results

observed in this study differ from those results obtained in prior studies. Initially, it

becomes apparent that industry type may play a crucial role in the realisation of

network rivalry. Should technological or regulatory imperatives characterise an

industry, these attributes may contribute to providing an external impetus for implicit

16

coordination by industry actors. These attributes in themselves provide an economic

incentive – for instance, in support of a specific technological standard central to a

firm’s ongoing viability – and this in itself provides participants to the industry an

ability to effectively organise their competitive intentions without direct reference to

other firms within the industry. The firms in this industry type benefit from a level of

transparency in the industry due to either subscription to, or against, a specified

standard. Thus, it is more likely that firms will readily identify those firms in the

industry that advocate the same product-specific attributes that are central to the on-

going economic viability of these firms in the industry, and on this basis are less

likely to challenge each other for competitive dominance. Rather, rivalry at this time

would be focused on reducing the opportunities for firms advocating an alternative

standard to prosper within the industry. Should the battle for a dominant design be

won by a specified standard, the competitive landscape of the industry would alter.

Strategic networks previously characterising the industry may dissolve as the

relevancy of network subscription (the advancement of a specific technological

standard) is no longer valid. In this respect the strategic network in itself may not be

responsible for the realisation of collective competitive action observed by

researchers prior to the success of a dominant design, but merely act as an

intraindustry analytical tool by which this action can be more readily defined.

In industries that are not characterised by such prevailing influences, it may be more

difficult for firms to discern the web of strategic relationships that ultimately comprise

their strategic network, and without an external rationale to align competitive

behaviours are less able to determine which firms constitute more pressing

competitive threats. This lack of recognition of network co-members provides little

opportunity for implicit coordination to develop, thus reducing the likelihood that

collective competitive action will eventuate. Further, the results of this research

would suggest that firms do not actively structure their product markets in light of

their collaborative relationships. Therefore this research is unable to support the

contention that firms develop their competitive strategy to accommodate the best

interests of collaborative partners.

Despite the value of this research to the strategic management field, this

investigation is not without limitations. The most prominent weakness of this

research relates to how the strategic networks were formed, given that past

17

research endeavours have utilised methods associated with positional versus

relational equivalence. The method employed for defining networks in this research

is in contrast to past empirical efforts, and therefore the findings of this research may

be challenged by researchers in the strategic network field. The second limitation of

this research relates to the measure of rivalry employed. Unlike prior study of the

strategic network – rivalry relationship, this research has sought to directly measure

rivalry. While this measure is essentially quite sound, there exists an opportunity for

this measure to be improved to capture a more detailed insight into competitive

dynamics.

1.8 DIRECTIONS FOR FUTURE RESEARCH

The area of strategic networks and rivalry offers considerable scope for future

research. Current theoretical conjecture in this field suggests that network

membership may act to crystalise the competitive intent of all network participants

(Gomes-Casseras, 1994). In this way, it is proposed, network rivalry is realised.

However, before this theoretical conjecture overwhelms empirical evidence,

research into different industry types – those dominated by technological and

regulatory imperatives and those that are not – should take place. At present, any

conclusions that are developed are based on limited empirical studies, and appear

to be generalised without respect to the details of the original study. This study

represents the first of its kind in that rivalry as an independent construct is directly

assessed, in conjunction with examining an industry type not previously

investigated. Additional studies are required in order to validate the findings

produced here.

The 70s, 80s and 90s were characterised by the strategic group construct as the

concept of choice when investigating intraindustry rivalry. There is the potential for

this concept to slowly give way to the strategic network rationale as a means to

research intraindustry rivalry. In part this potential to use the network rationale has

been prompted by the rapid proliferation of strategic alliances between firms in

contemporary industry environments, and the inability of the strategic group concept

to effectively encompass these collaborative partnerships in analysis. However, this

research suggests that the strategic network rationale may prove ineffective in

deciphering patterns of intraindustry rivalry, particularly in those industries that do

not demonstrate any subscription to technology or regulatory imperatives.

18

1.9 DISSERTATION STRUCTURE

This chapter has provided an overview of the rationale for research and the key

attributes defining this investigation. The remaining chapters of this thesis seek to

provide richer detail on each component of research, and in this regard these

outstanding chapters are completed according to a traditional structure associated

with thesis documents. A review of relevant theory is offered in Chapter 2,

establishing the foundation upon which empirical research is based. The

methodology underlying empirical research is presented in Chapter 3, while the

results of this research effort are provided in Chapter 4. Chapter 5 provides a

detailed discussion on the relevance of these research results. The conclusion to

this research effort is presented in Chapter 6.

19

CHAPTER 2CHAPTER 2

L I T E R A T U R E R E V I E WL I T E R A T U R E R E V I E W

20

2.0 INTRODUCTION

‘Over the past 20 years one basic question which has occupied the attention of both

strategy researchers and practitioners is ‘with whom and how do firms compete?’’

(Thomas & Pollock, 1999, p.127)

The basic question ‘with whom do firms compete?’ serves as the foundation upon which

this thesis is based. A repetitive fixation of strategy research, the dynamics of rivalry are

understood to differ greatly within any singular industry. While variations in firm

performance have dominated an increasingly significant proportion of strategy research

in recent decades (Mehra, 1996, Rumelt et al., 1991), the capacity to interpret patterns of

rivalry between organisations in competition has remained relatively stagnant. However,

without an understanding of rivalry and the dynamics of competition within industries,

study of the performance differences between firms remains relatively removed from one

of the implicit conditions that generates such differences – rivalry.

The phenomenon of rivalry within the context of industry operation yields significant

implications for the study of management, and strategy in particular. In order to undertake

relevant and meaningful research into the dynamics of an organisation’s strategy and

performance, an understanding of the influence of rivalry as instigating and generating

firm outcomes must be considered. From a historical perspective, the construct of rivalry

has been explored through the theoretical lens of neo-classical economics (oligopoly

theory), industrial organisation economics (product market competition), the resource-

based view of the firm (supply-oriented competition) and through psychology (cognitive

interpretations of rivalry).

However, despite the relative insight these paradigms have offered rivalry research, little

practical knowledge exists to explain or interpret the patterns of rivalry evidenced in

contemporary industry structures. The concept of strategic groups, as developed from

within the Industrial Organisation School, currently offers the only method by which

intraindustry rivalry can be readily examined.

Strategic groups are collections of firms in an industry that are distinguished based on

their relative measure according to a select number of competitive variables that

21

characterise the dimensions upon which competition is enacted within the industry. This

initial analysis allows for an appreciation of the firm-specific factors that are idiosyncratic

to each group, and facilitates examination of how and why distinct groups facilitate

differential performance. The principle assumption supporting the strategic group concept

is of heterogeneity between firms within an industry, as it is on this basis that groups are

devised. Traditionally guiding research in the strategic group – rivalry relationship has

been the empirical examination of the hypothesis posed by Caves and Porter (1977), that

rivalry will be greater between firms from different strategic groups as opposed to firms

within the same group. The results obtained from empirical research have generated

incompatible results, thus reducing the capacity to definitively argue the validity of the

strategic group rationale in explaining patterns of rivalry witnessed in contemporary

industry environments. As a consequence, validation of the presence or absence of any

hypothesised relationship between strategic group membership and rivalry is yet to be

conclusively ascertained. Further confounding the capacity for the strategic group

construct to account for patterns of intraindustry rivalry is the recognition that this concept

is unable to fully integrate the collaborative nature of competition currently characterising

contemporary industry settings.

Given this limitation in contemporary management knowledge and tools of analysis, this

thesis is concerned with examination of one practical aspect of the rivalry equation: from

a macro perspective, the role and relevance of strategic networks in influencing rivalry.

Specifically, this thesis seeks to investigate whether differences in rivalry can be

observed between and within firms engaged in strategic relationships via the rationale of

strategic networks in the United States Light Vehicle Industry over the timeframe 1993 –

1999.

The concept of strategic networks demonstrates a diverse history in the strategic

management literature, with these differences largely associated with the use of

terminology applied, theoretical foundations observed and methodology enacted. For the

purposes of this thesis, the pragmatic definition of ‘strategic blocks’ offered by Nohria and

Garcia-Pont (1991) is applied, in that strategic networks are recognised as sets of firms in

an industry that exhibit denser strategic linkages (interorganisational relationships)

among themselves than other firms within the same industry. Whilst this definition is

observed in undertaking this research investigation, it is necessary to note that

22

distinctions are drawn in respect to theoretical grounding and methodology to Nohria and

Garcia-Pont’s (1991) original research.

The relative influence of strategic networks in generating competitive outcomes for

participant firms has been a matter of conjecture within the strategic management

literature for some time (Ahuja, 2000; Boyd, 2004; Brass, Galaskiewicz, Greve & Tsai,

2004). The underlying logic of the strategic network concept is the contention that firms

which engage in strategic relationships with other firms within an industry will take into

account these relationships when formulating competitive strategy, or to a lesser degree,

some measure of competitive benefit is ascribed to members (Gomes-Casses, 1994,

1996; Vanhaverbeke and Noorderhaven, 2001). As a consequence, where a group of

firms are closely linked through a network of strategic relationships, variations may be

observed in the nature and intensity of competition between members of the same

strategic network and other networks within the industry.

To date, few studies have applied the construct of strategic networks to investigation of

intraindustry rivalry. Greater emphasis has been typically applied to the micro

implications associated with strategic relationship formation and management, such as

joint value creation, propensity for opportunistic behaviour, and pursuit of competitive

advantage (Hamel, 1991, Penrose, 1959, Pfeffer & Nowak, 1976, Porter & Fuller, 1986,

Rumelt, 1984). However, given the rapid proliferation of strategic relationships in recent

decades (Burgers et al., 1993, Colombo, 1998, Gulati, 1998), and the relative influence of

these relationships in generating competitive outcomes for participant firms (Hamel,

1991, Nohria & Garcia-Pont, 1991, Porter & Fuller, 1986), scope exists to apply this

conceptual approach to the study of intraindustry rivalry.

The purpose of this chapter is to establish the theoretical foundation upon which research

is based. To do so, this chapter will begin by presenting an overview of the development

of the strategy field and introduce the concept of competitive advantage for the purposes

of establishing the context upon which later discussion is embedded. From these

beginnings, the concept of rivalry will be discussed via referral to dominant models,

frameworks and conceptualisations put forward in contemporary management literature,

followed by a review of the strategic group construct. This review is followed by a

23

theoretical investigation of the strategic network concept, as the primary construct of

analysis.

The objective of this thesis is thus concerned with examination of the relative influence

strategic network membership plays in defining the dynamics of intraindustry rivalry. This

chapter concludes with summation of the focal points of review and discussion and

forwards the proposition that will guide research.

24

2.1 COMPETITIVE ADVANTAGE FROM A HISTORICAL PERSPECTIVE

‘The fluidity of many strategic issues requires strategy researchers to keep advancing the

extant body of knowledge’

(Hoskisson et al., 1999, p. 444).

Investigation into the origins of competitive advantage has occupied a central place in

management research dating back to Adam Smith’s treatise The Wealth of Nations,

published in 1776 (Besanko et al., 2000). Competitive advantage is defined as ‘attaining

a competitive position or series of competitive positions that lead to superior and

sustainable financial performance’ (Porter, 1991, p.96). Considered the Holy Grail in

strategic management literature and research, competitive advantage effectively implies

that organisations can achieve and sustain superior performance and returns over rivals.

The field of strategy is littered with theoretical and empirical examination of the

foundations of competitive advantage. Learned, Christensen, Andrews and Guth

proposed the analysis of environmental opportunities and threats and internal strengths

and weaknesses to identify and develop distinctive competencies with which to pursue

competitive advantage (Learned et al., 1969). However, many approaches emphasised

either an internal or external focus on the determinants of competitive advantage.

Michael Porter, for instance, suggests attention to industry dynamics as constituting the

basis upon which the sources of competitive advantage are determined (Porter, 1980).

More recent contributions, such as those offered by Wernerfelt (1984), Barney (1991)

and Peteraf (1993) prescribe to the view that sustained competitive advantage is

achieved through firm-specific attributes, specifically resources and capabilities.

The field of strategy is primarily focussed upon understanding how and why some firms

consistently outperform others. A historical analysis of the strategy field yields four

transitional stages through which the focus and development of the field can be

understood thus far. These frames of reference include Early Development, Industrial

Organisation Economics, Organisational Economics and the Resource-Based View

(Hoskisson et al., 1999). These four stages of strategic thought are reviewed here briefly,

in order to establish a historical and theoretical context to later deliberation. Of the brief

25

review included here, it is possible to note that the schools of thought differ in terms of

key focal variables as well as their basic prescription as to what drives firm success.

2.1.1 Early Development Conceptualisation of organisational phenomenon at a strategic level is principally

acknowledged by the publication of Alfred Chandler’s Strategy and Structure in 1962.

This publication mirrored the academic evolution of the business policy field from a

fragmented management discourse to an integrated approach to the whole-of-business

activities.

Firms implicitly operate within defined environmental structures. Chandler proposed that

attention to these structures could better assist firms in the development and deployment

of organisational capabilities, thus generating appropriate strategic responses more

inclined to result in firm success. Chandler’s seminal work dismantled and reassembled

the fragments of business history, framing historical business activities within an

institutional context of organisational and environmental changes to formulate a

relationship between strategy and structure (Bowman, 1990, Chandler, 1962). Chandler

is thus credited with the introduction of corporate strategy as a responsive and powerful

component of organisational functioning upon which other authors subsequently built

(Ansoff, 1965).

Rationalist perspectives soon followed the lead initially established by Chandler. In 1965

Igor Ansoff published Corporate Strategy which sought to make explicit the analytic

decisions pertaining to strategy at a corporate level. This was achieved, Ansoff argued,

through a formal planning process, which concurrently sought to guide expansion of

products into existing markets whilst also assisting in the development of new markets

and products (Ansoff, 1965). In 1969, Business Policy: Text and Cases by Learned,

Christensen, Andrews and Guth was published in which issues such as strategy

formulation and implementation were discussed in regard to corporate strategy (Learned

et al., 1969). This involved an analysis of environmental opportunities and threats and

internal strengths and weaknesses of the organisation (the well-known SWOT Analysis)

to identify and develop distinctive competencies with which to pursue competitive

advantage (Andrews, 1971).

26

The seminal works by Chandler (1962), Ansoff (1965) and Learned et al (1969) found

common ground in application of a contingency doctrine to articulate the relationship

between strategy and structure. These approaches emphasise internal strengths and

weaknesses consistent with the resource-based framework – many of the central tenets

of which were found in earlier works by Barnard (1938), Selznick (1957) and Penrose

(1959) (Hoskisson et al., 1999). Such books reshaped the field of strategy, promoting

movement away from traditional disparate perspectives of strategy which largely

emerged from the business policy arena or were encapsulated in organisational theory.

2.1.2 Industrial Organisation Economics Evolution of the strategy field continued throughout the 1970s. By 1982, Gluck, Kaufman

and Walleck proposed characteristic transitional stages of strategic thinking including

budgets, long-range planning and strategic planning, culminating in strategic

management (Bowman, 1990, Gluck et al., 1982). The dominant drive of this era was the

concept of strategy formulation in consideration of, and within the context of, specific

industries.

This orientation drew substantially from the policy oriented industrial organisation

economics approach, the conceptual foundations of which were laid by Mason (1939)

and Bain (1956, 1968). Articulation of the Structure-Conduct-Performance paradigm

(Bain, 1956, 1959, Mason, 1939) effectively transferred the focus of theory and research

in the strategy field from the firm to the broader concerns of market structure (Hoskisson

et al., 1999). Industry structure, as an outcome of this movement, was considered the

primary determinant of firm performance (Porter, 1981).

Significant contributions of this time included work authored by Michael Porter,

particularly Competitive Strategy (1980), which focused upon the importance of

firm/industry dynamics and later Competitive Advantage: Creating and Sustaining

Superior Performance (1985) which introduced a disaggregated production function in

the form of a value chain. Relationships between firms and significant economic actors –

suppliers, customers, potential industry entrants, competitors, government agencies and

the relevance of substitute products became the focus of scrutiny, particularly in the

search to seek maximisation of firm performance (Bowman, 1990, Porter, 1985). Porter is

credited with generating a new approach to examining competition, by successfully

27

transferring the prior economic focus on maximising competition into profiteering through

models of competition reduction (eg. monopolistic rents). Porter effectively transcribed

economic theory into approaches that could be used by the academic and practitioner

masses.

Emergent strategies of this time focussed largely on environmental circumstances

affecting the United States, generating economically ascertained perspectives focused on

industry structure, and to a lesser extent, competition (Bowman, 1990, Hoskisson et al.,

1999).

However, the growing dominance of the IO economic emphasis on industry structure as

the primary factor influencing the performance outcomes of firms was met with some

resistance. The failure to consider that firm-oriented factors could likewise affect

organisational performance created perhaps the first major paradox in the strategy field,

and was at odds with earlier works by Chandler (1962), Ansoff (1965) and Learned et al

(1969). As a consequence, the mid-1970s saw the development of organisational

economics, a counter measure to the industrial organisation approach, and later, the re-

articulation of the resource-based view of the firm.

2.1.3 Organisational Economics A sub-field of the economics discipline, organisational economics focuses upon the

organisation, and considers ‘how the firms’ internal mechanisms and attributes influence

firm strategy and performance’ (Hoskisson et al., 1999, p. 436). Notable theoretical

approaches to have emerged from this school have included transaction-cost economics,

as proposed by Williamson (1975, 1985), and agency theory as articulated by Jensen

and Meckling (1976). In common, both approaches express concern with governance

mechanisms of the firm, although from competing perspectives. Transaction cost

economics finds substantive issue with the economic costs associated with disparate

organisational transactions, and how these costs can be minimised through alternative

arrangements. Such arrangements may include the organisation contracting out activities

to other firms that have cost advantages, or reorganising the structure of activities within

the organisation itself (Williamson, 1975). Agency theory, in contrast, posits that conflict

arises through the competing interests of shareholders (principals) and managers

(agents) due to the separation of ownership and control in modern organisations

28

(Eisenhardt, 1989, Jensen & Meckling, 1976). Consequently, organisational economics

finds divergence from industrial organisation in concern for opportunism, bounded

rationality, performance evaluation, contracts enforcement and the transaction

relationships between two parties (Hoskisson et al., 1999).

The organisational economics approach addressed the significance of the internal

arrangement within organisations, and how these arrangements influenced performance

outcomes from a transaction-cost and agency perspective.

2.1.4 The Resource-Based View The conceptualisation of organisations as unique collections of heterogeneous resources

bundled together and able to deliver variable production is attributed to Penrose (1959).

With renewed popularity, the conceptual basis of the resource-based view (RBV) was

reintroduced in the 1980s (Wernerfelt, 1984), and has since exerted a substantial

influence on strategy discourse and research (Hoskisson et al., 1999).

Central to the notion of the RBV is the position that firms are by nature heterogeneous,

according to the stocks of resources and capabilities held by the firm (Barney, 1991,

Wernerfelt, 1984). The term ‘resources’ is often broadly applied, and can include tangible

and intangible resources of the firm. Tangible resources are those that have a physical

presence within the organisation, whereas intangible resources are those organisational

components less easily identified, such as brand reputation and knowledge. The

interaction of these resource types over time generate organisational capabilities, which

represent the accumulated and specialised ability of the firm (Sanchez et al., 1996). This

in turn may stimulate the creation of core competencies within the organisation, whereby

through collective learning, experience and interplay between tangible and intangible

resources, the efficiency of the organisation is heightened (Collis, 1991, Prahalad &

Hamel, 1990).

In contrast to prior schools of thought, the RBV contends that competition is a function of

demand for resources and capabilities in factor markets as opposed to product market

competition. This is illustrated by the presence of imperfect factor markets, where not all

firms are able to secure access to identical resources upon which to base competition. As

a consequence, firm resources (both tangible and intangible) and organisational

29

capabilities are argued to generate the distinctive competencies upon which firms

generate production and performance differentials, and have the capacity to generate

superior performance over rivals (Barney, 1991).

Providing a significant point of divergence from the traditional industrial organisation

perspective, the RBV has spawned a number of splinter theories, including dynamic

capability theory (Teece, Pisano & Shuen, 1997), the knowledge-based view of the firm

(Grant, 1996) and competency research (Sanchez, Heene & Thomas, 1996). In addition,

the RBV has provided leverage into specialised research areas such as knowledge,

innovation, technology, strategic leadership and strategic decision theory (Hoskisson et

al., 1999).

Figure 2.1: The Four Stages of Strategy (Adapted in part from Hoskisson et al., 1999, p.

421).

The Resource Based View

Theorists: Penrose (1959) Wernerfelt (1984) Barney (1991) Peteraf (1993) Perspective: Internal Central Concepts: resources, capabilities, factor markets, competencies, dynamic capabilities, heterogeneity

Organisational Economics

Theorists: Williamson (1975,1981) Jensen (1976) Perspective: Internal and External Central Concepts: transaction costs, contracts, governance, agency costs, opportunism, bounded rationality, principal, agent

Industrial Organisation Economics

Theorists:Mason (1939) Bain (1956, 1959) Porter (1980,1985) Perspective: External Central Concepts: mobility barriers, economies of scale, industry structure, conduct, performance, firm size, production, homogeneity

Early Development

Theorists: Chandler (1962) Ansoff (1965) Learned etal (1969) Penrose (1959) Perspective: Internal & External Central Concepts: strategy, structure, “best practices”, resources, SWOT Analysis

30

2.1.5 Discussion Given that the 20th century has witnessed rapid industrial and economic change, it is not

surprising that the field of strategy has evolved to account for the altered circumstances

affecting business and industry. In recent decades, the strategic implications stemming

from a rapidly changing global environment have resulted in re-examination and analysis

of the competitive and structural arrangements of firms, industries, economies, markets,

cooperative arrangements, resources and capabilities.

As Figure 2.1 demonstrates, considerable deviation can be observed across the content

emphasis placed by the different fields of Early Development, Industrial Organisation

Economics, Organisational Economics and the Resource-Based View.

Albeit the seeming disparity between the stages defined in this brief review of strategic

management, a common presumption of all approaches has been toward identifying the

critical factors that contributed to the capacity of a firm to generate superior returns over

rivals, otherwise referred to as competitive advantage.

Within the strategy literature, two prominent yet distinct models exist to explain

sustainable competitive advantage in strategic management literature. The first is

embedded in neoclassical economics and is more definitely explored within the literature

on industrial organisation (Bain, 1956; Porter, 1980, 1981). The second perspective is

derived from the resource-based view of the firm (Barney, 1986; Wernerfelt, 1984).

31

2.2 COMPETITIVE ADVANTAGE

‘Attaining a competitive position or series of competitive positions that lead to superior

and sustainable financial performance’

(Porter, 1991, p.96)

Two distinct branches of strategic management discourse provide inside into the

foundations of competitive advantage. The first, Industrial Organisation, was reviewed

within the previous section of this chapter, but is explored here in greater detail. The

second perspective to be reviewed here is the resource-based view of the firm (RBV),

which offers a contrasting view as to the basis of competitive advantage.

2.2.1 Industrial Organisation Strategic management as a distinct discipline exists to build theory and test prediction

concerning the imperatives required for organisational success and failure (Rumelt et al.,

1991). Historically, the economic tradition has provided significant frameworks and issued

methodologies appropriate for such activities. In this respect, the economic tradition has

contributed substantially to the development of the strategy field, constituting a corner

stone of much of the research and literature generated in the field following inception. Of

relevance in this work is the collective field of Industrial Organisation, found within

neoclassical economic theory.

Figure 2.2: The Economic Tradition

Foundations of the Economic Tradition

Neoclassical Theory

Mason-Bain

IO

Schumpeterian Innovation

Chicago Revisionist

School

Williamson Transaction Cost

Economics

Competitive Dynamics

Evolutionary Theory of Growth Competitive Theory Advantage Game Theory

Network Theory

1930s-1950s 1960s 1970s 1980s-1990s

32

An Industrial Organisation economics perspective represents one of the most prominent

paradigms used to investigate competition in particular industries. Two distinct periods

can be observed in the Industrial Organisation school, often referred to as (classic)

Industrial Organisation and the new IO (Hoskisson et al., 1999, Rumelt et al., 1991,

Williamson, 1996). Porter (1981) posits the conceptual differences underlying these

periods to include unit of analysis, determinism, and a re-evaluation of the veracity of the

traditional structure-conduct-performance paradigm.

2.2.1.1 Classic Industrial Organisation Theory

Industrial Organisation economics theory, conceptually developed by Mason (1939) and

Bain (1956, 1959), and later adapted by Porter (forming the New IO) (1979, 1980, 1981,

1985), suggests that firm performance is critically determined by the idiosyncratic

characteristics of industry structure. According to the logic prescribed by this perspective,

the structure of an industry determines firm behaviour, culminating in the collective

performance of firms in the marketplace, as articulated by Mason and Bain in the

structure-conduct-performance (SCP) paradigm. As a consequence of the predominance

of industry structure as the initial and greatest moderator of firm performance within this

perspective, firm conduct, or strategy, is largely ignored by this tradition (Porter, 1981).

A defining and relatively enduring component of the early IO perspective has been the

contention of homogeneity across products, consumers, information, demand and

organisations. This contention is largely based on the assumption that little long term

variation exists between organisations due to the high mobility of resources (Spanos &

Lioukas, 2001). Therefore firms pursue maximisation of economic rents through the

factors of production, which the IO perspective posits are relatively homogeneous

between firms. Similarly, demand for products is postulated to be homogeneous between

industries, as are consumer preferences for product features and characteristics. Perfect

information is likewise argued to be homogeneous and costless, readily available to both

producers and consumers (Sampler, 1998).

Particularly accentuated within the traditional SCP paradigm is the relevance of firm size

and industry concentration. Large organisations are said to obtain profit and structural

advantage derived through the interplay of industry entry barriers and concentration

levels, supporting environments where collusion, oligopoly or monopoly can foster

33

(Martin, 1993, Mason, 1939). Conner (1991) contends that the resulting restriction in

competition generated through monopolistic practices by some organisations serves to

artificially inflate the market value of goods offered, therefore increasing the profit margin

between cost and sale prices for firms.

Figure 2.3: The Traditional Mason-Bain Structure-Conduct-Performance (SPC) Paradigm

(Porter, 1981)

The principle premise of the IO economic school of thought was in the allocative

efficiency of economies, which translates into an emphasis upon the collective entity of

industry, to the exclusion of the individual organisation (Porter, 1981). Further, this

school, not unlike other economic schools of thought, subscribes to the view of prevailing

rationality as the mechanism upon which firm behaviour is based (Nelson, 1991). This

perspective views market environments as stable and static, therefore positioning the

early IO school of thought within a fixed scope of application.

2.2.1.2 The New IO

The work of theorists such as Caves, Hunt and Porter in the 60s, 70s and 80s triggered

continued interest in the IO field. Challenging many of the traditional assumptions

articulated by earlier theorists, the conventional notion of monopolistic and oligopolistic

firm behaviours were relaxed, as were assumptions of firm homogeneity (McKiernan,

1997). As a consequence, the central focus on industry structure as the precursor to

performance outcomes by firms as articulated by the classic industrial organisation and

traditional SCP paradigm was in large relinquished. This focus was replaced by a

reorientation to firm-level factors within the broader context of industry activity (Barney &

Ouchi, 1986, Porter, 1981, Rumelt et al., 1991).

In light of the changing frame of reference in the industrial organisation school, and given

the growing objections to the theoretical validity of the traditional SCP paradigm (for

Industry Structure Performance

Conduct (Strategy)

34

instance, the suggestion that the strategic choices [conduct / strategy] made by firms do

not influence performance outcomes), a new conceptualisation of the SCP paradigm was

developed. This new conceptualisation, whilst maintaining features of the classic

paradigm, suggests the integrative and interdependent nature of industry structure,

conduct (strategy) and performance, as indicated in Figure 2.4.

Figure 2.4: An Updated Version of the Industrial Organisation Paradigm (Porter, 1981).

This paradigm is distinguished from its traditional purpose of guiding microeconomic

policy by subsequent developments in microeconomic theory which focus on the firm

rather than the industry. This perspective has advanced to become a collective paradigm

which has been informed by theories developed in agency and transaction cost

economics, business strategy, team production, and evolutionary theories of the firm

(Barney & Ouchi, 1986, Donaldson, 1995).

An additional benefit to arise from the new IO was the recognition that the heterogeneity

identified between firms could define discrete similarities and differences in organizations

according to firm-specific variables within the industry, thus providing the basis for intra-

industry stratification. These collections of firms were initially identified by Hunt (1972) in

a study of the white-goods industry. This recognition of a method of intra-industry

stratification and the resulting collections of firms became known as ‘strategic groups’

and has become a staple of strategic management theory and research. Prominent

studies have included utilizing the strategic group paradigm to investigate performance

differentials between firms and intra-industry rivalry, among other research agendas

(Cool & Schendel, 1988; Mascarenhas, 1989; Cool & Dierickx, 1993; Peteraf, 1993;

Smith, Grimm, Wally & Young, 1997). An important inclusion into the strategic group

theoretical and research debate concerned the proposition posited by Caves and Porter

Industry Structure

Performance Conduct (Strategy)

35

(1977) who suggested that rivalry between strategic groups would be greater than

competition within strategic groups. The underlying logic of this contention was that those

firms occupying the same strategic group would be more inclined to deploy the same

strategic intent, and through either overt or implicit collusion direct their competitive

intentions to those firms found in other strategic groups in order to obtain greater market

share and economic gains. Multiple research investigations have been conducted in

order to try to prove or disprove this hypothesis (eg., Peteraf, 1993; Cool & Dierixkx,

1993), however at this stage a conclusive outcome to this proposition is yet to be

determined.

Despite the substantive gains made in addressing many of the limitations of the classic

industrial organisation school, the new IO is faced with significant challenges in

articulating a cogent and problem-free framework for strategy research (Porter, 1981).

One such challenge lies in developing a dynamic model of competition, however

meaningful research has emerged in recent years addressing multi-market competition

and competitor action-reaction studies (Baum & Korn, 1996, Gimeno & Woo, 1999,

Grimm & Smith, 1997).

2.2.1.3 Industrial Organisation and Competitive Advantage

In contrast to early strategy discourse where Selznick (1957), Andrews (1971) and

Chandler (1962) collectively implied that the source of competitive advantage was

internalised within the firm, Classic Industrial Organisation contends that competitive

advantage is derived from sources external to the organisation. The New IO, while

testifying to many of the central tenets of the Classic paradigm, makes greater

allowances for the relevance of firm heterogeneity.

The classic industrial organisation perspective contends that competitive advantage (in

this instance referring to superior performance and profit) is achieved through the

command of monopolistic power or colluding behaviours between large firms within an

industry (Bain, 1959). Superior rents are generated through curtailing production to

artificially increase market price, thereby augmenting the profit potential of the firm. The

traditional SCP paradigm therefore suggests that economic performance (firm

profitability) serves as a function of industry structure (including barriers to entry, vertical

integration, number of buyers and sellers, product differentiation, degree of fixed versus

36

variable costs) which collectively influence the capacity for firms to achieve competitive

advantage within a specific industry (Conner, 1991).

Arguably a paradox in the theoretical logic of the classic industrial organisation school

lies in the fundamental discrepancy which exists between the contention that conduct

(firm strategy) is irrelevant due to the relative influence of industry structure on

performance outcomes. This contention is in direct contrast to the overt suggestion that

competitive advantage is inherently linked to the deliberate intent of firms to engage in

either monopolistic or collusive behaviours (implying some measure of conduct / strategy

articulation by firms that clearly does influence outcomes). Another point of divergence is

the implicit suggestion that enduring above-normal returns are possible and are

inherently linked to the limited types of heterogeneity between firms such as entry

barriers and firm size (Conner, 1991). This conclusion is in opposition to claims made

regarding the classical IO perception of homogeneity across products, consumers,

information, demand and organisations. This contention is largely based on the

assumption that little long term variation exists between organisations due to the high

mobility of resources (Spanos & Lioukas, 2001). However, given the advent of market

power through monopolistic or collusive arrangements between firms, some variability

between firms must exist.

Drawing on the somewhat diverse origins of the economic tradition, and in recognition of

the classical IO perspective, the new IO bears the hallmarks of these theoretical positions

in discussing the sources of competitive advantage. It is recognised, however, that some

central tenets of the classic IO perspective have been abandoned.

The current perspective of competitive advantage in IO is largely attributed to Porter

(1980, 1985). Consistent with the majority of prior economic theories, Porter maintains

outward focus toward the macroeconomic environment (market-driven factors or industry

structure) of the firm as the primary instigator of competitive advantage. Incorporated

into this perspective is firm behaviour and mobility, thus departing from the conventional

monopolistic and oligopolistic thinking characterising earlier classic IO theories.

Consequently, the assumption of firm homogeneity has been replaced with

heterogeneity.

37

The foundation of Porter’s work (and thus reflective of the new IO) encompasses three

aspects:

1. Industry structure (the ‘five forces’ model)

2. Generic strategies; and

3. The value chain framework

Porter’s work maintains the focus on industry structure as the predominant determinant of

competitive advantage. The five forces model is concerned with the external competitive

environment, identifying five forces that influence competition – the culmination of which

determine the level of rivalry evidenced within the industry (this framework will be

examined in greater detail in section 2.3 of this chapter) (Porter, 1980). The next advance

in theory attributed to Porter was the articulation of ‘generic strategies’ that broadly

encompassed all the different strategic options that could be pursued by firms.

Generic strategies focus on the position a firm must adopt in order to remain

competitively viable in any given industry, and departs substantially from the classic IO

presumption that independent managerial decisions are irrelevant. Porter contends that

despite the range of competitive circumstances that could affect the firm, only three

generic strategies are applicable or necessary to counter these competitive forces and to

position the firm advantageously within the industry. These generic strategies cover cost

leadership, differentiation and focus (Porter, 1985).

The final component is the value chain framework. Porter (1990) suggests that firms

must organise and perform idiosyncratic primary and secondary activities which comprise

the value chain in any organisation. The value chain framework focuses on the bundle of

activities that the firm must perform well in order to gain superior value and performance

and is argued to serve as the basis upon which competitive advantage can be achieved

within any given industry (Porter, 1985). The collective outcome of these three

models/frameworks reflect the current and popularised understanding of competitive

advantage within the New IO.

Despite Porter’s work in respect to generic strategies and the value chain framework

(1985), the IO perspective still initially advocates attention to industry structure above

other considerations. Whilst firm heterogeneity is considered an accepted and enduring

38

component of the IO paradigm as it is understood today, it remains overshadowed in

comparison to the broader concerns of the industry environment when determining the

capacity for firms to achieve competitive advantage. This approach lies in direct contrast

to the second model of competitive advantage to be examined: the resource-based view

of the firm.

2.2.2 The Resource-Based View of the Firm Early contributors to the RBV paradigm include Selznick (1957), Chandler (1962), Ansoff

(1965) and Learned et al (1969) who collectively implied some measure of internalisation

of the sources of competitive advantage within the firm. However, the conceptualisation

of organisations as unique collections of heterogeneous resources bundled together and

able to deliver variable production is attributed to Penrose (1959). With renewed

popularity and revision, the conceptual basis of the RBV was reintroduced in the 1980s,

(Wernerfelt, 1984) and combines internally and externally focused theories including

organisational behaviour, industrial organisation and transaction cost theory (Collis, 1991,

Maijoor & Witteloostuijn, 1996, Majumdar, 1998, Wernerfelt, 1984). The focal level of

analysis distinguishes the resource-based paradigm from others, in that the firm is

considered to be heterogenous (Mahoney & Pandian, 1992, Maijoor & Witteloostuijn,

1996).

One of the principal and most basic assumptions underlying the resource-based

paradigm is the concept of firm heterogeneity (Mahoney & Pandian, 1992, McGrath,

1995, Peteraf, 1993a). While firm heterogeneity has been acknowledged in prior

theoretical treatments such as neoclassical and industrial organisation theories, it has

often been discounted as a viable source of advantage as opposed to the imperatives of

industry and market structure (Lado et al., 1992). These theories have traditionally

adopted the perspective that above normal returns can be largely explained through

analysis of industry effects on firms in competition (McGrath, 1995). The shift from this

structural perspective of strategy to the resource-based paradigm has necessitated the

reappraisal of the sources of superior performance, particularly those idiosyncratic to

firms (Miller & Shamsie, 1996). As a consequence, factors that contribute to the

differences between organisations must be considered, including resources, capabilities

and competencies. Adoption of this approach necessarily requires focus toward factor

39

market resource flows, away from the traditional perspective of product market obstacles

to competition (Mehra, 1996, Wernerfelt, 1984).

In order to provide a critique of the RBV, three central elements of the paradigm will be

discussed, including firm heterogeneity, resources and organisational capabilities.

2.2.2.1 Firm Heterogeneity

Firm heterogeneity is the recognition that firms are intrinsically different in respect to the

characteristics that define their existence. Unique historicity, social complexity, variable

rationality, causal ambiguity, tacit knowledge and future uncertainty are distinct

mechanisms typical to every firm, however in fundamentally disparate conditions (Maijoor

& Witteloostuijn, 1996, Lippman & Rumelt, 1982). These collective mechanisms affect

behavioural and social phenomenon inside a firm such that organisations, over time,

develop and sustain internal dynamic routines, pools of tacit knowledge, culture and

interpretation systems (Barney & Zajac, 1994). Managerial prerogatives, organisational

structure, resource deployment choices, acquisitions from factor markets and strategy

choices are influenced as a result, therefore establishing unique collections of resources

and capabilities underlying production across all firms (Barney, 1991, Peteraf, 1993).

2.2.2.2 Resources

Wernerfelt (1984) proposes that both intangible and tangible assets must be considered

resources of the firm, some of which, among others, may include machinery, intellectual

property, human resources, brand names, culture, technology, efficient procedures,

distribution channels, financial capital and organisational processes (Maijoor &

Witteloostuijn, 1996, Miller & Shamsie, 1996, Wernerfelt, 1984). As not all resources are

readily available in factor markets, differences exist across resources available to firms

within an industry, thus creating the basis for heterogeneity to arise between firms.

Sanchez, Heene and Thomas (1996) contend that over time, the interplay between

tangible and intangible resources generates organisational capabilities through which the

competitive agility of the firm is heightened.

2.2.2.3 Organisational Capabilities

Capabilities represent the accumulated ability of the firm, derived over time from complex

interactions between an organisation’s tangible and intangible resources, enacted by

40

employees of a firm through the development, transmission and exchange of information

and knowledge (Sanchez et al., 1996). These resources in sum constitute ‘a nexus or

bundle of specialised resources that are deployed to create a privileged market position’

for the firm (Lado et al., 1992, p. 78, Wernerfelt, 1984). Due to the inherent differences in

tangible and intangible resources across organisations, capabilities are considered to be

idiosyncratic, and therefore unable to be replicated by other firms. Organisational

capabilities are thus understood to broaden differentiation between firms, and further

contribute to the variable performance evidenced between firms within an industry.

2.2.2.4 Discussion

The underlying premise of the resource-based paradigm is the proposition that over a

period of time firms accumulate idiosyncratic combinations of skills and resources which

facilitate the collection of rents on the basis of ‘distinctive competencies’ (Selznick, 1957).

Such assets, unable to be replicated, purchased, substituted or stolen, create the

foundation for advantage (McGrath, 1995, Peteraf, 1993a). Prahalad and Hamel (1990)

and Nelson and Winter (1982) posit that the most cogent of these resources are those

that reside in a firm’s collective tacit knowledge, inhibiting ready replication and

homogeneity across firms due to path dependencies and causal ambiguity (Prahalad,

1990, Nelson & Winter, 1982, Collis, 1994). It is on the basis of these organisational

attributes (resources, capabilities and competencies), that firms are said to generate the

capacity to achieve competitive advantage.

2.2.2.5 The RBV and Competitive Advantage

Within RBV theory, two similar models exist to explain the capacity of firms to achieve

sustained competitive advantage. The first model, proposed by Barney, articulates the

characteristics necessary for resources to deliver advantage to firms (Barney, 1991). The

second model, by Peteraf, signifies the first attempt to offer a unifying framework for

sustainable competitive advantage within the resource-based paradigm (Peteraf, 1993a).

In the first model, Barney (1991) suggests that ‘resources must be difficult to create, buy,

substitute, or imitate’ in order to contribute superior returns to the firm (Miller & Shamsie,

1996, p. 520). Barney (1991) posits that for resources to contribute to sustainable

competitive advantage they must fulfil four criteria: they must be imperfectly substitutable,

rare, valuable and inimitable. These resource characteristics act as a reflection of

41

imperfect factor markets, where access to and the capacity to trade in specific resources

is impaired (Dierickx & Cool, 1989). Rumelt (1984) suggests that these resources then

act as ‘isolating mechanisms’ which prevent the opportunity for competitors to derive rent

from these already monopolised resources. As a consequence, firms develop advantage

based on ownership or access to these resources, capabilities and competencies, which

cannot be replicated by other firms within the industry.

The second model, developed by Peteraf (1993a), contends that four conditions are

necessary to achieve competitive advantage within the resource-based paradigm. These

conditions include heterogeneity, ex post limits to competition, imperfect mobility, and ex

ante limits to competition.

Within this model, the concept of heterogeneity assumes that, despite the fundamental

differences that exist across the resource base of organisations, firms are still able to

participate in the market (Peteraf, 1993a). One of the primary objectives of all

organisations is to secure income in the form of rents, which may be richardian or

monopolistic in nature. Despite the type of rent accrued, heterogeneity across firms must

remain in order to maintain sustained competitive advantage and to achieve ex post limits

to competition.

Imperfectly mobile resources are those that can be exchanged but are of more value

within the firm than they would be in other employ due to semi-specialisation, firm

specificity or existence as cospecialised assets. Ex ante limits to competition explains the

presence of limited competition for a market position prior to any organisation’s founding

a superior resource position. By this Peteraf (1993a) refers to imperfections that exist in

strategic market factors that ensure that a firm is able to achieve above normal returns.

Traditionally the relevance of theoretical paradigms in strategy research has been

dependent on the capacity of theory to explain competitive advantage, or at minimum

divulge the basis upon which the sources of competitive advantage may be derived.

Peteraf’s model demonstrates the capacity of the resource-based paradigm to elicit a

framework of advantage, while Barney’s (1991) work articulates the necessary

characteristics that resources must fulfil in order to provide a source of advantage for the

firm. These authors deliver a cogent argument supporting the inherent rationale of the

RBV to explain competitive advantage.

42

Despite this, the theoretical and empirical validity of the resource-based view of the firm

has been subject to considerable debate in strategic management literature. On one

level, the conceptual foundations underpinning the paradigm have been charged with

failing to elicit a cogent theoretical structure which can support empirical research (Priem

& Butler, 2001). On another level, the paradigm has been charged with adopting an

exclusionary position in analysis of resources and capabilities to the detriment of product

market variables and influences. This has led some theorists to question the efficacy of

resources as a source of sustainable competitive advantage (Collis & Montgomery,

1997).

At a much broader conceptual level, debate surrounds the proposition that resources and

capabilities may serve as the basis of sustainable competitive advantage (Collis, 1991,

Collis, 1994, Collis & Montgomery, 1997). Barney does acknowledge that to derive a

complete model of strategic advantage it would be necessary to integrate both product

market and factor market models. However the purpose of the resource-based view is to

encapsulate the supply oriented competitive environment previously under-examined in

strategic management literature (Barney, 2001).

As a consequence of these debates, and due to the lack of significant levels of empirical

validation, the cogency of the RBV approach to competitive advantage is weakened.

Clearly this paradigm suffers from a lack of attention to industry and product market

considerations, which have been empirically proven in IO research to contribute to the

capacity of an organisaiton to achieve advantage. Despite these limitations, however, the

resource-based rationale to competitive advantage remains compelling in contemporary

strategy literature.

2.2.3 Discussion As demonstrated by both paradigms, differences exist between how competitive

advantage is perceived and approached by each perspective. According to the logic of

industrial organisation economics, competitive advantage is principally attained through

attention to factors external to the organisation, such as industry structure (Porter, 1980).

Incorporated into this view is consideration of firm strategy and value chain organisation,

however the emphasis still remains on industry structure as a fundamental determinant of

competitive advantage.

43

Alternatively, the resource-based view contends that the manifestation of competitive

strategy and pursuit of competitive advantage is idiosyncratic to each organisation, if

indeed competitive strategy is derived from an organisation’s stock of resources and

capabilities (Lado et al., 1992, Miller & Shamsie, 1996, Wernerfelt, 1984). The RBV

considers the heterogenous nature of organisations as the predominant determinant of

competitive advantage within the broader context of the industry environment.

Figure 2.5: Conceptual Differences in Perspectives and Sources of Competitive

Advantage

As demonstrated by Figure 2.5, the contrary emphasis placed on industry structure and

the firm clearly distinguish the conceptual differences that underlie the determination of

the sources of competitive advantage across the IO economic and RBV perspectives.

Common to this investigation of competitive advantage, however, has been the collective

perception that competition, whether in supply or product markets, represents the most

profound obstruction to firms attaining advantage.

As a consequence of the divergence in theoretical tenets, the IO economic and RBV

posture competition and rivalry according to different models, frameworks and

conceptualisations. However, as observed in the following section, translation of the

theoretical merit associated with these tenets does not easily convert to models,

frameworks or conceptualisations of rivalry that can be readily applied to the study of

intraindustry rivalry.

THE FIRM

THE FIRM

INDUSTRY STRUCTURE

INDUSTRY STRUCTURE

INDUSTRY STRUCTURE

THE FIRM

INDUSTRIAL ORGANISATION THE RESOURCE-BASED VIEW

44

2.3 COMPETITION AND RIVALRY

‘The essence of strategy formulation is coping with competition’

(Porter, 1979, p.137)

Knowledge of the determinants of competition and the nature of rivalry within an industry

is of paramount importance in the articulation of effective strategy. Who competes with

who is becoming an increasingly difficult question to answer due to the erosion of clearly

defined market boundaries, improved communication and production technologies,

deregulation of industries and the increasing numbers of global competitors (Garcia-Pont,

1992, Porter, 1986). These issues collectively act to reduce the capacity to which the

foundations of rivalry and competitive dynamics in industry environments can be readily

defined. This in turn enhances reliance on models or frameworks of competition.

However, the changing competitive landscape does not constitute the only concern for

theorists and practitioners of strategy. Many theorists argue that the very mechanisms

upon which competition is enacted are undergoing subtle yet profound change.

Traditional and recognised mechanisms of competition include price, distribution,

marketing and the use of superior technologies. Recent theoretical and empirical

evidence would suggest that while emphasis still remains on these conventional

competitive mechanisms, the locus of rivalry has shifted to collaborative orchestration

between organisations ( Lazzarini, 2007; Ahuja, 2000, Blankenburg Holm et al., 1999,

Chung, 1993, Dyer, 1997, Gomes-Casseres, 1996, Haugland & Gronhaug, 1996,

Vanhaverbeke & Noorderhaven, 2001). Given the proliferation of collaborative ties

between organisations in recent decades (Burgers et al., 1993, Colombo, 1998, Gulati,

1998), it becomes imperative that how competition is understood and interpreted within

theory and practice recognises both the traditional mechanisms of rivalry in conjunction

with collaborative dimensions.

As suggested in the prior section on competitive advantage, IO economics and the RBV

provide the basis upon which two streams of argument emerge as to the basis of

competitive advantage in industrial settings. Whilst IO economics positions competition

as predominantly a function of the characteristics of industry structure, the RBV contends

that rivalry is largely an outcome of demand and supply of resources and capabilities in

factor markets.

45

Within the strategic management field, a range of analytical models and frameworks exist

upon which the competitive dynamics within industries can be understood. According to

Furrer and Thomas (2000) (and as demonstrated in Figure 2.6), the most prominent of

these include oligopoly theory (Shapiro, 1989), game theory (Camerer, 1991), scenario

analysis (Schoemaker, 1993), ‘warfare’ models (Chen, 1996), simulation and system

dynamic models (Warren, 1999) and framework approaches (Porter, 1980). In addition,

the RBV, via the work of Sanchez, Heene and Thomas (1996), provides an alternative

conceptualisation of rivalry.

Figure 2.6: The Rivalry Matrix (Furrer & Thomas, 2000, p. 620)

This section reviews these models, frameworks and conceptualisations of rivalry,

providing a critique of their relative strengths and weaknesses in application. The

purpose of undertaking this broad review is to provide the theoretical grounding upon

which rivalry is enacted later within this research.

2.3.1 Models of Rivalry and Competitive Dynamics

2.3.1.1 Oligopoly Theory

Concerned with the outcome of competitive action/response interactions by firms in an

industry, oligopoly theory seeks to address the spectrum of firm activity that lies within the

economic extremes of pure competition and monopoly (Porter, 1981). Unlike models of

competition which suggest that unique equilibriums can be demonstrated in the market,

Decision Variables

Few Many

Unc

erta

in

Pre

dict

able

Nat

ure

of th

e En

viro

nmen

t

Game Theory

(eg. Camerer, 1991; Oster, 1999)

Scenarios, Simulation, and Systems Dynamics

(eg. Porter and Spence, 1982; Mezias and Eisner, 1997)

Warfare Models, Multipoint Competition

(eg. Karnani and Wernerfelt, 1985; Chen, 1996;

D’Aveni, 1994)

Frameworks

(eg. Porter, 1980, 1991)

46

oligopoly theory suggests that such equilibriums are unlikely, given the influence of

individual firm strategy choices and contingency factors. Rather, oligopoly theory

contends that each firm, in order to secure maximum profit, is tempted to aggressively

compete with rivals. However, such action would threaten the viability of each firm in the

market, such that profit maximisation, as a goal, would suffer. Shapiro (1989) suggests

that without any explicit structure, firms acting in rivalrous interaction can tacitly support

collusive behaviours, in such a way that collusive industry configurations may result.

Unlike other models of competition, oligopoly theory considers collaboration as a

mechanism of rivalry, and postures this assumption within its framework. The relative

success of applying this theory to the study of competition is however difficult.

Encompassing a broad set of variables and complexity in application, oligopoly theory is

generally considered regulated to use by established economists and the academic

community.

2.3.1.2 Game Theory

Game theory is preoccupied with assessing the rationality of decision-makers, according

to conflict and cooperation options (Dixit & Nalebuff, 1991). Used to understand the

projected behaviour of firms in response to the competitive actions of other organisations,

game theory postures scenarios in which the interdependence of outcomes is monitored

– the outcome is dependent on the choices made by the decision-makers (Camerer,

1991). Two types of games exist; rule-based games whereby ‘rules of engagement’ must

be observed, and ‘freewheeling games’ where no rules are applied (Furrer & Thomas,

2000). Despite game theory’s success in generating suitable outcomes for organisations

when applied in respect to acquisitions, bidding and negotiations (Oster, 1999), several

limitations exist in respect to application and it’s usefulness for business. Furrer and

Thomas (2000) contend that these limitations include restrictive assumptions (assumed

rationality of decision-makers, financial consequences and finite choices), prompting

problems in applying game outcomes to unpredictable environments and large numbers

of players.

2.3.1.3 Scenarios, Simulations and System Dynamic Modelling

Scenarios, simulations and system dynamic modelling (Schoemaker, 1993, Warren,

1999) are ‘based on the study of interaction between a limited number of known variables

in situations of uncertainty, interdependence, and complexity’ (Furrer & Thomas, 2000, p.

47

620). Scenarios consist of the presentation of radically contrary forecasts in a narrative

approach (Schoemaker, 1993), whereas simulations analyse the outcomes of divergent

strategies. System models (Warren, 1999) operationalise cause and effect relationships

to understand the interplay between variables using feedback loops and networks (Furrer

& Thomas, 2000). These models are predominantly useful in appraising the influence of

environmental factors and in predicting the possible consequences of rival’s moves on

the organisation’s strategy (Furrer & Thomas, 2000).

2.3.1.4 Warfare Models

Warfare models draw their foundation from military strategies where the capacity to

constantly disturb the competitive ‘playing field’ to produce unconventional environments

is the objective. Warfare models contend that organisations who are adept at changing

the competitive playing field will outperform rivals (Furrer & Thomas, 2000). Multipoint

competition is one such model which acts to predict the potential competitive behaviour

of rivals within defined markets that are presumed stable (Chen, 1996). Much current

research can be found in the realm of multipoint competition and also in the field of

action-reaction studies.

2.3.1.5 Limitations

The rivalry matrix (Furrer & Thomas, 2000), presented in Figure 2.6, provides a

schematic representation of the relevance of different models and frameworks, according

to the number of decision variables and environmental conditions. The normative worth of

each model defined is dependent on the assumptions characterising the model and how

reflective these assumptions correspond with reality. Each model employs only a

restrictive set of variables into analysis, therefore reducing the relevance of model

outcomes to a definitive range where model assumptions correlate to industry

characteristics (Furrer & Thomas, 2000, Porter, 1991). As a consequence, this inhibits

the veracity of the outcomes obtained through application of the various models in

undertaking analysis of rivalry within an industry.

.

Within the rivalry matrix (Figure 2.6), frameworks are identified as demonstrating a

broader scope of application across uncertain environments and many decision

variables. Recognised within this matrix is the framework proffered by Michael Porter

(1980), which is theoretically embedded in the IO economic school of thought.

48

2.3.2 Frameworks of Rivalry and Competitive Dynamics 2.3.2.1 Porter’s Five Forces of Competitive Rivalry The traditional method of assessing industry rivalry derived from traditional IO literature is

Porter’s Five Forces of Competitive Rivalry which provides linkage between the economic

notions of competition and rivalry (Davis & Devinney, 1997). Developed during the late

1970s, Porter’s five forces framework reflected the shift in strategy research when

attention to the firm-derived sources of competitive advantage were displaced by

concentration on external environmental conditions. At this time the focus lay in

determining the environmental constraints to a firm attaining competitive advantage and

achieving profit maximisation.

Porter’s model incorporates influences found in typical industry structures, including

supplier/buyer power, threat of substitutes and industry entry. The agglomeration of these

Figure 2.7: Forces Driving Industry Competition (Porter, 1980, p. 4). Additional information obtained from Porter’s Five Forces of Competitive Rivalry (Grant, 1998, p. 58)

THREAT OF NEW ENTRANTS •Economies of Scale •Absolute Cost Advantages

•Capital Requirements •Product Differentiation • Access to Distribution Channels •Government and

Legal Barriers •Retaliation by Established Producers

BARGAINING POWER OF BUYERS •Price Sensitivity •Cost of product relative to total cost •Product Differentiation •Competition between Buyers •Size and concentration of buyer relative to suppliers •Buyers’ switching Costs •Buyers’ Information

BARGAINING POWER OF SUPPLIERS •Size and concentration of suppliers relative to buyers •Competition between Suppliers •Product Differentiation •Supplier switching Costs •Suppliers’ ability to forward integrate •Suppliers’ Information

THREAT OF SUBSTITUTE PRODUCTS OR SERVICES

•Buyer propensity to substitute •Relative price performance of

substitutes

RIVALRY AMONG EXISTING COMPETITORS

•Concentration •Diversity of Competitors

•Product Differentiation •Cost Conditions •Excess Capacity & Exit

Barriers

49

influences is said to indicate the nature or intensity of rivalry in the industry and the

constraints under which a firm will compete (see Figure 2.7).

As evidenced by Figure 2.7, various aspects of the industry environment can either

contribute or limit the capacity to which a firm may maintain competitive agility. The threat

of new entrants is determined by the structural and economic barriers that an incumbent

must overcome in order to enter and participate within a particular industry. These

barriers may consist of an entrant’s propensity to achieve economies of scale, the

absolute cost advantages of established competitors, and the capital requirements

associated with industry entry. Additional barriers to entry may include the extent of

product differentiation inherent in the industry, likely access to distribution channels,

government or legal requirements associated with operation, and the likelihood of

retaliation by established producers.

Within this framework, supplier power is dependent upon the level of competition

between suppliers. This may incorporate consideration of the switching costs for

suppliers or firms, whether products supplied are differentiated in nature, and the

likelihood of forward integration by the supplier into the market arena. The size and

concentration of suppliers relative to buyers may further affect supplier power, as may the

extent of information the supplier has access to (Porter, 1980).

Alternatively, buyer power is associated with the price sensitivity of consumers, the

degree of differentiation in the product or service provided by producers for sale within

the industry, the relative cost associated with buyer’s switching between different product

offerings and the ability of the buyer to backward integrate in terms of producing the

product or service themselves rather than purchasing the product or service from industry

producers. In addition, buyer power is influenced by the product cost relative to the entire

cost, the size and concentration of the buyer relative to the supplier, available

information, and the degree of competition between buyers (Porter, 1980).

The threat of substitutes refers to the availability of substitutes within the market and

whether such substitutes offer a credible threat to potential revenue generation. The

threat of substitutes is affected by the price performance of available substitute products

and services, and the propensity of buyers to switch between alternative products or

50

services dependent upon the characteristics associated with the product and its relative

price performance (Porter, 1980).

Industry rivalry, according to Porter’s framework, is dependent on competitor

concentration within an industry, and how this concentration defines market share and

the potential for achieving above average returns. The capacity of competitors to

compete on levels other than price is reliant on the diversity of competitors, and whether

distinctions can be determined in relation to costs and strategies (Porter, 1980).

Competition within the industry is further stimulated by the level of product differentiation,

and whether the products or services offered by firms can be distinguished from each

other, or share the same characteristics such that they are considered interchangeable

commodities whereby price becomes the only method of competition. Excess capacity,

where price competition is the only means of expelling excess stock, can influence the

level and intensity of rivalry within an industry. Finally, barriers to exit – the cost

associated with leaving the industry – can compel competitors to remain within the

industry, and heighten competition due to the inability of firms to leave the industry

without incurring substantial costs (Grant, 1998, Porter, 1980).

2.3.2.2 Limitations

Porter’s framework possesses limitations derived from both the theoretical position

adopted and in regard to the omission of relevant variables. Unless a potential entrant

has perfect knowledge (which is unlikely), it is difficult to adequately assess the chances

associated with successful entry and profit achievement. In addition, the framework,

despite offering insight into the context of industry environments, cannot be appropriately

operationalised, and fails to recognise the industry as dynamic in nature. Another

limitation of this framework relates to its inability to incorporate collaborative

interorganisational relationships into assessment of rivalry. Given the propensity for firms

to engage in these relationships as a means to navigate the competitive environment,

these relationships could be construed to constitute forces which influence the

competitive interaction of firms in industry environments.

Porter’s Five Forces of competitive rivalry can therefore be defined only in terms of a

framework for analysis, rather than a predictive or causal model of industry dynamics.

Successful application of the framework is dependent on the knowledge, skills and

51

abilities of the practitioner in respect to the specific industry at the centre of analysis. Like

other frameworks derived from economics, Porter’s five forces assumes that all strategic

choices are known, and that the ultimate choice will result in the maximisation of positive

outcomes.

2.3.3 Conceptualisations of Rivalry and Competitive Dynamics

2.3.3.1 Competence-Based Competition

Sanchez, Heene and Thomas (1996) contend that the conceptualisation of competence-

based competition is ‘intentionally dynamic, systemic, cognitive, and holistic’ (Sanchez et

al., 1996, p. 11). This conceptualisation incorporates the logic underlying the resource-

based paradigm as evidenced by the inclusion of resources and capabilities and adoption

of an open system perspective of firms, through which, the authors argue, the formation

of intentional strategic goals is developed (Sanchez et al., 1996).

Figure 2.8: The Hypothesised Relationship Between Organisational and Environmental Variables in Determining Industry Structure in the Competence-Based Paradigm (derived from Sanchez et al., 1996).

Within this conceptualisation, each firm deploys both firm-specific (those resources

owned by the firm) and firm-addressable resources (those resources accessible to the

firm through strategic relationships) that form distinctive configurations of competence

leveraging and competence formation (Sanchez et al., 1996). As competitive conditions

change due to the actions of one firm’s competence leveraging or building, a tandem

Managerial Cognition & Organisational Learning

Strategic Goals

Resource Endowments

Competence Leveraging & Building

Hypothesised Causal Relationship Hypothesised Extraneous Influences

ORGANISATIONAL DOMAIN

INDUSTRIAL ENVIRONMENT

INDUSTRIAL ENVIRONMENT

Competitor Interaction Industry Structure

52

response will usually be forthcoming in the competence leveraging and building of other

organisations, thus stimulating rivalry between firms within an industry (Sanchez et al.,

1996).

In pursuit of the necessary resources or markets for both a firms’ inputs and outputs, an

organisation may engage in competition or collaboration, which are not considered

mutually exclusive strategic choices of the firm. Industry dynamics and evolutionary

patterns result from the competence leveraging and building activities of firms, and is

influenced directly by managerial cognition and causal ambiguity (Sanchez et al., 1996).

While prior theories of competition regard industry structure as exogenously determined,

competence theory supports the manipulation of the structural attributes of industry by

the impact of competence leveraging on industry asset structures (Sanchez et al., 1996).

2.3.3.2 Limitations

Developed only recently in 1996, the proposal of a competence based paradigm by

Sanchez, Heene and Thomas still remains subject to theoretical acceptance by the

academic and research community at large. It is, however, a valid effort by the authors to

elicit a cogent and systematic paradigm that unambiguously dictates the relationships

forthcoming between organisations and the environment, consistent with many of the

tenets of the resource-based view. The authors readily identify competition as a central

construct in any attempt to formulate a viable theory (Sanchez et al., 1996). However, the

translation of this theoretic logic into a conceptualisation of competition that can be

readily applied is not apparent. Further, the variable of competition in this model appears

to occupy both a reactive and proactive presence. Competition can be considered both

proactive and reactive in that the authors attribute the changes in one organisation’s

leveraging and building of competences as stimulating a responding modification in the

competence activities of other firms – what Sanchez, Heene and Thomas entitle

‘competitive dynamics’ (Sanchez et al., 1996, p. 13). Confusion stems from the absence

of any addressable claim as to what competition is, how it transpires, or indeed how it

maintains ongoing veracity in the conceptualisation detailed. The inability of the

conceptualisation specified to adequately provide greater detail concerning the variable

of competition limits its application, however this weakness may well be explained by the

infancy of the conceptualisation itself which will undoubtedly be subject to greater

evolution.

53

2.3.4 Discussion It can be observed that the models, frameworks and conceptualisations currently utilised

in strategy research demonstrate limitations in the study of rivalry and competitive

dynamics (see Table 2.1 for an overview). Oligopoly theory, game theory, scenario

analysis, ‘warfare’ models, simulation and system dynamics are able to only account for a

limited number of decision variables and environmental conditions. Therefore, the

veracity of these models finds only a fixed scope of application. Despite this, however,

some streams of oligopoly theory do provide the foundation upon which inferences of

rivalry can be effectively surmised (as demonstrated through the application of

concentration measures; see Cool & Dierickx, 1993).

The framework proffered by Porter (1980) has to date been the most influential in some

academic and many practitioner studies of rivalry and competitive dynamics in strategic

management. This success has largely been due to its capacity to account for a number

of influences that impact upon the firm and determine the generic level of rivalry

evidenced within the industry at a given point in time. This framework, as previously

discussed, does demonstrate a number of limitations in analysis, including its inability to

account for the effect of collaborative arrangements between firms.

Conceptualisations of competition, such as that offered by Sanchez, Heene and Thomas

(1996) provide a explanation of rivalry and competitive dynamics in contrast to alternative

models. It does so by exploring the relevance of resources, capabilities and

competencies, and posturing competition as occurring between firms according to these

firm-specific (those resources and capabilities held by the firm) attributes. Due to its

conceptual nature, it is limited in terms of practical applicability to the study of rivalry and

competitive dynamics.

While the models, frameworks and conceptualizations of rivalry reviewed here offer

limitations as to their practical application in rivalry research, strategic group theory – as

emergent from the Industrial Organisation School – provides an alternative conceptual

approach to the investigation of intraindustry rivalry. This approach is based on the

capacity for strategic group theory to distinguish groups of firms within an industry

according to firm-specific attributes, allowing for the study of patterns of rivalry within the

industry.

54

Model /

Framework

Benefits / Limitations

Oligopoly Theory • Assumes that all firms within an industry will compete aggressively with each other;

• Positions economic actions within the extremes of pure competition and monopoly (Porter, 1981);

• Does acknowledge that collaborative or collusive arrangements may develop between firms (Shapiro, 1989);

• Difficulty in successful application to study due to a broad set of variables and complexity in application; and

• Some streams of oligopoly theory do provide the foundation upon which inferences of rivalry can be effectively surmised (as demonstrated through the application of concentration measures; see Cool & Dierickx, 1993).

Game Theory • Based on the rationality of decision-makers, therefore dependent on the decision-maker’s capacity to make rational choices; and

• Concerns raised regarding it’s usefulness for business, particularly in unpredictable environments where there are a finite number of options and potentially a large number of participants (Furrer & Thomas, 2000).

Scenarios, Simulations and System Dynamic

Modelling

• Are ‘based on the of interactions between a limited number of known variables in situations of uncertainty, interdependence , and complexity’ (Furrer & Thomas, 2000, p.620);

• Predominantly useful in appraising the influence of environmental factors and in predicting the possible consequences of rival’s moves on the organisational strategy (Furrer & Thomas, 2000);

• While recent research suggests the promising nature of these methods to understand rivalry, they are not easily applicable to study and lack the ability to include a complex array of variables into analysis.

Warfare Models • Draw foundation from military strategies where the capacity to constantly disturb the competitive ‘playing field’ to produce unconventional environments is the objective; and

• Dependent on the assumption that the competitive ‘playing field’ is stable (Chen, 1996).

Porter’s Five Forces of Competition

• Dependent on the rationality of the practitioner and understanding of the model;

• Fails to consider the relevance of collaborative arrangements within the application of the framework;

• Limited considerations given to the impact of government policies and practices on the role of industry and organizations; and

• The framework is ‘static’ and therefore not easily operationalised within the context of a research investigation.

Competence-Based Competition

• A conceptualization, without the capacity to operationalise effectively at the present time.

Table 2.1: Overview of the Benefits and Limitations of Rivalry Models, Frameworks and Conceptualisations

55

2.4 STRATEGIC GROUP THEORY AND THE STUDY OF INTRAINDUSTRY RIVALRY

‘Whatever the historical genesis of strategic groups, the essential characteristic is

similarity along key strategic dimensions. The patterns of similarity and the extent of

variety in an industry will have consequences along three dimensions: the structure of the

industry and its evolution over time, the nature of competition, and implications for the

relative performance of firms’

(McGee in Faulkner & Campbell, 2006, p. 273).

The identification of subsets of firms (strategic groups) in particular industries, most

notably ‘asymmetries’ that prevented industry-wide oligopolisitic consensus promoting

interfirm rivalry, was first identified by Hunt (1972) in an analysis of the United States

home appliance industry during the 1960s. By distinguishing firms within the industry on a

product line basis incorporating degree of product diversification, differences in product

differentiation, and extent of vertical integration, Hunt concluded that such firm relevant

distinctions served as critical dimensions that could instigate intraindustry group

stratification (McGee & Thomas, 1986). Such groupings were interpreted by Hunt as

minimising the economic asymmetry within such groups, the outcome of which promoted

differing barriers to entry for potential entrants into the industry (McGee & Thomas,

1986).

The concept of strategic groups emerged principally as a method by which performance

differences between firms could be ascertained. Following inception of the strategic

group concept by Hunt (1972), substantial literature and research has been devoted to

applying this conceptual tool in the study of group determination, performance differences

between firms, group dynamics and most significantly in examination of intraindustry

rivalry (Nath & Gruca, 1997). Indeed, the strategic group rationale to discerning patterns

of rivalry has become the pre-eminent method for investigating intraindustry rivalry in

strategic management research (Thomas & Pollock, 1999). Psychological and economic

approaches underlie this literature and research, which has resulted in two broad schools

of thought developing in the strategic group domain.

Psychological interpretations conceptualise strategic groups along cognitive dimensions,

whereas the economic perspective on strategic groups refers to these groups as

56

collections of firms within a single industry, which share common elements in their

strategic dimensions (Porter, 1980). While the economic perspective exerts dominance in

research and literature, the psychological interpretation is reviewed here in order to

distinguish the conceptual differences that underlie both approaches.

2.4.1 The Psychological Interpretation

It can be argued that the concept of strategic groups stems readily from psychological

research, as evidenced by research utilising the cognitive paradigm, reference point

theory and social identification to perceived strategic groupings.

The cognitive perspective of strategic groups argues that the role of individual or

collective perception and intent of firm members (whereby some form of categorisation is

implied) may exhibit profound influence on firm activity (Dutton & Jackson, 1987).

Deliberately or unintentionally, perceptual influences, such as those exhibited by

management, may segment firms, placing them within perceived intraindustry groupings.

Similarly, competitor definition lies within the cognitive interpretation of individuals and

collective groups, and has been argued to constitute an important role in the competitive

dynamics instigated by key decision makers in response to competitor analysis and

strategy formulation (Porac & Thomas, 1990).

Cognitive theoretical traditions further identify strategic groups as reference groups, as

explored by Fiegenbaum & Thomas (1995). Within this framework, a strategic group or

set of strategic groups may act as a reference point in the formulation and

implementation of competitive strategy decisions (Fiegenbaum et al., 1996, Fiegenbaum

& Thomas, 1995). Through a process of interorganisational signalling and imitation, firms

display a tendency to adjust their strategic behaviour in accordance with a recognised

group reference point (Fiegenbaum & Thomas, 1995). It is further postulated that the

realignment or repositioning of firm strategy can be, in part, attributed to the role of

strategic groups as normative and comparative industry benchmarks (Fiegenbaum et al.,

1996, Fiegenbaum & Thomas, 1995). Similar arguments are offered by Nelson and

Winter (1982) who adopt an evolutionary economics perspective in discussion of

‘imitation’ and its role in ensuring that followers survive the innovation of ‘first movers’.

57

The final psychological approach to strategic groups to be reviewed is social

identification, as explored by Peteraf and Shanley (1997). In applying the concept to

strategic group discourse and research, it can be postulated that social identification

theory contends that individuals manifest an internal system of categorisation when

perceiving of the social world, or the world external to the immediate environment. In

doing so, Peteraf and Shanley (1997) argue that decision makers of an organisation then

impose this system of classification and categorisation on percieved intraindustry

groupings within their competitive environment.

2.4.1.1 Limitations of the Psychological Interpretation

Of particular relevance in discussion of the various frameworks that are found within the

psychological perspective is the recognition of the cognitive emphasis placed on

determining strategic group membership. Interpretations stem from the individual or

mental mode of individuals or groups within the industry environment, as opposed to the

objective and dispassionate analysis of the industry setting.

The strengths associated with pursuing a psychological understanding of strategic group

formation are best understood when considered in light of the human element found in

firms and in the subjective arena the formulation of competitive strategy takes place in.

The weaknesses of utilising such an approach include the inability to adequately

measure, impartially, the subjective realm of human perception, information processing,

social learning, and referencing skills.

The focus of these approaches is therefore found in the individual or collective group, and

how the strategist(s) interpret the external environment in which they are embedded. How

this is then translated into perceptions of the industry, competitor definition and rivalrous

activity is yet to be fully understood.

The context of psychological strategic group research, namely the individual cognitive

basis from which it is derived, limits the degree to which rivalry, as an objective action of

firm based activity, can be determined. Investigation into the strategic group – rivalry

relationship has yet to be significantly explored within the context of this particular span of

frameworks. It is envisioned that should such investigation be instigated, that focus will

58

rest with the subjective cognitive interpretation of individual firms and their perceived

recognition of strategic groupings, competitive environmental interface, and interaction.

From this brief review, it is possible to determine that the psychological approach to

strategic groups demonstrates several limitations in the study of intraindustry rivalry. In

contrast to the psychological approach, the economic perspective to strategic groups

provides a substantive basis upon which intraindustry rivalry has been investigated.

Central to this stream of research has been the Caves-Porter hypothesis that greater

rivalry will evidenced between strategic groups as opposed to within strategic groups.

2.4.2 The Economic Interpretation

According to the economic perspective, strategic groups constitute ‘a group of firms in an

industry following the same or similar strategy along the strategic dimensions’ (Porter,

1980, p.129). Since the initial work of Hunt in 1972, an economic approach to the concept

of strategic groups has dominated research, with the concept receiving significant

attention.

The value ascribed the strategic group construct stems from the capacity of the concept

to elucidate the differences underlying organisations, according to the critical competitive

dimensions that characterise the industry under investigation. In application of the

concept, it is possible to distinguish firms into groups, based on their relative measure

according to a select number of competitive variables that characterise the dimensions

upon which competition in the industry is enacted. This initial analysis allows for an

appreciation of the firm-specific factors that are idiosyncratic to each group, and

facilitates examination of how and why distinct groups generate differential performance.

The principle assumption supporting the strategic group concept is of heterogeneity

between firms within an industry, as it is on this basis that groups are devised.

Underlying the interest in the strategic group construct is the capacity of the concept to

generate insight into the competitive arrangement of firms within an industry. Further, the

strategic group rationale is credited with the capacity to offer structural analysis of

industry environments and contribute to the development of theories of competition

(Sudharshan et al., 1991).

59

Substantial research has been devoted to utilising the strategic group construct.

Research agendas have varied from investigation of performance differentials evidenced

between organisations (Cool & Dierickx, 1993, Dierickx & Cool, 1994, Lewis & Thomas,

1994) to group dynamics (Bogner et al., 1994, Fiegenbaum & Thomas, 1993,

Mascarenhas & Aaker, 1989), and in the study of intraindustry rivalry (Cool & Dierickx,

1993, Peteraf, 1993b).

Despite the popularity of this concept in research, debate still surrounds the appropriate

definition of strategic groups and the methodological approach to group determination.

2.4.2.1 Defining Strategic Groups and Group Membership

The problem of defining strategic groups has experienced considerable attention, as

alternative theoretical rationales support different approaches to defining the concept.

The definition chosen by the researcher tends to dictate the way in which groups are

determined, and emphasis therefore rests on securing a suitable definition upon which

groups can be formulated.

The origin of strategic group definition is found with Hunt, who, in 1972, referred to

strategic groups as ‘a group of firms within an industry that are highly symmetric…with

respect to cost structure, degree of product diversification…formal organization, control

systems, and management rewards and punishments…(and) the personal views and

preferences for various outcomes…’ (cited in Thomas & Venkatraman, 1988, p. 538).

This definition differs somewhat with the most cited definition used in industrial

organisation economics offered by Michael Porter, who defines strategic groups as

constituting ‘a group of firms in an industry following the same or similar strategy along

the strategic dimensions’ (Porter, 1980, p. 129). An alternative definition emerges from

the resource-based view, with Cool and Schendel (1987) proposing that strategic groups

are ‘a set of firms competing within an industry on the basis of similar combinations of

scope and resource commitments’ (p. 1106).

Distinctions between alternative definitions herald implications both for determination of

strategic groups and outcomes in research. These definitional differences appear largely

to be generated by opposing paradigms or schools of thought. Thomas and Venkatraman

(1988) suggest the classification offered by Hunt is drawn predominantly from a strategic

60

management perspective, which in this instance incorporates notions of managerial

function and interpretation as inherent and significant determinants of the strategic group.

This definition can be seen to adopt a business policy approach, promoting inferences of

subjective managerial qualities upon strategic groups.

In contrast, Porter’s classification stems from within the realm of industrial organisation

economics, and as such is a reflection of a more objective firm-oriented and industry-

dependent view of strategic groups. The definition offered by Porter further accentuates

the divide between alternative classifications in that the term strategic dimensions creates

a vast spectrum of possibilities when approaching analysis of firms and the multitude of

dimensions that could be employed to distinguish groupings within an industry. It could

be argued that strategy interpretation could be dependent upon such dimensions

employed. The ability to operationalise such a non-specific definition poses obvious

limitations.

Adopting a resource based approach, the definition proposed by Cool and Schendel is,

according to the authors, emergent within the context of business level strategies. In

assessment of the inclusion of resource and scope commitments, it could be argued that

such a classification scheme is of greater definitive value than that offered by Porter, or

less if corporate strategy is contemplated. Ambiguity stems from precisely what specific

resource and scope commitments are to be utilised, and necessarily how such

dimensions are measured. It could be proposed that this ambiguity is a reflection on the

variety and subsequent diversity of industry environments.

In line with the various strategic group definitions, notable divergences are evidenced in

the formation of groups. Hunt (1972) and Oster (1981) determine groups according to a

product line/product strategy basis (Oster, 1981). Alternatively, Newman (1973, 1978)

defined groups by degree of vertical integration. Mobility barriers have been used by

Caves and Porter (1977), Mascarenhas and Aaker (1989) and McGee and Thomas

(1986). Scope and resource commitments, as first proposed by Cool and Schendel in

1987, have additionally provided discretion to group determination (Cool & Schendel,

1987).

61

2.4.2.2 Discussion

It is evident that no consensually derived definition exists upon which to define strategic

groups. Rather, a host of alternative definitions are available, each of which generate

implications for which dimensions are employed to formulate strategic groups. As a

consequence, the strategic group concept and related discourse have become subject to

debate within the strategic management field.

Recognition of the differences that exist in regard to the conceptualisation of strategic

groups does not diminish the validity of the concept itself. Given the theoretical

foundation upon which groups are distinguished, namely the heterogeneity of firms within

an industry, it is possible to suggest that at the basis of these differences lie in

recognition of firm-specific resources and capabilities as argued by Cool and Dierickx

(1989) and Amit and Schoemaker (1993). It is these very resources and capabilities that

are used by the organisation to transform inputs into outputs and therefore deliver goods

and services to product markets where overt competition is then enacted between firms.

It is also on the basis of these resources and capabilities that firms are able to formulate

and then execute competitive strategy. In this regard it is possible to align the strategic

group rationale with the resource-based paradigm.

Guiding research in the strategic group-rivalry field, and derived from the economic

perspective, has been the Caves-Porter hypothesis. This proposition suggests that

strategic group membership provides a substantial basis upon which conclusions

pertaining to intraindustry rivalry may be drawn.

2.4.3 Strategic Groups and Rivalry

Considerable interest in the strategic group concept has emanated from the theoretical

link between group membership and profitability (Caves & Porter, 1977, Porter, 1979).

Central to this link is the premise that firms cannot easily switch between strategic groups

due to mobility barriers, making members of certain groups persistently more profitable

than those of other groups (Porter, 1979).

Implicit in the concept of mobility barriers is the notion that rivalry differs within and

between groups. Derived from IO economics, the Caves and Porter (1977) hypothesis of

competition postulates that rivalrous behaviour between firms within different strategic

62

groups is greater than the rivalry witnessed between firms within the same group. The

following section is devoted to exploring the arguments found to support or dismiss this

hypothesis.

2.4.3.1 The Case For and Against the Caves-Porter Hypothesis of Strategic Group

Rivalry

Strategic groups are determined based on the categorisation of firms according to the

key competitive dimensions of the industry under analysis. As a consequence, firms

within a single group are expected to display homogeneous characteristics based on

their congruence to the key dimensions upon which the firms were categorised.

Heterogeneity is expected to be found between groups, in that one group should bear

distinct differences from other strategic groups formulated within the industry.

Upon this basis, the case for the Caves-Porter hypothesis is positioned. Similarities in

competitive posture and strategy are expected within each strategic group, with distinct

differences in firm-specific attributes and strategy evidenced between groups. Therefore,

‘structural similarities among firms predisposes them to respond in similar ways to

disturbances from inside or outside the group’ (Peteraf, 1993b, p.520). This recognition of

mutual dependency between organisations is then said to foster predictability in rivalrous

interactions among firms within a particular industry (Cool & Dierickx, 1993). Based on

this reasoning, firms are more inclined to direct rivalry toward other firms within other

strategic groups in the industry. Such action is undertaken in order to accrue greater

market (and therefore economic) gains, reducing the market share held by other strategic

groups.

However, arguments can be positioned against the accuracy of the Caves-Porter

hypothesis. Homogeneity within a single strategic group suggests that firms are inclined

to share similarities across a spectrum of dimensions upon which competition is enacted

within the industry. Such similarities would suggest that firms within the same strategic

group would in effect be vying for the same factor market resources and competing for

the same market segment of the industry. As a consequence, it is entirely plausible that

these firms would engage in direct competition with each other, as opposed to directing

their competitive intent to other perceived strategic groupings within the industry, in order

to gain greater market share within their defined market segment.

63

It is apparent that theoretical arguments can be found to support or dismiss the Caves-

Porter hypothesis. Despite its longevity in strategic group discourse, few empirical studies

have sought to test the validity of this hypothesis, or to discern whether strategic groups

have the capacity to interpret patterns of intraindustry rivalry.

2.4.3.2 Empirical Studies of the Strategic Group – Rivalry Relationship

Three prominent studies have been undertaken to examine the theoretical and practical

relationship between strategic groups and rivalry. Implicit in these research

investigations, the Caves-Porter hypothesis (1979) has been explored, which suggests

that the strategic group rationale provides the framework upon which intraindustry rivalry

can be understood.

In analysis of the domestic US airline industry, Peteraf (1993) sought to partially test the

(until that time untested) Caves-Porter hypothesis. In determination of strategic groupings

within the industry, Peteraf segmented the industry in terms of formerly regulated carriers

and new-entrant carriers following the deregulation of the industry. As a measure of

rivalry between these groupings, Peteraf examined pricing behaviour, particularly the

response by monopolist carrier firms towards new entrants, to determine the degree to

which rivalry, manifested as price, influenced rivalrous interaction within and between

strategic groupings. The results of this study provided limited support to validate the

Caves-Porter hypothesis (Peteraf, 1993b).

Similarly, Cool and Dierickx (1993) sought to determine the nature of within and between

group rivalry, with the focus upon the implications this relationship, if any, may have on

firm profitability. In analysis of the US pharmaceutical industry (1963-1982), the authors

determine strategic grouping of firms using a mix of variables including profitability,

rivalry, concentration, segment interdependence and strategic distance (Cool & Dierickx,

1993). The findings of this longitudinal study observed rivalry to shift from within group

rivalry to between group rivalry. The findings therefore generated inconsistent outcomes

in comparison with the hypothesis proposed by Caves-Porter, in that rivalry was not

consistently observed to be greater between strategic groups, but rather varied from

between to within groups (Cool & Dierickx, 1993).

64

In criticism of the Peteraf (1993) and Cool and Dierickx (1993) studies, Smith, Grimm,

Wally and Young (1997) suggest that neither investigation directly measured interfirm

rivalry, relying instead on assumptions to infer measures of rivalry. In classifications of

strategic groups in the domestic US airline industry, Smith et al explored resource

deployment variables as the basis upon which cluster analysis was used. Competitive

behaviour manifested as rivalry was determined through action-response variables which

included competitive activity, degree of rivalry instigation, proclivity toward price cutting,

speed of response, and tit-for-tat imitation (Smith, Grimm, Wally & Young, 1997). The

findings of this study led Smith et al to conclude that while strategic group membership

offered prediction as to the manner in which individual firms compete with one another,

competitive response-action interaction could not be predicted on the basis of strategic

group membership.

As evidenced by prior research into rivalry using the strategic group concept, the validity

of the Caves-Porter hypothesis is yet to be conclusively ascertained. Researchers, in

undertaking investigation, have utilised either economic or resource-based rationales to

guide research, however the economic perspective has to date exerted dominance in this

area of strategic group research.

Given that arguments can be found to either support or dismiss the Caves-Porter

hypothesis (1979), and due to the limited research that has been conducted, the

relevance of the strategic group rationale in explaining patterns of intraindustry rivalry is

yet to be determined. Significant scope therefore exists in speculating the cogency of the

strategic group concept in explaining rivalry within singular industry environments.

2.4.4 Discussion

Theoretical and empirical evidence suggests the validity of the strategic group concept

(see McGee & Thomas, 1986; McGee, 1985; Thomas & Venkatraman, 1988). The

conjectured relationship between the concept of strategic groups and rivalry has received

minimal empirical investigation with consequent research outcomes eliciting divergent

results. Clearly, the most significant contribution the concept of strategic groups could

yield in strategic management relates entirely on its predisposition to interpret rivalry in

industry environments. However, distinct problems arise in the use of the strategic group

65

construct to investigate rivalry. A lack of methodological consensus reduces the capacity

of the strategic group rationale to effectively account for patterns of rivalry observed in

contemporary industry environments. In addition, a further limitation of the strategic group

approach relates to its inability to readily account for the impact of interorganisational

relationships on competitive behaviour between firms. As a consequence, it is possible to

suggest that the concept of strategic groups does not alone provide potential for

discerning patterns of rivalry in industry environments. The concept of strategic networks

– sets of firms in an industry that exhibit denser strategic linkages among themselves

than other firms within the same industry – provides for an alternative approach to

investigate rivalry within contemporary industry settings.

66

2.5 STRATEGIC NETWORKS

‘The image of atomistic actors competing for profits against each other in an impersonal

marketplace is increasingly inadequate in a world in which firms are embedded in

networks of social, professional, and exchange relationships with other individual and

organizational actors’

(Gulati, Nohria & Zaheer, 2000, p. 205).

Interest in interorganisational relationships represents a growing recognition that the

traditional boundaries of the firm have experienced significant change in recent decades.

Practically, one of the most prominent examples of this phenomenon has been the

increased incidence of collaborative relationships between organisations (Burgers et al.,

1993, Colombo, 1998, Gomes-Casseres, 1996, Gulati, 1998, Stuart, 1998, Boyd, 2004).

Such strategic linkages may adopt multiple forms, including joint venture agreements,

strategic alliances, mergers, acquisitions, technology licensing and development

arrangements, equity partnerships, and manufacturing, marketing and distribution

collaborations (Nohria & Garcia-Pont, 1991; Contractor & Lorange, 1988). These

relationships are said to significantly influence firm-level performance outcomes

(Rosenkopf & Schilling, 2007).

While the motivations for strategic alliance formation vary from firm to firm and from

industry to industry, in common these motivations appear to be generated according to

resource constraints, institutional regulations, environmental uncertainty, mobility

barriers, inefficiencies in production and distribution technologies, technology

development, failures in economies of scale and scope, knowledge disadvantages,

increased market power, demand for innovation, market development and ultimately in

pursuit of competitive advantage (Caves & Porter, 1977, Hamel, 1991, Penrose, 1959,

Pfeffer & Nowak, 1976, Porter & Fuller, 1986, Rumelt, 1984, Glaister & Buckley, 1990,

Ebbers & Jarillo, 1998, Vanhaverbeke & Noorderhaven, 2001). These relationships are

said to create different dynamics of strategic interaction between competitors, challenging

many of the traditional assumptions of competition (Kogut, 1988, Nohria & Garcia-Pont,

1991).

As a consequence of the increased incidence of collaborative arrangements between

firms, the competitive environment characterising many industries has undergone

67

profound change. It is suggested that rivalry is not necessarily enacted by individual firms

according to the traditional mechanisms of direct confrontation in factor and product

markets, but rather as collaborative orchestration between a number of participants or

network members (Ahuja, 2000, Blankenburg Holm et al., 1999, Chung, 1993, Dyer,

1997, Gomes-Casseres, 1996, Haugland & Gronhaug, 1996, Vanhaverbeke &

Noorderhaven, 2001). Arguably, the collective outcome of these strategic relationships

engineered between firms suggest that the collaborative benefits ascribed

interorganisational relationships require closer examination in respect to their propensity

to influence rivalry in intraindustry environments.

Strategic networks are one such vehicle upon which this examination can take place.

Theorists have offered a number of different conceptualisations of what characterises the

generic form of a network, based on competing theoretical paradigms (Ebers & Jarillo,

1998). Using the pragmatic definition employed by Nohria and Garcia-Pont (1991) in

definition of strategic blocks, strategic networks are recognised as sets of firms in an

industry that exhibit denser strategic linkages among themselves than other firms within

the same industry. Based on this definition, strategic networks are determined according

to evidence of strategic linkages between firms comprising the industry. As a result, a

single strategic network represents a group of firms closely linked according to

collaborative ties. These ties represent cooperative relationships facilitating the exchange

of resources (capital, technology and information, among others) between organizations.

Prior theoretical and empirical enterprise in alliance research has largely focussed upon

the micro perspective of these alliance relationships in respect to how they influence

organisations and strategy (Madhavan et al., 1998). Derived in large from the social

sciences, network theory additionally allows for the macro examination of the

opportunities and constraints inherent in the structure of relationships in strategic

networks, establishing a relational approach upon which the conduct and performance of

firms can be more fully understood (Gulati et al., 2000, Madhavan, 1996).

Current strategic management literature suggests the strategic network concept bears

close association with what has been referred to in the literature as ‘strategic blocks’

(Garcia-Pont, 1992), ‘alliance networks’ (Gomes-Casseres, 1994) and ‘alliance blocks’

(Vanhaverbeke & Noorderhaven, 2001). However, underlying these apparent similarities

are significant methodological differences which clearly distinguish the concepts of

68

‘strategic blocks’ (alliance blocks) from ‘strategic networks’ (alliance networks). Strategic

blocks are formulated based on the analysis of strategic relationship data utilizing

positional equivalence clustering techniques, whereas the identification of strategic

networks is based on the analysis of strategic relationship data utilizing relational

equivalence clustering techniques. Strategic blocks, as a consequence, represent

collections of firms that occupy the same relative position in network structures across an

industry, and therefore the firms comprising these ‘blocks’ are not necessarily related by

strategic relationships. Strategic networks, in contrast, are based on identifying those

firms that are directly or indirectly linked to each other based on the presence of strategic

relationships. Therefore, those firms densely linked through the presence of these

relationships are regarded as a strategic network.

2.5.1 Origins of the Strategic Network Concept

No clear consensus exists assigning any specific individual, or indeed social science

discipline with direct credit in creation of the network perspective. Early research (1930s

– 1940s) was undertaken utilizing the network construct in anthropology, social

psychology and sociology, including investigation into social organization, individual and

group perception, social construction, group structure and dynamics, among other

interests (Wasserman & Faust, 1999; Scott, 2005). Since this time, the network rationale

has grown to include alternative avenues of theoretical investigation, including political

and economic perspectives (Tichy, Tushman & Fombrun, 1979; Wasserman & Faust,

1999; Scott, 2005). Collectively, the relevance of the network construct to research has

been immense, and dependent on the objective of the researcher can be used to

examine social, political and economic realms of investigation from both micro and macro

perspectives.

Within strategy research and literature, the focus of much research attention has been

devoted to understanding and evaluating the formation, structure, governance, evolution

and relative performance of singular strategic relationships between organizations. Not

until recently has this work given way to a network perspective, whereby the focus of

analysis has shifted from the singular alliance elation to the collective interpretation of all

relations found between organizations within any defined field of investigation. This

embrace of the network perspective can be attributed to a number of sources: the

inherent capacity of the network rationale to account for multilevel analyses, from micro

69

research settings to those at a macro scale; the acceptance and later popularity of this

approach in organizational studies, particularly in investigation of social and political

dimensions of leadership, affect, power and communication; and perhaps, most

importantly, from the perspective of the strategist, the growing recognition that we are

witnessing the evolution of selected industry environments through the rejection of

traditional and independent organizational forms for more broadly embedded relational

systems of structure between firms, characterized by differing levels of interdependency.

Theorists contend that strategic networks facilitate social, political and economic

exchange (Araujo & Brito, 1998). Network analysis provides the methodology by which

the work of theorists is enhanced. Within strategy research, three dominant perspectives

dominate network investigation: the social, political and economic perspectives.

2.5.1.1 The Social Perspective

Investigation of the social dimension of organizational and industry environments has

dominated a significant portion of strategy research, particularly in the study of the

determinants of knowledge creation and innovative activity within the firm and among

closely clustered organisations (for example see Maarten de Vet and Scott, 1992;

Saxenian, 1990;1991;1994; and Glasmeier, 1991). In addition, Davis (1991) proposes

that strategic networks serve as channels for socialisation, which may promote

behavioural conformity. As social relationships typically entail informal (non-contractual)

linkages between individuals within and between organizations, these relationships are

much more difficult to identify, reducing the capacity to apply the network perspective to

induce holistic, reliable and viable results that can be generalised across an entire

population. As a consequence, the social dimension of networks is discussed with only

limited scope in the remainder of this review. More appropriate to this review, and the

purpose of this thesis, is consideration of the application of the network rationale in

defining economic and political benefits to member participants.

2.5.1.2 The Political Perspective

Burt (1992) has argued for the propensity of networks to deliver both control and

information benefits, culminating in gains in power and political strength. Control benefits

derive from the compromised autonomy of firms in the relationship, and due to the

interdependent investments and commitments of network members through participation

in strategic networks. Within this context, it is therefore possible to conceive of two

70

alternative forms of control developing leading to differentials in power amongst

organization members comprising the strategic network. The first form of control is by a

dominant participant or small collective group within the network who are able to

coordinate and navigate the broader web of relationships and manipulate the information

other participants receive. The second form of control is an outcome of interdependency

itself – a type of proxy control, whereby firms are held to certain behaviours through the

behaviour of other organisations in the system (Burt, 1992; Gulati, 1998).

This latter form of control is perhaps more implied than the first in consideration that

through the linking of certain strategic actions, firms would have available to them less

choice than if independent. Benson (1975) proposed that network structures are in effect

a political economy with relationships characterised by power and resource differentials.

Adopting this perspective, control is derived from those organisations that boast

ownership of valued resources or who are advantageously positioned in the network

(Benson, 1975). Similarly, Pfeffer and Salancik (1978) of the resource dependency

school would contend that ownership of valued resources upon which other firms are

reliant allows an organisation to exercise political power and control over dependent

firms. Coleman et al. (1996), arguing from a socially-oriented perspective, propose that

those firms strongly linked to each other within the strategic network tend to develop a

common understanding of the utility of certain behaviours over time through socialisation

mechanisms. This cohesion of behaviours is said to both reduce uncertainty and promote

trust between network members (Gulati, 1998).

Given that the political and resource dependency perspectives acknowledge the

existence of relationships based on differential power, it is possible to propose that this

power becomes manifested as control mechanisms, dictating the behaviour of

subservient network members. These mechanisms may be transmitted socially, as

posited by Coleman et al. (1996), leading to behavioural conformity (Davis, 1991). One

could assume that organisations exercising this power and control would be reluctant to

relinquish the basis upon which this authority was derived, therefore acting to prevent any

internal disturbance, such as rivalry, from developing between network members. Rather,

and in pursuit of greater power, authority and economic gains, logic suggests that rivalry

be directed away from members of the strategic network.

71

2.5.1.3 The Economic Perspective

Recognition of alternative forms of economic organisation is attributed to Coase (1937)

and Williamson (1975), in the distinction made between ‘markets’ and ‘hierarchies’

(Jarillo, 1988). Ouchi later proposed the further categorisation of ‘hierarchies’ into

bureaucracies and clans, the former embodying some characteristics of markets, but

within the confines of a recognised ‘firm’. Clans, in contrast, remove the market

mechanism typical of the industrial environment, instead arriving at a hierarchical

prescription to facilitate collective effort – the alliance (Jarillo, 1988, Ouchi, 1980). These

initial propositions arguably precipitate the modern economic interpretation of networks in

general, and strategic networks in particular.

Garcia-Pont, who proposed the strategic block rationale, suggests the basis of the

network concept was first anticipated by Harrigan, who termed these formations of

strategic linkages ‘constellations’ (Garcia-Pont, 1992, Harrigan, 1985a). Prior theoretical

and empirical research into the realm of interogranisational strategic linkages (regardless

of form), had, until this time, focussed almost exclusively on the pre-conditions, formation,

management, performance implications and economic impact such arrangements yielded

on the firm (Auster, 1994, Chung, 1993, Hamel, 1991, Porter & Fuller, 1986). Analysis at

the macro level – of firms linked together through a vast web of interorganisational

relationships – was largely overlooked as a consequence until recently.

One stream of argument with significant appeal within this area is the work of Thorelli

(1986) which is considered seminal in this regard. Thorelli proposes the conception of

network structures as an alternative means of accruing or subscribing necessary

resources and capabilities rather than through market derived sources or internal

development (Thorelli, 1986). This interpretation equates with the theoretical logic of

transaction-cost economics which emphasises minimisation of transaction costs

associated with particular structures of exchange (Williamson, 1985). Within this frame of

reference, networks denote all kinds of intentional ties between organisations,

encompassing both formal (contractual) and informal (non-contractual) forms (Chung,

1993).

Theoretically this shift in the organisational form from individualistic enterprise to strategic

network configuration can be associated with the recognition that strategic

72

interdependency exists between organisations whose inputs and outputs are similar

(Domke-Danonte, 1998, Pennings, 1981). The resource-based approach would argue

that such interorganisational relationships facilitate access to resources or capabilites

that are unable to be easily replicated due to causal ambiguity or which are already

monopolised in factor markets and which are necessary for competition (Lippman &

Rumelt, 1982; Dierickx & Cool, 1989; Peteraf, 1993a; Domke-Danonte, 1998). In effect

then, these strategic relationships can be considered resources for the firms in their own

right (Madhavan et al., 1998).

2.5.2 Strategic Linkages As industries have become more susceptible to the process of globalisation,

environmental discontinuities have altered prior structural and competitive frameworks

often associated with particular industries (Tushman & Anderson, 1986). Responding to

these changes, many organisations are currently faced with uncertain environments

(Pfeffer & Nowak, 1976). The creation of negotiated environments (Hirsch, 1975) through

strategic linkages between competitors reduces the uncertainty and risk firms would

otherwise face alone, and provides access to accumulated resources and capabilities

that the relationship members contribute (Nohria & Garcia-Pont, 1991, Porter & Fuller,

1986). Such linkages are seen to create an ‘opportunity structure’ that delivers greater

access to strategic resources, improving the capacity of firms to engage in competition

(Garcia-Pont, 1992). These lalliances have the effect of changing the traditional

boundaries of the firm (Burgers et al., 1993, Colombo, 1998, Gulati, 1998).

2.5.2.1 Linkage Forms

As with all exchange relationships between firms, it is possible to distinguish these

strategic linkages according to horizontal or vertical ties. Those relationships limited to

exchange within the same value chain activity are associated with horizontal linkages,

whereas those linkages that span across multiple activities of the value chain are

regarded as vertically aligned relationships (Nohria & Garcia-Pont, 1991). Collectively,

these horizontal and vertical linkages are considered strategic linkages, as despite their

alignment they all constitute competitive relationships affecting or influencing the specific

industry environment in which they occur. Considerable scope exists in which inter-firm

relations can be engineered between firms (for example, see Thorelli, 1986) but may

include joint venture agreements, licensing and development arrangements and

73

interlocking directorates, among other forms. In sum, these different forms of

collaborative arrangements are collectively referred to, most commonly, as strategic

alliances.

Strategic alliances are, in essence, formally derived interfirm cooperative relationships

which facilitate the (ideally) concurrent flow of knowledge and resources to members

(Madhavan et al., 1998). Gomes-Casseres (1996) contends that strategic alliances

constitute a powerful strategic tool, used by organisations to navigate uncertain business

environments. In this respect, the propagation of strategic alliances in business

constitutes a revolution in the formulation of competitive strategy (Gomes-Casseres,

1994, Gomes-Casseres, 1996). The vast plethora of theory and empirical literature on

strategic alliances support this proposition, detailing a significant upward trend in

strategic alliance creation between firms (Lazzarini, 2007; Rowley, Baum, Shiplov, Greve

& Rao, 2004; Burgers et al., 1993, Harrigan, 1985b). Indeed, the expediency of strategic

alliances, specifically in knowledge dissemination, have generated what is now

recognised as ‘alliance capitalism’ (Dunning, 1995). Ritcher (2000) contends that

alliance capitalism is ‘…capitalism without capitalists’ due to the interrelated interests of

network participants in attaining profit.

At the most fundamental level then, strategic alliances represent the foundation of

networks of strategic linkages, and more specifically, strategic networks.

2.5.3 Networks of Strategic Linkages

A recurrent theme in organisational theory has been the perception of organisations

existing within a larger network of exchange (Chung, 1993, Levine & White, 1961,

Perrow, 1986). However, unlike strategic linkages, the conceptualisation of strategic

networks precludes conceiving of interorganisational relationships as isolated

mechanisms (Axelsson & Easton, 1994, Burt, 1980, Chung, 1993, Perrow, 1986). The

singular tie between two organisations is preempted by a larger agglomeration of direct

and indirect relationships between firms that comprise the network, the sum of which

Easton (1994) contends must be considered as structural attributes of industry

environments (Blankenburg Holm et al., 1999).

74

Within the strategy discipline, much of the attention of researchers utilizing network

analysis has been on the governance, structure and evolution of strategic networks within

the broader context of industry environments (Gulati and Singh, 1998; Ebers & Jarillo,

1998; Madhavan, Koka & Prescott, 1998; Gulati, Nohria & Zaheer, 2000).

2.5.3.1 Governance

The recent proliferation of network forms that do not fit cleanly into either the hierarchy or

market frameworks proposed by Coase (1952) to explain economic exchange has

resulted in ambiguity in defining how strategic networks are governed. Many researchers

readily acknowledge the presence of governance structures in strategic networks,

however the dynamics of such structures has remained largely unexplored (Gulati &

Singh, 1998). Within the literature, two forms of coordination and control are articulated to

exist: formalised contractual structures that exhibit elements traditionally associated with

hierarchy, and informal self-enforcement structures (Dyer & Singh, 1998).

Within the literature, greater understanding is ascribed the governance attributes related

to formalised contractual arrangements typically associated with the study of singular

strategic alliance relationships between firms, and largely associated with hierarchical

control features often employed within the setting of the organization (Gulati & Singh,

1998). Research suggests that such formal governance mechanisms are introduced on

the basis of coordination and appropriation concerns to parties involved in the

relationship (Williamson, 1985, 1991). Such formalised contractual governance structures

incorporate the capacity to refer disputes to third party enforcement agencies (Dyer &

Singh, 1998). The specific types of hierarchical controls – encompassing agency and

coordination features – are typically evident in all relationships of this type and include:

‘command structure and authority systems, incentive systems, standard operating

procedures, dispute resolution procedures, and non-market pricing systems’ (Gulati &

Singh, 1998, p. 792). Whether the alliance signifies an equity or non-equity relationship

between firms, or involves the creation of a joint venture necessitating the formation of a

new enterprise between partners to the relationship, are arguably key elements that

define the boundaries of the governance structure employed. Gulati & Singh (1998)

propose formalised alliances fall into one of three categories, dependent on these

elements. The first is a minority alliance, characterised by one partner taking a minor

equity position in the other (or others), working together without the formation of a new

75

entity. According to the authors the level of control exhibited in this form of relationship is

intermediate with that demonstrated in joint ventures (highly controlled) and the level of

controls associated with contractual alliances (less controlled). Joint ventures generally

typify highly controlled enterprises which typically encompass strong hierarchical

elements inherent in their structure. Alternatively, contractual alliances are alliances

achieved without the exchange of equity or creation of new organisational entities,

usually entailing the unidirectional arrangement between partners for such activities as

second-sourcing, distribution and licensing (Gulati & Singh, 1998).

In contrast to the transaction cost explanation of control and governance, research into

the nature of governance and networks (Coser, Kadushin & Powell, 1982; Hakansson,

1987; Lorenzoni & Ornati, 1988; Jarillo, 1988) point to reputation, reciprocity norms,

personal relationships, reputation, and trust as important factors explaining the duration

and stability of the exchange structures (Larson, 1992). In contrast to formalised

contractual governance structures, informal governance structures are characterised by

the absence of a third party (contractual) presence in the relationship. Such structures

are referred to as self-enforcement governance due to the lack of formalised methods of

dispute resolution available to partners. Such relationships allow for self-enforcement by

partners to the agreement incorporating safeguards that encapsulate both formal and

informal dimensions (Dyer & Singh, 1998). Formal self-enforcing safeguards are

intentionally created economic hostages such as financial and investment hostages that

are designed to prevent opportunism by partners to the relationship and act to align the

economic incentives of parties to the operation (Klein, 1980; Williamson, 1983). Informal

self-enforcing safeguards include reputation, trust or embeddedness (Powell, 1990;

Larson, 1992; Gulati, 1995). Such informal safeguards are socially complex and

idiosyncratic in that such safeguards require a history of interactions over time to develop

and necessitate the development of trust between partners to establish the norms and

expectations about appropriate behaviour to the relationship (Granovetter, 1985; Dyer &

Singh, 1998).

Despite these findings, much research into governance mechanisms in strategic

networks has focused on the singular alliance as the unit of analysis. As a consequence

it becomes difficult to generalize the findings on governance mechanisms to encompass

the entire strategic network. This results in continued ambiguity in clearly articulating the

76

implications the role of governance in strategic networks. Consensus suggests that a

combination of formal and informal governance mechanisms are employed

simultaneously (Borch, 1994). Due to this, network governance is typically examined in

light of economic exchange and the social network theories (Jones, Hesterly & Borgatti,

1997).

2.5.3.2 Structure & Evolution of Strategic Networks

‘The tradition in network analysis has been to view networks as given contexts for action,

rather than as being subject to deliberate design’ (Madhavan, Koka & Prescott, 1998, p.

439). In challenging this prescriptive view, recent research has identified that network

structure plays a significant role in defining the welfare and performance of firms

comprising the network in conjunction with advocating industry structure and evolution

(Ebers & Jarillo, 1998; Madhavan, Koka & Prescott, 1998; Gulati, Nohria & Zaheer,

2000).

Network structure refers to the overall and relatively enduring pattern of relationships

between actors (firms) comprising the network. Similar to industry evolution, structural

change in the network emerges over a period of time, as evidenced by significant

variation in the underlying pattern of relationships that connect this given set of actors.

The structural elements of a network do not change due to an increase or decrease in the

frequency of activity between actors, nor due to positional changes between actors

comprising the network. Structural change in the network would be observed as changing

relations between individual firms, as well as between groups of firms (Madhavan, Koka

& Prescott, 1998; Gulati, Nohria & Zaheer, 2000; Rowley, Baum, Shiplov, Greve & Rao,

2004).

The importance of network structure has been explored by Burt (1992) and Galaskiewicz

(1979) who propose the notion of centrality to explain those actors in a network that

occupy a significant position in the network. Centrality occurs when one actor is more

prominently and frequently linked to other actors in the same network than other actors

comprising the network. Actor centrality has been empirically associated with political

prestige and power (Krackhardt, 1990), reputation (Galaskiewicz, 1979) and in the early

adoption of innovation (Rogers, 1971). As such, actor centrality in a network is an

important strategic resource to the firm, with each linkage the actor has with other

77

members of the network providing a potential conduit for timely and relevant information,

political influence and resources (Madhavan, Koka & Prescott, 1998).

Ebers and Jarillo (1998) suggest that benefits ascribed to members of strategic networks

are dependent on the scope of interests that firms comprising the network seek to further

via their membership, and how collaborative endeavours between firms are organized.

Thus, the pattern of network linkages can have ‘important implications for the goal

accomplishment of individual network members and their collaborative welfare’ (p. 4).

Research is only beginning to articulate how individual firm goals and their choice of

collaborative organisational form within the broader context of the interaction of all

strategic network members create the foundation for different network structures and

outcomes under diverse circumstances (Ebers, 1997; Jarillo, 1993; Nohria and Eccles,

1992).

Madhavan, Koka and Prescott (1998) promote the importance of investigating what

factors shape and constrain networks, as opposed to the traditional view of asking how

networks shape and constrain action. These authors contend that due to the influence

network structure has in defining firm performance (and as a consequence, industry

evolution), firms seek to deliberately maneuver their position within their network by

constructing additional strategic alliances to access key resources and information, thus

seeking to improve their relative centrality in comparison to other actors comprising the

network. As a result, those actors (firms) that exhibit greater centrality within the network

have greater scope to define the parameters of competition enacted within the industry,

and direct the future evolution of the industry (Madhavan, Koka & Prescott, 1998; Gulati,

Nohria & Zaheer, 2000).

2.5.3.3 Discussion

The governance, structure and evolution of strategic networks are important topics in

strategic management research. Underlying the relevancy of these issues, however, is

the importance of understanding how the presence of strategic networks in contemporary

industry environments influence the nature of competition observed. Determining whether

such strategic networks influence patterns of rivalry in the industry assists in clarifying

whether network members are acting in an individualistic or collective manner in pursuit

of their economic goals. Should firms engage or not engage in collective action helps to

78

define whether governance structures are in place across the network. Similarly, the

ability to assign network members as engaging in individualistic or collective action as a

rationale for operation illustrates the relative importance and commitment ascribed to

network membership and opportunities for stability within the network and evolution over

time. It is therefore necessary to investigate the relationship between strategic network

membership and rivalry.

2.5.4 Strategic Networks and Rivalry

The conceptual and empirical value of network theory in discerning the competitive

dynamics of industrial environments is considered a relatively new addition to strategic

management research (Thomas & Pollock, 1999). At the most basic level, the

competitive benefits to be achieved through participation in a strategic network are often

cited to include increased access to resources and capabilities held by other participants

in the network (Dyer, 1997, Dyer & Singh, 1998, Normann & Ramirez, 1993), improved

access to relevant and timely information (Rosenkopf & Schilling, 2007), enchanced

opportunities to realise economies of scale (Gomes-Casseres, 1994), access to new

markets or further exploitation of established markets (Vanhaverbeke & Noorderhaven,

2001), greater market power, and heightened competitive agility (Gomes-Casseres,

1994, Gomes-Casseres, 1996). As compelling as these benefits may be, there is little

evidence to suggest that these benefits are acquired as a result of the strategic network

operating as a coordinated unit. Indeed, it is possible that many of the competitive

advantages that are said to transpire through strategic network membership are merely

artefacts of the benefits derived from interorganisational linkages.

Strategic network research yields evidence of empirical efforts to unite network activity

with competitive patterns. Madhavan, Koka and Prescott (1998) argue that ‘the strategic

conduct of firms in an industry is influenced not only by the properties of their

relationships taken one at a time, but also by the overall structure of interfirm relationship

networks’ (Madhavan et al., 1998, p. 439-459). In this light, strategic networks are often

conceived as a ‘mode of organisation’ (Jarillo, 1988, p.31), where it is possible to

conceive that this organisation extends beyond the traditional conceptualisation of the

strategic network as facilitating technology, supply, production and resource exchange, to

the active mobilisation and institutionalisation of the competitive focus of the participant

members of the network.

79

Arising from the rich heritage of research on interorganisational relationships, a common

presumption has been that the competitive characteristics defining singular inter-firm

relationships transcend to encompass the entire horizontal network (Gomes-Casseres,

1994, Gomes-Casseres, 1996, Vanhaverbeke & Noorderhaven, 2001). This has led to

some researchers proposing the relevance of strategic networks competing as collective

competitive units against other networks within industry environments (Gomes-Casseres,

1994, Vanhaverbeke & Noorderhaven, 2001). For instance, Gulati, Nohria & Zaheer

(2000, p. 204) contend that ‘the location of firms in interfirm networks is another important

element of competition, since competition is more intense among actors who occupy a

similar location relative to others but is mitigated if actors are tied to each other’.

Regardless of whether the benefits cited above are acquired through the advent of

network rivalry via the vehicle of strategic networks, or through the disparate collection of

interorganisational relationships, a number of impediments exist in realising coordinated

competitive intent across the strategic network. These constraints are associated with

network evolution and longevity (the duration and stability of network relationships),

internal competition (the extent and nature of internal competition evidenced by

participants comprising the strategic network, influencing the capacity for firms to operate

in an orchestrated manner), and also according to the difficulty of instituting governance

and coordination across all members of the strategic network.

Research to date has yet to comprehensively clarify whether membership in a strategic

network elicits competitive benefits in respect to product market rivalry, particularly in

relation to examining industries not dominated by technical or regulatory imperatives (it is

postulated that technological standards and regulatory requirements may provide a locus

of subscription irregardless of network affiliation).

2.5.4.1 Network Research as Distinct from Block Research

Before proceeding further, it is necessary to emphasise that the strategic network

rationale forwarded within this work lies in contrast with selected research completed by

other academics, despite at times this research finding itself grouped under the same

umbrella of ‘strategic network’ research. This has led, in some instances, to key

terminology being used interchangeably in management dialogue, further confounding

80

the fields themselves and in effect reducing the opportunities each distinct stream of

research may have to deliver insights into strategy discourse. A brief review of these

differences is provided here, so as to avoid confusion when reviewing later work within

this chapter.

The initial goal of any researcher seeking to examine the relevancy of a network of firms

linked together by varying types of relationships, will pursue a range of relevant data

sources that will elicit the detail of these relationships. Typically the population boundary

will include all firms active in a particular segment of the value chain, although it is

entirely plausible to design this analysis to incorporate vertical relationships (relationships

between firms across different activities in the value chain). The research objective

developed by the researcher is critical, as this will dictate whether horizontal, vertical, or a

combination of horizontal and vertical relationships are sought for analysis.

Investigation of the horizontal relationships between firms allows for a number of strategic

issues to be analysed. In the empirical study of rivalry, for instance, it makes much more

implicit sense to target those firms (and their relationships) that participate within a well-

defined boundary, such as motor vehicle manufacturing and sale. In this example, the

products offered for sale – cars – are comparable across different members of the

defined population. Another example would be the airline industry, where these airlines

operate a relatively standardised product and service within defined fields of operation. In

order to overcome a variety of regulatory requirements (amongst others), these firms

develop horizontal strategic alliances amongst themselves in order to facilitate code-

share arrangements, customer loyalty programs, and effectively overcome regulatory

limits on where they can and cannot travel to.

Firms that participate in different activities across a value chain may consist of such

organisaitonal groupings (dependent on the industry examined) including chip

manufacturers, component installers, hardware developers, software developers,

assembly teams, and manufacturers. No two activities within the value chain are the

same, and the outputs of each stage are different. Each stage of the value chain adds

value to their input, and passes along this value-added input to the next stage of

production, whereby ultimately a finished product is produced. Relationship data

collected from this perspective does not have a clearly defined population due to the

81

complexities often involved in the value chain of manufacturing activities, and is less

likely to have a defined boundary for analysis.

How this data is evaluated provides the critical basis upon which the outcomes of

analysis can be defined as either constituting strategic networks or strategic blocks. The

relationships the researcher is able to identify between different firms can be analysed

according to a number of equations or protocols by such programs as UCInet (Borgatti,

Everett & Freeman, 2002). This program analyses the data it elicits from the relationship

information sourced through examining all the strategic relationships held between firms

comprising the population. It is entirely feasible that the data can be analysed according

to several different analytical techniques, however it is dependent on the researcher to

decide which analytical technique is most appropriate given the objective of their

research. If a researcher seeks to obtain a ‘block’ (CONCOR – convergent correlations)

or positional equivalence clustering of all the relationships within the dataset, this will

produce an output of firms, who, while not necessarily directly or indirectly linked to one

another via a relationship, share similarities in terms of where they are positioned within a

network. If we were to apply this same positional logic to the airline industry, we may find

that Singapore Airlines and Air New Zealand are engaged in a direct alliance with each

other (the Star Alliance). Despite the direct relationship these carriers have with each

another, due to their similarities in their relative position within the network and the

relationship types held with other firms, Singapore Airlines and Air New Zealand may be

relegated to different network structures, occupying similar ‘positions’ within this ‘block’

(network) of firms. Therefore ‘block’ or positional equivalence clustering produces what

are technically known as networks, however the actors within these defined networks do

not necessary have any direct or indirect relationships between other members of the

same ‘block’.

In contrast, within the UCInet program (Borgatti, Everett & Freeman, 2002) it is possible

to analyse for cohesive groupings of firms, whereby firms are either directly or indirectly

related to each other. Once again, the primary unit of analysis is the strategic

relationship. All relationships observed between all firms within the population are

entered into the UCInet (Borgatti, Everett & Freeman, 2002) program, however instead of

examining this data for ‘blocks’ (CONCOR – Convergent Correlations), the program looks

to identify groupings of firms that are more closely linked via these relationships than

82

other firms within the same dataset. As a result, a number of networks are identified,

each of which is more closely aligned with other firms in their own network. This type of

analysis is based on relational equivalence clustering, whereby those actors more closely

associated to each other by strategic relationships are grouped together. As these data

collections are groups of firms linked closely to one another by virtue of their strategic

relationship, the outcomes of this type of analysis are called strategic networks. This

analysis of horizontal cohesive relationships is what is referred to within this thesis as

‘strategic networks’ and forms the focus of this research effort.

While the same data input (relationships) is analysed, the method of analysis defines

whether the data outcomes constitute ‘blocks’ or ‘networks’. On a technical level, both

outcomes do represent networks but of different kinds. Those actors that comprise

‘blocks’ can demonstrate only minimal linkage between each other, whereas those actors

that comprise ‘networks’ demonstrate strong direct and indirect ties to each other.

A selected study that has used positional equivalence clustering or the ‘block’ approach

is provided here to distinguish this work from the strategic network approach which

comprises the main focus of this research stream. Empirical rivalry studies, utilising the

block and strategic network rationales, have been slow to develop.

2.5.4.1.1 Associated Research Utilising the ‘Block’ Methodology

Vanhaverbeke and Noorderhaven (2001), adopting an quantiative approach, investigate

the advent of alliance network competition and the nature of technical standard wars in

the RISC (reduced instruction-set computing) Microprocessor industry over the period

1980-1989. Alliance blocks were determined based on evidence of vertical and horizontal

strategic relationships between industry participants, and utilising a positional

equivalence clustering technique (CONCOR) found the presence of competitive blocks of

firms structured around alliance configurations (Vanhaverbeke & Noorderhaven, 2001).

The authors argue that competitive advantage within the industry must be understood as

‘not only the results of company-based characteristics but also of features of the alliance

block to which the firm belongs’ (Vanhaverbeke & Noorderhaven, 2001, p.1-2). Rivalry,

as an independent construct, was not examined.

83

2.5.4.3 Studies of the Strategic Network – Rivalry Relationship

Perhaps the most recognised work in relation to strategic networks is found with Gomes-

Casseres (1994), who has referred to strategic networks as ‘alliance networks / groups

and/or blocks’. Gomes-Casseres suggests that the conventional two-company alliance is

being superseded by organisations who engage in multi-partner alliances in pursuit of

competitive advantage. This process, Gomes-Casseres claims, is deliberate and

purposeful, with alliance participants fulfilling specific roles, designed to facilitate

competitive strength at the group (as opposed to individual) level. Qualitative examples

cited by Gomes-Casseres include the microprocessor and airline industries (1994) –

industries that are either dominated by technical standards or government regulation.

Boyd (2004) proposed the integration of the strategic group and strategic network

constructs to investigate intraindustry rivalry. With empirical research based on the airline

industry, network structures were based on the overt alliance collectives characterising

the industry. Therefore Boyd did not engage in analytical determination of strategic

network structures. Using return on sales (ROS) as a proxy for rivalry, Boyd found that

the strategic networks identified in the airline industry offered a limited predictive ability to

account for patterns of rivalry.

An important distinction, when considering investigation, must be drawn in respect to the

proposed industries where a relationship between strategic networks and rivalry is more

likely to be a reality. Industries dominated by technical standards are more likely to

demonstrate support for this relationship, if only due to the likelihood that firms will find

commonalities due to technical standards characterising the industry (microprocessor

industries, for example). Likewise, in industries overtly influenced by regulatory

imperatives (such as the airline industry), firms would be more inclined to demonstrate

overt strategic networks based on delivering rivalrous benefits. However, the more

interesting question lies in whether horizontal strategic networks can be associated with

rivalry in product-oriented industries that operate without the presence of a coordinating

force such as technological standards or regulatory imperatives. Research to date has

largely concentrated on examination of the predominantly micro context of pre- and post-

collaborative endeavours (Gulati, 1998, Hall et al., 1977, Jarillo, 1988, Madhavan, 1996,

Richter, 2000). As a consequence, less is known about the macro competitive attributes

of strategic network structures. As a result, academics and practitioners alike are divided

84

in their perception as to whether coordinated competitive intent (as defined by strategic

network membership) exists.

2.5.5 Discussion

The theoretical argument underlying the concept of strategic networks suggests that

organisations will engage in strategic alliances to secure access to necessary resources

and capabilities by which competition within the industry is defined. Despite limited

empirical research to validate the strategic network concept in the study of rivalry, it is

suggested that strategic networks are becoming a feature of contemporary industry

environments evidenced by the proliferation of collaborative endeavours between

organisations traditionally understood to be in contention with one another. However, as

indicated, a number of practical constraints inhibit opportunities for firms to realise

coordinated rivalry across the strategic network, including network evolution and

longevity, internal competition between participants of the network, and according to

governance and coordination mechanisms.

The research undertaken to date in the realm of strategic networks and rivalry have either

provided a qualitative perspective (Gomes-Casseres, 1994, Gomes-Casseres, 1996), or

a quantitative approach without the direct measurement of rivalry (Boyd, 2004). Therefore

scope exists in which to research the veracity of this relationship. The most profound

contribution the strategic network concept could deliver in strategic management

discourse is as an alternative means of interpreting competition in networked

environments.

85

2.6 SUMMARY AND RESEARCH PROPOSITION

As indicated at the beginning of this Chapter, the objective of this review was to establish

the basis upon which the strategic network rationale could be positioned as an alternative

conceptual tool in the study of intraindustry rivalry. In order to arrive at this conclusion,

the sources of competitive advantage were examined from the IO and RBV perspectives.

Comparison of the disparate approaches adopted by these dominant theories in strategic

management discourse highlighted the vast spectrum of possible variables upon which

intraindustry rivalry could be investigated.

Examination of current models, frameworks and conceptualisations of competition

determined a number of limitations characterising each approach. Whether these

weaknesses were derived from a theoretical or practical basis, the outcome of this

critique illustrated the reliance placed on the strategic group construct in the study of

intraindustry rivalry. Given the limitations of this construct to successfully interpret

patterns of rivalry evident in the industry, and in light of the rapid growth of alliance

relationships between firms, strategic network analysis was posited as a viable

alternative to examine intraindustry rivalry in contemporary industry environments.

Strategic networks represent a group of firms in an industry that have denser strategic

linkages amongst themselves than other firms within the same industry (Nohria & Garcia-

Pont, 1991). The rapid proliferation of collaborative ties between organisations in recent

decades (Colombo, 1998) and subsequent research into this phenomenon has

established that these relationships elicit competitive benefits to participant organisations

(Nohria & Garcia-Pont, 1991, Dyer & Singh, 1998, Normann & Ramirez, 1993, Gomes-

Casseres, 1994, Vanhaverbeke & Noorderhaven, 2001).

The proposition of coordinated rivalry – where groups of firms linked together by

interorganisational relationships engage in orchestrated competition – is not new.

Arguments can be found for and against the contention of collective rivalry via the vehicle

of strategic networks. However, research into the relevancy of this proposition has been

limited, comprising qualitative analysis (Gomes-Casseres, 1994, 1996), and a single

empirical study (Boyd, 2004). As a consequence of the continued incidence of strategic

linkage formation, and in light of their relative influence in generating competitive benefits

86

for participant firms, the study of the relationship between strategic networks and

intraindustry rivalry was proposed.

From this discussion it is possible to identify the central proposition guiding this research

effort:

Are patterns of competition predicted by strategic network membership?

The strategic network concept is utilised within the framework of this thesis to determine

the predictive ability of horizontal strategic network membership to decipher patterns of

intraindustry rivalry. Fundamental to this research is assessment of the strategic network

rationale in defining patterns of rivalry in the US Light Vehicles Industry over the period

1993 – 1999. The significance of this research lies in deciphering the competitive

dynamics and patterns of rivalry within industry settings. This dissertation topic area finds

relevance and significance in strategic management research and literature based upon

the propensity for competitive strategy to be built upon the interpretation of competition

and outcomes of structural analysis.

87

CHAPTER 3CHAPTER 3

M E T H O D S O F R E S E A R C HM E T H O D S O F R E S E A R C H

88

3.0 INTRODUCTION & RESEARCH QUESTION

As stated in the conclusions of Chapter 2, this thesis is focused toward assessing

whether strategic network membership predicts patterns of rivalry evidenced in the

United States Light Vehicles Industry over the timeframe 1993 – 1999. Specifically, the

research question guiding this research is:

Are patterns of rivalry predicted by strategic network membership?

The relative importance of this research lies in identifying an approach to examining intra-

industry rivalry whereby the question ‘with whom do firms compete?’ within the broad

context of industry is defined (Thomas, 1999, p.127). This chapter provides an overview

of the thesis, methodology and design employed in this research investigation. This

chapter concludes with outlining each of the studies that comprise this research

investigation.

3.1 THESIS OVERVIEW

In order to test the central proposition of research – whether strategic network

membership can account for patterns of rivalry observed in the industry – it was

necessary to investigate rivalry from the complimentary perspectives of between network

rivalry, and within network rivalry. Between network rivalry was concerned with assessing

the level of rivalry between defined network structures, whereas within network rivalry

was focused on determining the level of rivalry observed between firms comprising the

same network. If indeed the argument that strategic networks elicit competitive benefit

holds true, then ideally the level of rivalry observed between network structures should be

greater than the level of rivalry observed within the network and between co-members.

While this thesis research is principally concerned with identification of whether network

membership delivers competitive benefits in the product market (reduced levels of rivalry

from co-members of the same strategic network), both patterns and levels of rivalry are

examined.

Three studies were undertaken to address the research question. Study 1 is concerned

with defining the measure of rivalry that was utilized in research over the timeframe 1993-

1999. Study 2 is concerned with detailing the process by which strategic networks were

formulated for the period 1993-1999 via network analysis. Study 3 explores the statistical

89

relationship that exists between the defined rivalry measure (Study 1) and the strategic

networks defined for each period of study (Study 2).

Chapter 4 details the results of studies 1 and 2, and presents the outcomes of empirical

assessment of the strategic network – rivalry relationship (Study 3). Chapter 4 will

therefore examine the predictive ability of strategic networks to decipher patterns of

rivalry in the traditional setting of industry environments – that is, those industries not

overtly dominated by regulatory and/or technical imperatives.

Chapter 5 discusses the findings of research, incorporating discussion on the theoretical

and practical implications these findings generate for current and future rivalry research.

90

3.2 METHODOLOGY

3.2.1 Data Collection

This thesis is characterised by the use of secondary data as the method of data collection

employed. Obtaining the data required to undertake strategic network and rivalry analysis

in the automotive industry necessitated two distinct data sets. Strategic networks can be

devised in a number of ways – either to encompass horizontal ties, vertical ties, or the

entire web of horizontal and vertical ties. The goal of this research was to investigate

whether strategic networks could predict patterns of rivalry.

In order to minimise complexity in terms of the research design and required data, it was

concluded that horizontal networks, whereby the firm sample included those firms who

occupy the same relative position in the value chain and whose inputs and outputs are

similar, would be investigated. The sample therefore included all auto producers who

offer vehicles for sale within the light vehicles component of the United States auto

industry. To compile the first dataset required extensive information to be collated on the

advent and decline of strategic relationships between producers in the auto industry. The

primary data source identified for this information was How the World’s Automakers are

Related. The second dataset required information to be collated on all firms active in the

automotive industry, their production and sales figures and detailed information on

product specifications and product market segments. The primary source for this data

was obtained through Ward’s Automotive Yearbook. Additional data was obtained from a

variety of sources in order to complete the datasets and also to ensure the reliability and

validity of the data collected.

The data sets provide the numeric information required for undertaking longitudinal

research of strategic networks and rivalry. This data was obtained from a number of

sources, including Wards Automotive Yearbook, Interrelationships Among the World’s

Major Automakers / How the World’s Automakers are Related, Hoovers Handbook:

Profiles of Over 500 Major Corporations, Hoovers Handbook of World Business, Hoovers

Handbook of American Business, Worldscope Industrial Profiles and World Motor Vehicle

Data. The collection of data from a range of publications and online sources ensured that

many reliability and validity considerations often characterising secondary data collection

were overcome.

91

Data pertaining to inter-firm relationships – the fundamental basis of compiling strategic

networks – was principally obtained via reference to How the World’s Automakers are

Related which details a host of inter-firm relationships found between participants in the

Automotive Industry. To ensure the reliability and validity of this data, a random selection

of relationships included in this publication (featuring those manufacturers participating in

the United States Light Vehicle Industry) were confirmed against reporting of these

relationships in the popular media and business press (The Wall Street Journal, Business

Review Weekly, Financial Times and Automotive News) (Nohria and Garcia-Pont, 1991).

On average, the accuracy of twenty-two relationships were verified per year according to

this approach.

In addition, independent information relating to inter-firm relationships was collated for

selected firms comprising the sample to contrast against the listings featured in How the

World’s Automakers are Related to ensure that all relationships were captured in the data

presented in this publication (on average four firms per year – 25% of each yearly

dataset).

The data required to operationalise the construct of rivalry was obtained exclusively

through Ward’s Automotive Yearbook. This data included information relating to market

segment distinctions, vehicle price, production figures for each producer by vehicle,

sales, and market share.

The culmination of data from these sources provided rich data sets upon which research

could be undertaken. Given the depth and breadth of meaningful data required, and the

necessity for data to historically define identifiable constructs (firms) within the United

States Light Vehicle Industry, numerical data, captured yearly, provided the most

transparent, accurate and unprejudiced data source available for this research.

3.2.2 Timeframe of Research

The decision to examine strategic networks in this industry over the period 1993-1999

stems from a number of factors. Initially, in order to undertake meaningful analysis,

sufficient secondary data had to be available upon which the criteria guiding research

could be examined. Secondly, sufficient levels of inter-firm relationships had to be

observed within the industry upon which the strategic network component of investigation

92

could be based. Whilst the 1980s did see a significant rise in this behaviour, the level of

reporting of these relationships in the media was not comprehensive until the early

1990s. Given reliability and validity considerations, the timeframe 1993-1999 was chosen

which provided a sound time period over which the concept of strategic networks could

be examined.

3.2.2.1 Industry Context as a Moderating Consideration

The decision to embed research within the United States Automotive Industry was based

on establishing a distinction between this and past research efforts. Prior research has

investigated the role of strategic networks in facilitating what has been described as

collective rivalry by firms, in that firms argued to constitute the same strategic network

tend to exhibit unified competitive action. These results have been forthcoming in industry

environments that are either technology intensive or heavily regulated, such as the

microprocessor or airline industries. These industry types demonstrate overt and

significant imperatives (such as technology standards or regulatory requirements) that

may act to independently organize firms and predispose them to channel their

competitive intent in predefined ways regardless of whether firms subscribe to a strategic

network or otherwise. For instance, firms that champion different technological standards

are more likely to perceive of each other as competitors in the pursuit of realizing their

technological standard as the ultimate winner in the battle for a dominant design in their

industry. In contrast to this, the automotive industry demonstrates no such overt rationale

for predisposing firms to behave in any prescribed competitive manner. As such, the role

of strategic networks as facilitating collective competitive action can be effectively

investigated without the influence of industry specific imperatives inadvertently

confounding analysis.

Throughout the 1990s, it is speculated that organisations in the automotive industry were

‘feeling’ their way in terms of establishing strategic networks. During this period of time, it

was not uncommon to witness alterations in strategic allegiances between firms. While

some relationships between actors remained constant throughout the timeframe of

analysis, other relationships were terminated and other relationships instigated between

the actors comprising the sample. As a consequence, it is not anticipated that stable

strategic networks would be identified throughout the entire period of analysis. Rather, it

was posited that analysis would illustrate an evolution of strategic networks in the

93

industry as firms became more conscious of the relevance and implications associated

with their strategic relationships. It is speculated that some key ‘players’ in the industry –

those firms that constitute the dominant participants in the industry -would act as ‘hubs’ to

other less dominant firms.

3.2.2.2 Years of Analysis

The timeframe of research extends from 1993 to 1999. Within this timeframe, it was

necessary to choose specific years upon which to undertake analysis in order to monitor

changes that transpired in strategic network membership. Due to the generally assumed

stability and longevity associated with inter-firm relationships, analysis on a yearly basis

would have proved redundant and would not have elicited results of great variability to

prior years. As a consequence, analysis begins in 1993 and occurs on a biannual basis

thereafter (1995, 1997 and 1999).

3.3.3 Population and Sample

Organisations participating in the United States Light Vehicles Industry over the

timeframe 1993-1999 represent the research population under investigation (Table 3.1).

Table 3.2 indicates the total population of firms available for analysis for each period of

study.

Firms in Analysis Chrysler General Motors Honda Hyundai

Mitsubishi Suzuki Volvo BMW

Daimler Benz Ford Mazda Nissan

Porsche Subaru Toyota Volkswagon

Table 3.1: Firms Comprising Sample – 1993, 1995, 1997, 1999

94

Period of Study Number of Firms in Analysis 1993 20 1995 20 1997 18 1999 18

Table 3.2: Population – Total Number of Producers Available for Analysis in the United States Light Vehicles Industry 1993-1999

Period of Study Number of Firms in Analysis 1993 16 1995 16 1997 16 1999 16

Table 3.3: Sample - Number of Subjects in Analysis per Period of Study

It can be noted that the number of firms participating in the Light Vehicles Industry in the

United States (Table 3.2) is greater than those firms chosen for the defined sample of

analysis in this research (Table 3.3). As evidenced in Table 3.3, sample numbers

comprising each year of study remain consistent. In order to undertake network analysis

it is necessary to retain an equal number of actors (firms) in analysis, otherwise the

resulting strategic networks are not considered valid when compared across time

periods. In this respect, the addition or exclusion of an actor (firm) in any given timeframe

would confound analysis (Hanneman, 2000; Wasserman & Faust, 1999).

3.3.3.1 Exclusions

Based on the requirements of network analysis – that is, the need to retain equal and

identical actors in analysis across all periods of study - It became necessary to exclude

some organisations from analysis in Studies 1 and 2 or otherwise risk confounding the

network component of study (Hanneman, 2000; Wasserman & Faust, 1999). Therefore, if

firms did not participate throughout the entire period of study (1993-1999), whether this

was due to new entry into the industry (largely characteristic of Asian Producers) or

acquisition by other players in the industry (largely characteristic of European Producers),

it was not possible to include these firms in analysis. Where firms simply engaged in a

merger, it was possible to retain these firms in analysis.

95

Over the period 1993-1997, five firms were excluded from analysis due to the

requirements of network analysis. These producers included Isuzu, Alfa, Peugeot, Saab

and Kia, encompassing both Asian and European manufacturers. While Isuzu did

participate in the market across these years, its level of production was highly

insignificant across the market segments it participated in and was excluded based on

this fact. Across these years of analysis (1993-1997), the contribution of these producers

to the United States Light Vehicle Industry was 0.4685679% of the total vehicles offered

for sale (See Table 3.4).

Year Segment Firm Individual Firm Percentage

Total Segment

Percentage

Total Yearly Percentage Excluded

1993 Lower Small Isuzu No Firm Output -- Small Specialty Isuzu 0.001189647 0.001189647 Upper Middle Alfa 4.65E-07 Peugeot No Firm Output Saab 0.000324059 0.000324524 Lower Luxury Saab 0.039798135 0.039798135 Middle Luxury Alfa 0.001426774 Peugeot No Firm Output Saab 0.014722879 0.021451048 Luxury Sport Alfa 0.010185076 0.010185076 0.07294843

1995 Lower Small Kia 0.063870007 0.063870007

Small Specialty Isuzu No Firm Output -- Lower Luxury Saab 0.063730778 0.063730778 Middle Luxury Alfa 0.000684531 Saab 0.010758189 0.01144272 Upper Small Isuzu No Firm Output -- Luxury Sport Alfa 0.000490765 0.000490765 0.13953427

1997 Lower Small Kia 0.181095064 0.181095064

Lower Luxury Saab 0.064145808 0.064145808 Middle Luxury Saab 0.010844329 0.010844329 0.2560852

1999 Lower Middle Daewoo 0.008266013 0.008266013

Lower Small Daewoo 0.052502028 Kia 0.463066646 0.515568674 Upper Small Daewoo 0.004782276 0.004782276 0.528616963

Table 3.4: Firms Excluded from Analysis – 1993, 1995, 1997, 1999 and Their Percentage Input Into Sales for those Years

96

In 1999, the majority of these previously excluded firms (excluding Isuzu and Kia) were

included in analysis due to the change in firm ownership (mergers with the major

producers included in Table 3.3) that occurred over this brief timeframe. Isuzu, however,

did not participate in the United States Light Vehicles Industry beyond 1997. In addition,

Saab production figures were included in 1999 as General Motors purchased Saab

outright in 1999. Daewoo was new to the market in 1999, and as this producer did not

participate throughout all years of analysis it was automatically excluded from analysis.

The contribution to total production of vehicles offered for sales by the producers

excluded from analysis in 1999 (Daewoo and Kia) was 0.528616963%.

All firms excluded from analysis and a breakdown of the percentages of each producer’s

contribution to vehicles offered for sale excluded from analysis for each year of research

(1993, 1995, 1997, 1999) can be found in Table 3.4.

Given the low percentage average of the contribution made by each producer excluded

from analysis, it is reasonable to surmise that the retained sample remained relatively

robust and representative of firms participating within the industry.

97

3.4 RESEARCH DESIGN

A total of three studies were completed in order to investigate the central question of

research. Study 1 was concerned with developing the rivalry measure to be employed

within the context of later research. Study 2 identified the strategic networks operational

in the United States Light Vehicles Industry over the timeframe 1993 – 1999. The

outcomes of Study 2, in conjunction with the outcomes of Study 1, provided the data

input into Study 3. Study 3 was concerned with identifying whether a statistical

relationship could be identified between strategic networks and rivalry.

3.4.1 Study 1: The Rivalry Measure

As detailed in Chapter 2 (Table 2.1), a number of different conceptualizations and models

exist upon which the study of rivalry could be based. Whilst a selection of these models

acknowledge the relevance of collaborative arrangements between firms within their

scope of analysis, many lack the practical advantage of actually implementing such

acknowledgement into a form which can be readily applied by theorists and practitioners

in real-world situations. Given that this thesis is primarily concerned with discerning

whether a relationship exists between strategic network membership and rivalry, the most

appropriate rivalry measure to employ emerges from within neo-classical economics,

namely a branch of oligopoly theory which focuses on concentration measures between

firms. In particular, the rivalry measure to be utilized in this research stems from the

Herfindahl Index which incorporates some modifications in order to ensure it is entirely

relevant to the industry under examination.

3.4.1.1 The Herfindahl Index

The Herfindahl Index, also known as Herfindahl-Hirschman Index or HHI, is a measure of

the size of firms in relationship to the industry and an indicator of the amount of

competition among them. Named after economists Orris C. Herfindahl and Albert O.

Hirschman, it is an economic concept but widely applied in studies of antitrust and

competition. The Herfindahl Index is defined as the sum of the squares of the market

shares of each individual firm and the level of competition amongst those firms (George,

Joll & Lynk, 1992).

98

The formula for the Herfindahl Index is represented as:

where si is the market share of firm i in the market, and n is the number of firms.

The formula represents the sum of the squares of the market shares of all firms within an

industry. As a result, the range of the Index can vary from 0 to 10,000, dependent on the

market share of the firms analysed within the industry. Lower Herfindahl scores generally

indicate a lower degree of concentration, loss of the pricing power of firms and an

increase in competition. Further, lower Herfindahl scores suggest that a significant

number of firms participate within the industry. Higher Herfindahl scores indicate the

industry potentially has fewer firm participants, greater pricing power and competitive

influence, or a single or small collection of firms occupying dominance in the industry. In

essence, a Herfindahl score of 10,000 indicates a pure monopoly, whereas the lower the

Herfindahl score, the greater level of competition within the industry (Kelly, 1981; George,

Joll & Lynk, 1992; Cool & Dierickx, 1993; Shepherd, 1972). The use of the Herfindahl

Index is well accepted in studies concerned with industry concentration issues, but is less

widely used in rivalry studies.

3.4.1.1.1 Limitations of the Herfindahl Index

According to Kelly (1981), the Herfindahl Index has not had wide appeal as a method of

examining measures of concentration and rivalry in research for a variety of reasons,

despite the fact that the concentration rationale is still considered valuable by researchers

on both theoretical and empirical grounds. Kelly (1981, p.50) provides three key reasons

for this, two of which are relevant within the context of this research:

1. A lack of longitudinal empirical data in which market share information could be

collated from, therefore limiting the ability of researchers to utilize this measure;

and

2. The Herfindahl Index (as a concentration measure) does not appear to provide a

clear intuitive meaning for researchers.

99

The initial concern raised by Kelly (1981) has been overcome given the range and nature

of the secondary data collected for this research investigation (Weinstock, 1982) (see

Section 3.3.1).

The outstanding limitation associated with applying the Herfindahl Index to the study of

concentration and/or rivalry is the second concern as raised by Kelly (1981). This

concern is based on the implicit assumption that all firms within an industry are vying for

the same consumers for the products or services offered, which, in many instances, may

not necessarily be accurate. For instance, many firms target multiple defined market

segments within the boundaries of the same industry sector, offering differing value

propositions through products and/or services to consumers. This could generate two

plausible flawed propositions.

In the first proposition, Firm A and Firm B could both compete in the same industry which

is sub-divided into multiple product market segments. In market segment 1, Firm A

outperforms Firm B, while in market segment 2, Firm B outperforms Firm A. Within the

context of the entire industry, market segment 1 may be of greater size within the

industry, therefore positioning Firm A as achieving more substantial results in relation to

the Herfindahl analysis. With this consideration in mind, the traditional Herfindahl Index

Formula (as listed in section 3.4.1) would provide distorted outcomes dependent on

which market segment dominated the industry.

In scenario two, Firm A and Firm B operate within the same industry. In market segment

1, Firm A has a dominant 75% share of the market, whereas Firm B has a 25% share. In

market segment 2, Firm A has a 25% share of the market, versus Firm B who possesses

a 75% share of the market. Assuming that both the market segments are of equal value

within the industry, any measure of concentration would correctly show that both firms

have the same degree of concentration in the industry. In this respect, the concentration

measure would not reflect the significant difference in rivalry that Firm A and Firm B face

in each market segment which could prove fundamental in any rivalry research

investigation (Cool & Dierickx, 1993).

100

Given consideration of the above two scenarios, the application of the traditional

Herfindahl Index would not be appropriate to assess the degree of rivalry a specific firm

faces within a given industry (Shepherd, 1972).

In light of the above arguments, it is necessary to modify the Herfindahl Index to account

for the differences in rivalry faced by firms in different market segments.

3.4.1.2 The Modified Herfindahl Index Utilised in this Research

To determine the degree to which rivalry from other firms impact into a given firm’s

profits, it is necessary to exclude that firm’s own market share from the traditional

concentration measure (Shepherd, 1972). In the instance of the Herfindahl Index, an

effective measure of rivalry can be obtained by excluding a firm’s own market share from

the overall industry market segment Herfindahl. ‘A negative correlation between this

rivalry index and return suggests that firms adversely affect each other’s profits;

conversely, a positive correlation indicates the absence of rivalry’ (Cool & Dierickx, 1993,

p. 50). This approach to assessing the degree of rivalry experienced by the firm at the

level of the product market segment was successfully applied by Cool and Dierickx in the

study of strategic group rivalry in the United States Pharmaceutical Industry (1993). The

use of this modified Herfindahl Index was later used as a rivalry measure in an empirical

study by Durisin & Von Krogh (2005) to investigate knowledge assets of global

investment banking. The use of this modified Herfindahl Index as used by Cool and

Dierickx (1993) and Durisin and Von Krogh (2005) overcomes the limitations as detailed

in Section 3.4.1.1.

Given that the United States Light Vehicles Industry has clearly distinct product market

segments (see Section 3.4.3), it was possible to assess firm rivalry at the market

segment level by utilising the modified Herfindahl Index as applied by Cool and Dierickx

(1993) and Durisin and Von Krogh (2005).

101

Specifically, the aggregate measure of rivalry, RIVj, was computed for each firm j as:

!

RIVj = wiji" RIVij; i =1, 11segments

with

wij = the ratio of the sales of firm j in segment i to its overall sales (segment

weight)

RIVij = the rivalry index for firm j in segment i (segment rivalry), i.e., the

overall segment Herfindahl from which the squared segment share of

firm j has been subtracted.

RIVij measures the rivalry a firm faces from all other firms in segment

i.

Based on this formula, the level of rivalry experienced by firms at the product market

segment of the United States Light Vehicles Industry was determined.

3.4.1.3 Product Market Segmentation

As indicated in section 3.4.1.2 (The Modified Herfindahl Index utilised in this Research) it

is necessary to classify data relating to the rivalry dimension of analysis according to

distinct product market segments.

When determining product market segments, it is necessary to take into consideration

features of the products offered within the industry that serve as points of differentiation

between product classes (Cool & Dierickx, 1993; Hatten, 1987). Given the volume of

vehicles offered for sale within the United States Light Vehicle Industry, the number of

producers participating within the industry and the duration over which this industry is

analysed for this thesis, it became necessary to defer product market classifications to an

expert source.

Wards Automotive Yearbook is a yearly publication specialising in the automotive

industry, especially the United States. In publication for over 100 years, this yearbook

serves as an authoritative reference guide to the industry, and contains up-to-date

102

information regarding vehicle specifics, as well as industry and firm specific data. This

publication additionally provides a substantial review of the industry on an annual basis.

The product market classifications utilised by Wards Automotive Yearbook serve as the

product market segments used for analysis purposes in this thesis. These product market

classifications stem from analysis of the different product features associated with each

vehicle offered for sale within the United States market, involving such vehicle-specific

attributes as engine type, technology, size, performance and the like (Ward’s Automotive

Yearbook). On the basis of these attributes, vehicles are classified to one of the following

10 categories:

1. Lower Small

2. Upper Small

3. Small Specialty

4. Lower Middle

5. Upper Middle

6. Middle Specialty

7. Lower Luxury

8. Middle Luxury

9. Upper Luxury

10. Luxury Specialty

11. Luxury Sport

Due to low participation (in respect to vehicles and producers), the product market

segments of ‘large’ and ‘large speciality’ have been omitted from analysis across all years

of investigation. These market segments collectively sold fewer than six vehicle types,

with only two producers active in these market segments.

These product categories and the attributes upon which vehicles were classified by

Ward’s Automotive Yearbook remain consistent throughout the timeframe of analysis,

therefore eliminating validity and reliability concerns pertaining to data classification.

103

3.4.2 Study 2: Network Configuration Determination

3.4.2.1 Defining the Network

Network research is unique in that the objective of research lies with examination of the

relations (ties) between actors or agents (nodes). Whereas paradigms of research

examine isolated and individualistic actors, the substantive focus of the network

perspective lies in discerning the ‘structured patterns of interaction’ between actors

(Brass et al, 2004). In this regard, a network can be defined as consisting of a finite set or

sets of actors and the relation or relations defined on them (Wasserman and Faust,

1999).

Actors comprising any given study utilizing the network perspective do not fundamentally

differ from traditional conceptualizations or even concepts (Borgatti and Foster, 2003).

The focus of research, however, is not based on determining or classifying the attributes

of actors, but rather on discerning the relations that either link or do not link actors within

a defined field.

A tie is a relation that exists between two actors (Scott, 2005). The tie between a

connected pair may be one-dimensional (eg. economic aid from one country to another),

undirected (eg. mutual communication between two individuals), dichotomous (eg. the

relation between two actors is either present or absent), or valued (eg. measured on a

scale, such as affect between team members) (Borgatti and Foster, 2003). Typically,

more than two actors are investigated, generating an interrelated arrangement of

relations between multiple actors. These relations do not specify the properties, attributes

or qualities of individual actors, but instead speak of the relations that characterize the

broader system to which actors belong (Wasserman and Faust, 1999; Scott, 2005).

At a fundamental level, network analysis is concerned with modeling the relationships

that exist among systems of actors (firms) comprising a population. Of central

importance, then, is the presence or absence of relations that may or may not exist

between actors within this defined population in order to determine the underlying

structure of the examined population (Knoke & Kuklinski, 1982).

This component of research is concerned with defining cohesive subsets of firms in the

Light Vehicle Industry that are homogeneous with respect to some aspect of network

104

properties. In the instance of this research, the network property to be investigated is the

horizontal relational ties that link collections of discrete corporate units – firms – to one

another. The relational tie, as defined in this research, represents the formal horizontal

business relationships that exist between firms within the industry that deliver finalized

vehicles for sale in the Light Vehicles Industry of the United States Automotive Industry.

The specific relationships of concern are those that facilitate either the transaction of

material or non-material property from one actor to another.

3.4.2.1.1 Classifying Network Data: Valuing Strategic Relationships as a Moderating

Consideration in Determining Strategic Network Configurations

Strategic linkages signify formal relationships between firms traditionally understood to

operate in competition with one another. Such linkages adopt multiple forms including

(but not limited to) joint venture agreements, strategic alliances, mergers/acquisitions,

technology licensing, development agreements, equity partnerships, manufacturing,

marketing or distribution collaborations (Nohria & Garcia-Pont, 1991).

The value of these relationships to participant firms varies depending on the type of

strategic relationship employed. For instance, a marketing collaboration between firms

engenders far different responsibilities, possible benefits and interdependencies for

participating firms than those associated with a joint venture agreement. In essence, the

risk/return ratios and the level of integration or participation by firms to different types of

collaboration bear different implications for the firms involved – some relatively minor,

while others far more substantial. (See Appendix A for the range of strategic relationships

assessed and their working definition).

It is therefore necessary to distinguish between the value differences inherent in

collaborative relationships. In order to account for these divergent relationships and their

variable value to the participant firms, it is necessary to classify the strength and

weakness of these ties between firms. Table 3.5, based on the conceptual and practical

work of Contractor and Lorange (1988) and later adapted by Nohria and Garcia-Pont

(1991), signifies one such way in which these differences can be accommodated. In

essence, this table provides an indication of the level of interdependence between firms

engaged in collaborative relationships. Collectively, the scope of these relationships were

105

found to provide a sound coverage of those strategic relationships found in the United

States automotive industry over the period of investigation.

TYPE

EXTENT OF INTERDEPENDENCE

SCORE

Mergers & Acquisitions

Very High

9

Independent Joint Ventures 8 Limited Cross Equity Ownership 7 Minority Equity 6 Broad R& D Agreements Moderate 5 Second Source Agreements 4 Component Sourcing Agreements 3 Know-how and Patent Licensing Agreements 2 Distribution Agreements Low 1

Table 3.5: Rating Criteria for the Strength of Strategic Linkages (adapted from Contractor & Lorange, 1988 by Nohria & Garcia-Pont, 1991). 3.4.2.1.2 Data Classification

According to the scale presented in Table 3.5, each strategic relationship between firms

participating in the United States Light Vehicles Industry was weighted against the criteria

modified by Nohria and Garcia-Pont (1991), based on the initial scale developed by

Contractor and Lorange (1988). Across all years of analysis, a total of 216 relationships

were classified. It was not uncommon to discover that a selection of firms demonstrated

multiple relationships with each other across the range of possible classifications. In such

instances, the strongest value (or relationship indicative of greatest interdependence)

was utilized (Nohria & Garcia-Pont, 1991). While the data was assigned values, the

direction of the relationship (the flow of resources or information from one firm to another)

was not assessed. The directionality of relations between participant firms was

considered redundant and not necessary to the determination of the final network

solutions – the presence or absence of relations was perceived to be the most important

consideration.

As the relationship information was presented in a qualitative format, it was necessary to

ensure the reliability of the value scores assigned to relationships during the initial data

classification process. In order to do so, an inter-reliability test was performed. Five

tertiary educated professionals from the fields of Commerce, Science, Psychology,

106

Mathematics and Engineering were given a sample of the qualitative relationship

information and a copy of the Rating Criteria for the Strength of Strategic Linkages

(adapted from Contractor & Lorange, 1988 by Nohria & Garcia-Pont, 1991), and were

requested to assign a value from this scale to a total of 45 randomly selected strategic

relationships as presented in their original form (qualitative). Within the context of this

inter-reliability testing, the qualitative information on inter-firm relationships given to

testers was random in nature, capturing a broad selection of the sample firms in analysis.

The mean result of this testing was 79.23% in comparison to initial classification,

ensuring that a strong consistency was evidenced in the assigning of values to inter-firm

relationships (Garcia-Pont, 1992; Scott, 2005).

3.4.2.1.3 Data Entry

A one-mode network n x n matrix configuration S was established to enter valued

relational data for each period of analysis (1993, 1995, 1997 and 1999), where n equals

the number of nodes in the network. In the instance of this research, n represents the

finite set of actors (16 in total) that comprised the final sample. Each cell, Sij, indicates

the strength of the relationship between nodes i and j. The data was entered on an actor

(firm) x actor (firm) matrix, with the value of ‘10’ assigned Firm A x Firm A to indicate that

the strongest relationship held by any firm was that of the firm to itself. Matrix S is

symmetrical (Sij =Sji) due to the non-directionality of the valued relations, aside from the

diagonal values of ‘10’ ascribed from Firm A x Firm A. The substantive valued relations

between firms – based on analysis of the strategic relationships between firms –

comprised the input into the data matrix (Scott, 2005; Wasserman and Faust, 1999).

3.4.2.2 Commentary on Analytical Approaches

Differing approaches have been adopted by theorists in the pursuit of defining strategic

blocks, alliance blocks, and strategic networks (Gulati et al, 2000; Boyd, 2004;

Vanhaverbeke & Noorderhaven, 2001; Nohria and Garcia-Pont, 1991). In common, a

central point of divergence amongst theorists has rested on whether such configurations

should be determined via structural (positional) or regular equivalence modeling or

relational equivalence modeling. Such modeling is performed via UCInet (Borgatti,

Everett & Freeman, 2002), an analytical program designed to study social network

arrangements, largely within the fields of sociology, anthropology and social psychology

(Wasserman & Faust, 1999; Scott, 2005).

107

Structural equivalence modeling, in its most basic form recognizes firms that share

similarities in terms of their structural position within the industry in relation to their

arrangement of strategic relationships with other firms in the same industry. In essence,

these ‘block’ configurations represent a collection of firms that, while not necessarily

related by interorganisational relations, have at their disposal a similar range of

relationships, and share in common ‘positional’ or structural equivalence with the other

firms they have been grouped with. In effect, these ‘block’ configurations do not

necessarily share a network of direct or indirect relations with their ‘block’ counterparts,

but rather occupy the same structural field within the industry according to the number

and types of strategic ties they share with other industry participants. In sum, while

structural equivalence modeling demonstrates many benefits in defining the structural

attributes of an industry, it fails to clearly distinguish strategic networks – those firms

directly or indirectly related to each other by interorganisational ties (Wasserman & Faust,

1999; Scott, 2005).

In contrast, relational equivalence modeling seeks to determine relationships between

firms based on their direct or indirect ties to one another within the industry. As such,

firms that share joint venture agreements, equity relationships, strategic alliances,

research and technology partnerships and distribution agreements (among other similar

formal strategic relationships) are grouped together to ultimately form the final

configuration of firm participants within the greater context of the entire industry, thus

forming ‘strategic networks’. A number of different analytical procedures exist that can be

operationalised to examine relational structures and cohesive subsets in particular. The

difficulty that can arise in this regard rests on whether the researcher has chosen to

utilize binary or valued data. This decision can define the scope of analytical options

available to the researcher (Wasserman& Faust, 1999; Scott, 2005). (For greater detail,

please refer to Appendix B).

3.4.2.3 Network Data Analysis Methods

To identify cohesive subgroups within a population of actors, a number of different

analytical procedures exist (please see Appendix B for a listing of possible analysis

methods). It is important to note that no definitive methodological approach exists by

which to analyse valued non-directional network data, therefore it was necessary to run a

range of procedures to determine the most appropriate method to employ. These

108

approaches included n-cliques, n-clans, k plex, k core and clustering, all of which are

based on the presumption of identifying cohesive sub-sets within the broader population

of actors in the network matrix.

The most definitive form of a cohesive subgroup in network methodology is known as a

clique which represents a sub-set of actors who are directly tied to each and every other

actor comprising the sub-set, and whose actors are not contained within any other clique

(Scott, 2005). As such, finding a population of actors who exhibit such network properties

is difficult given the strict guidelines guiding clique membership. Given the inter-

connected nature of many of the strategic relationships identified in the Light Vehicles

segment of the United States Automotive Industry (see Figures 4.2, 4.4, 4.6 and 4.8)

demonstrating the complex interrelationships between the actors under analysis, such

analysis would prove redundant, therefore eliminating this method of relational analysis

from being utilized.

The second form of analysis attempted was the process known as n-clique analysis. In

this procedure, the strict guidelines applicable to clique analysis are relaxed so that sub-

sets of cohesive actors can be determined on the condition that actors are connected to

every other actor in the sub-set by 1 or 2 connections from all other members (Scott,

1985). A limitation to this approach resides in the potential that ‘loose’ groups could be

identified, rather than discrete tight collections of actors. An additional limitation exists in

that members of the resulting n-clique may be designated into the n-clique through ties

with actors residing outside the n-clique itself (Wasserman & Faust, 1999). The

application of the n-clique approach to the analysis of the network data provided loose

groupings of actors. When compared against the raw data on strategic relationships, the

groupings identified by the n-clique procedure proved inconclusive, and in several cases

inconsistent with the raw data itself – For instance, the output clusters from the analysis

did not correlate with alliance relationships evidenced in the raw data. As a consequence,

the n-clique approach to network data analysis was dismissed.

The third approach to be employed was the n-clan procedure for clustering cohesive

subgroups from the broader population. Once again, the n-clan method relaxes the strict

criteria guiding the formal clique identification process. Unlike the n-clique procedure, the

n-clan analysis method insists ‘that all ties among actors occur through other members of

109

the group’ (Hanneman, 2000, p. 83) as opposed to the n-clique process of potentially

incorporating actors that reside beyond the cohesive sub-set partition. Reservations in

utilizing the n-clan approach exist, however, as few researchers have successfully

applied this approach (Sprenger & Stokman, 1989 in Wasserman & Faust, 1999, p. 262).

Application of the n-clan method to define cohesive sub-sets within the network

populations under analysis (1993, 1995, 1997, 1999) derived from the United States Light

Vehicles Industry proved difficult, with the sub-set outcomes proving inconsistent with the

raw data collated on the strategic relationships observed in the industry. This problem

was akin to that identified previously in the discussion of the n-clique approach to network

determination.

K Plex analysis is an alternative method of relaxing the strict assumptions of the clique,

demonstrating similarities to the n-clique approach. The K Plex procedure allows ‘that

actors may be members of a clique [cohesive sub-set] even if they have links to all but k

other members’ (Hanneman, 2000, p. 84). The output of K Plex procedures tend to

deliver distinctly different conceptualizations to those produced by alternative methods

due to the tendency for the algorithm to locate large numbers of smaller groupings. This

in large rests on the compulsion of the K Plex approach to focus on solidarity and

overlaps in cohesive group membership (overlap in cohesive subgroups is not

necessarily uncommon when dealing with large populations) (Scott, 2005). Researcher

discretion in specifying the appropriate value of k is considered fundamental in deriving

robust results. Despite varying the value of k, the outcomes of analysis delivered large

numbers of cohesive subgroups comprising limited group membership that while possibly

valid, were not functionally useful in completing the broader research question guiding

this thesis.

K Core analysis tends to be more inclusive than the K Plex approach. The K Core

procedure defines a group of actors within the population that demonstrate connection to

some specified number of other actors within the sub-set. Therefore, for an actor to

become a member of a group, it must be linked to all but k other actors in the group.

Researcher discretion determines the value of k, and as k becomes smaller, group sizes

increase. The outcomes of this approach tend to reveal subgroups of relatively high

cohesion, however these groups may be connected to each other rather loosely

(Hanneman, 2000; Wasserman & Faust, 1999; Scott, 2005). After running K Core

110

analysis on the network datasets, and providing for a variation in the value of k, the

cohesive subgroup results were not definitive, and when compared with the raw data,

failed to correspond with the overt relationships that were evidenced in the data sets.

3.4.2.3.1 Clustering as the Method of Analysis of Network Data Employed

Given the weaknesses associated with above approaches (and as detailed in Appendix

B), Johnson’s Hierarchical Clustering Procedure (1967) was employed to define strategic

networks in the United States Light Vehicles Industry over the period 1993 – 1999. The

clustering procedure has been previously used in network analysis (Lazzarini, 2007).

Cluster analysis is a procedure for locating groups of similar (or dissimilar) entities in the

data population, finding those collections of actors from the network population that best

represent their measured relations. As such, ‘the procedure is explicit’ (Wasserman &

Faust, 1999, p. 385). Given a symmetric n-by-n matrix, the clustering procedure finds

series of nested partitions from within the population of actors in the data, with each

identified partition ordered according to increasing (or decreasing) levels of similarity (or

dissimilarity). From the initial partition of actors, the algorithm proceeds to then join with

the next partition that is most similar (or dissimilar) which then forms a single entity and

continues to do so until all partitions have been joined and constitute a single cluster

whereby the primary output is either a dendogram or tree diagram (Johnson, 1967). ‘The

intuitive idea of a cluster corresponds to the idea of an area of relatively high density in a

graph’ (Scott, 2005, p. 126-127).

The use of clustering in network analysis can be performed according to two alternatives

(similarities or dissimilarities), and based on the criteria of either single linkage, complete

linkage or average linking methods. In an effort to identify cohesive sub-sets of actors

from the network population, the option of similarities is automatically chosen (Scott,

2005). The single linking method is considered least preferable, at times producing

‘chains’ where single actors are added one at a time, leading to difficulties for the

researcher in terms of determining clearly defined clusters. The complete linking method

tends to create more stable and homogeneous groups, however this method can produce

highly restrictive final cluster solutions that can be quite difficult to operationalise in

further research (Wasserman & Faust, 1999; Scott, 2005). The use of the average linking

method is postured to define the average similarity between cluster members (Borgatti,

111

Everett & Freeman, 1992). Thus, the use of the average linkage method overcomes the

limitations imposed by the single link approach, and reduces the restrictive outcomes

often produced by the complete linkage method.

In the instance of this component of research – that is, defining strategic networks within

the United States Light Vehicles Industry – Johnson’s Hierarchical Clustering Procedure

(1967) was utilized on the 1993, 1995, 1997 and 1999 proximity n x n matrices detailing

the valued relations of inter-organisational relationships amongst all firms in the industry.

UCInet provided the context upon which this clustering procedure was operationalised

(Borgatti, Everett & Freeman, 2002). Johnson’s Hierarchical Clustering Procedure was

set to identify similarities, where actors i and j are clustered together if X(i,j) is large

(Johnson, 1967). (This is in contrast to assigning the procedure to cluster dissimilarities,

where actors i and j are grouped together if X(i,j) is very small). To overcome the

limitations associated with both the use of single link and complete link methods, the

criteria of average linkage was employed (Hanneman, 2005).

3.4.2.3.2 Limitations

Several disadvantages are associated with the use of hierarchical clustering. The initial

and most distinct disadvantage resides in the fact that once an initial grouping is created

at the early stage of the procedure, this grouping cannot be ‘undone’ at a later stage, and

all consequent groupings are based on this initial cluster classification (Wasserman &

Faust, 1999). Secondly, the researcher is often compelled to make arbitrary choices

(single link, average link or complete link), and on the basis of these choices the final

solutions are generated. Finally, the clustering procedure does not always deliver unique

solutions, requiring the researcher to have a thorough understanding of the subject under

examination to formulate robust conclusions (Scott, 2005; Breiger, Boorman & Arabie,

1975).

3.4.3 Study 3: Testing for Within and Between Network Rivalry

The primary goal of Study 3 is to discern whether a relationship exists between the

presence of industry-embedded strategic network configurations and the patterns of

rivalry observed in the United States Light Vehicles Industry over the timeframe 1993-

1999. In essence, Study 3 explores the statistical relationship that exists between the

112

defined rivalry measure (Study 1), and the strategic networks identified for each period of

study (Study 2).

3.4.3.1 Testing for Within and Between Network Rivalry

In order to effectively investigate the research question guiding this research - Are

patterns of rivalry predicted by strategic network membership? – it is necessary to

examine the relationship between strategic network membership and rivalry from two

different, yet complementary, perspectives. Initially, it is imperative to assess whether the

defined network configurations compete as collective entities against each other in

defining intra-industry rivalry. Secondly, it is fundamental to ascertain whether the level of

rivalry observed between networks is a consequence of reduced rivalry within each

network, or whether, despite membership in a specified network, firms act as singular

entities in defining rivalry within the industry.

Section 3.4.1.2 specified the modified Herfindahl Index used to calculate rivalry at the

market segment level, whereby to determine the level of rivalry a firm faced in a given

market segment the firm’s individual market segment Herfindahl score was subtracted

from the overall market segment Herfindahl (Cool & Dierickx, 1993) (Study 1). Following

determination of the strategic networks defining each period of analysis – 1993, 1995,

1997 and 1999 (Study 2) – it becomes possible to assess the statistical relationship that

exists between the defined strategic networks and rivalry.

The modified Herfindahl formula detailed in 3.4.1.2 provides the basis upon which within

and between network rivalry could be determined. Specifically, this index could be

disaggregated to distinguish rivalry from firms belonging to the same strategic network,

and rivalry from network outsiders. These separate rivalry measures RIVjw (within

network rivalry) and RIVjb (between network rivalry) were calculated as follows:

!

RIVjw = wiji" RIVij

w ; i =1, 11segments

segments111,i ;RIVi ijwRIV bij

bj =!=

with

113

RIVijw = the within network rivalry index for firm j in segment i (i.e., summed

over all members of firm j’s strategic network, except firm j).

RIVijb = the between network rivalry index for firm j in segment i (i.e., summed over all

firms not in the strategic network of firm j).

Following this analysis which enabled the statistical assessment of the relationship

between strategic network membership and patterns of rivalry observed within the United

States Light Vehicles Industry for the years 1993, 1995, 1997 and 1999, closer

examination of the results was achieved via multiple MANOVA (Multivariate Analysis of

Variance) analyses. MANOVA analysis allows for the testing of mean differences among

groups across multiple dependent variables simultaneously by utilizing the sums of

squares and cross-product matrices, thereby circumventing the bias traditionally

associated with ANOVA tests (Sekaran, 2000). The MANOVA analyses were completed

via the analytical program SPSS to investigate the relevance of moderating influences

such as firms, number of participant networks, market segments and years.

3.5 CONCLUSION

This chapter has described the three studies undertaken in order to address the primary

research question characterizing this investigation – Are patterns of rivalry predicted by

strategic network membership? This chapter reviews the source of data, the rationale of

the time period chosen for investigation and the method of analysis. Given the limited use

of network methodology used within the strategic management domain, considerable

effort was made to explain the different approaches available and their relative

limitations. As a consequence of this review, Johnson’s Hierarchical Clustering

Technique (1967) proved the most appropriate and reliable method upon which to base

strategic network formation.

114

CHAPTER 4CHAPTER 4

R E S U L T SR E S U L T S

115

4.0 INTRODUCTION This chapter is concerned with detailing the results of analysis across the three studies

comprising the scope of this research endeavour. These three studies were completed in

order to initially determine whether a statistical relationship exists between strategic

network configurations and rivalry in an industry not dominated by technological or

regulatory imperatives, and to answer the primary question of research guiding

investigation – Are patterns of rivalry predicted by strategic network membership? Study

1 was concerned with defining the measure of rivalry that was to be utilised in research

over the timeframe 1993-1999. Study 2 was concerned with detailing the process by

which strategic networks were formulated for the same time period. Study 3 was focused

on exploring the statistical relationship that existed between the defined rivalry measures

generated from Study 1, and the strategic networks formulated for the years 1993, 1995,

1997 and 1999 that were the outcome of Study 2.

4.1 CHAPTER OVERVIEW

This chapter consists of 5 sections, and is primarily concerned with detailing the results of

the analyses completed in Study 1, Study 2 and Study 3. Section 4.2 provides a brief

review of the theoretical and practical origins by which the measure of rivalry was defined

for the purposes of achieving the desired outcomes of Study 1. The results of this Study

are detailed in Tables 4.1 – 4.4, which detail the vehicle manufacturer, market segment

Herfindahl Index, and the level of rivalry firms face from competing firms participating in

same product market segment. These analytical results constitute the rivalry input into

Study 3. Section 4.3 establishes the parameters under which strategic networks were

determined for the years 1993, 1995, 1997 and 1999, achieved via the use of Johnson’s

Hierarchical Clustering (1967). Tables 4.5 – 4.8 detail the outcomes of this analysis,

identifying the strategic networks operating in the United States Light Vehicles Industry

throughout the period of study. These strategic network configurations provided the

foundation for the strategic network component of analysis utilised in Study 3.

Section 4.4 is concerned with the calculation of the level of within and between network

rivalry at the product market segment for the years 1993, 1995, 1997 and 1999 (Tables

4.9 – 4.12), comprising the basis of Study 3. In order to determine whether strategic

networks act as entities of collective rivalry, it is necessary to ascertain whether the level

of rivalry observed within the identified strategic network configurations is lower than that

116

observed between the strategic networks operating in the industry, therefore the need to

determine within and between measures of network rivalry. Statistical analysis of the

relationship between the patterns of firm rivalry observed at the level of the product

market segment was then assessed against the prescribed strategic networks defined for

each period of study, and in conjunction with the application of MANOVA analysis, the

outcomes of analysis produce a conclusion to the research question guiding investigation

– Are patterns of rivalry predicted by strategic network membership? Section 4.5 provides

a combined summary of the results evidenced from Study 1, Study 2 and Study 3.

4.2 STUDY 1: THE RIVALRY MEASURE – RESULTS

As detailed in Chapter 3, Methods of Research, the measure of rivalry employed in this

research draws its origins from neo-classical economics, specifically a branch of

oligopoly theory. The Herfindahl Index – traditionally utilised as a measure of

concentration and competition within an industry – served as the foundation for the

formulation of the rivalry measure. In order to capitalise on the capacity for the Herfindahl

Index to be used as an effective measure of rivalry, it was necessary to modify the

traditional formula by initially operationalising the measure at the level of the market

segment. Second, it was necessary to exclude the firm’s own market share from the

overall industry segment Herfindahl measure (Shepherd, 1972) as successfully applied

by Cool and Dierickx (1993) and Durisin and Von Krogh (2005). In this way, it was

possible to determine the level of rivalry firms experienced at the product market

segment.

Rivalry scores were determined across 11 market segments of the United States Light

Vehicle Industry – lower small, upper small, small specialty, lower middle, upper middle,

middle specialty, lower luxury, middle luxury, upper luxury, luxury specialty and luxury

sport (Ward’s Automotive Yearbook). Tables 4. 1, 4.2, 4.3 and 4.4 detail the vehicle

manufacturer, total market segment Herfindahl Index, and the level of rivalry faced by

firms participating in the specified market segments. These rivalry scores, in conjunction

with the outcomes of Study 2, serve as the foundation of the data input for Study 3.

117

MARKET

SEGMENT

SEGMENT

HERFINDAHL SCORE

PRODUCER

RIVALRY SCORE

MARKET

SEGMENT

SEGMENT

HERFINDAHL SCORE

PRODUCER

RIVALRY SCORE

MARKET

SEGMENT

SEGMENT

HERFINDAHL SCORE

PRODUCER

RIVALRY SCORE

Lower Small 0.194750619 Chrysler -0.023619248 Upper Middle 0.231140621 Chrysler 0.228721584 Upper Luxury 0.200983686 BMW 0.16618664 General Motors 0.029891737 General Motors 0.109672757 General Motors 0.156068277 Mitsubishi 0.040087452 Honda 0.20568209 Daimler Benz 0.148485005 Subaru 0.041038451 Mitsubishi 0.231140621 Ford 0.19855296 Suzuki 0.040991663 Volvo 0.231140481 Nissan 0.1960296 Honda -0.035836586 Ford 0.172330012 Toyota 0.139620941 Ford 0.039562595 Nissan 0.228525193 Volkswagon 0.200958695 Mazda 0.040977945 Toyota 0.210809096 Luxury Specialty 0.287265776 BMW 0.287140696 Nissan 0.009442874 Volkswagon 0.231103131 General Motors 0.159667151 Volkswagon 0.040946781 Middle Specialty 0.272105812 Chrysler 0.254036889 Daimler Benz 0.286134568 Hyundai 0.03324357 General Motors 0.269607306 Subaru 0.284510469 Upper Small 0.27878484 General Motors 0.202815088 Honda 0.267729471 Ford 0.173854524 Honda 0.277407839 Subaru 0.272105812 Nissan 0.287255491 Subaru 0.278212877 Ford 0.018166161 Toyota 0.245031759 Ford 0.121262199 Mazda 0.266054064 Volkswagon 0.287265776 Mazda 0.275975888 Nissan 0.268374544 Luxury Sport 0.204703862 Chrysler 0.204500018 Nissan 0.278775501 Toyota 0.2639981 General Motors 0.075664982 Toyota 0.238346327 Volkswagon 0.27183038 Honda 0.204570245 Volkswagon 0.27869816 Lower Luxury 0.173789438 BMW 0.159715985 Daimler Benz 0.199504901 Small Specialty 0.045037407 Chrysler 0.001886717 Chrysler 0.117412629 Ford 0.203056152 General Motors 0.010514941 General Motors 0.137607571 Mazda 0.197699343 Honda 0.045037407 Honda 0.170621709 Mitsubishi 0.165570102 Mitsubishi 0.000334551 Mitsubishi 0.156332144 Nissan 0.18625424 Ford 0.043557162 Volvo 0.170619323 Porsche 0.201737299 Mazda 0.015663899 Toyota 0.130427858 Toyota 0.20377748 Nissan 0.044037539 Volkswagon 0.173788848 Volkswagon 0.204703862 Toyota 0.034391856 Middle Luxury 0.24235455 BMW 0.240721374 Hyundai 0.042499174 Chrysler 0.24223824 Lower Middle 0.316990747 Chrysler 0.282727959 General Motors 0.088509084 General Motors 0.060120088 Honda 0.236601062 Mitsubishi 0.315447313 Daimler Benz 0.241675957 Subaru 0.313584134 Volvo 0.230868158 Mazda 0.312343063 Ford 0.176359153 Nissan 0.300977823 Mazda 0.241357982 Hyundai 0.316744102 Nissan 0.240998538 Volkswagon 0.2418614

Table 4.1: 1993 Market Segment Herfindahl Scores and Producer Rivalry Scores

118

MARKET SEGMENT

SEGMENT

HERFINDAHL SCORE

PRODUCER

RIVALRY SCORE

MARKET

SEGMENT

SEGMENT

HERFINDAHL SCORE

PRODUCER

RIVALRY SCORE

MARKET

SEGMENT

SEGMENT

HERFINDAHL SCORE

PRODUCER

RIVALRY SCORE

Lower Small 0.263130111 Chrysler 0.262458455 Upper Middle 0.229125634 Chrysler 0.228395619 Upper Luxury 0.199915419 BMW 0.150741077 General Motors 0.167774404 General Motors 0.093617094 General Motors 0.152337397 Mitsubishi 0.232500292 Honda 0.205923258 Daimler Benz 0.136726463 Suzuki 0.262664065 Mitsubishi 0.229125634 Volkswagon 0.199884162 Ford 0.24894975 Ford 0.185365486 Ford 0.193543139 Mazda 0.263130099 Nissan 0.225833168 Nissan 0.197802225 Subaru 0.263130111 Subaru 0.22802979 Toyota 0.168458052 Volkswagon 0.263129902 Toyota 0.207628466 Luxury Specialty 0.457244602 BMW 0.457082472 Hyundai 0.14130381 Volkswagon 0.229086557 General Motors 0.066325734 Upper Small 0.197790679 Chrysler 0.182175855 Middle Specialty 0.291254067 Chrysler 0.152213981 Ford 0.402417976 General Motors 0.079559342 General Motors 0.284427396 Nissan 0.457244602 Honda 0.196376269 Honda 0.290656361 Subaru 0.456659442 Suzuki 0.197788029 Ford 0.154030232 Toyota 0.446492785 Ford 0.166550281 Mazda 0.286219684 Luxury Sport 0.167784387 Chrysler 0.16021998 Mazda 0.196721556 Nissan 0.290591868 General Motors 0.071427383 Nissan 0.192868341 Toyota 0.289509265 Honda 0.167575054 Subaru 0.197629338 Volkswagon 0.291129685 Daimler Benz 0.154793185 Toyota 0.175038962 Lower Luxury 0.158438859 BMW 0.122165761 Mitsubishi 0.138643658 Volkswagon 0.195408139 Chrysler 0.155025868 Ford 0.162275214 Small Specialty 0.282482491 Chrysler 0.254528691 General Motors 0.150724514 Mazda 0.167260102 General Motors 0.282482323 Honda 0.155023021 Nissan 0.163112925 Mitsubishi 0.104433049 Volvo 0.114247477 Porsche 0.158862967 Ford 0.282482491 Volkswagon 0.157552042 Toyota 0.165889012 Mazda 0.27827808 Mazda 0.152807279 Nissan 0.213704217 Nissan 0.155012435 Toyota 0.280447905 Toyota 0.104952477 Nissan 0.28102068 Middle Luxury 0.233951282 Chrysler 0.231087367 Lower Middle 0.267359046 Chrysler 0.263206038 General Motors 0.076742862 General Motors 0.138616729 Honda 0.232879012 Honda 0.229850617 Daimler Benz 0.231559798 Mitsubishi 0.265672636 Mitsubishi 0.233603953 Ford 0.240229361 Volvo 0.23138237 Mazda 0.263467052 Volkswagon 0.233709717 Nissan 0.25673893 Ford 0.176100653 Hyundai 0.262989409 Mazda 0.233904991 Nissan 0.230193338 Toyota 0.228348757


119

MARKET

SEGMENT

SEGMENT

HERFINDAHL SCORE

PRODUCER

RIVALRY SCORE

MARKET

SEGMENT

SEGMENT

HERFINDAHL SCORE

PRODUCER

RIVALRY SCORE

MARKET

SEGMENT

SEGMENT

HERFINDAHL SCORE

PRODUCER

RIVALRY SCORE

Lower Small 0.25871687 Chrysler 0.25871687 Upper Middle 0.209550981 Chrysler 0.209351409 Upper Luxury 0.218802959 BMW 0.148456074 General Motors 0.141611955 General Motors 0.117098082 General Motors 0.193367378 Ford 0.216897928 Ford 0.164964329 Daimler Benz 0.119339212 Mitsubishi 0.220649154 Honda 0.173684829 Ford 0.214275601 Suzuki 0.258618048 Nissan 0.206506912 Nissan 0.21568707 Volkswagon 0.25871687 Subaru 0.207820048 Toyota 0.203013665 Hyundai 0.197090396 Toyota 0.177924599 Volkswagon 0.218678753 Upper Small 0.187042206 Chrysler 0.16048262 Volkswagon 0.209506658 Luxury Specialty 0.297506203 BMW 0.297457542 General Motors 0.082730033 Middle Specialty 0.253853055 Chrysler 0.163924049 General Motors 0.143611727 Ford 0.157556341 General Motors 0.205237181 Daimler Benz 0.297310926 Honda 0.186552827 Ford 0.144095386 Ford 0.264688981 Mazda 0.18624258 Honda 0.252126387 Honda 0.190457555 Nissan 0.182918658 Mazda 0.251176704 Subaru 0.297453846 Subaru 0.186880648 Nissan 0.253775572 Toyota 0.294257967 Suzuki 0.187028857 Toyota 0.253347893 Volkswagon 0.297304876 Toyota 0.169843538 Volkswagon 0.253288213 Luxury Sport 0.18064336 BMW 0.130311962 Volkswagon 0.183612766 Lower Luxury 0.205591804 BMW 0.181245215 Chrysler 0.180322379 Hyundai 0.186573188 Chrysler 0.205591804 General Motors 0.112376239 Small Specialty 0.342286535 Chrysler 0.334070737 General Motors 0.198352746 Daimler Benz 0.151967818 Honda 0.339814786 Volvo 0.201989085 Ford 0.174479546 Mazda 0.342286532 Honda 0.200808962 Honda 0.180621159 Mitsubishi 0.071719885 Mazda 0.202720194 Mazda 0.180643341 Nissan 0.288835796 Mitsubishi 0.204442121 Mitsubishi 0.175868847 Toyota 0.341674325 Nissan 0.196692888 Nissan 0.180511865 Hyundai 0.335317152 Toyota 0.056743564 Toyota 0.18039143 Lower Middle 0.2069046 Chrysler 0.192272608 Volkswagon 0.20173966 Porsche 0.15893901 General Motors 0.08452151 Middle Luxury 0.260329855 Chrysler 0.256578892 Ford 0.188555136 General Motors 0.093088164 Honda 0.169748089 Volvo 0.239751672 Hyundai 0.206661121 Daimler Benz 0.255971119 Mazda 0.204047562 Ford 0.197538648 Mitsubishi 0.206001908 Honda 0.259275178 Nissan 0.196524269 Mazda 0.260329855 Mitsubishi 0.260329855 Nissan 0.260242993 Toyota 0.260269702 Volkswagon 0.259922471


120

MARKET

SEGMENT

SEGMENT

HERFINDAHL SCORE

PRODUCER

RIVALRY SCORE

MARKET

SEGMENT

SEGMENT

HERFINDAHL SCORE

PRODUCER

RIVALRY SCORE

MARKET

SEGMENT

SEGMENT HERFINDAHL SCORE

PRODUCER

RIVALRY SCORE

Lower Small 0.383807664 General Motors 0.245462486 Upper Middle 0.177984515 Chrysler 0.177833331 Middle Luxury 0.227104117 BMW 0.222368594 Ford 0.368930721 General Motors 0.097515246 Chrysler 0.227104117 Mitsubishi 0.383807664 Toyota 0.148067901 Daimler Benz 0.206362707 Suzuki 0.383098684 Volkswagon 0.177292705 General Motors 0.095018624 Hyundai 0.1539311 Ford 0.145158701 Toyota 0.225079399 Upper Small 0.19483548 Chrysler 0.184172639 Volvo 0.177963109 Volkswagon 0.224890288 General Motors 0.07560628 Honda 0.150395356 Ford 0.166036293 Toyota 0.17523794 Mazda 0.176863966 Volvo 0.223448007 Volkswagon 0.191524172 Mitsubishi 0.177151532 Honda 0.226524908 Ford 0.158459109 Nissan 0.174753129 Nissan 0.227104116 Honda 0.194835479 Subaru 0.184193801 Upper Luxury 0.185328139 BMW 0.168779042 Mazda 0.19349194 Middle Specialty 0.328976578 Chrysler 0.318883423 Daimler Benz 0.143231556 Mitsubishi 0.194134184 General Motors 0.286418285 General Motors 0.129355349 Suzuki 0.194788268 Toyota 0.3275214 Toyota 0.158416428 Nissan 0.193577358 Volkswagon 0.328257772 Volkswagon 0.185021723 Subaru 0.194717223 Ford 0.058713813 Ford 0.143794232 Hyundai 0.192645689 Honda 0.32827769 Nissan 0.183370504 Small Specialty 0.680627943 Chrysler 0.680627943 Mazda 0.32727532 Luxury Specialty 0.309424003 BMW 0.309423999 Toyota 0.680627935 Mitsubishi 0.327493257 Chrysler 0.086062494 Honda 0.68062784 Nissan 0.328971665 Daimler Benz 0.286124405 Mitsubishi 0.029927366 Lower Luxury 0.138525557 BMW 0.116009331 General Motors 0.283875645 Nissan 0.680176603 Chrysler 0.114141956 Toyota 0.308878686 Hyundai 0.651152028 General Motors 0.136573775 Ford 0.309424003 Lower Middle 0.229435471 Chrysler 0.220497745 Toyota 0.089435781 Honda 0.272754785 General Motors 0.081009012 Volkswagon 0.134600135 Luxury Sport 0.175674983 BMW 0.143352428 Volkswagon 0.222388623 Volvo 0.119383924 Porsche 0.140679645 Ford 0.216819061 Honda 0.126421911 Chrysler 0.174587424 Honda 0.187222826 Mazda 0.137130889 Daimler Benz 0.148329148 Mazda 0.229435471 Mitsubishi 0.138153106 General Motors 0.102915397 Mitsubishi 0.229435471 Nissan 0.134879208 Toyota 0.175674937 Nissan 0.219615603 Volkswagon 0.173554111 Hyundai 0.229059957 Ford 0.172633592 Honda 0.174612107 Mitsubishi 0.174736221 Nissan 0.17567482


121

4.3 STUDY 2: STRATEGIC NETWORK DETERMINATION - RESULTS

At a fundamental level, network analysis is concerned with modelling the relationships that

exist among systems of actors (firms) comprising the population under investigation. In an

effort to identify cohesive subsets of actors within the population, the network property to

be assessed is the relational tie that links collections of discrete corporate units – firms – to

one another. The relational tie examined in the instance of this research was the strongest

horizontal strategic relationship connecting firms within the defined sample. Strategic

relationships were rated according to their level of interdependence the relationship had

between the participant firms, according to a scale developed by Contractor and Lorange

(1988), and later modified by Nohria and Garcia-Pont (1991). The greater the level of

interdependence observed between firms, the greater the value assigned. Based on a

rating scale from 1 (low) to 9 (very high), the valued relations formed the basis for the one-

mode network n x n matrix configuration developed for each year of analysis – 1993, 1995,

1997 and 1999.

As detailed in Chapter 3, Section 3.5.3, no definitive methodological approach exists by

which to analyse valued non-directional network data. After pursuing a range of analytic

procedures for identifying cohesive subsets of actors within the defined sample, the most

appropriate and reliable method proved to be Johnson’s Hierarchical Clustering Technique

(1967), operationalised using UCInet, networking analytical software created by Borgatti,

Everett and Freeman (2002).

The clustering procedures were performed based on identifying similarities, utilising the

average linking method. The analysis outcomes and measures of cluster adequacy can be

found in Appendix C.

Determination of final cluster solutions (networks) were based on assessment of the

resulting dendograms generated from the Johnson’s Hierarchical Clustering Procedure for

each time period against close examination of the raw relational data. In conjunction,

secondary reference was made to the UCInet Netdraw simulations of the strategic

relationships detailed to exist between the firms comprising the sample for each time

period (see Figures 4.2, 4.4, 4.6 and 4.8). While the dendograms for each period (see

Figures 4.1, 4.3, 4.5 and 4.7) provided the foundation for determination of the final network

configurations listed in Tables 4.5 – 4.8, comparison to the raw relational data was

122

imperative in terms of distinguishing where cut-offs in cluster partitions were made in the

hierarchical clustering outcomes. The raw data, comprising matrices of valued relations –

indicative of the level of interdependence between firms involved in the strategic

relationship – allowed for decision-making to occur with reference to an informative source.

4.3.1 1993 Strategic Network Configurations

Network Firms Comprising Network Network 1 BMW (isolate) Network 2 Chrysler, General Motors, Suzuki, Daimler Benz, Mitsubishi, Honda,

Subaru, Volvo Network 3 Mazda, Ford, Nissan, Toyota, Volkswagon, Porsche Network 4 Hyundai (isolate)

Table 4.5: 1993 Strategic Network Configurations for the United States Light Vehicles Industry 4.3.2 1995 Strategic Network Configurations

Network Firms Comprising Network Network 1 BMW, Honda, General Motors, Chrysler, Suzuki, Daimler Benz,

Mitsubishi, Volvo Network 2 Ford, Mazda, Nissan, Subaru, Porsche, Toyota, Volkswagon Network 3 Hyundai (isolate)

Table 4.6: 1995 Strategic Network Configurations for the United States Light Vehicles Industry 4.3.3 1997 Strategic Network Configurations

Network Firms Comprising Networks Network 1 BMW, Chrysler, Volvo, General Motors Network 2 Daimler Benz, Mitsubishi, Honda, Toyota, Volkswagan, Ford, Mazda,

Suzuki, Nissan, Subaru Network 3 Hyundai (isolate) Network 4 Porsche (isolate)

Table 4.7: 1997 Strategic Network Configurations for the United States Light Vehicles Industry

123

Figure 4.1: 1993 Network Data Output Dendogram

124

Figure 4.2: 1993 Netdraw Simulation of Strategic Relationships in the United States Automotive Industry

125


126


127


128


129


130


131

4.3.4 1999 Strategic Network Configurations

Network Firms Comprising Networks Network 1 BMW, Porsche Network 2 Chrysler-Daimler Benz (Daimler Chrysler), General Motors, Toyota,

Volkswagon Network 3 Ford, Volvo, Mazda, Honda, Mitsubishi, Suzuki Network 4 Nissan, Subaru Network 5 Hyundai (isolate)

Table 4.8: 1999 Strategic Network Configurations for the United States Light Vehicles Industry 4.4 STUDY 3: TESTING FOR WITHIN AND BETWEEN NETWORK RIVALRY - RESULTS As defined in Section 4.1, the purpose of Study 3 is to utilise the results obtained from

Study 1 (rivalry at the market segment level) and Study 2 (strategic network configurations)

to statistically determine the relationship between these constructs in the Light Vehicles

Industry of the United States over the period 1993 – 1999. Fundamental to Study 3 is

ascertaining whether strategic networks act in a collective manner when competing against

other networks identified in the industry. In order to assess whether this is the case, it is

necessary to derive rivalry measures for individual firms and networks at the level of within

the network and between the participant networks. By doing so, it is possible to either

validate or invalidate the proposition that network membership plays a critical role in rivalry

outcomes in the industry – particularly an industry not dominated by technological or

regulatory imperatives – but further, to answer the central question of research: Are

patterns of rivalry predicted by strategic network membership?

The following analyses first details the results of assessing within and between rivalry

measures for individual firms categorised by market segment and network membership

(Tables 4.9 – 4.12). Following these analyses for the years 1933, 1995, 1997 and 1999, an

initial summary conclusion on whether network membership plays a role in rivalry

outcomes is provided. To further validate these results, MANOVA analyses are presented,

finalising the outcome to the research endeavour at hand.

132

MARKET SEGMENT

NETWORK

PRODUCER

TOTAL WITHIN NETWORK SEGMENT HERFINDAHL

TOTAL WITHIN NETWORK SEGMENT RIVALRY

TOTAL BETWEEN NETWORK SEGMENT HERFINDHAL

TOTAL BETWEEN NETWORK SEGMENT RIVALRY

MARKET SEGMENT

NETWORK

PRODUCER





Lower Small 2 Chrysler 0.859129304 0.045850512 Middle Specialty 2 Chrysler 0.109506701 0.614394408 2 General Motors 0.97570549 0.045850512 2 General Motors 0.357521677 0.614394408 2 Mitsubishi 0.997917333 0.045850512 2 Honda 0.327610391 0.614394408 2 Subaru 0.999989129 0.045850512 2 Subaru 0.397319384 0.397319384 0.033617971 0.614394408 2 Suzuki 0.9998872 0.045850512 3 Ford 0.03234365 0.033617971 2 Honda 0.334858283 0.832513261 0.477128725 0.045850512 3 Mazda 0.473691703 0.033617971 3 Ford 0.972995214 0.484928597 3 Nissan 0.477823164 0.033617971 3 Mazda 0.998805615 0.484928597 3 Toyota 0.470031195 0.033617971 3 Nissan 0.423730464 0.484928597 3 Volkswagon 0.484466441 0.483976053 0.614394408 0.033617971 3 Volkswagon 0.606231414 0.998237293 0.03805064 0.484928597 Lower Luxury 1 BMW 1 0 0.014073453 0.26965757 4 Hyundai 1 0 0.007799872 0.515179365 2 Chrysler 0.132670274 0.165078352 Upper Small 2 General Motors 0.017196773 0.451898864 2 General Motors 0.177341868 0.165078352 2 Honda 0.675369485 0.451898864 2 Honda 0.250369767 0.165078352 2 Subaru 0.687519519 0.682472779 0.090182569 0.451898864 2 Mitsubishi 0.218760978 0.165078352 3 Ford 0.098500424 0.090182569 2 Volvo 0.257376844 0.250364489 0.118652672 0.165078352 3 Mazda 0.450095698 0.090182569 3 Toyota 1.3511E-05 0.132726125 3 Nissan 0.45645797 0.090182569 3 Volkswagon 0.992675572 0.992662061 0.151004899 0.283730082 3 Toyota 0.364580473 0.090182569 Middle Luxury 1 BMW 1 0 0.001633176 0.416951358 3 Volkswagon 0.456479193 0.456282209 0.451898864 0.090182569 2 Chrysler 0.45845661 0.087499858 Small Specialty 2 Chrysler 0.216476351 0.069211164 2 General Motors 0.048136793 0.087499858 2 General Motors 0.240052228 0.069211164 2 Honda 0.443410369 0.087499858 2 Honda 0.334381885 0.069211164 2 Daimler Benz 0.456955815 0.087499858 2 Mitsubishi 0.334381885 0.212235191 0.342630056 0.069211164 2 Volvo 0.458767053 0.428108625 0.331084676 0.430966585 3 Ford 0.345306849 0.345168289 3 Ford 0.023566544 0.332717852 3 Mazda 0.110494894 0.345168289 3 Mazda 0.561846188 0.332717852 3 Nissan 0.349350769 0.345168289 3 Nissan 0.558869499 0.332717852 3 Toyota 0.357767895 0.268151174 0.06667293 0.345168289 3 Volkswagon 0.570099138 0.566015182 0.570099138 0.332717852 4 Hyundai 1 0 0.002538233 0.409302986 Upper Luxury 1 BMW 1 0 0.034797046 0.255742115 Lower Middle 2 Chrysler 0.419964774 0.024780244 2 General Motors 0.269870909 0.130423313 2 General Motors 0.06289804 0.024780244 2 Daimler Benz 0.500759799 0.23088889 0.160115848 0.130423313 2 Mitsubishi 0.472447162 0.024780244 3 Ford 0.748240643 0.194912894 2 Subaru 0.474922856 0.469458593 0.790243283 0.024780244 3 Nissan 0.730045496 0.194912894 3 Mazda 0.422344535 0.790489928 3 Toyota 0.323300646 0.194912894 3 Nissan 0.544928269 0.122583734 0.024533598 0.790489928 3 Volkswagon 0.765767835 0.765587629 0.095626267 0.194912894 4 Hyundai 1 0 0.000246645 0.814776881 Luxury Specialty 1 BMW 1 0 0.00012508 0.441581145 Lower Small 2 Chrysler 0.193987587 0.045850512 2 General Motors 0.019774176 0.278252709 2 General Motors 0.310563773 0.045850512 2 Daimler Benz 0.66322692 0.278252709 2 Mitsubishi 0.332775617 0.045850512 2 Subaru 0.668982387 0.654963677 0.163453517 0.278252709 2 Subaru 0.334847412 0.045850512 3 Ford 0.141973446 0.163578597 2 Suzuki 0.334745483 0.045850512 3 Nissan 0.523088176 0.163578597 2 Honda 0.334858283 0.167371544 0.477128725 0.045850512 3 Toyota 0.381183864 0.163578597 3 Ford 0.579226628 0.484928597 3 Volkswagon 0.523122743 0.523122743 0.278127629 0.163578597 3 Mazda 0.605037029 0.484928597 Luxury Sport 2 Chrysler 0.642939216 0.189513427 3 Nissan 0.029961878 0.484928597 2 General Motors 0.026490623 0.189513427 3 Volkswagon 0.606231414 0.472492705 0.03805064 0.484928597 2 Honda 0.643275234 0.189513427 Upper Middle 2 Chrysler 0.089650336 0.140498774 2 Daimler Benz 0.643914567 0.619038628 0.173420367 0.189513427 2 General Motors 0.398401305 0.140498774 3 Ford 0.232394589 0.173420367 2 Honda 0.480271948 0.140498774 3 Mazda 0.214215882 0.173420367 2 Mitsubishi 0.480271498 0.140498774 3 Mitsubishi 0.105183035 0.173420367 2 Volvo 0.480271948 0.117456982 0.274474448 0.140498774 3 Nissan 0.175376116 0.173420367 3 Ford 0.404630216 0.274474448 3 Porsche 0.22791897 0.173420367 3 Nissan 0.314095959 0.274474448 3 Toyota 0.234842467 0.173420367 3 Toyota 0.417804208 0.274474448 3 Volkswagon 0.237986212 0.237986212 0.189513427 0.173420367 3 Volkswagon 0.417995788 0.472492705 0.140498774 0.274474448

Table 4.9: 1993 Market Segment Network Within and Between Herfindahl Scores and Rivalry Scores

133

MARKET SEGMENT

NETWORK

PRODUCER





MARKET SEGMENT

NETWORK

PRODUCER





Lower Small 1 Chrysler 0.447939569 0.136023042 Lower Luxury 1 BMW 0.16027 0.098548806 1 General Motors 0.112532645 0.136023042 1 Chrysler 0.24993594 0.098548806 1 Mitsubishi 0.341816361 0.136023042 1 General Motors 0.23819876 0.098548806 1 Suzuki 0.450318831 0.448667918 0.236356357 0.136023042 1 Honda 0.24992817 0.098548806 2 Ford 1.54293E-05 0.358182657 1 Volvo 0.259249027 0.138663241 0.293164898 0.098548806 2 Mazda 0.990558692 0.358182657 2 Volkswagon 0.401612891 0.293164898 2 Subaru 0.990559539 0.358182657 2 Mazda 0.3711456 0.293164898 2 Volkswagon 0.990559539 0.990544957 0.014196741 0.358182657 2 Nissan 0.385305447 0.293164898 3 Hyundai 1 0 0.121826301 0.250553098 2 Toyota 0.407307361 0.063858146 0.098548806 0.293164898 Upper Small 1 Chrysler 0.463558805 0.137282865 Middle Luxury 1 Chrysler 0.452931091 0.103290157 1 General Motors 0.065987343 0.137282865 1 General Motors 0.025593829 0.103290157 1 Honda 0.518576066 0.137282865 1 Honda 0.457891662 0.103290157 1 Suzuki 0.524055974 0.524045707 0.215330909 0.137282865 1 Daimler Benz 0.454239122 0.103290157 2 Ford 0.129274122 0.215330909 1 Mitsubishi 0.459898822 0.103290157 2 Mazda 0.25393797 0.215330909 1 Volvo 0.46086048 0.453747874 0.294765814 0.103290157 2 Nissan 0.238016979 0.215330909 2 Volkswagon 0.422426567 0.294765814 2 Subaru 0.257688809 0.215330909 2 Ford 0.060598669 0.294765814 2 Toyota 0.164348266 0.215330909 2 Mazda 0.423653034 0.294765814 2 Volkswagon 0.258355449 0.248511098 0.137282865 0.215330909 2 Nissan 0.400341087 0.294765814 Small Specialty 1 Chrysler 0.512249371 0.1015391 2 Toyota 0.423943775 0.388755742 0.103290157 0.294765814 1 General Motors 0.59267207 0.1015391 Upper Luxury 1 BMW 0.231813595 0.056363785 1 Mitsubishi 0.592672553 0.080423665 0.340749322 0.1015391 1 General Motors 0.235154379 0.056363785 2 Ford 0.541501094 0.342211133 1 Daimler Benz 0.334725863 0.202483752 0.547529119 0.056363785 2 Mazda 0.511152174 0.342211133 2 Volkswagon 0.41901303 0.547529119 2 Nissan 0.045035277 0.342211133 2 Ford 0.352493697 0.547529119 2 Toyota 0.541501094 0.526814737 0.100077289 0.342211133 2 Nissan 0.397172849 0.547529119 Lower Middle 1 Chrysler 0.387892619 0.064129417 2 Toyota 0.419340927 0.089343205 0.056363785 0.547529119 1 General Motors 0.100122634 0.064129417 Luxury Specialty 1 BMW 0.960480881 0.092965491 1 Honda 0.310849982 0.064129417 1 General Motors 0.960879232 0.000398351 0.402221829 0.092965491 1 Mitsubishi 0.397485026 0.393589842 0.4294666 0.064129417 2 Ford 0.0864975 0.402221829 2 Ford 0.13313925 0.429607302 2 Nissan 0.504807082 0.402221829 2 Mazda 0.346330064 0.429607302 2 Subaru 0.500342498 0.402221829 2 Nissan 0.3820366 0.284603885 0.063988715 0.429607302 2 Toyota 0.504807082 0.422774165 0.092965491 0.402221829 3 Hyundai 1 0 0.000140702 0.493455315 Luxury Sport 1 Chrysler 0.285875422 0.035990457 Upper Middle 1 Chrysler 0.529549952 0.133437789 1 General Motors 0.102862168 0.035990457 1 General Motors 0.079852079 0.133437789 1 Honda 0.301035201 0.035990457 1 Honda 0.454569365 0.133437789 1 Daimler Benz 0.274690083 0.035990457 1 Mitsubishi 0.531985698 0.531985698 0.267799053 0.133437789 1 Mitsubishi 0.301466663 0.241403776 0.511556096 0.035990457 2 Ford 0.126587672 0.267799053 2 Ford 0.173885003 0.511556096 2 Nissan 0.324188347 0.267799053 2 Mazda 0.228017403 0.511556096 2 Subaru 0.334914292 0.267799053 2 Nissan 0.18298196 0.511556096 2 Toyota 0.235296143 0.267799053 2 Porsche 0.136830388 0.511556096 2 Volkswagon 0.340265214 0.340074404 0.133437789 0.267799053 2 Toyota 0.23371077 0.213128325 0.035990457 0.511556096 Middle Specialty 1 Chrysler 0.032230241 0.21585838 1 General Motors 0.606186778 0.21585838 1 Honda 0.635822295 0.633227571 0.635822295 0.21585838 2 Ford 0.030569523 0.195195382 2 Mazda 0.519346538 0.195195382 2 Nissan 0.535512906 0.195195382 2 Toyota 0.531509925 0.195195382 2 Volkswagon 0.537961419 0.537501509 0.537961419 0.195195382


134

MARKET SEGMENT

NETWORK

PRODUCER





MARKET SEGMENT

NETWORK

PRODUCER





Lower Small 1 Chrysler 1 0.189762891 Lower Luxury 1 BMW 0.119554458 0.469468388 1 General Motors 1 0 0.117104915 0.189762891 1 Chrysler 0.388029176 0.469468388 2 Ford 0.22754891 0.17873139 1 General Motors 0.30820263 0.469468388 2 Mitsubishi 0.249913717 0.17873139 1 Volvo 0.388029176 0.348301264 0.051347814 0.469468388 2 Suzuki 0.476284268 0.17873139 2 Honda 0.339104102 0.051347814 2 Volkswagon 0.476873447 0.476873447 0.128136416 0.17873139 2 Mazda 0.343017301 0.051347814 3 Hyundai 1 0 0.061626474 0.245241332 2 Mitsubishi 0.346542906 0.051347814 Upper Small 1 Chrysler 0.4417338 0.135004955 2 Nissan 0.330676541 0.051347814 1 General Motors 0.554206452 0.112472652 0.206830359 0.135004955 2 Toyota 0.044133735 0.051347814 2 Ford 0.093980489 0.207299377 2 Volkswagon 0.348896854 0.341009684 0.469468388 0.051347814 2 Honda 0.213575373 0.207299377 Middle Luxury 1 Chrysler 0.49877291 0.097425352 2 Mazda 0.212295771 0.207299377 1 General Motors 0.064608281 0.097425352 2 Nissan 0.198586384 0.207299377 1 Volvo 0.508733937 0.454086683 0.356445502 0.097425352 2 Subaru 0.214927458 0.207299377 2 Daimler Benz 0.43144109 0.356445502 2 Suzuki 0.215538739 0.207299377 2 Ford 0.039980554 0.356445502 2 Toyota 0.144658558 0.207299377 2 Honda 0.45357619 0.356445502 2 Volkswagon 0.215593795 0.201449203 0.134535937 0.207299377 2 Mazda 0.460641856 0.356445502 3 Hyundai 1 0 0.000469017 0.341366297 2 Mitsubishi 0.460641858 0.356445502 Small Specialty 1 Chrysler 1 0 0.008215798 0.958445972 2 Nissan 0.460059941 0.356445502 2 Honda 0.475946677 0.015185182 2 Toyota 0.460238874 0.356445502 2 Mazda 0.479570558 0.015185182 2 Volkswagon 0.460641858 0.457912642 0.097425352 0.356445502 2 Mitsubishi 0.082886785 0.015185182 Upper Luxury 1 BMW 0.141008724 0.266565961 2 Nissan 0.401205247 0.015185182 1 General Motors 0.530994881 0.389986158 0.146688689 0.266565961 2 Toyota 0.479570564 0.478672988 0.951476588 0.015185182 2 Daimler Benz 0.07117862 0.146688689 3 Hyundai 1 0 0.006969383 0.959692387 2 Ford 0.358036616 0.146688689 Lower Middle 1 Chrysler 0.55214915 0.180986362 2 Nissan 0.362301485 0.146688689 1 General Motors 0.618163515 0.066014365 0.192996526 0.180986362 2 Toyota 0.324007763 0.146688689 2 Ford 0.18403404 0.192996526 2 Volkswagon 0.371716395 0.371341096 0.266565961 0.146688689 2 Honda 0.116879728 0.192996526 Luxury Specialty 1 BMW 0.965362588 0.318498896 2 Hyundai 0.248685073 0.192996526 1 General Motors 0.965667836 0.000305247 0.156195982 0.318498896 2 Mazda 0.239352838 0.192996526 2 Daimler Benz 0.397276082 0.156195982 2 Mitsubishi 0.246331222 0.192996526 2 Ford 0.306879777 0.156195982 2 Nissan 0.249554465 0.212489421 0.180986362 0.192996526 2 Honda 0.101182456 0.156195982 Upper Middle 1 Chrysler 0.913174696 0.462507966 2 Subaru 0.397672117 0.156195982 1 General Motors 0.915145901 0.001971205 0.095533529 0.462507966 2 Toyota 0.388816249 0.156195982 2 Ford 0.155553508 0.095533529 2 Volkswagon 0.397817199 0.397259317 0.318498896 0.156195982 2 Honda 0.174312594 0.095533529 Luxury Sport 1 BMW 0.27050596 0.086045121 2 Nissan 0.244917752 0.095533529 1 Chrysler 0.467743025 0.086045121 2 Subaru 0.247742501 0.095533529 1 General Motors 0.469008947 0.19976891 0.216984378 0.086045121 2 Toyota 0.183432966 0.095533529 2 Daimler Benz 0.093063939 0.238688728 2 Volkswagon 0.251465994 0.251370648 0.462507966 0.095533529 2 Ford 0.277746222 0.238688728 Middle Specialty 1 Chrysler 0.179535654 0.168862914 2 Honda 0.328130942 0.238688728 1 General Motors 0.511638351 0.332102697 0.247180753 0.168862914 2 Mazda 0.328312918 0.238688728 2 Ford 0.024128094 0.247180753 2 Mitsubishi 0.289143806 0.238688728 2 Honda 0.493739734 0.247180753 2 Nissan 0.327234312 0.238688728 2 Mazda 0.489611457 0.247180753 2 Toyota 0.32831307 0.326246283 0.064340771 0.238688728 2 Nissan 0.500908755 0.247180753 4 Porsche 1 0 0.02170435 0.28132515 2 Toyota 0.499049634 0.247180753 2 Volkswagon 0.501245576 0.498790205 0.168862914 0.247180753


135

MARKET SEGMENT

NETWORK

PRODUCER





MARKET SEGMENT

NETWORK

PRODUCER





Lower Small 2 General Motors 1 0 0.138345178 0.246498193 Lower Luxury 1 BMW 1 0 0.022516226 0.134250111 3 Ford 0.032107704 0.368221742 2 Chrysler 0.234115647 0.075734275 3 Mitsubishi 0.705842952 0.368221742 2 General Motors 0.329657365 0.075734275 3 Suzuki 0.705842952 0.673735248 0.016621629 0.368221742 2 Toyota 0.128886979 0.075734275 5 Hyundai 1 0 0.229876564 0.154966808 2 Volkswagon 0.337970406 0.321251228 0.081032063 0.075734275 Upper Small 2 Chrysler 0.340503501 0.049177848 3 Volvo 0.149094131 0.107194638 2 General Motors 0.080423769 0.049177848 3 Honda 0.224744065 0.107194638 2 Toyota 0.319099688 0.049177848 3 Mazda 0.339852766 0.107194638 2 Volkswagon 0.366047223 0.35811471 0.394795295 0.049177848 3 Mitsubishi 0.354843777 0.350840369 0.049571699 0.107194638 3 Ford 0.030765419 0.398398139 4 Nissan 1 0 0.003646349 0.153119988 3 Honda 0.565711843 0.398398139 Middle Luxury 1 BMW 1 0 0.004735523 0.398497932 3 Mazda 0.545953925 0.398398139 2 Chrysler 0.437017081 0.085693028 3 Mitsubishi 0.555398683 0.398398139 2 Daimler Benz 0.379306423 0.085693028 3 Suzuki 0.565711861 0.565017572 0.045575004 0.398398139 2 General Motors 0.069503942 0.085693028 4 Nissan 0.055059038 0.44256009 2 Toyota 0.431383529 0.085693028 4 Subaru 0.640824828 0.58576579 0.001413053 0.44256009 2 Volkswagon 0.437017081 0.430857348 0.317540427 0.085693028 5 Hyundai 1 0 0.002189791 0.441783351 3 Ford 0.03850543 0.322275951 Small Specialty 2 Chrysler 1 0.681047935 3 Volvo 0.560464411 0.322275951 2 Toyota 1 0 7.92799E-09 0.681047935 3 Honda 0.593703962 0.588438084 0.080957504 0.322275951 3 Honda 0.999205736 0.029927264 4 Nissan 1 0 9.36979E-10 0.403233454 3 Mitsubishi 0.999205894 1.57776E-07 0.651120679 0.029927264 Upper Luxury 1 BMW 1 0 0.016549097 0.410664374 4 Nissan 1 0 0.00045134 0.680596602 2 Daimler Benz 0.214123214 0.060040639 5 Hyundai 1 0 0.029475915 0.651572027 2 General Motors 0.17840756 0.060040639 Lower Middle 2 Chrysler 0.489202 0.083751576 2 Toyota 0.253207211 0.060040639 2 General Motors 0.022173131 0.083751576 2 Volkswagon 0.322474654 0.321685977 0.367172833 0.060040639 2 Volkswagon 0.517324551 0.467028581 0.277692013 0.083751576 3 Ford 1 0 0.041533907 0.385679564 3 Ford 0.418012784 0.287887396 4 Nissan 1 0 0.001957635 0.425255836 3 Honda 0.124934621 0.287887396 Luxury Specialty 1 BMW 1 0 4.0868E-09 0.95840214 3 Mazda 0.542947405 0.287887396 2 Chrysler 0.07557322 0.036669222 3 Mitsubishi 0.542947405 0.542947405 0.073556193 0.287887396 2 Daimler Benz 0.381674073 0.036669222 4 Nissan 1 0 0.009819868 0.351623721 2 General Motors 0.378233401 0.036669222 5 Hyundai 1 0 0.000375514 0.361068075 2 Toyota 0.417323171 0.416488819 0.921732923 0.036669222 Upper Middle 2 Chrysler 0.45290517 0.115446825 3 Ford 1 0.921732927 2 General Motors 0.125418392 0.115446825 3 Honda 1 0 0.036669217 0.921732927 2 Toyota 0.331540415 0.115446825 Luxury Sport 1 BMW 0.260028495 0.20944552 2 Volkswagon 0.453521604 0.450700837 0.197598864 0.115446825 1 Porsche 0.500197206 0.24016871 0.100928346 0.20944552 3 Ford 0.172288921 0.203243775 2 Chrysler 0.386404635 0.106794174 3 Volvo 0.3634612 0.203243775 2 Daimler Benz 0.287151159 0.106794174 3 Honda 0.202806262 0.203243775 2 General Motors 0.115492035 0.106794174 3 Mazda 0.357055786 0.203243775 2 Toyota 0.390515318 0.106794174 3 Mitsubishi 0.363585946 0.358731617 0.109801914 0.203243775 2 Volkswagon 0.390515493 0.382498823 0.203579692 0.106794174 4 Nissan 0.138421419 0.307400778 3 Ford 0.142809434 0.3045082 4 Subaru 0.532742256 0.394320838 0.005644911 0.307400778 3 Honda 0.283969112 0.3045082 Middle Specialty 2 Chrysler 0.323736939 0.36125413 3 Mitsubishi 0.359801373 0.292824199 0.005865665 0.3045082 2 General Motors 0.088779879 0.36125413 4 Nissan 1 0 1.62623E-07 0.110837383 2 Toyota 0.386251801 0.36125413 2 Volkswagon 0.396783232 0.391581077 0.085058052 0.36125413 3 Ford 0.009907889 0.425295173 3 Honda 0.697646197 0.425295173 3 Mazda 0.69508885 0.425295173 3 Mitsubishi 0.69942927 0.695644873 0.361249217 0.425295173 4 Nissan 1 0 4.91329E-06 0.786539476


136

4.4.1 Rivalry Results

Since not all firms were producing vehicles for sale in all market segments across all years

of the study and had presence in all networks, only 385 cases were available for statistical

modelling. (If all 16 producers produced vehicles for all 11 market segments across all four

year periods studied, a total of 704 cases would be available for analysis.) Table 4.13 shows

the numbers of firms producing vehicles for various market segments across the four time

periods of the study.

Year Market

segment 1993 1995 1997 1999 Total

Lower Luxury 8 9 10 10 37 Lower Middle 7 8 8 9 32 Lower Small 11 9 7 5 32 Luxury Spec 8 6 8 7 29 Luxury Sport 11 10 11 11 43 Middle Luxury 10 11 11 10 42 Middle Spec 9 8 8 9 34 Small Spec 9 8 7 6 30 Upper Luxury 7 7 7 7 28 Upper Middle 9 9 8 11 37 Upper Small 8 10 11 12 41 Total 97 95 96 97 385 Table 4.13: Market Segment Count Cross-Tabulation by Year

Rivalry-Within network

Rivalry-Between network Firm

mean std. dev. mean std. dev. BMW 0.194 0.317 0.305 0.223 Chrysler 0.405 0.236 0.192 0.207 Mercedes-Benz 0.344 0.171 0.138 0.093 Ford 0.202 0.222 0.300 0.160 GM 0.159 0.154 0.166 0.135 Honda 0.380 0.212 0.226 0.191 Hyundai 0.018 0.066 0.448 0.231 Mazda 0.427 0.178 0.282 0.168 Mitsubishi 0.348 0.181 0.167 0.123 Nissan 0.273 0.201 0.305 0.189 Porsche 0.151 0.111 0.294 0.152 Subaru 0.462 0.210 0.276 0.168 Suzuki 0.463 0.151 0.210 0.129 Toyota 0.300 0.151 0.216 0.157 Volvo 0.363 0.142 0.214 0.142 Volkswagen 0.455 0.181 0.206 0.128 Total 0.310 0.222 0.238 0.177

Table 4.14: Within and Between Network Rivalry Indices With Respect to Firms

137

The overall averages for the two dependent variables appear to be significantly different,

with the average for within network rivalry appearing to be much higher at 0.310 (standard

deviation = 0.222) compared with the average for between network rivalry at 0.238 (standard

deviation = 0.177). Closer examination of this result, including the moderating influence of

factors such as firms, networks, market segments and years, was carried out with the

assistance of multivariate analysis of variance (MANOVA) data analysis procedure.

4.4.2 Manova Results

The MANOVA procedure compensates for variable intercorrelations and provides an

omnibus test of any multivariate effect. In the ideal case, a single MANOVA would have

involved a 16 (firms) x 11 (segments) x 16 (networks) factorial design, with within and

between rivalry measures as dependent variables. This would result in a very large number

of groups (2816 groups in total). Since only 385 cases were available, this factorial design

would clearly violate the minimum groups to cases ratio of 1:20 for MANOVA (Hair Jr. et al.,

2006). As a result, a series of MANOVA was conducted, with each of the factors tested in

separate MANOVA, but with both dependent variables included in each model. While this is

far from ideal in that the benefits of MANOVA are not fully realized (with the notable one

being the effects of interactions between the factors), nonetheless, the separate analyses

was not fatally flawed as the full factorial design would be.

4.4.2.1 MANOVA Results for ‘Firm’ as Controlling Factor

The first factor to be tested for its influence on rivalry through MANOVA was the 16 firms.

Table 4.14 shows the average values of the within and between network rivalry measures for

the firms. The four commonly used multivariate tests (Pillai’s criterion, Wilk’s lambda,

Hotelling’s trace and Roy’s largest root) all indicated significant differences in the two rivalry

measures when these measures were grouped into categories of firms (e.g., Pillai's trace =

0.403; F-statistic = 6.4, df = 30, 736, sig. = 0.000). However, this factor accounted for a

relatively small proportion (about 20 percent) of the variance in the dependent measures

(partial Eta squared = 0.201 associated with Pillai’s trace). In addition, univariate tests for

each dependent rivalry measure, when taken individually, indicated that both within and

between network rivalries differed significantly across firms (F = 8.9; sig. = 0.000 for within

network rivalry, F = 4.1; sig. = 0.000 for between network rivalry).

138

To establish which particular firm(s) contributed to differences existing the in the two rivalry

measures, comparison of means presented in Table 4.14 were needed. Post hoc methods

where equality of variance is not assumed had to be used because the critical MANOVA

assumption of homogeneity of variance-covariance matrices was not fully supported1.

The results of the post hoc methods where equality of variance is not assumed (e.g.,

Tamhane’s T2, Dunnett’s T3 and Games-Howell) all produced reasonably consistent results,

and showed that only one firm, Hyundai, had differences in rivalry with other firms that were

significant. Hyundai’s average within network rivalry index measure was the lowest of all

firms at 0.018, significantly lower than the overall average for all firms of 0.301. Similarly, the

between network rivalry for Hyundai was 0.448, this being the highest of all firms and

significantly higher than the overall average of 0.238. Examining these results more

specifically, the post-hoc tests showed that Hyundai’s rivalry measures were significantly

different from most other firms, and that there was no obvious pattern to these differences at

the firm level.

4.4.2.2 MANOVA Results for ‘Year’ as Controlling Factor

The second MANOVA tested for the effect of Year as the controlling factor. Since the

dataset consisted of cases from four time periods, there was a possibility of a temporal

relationship in the data being present and this would violate a critical assumption of

MANOVA, i.e., independence of observations. Both multivariate and univariate tests showed

that at 0.05 level of significance, there were no differences in within and between network

rivalries across the four-time periods. (Typical multivariate test result: Pillai's trace = 0.016;

F-statistic = 1.0, df = 6, 762, sig. = 0.416; partial Eta squared = 0.008) Hence, it can safely

be assumed that there is no detectable temporal dimension to rivalries within the industry.

The average values of the two rivalry measures categorised in four time periods are shown

in Table 4.15.

1 The multivariate Box’s M test for equality of covariance matrices produced negative results (Box’s M = 117, F = 2.4, df = 45, 9714, sig. = 0.000), suggesting the MANOVA assumption was violated. The univariate Levene’s test of equality of variances was mixed (F = 1.8, df = 15, 369, sig. = 0.028 for within network rivalry measure, and F = 1.0, df = 15, 369, sig. = 0.412 for between network rivalry measure). The significance value for within network rivalry measure is less than 0.05, indicating that the equal variances assumption is violated for this variable. On the other hand, the significance value for the test of between network rivalry measure is greater than 0.05, presenting no reason to believe that the equal variances assumption is violated for this variable.

139

Rivalry-Within

network Rivalry-Between network Year

mean std. dev. mean std. dev. 1993 0.329 0.222 0.238 0.186 1995 0.328 0.221 0.235 0.149 1997 0.297 0.197 0.217 0.156 1999 0.286 0.246 0.263 0.211 Total 0.310 0.222 0.238 0.177

Table 4.15: Within and Between Network Rivalry Indices With Respect to Years

4.4.2.3 MANOVA Results for ‘Segment’ as Controlling Factor

The third MANOVA model tested was for the effect of specific market segments on the

rivalry between firms. Table 4.16 shows the within and between network rivalry measures

categorized along the 11 market segments. MANOVA results showed that along all four

multivariate measures, the effect of the segments was significant, with all four multivariate

tests having significance levels less than 0.05 (e.g., Pillai's trace = 0.089; F-statistic = 1.7, df

= 20, 748, sig. = 0.024). However, market segment did not account for the large proportions

of variance in the two rivalry measures (Eta squared values associated with Pillai’s trace =

0.044). Further, univariate tests for each dependent rivalry measure showed that they do not

differ significantly across segments (F = 1.5; sig. = 0.123 for within network rivalry, F = 1.8;

sig. = 0.056 for between network rivalry).

To make sense of these mixed results, tests results for the assumption of MANOVA of

homogeneity of variance-covariance matrices were reviewed (Box’s M and Levene’s tests).

These were both negative (Box’s M = 129, F = 4.2, df = 30, 276781, sig. = 0.000; Levene’s

test of equality of variances for within network rivalry measure F = 5.1, df = 10, 374, sig. =

0.000, and for between network rivalry measure F = 5.8, df = 10, 374, sig. = 0.000),

suggesting the MANOVA assumption was violated. The MANOVA outcomes were therefore

unstable and unreliable. So, post-hoc tests for mean difference which assumed unequal

variances were used to determine if the two measures of network rivalry differed significantly

depending on market segment. This showed no consistent pattern. It was therefore

concluded that when viewed from the perspective of market segments, there were no

discernible differences in within and between network rivalries.

140

Rivalry-Within

network Rivalry-Between network Market sub-segment

mean std. dev. mean std. dev. Lower luxury 0.250 0.176 0.172 0.130 Lower middle 0.264 0.185 0.255 0.223 Lower small 0.364 0.327 0.244 0.152 Luxury sports 0.365 0.296 0.284 0.255 Luxury spec 0.260 0.145 0.207 0.135 Middle luxury 0.351 0.197 0.245 0.130 Middle spec 0.378 0.220 0.294 0.190 Small spec 0.293 0.282 0.298 0.285 Upper luxury 0.285 0.209 0.228 0.169 Upper middle 0.315 0.174 0.196 0.096 Upper small 0.296 0.200 0.230 0.134 Total 0.310 0.222 0.238 0.177

Table 4.16: Within and between network rivalry indices with respect to market segments

4.4.2.4 MANOVA Results for ‘Network’ as Controlling Factor

The final MANOVA tested for the effect of the 16 networks on the two dependent rivalry

measures. Note that since network memberships frequently changed over the years (see

Tables 4.5 – 4.8), it would be meaningless if one omnibus MANOVA was performed that had

all 16 networks from the four separate time periods specified as a single grouping factor. For

example, comparing the rivalry measures for network 1 (year 1993) with network 5 (1999)

would be meaningless from a practical point of view. It is much more meaningful to compare

and report MANOVA results for networks limited to the year in which the networks existed.

With this in mind, four separate MANOVAs were performed. Table 4.17 summaries the

mean values of the two dependent rivalry measures along the 16 network groups,

categorised into four time periods.

The MANOVA outcomes for the four time periods are summarised in Table 4.18. This shows

that all four MANOVA models were supported, suggesting that both rivalry measures were

affected by the networks.

141

Rivalry-Within network

Rivalry-Between network Year Network

mean std. dev. mean std. dev. 1993 1 0.000 0.000 0.346 0.097 2 0.338 0.193 0.199 0.179 3 0.372 0.225 0.245 0.174 4 0.000 0.000 0.580 0.210 Sub-total 0.329 0.222 0.238 0.186 1995 5 0.334 0.204 0.104 0.045 6 0.343 0.230 0.341 0.112 7 0.000 0.000 0.395 0.128 Sub-total 0.328 0.221 0.235 0.149 1997 8 0.329 0.294 0.273 0.203 9 0.303 0.136 0.182 0.095 10 0.000 0.000 0.515 0.388 11* 0.000 - 0.281 - Sub-total 0.297 0.197 0.217 0.156 1999 12 0.083 0.129 0.387 0.301 13 0.306 0.183 0.140 0.155 14 0.384 0.280 0.321 0.188 15 0.107 0.199 0.401 0.199 16 0.000 0.000 0.402 0.205 Sub-total 0.286 0.246 0.263 0.211 * Group n = 1

Table 4.17: Within and Between Network Rivalry Indices with Respect to Networks

However, all four MANOVA models failed to meet the assumptions of covariance-variance

homogeneity. These meant that the MANOVA results were unstable and unreliable. Indeed,

closer inspection of the post-hoc comparison of means (see Table 4.17 for mean values)

showed that the significant MANOVA results were caused by one or two zero mean values

of within network rivalry measures. (Note that these zero values were obtained in situations

where there was only one firm present in the network.) If this distortion is removed from the

models, it becomes evident that both within and between rivalry measures do not differ

significantly when these measures are viewed from the perspective of network groups.

142

MANOVA Tests Post-hoc Comparison of Means Omnibus

multivariate test outcome

Univariate tests outcome MANOVA assumptions met? Year Networks

(from Table 23)

Do significant differences exist in rivalry measures?

Does significant difference exist for within network rivalry measure?

Does significant difference exist for between network rivalry measure?

Box’s M test Levene’s test for within network rivalry measure

Levene’s test for between network rivalry measure

Pattern of differences in means observed:

1993 1,2,3,4 Yes Yes Yes Yes No

No Positive MANOVA result is due to one (network 4) within network rivalry measure being zero.

1995 5, 6,7 Yes Yes Yes No Yes No Positive MANOVA result is due to one (network 7) within network rivalry measure being zero.

1997 8, 9, 10, 11 Yes Yes Yes No No Yes Positive MANOVA result is due to two (networks 10 & 11) within network rivalry measures being zero.

1999 12, 13, 14, 14, 16

Yes Yes Yes No No No Positive MANOVA result is due to one (network 16) within network rivalry measure being zero.

Table 4.18: Summary of MANOVA results for effect of networks (grouped in four time periods) on within and between network rivalry measures

143

4.4.3 Summary of MANOVA Results

The overall average value of within network rivalry was 0.310, seemingly significantly higher

than the between network rivalry average of 0.238. In investigating these results further, a

number of more specific conclusions could be drawn. First, the headline MANOVA result

was that ‘firms’ had an influence on the extent of rivalry in the industry; however, this

influence was mainly due to one firm and the differences existed in both the between and

within network rivalry measures. Second, there did not appear to be a temporal effect

present, with year not showing a significant effect in differences in rivalry measures. This

enhanced the case for independence of data cases used in this study. Third, with respect to

market segments, the MANOVA model tests did not produce clear-cut results. On balance

however, it appeared that market segment was a non-influential factor in affecting both types

of rivalries. Finally, it appeared from the MANOVA models that network membership was an

influential differentiation factor for both forms of rivalry. However, on closer inspection, this

was due to the distortionary effect of some single-firm networks which resulted in zero within

network rivalry scores. When these networks were removed, it became evident that network

membership did not significantly affect either types of rivalry.

4.5: SUMMARY AND CONCLUSIONS

As demonstrated by the results of this investigation, it appears evident that strategic network

membership did not contribute to defining patterns of rivalry within the target industry under

study – the Light Vehicles Industry in the United States. Rather, in contrast to current

theorising, these results testify to the fact that strategic network membership does not

necessarily elicit overt benefits in facilitating collective rivalry and accompanying market-

induced rewards. Unlike prior studies that have examined the role of strategic networks in

technological and regulatory intense environments which provided evidence of the collective

nature of network participation in market derived activities, this research indicates that such

benefits may be confined only to those market environments where technological or

regulatory standards apply. Indeed, the results generated in this research suggest that

rivalry between firms occupying the same network were of greater significance to those

rivalries found between competing networks. The results generated within the context of this

research have implications for current theorising in the realm of strategic networks, which will

be discussed in detail in the following Chapter.

144

CHAPTER 5CHAPTER 5

D I S C U S S I O ND I S C U S S I O N

145

5.0 INTRODUCTION

This chapter is written with the intent of articulating the results generated through the study

of horizontal strategic networks and rivalry, and in particular, presents an outcome in relation

to the central question guiding research: Are patterns of rivalry predicted by strategic

network membership? In determining the relative value of this research it is imperative to

address the relevancy of these results in light of prevailing theory in the strategic network

and rivalry realm, and how the findings generated in this research correlate to prior research

in this field. The contribution of this research to theory and the practical context of business

will be identified, leading to the presentation of key findings as emergent from this research

investigation. This chapter will conclude by positing the limitations characterising this

research, followed by suggestions for future research.

5.1 ASSESSING THE RESULTS OF THE RESEARCH

The purpose of this research was to ascertain whether the presence of strategic networks in

an industry predicted the patterns and levels of rivalry experienced by firms. The relative

influence of strategic networks in generating competitive outcomes for participant firms has

been a matter of conjecture within the strategic management literature for some time. The

underlying logic of the strategic network concept in this thesis is the contention that firms

which engage in strategic relationships with other firms within an industry will take into

account these relationships when formulating competitive strategy, or to a lesser degree,

some measure of competitive benefit is ascribed to members (Gomes-Casses, 1994, 1996;

Vanhaverbeke and Noorderhaven, 2001). These authors contend that, where a group of

firms are closely linked through a network of strategic relationships, variations may be

observed in the nature and intensity of rivalry between members of the same strategic

network and other networks within the industry. As individual strategic alliances –

representing the building blocks of strategic networks – are formulated by partners to the

relationship to attain some form of competitive benefit, it has been argued that this

competitive intent should in some measure characterises the resulting strategic network. In

contrast, it could be argued that while membership in a strategic network elicits competitive

benefits to participant firms, this benefit does not translate into reduced levels of rivalry

experienced by firms comprising the strategic network.

Another significant issue relates to whether the industry environment is a relevant factor,

either restricting or facilitating rivalry at the network level. Strategic networks can be

146

identified as present across a broad range of industry types, including the airline,

microprocessor, banking and automotive industries (Nohria & Garcia-Pont, 1991; Gomes-

Caserras, 1994; Vanhaverbeke & Noorderhaven, 2004; Rowley, Baum, Shiplov, Greve &

Rao, 2004). It is feasible to anticipate greater adherence to a model of network rivalry in

those industries that demonstrate strong competition along the lines of technological

standards or regulatory imperatives, such as the microprocessor or airline industries. In the

instance of technological industries, compliance to a particular standard has the propensity

to define which strategic network firms are aligned to, and consequently firms – on the basis

of their technological subscription to a particular standard – channel their rivalry away from

those demonstrating the same allegiance toward those firms in the industry championing a

competing technology. In the highly regulated airline industry, strategic networks are overtly

positioned in the industry, such as the Oneworld and Star alliance networks, which facilitate

the sharing of products and services across member firms. In industries which do not have

such identifiable and observable imperatives, such as the automobile industry, it is

questionable as to whether firms comprising strategic networks will have significant external

focus to assume specific competitive positions, and therefore will not have the impetus to

collectively target rivalry away or to any specific firm or groups of firms. As a consequence,

the likelihood of observing network rivalry in such industries is low.

5.1.1: The Research Proposition

It is on the basis of this conjecture that the central question guiding research was developed:

Are patterns of rivalry predicted by strategic network membership?

To investigate the principle question of research it was necessary to engage in three studies.

The industry examined was the United States Light Vehicles Industry, over the period 1993-

1999. The initial study (Study 1) was concerned with calculating individual rivalry measures

for each firm participating in the automobile industry over the years 1993-1999. Study 2 was

focussed on defining which firms were a part of which strategic networks over the timeframe

1993-1999. The final study, Study 3, examined the level of rivalry experienced between

strategic networks identified as operating within the auto industry over the specified time

period. In addition, the level of rivalry experienced by firms within (comprising) the network

was also measured. The purpose of obtaining this dual measure of rivalry levels was to

determine if the presence of strategic networks bear any influence on the levels or patterns

of rivalry in the United States automotive industry.

147

5.1.2: Key Findings

The key finding from this research was that strategic network membership did not predict the

patterns or levels of rivalry observed in the United States Light Vehicles Industry from 1993-

1999 (Kelly, 1981). From this outcome it was possible to conclude that while horizontal

strategic networks did operate in the industry during this time, these networks yielded no

benefit to firms in terms of product market benefits. Indeed rivalry was found to be greatest

between firms participating in the same strategic network, as opposed to the level of rivalry

observed between firms comprising different strategic networks. In this respect, the

contention of network rivalry – that is, firms in a single strategic network competing as a

collective entity against other firms or networks in an industry – is unfounded.

5.2: STUDY FINDINGS

In total, three studies were undertaken in order to achieve an answer to the research

question: Are patterns of rivalry predicted by strategic network membership? These studies

are reviewed here with the intent of offering a brief review of each study, and providing,

where appropriate, discussion of the study findings.

5.2.1: Study 1 Findings: Rivalry

As detailed in Chapter 4, the measure of rivalry utilised in this research was derived from the

Herfindahl Index, traditionally applied to the study of industry concentration. Rivalry was

determined to encapsulate the level of competitive opposition a firm faced from rivals in

offering a product to market. Therefore rivalry was observed at the product market segment,

where firms engage in direct competition with each other based on similarities in product

attributes. The specific measure used within the context of this research was appropriated

from the work of Cool & Dierickx (1993) and Durisin & Von Krogh (2005) who modified the

Herfindahl index in their respective rivalry studies. An outcome of this formula allowed for the

level of rivalry a firm faced from other firms participating in the same product market

segment to be captured in numerical form.

The numerical outcomes derived from the use of the modified Herfindahl formula provide

little insight when considered in isolation to the broader context of research. Each

calcultation – representing the level of rivalry a firm faced from other firms who produce a

vehicle or vehicles in the same product market segment of the United States Light Vehicle

148

Industry within the same period of research – could be considered as a ‘snapshot’ of rivalry

levels firms faced during that year. When considered in conjunction with the outcomes of

Study 2 (which investigated strategic network membership), these calculations attain greater

significance in Study 3 in terms of defining the level of rivalry experienced by firms in relation

to the status of their strategic network membership.

5.2.2: Study 2 Findings: Strategic Network Membership

In order to determine strategic network configurations in the years 1993, 1995, 1997 and

1999, the horizontal strategic relations between all firms participating in the United States

Light Vehicle Industry were identified and categorised according to the framework for

assessing the interdependency of strategic relations by Contractor and Lorange (1988), and

later adapted by Nohria and Garcia-Pont (1992) (this framework is provided in Chapter

3.5.1). As a result of this categorisation it was possible to define strategic networks

participating in the automotive industry in the years 1993, 1995, 1997 and 1999 by applying

Johnson’s Hierarchical Clustering Technique (1967) operationalised using UCInet

networking analytical software created by Borgatti, Everett and Freeman (2002). The

outcomes of this clustering analysis were then contrasted with the raw relational data and

UCInet Netdraw simulations to collectively define the final network configurations for each

period of analysis.

In sum, the strategic networks identified across the timeframe of research showed great

variability in terms of the number of active networks found in each period (1993, 1995, 1997

and 1999), and in terms of the lack of consistency in network membership when compared

across all years of analysis. Four networks were identified in 1993. During this period, BMW

and Hyundai participated in very few strategic relationships, therefore defining each of these

firms as isolates, with each firm essentially acting as its own network. At this time it is

possible to note the emergence of two strong networks, centred around Chrysler and

General Motors in one network and Ford and Toyota in another. The dominance of these

firms is not surprising: these firms were (and still are) substantial in nature, encompassing

broad geographic scope, with strong brand recognition and significant presence in all

product market segments of the United States auto industry.

149

Only three networks were observed in 1995 and showed the continued dominance of

Chrysler and General Motors in one network while Ford and Toyota accounted for central

positions in the other major network. Once again, Hyundai was an isolate.

In 1997, a total of four networks were identified. Once again it was possible to define the

presence of strong industry players in two of the networks – Chrysler and General Motors in

one network with Ford and Toyota in the other. The two isolates in this year consisted of

Hyundai and Porsche – as a consequence, these firms were the only members in their

defined network.

The network results of 1999 demonstrated some variability in comparison with the networks

characterising prior years. During this period five networks were identified, with the newly

merged Chrysler-Daimler Benz and General Motors comprising one network, and Ford in

another. Toyota during this time had realigned itself with the Chrysler-Daimler Benz and

General Motors group, with Mitsubishi and Volvo demonstrating increased prominence in the

‘Ford’ network. Nissan and Subaru had broken away from their prior network with the Ford

and Toyota group, creating in effect their own network. Porsche during this period moved

from being an isolate to aligning itself with BMW, who had previously been embedded in the

Chrysler – General Motors network in the 1995 and 1997 networks.

5.2.2.1: Strategic Network Structure & Evolution: Change in Strategic Network Membership

Over Time

Network membership and the number of strategic networks active in each year of analysis

underwent considerable change throughout the total period of analysis. This change was

anticipated: as detailed in Chapter 3, the industry was in a state of transition which in turn

created different structural dynamics in the industry. The entrance of Asian producers

(Hyundai, Kia) into the market during the 1980s, in conjunction with the increased presence

of firms such as Mazda, Subaru, Suzuki and Nissan heightened the competitive pressure

inherent in the industry leading to the erosion of the market share held by leading producers

such as Chrysler, General Motors and Ford. At this time the industry was characterised by

increased over-capacity, with pressures for cost minimisation and demands for increased

product development affecting all producers. These larger producers, believing themselves

protected by past demonstrations of strong brand loyalty trends, were suddenly faced with

the need for economic rationalisation.

150

Buyer power during this time increased exponentially due to excess capacity, and the

relevance of consumer preferences became significant to firms trying to quit their excess

stocks and in light of future production choices. One means of circumventing these collective

pressures was to engage in strategic alliances with suppliers and other producers in the

industry, developing (where feasible) modular architecture forms across brands to minimise

the need for specialist components and parts, and reduce individual expenditures on product

development. This state of industry flux had a consequent influence on the creation and

dissolution of strategic relationships between firms active in the industry during this period,

thereby influencing the number of networks present at each period of analysis.

Whilst the circumstances of the 1980s instigated the foundations for change in the

automotive industry, the 1990s represented the period whereby the ramifications of this

change were evident in the industry. The evolution of an industry has inevitable

consequences for the creation and sustainability of networks, especially during the period of

transition. It was noted that from the late 1980s through to the late 1990s, the advent and

decline of strategic relationships was significant – indeed, the advent of some relationships

in conjunction with the decline of others substantially influenced the number of networks

evident at each period of analysis and the membership of these networks. At times, the

decline of one relationship had implications not just for the focal firm, but for other firms in

alliance with the focal firm who suddenly found themselves realigned in light of the structure

of the industry. A common feature throughout the entire period of analysis was the presence

of ‘hub’ firms – firms that consistently acted as the dominant actors in each network in which

they were affiliated. These firms, including Ford, Chrysler (later known as Daimler-Chrysler)

and General Motors, signify the largest producers within the industry, and as evidenced by

the resulting network configurations for each period, these firms and their individual

relationships with other industry producers acted to define each network structure and

determined the framework by which smaller firms within the industry were designated to

specific networks.

5.2.3 Study Findings: The Relationship Between Strategic Network Membership and

Rivalry

The purpose of Study 3 was to utilise the results obtained in Study 1 (rivalry at the market

segment level) and Study 2 (strategic network configurations) to statistically determine the

151

relationship between these constructs in the Light Vehicles Industry of the United States

over the period 1993-1999. Central to this component of research was testing for within

network rivalry (whether firms comprising the same strategic network engaged in direct

competition with each other) and between network rivalry (the level of rivalry observed

between different networks). The results of these analyses allowed for the determination of

whether members of strategic networks act as collective entities in terms of directly their

rivalry away from themselves and towards other strategic networks within the industry.

Initial statistical analysis outcomes found the level of within network rivalry to be greater than

between network rivalry (0.310 versus 0.238). Extended analysis via the MANOVA model

tested for the effect of controlling factors including firm, year, market segment and strategic

network. The culmination of these results allowed for the conclusion that strategic network

membership was unable to account for the patterns or levels of rivalry observed in the

United States Light Vehicles Industry over the period 1993-1999.

5.2.4 Research Outcome: Answering the Central Question of Research

The most significant finding to emerge from this research was that strategic network

membership exhibited no predictive ability to determine the patterns of rivalry evidenced in

the United States light vehicles over the timeframe 1993 – 1999 industry. This finding can

support a number of different potential scenarios, including:

Without the presence of a strong industry-centric rationale for collective behaviour

(such as technological standards or regulatory imperatives as demonstrated in the

microprocessor or airline industries), the likelihood that firms will independently align

their rivalry behaviour in the product market is limited.

Despite the dense nature of interorganisational linkages between participants in the

same network, these relationships do not offer opportunities for coordination in

terms of how to structure their product market output to minimise competition

between themselves. Further, given that the firms in a strategic network fail to

demonstrate significant interconnectivity – where all the firms are in some way

directly linked to each other through the advent of a collective alliance, for instance –

the opportunity for governance mechanisms (beyond those characterising the

singular relationship between partners) to be installed is limited. Given the results

generated in this research, there is little observable coordination between strategic

network members.

152

Whether firms are aware of which other firms in the industry constitute their strategic

network is questionable. It is entirely possible that firms are aware only of those

firms they are in direct relationship with and do not have a broader perspective of

which firms constitute their strategic network. That said, the results of this research

suggest that even those firms who are partners in alliances engage in direct

competition with each other in product markets. The conclusion to be drawn here is

of the difficulties associated with a firm obtaining a broad perspective as to which

other firms in the industry comprise their network. The second conclusion relates to

the benefits ascribed strategic alliances in general – it is entirely possible that the

scope of these relationships is very much related to collaborative engagement in

terms of producing products for market, but does not extend beyond this point in

terms of defining how to structure the product market post collaborative effort. An

outcome of these relationships presents a paradox – on one side of the equation,

firms engage in collaboration which is deemed beneficial to partner firms. On the

other side of the equation, these firms engage in direct competition with each other,

therefore arguably undermining the very nature and purpose of the collaborative

arrangement.

An alternative explanation of the results could lie with how managers perceive their

environment. Through regular interaction via their strategic relationships with each

other, managers begin to cognitively associate those firms – due to similarities in

goals, firm characteristics and shared product attributes (as derived from their

collaborative efforts) – as their direct competitors. Due to this perception, managers

may target those firms they consider most alike to their own and who they consider

more likely to impinge on their target market. The relevance of cognitive

interpretations to rivalry has been explored, to some degree, in the work on strategic

groups (see Porac, Thomas & Baden-Fuller, 1989; Reger & Huff, 1993).

A final scenario presents itself. The results derived from this research could be

associated with the turbulence of the industry throughout the late 1980s and 1990s.

As firms were just embarking on establishing strategic relationships with each other

during this time, it is possible that firms were yet to entirely finalise these

arrangements and the ramifications of these arrangements in terms of structuring

their product market output. It would arguably take considerable time to reformulate

product market output to reflect collaborative arrangements between firms, and it is

entirely possible that for these reasons this research was unable to capture this

153

effect in the product market. It is possible that research that investigates strategic

network membership and rivalry beyond 1999 may find differences in terms of

product market output by firms that is more reflective of which firms are engaged in

strategic relationships with each other.

In the instance of the above propositions, greater research is required to ascertain which, if

any, of these conclusions offers the most appropriate approximation of the dynamics of the

United States Light Vehicles Industry.

5.3 RESEARCH FINDINGS IN LIGHT OF PRIOR RESEARCH

Network research has become increasingly popular with strategy researchers over the past

decade (Thomas & Powell, 1999). Much of this research has found focus with exploring how

strategic network analysis offers an alternative structural interpretation of industry compared

with the application of strategic group theory (Nohria & Garcia-Pont, 1992). Another avenue

of research has included investigating whether strategic networks are competitive constructs

that define patterns of rivalry in the industry under examination. The first of these studies,

utilising the strategic block concept (refer to page 65 for detail regarding the methodological

distinctions associated with the strategic block and strategic network constructs) by

Vanhaverbeke and Noorderhaven (2001) examined whether network configurations

influenced rivalry in the RISC microprocessor industry. While these authors found that

strategic blocks were evident in the industry, each structured around the presence of

competing technological standards, they did not directly measure rivalry itself. The second

study by Boyd (2004) proposed an integration of the strategic group and strategic ‘block’

concepts (utilising the methodology associated with the strategic network construct) to

achieve a more accurate prediction of intra-industry structure and firm performance. This

study drew strongly on the cognitive tradition of strategic management research (see the

references on the cognitive interpretation of strategic groups provided previously for an

introduction to this work) in order to define the methodology utilised to form both strategic

groups and strategic blocks for the purposes of research. The measure of performance used

in this research was the financial ratio return on sales (otherwise known as ROS). The

results suggest that the integrated use of both the strategic group and strategic block

constructs offered some predictive ability in accounting for performance differentials between

firms.

154

What characterises these studies is a failure to directly measure rivalry as an independent

construct. Furthermore, another common element in these research efforts has been the

industry type examined: both industries can be characterised as being substantially

influenced by the presence of overriding features such as technological standards or

regulatory imperatives, which can act as an external stimulus to coordinate firms into

clusters that independently may act to instigate what may otherwise be perceived as

collective action in terms of observable competitive intent.

In contrast to these prior studies, the research undertaken here focussed on directly

measuring rivalry within an industry that did not demonstrate such potentially confounding

features as technological standards or regulatory imperatives as evidenced in the RISC

microprocessor or airline industries. An outcome of this industry choice allowed for the

examination of whether horizontal strategic network membership demonstrated a direct

correlation with the patterns of rivalry observed within the industry, or whether the industry

environment in itself was a contributing factor to the advent of what is termed ‘network

rivalry’. The results presented here indicate that horizontal strategic network membership

was unable to predict the patterns or levels of rivalry evidenced in the United States Light

Vehicles Industry, suggesting that contentions of ‘network rivalry’ in industries not dominated

by technological standards or regulatory imperatives is perhaps premature. Further, the

results suggest that in industries not dominated by these imperatives, clear and ready

interpretation of network configurations is subject to some difficulty, therefore impacting on

the capacity of firms comprising the network to avail themselves of this membership for

benefit beyond the scope of their individual relationships taken one at a time.

5.4 THEORETICAL CONTRIBUTION OF RESEARCH

This research represents the first empirical investigation of horizontal strategic networks and

rivalry in the United States Light Vehicles Industry, and the first study to investigate such

constructs in an industry environment not dominated by technological or regulatory

imperatives. The results of this research suggest that strategic network membership cannot

account for the patterns of rivalry observed in the United States Light Vehicles industry. The

relevance of the industry environment was found to be significant – other industries such as

the microprocessor and airline industries appear more inclined to demonstrate an external

rationale for strategic network formation that enhances the likelihood that resulting networks

would find commonality in terms of how they enact rivalry. Suggestions of network rivalry in

155

these environments is more likely to be supported by empirical research based on these

dominating industry characteristics.

The work in this thesis finds some similarities with that completed in the realm of strategic

groups. Strategic groups represent groups of firms within an industry that demonstrate

similarities in terms of their scope and resource commitments (Cool & Schendel, 1987), with

both strategic groups and strategic networks constituting forms of structural analysis of

industry environments. Following the initial identification of the strategic group construct by

Hunt (1972), Caves and Porter (1977) proposed the hypothesis that greater rivalry would be

observed between strategic groups as opposed to within the strategic group. Despite several

research projects designed to test this hypothesis, a conclusive outcome has yet to be

determined. The failure of these studies to collectively articulate a clear finding in relation to

this hypothesis was blamed on the lack of a consistent methodology used to formulate

resultant strategic groups. A similar hypothesis could be postured when assessing strategic

networks and rivalry, that greater rivalry would be observed between strategic networks as

opposed to within the strategic network. The results from this research would suggest

otherwise, in that greater rivalry was found within the strategic network rather than between

strategic networks. However, not unlike the work in strategic group research, the

methodology underlying strategic network determination needs to be clarified, and an

accepted approach clearly articulated. Until this is the case, inconsistent research results are

likely to continue.

One finding of this research suggests that firms place little store in strategic alliances when

formulating and enacting competitive strategy. As evidenced in this research, an outcome to

this is that firms who collaborate with each other via strategic relationships tend to then

compete more aggressively with each other in the product market versus other firms in the

industry. The dual nature of this relationship – that of collaborator and then competitor – is

almost counter-intuitive – the alliance enables the realisation of each firm having the

capacity to offer products in the market, however at the same time these same firms engage

in direct product market rivalry with each other, therefore potentially eroding any benefits that

may have been forthcoming from engaging in the alliance in the first place. Perhaps in those

industries that have an overt means of identifying allies and rivals (such as those industries

that have dominant standards or regulations), observers are more likely to note more distinct

and enduring patterns of rivalry tied to such industry attributes, and consequently, network

156

structures. In industries that do not demonstrate such clearly defined unifying attributes,

such as the automotive and white goods industries, firms are less able to identify which firms

participate in which networks, and therefore identify which firms constitute allies and which

firms constitute rivals.

It is important to note at this juncture that significant differences exist in terms of the level of

interdependency different alliance types entail between firms. It is possible that this level of

interdependency implies implications for understanding strategic networks and how rivalry

then develops. For instance, firms engaged in low-level interdependency alliances (for

example distribution or marketing alliances) are more likely to engage in direct product

market competition with their alliance partner, given that these relationships require limited

on-going interaction between partners in order to maintain the alliance. The importance of

these relationships over the long term are limited given that they are utilised only to fulfil

short-term resource and/or capability inefficiencies of one of the partner firms to the alliance.

As a consequence, it could be argued that these relationships are not considered significant

enough as to factor into strategy formulation by managers. Utilising this same logic, it is

possible to suggest that perhaps those relationships that entail greater interdependency

between firms (for instance, joint ventures or joint R & D efforts) are more likely to be

considered by management when formulating competitive strategy in that these relationships

tend to deliver more significant medium to long term outcomes for the partners involved.

Directing rivalry toward a partner firm in this scenario may threaten the on-going viability of

their shared project, and endanger more substantial resources than a simple distribution or

marketing alliances would require. It is also possible that due to the interdependency of the

relationship and the increased level of interaction the project requires between management

of partner firms, the interpretation of the partner as a competitor is eroded over time to be

replaced with the recognition of the partner firm as an ally. With this logic in mind, it is

relevant to forward the contention that the interdependency associated with alliances may in

fact represent a significant factor in understanding how and why some networks may

develop collective action initiatives over time.

Perhaps the most significant finding to emerge from this research relates to the general

acceptance that strategic networks act to facilitate rivalry. An interpretation that seems to

feature somewhat prominently – and due in some respects to the lack of specificity by prior

researchers – is the contention that strategic networks act as collective units of rivalry

157

(Gomes-Casseras, 1994, 1996). Strategic networks do elicit competitive benefits for firms,

however it important that this benefit first be assessed in terms of whether it resides in the

individual context of firm operation or in the collective context of network activity. It is

necessary to approach with caution any argument implying a collectivist rationale given the

reality that such action would be fraught with difficulties, given that different industry

environments create varying opportunities for such collective action to be realised.

5.5 PRACTICAL RELEVANCE OF RESEARCH FINDINGS

The findings from this research found that those firms engaged in strategic alliances with

each other are more likely to target these same firms when formulating their competitive

strategy. This suggests that strategic relationships provide little protection from rivalry, and

may in fact heighten the level of rivalry the firm faces from other firms in the industry. It is

entirely possible to conclude that managers are not aware of the greater scope of the

strategic relationships characterising the industry, and have little knowledge as to what

strategic network they may form a part of.

Given these findings, several potential avenues for managing the competitive environment of

industries not dominated by technological and regulatory imperatives are proffered. Initially,

the nature of the collaborative arrangement engineered by partner firms should broker

concerns beyond the parameters of the goals of the relationship itself. Managers should be

educated as to the tendency for strategic partners to target their partner in the product

market, therefore eroding any benefits achieved in the earlier collaborative effort. With this

knowledge, managers may show greater foresight in terms of structuring their product

market to avoid the losses accrued by engaging in direct competition with collaborative

partners. Further, it may benefit managers to learn how to determine with whom they are

indirectly linked to through strategic relationships. Through advancing their understanding of

the structural nature of their industry via the strategic network rationale, managers may be

able to develop a sustainable opportunity structure based on their potential capacity to

access other firm’s resources and capabilities, in conjunction with advocating a system

whereby their direct rivalry from network participants is minimised.

5.5.1 The Significance of the Industry Context

In light of the outcomes of this research, it becomes apparent that industry context

constitutes a significant factor when considering the role of strategic network rivalry. In those

158

industries not dominated by a prevailing technological or regulatory imperative, the argument

for overt or implied collusive organising product market agendas between firms comprising a

strategic network appear non-existent on the basis of study outcomes generated in this

research. In those industries where strategic network formation develops as a consequence

of firms prescribing to a specific technological standard – such as the research conducted by

Vanhaverbeke and Noorderhaven (2001) – strategic network members may find collective

competitive focus on the basis of advancing the technological standard that is central to their

product market output and continued viability of their firm within the industry. The unifying

factor underscoring the action of individual firms is to advance the technological standard

that they advocate, as demonstrated by their inclination to engage in alliances with other

firms sharing this same technological platform. This product specificity represents an internal

and external commonality shared by those firms comprising the network, and thus it is more

likely that on the basis of each firm’s competitive intent to further their own economic

interests does the argument of collective rivalry develop. It is possible that this ‘collective’

competitive action as observed by some researchers is in reality an artefact purely derived

from the individual firm’s logic of economic survival. It may be that over time an implied

agreement between firms comprising the network develops, however preceeding this

common understanding would be the acknowledgement of commonalities shared between

firms in terms of the reliance on a specific technological standard.

In contrast, industries dominated by regulatory imperatives seem to imply a different set of

characteristics on horizontal strategic networks. The most prominent example here is the

Airline Industry which has been the setting for numerous research investigations over the

past two decades. Once again, economic viability is central to the advent of alliance (and

subsequently network) relationships established, however the benefits associated with these

alliances tend to offer greater transparency than those developed in other industries. Pursuit

of alliance arrangements between firms in the airline industry is largely motivated by the

desire of industry players to extend their market scale and scope via offering an increased

number of destinations and access to consumer privilege schemes as opposed to research

and development agreements. Here the presence of regulatory requirements – defining what

activities can and cannot be performed and what product / service markets can be

participated in by different carriers as dictated by governments and international bodies –

creates a universal framework for the industry as a whole, thus elevating the transparency of

horizontal alliances created in this context. Determining who is and who is not a participant

159

in the resulting strategic networks in the industry is simplified due to the lack of network

membership overlap. In industries that demonstrate these strong regulatory imperatives, it

becomes easier to ascertain the motives of the partners to the relationship, and observe,

from an outsider position, the role of each partner to the relationship. Alliances are typically

scale and scope related, not research and development related which implies the creation of

the alliance simply to fulfil performance related activities of the firms associated with the

relationship. These relationships imply a level of governance and coordination between the

partners that further delineates the greater collection of relationships comprising the network,

providing little confusion in terms of assessing the outcomes of the relationship.

In industries that do not demonstrate such strong technological or regulatory imperatives,

identifying the strategic network to which a firm may belong is fraught with difficulties.

Lacking a clear external rationale for focusing competitive intent, firms may find it

problematic differentiating one firm from another unless managers possess an intimate

knowledge of the strategic relationships characterising the industry. Without this knowledge,

managers may be unable to effectively ‘map’ firms within the industry, creating significant

problems in network identification. Therefore, the benefits associated with strategic networks

are more likely to be observed in those industries dominated by technological or regulatory

imperatives, and are less likely to be realised in those industries that do not have these

features.

5.6 DISCUSSION

It becomes evident that the benefits often ascribed horizontal strategic networks are only

forthcoming should all participant members of a network be aware of their affiliation. Without

such knowledge, firms are unable to formulate their competitive strategy in light of the

broader web of strategic linkages they are a part of. As a consequence, firms are likely to

erode benefits attained via their collaborative efforts by engaging in direct rivalry with related

members of their network. It is evident that greater benefits could be achieved by all firms

should they be aware of their network and how active subscription to this network could

facilitate the competitive standing of all participant members.

It is questionable as to whether strategic networks in industry environments that are

dominated by technological and regulatory imperatives engage in active forms of

governance as opposed to simply responding to an external imperative. The strategic

160

networks that appear to develop in these industries tend to do so on the basis of their

product characteristics (advocating a specific technological standard) or in terms of

increasing their scale and scope capabilities (to overcome regulatory limitations). This

external impetus orients firms toward advocating a specific objective or achieving a specified

performance goal, thereby allowing firms in such networks to easily identify their direct

competition and target these firms accordingly.

5.7 LIMITATIONS OF RESEARCH

Several limitations characterise this research effort. These limitations relate to the measure

of rivalry employed and the method of network formation. These limitations do not represent

fatal flaws of the research project, but simply represent the areas in which improvement

could be based.

5.7.1 The Rivalry Measure

One limitation associated with the research into strategic networks and rivalry completed

here relates to the rivalry measure employed. The use of the modified Herfindahl index

elicited both benefits and weaknesses in application. Initially, the benefits ascribed use of the

modified Herfindahl index include the use of real production figures of each firm, which

represents a direct reflection of their product market output. In addition, the use of the

modified Herfindahl formula allowed for the relevance of product market segments to be

taken directly into consideration. In this respect, this measure provides for a sound

representation of what level of rivalry one firm faces from other firms participating in the

same product market segment. The weaknesses associated with the use of the modified

Herfindahl formula include the failure of this measure to incorporate dynamic elements such

as tit-for-tat rivalry, marketing strategies and competitive attacks in terms of instigating price

competition between firms.

5.7.2 Strategic Network Formation

The method of strategic network formation included in this research is not the method most

advocated by academics in the field who encourage use of the CONCOR (convergence

correlations) algorithm to produce ‘strategic blocks’. ‘Strategic blocks’ offer an interpretation

of networks in which firms demonstrating similarities in terms of their position in their network

are grouped together to form a strategic block. In contrast, the logic behind strategic

networks is to identify those firms most closely linked to each other through engagement in

161

strategic relationships. Therefore, advocating the use of the CONCOR algorithm fails to

address the relational equivalence that is sought in the identification of strategic networks.

Therefore the approach used within this research focuses instead on identifying collections

of actors that demonstrate denser ties with each than with other firms in the industry. In this

respect this research deviates from the approach advocated by prominent academics in the

field, however it does so with sufficient and relevant justification. In order to avoid the

difficulties found in the strategic group realm of research, where there exists no prescribed

method for determining strategic groups, a clarification of standard network methods needs

to be available to future researchers in this field.

5.7.3 Sample Size

As with other studies of this kind, finding suitably detailed and available data with which to

conduct research is a significant problem. Publicly available data – such as that relating to

the airline industry for example – tends to be relied on excessively given the difficulties

associated with securing data on other industries. The issue of data availability necessarily

dictates which industries will most often provide the testing ground for research investigation,

regardless of whether these industries are representative or not. This creates significant

problems in terms of accounting for the relevancy and applicability of some research

outcomes across a broad range of industry types. Further, this impacts on the capacity for

sound theory to be developed over the long term in the strategic management field.

Despite obtaining the required data from the automotive industry upon which to undertake

this research, the scope of complete data allowed only for the light vehicles component of

the United States vehicle industry to be investigated. After necessary actor exclusions took

place, a total of only sixteen actors were included in the analysis. This sample size is

considered relatively small, however given the total population of this industry segment, and

adherence to proper methodology, this relatively small sample size could not be avoided. An

ideal scenario would be to undertake research using a greater sample size where possible.

5.8 DIRECTIONS FOR FUTURE RESEARCH

The need for continued research into the strategic network construct coupled with the

influence this contemporary industry phenomenon has on rivalry is abundantly clear. Prior

empirical research into this area is sparse, and the few studies that do exist have been

completed either without directly measuring rivalry, or failing to follow suitable methodology

162

in defining network structures. While this research project seeks to address some of the

current gaps in contemporary knowledge of the strategic network – rivalry relationship,

significant scope exists through which additional research efforts could further clarify the

nature of this relationship, specifically in relation to the relevance of the industry context.

5.8.1 Industry Context

It becomes evident that the relevance of industry context is a significant variable when

engaging in strategic network research. Typical choices seem to revolve around technology

intensive industries where different standards are engaged in a fixed competition until a

dominant design is determined, or in highly regulated industries, such as the airline industry.

The choice of industry setting when pursuing network research is decided almost by virtue of

suitably available firm and industry specific data required to operationalise the necessary

research constructs. It is perhaps not surprising then that industries that offer greater

transparency in terms of regulatory requirements and competing technologies attract the

greatest amount of research attention. However the problem with these industries

representing the greatest research exposure is that the majority of findings generated are

specific only to these industries which may in fact constitute the lesser prevalent industry

type evident in contemporary environments. More challenging and greater value can be

found in attempting to research those industries that are more common, such as the

automotive or white goods industries, where the results of investigation may offer greater

insight for academics in expanding the extant knowledge of strategic management, or for

managers who find themselves entrenched in the practical context of such industries.

Obtaining data for such industries can be fraught with difficulty, however this should not

preclude academics from opting for this ‘less travelled’ option. The pursuit of research

results that offer a greater representation of the industry majority should be a goal rather

than an exception.

That said, greater research needs to be undertaken in industries characterised by

technological and regulatory imperatives and in industries that do not have these

characteristics in order to contribute to the broader understanding of the role of industry

types in strategic network – rivalry research. While greater research has been undertaken in

those industries demonstrating technological or regulatory imperatives, in sum, these studies

are relatively few, and suffer from the two general problems associated with research in this

area – either a failure to directly measure rivalry, or a failure to follow suitable methodology

163

to define strategic networks. It will only be through investigating strategic networks and

rivalry in differing industry types will it be possible to determine if the industry environment

plays a critical role in predisposing strategic networks to act in a collectivist manner in

regards to enacting rivalry.

Does strategic network membership predict patterns of rivalry? In those industries that

demonstrate an overt unifying rationale such as technology or regulatory imperatives, the

answer may indeed be ‘yes’. In industries where these features are absent, research has

largely failed to investigate. Before a conclusive response can be given as to the relevancy

of strategic network membership and rivalry, greater research needs to be undertaken

across a variety of industries that capture all significant variables.

5.8.2 Measure of Rivalry

The use of the modified Herfindahl Index utilised in this research presents some limitations,

as discussed in section 5.6.1 of this chapter. Industrial organization economics and the

resource- based view of the firm informs us that competition occurs in both product and

supply markets. According to some theorists, collaboration simply represents a variation of

what is traditionally recognised as competition. If we acknowledge that this premise is true

and remove ourselves from the dominance of any one theoretical framework, how can we

effectively examine competition given the prevalence of current models of rivalry to preclude

one of these markets? Given that the research on strategic networks and rivalry is very

much concerned with supply (collaboration) and product outcomes, the very nature of this

research necessitates that researchers define ways in which both the collaborative and

product market dimensions are simultaneously captured in reliable measures.

An important issue in this regard relates to the value associated with a collaborative

endeavour between partners, and whether this value is eroded due to how partner firms

structure their product markets. For instance, in the United States Light Vehicles Industry,

this research showed that firms which engage in collaborative efforts tend to then target their

collaborative partners in direct competition in the product market. This logic is counter-

intuitive, and if the results generated via this research are replicated in further research into

strategic networks and rivalry (where industry type is taken into account), an important

avenue of how research can inform management practice may develop. However, before

164

this possible outcome can be considered, the dilemma of developing effective measures of

rivalry (both in collaboration and product market outcomes) must be addressed.

Adopting a general perspective, it becomes evident that current rivalry research tends in

large to be based within the industrial organization paradigm, whereby models of rivalry are

embedded in product market outcomes. Within this paradigm, product markets represent the

most overt and practical expression of rivalry available for analysis, however no

comprehensive and readily applicable measure/s of rivalry have been developed that satisfy

all attributes associated with competition. A further difficulty arises when collaborative

arrangements between firms traditionally understood to be competitors is factored into

contemporary industry environments.

Clearly significant scope exists in which future research may look at either enhancing current

measures or developing new measures that effectively capture the rivalry construct. It is

important that such measures are designed to be readily applicable, given that the limitation

ascribed many current rivalry measures relates to the inability to operationalise such

measures appropriately. Further, the development of a means by which collaborative

engagements (strategic alliances) could be valued in terms of their competitive benefit to

partner firms, especially given the likelihood of large sample numbers, would be highly

beneficial not only to strategic network studies in particular, but to strategic management

research as a whole.

5.8.3 Strategic Network Determination

As detailed in Section 5.6.2 of this Chapter, consensus needs to arise as to accepted and

appropriate methods for strategic network determination for research within the business

discipline. As it currently exists, confusion appears to revolve around the concept of strategic

networks and ‘strategic blocks’, each of which refer to different conceptualisations of the

network concept, and yet prescribed the same methodology to determine. Strategic

networks represent collections of firms in an industry that exhibit denser (horizontal) strategic

linkages among themselves in comparison with other firms (or collection of firms) in the

same industry. In this regard, firms that are closely associated with other firms in an industry

due to the presence of strategic alliances are grouped together to form a single strategic

network. Strategic blocks, in contrast, represent a grouping of firms in an industry that

occupy the same relative position in a strategic network. Therefore the idea associated with

165

strategic blocks is that of positional equivalence as opposed to relational equivalence as

associated with strategic networks. Despite these distinct differences, confusion still

surrounds the appropriate methods by which strategic networks should be determined, often

with the methodology ascribed strategic block determination prescribed for strategic network

determination. Further, due to this lack of clarity in strategic network determination, it is

possible to interpret and re-interpret the data according to the application of different

equations. A goal for future research may relate to clearly defining the methods by which

strategic networks are determined, in conjunction with minimising the complexity currently

inherent in current network methodology practices.

5.8.4 Research Agendas

A number of specific research agendas arise when reviewing this research project. These

include, but are not limited to:

There exists a clear need to investigate the relationship between horizontal strategic

networks and rivalry, mindful of the need to directly measure rivalry. Some prior

research into the strategic network and rivalry domain has failed to directly measure

rivalry, therefore limiting any conclusions such research may reach in relation to the

relationship between strategic networks and rivalry. Gaps in current strategic

management knowledge extend to the role that strategic networks may play in

influencing competitive outcomes in both industries dominated by technological and

regulatory imperatives (such as the microprocessor or airline industries), and those

industries that do not have such dominant industry attributes (such as the

whitegoods or automobile industries).

o This particular research agenda necessarily creates the opportunity to help

determine the role that industry type may play in defining the relevance of

strategic networks in industries and whether such structures directly

influence the nature of rivalry observed between firms.

Whether all industry types are inclined to support network configurations is

unknown. It is possible to foresee that some industry types are not predisposed to

strategic alliances of any kind, and therefore would fail to be suitable environments

for strategic networks to prosper. In this regard, a useful research agenda would be

to determine those industry attributes or characteristics that predispose some

industry types to foster strategic networks, and define those industry attributes or

166

characteristics that characterise those industry environments where strategic

networks are less likely to develop.

5.9 CONCLUSION

This chapter has offered a brief review of the three studies undertaken that together

provided a conclusion to the primary research question central to this investigation: Are

patterns of rivalry predicted by strategic network membership? The results of this research

propose that strategic network membership is unable to account for the patterns of rivalry

observed in the United States Light Vehicles Industry over the timeframe 1993-1999. This

conclusion is in contrast to the contention that strategic networks act to facilitate collective

action, specifically rivalry. In light of prior research, it becomes evident that the industry type

may be a mediating factor in determining whether strategic networks have the capacity to

directly influence rivalry. Prior research on horizontal strategic networks and rivalry have

investigated this relationship only in technological or regulatory intense industry settings,

which due to the presence of either competing technological standards or regulatory

necessity may act to provide the economic rationale that better explains the contention of

collective rivalry as opposed to the concept of strategic networks. The presence and

relevance of strategic networks in industry settings that do not have such dominant

imperatives requires further investigation, first to test the proposition that strategic networks

can be empirically identified across a broad range of industries as argued by prominent

theorists in the strategic management field (Gomes-Cassares, 1994, 1996; Gulati, 1995;

Vanhaverbeke and Noorderhaven, 2001), and further to clarify whether such industry

structures can offer any predictive scope to distinguish patterns of rivalry independently

observed to transpire between firms.

Alternative explanations were postured to explore the results derived from this research,

suggesting that industry type, cognitive interpretations by managers of which firms may

constitute rivals and incomplete knowledge as to which firms comprise members of a

strategic network may represent significant factors to interpreting these research findings.

The significance of this research was proposed, in that the direct measurement of rivalry

coupled with the choice of industry setting were key differentials observed between this

investigation and those previously undertaken in the strategic network realm. The limitations

characterising this research were presented, and directions for future research were

outlined.

167

CHAPTER 6CHAPTER 6

C O N C L U S I O NC O N C L U S I O N

168

My interest in competitive dynamics began when I commenced my Honours study. In the

thesis completed for the award of the Bachelor of Business Honours degree, I examined

whether the strategic group construct could account for patterns in rivalry observed in the

airline industry. While completing this work I became aware of the relative importance of the

strategic alliance tool used by firms to overcome regulatory restrictions in the industry which

constrained a firm’s capacity to increase market share. The strategic group construct

allowed for elements of these alliances to be captured in analysis, however I was aware that

these alliances in themselves told another story. It was apparent that these strategic

alliances effectively united firms in a network of activity which collectively defined the

competitive dynamics of the industry. Further, it became evident that these strategic

alliances, given their capacity to determine the opportunity for partner firms to build market

share, represented perhaps the most significant aspect of an individual firm’s competitive

strategy. These alliances effectively acted to channel the competitive intent of individual

firms away from network members and towards competing networks in the industry,

suggesting that these network structures provided the framework whereby collective rivalry

could be realised.

This interest in how network structures could facilitate rivalry led me to investigate the

concept of strategic networks further. Perhaps the most significant research reviewed at this

time was written by Nohria and Garcia-Pont (1991), based on Garcia-Pont’s empirical

investigation of the auto industry. Garcia-Pont identified what was termed ‘strategic blocks’

based on the collation of information on strategic relationships between producers in the

industry. On close inspection it became evident that while strategic blocks were determined

using data on strategic relationships between producers in the industry, the method of

network analysis employed produced networks based on positional equivalence rather than

relational equivalence. Positional equivalence is concerned with identifying and grouping

together those actors that demonstrate the same relative position in the broader network

they are affiliated to. As a consequence, firms who occupied similar positions in their

respective network were grouped together to comprise a single strategic block. These

resultant blocks were therefore not reflective of strategic networks – groups of firms that

demonstrate denser strategic linkages amongst themselves in comparison to other firms in

the industry. Thus, the outcome of relational equivalence are collections of firms that

demonstrate close strategic relationships with each other. It was this conceptualisation of

strategic network structures that I was interested in studying further.

169

Gomes-Cassares (1994, 1996) proposed, through qualitative reasoning, the concept of

‘alliance blocks’ (equivalent to strategic networks) that represented groups of firms closely

linked to each other via strategic relationships. With examples based on the airline industry,

these alliance blocks, Gomes-Cassares argued, demonstrated some capacity to engage in

collective action. This confirmed the observations made during the completion of my

Honours thesis.

It soon became evident that few empirical studies utilising the strategic network rationale to

investigate rivalry had been undertaken. Further, of those studies completed, it was apparent

that the methodology proposed by Nohria and Garcia-Pont in identification of strategic

blocks (1991) had been widely accepted as the process by which strategic networks were

defined. Indeed, the concepts of strategic blocks, alliance blocks and strategic networks had

become interchangeable in the literature, further confounding this area of research. Clearly a

divide existed in the generalised theory relating to network structures and the relationship

that these structures had in influencing rivalry. At the core of this divide was the generally

accepted perception of networks as comprised of actors that are strategically linked to each

other, whereas the methodology underlying the empirical identification of these networks

failed to identify cohesive subsets of firms that demonstrated strategic relationships with

each other. As a result, theory and research objectives did not correlate with the

methodology employed. The conclusions generated by research endeavours demonstrating

this flawed research design began to obtain precedence and popularity in the literature. In

effect, what emerged over time in the literature was a line of theoretical conjecture not

actually based on the reality of the research undertaken, given that strategic blocks were

being misrepresented as strategic networks.

Based on these observations, it became apparent that scope existed upon which to

investigate the relationship between horizontal strategic networks and rivalry, as this had yet

to take place despite several academic articles that proposed otherwise. Before the strategic

network rationale could be positioned as a conceptual approach to examining intraindustry

rivalry as some researchers have posited, it was necessary to complete research utilising

the appropriate methodology required for strategic network determination in conjunction with

the direct measurement of rivalry. Without question the most challenging aspect of this work

has rested with the methodological requirements of this topic, validating that prior research

170

did indeed utilise methods that did not generate what is recognised as ‘strategic networks’,

and ensuring that the methods appropriated in this research identified networks whose

members were strategically aligned to each other by virtue of the strategic relationships

evident in the industry.

Secondly, it became apparent that the typical industries investigated thus far were those

inclined to demonstrate an internal rationale for collective competition to transpire in the

industry regardless of overt network affiliation. The airline industry is highly regulated,

requiring firms to engage in strategic alliances in order to overcome the constraints that

these regulations have on an individual firm’s capacity to increase market share. As a result,

strategic alliances usually represent a means by which firms increase their ability to offer

greater products and services to consumers and enhance the attractiveness of their

respective loyalty schemes. Another setting of empirical investigation has been technology

intensive industries. These industries are associated with the battle of technological

standards, and thus this provides an economic reasoning as to why firms affiliated with

advocating a specific standard may appear to be engaging in collective competitive action. A

true test of the strategic network – rivalry relationship would be to assess this relationship in

an industry that did not demonstrate such overt rationales for collective action to develop,

such as the automotive industry.

From these foundations it was possible to identify the central question of research: Are

patterns of rivalry predicted by strategic network membership? In order to comprehensively

answer this question it was necessary to assess the level of rivalry observed between and

within networks. Between network rivalry is based on the level of the rivalry observed

between firms comprising different strategic networks within the industry. Within network

rivalry refers to the level of rivalry observed to transpire between members of the same

strategic network. If firms do engage in some form of collective competitive action based on

their membership to a strategic network, we should find that the level of rivalry identified as

transpiring between strategic networks should be greater than that identified at the within

network level. The setting for this research was the automotive industry for the reasons

discussed above, however the parameters of this research would be based on the scope of

available data. Restrictions in available data would ultimately see the investigation

embedded within the light vehicles component of the United States automotive industry.

171

Obtaining the data required to undertake strategic network and rivalry analysis in the

automotive industry necessitated two distinct data sets. Strategic networks can be devised in

a number of ways – either to encompass horizontal ties, vertical ties, or the entire web of

horizontal and vertical ties. The goal of this research was to investigate whether strategic

networks could predict patterns of rivalry, and in order to minimise complexity in terms of the

research design and required data, it was concluded that horizontal networks, whereby the

firm sample included those firms who occupy the same relative position in the value chain

and whose inputs and outputs are similar, would be investigated. The sample therefore

included all auto producers who offer vehicles for sale within the light vehicles component of

the auto industry. To compile the first dataset required extensive information to be collated

on the advent and decline of strategic relationships between producers in the auto industry.

The primary data source identified for this information was How the World’s Automakers are

Related. The second dataset required information to be collated on all firms active in the

automotive industry, their production and sales figures and detailed information on product

specifications and product market segments. The primary source for this data was obtained

through Ward’s Automotive Yearbook. Additional data was obtained from a variety of

sources in order to complete the datasets and also to ensure the reliability and validity of the

data collected.

The resulting data was assessed to determine where data gaps existed (for example, it was

not uncommon for some privately held firms to withhold production figures specific to vehicle

types offered in the market). The most robust data emerged from the United States,

particularly the Light Vehicles Segment of the Auto Industry. Due to this, the parameter for

investigation was decided. In addition, data availability determined the timeframe of

investigation, with complete datasets available from 1993 – 1999. The final sample

incorporated all firms for which complete data was available in terms of the strategic network

and rivalry components of research. It was necessary that some producers had to be

excluded from the study based on their failure to participate throughout the entire period of

study. Strategic network research requires that consistency be observed in the number of

actors included in analysis from period to period, otherwise the resulting networks can not be

effectively compared across time frames.

Due to the relatively limited change expected to occur in network membership over the short

term, it was decided that analysis undertaken on an annual basis would prove redundant.

172

Instead, analysis every second year provided the foundation upon which the goal of

research could be realised. Thus, strategic networks were defined for the years 1993, 1995,

1997 and 1999. In accordance with this, rivalry measures, based on the data acquired on

product market segments, vehicle types, production and sales figures, were calculated.

The results obtained from analysis found little evidence to support the contention that

strategic network membership predicted patterns of rivalry in the United States Light

Vehicles Industry over the period 1993 – 1999. Indeed, the level of rivalry observed at the

within network level exceeded that observed at the between network level. These results

therefore suggest that firms are more inclined to engage in direct rivalry with those firms to

whom they are strategically aligned.

It is possible to infer a number of plausible scenarios to explain why the results observed in

this study differ from those results obtained in prior studies. Initially, it becomes apparent that

industry type may play a crucial role in the realisation of network rivalry. Should

technological or regulatory imperatives characterise an industry, these attributes may

contribute to providing an external impetus for implicit coordination by industry actors. These

attributes in themselves provide an economic incentive – for instance, in support of a specific

technological standard central to a firm’s ongoing viability – and this in itself provides

participants to the industry an ability to effectively organise their competitive intentions

without direct reference to other firms within the industry. The firms in this industry type

benefit from a level of transparency in the industry due to either subscription to, or against, a

specified standard. Thus, it is more likely that firms will readily identify those firms in the

industry that advocate the same product-specific attributes that are central to the on-going

economic viability of these firms in the industry, and on this basis are less likely to challenge

each other for competitive dominance. Rather, rivalry at this time may be focused on

reducing the opportunities for firms advocating an alternative standard to prosper within the

industry. Should the battle for a dominant design be won by a specified standard, the

competitive landscape of the industry would alter. Strategic networks previously

characterising the industry may dissolve as the relevancy of network subscription (the

advancement of a specific technological standard) is no longer valid. In this respect the

strategic network in itself may not be responsible for the realisation of collective competitive

action observed by researchers prior to the success of a dominant design, but merely act as

an intraindustry analytical tool by which this action can be more readily defined.

173

Despite the contribution of this research to strategic management research and literature,

several limitations characterise this study. Initially, it would not be surprising to discover that

some researchers in the strategic network field find issue with the methodology employed to

define strategic networks in this research. As previously discussed, the methodology

underlying work in the strategic network area is fraught with some difficulty, with confusion

stemming from the concepts of strategic blocks and strategic networks. These two concepts

are in reality two very different things, and yet the methodology underlying these concepts

has been applied to develop both constructs. The second weakness of this research relates

to the rivalry measure employed. While the measure used in this research is quite sound,

scope exists by which this measure could be further developed to incorporate other

indications of rivalry, such as tit-for-tat imitation. The final weakness of this work relates to

the sample size. It would be interesting to see if the results generated within the context of

this research could be replicated if a larger sample size was used.

The findings of this research provide the basis for renewed discussion of the strategic

network concept in strategic management theory and research. This work provides the

platform upon which further research into the strategic network and rivalry relationship can

proceed, particularly in investigating this relationship in other industries that do not

demonstrate overt subscription to technological or regulatory imperatives. Further research

into this relationship in those industries that do demonstrate these overt imperatives should

ensure that rivalry is directly measured, as this is a weakness evident in past empirical

efforts. It is postulated here that industry type may represent a significant factor in the

realisation of strategic network rivalry. If this is indeed the case, it may be necessary to

examine whether it is the network structure that facilitates what is observed as collective

action, as opposed to the overt influence of technological or regulatory imperatives that

institute an economic rationale for firms to behave in a particular competitive fashion.

It is evident that the relationship between strategic networks and rivalry is far from

conclusively determined. This research provides an initial step toward attaining a clear

conclusion on the nature of this relationship, if indeed one is found to exist across a broad

range of industry types. This research identifies strategic network determination, the

inclusion of the direct measurement of rivalry, and awareness of industry type as important

components of any future research endeavour.

174

REFERENCES

Ahuja, G. 2000. The duality of collaboration: Inducements and opportunities in the formation of interfirm linkages. Strategic Management Journal, 21: 317-343. Amit, R. & Schoemaker, P. 1993. Strategic assets and organizational rent. Strategic Management Journal, 14(1): 33-46. Andrews, K. 1971. The Concept of Corporate Strategy. IL: US: Irwin. Ansoff, I. 1965. Corporate Strategy. NY: US: McGraw-Hill. Ansoff, I. 1991. Strategic management in a historical perspective. International Review of Strategic Management, 12: 3-69. Araujo, L. & Brito, C. 1998. Agency and constitutional ordering in networks. International Studies of Management & Organization, 27(4): 22-46. Auster, E. 1994. Macro and strategic perspectives on interorganizational linkages: A comparative analysis and review with suggestions for reorientation. In H. D. Shrivastava (Ed.), Advances in Strategic Management, Vol. 10B: 3-40: JAI Press Inc. Bain, J. 1956. Barriers to New Competition. MA: US: Harvard University Press. Bain, J. 1959. Industrial Organization. NY: US: John Wiley & Sons. Barney, J. & Ouchi, W. 1986. Organizational Economics: Toward a New Paradigm for Understanding and Studying Organizations. California; US: Jossey-Bass Inc. Barney, J. 1991. Firm resources and sustained competitive advantage. Journal of Management, 17: 99-120. Barney, J. & Zajac, E. 1994. Competitive organizational behavior: Toward an organizationally-based theory of competitive advantage. Strategic Management Journal, 15: 5-9. Barney, J. 2001. Is the resource-based 'view' a useful perspective for strategic management research? Yes. Academy of Management Review, 26(1): 41-56. Baum, J. & Korn, H. 1996. Competitive dynamics of interfirm rivalry. Academy of Management Journal, 39(2): 255-291. Benson, J. 1975. The interorganizational network as a political economy. Administrative Science Quarterly, 20: 229-249. Besanko, D., Dranove, D., & Shanley, M. 2000. Economics of Strategy. (2nd ed.). MA: US: John Wiley & Sons, Inc. Blankenburg Holm, D., Eriksson, K., & Johanson, J. 1999. Creating value through mutual commitment to business network relationships. Strategic Management Journal, 20: 467-486.

175

Bogner, W., Pandian, J., & Thomas, H. 1994. The firm-specific aspects of strategic group dynamics. In H. D. H. Thomas (Ed.), Strategic Groups, Strategic Moves and Performance: 299-329. London; UK: Elsevier Science. Borch, O. 1994. The process of relational contracting. Developing trust-based strategic alliances among small business enterprises. I Shrivastava, P. Dutton, J. & Huff, A. (eds.): Advances in Strategic Management, JAI Press Inc., Connecticut: US. Borgatti, S. & Foster, P. 2003. The network paradigm in organizational research: A review and typology. Journal of Management. 29(6): 991-1013. Borgatti, S. P., Everett, M. G., & Freeman, L. C. 2002. Ucinet for Windows: Software for Social Network Analysis (Version 6.39). Harvard: Analytic Technologies. Boyd, J. 2004. Intra-industry structure and performance : strategic groups and strategic blocks in the worldwide airline industry. European Management Review, 1 : 132-144. Bowman, E. 1990. Strategy changes: Possible worlds and actual minds. In J. Fredrickson (Ed.), Perspectives on Strategic Management: 9-37. San Francisco: Harper Business. Brass, D., Galaskiewicz, J., Greve, H. & Tsai, W. 2004. Taking stock of networks and organizations: A multilevel perspective. Academy of Management Journal, 47(6), 795-817. Breiger, R., Boorman, S., & Arabie, P. 1975. An algorithm for clustering relational data with applications to social network analysis and comparison with multidimensional scaling. Journal of Mathematical Psychology, 12: 328-383. Burgers, W., Hill, C., & Kim, W. 1993. A theory of global strategic alliances: The case of the global auto industry. Strategic Management Journal, 14: 419-432. Burt, R. 1980. Cooptive corporate actor networks: A reconsideration of interlocking directorates involving American manufacturing. Administrative Science Quarterly, 25: 557-582. Burt, R. 1992. Structural Holes: The Social Structure of Competition. MA: US: Harvard University Press. Camerer, C. 1991. Does strategy research need game theory? Strategic Management Journal, 12: 137-152. Caves, R. & Porter, M. 1977. From entry barriers to mobility barriers: Conjectural decisions and contrived deterrence to new competition. Quarterly Journal of Economics, 91: 241-262. Chandler, A. 1962. Strategy and Structure: Chapters in the History of the Industrial Enterprise. Mass: US: MIT Press. Chen, M. 1996. Competitor analysis and interfirm rivalry: Toward a theoretical integration. Academy of Management Review, 21(1): 100-134.

176

Chung, S. 1993. Markets and Networks of Organizations: A Longitudinal Study on Collaboration of Organizations in Competition. Unpublished Doctor of Philosophy, University of Pennsylvania, Pennsylvania: US. Coase, R. 1937. The nature of the firm. Economica, 4(16): 386-405.

Collis, D. 1991. A resource-based analysis of global competition: The case of the bearings industry. Strategic Management Journal, 12: 49-68. Collis, D. & Montgomery, C. 1997. Corporate Strategy: Resources and the Scope of the Firm. Chicago; US: Irwin. Colombo, M. E. 1998. The Changing Boundaries of the Firm: Explaining Evolving Inter-Firm Relations. New York; US: Routledge. Conner, K. 1991. A historical comparison of resource-based theory and five schools of thought with industrial organization economics: Do we have a new theory of the firm? Journal of Management, 17: 121-154. Contractor, F. & Lorange, P. 1988. Cooperative Strategies in International Business: Joint Ventures and Technology Partnerships between Firms. Mass; US: Lexington Books. Contractor, F. & Lorange, P. 1998. Competition vs. cooperation: A benefit/cost framework for choosing between fully-owned investments and cooperative relationships. In P. Beamish (Ed.), Strategic Alliances.: 139-152. Cornwall: UK: Edward Elgar Publishing Limited. Cool, K. & Schendel, D. 1987. Strategic group formation and performance: The case of the US pharmaceutical indsutry, 1963-1982., 33(9): 1102-1124. Cool, K. & Schendel, D. 1988. Performance differences among strategic group members. Strategic Management Journal, 9: 207-223. Cool, K. & Dierickx, I. 1993. Rivalry, strategic groups and firm profitability. Strategic Management Journal, 14: 47-59. Coser, L., Kadushin, C. & Powell, W. 1982. The Culture and Commerce of Publishing. New York: Basic Books. Davis, G. 1991. Agents without principles? The spread of the poison pill through the intercorporate network. Administrative Science Quarterly, 36: 583-613. Davis, J. & Devinney, T. 1997. The Essence of Corporate Strategy: Theory for Modern Decision Making. Sydney: Allen & Unwin. Dierickx, I. & Cool, K. 1989. Asset stock accumulation and sustainability of competitive advantage. Management Science, 35(12): 1504-1511. Dierickx, I. & Cool, K. 1994. Competitive strategy, asset accumulation and firm performance. In H. D. H. Thomas (Ed.), Strategic Groups, Strategic Moves and Performance: 63-. London; UK: Elsevier Science.

177

Dixit, A. & Nalebuff, B. 1991. Thinking Strategically: The Competitive Edge in Business, Politics, and Everyday Life. NY: US: W.W. Norton and Company. Domke-Danonte, D. 1998. An Investigation of Antecedents and Outcomes of Cooperative and Competitive Strategic Repertoire: The Case of the Commercial Airline Industry. Unpublished Doctor of Philosophy, The Florida State University, Florida; US. Donaldson, L. 1995. American Anti-Management Theories of Organization: A Critique of Paradigm Proliferation. Melbourne; Australia: Cambridge University Press. Dunning, J. 1995. Reappraising the eclectic paradigm in an age of alliance capitalism. Journal of International Business Studies, 26(3): 461-491. Dunning, J. 1998. Reappraising the eclectic paradigm in an age of alliance capitalism. In P. Beamish (Ed.), Strategic Alliances: 153-183. Cornwall: UK: Edward Elgar Publishing Limited. Durisin, B. & Von Krogh, G. (2005). Competitive advantage, knowledge assets & group level effects: An empirical study of global investment banking. In R. Bettis (Ed.), Strategy in Transition. NY: US: Blackwell, p. 35-80. Dutton, J. & Jackson, S. 1987. Categorizing strategic issues: Links to organizational action. Academy of Management Review, 12(1): 76-90. Dyer, J. 1997. Effective interfirm collaboration: How firms minimize transaction costs and maximize transaction value. Strategic Management Journal, 18(7): 535-556. Dyer, J. & Singh, H. 1998. The relational view: Cooperative strategy and sources of interorganizational competitive advantage. Academy of Management Review, 23(4): 660-679. Easton, G. 1994. Industrial networks: A review. In B. E. Axelsson, G. (Ed.), Industrial Networks: A New View of Reality.: 3-27. NY: US: Routledge. Ebers, M. & Jarillo, J. 1997. The construction, forms, and consequences of industry networks. International Studies of Management & Organization. 27(4), 3-21. Eisenhardt, K. 1989. Agency theory: An assessment and review. Academy of Management Review, 14(1): 57-74. Faulkner, D. & Campbell, A. (Ed). 2006. The Oxford Book of Strategy. Oxford University Press, UK. Fiegenbaum, A. & Thomas, H. 1995. Strategic groups as reference groups: Theory, modeling and empirical examination of industry and competitive strategy. Strategic Management Journal, 16(6): 461-476. Fiegenbaum, A., Hart, S., & Schendel, D. 1996. Strategic reference point theory. Strategic Management Journal, 17: 219-235. Fredrickson, J. (Ed.). 1990. Perspectives on Strategic Management. San Francisco; US: Harper Business.

178

Furrer, O. & Thomas, H. 2000. The rivalry matrix: Understanding rivalry and competitive dynamics. European Management Journal, 18(6): 619-637. Galaskiewicz, J. 1979. Exchange Networks and Community Politics. Sage Publications; California: US. Garcia-Pont, C. 1992. Strategic Linkages Within an Industry: The Emergence of Strategic Blocks. Unpublished Doctor of Philosophy, Massachusetts Institute of Technology, Massachusetts; US. Gimeno, J. & Woo, C. 1999. Multimarket contact, economies of scope, and firm performance. Academy of Management Journal, 42(3): 239-259. Glasimeier, A. 1991. Technological discontinuities and flexible production networks: The case of Switzerland and the world watch industry. Research Policy, 20(5): 469-485. Gluck, F., Kaufman, S., & Walleck, S. 1982. The four phases of strategic management. The Journal of Business Strategy, 2(3): 10-21. Gomes-Casseres, B. 1994. Group versus group: How alliance networks compete. Harvard Business Review, July-August: 62-74. Gomes-Casseres, B. 1996. The Alliance Revolution: The New Shape of Business Rivalry. Massachusetts; US: Harvard University Press. Granovetter, M. 1985. Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91(3): 481-510. Grant, R. 1998. Contemporary Strategic Analysis. (3rd Edition ed.). Oxford:UK: Blackwell Publishers. Grimm, C. & Smith, K. 1997. Strategy as Action: Industry Rivalry and Coordination. Cincinnati: US: South-Western College Publishing. Gulati, R. 1995. Does familiarity breed trust? The implications of repeated ties for contractual choice in alliances. The Academy of Management Journal, 38(1): 85-112.

Gulati, R. 1998. Alliances and networks. Strategic Management Journal, 19: 293-317. Gulati, R. & Singh, H. 1998. The architecture of cooperation: Managing coordination costs and appropriation concerns in strategic alliances. Administrative Science Quarterly, 43: 781-814. Gulati, R., Nohria, N., & Zaheer, A. 2000. Strategic networks. Strategic Management Journal, 21: 203-215. Hakansson, H. (Ed.). 1987. Industrial technological development: A network approach. England: Croom Helm.

179

Hamel, G. 1991. Competition for competence and inter-partner learning within international strategic alliances. Strategic Management Journal, 12: 83-103. Hanneman, R. 2000. Introductory Textbook on Social Network Analysis. Retrieved 14 August, 2002 from http://faculty.ucr.edu/~hanneman/networks/nettext.pdf Harrigan, K. 1985. Joint ventures and competitive strategy. First Boston Working Paper Series: Money, Economics and Finance, Graduate School of Business, Columbia University; US. Harrigan, K. 1985. Strategies for Joint Ventures. MA; US: Lexington Books. Haugland, S. & Gronhaug, K. 1996. Cooperative relationships in competitive markets. Journal of Socio-Economics, 25(3): 359-371. Hirsch, P. 1975. Organizational effectiveness and the institutional environment. Administrative Science Quarterly, 20: 327-344. Hoskisson, R., Hitt, M., Wan, W., & Yiu, D. 1999. Theory and research in strategic management: Swings of a pendulum. Journal of Management, 25(3): 417-.443. Hatten, K. & Hatten, M. 1987. Strategic groups, asymmetrical mobility barriers and contestability. Strategic Management Journal, July-August: 329-342. Hunt, M. 1972. Competition in the Major Home Appliance Industry. Doctoral Dissertation, Harvard University. Jarillo, J. 1988. On strategic networks. Strategic Management Journal, 9: 31-41. Jensen, M. & Meckling, W. 1976. Theory of the firm: Managerial behavior, agency costs, and ownership structure. Journal of Financial Economics, 3, 305-360. Johnson, S. 1967. Hierarchical clustering schemes. Psychometrika, 32(3): 241-254. Kelly, W. 1981. A generalized interpretation of the Herfindahl Index. Southern Economic Journal, 481(1): 50-57. Klein, B. 1980. Transaction cost determinants of “unfair” contractual arrangements. The American Economic Review, 70(2), 356-362.

Knoke, D. & Kuklinski, J. 1982. Network Analysis. Sage Publications, US.

Kogut, B. 1988. Joint ventures: Theoretical and empirical perspectives. Strategic Management Journal, 9: 319-332. Kwoka, J. 1979. The effect of market share distribution on industry performance. Review of Economics and Statistics, February: 101-109. Kwoka, J. 1985. The Herfindahl Index in theory and practice. The Antitrust Bulletin, 30(4): 915-947.

180

Lado, A., Boyd, N., & Wright, P. 1992. A competency-based model of sustainable competitive advantage: Toward a conceptual integration. Journal of Management, 18(1): 77-91. Larson, A. 1992. Network Dyads in Entrepreneurial Settings: A Study of the Governance of Exchange Relationships. Administrative Science Quarterly, 37. Lazzarini, S. (2007). The impact of membership in competing alliance constellations: Evidence on the operational performance of global airlines. Strategic Management Journal, 28: 345-367. Learned, E., Christensen, C., Andrews, K., & Guth, W. 1969. Business Policy: Text and Cases. Ill: US: R.D. Irwin. Levine, S. & White, P. 1961. Exchange as a conceptual framework for the study of interorganizational relationships. Administrative Science Quarterly, 5: 583-601. Lewis, P. & Thomas, H. 1994. The linkage between strategy, strategic groups and performance in two contrasting U.K. Industries. In H. D. H. Thomas (Ed.), Strategic Groups, Strategic Moves and Performance: 261-278. London; UK: Elsevier Science. Lippman, S. & Rumelt, R. 1982. Uncertain imitability: An analysis of interfirm differences in efficiency under competition. Bell Journal of Economics, 13(2): 418-438. Lorenzoni, G. & Ornati, O. 1988. Constellations of firms and new ventures. Journal of Business Venturing. 3(1), 41-57. Madhavan, R. 1996. Strategic Flexibility and Performance in the Global Steel Industry. Unpublished Doctor of Philosophy, University of Pittsburgh, Pittsburgh; US. Madhavan, R., Koka, B., & Prescott, J. 1998. Networks in transition: How industry events (re)shape interfirm relationships. Strategic Management Journal, 19: 439-459. Mahoney, J. & Pandian, J. 1992. The resource-based view within the conversation of strategic management. Strategic Management Journal, 13: 363-380. Maijoor, S. & Witteloostuijn, A. 1996. The empirical test of the resource-based theory: Strategic regulation in the Dutch audit industry. Strategic Management Journal, 17: 549-569. Majumdar, S. 1998. On the utilization of resources: Perspectives from the U.S. telecommunications industry. Strategic Management Journal, 19: 809-831. Martin, S. 1993. Advanced Industrial Economics. Oxford: UK: Blackwell Publishers. Mascarenhas, B. 1989. Strategic group dynamics. Academy of Management Journal, 32(2): 333-352. Mascarenhas, B. & Aaker, D. 1989. Mobility barriers and strategic groups. Strategic Management Journal, 10: 475-485.

181

McGee, J. 1985. Strategic groups: A bridge between industry structure and strategic management? In H. G. Thomas, D. (Ed.), Strategic Marketing and Management: 293-313: John Wiley & Sons. McGee, J. & Thomas, H. 1986. Strategic groups: Theory, research and taxonomy. Strategic Management Journal, 7: 141-160. McGrath, R. 1995. Defining and developing competence: A strategic process paradigm. Strategic Management Journal, 16: 251-275. McKiernan, P. 1997. Strategy past: Strategy futures. Long Range Planning, 30(5): 790-798. Mehra, A. 1996. Resource and market based determinants of performance in the U.S. banking industry. Strategic Management Journal, 17: 307-322. Miller, D. & Shamsie, J. 1996. The resource-based view of the firm in two environments: The Hollywood film studios from 1936 to 1965. Academy of Management Journal, 39(3): 519-543. Mintzberg, H. 1990. Strategy formulation: Schools of thought. In J. Fredrickson (Ed.), Perspectives on Strategic Management: 105-235. San Francisco; US: Harper Business. Nelson, R. & Winter, S. 1982. An Evolutionary Theory of Economic Change. Cambridge, MA: Belknap Press. Nelson, R. 1991. Why do firms differ, and how does it matter? Strategic Management Journal, 12: 61-74. Nohria, N. & Garcia-Pont, C. 1991. Global strategic linkages and industry structure. Strategic Management Journal, 12: 105-124. Normann, R. & Ramirez, R. 1993. From value chain to value constellation: Designing interactive strategy. Harvard Business Review, 71(4): 65-77. Oster, S. 1981. Intraindustry structure and the ease of strategic change. The Review of Economics and Statistics, LXIV(3): 376-384. Oster, S. 1999. Modern Competitive Analysis. (3rd ed.). NY: US: Oxford University Press. Ouchi, W. 1980. Markets, bureaucracies, and clans. Administrative Science Quarterly, 25: 129-141. Penrose, E. 1959. The Theory of Growth of the Firm. Oxford:UK: Oxford University Press. Perrow, C. 1986. Complex Organizations: A Critical Essay. (3rd ed.). NY: US: Random House. Peteraf, M. 1993a. The conerstones of competitive advantage: A resource-based view. Strategic Management Journal, 14: 179-191.

182

Peteraf, M. 1993. Intra-industry structure and the response toward rivals. Managerial and Decision Economics, 14: 519-528. Peteraf, M. & Shanley, M. 1997. Getting to know you: A theory of strategic group identity. Strategic Management Journal, 18(Summer Special Issue): 165-186. Pfeffer, J. & Nowak, P. 1976. Joint ventures and interorganizational interdependence. Administrative Science Quarterly, 21: 398-418. Pfeffer, J. & Salancik, G. 1978. The External Control of Organizations: A Resource Dependence Perspective. NY: US: Harper and Row. Porac, J. & Thomas, H. 1990. Taxonomic mental models in competitor definition. Academy of Management Review, 15(2): 224-240. Porter, M. 1979. How competitive forces shape strategy. Harvard Business Review (March-April). Porter, M. 1980. Competitive Strategy: Techniques for Analyzing Industries and Competitors. New York; US: The Free Press. Porter, M. 1981. The contributions of industrial organization to strategic management. Academy of Management Review, 6(4): 609-620. Porter, M. 1985. Competitive Advantage: Creating and Sustaining Superior Performance. NY: US: Free Press. Porter, M. (Ed.). 1986. Competition in Global Industries. Boston; US: Harvard Business School Press. Porter, M. & Fuller, M. 1986. Coalitions and global strategy. In M. Porter (Ed.), Competition in Global Industries: 315-343. Boston; US: Harvard Business School Press. Porter, M. 1991. Toward a dynamic theory of strategy. Strategic Management Journal, 12: 95-117. Powell, W. 1990. Neither market nor hierarchy: Network forms of organization. Research in Organizational Behavior, 12: 295-336. Prahalad, C. & Hamel, G. 1990. The core competence of the corporation. Harvard Business Review(May-June): 79-91. Priem, R. & Butler, J. 2001. Is the resource-based 'view' a useful perspective for strategic management research? Academy of Management Review, 26(1): 22-40. Richter, F. 2000. Strategic Networks: The Art of Japanese Interfirm Cooperation. NY: US: International Business Press. Rosenkopf, L. & Schilling, M. 2007. Comparing alliance network structure across industries: Observations and explanations. Strategic Entrepreneurship Journal, 1:191-209.

183

Rowley, T., Baum, J., Shipilov, A., Greve, H. & Rao, H. (2004). Competing in groups. Managerial and Decision Economics, 25: 453-471. Rumelt, R. 1984. Toward a strategic theory of the firm. In R. Lamb (Ed.), Competitive Strategic Management: 556-570. NJ:US: Prentice-Hall. Rumelt, R., Schendel, D., & Teece, D. 1991. Strategic management and economics. Strategic Management Journal, 12: 5-29. Sampler, J. 1998. Redefining industry structure for the information age. Strategic Management Journal, 19: 343-355. Sanchez, R., Heene, A., & Thomas, H. 1996. Introduction: Towards the theory and practice of competence-based competition. In R. Sanchez, Heene, A. & Thomas, H. (Ed.), Dynamics of Competence-Based Competition: 1-35. Oxford: UK: Elsevier Science Ltd. Saxenian, A. 1990. Regional networks and the resurgence of Silicon Valley. California Management Review(Fall): 89-112. Saxenian, A. 1991. The origins and dynamics of production networks in Silicon Valley. Research Policy, 20: 423-437. Schoemaker, P. 1993. Multiple scenario development: Its conceptual and behavioural foundation. Strategic Management Journal, 14(3): 193-213. Scott, J. 2005. Social Network Analysis: A Handbook. Sage Publications, London, UK. Selznick, P. 1948. Foundations of the theory of organization. American Sociology Review, 13: 25-35. Shapiro, C. 1989. Theories of oligopoly behavior. In R. S. R. Willig (Ed.), Handbook of Industrial Organization, Vol. Volume 1. New York; NY: Elsevier Science Publishing Company. Shapiro, C. 1989. The theory of business strategy. RAND Journal of Economics, 20(1): 125-137. Shepherd, W. 1972. The elements of market structure. Review of Economics and Statistics, February: 25-37. Sleuwaegen, L. & Dehandschutter, W. 1986. The critical choice between the Concentration Ratio and the H-Index in assessing industry performance. The Journal of Industrial Economics, 35(2): 193-208. Sleuwaegen, L., DeBondt, R., & Dehandschutter, W. 1989. The Herfindahl Index and concentration ratios revisted. The Antitrust Bulletin, 34(3): 625-640. Smith, K., Grimm, C., Wally, S., & Young, G. 1997. Strategic groups and rivalrous firm behaviour: Towards a reconciliation. Strategic Management Journal, 18(2): 149-157.

184

Spanos, Y. & Lioukas, S. 2001. An examination of the causal logic of rent generation: Contrasting Porter's competitive strategy framework and the resource-based perspective. Strategic Management Journal, 22: 907-934. Stuart, T. 1998. Network positions and propensities to collaborate: An investigation of strategic alliance formation in a high-technology industry. Administrative Science Quarterly, 43: 668-698. Sudharshan, D., Thomas, H., & Fiegenbaum, A. 1991. Assessing mobility barriers in dynamic strategic groups analysis. Journal of Management Studies., 28(5): 429-438. Teece, D., Pisano, G. & Shuen, A. 1998. Dynamic capabilities and strategic management. Strategic Management Journal, 18(7) 509-533. Thomas, H. & Venkatraman, N. 1988. Research on strategic groups: Progress and prognosis. Journal of Management Studies, 25(6): 537-555. Thomas, H. & Pollock, T. 1999. From I-O economics' S-C-P paradigm through strategic groups to competence-based competition: Reflections on the puzzle of competitive strategy. British Journal of Managment, 10: 127-140. Thorelli, H. 1986. Networks: Between markets and hierarchies. Strategic Management Journal, 7: 37-51. Tichy, N., Tushman, M. & Fombrun, C. 1979. Social Network Analysis for Organisations. The Academy of Management Review, 4(4): 507-519. Tushman, M. & Anderson, P. 1986. Technological discontinuities and organizational environments. Administrative Science Quarterly, 31: 439-465. Vanhaverbeke, W. & Noorderhaven, N. 2001. Competition between alliance blocks: The case of the RISC microprocessor technology. Organization Studies, 22(1): 1-30. How the World’s Automakers Are Related. MI:US: Wards Communications. Ward's Automotive Yearbook. MI: US: Wards Communications. Warren, K. 1999. The dynamics of rivalry. Business Strategy Review, 10(4): 41-54. Wasserman, S. & Faust, K. 1999. Social Network Analysis: Methods and Applications. Cambridge ; New York : Cambridge University Press. Weinstock, D. 1982. Using the Herfindahl Index to measure concentration. The Antitrust Bulletin, 27(2): 285-301. Wernerfelt, B. 1984. A resource-based view of the firm. Strategic Management Journal, 5: 171-180. Williamson, O. 1975. Markets and Hierarchies. NY: US: Free Press. Williamson, O. 1985. The Economic Institutions of Capitalism. New York: US: Free Press.

185

Williamson, O. 1996. The mechanisms of Governance. New York: Oxford University Press.

186

APPENDIX A: Types of Strategic Relationships and Generic Definitions

As applied to the scale presented in Table 3.5, the following generic definitions were used to

categorise the raw data collated on strategic relationships between firms within the United

States Automotive Industry:

Merger or Acquisition: Where one company has taken over financial control of another; or

where two companies (or more) have joined together in operation and are now recognised

as a single company with joint financial assets.

Independent Joint Venture: Where two companies remain financially independent of each

other, however agree to work on a specified task together. The contribution or level of

investment of each company is clearly defined prior to the project commencing, and an

agreement usually exists which dictates how any benefits or profits generated by the project

will be distributed (money) or used (innovations) by the participating companies.

Limited Cross Equity Ownership: Where Company A owns a certain proportion of the stock

or shares of Company B, and vice versa. Each company therefore has a vested interest in

the activities and performance of the other company as they derive financial dividends.

Minority Equity: Where a company has a financial interest or owns a proportion of the stocks

or shares in another company. However, this interest is limited in that it doesn’t allow the

organisation holding the shares or financial interest to exert any power or control over the

activities of the other company.

Broad R&D Agreements: Where two or more companies agree to work together in a

collaborative fashion in order to undertake research or design efforts. Usually the project that

these companies work on relates to a specified project that if successful will generate

benefits for those associated with the agreement.

Second Source Agreements: Where a company has a choice of suppliers for provision of

specific parts required for the manufacture of certain products.

187

Production Agreements: Where a contractual arrangement exists between two companies

(Company A and Company B) for one company (Company A) to produce an entire product

for sale by another company (Company B) to the consumer under the brandname of

Company B.

Component Sourcing Agreements: Where firms agree to obtain specific parts or components

for a certain product from a certain company.

Know-How and Patent Licensing Agreements: Where two firms share ownership or control

of the knowledge of how a particular innovation or product works, and by law are the only

companies allowed to produce it according to defined specifications. Sometimes the ability to

produce this specific product is given to another company via the creation of a contractual

arrangement, allowing another company to use the innovation or the product also.

Distribution Agreements: Where one company will sell its products in a particular market via

the assistance of another company, or, alternatively, under the other company’s brand

name.

188

APPENDIX B: Approaches to Network Analysis and Associated Limitations

Analytical methods for defining cohesive subgroups within social network theory:

• Clique: a sub-set of points in which every possible pair of points is directly

connected by a line and the members of the clique are not contained in any other

clique (Scott, 2005, p. 114). This definition in social network analysis is considered

quite ‘strict’ in that ‘it insists that every member…have a direct tie with each and

every other member’ (Hanneman, 2000, p. 81), and ultimately too strong of an

approach for the meaningful analysis of data.

Limitation: Due to the nature of strategic relationships analysed in the United States

Light Vehicles Industry, there exist a number of interrelated linkages across multiple

members of the industry that do not allow exclusivity in terms of the very strict

definition of clique configuration, therefore prohibiting the use of this approach to

determine strategic networks.

• n-clique: A more relaxed approach to defining subgroups in a population than the

narrow clique approach. Allows the formation of cliques where an actor is defined

‘as a member if they are connected to every other member of the group at a

distance greater than one’ (usually 2) from all other members of the clique

(Hanneman, 2000, p. 81; Wasserman & Faust, 1999).

Limitation: Due to the procedure for identifying n-cliques, long and stringy groups

are often identified rather than tight and discrete actor collections. In addition, it is

possible for members of the resulting n-cliques to be connected by actors who are

not themselves members of the clique (Hanneman, 2000, p.82; Scott, 2005).

• n-clan: Associated with the n-clique approach in that a more ‘relaxed’ application of

the clique rationale is applied. The n-clan approach represents a restriction on the n-

clique method of sub-grouping population members by insisting ‘that all ties among

actors occur through other members of the group’ (Hanneman, 2000, p. 83).

Limitations: According to Sprenger and Stokman (1989) ‘”hardly anybody” has used

n-clans… and more research is needed on these cohesive subgroup ideas’ (in

Wasserman & Faust, 1999, p. 262).

• Cluster: Can be operationalised as either agglomerative or divisive, both of which

are hierarchical in nature. In the agglomerative method, the concept of the cluster

corresponds to an area of relatively high density in the population under analysis. In

the divisive (or partitioning) approach, analysis commences by considering the

189

entire population as a single cluster, with sub-sets split from the main cluster as

reducing levels of similarity (Scott, 2005).

Limitations: The boundaries of clusters cannot always be clearly drawn. In addition,

‘the composition of the clusters identified in a cluster analysis will depend upon the

density level that is chosen by the researcher, and on the assumptions made by the

particular clustering method’ (Scott, 2005, p. 127).

• Factions: Based on binary network datasets, faction analysis partitions the

‘adjacencies into n groups, then [performs] a count of the number of missing ties

within each group summed with the ties between the groups [which then] gives a

measure of the extent to which the groups form separate clique like structures’

(Borgatti, Everett & Freeman, 2002).

Limitations: Highly dependent on researcher discretion and familiarity with the data.

Further, the algorithm used for faction analysis may produce differing group

solutions when re-run.

• Components: ‘Components of a graph are parts that are connected within, but

disconnected between sub-graphs. If a graph contains one or more ‘isolates’, these

actors are components. Components act to divide the network into separate parts’

(Hanneman, 2000, p.86).

Limitations: While components divide the network population into separate parts, the

assumption is that the actors in these separate parts are connected. Their level of

connectivity or closeness cannot be assessed.

• K-Plex: An alternative method of relaxing the strict assumptions of the clique to

allow ‘that actors may be members of a clique even if they have links to all but k

other members’ (Hanneman, 2000, p. 84). This method of analysis has similarities to

the n-clique approach, however k-plex analysis generally delivers distinctly different

conceptualizations of subgroups in the analysed population due to the tendency for

the analysis to find relatively large numbers of smaller groupings. Unlike the n-clique

approach, ties that act simply as intermediaries do not qualify for inclusion into the

final group membership solutions.

Limitations: This approach ‘tends to focus attention on overlaps and co-presence

(centralization) more than solidarity and reach’ (Hanneman, 2000, p. 84). With this in

mind, however, Scott (2005) states that it is not uncommon for complex populations

to contain levels of over-lap and co-presence. Researcher discretion in specifying

the appropriate value of k is considered critical to the production of robust results.

190

• K-Core: K-Cores are usually more inclusive than k-plex analysis. ‘A k-core is a

maximal group of actors, all of whom are connected to some number (k) of other

members of the group. Therefore, for an actor to become a member of a group, it

must be linked to all but k (the number of members designated by the researcher)

other actors in the group. As k becomes smaller, group sizes will increase.

Outcomes represent areas of relatively high cohesion (Hanneman, 2000;

Wasserman & Faust, 1999).

• Limitations: Analysis using k-cores may produce areas that represent segments of

relatively high cohesion, however the actors in these segments may be connected to

each other rather loosely (Scott, 2005).

191

APPENDIX C: Supporting Clustering Outcome Data

1993 Analysis Outcomes:

B M W

H Y U N D A I

H O N D A

M I T S U B I S H I

D A I

M L E R

B E N Z

C H R Y S L E R

G E N E R A L

M O T O R S

S U Z U K I

S U B A R U

V O L V O

P O R S C H E

M A Z D A

F O R D

N I S S A N

T O Y O T A

V O L K S W A G O N

Level

1

7

6

9

3

2

5

1 3

1 2

1 5

1 1

8

4

1 0

1 4

1 6

8.0000

. . . XXXX

XXXX

XXXX

XXXX

. . . . XXXX

XXXX

. . .

6.5000

. . . XXXX

XXXX

XXXX

XXXX

. . . . XXXX

XXXX

XXXX

. .

5.0000

. . XXXX

XXXX

XXXX

XXXX

XXXX

. XXXX

XXXX

. XXXX

XXXX

XXXX

XXXX

XXXX

4.0000

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

. XXXX

XXXX

XXXX

XXXX

XXXX

2.6667

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

. XXXX

XXXX

XXXX

XXXX

XXXX

2.5556

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

. XXXX

XXXX

XXXX

XXXX

XXXX

1.6000

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

. XXXX

XXXX

XXXX

XXXX

XXXX

1.5833

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

1.0208

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

0.4286

. XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

0.0000

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

Measures of cluster adequacy:

1 ---------

--

2 ---------

--

3 ---------

--

4 ---------

--

5 ---------

--

6 ---------

--

7 ---------

--

8 ---------

--

9 ---------

--

10 ---------

-- Eta 0.380 0.449 0.501 0.510 0.481 0.474 0.448 0.412 0.279 0.216

Q 0.004 0.048 0.126 0.149 0.172 0.184 0.205 0.203 -0.001 0.000 Q-prime 0.005 0.052 0.142 0.170 0.201 0.221 0.257 0.271 -0.001 0.000

E-I 0.742 0.602 0.387 0.301 0.129 -0.118 -0.204 -0.409 -0.935 -1.000

192

Size of each cluster, expressed as a proportion of the total population clustered: 1

----------

2 --------

--

3 --------

--

4 --------

--

5 --------

--

6 --------

--

7 --------

--

8 --------

--

9 --------

--

10 --------

--

11 --------

-- CL1 0.063 0.063 0.063 0.063 0.063 0.063 0.063 0.063 0.063 0.063 1.000 CL2 0.125 0.125 0.125 0.188 0.188 0.375 0.375 0.500 0.875 0.938 CL3 0.125 0.125 0.188 0.188 0.188 0.313 0.375 0.375 0.063 CL4 0.125 0.188 0.188 0.188 0.313 0.063 0.063 0.063 CL5 0.063 0.063 0.063 0.063 0.063 0.063 0.125 CL6 0.063 0.063 0.063 0.063 0.063 0.125 CL7 0.063 0.063 0.125 0.125 0.125 CL8 0.063 0.063 0.063 0.125 CL9 0.063 0.063 0.125

CL10 0.063 0.063 CL11 0.063 0.063 CL12 0.063 0.063 CL13 0.063

193


H Y U N D A I

B M W

H O N D A

C H R Y S L E R

G E N E R A L

M O T O R S

S U Z U K I

D A I

M L E R

B E N Z

M I T S U B I S H I

V O L V O

F O R D

M A Z D A

N I S S A N

S U B A R U

P O R S C H E

T O Y O T A

V O L K S W A G O N

Level

7

1

6

2

5

1 3

3

9

1 5

4

8

1 0

1 2

1 1

1 4

1 6

8.0000

. . . XXXX

XXXX

. XXXX

XXXX

. XXXX

XXXX

. . . . .

6.0000

. . . XXXX

XXXX

. XXXX

XXXX

. XXXX

XXXX

XXXX

XXXX

. . .

5.0000

. XXXX

XXXX

XXXX

XXXX

. XXXX

XXXX

. XXXX

XXXX

XXXX

XXXX

. XXXX

XXXX

4.0000

. XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

. XXXX

XXXX

3.2500

. XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

. XXXX

XXXX

2.3333

. XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

. XXXX

XXXX

2.0000

. XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

1.8333

. XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

1.7500

. XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

0.9821

. XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

0.4000

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX


1 ---------

--

2 ---------

--

3 ---------

--

4 ---------

--

5 ---------

--

6 ---------

--

7 ---------

--

8 ---------

--

9 ---------

--

10 ---------

-- Eta 0.390 0.416 0.441 0.456 0.457 0.422 0.415 0.380 0.365 0.181

Q 0.008 0.033 0.071 0.113 0.144 0.145 0.157 0.178 0.198 -0.000 Q-prime 0.009 0.036 0.078 0.129 0.168 0.174 0.196 0.237 0.297 -0.001

E-I 0.758 0.697 0.596 0.434 0.303 0.091 0.051 -0.172 -0.384 -0.939

194

Size of each cluster, expressed as a proportion of the total population clustered 1

----------

2 --------

--

3 --------

--

4 --------

--

5 --------

--

6 --------

--

7 --------

--

8 --------

--

9 --------

--

10 --------

--

11 --------


CL10 0.063 0.063 0.063 CL11 0.063 0.063 CL12 0.063 0.063 CL13 0.063

195


P O R S C H E

H Y U N D A I

C H R Y S L E R

B M W

G E N E R A L

M O T O R S

V O L V O

N I S S A N

S U B A R U

M A Z D A

F O R D

S U Z U K I

H O N D A

M I T S U B I S H I

D A I

M L E R

B E N Z

T O Y O T A

V O L K S W A G O N

Level

1 1

7

2

1

5

1 5

1 0

1 2

8

4

1 3

6

9

3

1 4

1 6

8.0000

. . XXXX

XXXX

XXXX

XXXX

. . XXXX

XXXX

. . XXXX

XXXX

. .

6.0000

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

. . XXXX

XXXX

. .

5.0000

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

. XXXX

XXXX

XXXX

XXXX

.

4.0000

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

.

3.2500

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

2.7500

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

2.4000

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

2.0000

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

1.2750

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

0.4286

. . XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

0.2667

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX

XXXX


1 ---------

--

2 ---------

--

3 ---------

--

4 ---------

--

5 ---------

--

6 ---------

--

7 ---------

--

8 ---------

--

9 ---------

--

10 ---------

-- Eta 0.414 0.434 0.485 0.490 0.482 0.468 0.437 0.415 0.312 0.220

Q 0.024 0.045 0.084 0.104 0.114 0.135 0.130 0.129 -0.001 -0.000 Q-prime 0.026 0.050 0.095 0.119 0.133 0.162 0.162 0.172 -0.001 -0.000

E-I 0.714 0.661 0.438 0.366 0.250 0.152 -0.170 -0.455 -0.911 -0.964

196


----------

2 --------

--

3 --------

--

4 --------

--

5 --------

--

6 --------

--

7 --------

--

8 --------

--

9 --------

--

10 --------

--

11 --------


CL10 0.063 0.063 0.063 CL11 0.063 0.063 CL12 0.063

197


H Y U N D A I

B M W

P O R S C H E

N I S S A N

S U B A R U

H O N D A

M I T S U B I S H I

S U Z U K I

M A Z D A

F O R D

V O L V O

C H R Y S L E R

D A I M L E R

B E N Z

G E N E R A L

M O T O R S

T O Y O T A

V O L K S W A G O N

Level

7

1

1 1

1 0

1 2

6

9

1 3

8

4

1 5

2

3

5

1 4

1 6

9.0000 . . . . . . . . . XXXX XXXX XXXX XXXX . . . 8.0000 . . . . . XXXX XXXX . XXXX XXXX XXXX XXXX XXXX XXXX . . 6.0000 . . . XXXX XXXX XXXX XXXX . XXXX XXXX XXXX XXXX XXXX XXXX XXXX . 5.2500 . . . XXXX XXXX XXXX XXXX . XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX 4.0000 . XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX 3.8889 . XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX 2.7000 . XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX 1.9091 . XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX 0.8462 . XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX 0.4000 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX


1 ---------

--

2 ---------

--

3 ---------

--

4 ---------

--

5 ---------

--

6 ---------

--

7 ---------

--

8 ---------

--

9 ---------

--

10 ---------

-- Eta 0.281 0.478 0.523 0.548 0.542 0.546 0.489 0.438 0.279 0.265

Q -0.050 0.016 0.061 0.086 0.109 0.125 0.033 0.020 -0.001 -0.000 Q-prime -0.053 0.018 0.068 0.099 0.131 0.156 0.045 0.031 -0.001 -0.000

E-I 0.880 0.615 0.455 0.316 0.236 0.003 -0.535 -0.814 -0.935 -0.960

198


----------

2 ---------

-

3 ---------

-

4 ---------

-

5 ---------

-

6 ---------

-

7 ---------

-

8 ---------

-

9 ---------

-

10 ---------

- CL1 0.063 0.063 0.063 0.063 0.125 0.125 0.125 0.125 0.938 1.000 CL2 0.125 0.188 0.250 0.313 0.313 0.313 0.688 0.813 0.063 CL3 0.125 0.188 0.188 0.188 0.188 0.375 0.063 0.063 CL4 0.063 0.125 0.125 0.125 0.188 0.063 0.125 CL5 0.063 0.063 0.063 0.063 0.063 0.125 CL6 0.063 0.063 0.125 0.125 0.125 CL7 0.063 0.063 0.063 0.063 CL8 0.063 0.063 0.063 0.063 CL9 0.063 0.063 0.063

CL10 0.063 0.063 CL11 0.063 0.063 CL12 0.063 CL13 0.063 CL14 0.063

Rivalry Within and Between Strategic Networks: An ... · 2.3.1 Models of Rivalry and Competitive...

Documents

Transcript of Rivalry Within and Between Strategic Networks: An ... · 2.3.1 Models of Rivalry and Competitive...