Rivalry Within and Between Strategic Networks: An ... · 2.3.1 Models of Rivalry and Competitive...
Transcript of 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 4.2 1995 Market Segment Herfindahl Scores and Producer Rivalry Scores ………………… 111
Table 4.3 1997 Market Segment Herfindahl Scores and Producer Rivalry Scores ………………… 112
Table 4.4 1999 Market Segment Herfindahl Scores and Producer Rivalry Scores ………………… 113
Table 4.5 1993 Strategic Network Configurations ……………………………………………………… 115
Table 4.6 1995 Strategic Network Configurations ……………………………………………………… 115
Table 4.7 1997 Strategic Network Configurations ……………………………………………………… 115
Table 4.8 1999 Strategic Network Configurations ……………………………………………………… 124
Table 4.9 1993 Market Segment Network Within and Between Herfindahl Scores and Rivalry Scores 125
Table 4.10 1995 Market Segment Network Within and Between Herfindahl Scores and Rivalry Scores 126
Table 4.11 1997 Market Segment Network Within and Between Herfindahl Scores and Rivalry Scores 127
Table 4.12 1999 Market Segment Network Within and Between Herfindahl Scores and Rivalry Scores 128
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
Figure 4.3 1995 Network Data Output Dendogram ……………………………………………… 118
Figure 4.4 1995 Netdraw Simulation of Strategic Relationships ……………………………… 119
Figure 4.5 1997 Network Data Output Dendogram ……………………………………………… 120
Figure 4.6 1997 Netdraw Simulation of Strategic Relationships ……………………………… 121
Figure 4.7 1999 Network Data Output Dendogram ……………………………………………… 122
Figure 4.8 1999 Netdraw Simulation of Strategic Relationships ……………………………… 123
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
Table 4.2: 1995 Market Segment Herfindahl Scores and Producer Rivalry Scores
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
Table 4.3: 1997 Market Segment Herfindahl Scores and Producer Rivalry Scores
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
Table 4.4: 1999 Market Segment Herfindahl Scores and Producer Rivalry Scores
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
Figure 4.3: 1995 Network Data Output Dendogram
126
Figure 4.4: 1995 Netdraw Simulation of Strategic Relationships in the United States Automotive Industry
127
Figure 4.5: 1997 Network Data Output Dendogram
128
Figure 4.6: 1997 Netdraw Simulation of Strategic Relationships in the United States Automotive Industry
129
Figure 4.7: 1999 Network Data Output Dendogram
130
Figure 4.8: 1999 Netdraw Simulation of Strategic Relationships in the United States Automotive Industry
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
TOTAL WITHIN NETWORK SEGMENT HERFINDAHL
TOTAL WITHIN NETWORK SEGMENT RIVALRY
TOTAL BETWEEN NETWORK SEGMENT HERFINDHAL
TOTAL BETWEEN NETWORK SEGMENT RIVALRY
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
TOTAL WITHIN NETWORK SEGMENT HERFINDAHL
TOTAL WITHIN NETWORK SEGMENT RIVALRY
TOTAL BETWEEN NETWORK SEGMENT HERFINDHAL
TOTAL BETWEEN NETWORK SEGMENT RIVALRY
MARKET SEGMENT
NETWORK
PRODUCER
TOTAL WITHIN NETWORK SEGMENT HERFINDAHL
TOTAL WITHIN NETWORK SEGMENT RIVALRY
TOTAL BETWEEN NETWORK SEGMENT HERFINDHAL
TOTAL BETWEEN NETWORK SEGMENT RIVALRY
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
Table 4.10: 1995 Market Segment Network Within and Between Herfindahl Scores and Rivalry Scores
134
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
TOTAL WITHIN NETWORK SEGMENT HERFINDAHL
TOTAL WITHIN NETWORK SEGMENT RIVALRY
TOTAL BETWEEN NETWORK SEGMENT HERFINDHAL
TOTAL BETWEEN NETWORK SEGMENT RIVALRY
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
Table 4.11: 1997 Market Segment Network Within and Between Herfindahl Scores and Rivalry Scores
135
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
TOTAL WITHIN NETWORK SEGMENT HERFINDAHL
TOTAL WITHIN NETWORK SEGMENT RIVALRY
TOTAL BETWEEN NETWORK SEGMENT HERFINDHAL
TOTAL BETWEEN NETWORK SEGMENT RIVALRY
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
Table 4.12: 1999 Market Segment Network Within and Between Herfindahl Scores and Rivalry Scores
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
1995 Analysis Outcomes:
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
Measures of cluster adequacy:
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 --------
-- CL1 0.063 0.063 0.125 0.125 0.125 0.125 0.125 0.500 0.500 0.938 1.000 CL2 0.125 0.125 0.125 0.188 0.188 0.375 0.375 0.250 0.438 0.063 CL3 0.125 0.125 0.125 0.188 0.188 0.250 0.250 0.063 0.063 CL4 0.125 0.125 0.125 0.125 0.250 0.063 0.063 0.188 CL5 0.063 0.063 0.063 0.063 0.063 0.063 0.188 CL6 0.063 0.063 0.125 0.125 0.063 0.125 CL7 0.063 0.125 0.063 0.063 0.125 CL8 0.063 0.063 0.063 0.125 CL9 0.063 0.063 0.125
CL10 0.063 0.063 0.063 CL11 0.063 0.063 CL12 0.063 0.063 CL13 0.063
195
1997 Analysis Outcomes:
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
Measures of cluster adequacy:
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
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 0.125 0.125 0.125 0.125 0.125 0.250 0.250 0.250 0.875 0.938 1.000 CL2 0.125 0.125 0.250 0.250 0.313 0.313 0.500 0.625 0.063 0.063 CL3 0.125 0.125 0.125 0.188 0.188 0.188 0.063 0.063 0.063 CL4 0.125 0.125 0.125 0.125 0.125 0.063 0.125 0.063 CL5 0.063 0.063 0.063 0.063 0.063 0.125 0.063 CL6 0.063 0.063 0.125 0.125 0.125 0.063 CL7 0.063 0.125 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 0.063 CL11 0.063 0.063 CL12 0.063
197
1999 Analysis Outcomes:
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
Measures of cluster adequacy:
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
Size of each cluster, expressed as a proportion of the total population clustered: 1
----------
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