THREE ESSAYS ON SUPPLY NETWORK ARCHITECTURE:
A NETWORK MULTIPLEXITY APPROACH
By
Myung Kyo Kim

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Business Administration - Operations and Sourcing Management
- Doctor of Philosophy
2013

ABSTRACT
THREE ESSAYS ON SUPPLY NETWORK ARCHITECTURE:
A NETWORK MULTIPLEXITY APPROACH
By
Myung Kyo Kim
This dissertation consists of three essays. The first essay explores the existence of network
multiplexity in a supply network context and its association with supply network properties. The
primary data collected from a survey of 153 directed valued supply networks at the component
level is analyzed to find quantitative and statistically significant evidence on the existence of
supply network multiplexity. Empirical results confirm that different types of supply network
ties across multiple tiers of supply chain partners shape different supply network architectures.
This study further explores the “hidden” dynamics between socio-centric, social network
analysis indices used for characterizing multiplex supply networks.
The second essay addresses the unresolved question of the supply network antecedents in
a directed valued network setting. Specifically, anchoring on strategic network perspective, it
takes into account two major strategic intents (i.e. cost leadership and market responsiveness) of
an OEM. Empirical findings based on a network multiplexity approach suggest that a supply
network should be viewed as a consciously and purposely designed systematic outcome in
accordance with the OEM’s strategic goal(s). As a result, this essay attempts to address the
theoretical and empirical gap of supply network research by shedding light on the hereto
unaddressed strategic antecedents of different supply network architectures.
Extending network competence perspective to the context of supply network, the third

essay examines the impact of key indices quantifying supply network architecture on supply

chain performance of an OEM with consideration of the contingent effects of OEM intervention
in its immediate suppliers’ sourcing decisions. Depending on a key premise underlying network
strategy that a firm’s inimitable and non-substitutable resources lie outside its boundaries, this
essay aims to shed lights on the study of supply network, and consequently, to offer a supply
network competence perspective. Each essay of this dissertation research is concluded with
academic and managerial implications for supply network management.

Copyright by
MYUNG KYO KIM
2013

Dedicated to Young-Shin Angela Park
for her love, prayer, patience and selflessness throughout our marriage

v

ACKNOWLEDGEMENTS
If I have seen further, it is by standing on the shoulders of giants. — Sir Isaac Newton
While this dissertation carries my name, it is a record of support and assistance I have
received from many people. I owe a great deal of gratitude to my professors, friends, and family
who have been there to support my journey towards the completion of this dissertation research.
First and foremost, I would like to express my deepest gratitude to Dr. Ram Narasimhan
for his guidance, encouragement and inspiration he has generously given me not only regarding
this dissertation, but also throughout my doctoral studies at Michigan State University. As my
committee chair, a doctoral advisor, and a mentor, he has made a tremendous impact on my
academic and personal development over the past five years. His extensive knowledge,
experience, wisdom, and professionalism have truly helped me build a solid foundation for my
future as a researcher, a scholar, and an educator. I will forever be indebted to him for the many
gifts he has given me intellectually, professionally, and personally. I sincerely hope that I will
live up to his standards.
I would also like to acknowledge the profound contributions my committee members
have made to my dissertation: Dr. Roger Calantone, Dr. Sriram Narayanan, and Dr. Tobias
Schoenherr. I have benefited from their unique perspectives and experiences in developing some
of the core ideas presented in this work as well as preliminary research design and
methodologies. Their enthusiasm, patience, and collegiality have made every step of my
dissertation research an enjoyable learning experience and I deeply appreciate the invaluable
lesson they have shared with me throughout my doctoral studies. It has been such a privilege to
have this committee.

vi

My doctoral studies have been significantly enriched by insights from many outstanding
faculty members, particularly Dr. Srinivas Talluri, Dr. David Closs, Dr. Steven Melnyk, Dr.
Gary Ragatz, Dr. Brian Jacobs, Dr. Carol Prahinski, Dr. Claudia Rosales, and Dr. John
Macdonald. I have also been fortunate to be surrounded by my great fellow doctoral colleagues:
Ravi Srinivasan, David Dreyfus, Hugo Decampos, Christopher Fleming, Jiho Yoon, Matthew
Castel, Justin Lavadour, Gregory Sabin, Robert Wiedmer, Chang Seob Yeo, Hannah Lee,
Praneet Randawa, Malika Chauduri, and Whyatt Schrock. My thanks also go to the department
staffs who have always welcomed and helped me to resolve various administrative issues for the
last five years: Julie Alchin, Marlena Vertrees, Sheila Mead, Mary Metler, John Fitch, and Justin
Jagger.
Last but not least, this journey would not have been possible without the support and love
from my family. My parents, Jin Mook Kim and Myung Sook Kang have taught me the values of
love, honor, generosity, respect, dedication, discipline and so much more. They have also
imbued me with the lessons on how to give my best, be loyal and be grateful for the
opportunities that come my way. Their unconditional love and perpetual faith have been my
strengths in setting and achieving my dream to be a scholar, as well as the foundation upon
which I continue to build to this day. I will be forever grateful for their presence in my life. My
younger brother, Nam Woo Kim, has been an incredible friend, a silent fan, and an irreplaceable
pinch hitter throughout my life and studies. I would also like to thank my parents-in-law, Kyu
Chang Park and Hwa Ja Ahn for showing their trust in me from the very first day we met and
always cheering me with their warm hearts. Who could ask for more from family? I have been
truly blessed to have them in my life.

vii

And to conclude, my special thanks go to my wife, Young-Shin Angela Park. She has
every quality I could ever want in a wife, a best friend and a soul mate. Her genuine care, deep
affection, and her tolerance with the countless nights of sleep deprivation supporting her doctoral
candidate husband were what in the end have made this journey possible. I am thankful far
beyond any ability to express my gratitude and can only offer my love in return. Thank you, my
sweetheart. This dissertation is for you.

viii

TABLE OF CONTENTS
LIST OF TABLES ....................................................................................................................... xi
LIST OF FIGURES ................................................................................................................... xiii
1

INTRODUCTION ................................................................................................................. 1

2 SAME BOAT, DIFFERENT OCEAN: THE EFFECTS OF NETWORK
MULTIPLEXITY ON SUPPLY NETWORK ARCHITECTURE .......................................... 6
2.1
Introduction ..................................................................................................................... 6
2.2
Theoretical Background and Proposition ...................................................................... 9
2.2.1 Supply Network ......................................................................................................... 9
2.2.2 Multiplexity ............................................................................................................. 11
2.2.3 Supply Network Tie Types ..................................................................................... 13
2.3
Methodology .................................................................................................................. 18
2.3.1 Social Network Analysis ......................................................................................... 18
2.3.2 Directed Valued Network Indices.......................................................................... 19
2.4
Data and Analysis.......................................................................................................... 30
2.4.1 Boundary Specification .......................................................................................... 30
2.4.2 Data Source and Procedure ................................................................................... 32
2.4.3 Analysis .................................................................................................................... 34
2.5
Results and Interpretations ........................................................................................... 35
2.5.1 Descriptive Statistics ............................................................................................... 35
2.5.2 Correlations ............................................................................................................. 39
2.5.3 Tests.......................................................................................................................... 42
2.5.4 Comparisons ............................................................................................................ 45
2.6
Discussion ...................................................................................................................... 49
2.6.1 Contributions........................................................................................................... 49
2.6.2 Limitations and Directions for Future Research ................................................. 53
3 MOVING BEYOND SERENDIPITY: HOW SUPPLY NETWORK
ARCHITECTURE BECOMES STRATEGIC ........................................................................ 56
3.1
Introduction ................................................................................................................... 56
3.2
Theoretical Background and Hypotheses .................................................................... 59
3.2.1 Network Resource and Strategic Intent ................................................................ 59
3.2.2 Supply Network Tie Types ..................................................................................... 61
3.2.3 Indices for Network Characterization .................................................................. 64
3.2.4 Hypotheses ............................................................................................................... 65
3.3
Methodology .................................................................................................................. 71
3.3.1 Data .......................................................................................................................... 71
3.3.2 Measures .................................................................................................................. 75
ix

Capturing Strategic Intents............................................................................................75
3.3.3 Methods.................................................................................................................... 77
3.4
Results and Interpretations ........................................................................................... 80
3.4.1 Model 1: Strategic Intent and Betweenness Centralization ................................ 84
3.4.2 Model 2: Strategic Intent and In-degree Centralization ..................................... 86
3.4.3 Model 3: Strategic Intent and Out-degree Centralization .................................. 88
3.4.4 Model 4: Strategic Intent and Global Clustering Coefficient ............................. 89
3.5
Further Investigations................................................................................................... 91
3.5.1 Industry to Industry Comparison ......................................................................... 92
3.5.2 Firm to Firm Comparison ...................................................................................... 94
3.6
Discussion ...................................................................................................................... 99
3.6.1 Contributions........................................................................................................... 99
3.6.2 Limitations and Future Directions ...................................................................... 102
4 SUPPLY NETWORK ARCHITECTURE AND PERFORMANCE: A
CONTINGENCY PERSPECTIVE ......................................................................................... 104
4.1
Introduction ................................................................................................................. 104
4.2
Theoretical Background and Hypotheses .................................................................. 106
4.2.1 Network Competence Perspective ....................................................................... 106
4.2.2 Supply Network Tie Types and Characterization Indices ................................ 108
4.2.3 Hypotheses ............................................................................................................. 110
4.3
Methodology ................................................................................................................ 117
4.3.1 Data ........................................................................................................................ 117
4.3.2 Variables and Measures ....................................................................................... 119
Network Indices………….............................................................................................119
Performance Measures and OEM's Influence on Sourcing Decisions......................119
4.3.3 Methods.................................................................................................................. 123
4.4
Results and Interpretations ......................................................................................... 124
4.4.1 Cost Performance Effects of Supply Network Architecture ............................. 134
4.4.2 Quality Performance Effects of Supply Network Architecture ........................ 135
4.4.3 Delivery Performance Effects of Supply Network Architecture ...................... 135
4.4.4 Flexibility Performance Effects of Supply Network Architecture ................... 137
4.4.5 Innovation Performance Effects of Supply Network Architecture .................. 137
4.5
Discussion .................................................................................................................... 139
5

CONCLUSIONS ................................................................................................................ 142

APPENDIX ................................................................................................................................ 148
BIBLIOGRAPHY ..................................................................................................................... 210

x

LIST OF TABLES
Table 2.1 Development of Supply Network Perspective and Representative Literature ............. 10
Table 2.2 Conceptual Definitions, Item Measures, and Related Literature for Supply Network
Tie Types ...................................................................................................................................... 16
Table 2.3 Socio-Centric Indices, Conceptual Definitions, and Interpretations by Supply Network
Tie Type ........................................................................................................................................ 24
Table 2.4 Descriptive Statistics and ANOVA Results.................................................................. 37
Table 2.5 Bivariate Correlation Matrix ......................................................................................... 40
Table 2.6 Normality Test .............................................................................................................. 42
Table 2.7 Statistical Pairwise Comparisons between Socio-Centric Indices ................................ 43
Table 3.1 Sample Demographics and Descriptive Statistics ........................................................ 74
Table 3.2 Item Measures for Strategic Intents .............................................................................. 76
Table 3.3 Classification Results of K-Means Clustering and Accuracy ....................................... 77
Table 3.4 Descriptive Statistics for Variable ................................................................................ 78
Table 3.5 Results of Multinomial Logit Estimation ..................................................................... 82
Table 3.6 Panel 1 Comparison Results ......................................................................................... 97
Table 3.7 Panel 2 Comparison Results ......................................................................................... 98
Table 4.1 Confirmatory Factor Analysis Results........................................................................ 121
Table 4.2 Results of Hierarchical Regression Analyses: Performance Effects of Betweenness
Centralizations ............................................................................................................................ 126
Table 4.3 Results of Hierarchical Regression Analyses: Performance Effects of In-degree
Centralizations ............................................................................................................................ 128
Table 4.4 Results of Hierarchical Regression Analyses: Performance Effects of Out-degree
Centralizations ............................................................................................................................ 130

xi

Table 4.5 Results of Hierarchical Regression Analyses: Performance Effects of Global
Clustering Coefficients ............................................................................................................... 132

xii

LIST OF FIGURES
Figure 1.1: Conceptual Links among the Dissertation Essays........................................................ 5
Figure 2.1: Network Complexity with Varying Number of Entities/Tiers ................................... 31
Figure 2.2: Means of Socio-Centric SNA Indices by Network Tie Type..................................... 38
Figure 3.1 Strategic Intents and Corresponding Supply Network Types ..................................... 61
Figure 3.2 Data Collection Process ............................................................................................... 73
Figure 3.3 Framework for Further Investigations ......................................................................... 92
Figure 5.1 Simplified Example of the Supply Network ............................................................. 151

xiii

1 INTRODUCTION
Leading-edge companies have realized that the real competition is not company against
company, but rather supply chain against supply chain — Martin Christopher, Logistics and
Supply Chain Management: Strategies for Reducing Costs and Improving Services (1992, p.14).
Individual businesses no longer compete as standalone entities, but rather as collaborative
networks. We are now entering the era of ‘network competition’ where the prizes will go to those
organizations who can better structure, coordinate and manage relationships with their partners
in a network committed to creating customer and consumer value through collaboration —
Martin Christopher, Logistics and Supply Chain Management: Creating Value-Adding Networks
(2011, p.104).
A firm never exists in isolation. It is nested within other entities, including interorganizational partnerships, industry associations, international trade agreements; and a firm
itself has multiple nested components, including individuals, teams, task forces, business units,
geographic markets or offices. Given the highly interdependent and closely interlinked business
environment in recent years, even a vertically integrated conglomerate cannot compete alone in
that “…nothing happens in isolation …most events and phenomena are connected, caused by,
and interacting with a huge number of other pieces of a complex universal puzzle” ( ara si
2003). Weberian social theories have traditionally explained the organizational success and
survival in terms of its bureaucratic control such as personal observation or “files or records” that
enable behavior prediction and internal error correction (Weber et al. 1946). However, many
researchers in various disciplines of business including supply chain management (SCM) have
become increasingly inspired to employ a relational approach emphasizing the fact that a firm’s

1

critical resources may lie beyond the boundaries of the focal firm and be embedded in interfirm
linkages rather than transactional approach over the years (Dyer and Singh 1998; Koza and
Lewin 1998). In recent times, a network perspective of organizations has started to garner
attention in the literature. A network perspective primarily depicts the organizational structure in
terms of its multiple relationships and how they are patterned, whereas the transaction cost
economics stresses the appropriate governance structure for transactions between two parties
(Nohria 1998; Gulati and Gargiulo 1999; Gulati et al. 2000). From a network perspective, the
competitiveness of a firm depends vitally on its network architecture, thus neglecting a firm’s
overall network architecture and attributes can lead to an incomplete understanding of its sources
of competitive advantage and ensuing performance. For instance, OEMs heavily relying on their
tier-one suppliers are exposed to more risks, such as less control over costs, inability to respond
to changes in demand and new technology development, and inability to ensure their suppliers’
socially and environmentally responsible conduct (Choi and Linton 2011). A recent global
survey supports this view by showing that 40 percent of supply chain disruptions originate below
the immediate tier-one suppliers (Business Continuity Institute 2011). Accordingly, there has
been ample discussion on the urgency and necessity of adopting a network perspective in SCM
research during the past decade (e.g. Choi et al. 2001; Mills et al. 2004; Gupta et al. 2006;
Terpend et al. 2008; Nair et al. 2009). The two aforesaid descriptions on key aspects of business
competition by the same author reflect this radical paradigm shift.
The concept of supply network has evolved through a series of stages from dealing with
internal business functions of a firm such as inbound materials and outbound products; to dyadic
buyer-supplier relationships between a focal firm and its immediate suppliers; to triadic supply
chain which extends the dyad to buyer-supplier-supplier relationships; to supply networks which

2

investigate a network of interconnected business partners across multiple tiers of a supply
network. In spite of these recent conceptual advances, however, supply network research to date
suffers from several limitations in providing a more comprehensive understanding and analysis
of a supply network. First, most studies still tend to limit their focus on simplistic, linear, and
material/information flow control between two supply chain entities, taking less network-based
approaches such as buyer-supplier or buyer-supplier-supplier relationships (Harland et al. 2001;
Hofbauer and Wenninger 2011). Although useful for investigating a focal firm’s transactional or
relational exchanges with its immediate supply chain partners, these approaches fall short in
grasping the whole picture of complicated network in which manufacturers and their multiple
tiers of suppliers are nested (Parkhe et al. 2006; Choi and Kim 2008; Wassmer et al. 2010;
Wilhelm 2011). Further, there have been numerous findings in the organizational behavior and
strategic management literatures concerning the impact of network on a firm’s ehavior and
performance. For instance, the amount of a firm’s market asset and social capital depends on its
network position, and consequently, firms can enjoy advantages from their network position by
gaining access to the resources and capabilities of their business partners (Burt and Janicik 1996;
Gnyawali and Madhavan 2001; Kim et al. 2006). Because of this, firms often build and maintain
interfirm relationships not based on potential partners’ internal capabilities but their external
capabilities to initiate, handle, and utilize multiple external resources (Porter 1980; Anderson et
al. 1994). ). Li and Fung, a Hong Kong-based consumer goods sourcing and logistics company,
is a great example of this form of network management capability (Magretta 1998). Previous
SCM studies, hence, may be incomplete as they have largely overlooked the entire supply
network architecture. Relying upon network studies in sociology, a number of recent studies
have regarded supply networks as spontaneously created and informally maintained outcomes,

3

not as consciously and purposely designed organizational architecture on the basis of an OEM’s
specific strategic objectives to achieve its intended performance outcomes. Studies lacking such
considerations – antecedents of supply network formulation – can often give misleading answers
to both supply chain academics and practitioners about how different supply networks across
various contexts should be managed.
To fill the aforementioned gaps in the SCM literature, this dissertation is organized as
three separate essays (Chapters 2, 3, and 4), each of which is focused on specific research
questions following this introduction chapter (Chapter 1). The first essay explores how different
directed valued network ties across multiple tiers of supply chain partners shape different supply
network architectures. As a result, it aims to offer quantitative and statistically significant
evidence on the existence of supply network multiplexity and its effects on supply network
architecture and further provides meaningful hints to the question of why even firms with
outstanding buyer-supplier relationships still (sometimes) fail to maximize the network-wide
benefits from their supply chain partners or are vulnerable to external shocks. The second essay
investigates the strategic antecedents of different network architectures. More specifically, it
looks into the following questions: 1) Are an OEM’s strategic intent choices associated with
supply network architecture; and 2) If so, what differential effects do those strategic intents have
on architectural properties of the supply network. Anchoring on a strategic network perspective
emphasizing the importance of network design in achieving a firm’s strategic objectives (Gulati
et al. 2000), this study attempts to provide a strategic supply network perspective which views
the supply network architecture as an OEM’s strategic choice. The third essay examines the
impact of supply network architecture on an OEM’s supply chain performance with
consideration of the contingent effects of OEM intervention in selecting non-immediate suppliers.

4

Specifically, it aims to answer the following important questions in supply network research
domain: 1) Does the supply network architecture of a firm affect its supply chain performance?;
2) If so, what architectural properties of the supply network will enhance or degrade which
supply chain performance outcomes?; and 3) how does an OEM’s efforts to exert its influence on
its suppliers’ sourcing decisions interplay with such causalities? By addressing these questions,
this essay endeavors to facilitate further theoretical and empirical development of the network
competence perspective in a supply network context. The overall structure of the dissertation is
summarized in the Figure 1.1 below.
Essay 1
Supply Network Multiplexity

Strategic Intent

Supply Network
Architecture

Essay 2
Strategic Supply Network Perspective

Supply Chain
Performance

Essay 3
Supply Network Competence Perspective

Figure 1.1: Conceptual Links among the Dissertation Essays

5

2 SAME BOAT, DIFFERENT OCEAN: THE EFFECTS OF NETWORK
MULTIPLEXITY ON SUPPLY NETWORK ARCHITECTURE
2.1 Introduction
Analogous to mapping DNA on the human genome, understanding and redesigning a company’s
capabilities chain also begins with a map, one which identifies the organizations involved in that
company’s activities … Sometimes that understanding arrives from the most unexpected source
imaginable. — Charles H. Fine, Clockspeed (1998, p.105)
Previous supply chain management (SCM) studies have investigated the importance of
building and managing supply chain dyads between one buyer and its immediate supplier.
Regardless of the means by which supply chain dyads are managed, the literature has identified
mostly their positive (e.g. Lyons et al. 1990; Autry and Golicic 2010) or negative (e.g. Han et al.
1993; Villena et al. 2011) impacts on firm performance. More recently, this dyadic approach was
extended to the triadic case which consists of one focal firm or OEM and two immediate or tierone suppliers in a single supply chain (e.g. Park and Hartley 2002; Wu and Choi 2005; Dubois
and Fredriksson 2008; Mena et al. 2013). While previous dyadic and triadic approaches were
useful for investigating interfirm exchanges between a focal firm and its immediate supply chain
partners, in the era of “network competition” (Christopher 2011), they fall short of grasping the
whole picture of a complicated supply network in which a focal firm and its multiple tiers of
suppliers are nested (Parkhe et al. 2006; Choi and Kim 2008; Wassmer et al. 2010; Wilhelm
2011). In practice, Toyota and Nissan have managed their supply networks by establishing
Japanese first-tier suppliers’ associations (Kyohokai and Nishiokai, respectively) since mid-90s
(Sako 2004; Dyer and Hatch 2006). When earthquake and tsunami struck Japan’s Tohoku region

6

on March 2011, their entire assembly lines outside the region were completely stopped for over
two weeks. Tohoku region has been the biggest parts production base of Japanese automakers;
interestingly, most of the affected suppliers were second- or lower-tier suppliers (Fujimoto 2011).
Indeed, recent evidence indicates that an over-reliance on first-tier suppliers can present
dangerous vulnerabilities to OEMs (Choi and Linton 2011). This underscores the importance of
the need to study supply networks, consisting of a set of multiple supply chain partners connected
by a set of interactive ties, not just dyadic (or triadic) and unidimensional buyer-supplier
relationships. Furthermore, in order to overcome the shortcomings with current approaches to
studying supply networks, there have been calls for more detailed investigations of the supply
network architecture. The “architecture” of supply network is formulated from both exogenous
and endogenous sources for linking the members in the entire network, whereas its “structure” is
related to a firms better functional outcome or regional (i.e. local) collaboration (Snyder 1991;
Swamidass and Snyder 1994). In other words, the architecture of supply network can be
interpreted as the collective representation of multiple supply chain dyads or triads. This
architectural approach enables SCM researchers to better investigate supply networks which
consist of multiple independent entities pursuing their own interests.
Notwithstanding the aforementioned calls, the latest national survey conducted by
Firestorm and Genesis Management (2012) still stresses the urgent need for further research in
supply network architecture. The survey results show that firms do not fully understand, track, or
analyze the inherent risks within their supply network even though suppliers are their largest
source of supply chain failures. This unaddressed call for research reveals several limitations of
the existing studies in providing a comprehensive understanding and analysis of a supply
network architecture. First, current SCM literature has predominantly focused on developing

7

conceptual frameworks – taxonomies or typologies – to differentiate supply networks from
supply chains or identifying unique attributes of supply networks without empirical
substantiation (e.g. Lamming et al. 2000; Mills et al. 2004; Andersen and Christensen 2005).
These works have broadened the scope of traditional buyer-supplier relationship studies and have
garnered more research attention to the area of understudied supply network architecture by
introducing theoretical propositions. However, they lack empirical evidence to show the practical
applicability of the propositions developed in them. Second, a few recent empirical studies of
supply network architecture fall within the limited domain of descriptive case studies (e.g. Choi
and Hong 2002; Srai and Gregory 2008; Kim et al. 2011; Mena et al. 2013). Such exploratory
studies have provided some empirical support for the theoretical propositions concerning supply
network architecture. At the same time, however, these findings were heavily focused upon one
single industry (e.g. automotive, banking, etc.) or firm, and thus lack empirical evidence that can
be consistently applied across different supply networks. Last but most important, many studies
are still using overly simplified, ambiguous, or non-exclusive measures for different types of
network ties (e.g. Samaddar et al. 2006; Mason and Leek 2008; Wilhelm 2011). Granovetter
(1973, 1983) distinguishing strong and weak network ties on the basis of reciprocity, frequency,
emotional intensity, and intimacy in the relationship. This implies that both the direction and
strength of network ties should be taken into account to understand network phenomena. In this
vein, the same network can have completely different architectural properties with regard to
different kinds and attributes of network ties (commonly referred to as “multiplexity”)
(Wasserman and Faust 1994; Kenis and Knoke 2002). Prior studies that have overlooked such
inherent heterogeneity of a supply network can prevent SCM academics and practitioners from
fully grasping the multi-faceted supply network architecture.

8

To address these theoretical as well as practical issues in the supply network literature,
this study investigates how different directed valued network ties across multiple tiers of supply
chain partners shape different supply network architectures. Drawing upon social network
analysis (SNA), the primary survey data from 153 component-level supply networks is analyzed
to explore the associations between 12 network-level SNA indices for characterizing different
supply network architectures. This research offers, to the best of the author’s knowledge, the first
quantitative and statistically significant evidence on the existence of supply network multiplexity
and its effects on supply network architecture. This essay is organized as follows: the first
section sets out theoretical background and testable propositions on supply network and
multiplexity of various network ties in a supply chain context; sections two and three outline
methodology, measures, data source/analysis used to describe supply network architecture;
section four presents quantitative findings and interpretations examining the propositions,
followed by the final section on theoretical/managerial contributions, limitations, and directions
for future research.

2.2 Theoretical Background and Proposition
2.2.1 Supply Network
Sociologists described network as a single closed set of actors (or nodes) and one or more types
of ties (or edges) between them, and social network research has sought to understand observed
dynamics of multiple network entities (i.e. persons). At the firm level, network is the complex
organizational outcome that results from multiple strategic alliances that could have governance
and performance implications (Webster 1992; Heide 1994). SCM academics have, at least
conceptually, understood supply network as a composite entity comprising multiple supply chain

9

partners and various types of network ties among them (Nassimbeni 2004). Supply chains are
often even more complex and multi-faceted than interpersonal network (Cooper et al. 1997;
Mentzer et al. 2001; Borgatti and Li 2009; Lindgreen et al. 2013) in that they deal with both
inter-organizational and interpersonal factors such as “coordination and colla oration with
channel partners, which can be suppliers, intermediaries, third-party service providers, and
customers” (Gibson et al. 2005). The recent incorporation of the term “network” into SCM
research represents the urgency and necessity to view supply chains as a multi-level system
having a strict architecture ( ara si

; Ketchen and Hult 2007; Borgatti and Li 2009;

Galaskiewicz 2011). See Table 2.1 for a synopsis of the development of supply network
perspective and representative literature.
Table 2.1 Development of Supply Network Perspective and Representative Literature
Stage of
Development

Scope of Analysis

Representative Literature

Intra-Firm Chain

The management of internal business
functions involved in materials and
information flows from inbound to
outbound ends of a single supply chain

Supply Chain Dyad

The management of supply chain dyads
Bensaou (1999); Angeles
between one focal firm and its immediate
and Nath (2001); Grover
supplier, downstream distribution
and Malhotra (2003)
channel, or upstream production chain

Supply Chain Triad

The management of supply chain triads
consisting of one focal firm and two
immediate or tier-one suppliers in a
single supply chain

Park and Hartley (2002);
Wu and Choi (2005);
Dubois and Fredriksson
(2008); Choi and Wu
(2009); Mena et al. (2013)

Supply Network

The management of a network of
interconnected business partners across
multiple tiers of a single supply chain

Borgatti and Li (2009);
Galaskiewicz; (2011); Kim
et al. (2011)

Goodman et al. (1993);
Harland (1996)

Adopted and Revised from Van de Ven and Ferry (1980), Harland (1996), and Varga (2008)

10

2.2.2 Multiplexity
Pioneering scholars such as Jessop (2003) and Borgatti and Li (2009) have proposed multiplexity
in supply networks as a promising area for future research. Multiplexity in social networks is
defined as two or more types of exchange (i.e. layering) within the same network ties (Verbrugge
1979; Burt 1980; Feld 1981; Ibarra 1992). Social anthropologists and sociologists introduced the
term to denote coexistence of different normative elements in an interpersonal tie. In a familyowned business, for instance, a father-son relationship also can be viewed as an employeremployee relationship. Social ties containing only one such role represent “uniplex” or “singlestranded” ties, whereas those that involve two or more roles are “multiplex” or “many-stranded”
ties (Wasserman and Faust 1994; Kenis and Knoke 2002). The practical importance of the
concept in social network research was immediately recognized in that it helped to uncover
“hidden” network properties and clearly manifested the underlying dynamics of personal ties.
For instance, researchers found that multiplex ties were more likely to be intimate, supportive
and/or durable, especially during times of need, because: 1) they have multiple bases of
interaction (Wellman and Wortley 1990), and 2) there is less chance that one of them will be
unavailable (Morin and Seidman 1986).
The concept of network multiplexity can also complement the shortcomings of existing
buyer-supplier relationship literature. SCM researchers have stressed the importance of creating
trust and reciprocity from repeated transactions with the same supply chain partners. According
to the tenets of embeddedness theory (Granovetter 1985), an embedded relationship alleviates
information asymmetry and opportunistic behavior in relations between organizations and thus
leads to improved performance (Gulati 1995; Uzzi 1996; Rowley et al. 2000). However, growing
evidence indicates that repeated interactions do not necessarily result in trust or the expected

11

benefits; they might even have negative consequences (Sorenson and Waguespack 2006; Poppo
et al. 2008); rather, several studies present interdependent contingencies facilitating or
restraining the transition from repeated transactions to relational embeddedness in inter-firm
networks (Goerzen 2007; Sa Vinhas et al. 2012). The existence of multiplexity in supply
networks can provide a theoretical rationale for such inconclusive and equivocal findings by
showing that a given supply network with the same set of firms can be perceived differently
based on different tie types with different directions and strengths.
In spite of its apparent applicability and research gaps in existing SCM literature, there
have been surprisingly few attempts to provide a systematic empirical examination of
multiplexity in a supply networks. One imperfect, but notable, exception is Kim et al. (2011)
who depicted three supply networks of the center console assembly for Honda Accord, Acura
CL/TL, and DaimlerChrysler Grand Cherokee by using SNA indices. This study suffered from
the following limitations: 1) as an exploratory case study, it studied only three supply networks
all in the automotive industry, 2) their interpretation of results was not drawn from the
comparisons between two different network ties (i.e. contract and material flow), but
aforementioned three cases, 3) those comparisons were confined to simple description of SNA
index scores (e.g. higher or lower) without further statistical verification, 4) network ties were
measured on binary (or dichotomous) scales (i.e. “1” if two supply network entities were linked
either by contract or non-directional material exchange, “0” otherwise), and 5) other important
but invisible supply network ties at the individual level (e.g. professional and personal exchanges)
between network entities were overlooked. Despite these limitations, as a pioneering study
investigating more than one network tie type within the same network, it hinted at the existence

12

of multiplexity which remains to be tested. To enrich and generalize this skeletal finding on
supply network multiplexity, this essay proposes the following:
PROPOSITION 1. A supply network exhibits different architectural properties in terms
of different tie types which directly or indirectly link all the members of the network.

2.2.3 Supply Network Tie Types
The first question that should be considered by a network researcher interested in multiplexity is:
what are the different types of network ties that should be considered (Hartman and Johnson
1989)? Supply network entities are interlinked with various types of network ties having
different characteristics in accordance with different intentions to achieve different outcomes
(Carter et al. 2007; Ketchen and Hult 2007; Borgatti and Li 2009). This study covers four types
of supply network ties: contractual, transactional, professional, and personal ties. Table 2.2 offers
conceptual definitions of four supply network tie types under consideration and their
measurement items used based on the literature. The first two types represent visible network ties
for exchanging tangible goods and services, whereas the other two capture invisible exchanges
taking place among supply network entities.
Obviously, multiple supply network members are linked through visible ties such as
contract or delivery and receipt of goods and services as consistent with previous supply network
studies (e.g. Choi and Hong 2002; Kim et al. 2011). A formal written contract serves as the most
fundamental element of economic exchanges but can be differently interpreted by supply chain
partners. From a buyer’s perspective, a strong contractual tie (i.e. more complete contract)
including explicit work-related provisions and prescriptions can safeguard against opportunistic
behavior of its counterpart (Williamson 1985). From supplier’s perspective, however, an
interfirm contract specifying more control and legal rules might reflect distrust between

13

exchange partners because buyers may opportunistically utilize it by imposing terms and
conditions that are unreasonably difficult to comply with on the supplier (Ghoshal and Moran
1996; Woolthuis et al. 2005). In this vein, findings of prior research confirm that contractual and
transactional exchanges between supply network partners should be treated as separate network
ties, in which complete contract terms between firms do not necessarily imply the actual
exchange of goods or services and vice versa. For instance, transactional tie (i.e. the actual
exchange of goods and services) can be established without a formal written contract when both
parties share relational norms such as reciprocity, solidarity and information sharing (Williamson
1993; Poppo and Zenger 2002; Lazzarini et al. 2004).
Prior network research has pointed out that “much of commitment occurs at a personal
rather than organizational level” (Rylander et al. 1997, p.65) and “social capital is at the heart of
social network analysis” (Brass and Krackhardt 1999, p.180). Although personal (or social)-level
ties are usually invisible and often informal, they are significantly associated with organizational
(or network) outcomes such as innovation (Abrahamson and Rosenkopf 1997; Demirkan and
Demirkan 2012), knowledge transfer (Abrams et al. 2003; Inkpen and Tsang 2005), and trust
(Zaheer et al. 1998; Musteen et al. 2010). While visible and organization-level network ties (i.e.
contractual and transactional ties) representing economic exchange are widely discussed in the
supply network literature, invisible and personal-level network ties (i.e. professional and
personal ties) between supply network partners have received considerably less research
attention. However, some researchers have incorporated the personal dimension into
organization-level exchanges to uncover the invisible dynamics between network partners. Ulaga
and Eggert (2006), for example, found that the extensive interpersonal interaction is a greater
differentiator than lower cost for suppliers in achieving key supplier status. More recently, Ahuja

14

et al. (2012) conceptually argued that different types of personal and organizational network ties
can be embedded within the same business network. Grossman et al. (2012) also found the
interpersonal similarity between nascent entrepreneurs plays an amplifying role in forming new
ventures and their network structures. Extending these ideas from this emerging research stream
to the supply network context, this study additionally considers two invisible network ties
bridging supply chain personnel in partnering firms. When it is incorporated with social network
analysis, this consideration enables the inter- and intra-comparisons of different tie types and
comparable network indices, and consequently can provide invaluable insights concerning the
underlying network architecture

(Borgatti et al. 2009; Borgatti and Li 2009). As will be

explained in detail in the following sections, social network analysis offers quantitative indices
of network properties. To enrich and extend the findings from the first proposition, therefore, this
study investigates the following second proposition.
PROPOSITION 2. The supply network properties in terms of different tie types exhibit
significant positive or negative associations with one another.

15

Table 2.2 Conceptual Definitions, Item Measures, and Related Literature for Supply Network Tie Types
Network Tie
Type

Conceptual
Definition
The extent to
which a supply
network entity
perceives that it
Contractual
has a ‘complete’
tie
formal written
contract with its
immediate
counterpart
A supply network
entity’s amount of
‘monetary’
Transactional exchange (in
tie
percentage points)
with its
immediate
counterpart(s)

Professional
tie

A supply network
entity’s perceived
strength of the
interactions with
its immediate
counterpart in
performing ‘work
responsibilities’

Item Measure

Related
Literature

We have a formal written contract(s) detailing the operational requirements.
We have a formal written contract(s) that detail(s) how performance will be
monitored.
We have a formal written contract(s) detailing warranty policies.
We have a formal written contract(s) detailing how to handle complaints and
disputes (e.g. penalties for contract violations).
We have a formal written contract(s) detailing the level of service expected
from this supplier.

Podolny and
Page (1998);
Liu et al.
(2009); Carey
et al. (2011)

For OEMs (i.e. tier-0 firms): A percentage of total spend for each tier-1
supplier of the selected component
For tier-(N) (i.e. intermediate) suppliers where N=1 or 2: Percentages of total
sales derived from the tier-(N-1) buyer AND total spend for each tier-(N+1)
supplier in dealing with the OEM’s selected component
For tier-3 (i.e. end-tier) suppliers: A percentage of total sales derived from
tier-2 suppliers in dealing with the OEM’s selected component

Thorelli
(1986);
Knoke and
Yang (2008);
Liu et al.
(2009)

We regularly communicate (via face-to-face, conference calls, e-mails, etc.)
on work matters.
We widely share and welcome each other’s ideas or initiatives via open
communication (e.g. joint workshops, etc.).
The communication between us occurs at different levels of management and
cross-functional areas.
I (or our executives) receive periodic feedback (via face-to-face, conference
calls, e-mail, etc.) on progress, pro lems, and plans from this supplier’s
counterparts.
I (or our executives) do periodic on-site visits to this supplier’s plants.

Liu et al.
(2009); Carey
et al. (2011);
Wilhelm
(2011)

16

Table 2.2 (cont’d)
Network Tie
Type

Conceptual
Definition

Item Measure

Related
Literature

Personal
tie

A supply network
entity’s perceived
strength of the
interactions ‘not
directly related to
work’ with its
immediate
counterpart

We always invite each other to participate in various activities to socialize.
We do personal favors for each other.
We voluntarily exchange something of a personal nature to each other on
appropriate occasions (e.g. birthday cards, congratulations, condolences,
etc.).
We often communicate (via face-to-face, phone calls, e-mails, social network
services, etc.) during non-working time.
We often communicate (via face-to-face, phone calls, e-mails, social network
services, etc.) outside work places.

Nicholson et
al. (2001);
Lysons and
Gillingham
(2003); Burt
et al. (2009);
Gilgor and
Autry (2012)

17

2.3 Methodology
2.3.1 Social Network Analysis
To demonstrate different supply network architectures dependent upon network ties (i.e.
contractual, transactional, professional, and personal ties), this study adopts social network
analysis (SNA) which has long been used in analyzing any social network as a set of interrelated
actors and ties. This methodology has been widely adopted by business disciplines such as
organizational behavior and strategic management to describe inter-firm or interdepartmental
network properties and to investigate their performance effects. The field of SCM has stressed
the use of SNA methodology in a supply network context. For instance, Carter et al. (2007)
proposed SNA as a key approach to advance current knowledge on various relationships existing
within and beyond the supply chain by complementing traditional methodologies. This view was
echoed by Borgatti and Li (2009) who pointed out that supply chain settings are particularly
suitable to adopt SNA indices, which have been proven “highly portable” across other disciplines
from economics to physics. Most recently, Galaskiewicz (2011) also noted that SCM theories
mostly captured at the local level (e.g. dyad or triad) can be tested by using a supply network as
the primary unit of analysis.
In spite of repeated calls for the use of methodology, there are still very few empirical
studies that use SNA (e.g. Batallas and Yassine 2006; Schilling and Phelps 2007; Kim et al.
2011). Moreover, to the best of the author’s knowledge, all existing empirical studies on supply
network are case-based research that use SNA measures solely defined for binary (i.e. “1” if a tie
is exists between two supply network entities, “ ” otherwise) and non-directional ties (i.e. if one
supply network entity perceives a tie, its counterpart’s perception on the existence of the tie is
automatically assumed). This is commonly referred to as binary network approach, and most of
18

the existing SNA indices have been devised solely based on this approach (Wasserman and Faust
1994; Freeman 2004). The binary network approach specified by a symmetric adjacency matrix
is conceptually and computationally straightforward and especially appropriate when a
researcher focuses on cognitive ties (e.g. who knows whom). An important limitation of this
approach, however, is that it involves two counterintuitive and unrealistic premises – all ties are
completely homogeneous and symmetrical – which contradict previous findings in the literature.
For instance, strong social ties strengthen interpersonal obligations (Coleman 1988), facilitate
change in the face of uncertainty (Krackhardt 1992), and help to develop relationship-specific
heuristics (Uzzi 1997). Therefore, by using the binary network approach, network researchers
can inevitably overlook important information about network properties embedded in network
ties, and consequently arrive at limited or even misleading implications on network architecture.

2.3.2 Directed Valued Network Indices
From a methodological standpoint, the present study adopts directed valued network approach
represented by an asymmetric adjacency matrix to overcome the aforementioned shortcomings
of binary network approach (Wasserman and Faust 1994; Freeman 2004). This approach takes
into account the direction and strength (or magnitude) of each tie between different network
entities. In network terms, a directed valued network consists of a set of actors (or nodes)
{

}, a set of arcs (i.e. directional ties or links) {

{

} attached to the arcs, subject to

necessarily equal to

}, and a set of values
and

is not

. This is a more useful and realistic approach for exploring supply

network phenomena since it allows for the possibility that a focal firm may perceive much less
strong (or no) tie with its suppliers than those perceived by its suppliers. In this sense, there has

19

been a growing need for SNA indices that can be used in the directed valued network setting
when it is based on a different adjacency matrix.
SNA indices fall into one of two categories: ego-centric and socio-centric approaches
(Barnes 1974). The ego-centric approach focuses on a particular actor’s position within the
network and is particularly useful in dealing with a large network whose boundary cannot be
easily specified. In contrast, based on specific criterion of network boundaries, socio-centric
approach analyzes the overall pattern of multiple actors within a single, bounded network. This
approach can provide a better understanding of the directed valued network in that the network
architecture from one ego’s viewpoint can be markedly different from those of others linked
directly or indirectly (Scott 2000; Marsden 2002). It also fits perfectly with the purpose of this
study to explore the existence of supply network multiplexity and its effects on network
architecture. Thus, from a measurement perspective, this essay focuses on four socio-centric
SNA indices (i.e. betweenness centralization, in-degree centralization, out-degree centralization,
and global clustering coefficient) defined only at the network level.
First, betweenness centralization represents whether most network actors are equally
central or there are some (i.e. hubs) that are much more central than others. This index can be
calculated by the variation in the betweenness centrality divided by the maximum variation in
betweenness centrality scores possible in a network of the same size (Freeman 1979).
Betweenness centrality is an ego-centric index indicating how often an actor lies on the shortest
path between all combinations of pairs of other actors. The more a given actor has a higher
betweenness centrality; its immediate actors are more dependent on this actor to reach out to the
rest of the network. This index focuses on the role of an actor as an intermediary and posits that
this dependence of others makes the actor central in the network. Betweenness centralization, a

20

socio-centric version of betweenness centrality, ranges from 0 where all network actors have the
same betweenness centrality, to 1, where there exists one single actor connecting all the other
actors. This study calculates betweenness centralization of a directed valued supply network by
adopting the formula suggested by Opsahl et al. (2010) for betweenness centrality (
network actor

, defined as:

( )

( )

where

( )) for

is the total number of geodesics between two actors (

number of geodesics passing through actor

, and

and

),

( ) is the

is a positive tuning parameter that is set to

the benchmark value of 0.5 to equally value both the number of ties and their strengths ( ). Thus,
betweenness centralization can be formally expressed as:

∑

{
∑

(

where
; that is

( )}

( )
{

( )}

( )

) is the largest value of the betweenness centrality that occurs across the network
(

)

( ).

In the case of directed network, two additional degree indices are defined: in-degree, or
the number of links terminating at the actor (
originating from the actor (

); and out-degree, or the number of ties

) (Wasserman and Faust 1994). In-degree centralization

calculates the dispersion of or variation in in-degree centrality, the extent of individual actor’s

21

influence on other actors, and thus high in-degree centralization indicates the incoming flows of
different network resources are focused on a small group of actors in the overall network. In the
same sense, high out-degree centralization indicates that a small number of actors send out most
of the network resources for the rest of the network actors. This study derives in-degree and outdegree centralization of a supply network from in-degree centrality (
centrality (

( ) ) for actor

-

-

( )) and out-degree

of directed valued network by the following equations

(Opsahl et al. 2010):

( )

(

( )

where

)

(

and

)

are the total strengths attached to the incoming and outgoing ties,

respectively. Therefore, the general in-degree and out-degree centralizations ranging from 0 to 1
are respectively defined as:

∑

{

( )

∑
∑

{
{

∑

where

-

(

( )

) and

( )
{

-

( )
(

( )}
( )}
( )}
( )}

) are the largest in-degree and out-degree centrality values in

the network .
22

Lastly, this essay uses global clustering coefficient (GCC) varying from 0 to 1 to
measure the overall level of cohesion among network actors (Newman 2003; Schank and
Wagner 2005). In social network terms, this indicates the probability that network actors
and

are also connected to each other when

collectively represented as (

is connected to both of them,

). In a directed valued network setting, this socio-

centric index is defined as the total value of closed triplets (i.e. triples of network actors
where each actor is connected to the other two;

) divided by the total value of triplets

(i.e. triples where at least one actor is connected to the other two; ). Triplet value ( )
calculation is based on the geometric mean of the tie values for the nodes comprising the
triplet in that it: 1) captures differences between tie strengths, and 2) is robust to extreme
tie strength (Opsahl and Panzarasa 2009). Thus, the general GCC (

) can be formally

stated as:

∑

where

{

∑(
∑(

) {

(

}

(

) { }

)
}
)

is the number of possible triplets in network . Readers can refer to the recent study of

Opsahl and Panzarasa (2009) for more details on this technique.
SNA indices have been developed and used within a sociological context, which cannot
be directly applied and interpreted within an interfirm supply network context. Table 2.3,
consequently, proposes a new framework of the supply network implications of the socio-centric
SNA indices for directed valued networks used in this study for each of the four tie types
previously defined in Table 2.2.

23

Table 2.3 Socio-Centric Indices, Conceptual Definitions, and Interpretations by Supply Network Tie Type
SocioCentric
SNA Index

Conceptual Definition

Implications for Directed Valued Supply Network

Contractual
tie

The extent to which particular
Betweenness network actors serve as hubs
centralization relative to the rest of the
network

Tie Type

The extent to which there exist particular focal firms with unequally
complete (or specific) contract terms than other supply network
members
- The lower the index, the more firms there are which have more
equally complete contract terms with their supply network
counterparts
- The higher the index, the more firms there are which have more
unequally complete contract terms with their supply network
counterparts.

The extent to which there exist particular focal firms with unequal
percentage of monetary exchanges than other supply network
members (i.e. distribution of sales and spending in the network)
- The lower the index, the more firms there are which have equal
Transactional
percentage of monetary exchanges with their supply network
tie
counterparts.
- The higher the index, the more firms there are which have more or
less percentage of monetary exchanges with their supply network
counterparts.

Professional
tie

The extent to which there exist particular focal firms with unequal
amount of work-related interactions than other supply network
members
- The lower the index, the more firms there are which have equal
amount of work-related interactions with their supply network
counterparts.
- The higher the index, the more firms there are which have more or
less work-related interactions with their supply network
counterparts.

24

Table 2.3 (cont’d)
SocioCentric
SNA Index

Conceptual Definition

The extent to which network
In-degree
resources are converged on
centralization
particular network actors

Implications for Directed Valued Supply Network

Personal
tie

The extent to which there exist particular focal firms with unequal
amount of non-work-related interactions than other supply network
members
- The lower the index, the more firms there are which have equal
amount of non-work-related interactions with their supply
network counterparts.
- The higher the index, the more firms there are which have more or
less non-work-related interactions with their supply network
counterparts.

Contractual
tie

The extent to which particular
Betweenness
network actors serve as hubs
centralization
relative to the rest of the
(cont’d)
network

Tie Type

The extent to which particular focal firms have more complete (i.e.
less favorable) contract terms from the other supply network
members.
- The lower the index, the more firms there are which have fair
contract terms with their supply network counterparts.
- The higher the index, the fewer particular focal firms possess less
favorable contract terms with their supply network counterparts.

The extent to which particular focal firms take up more percentage
of the monetary exchanges occurring inside the supply network than
others.
Transactional
- The lower the index, the more firms there are which have equal
tie
percentage of monetary exchanges.
- The higher the index, the fewer particular focal firms account for
more percentage of monetary exchanges than the others.

25

Table 2.3 (cont’d)
SocioCentric
SNA Index

Conceptual Definition

Tie Type

Professional
tie

In-degree
The extent to which network
centralization resources are converged on
(cont’d)
particular network actors

Implications for Directed Valued Supply Network
The extent to which particular focal firms have more incoming
work-related interactions from the rest of the supply network
members
- The lower the index, then each of the supply network members
has more equal amount of work-related interactions with one
another.
- The higher the index, the more work-related interactions among
supply network members is focused on fewer particular focal
firms.

Personal
tie

The extent to which particular
Out-degree
actors disseminate network
centralization
resources to others

The extent to which particular focal firms have more incoming nonwork-related interactions from the rest of the supply network
members
- The lower the index, then each of the supply network members
has more equal amount of non-work-related interactions with one
another.
- The higher the index, the more non-work-related interactions
among supply network members is focused on fewer particular
focal firms.

Contractual
tie

The extent to which particular focal firms provide more complete
(i.e. less favorable) contract terms for the rest of the supply network
members.
- The lower the index, the more firms there are which have fair
contract terms with their supply network counterparts.
- The higher the index, the fewer particular focal firms yield less
favorable contract terms for their supply network counterparts.

26

Table 2.3 (cont’d)
SocioCentric
SNA Index

Conceptual Definition

Tie Type

Implications for Directed Valued Supply Network

The extent to which particular focal firms generate more percentage
of the monetary exchanges occurring inside the supply network than
others.
Transactional - The lower the index, the more firms there are which have equal
tie
percentage of monetary exchanges.
- The higher the index, the fewer particular focal firms send out
most of the percentage of monetary exchanges for the rest of the
supply network members.

Out-degree The extent to which particular
centralization actors disseminate network
(cont’d)
resources to others

Professional
tie

The extent to which particular focal firms have more outgoing
work-related interactions to the rest of the supply network members
- The lower the index, then each of the supply network members
has more equal amount of work-related interactions with one
another.
- The higher the index, the fewer particular focal firms send out
most of the work-related interactions to the rest of the supply
network members.

Personal
tie

The extent to which particular focal firms generate more outgoing
non-work-related interactions for the rest of the supply network
members
- The lower the index, then each of the supply network members
has more equal amount of non-work-related interactions with one
another.
- The higher the index, the fewer particular focal firms make more
non-work-related interactions for the rest of the supply network
members.

27

Table 2.3 (cont’d)
SocioCentric
SNA Index

The extent to which how
cliquish (or tightly knit) a
network is as a whole (i.e. the
degree to which all the network
actors tend to cluster together)

Tie Type

Implications for Directed Valued Supply Network

Contractual
tie

Global
clustering
coefficient

Conceptual Definition

The extent to which how the entire supply network members are
directly connected by contract relations
- The lower the index, the less proportion out of all supply network
members are directly connected by contract relations (i.e. the
supply network has a more “hierarchical” architecture as a whole).
- The higher the index, the more proportion out of all supply
network members are directly connected by contract relations (i.e.
the supply network has a more “lateral” architecture as a whole).

The extent to which how the entire supply network members are
directly connected by monetary exchanges
- The lower the index, the supply network as a whole has a more
Transactional
“hierarchical” architecture of monetary exchanges among supply
tie
network members.
- The higher the index, the supply network as a whole has a more
“lateral” architecture of monetary exchanges among supply
network members.

Professional
tie

The extent to which all the supply network members freely
communicate work-related subjects across firm boundaries
- The lower the index, the supply network as a whole has a more
“hierarchical” architecture of work-related interactions among
supply network members.
- The higher the index, the supply network as a whole has a more
“lateral” architecture of work-related interactions among supply
network members.

28

Table 2.3 (cont’d)
SocioCentric
SNA Index

Global
clustering
coefficient
(cont’d)

Conceptual Definition

The extent to which how
cliquish (or tightly knit) a
network is as a whole (i.e. the
degree to which all the network
actors tend to cluster together)

Tie Type

Personal
tie

Implications for Directed Valued Supply Network
The extent to which all the supply network members freely
communicate non-work-related subjects across firm boundaries
- The lower the index, the supply network as a whole has a more
“hierarchical” architecture of non-work-related interactions
among supply network members.
- The higher the index, the supply network as a whole has a more
“lateral” architecture of non-work-related interactions among
supply network members.

29

2.4 Data and Analysis
2.4.1 Boundary Specification
Survey is the most common method to collect social network data. Before starting a survey,
network researcher should first specify a network boundary around the data to be collected
(Laumann et al. 1989; Gulati 1995; Cross and Parker 2004). This is often one of the most
challenging task in that: 1) arbitrarily established network boundaries bear more risk of inflated
measurement error (Kossinets 2006), 2) SNA indices are sensitive to missing data (Knoke and
Yang 2008), and thus 3) a reasonably high response rate over 65% is required (Stork and
Richards 1992). Most of all, the overall complexity of a supply network rapidly escalates as
more suppliers and tiers (or echelons) are taken into consideration (Beamon 1999). As depicted
in Figure 2.1, the number of inter-organizational ties to be examined (N) increases from 1 to 3
when a researcher expands the area of interest from a buyer-supplier dyad to a buyer-suppliersupplier triad by adding one more supplier. If the researcher goes a step further to investigate a
tetrad with one buyer and three suppliers, then N grows from 3 to 6, and it rapidly increases as
the network boundary extends to pentad (N=9), hexad (N=15), and larger multi-term systems
including more suppliers or tiers. The same phenomenon is observed, assuming a purchasing
manager deals with only two unrelated suppliers, when the researcher expands the network
boundary from two-tier supply networks (N=2) to three-, four-, and five-tier supply network
(N=6, 14, and 30, respectively).

30

F
F

F
F

F

S

S

Dyad
Dyad
(N=1)
(N=1)

S

S

S

S

S

Triad
Triad
(N=3)
(N=3)

S

S

Tetrad
Tetrad
(N=6)
(N=6)

S

S

S

F

S

S

Pentad
Pentad
(N=9)
(N=9)

S

S

F

S

Hexad
Hexad
(N=15)
(N=15)
S

S
S

S

S
S

S

S

S
S

S

S

S

S

F

S

S

S
S

S

S
S
S

S
S

S

S
S

S

S S

S

S

S

F

S

S
S
4-Echelon
4-Echelon
(N=14)
(N=14)

3-Echelon
3-Echelon
(N=6)
(N=6)

2-Echelon
2-Echelon
(N=2)
(N=2)

S

S

S

S

S

S

S

S
S

S

S
S

S

S
5-Echelon
5-Echelon
(N=30)
(N=30)

Figure 2.1: Network Complexity with Varying Number of Entities/Tiers
To avoid those pitfalls, previous studies conveniently regarded a social network as an ego
network which consists of a focal actor and a set of network actors directly linked to the focal
actor. Researchers can save time and effort for data collection by adopting this ego network

31

approach, but it also has some serious limitations in describing the multiplex architecture of
directed valued supply network since: 1) it is prone to distortions as survey responses solely rely
on a focal actor’s perceptions (i.e. perceptions from its counterparts are not considered) (Mehra
et al. 2001; Marsden 2005), and 2) a clear determination of inter-firm network boundaries is
almost impossible in that a supply network is a complex system embracing numerous actors
linked with various types of ties across multiple tiers (or echelons) (Choi et al. 2001; Borgatti
and Halgin 2011). Therefore, for collecting supply network data, the current study takes a whole
network approach, which collects bidirectional responses stretching from a focal actor to its raw
materials suppliers (Wellman 1988; Kilduff and Tsai 2003; Provan et al. 2007), which
differentiates this study from prior studies. Although an arduous and time-consuming process is
required for collecting whole network data of inter-organizational ties (Scott 2000; Knoke and
Yang 2008), it has been repeatedly recommended as the most desirable approach for researchers
to investigate the holistic and systemic architecture of a supply network which includes all
activities and functions involved to bring a product or service to the market (Ketchen and Hult
2007; Borgatti and Li 2009).

2.4.2 Data Source and Procedure
This study focuses on the individual component-level supply network as the unit of analysis in
that a single product is mostly built up by incorporating a mix of functional and innovative
components (Huang et al. 2002; Vonderembse et al. 2006). For instance, although Apple’s iPad
released in March 2011 is regarded as a great example of an innovative product, it consists of a
combination of both new (e.g. dual-core Apple A5 system-on-a-chip processor, three-axis
gyroscope sensor, etc.) and existing (e.g. lithium-ion polymer battery, 9.7-inch LED-backlit
display, etc.) components. In this sense, prior research has examined supply networks by

32

component-level analysis focusing on a single component or module within a product (e.g.
Huang et al. 2005; Kim et al. 2011).
Three major South Korean automobile and consumer electronics manufacturers were
contacted to collect the component-level whole network survey data. To lessen the burden of
data collection, a combined sampling approach of fixed list and snowball selections was adopted
(Doreian and Woodard 1992; Stevenson and Greenberg 2000; Borgatti and Li 2009). First, to
keep the whole network perspective in data collection, initial contacts mostly at the executive
level were asked to select a strategically important component with manageable network sizes
(i.e. no more than 3 tiers and 5 suppliers per tier) and recommend the most knowledgeable
sourcing manager in charge of the selected component. This step also contributed to minimize
key informant bias (Kumar et al. 1993). Secondly, sourcing managers were asked to evaluate
their perceptions on different types of ties (i.e. contractual, transactional, professional, and
personal) with their major immediate suppliers mostly listed as the OEM’s preferred supplier.
Contractual, professional, and personal ties were evaluated using a five-point scale, anchored by
“1” (strongly disagree), “3” (neither disagree not agree), and “5” (strongly agree), and
transactional tie was assessed by percentages of total spend (or sales) for each supplier (or buyer)
for the selected component. Next, the same questions were given to the OEM’s counterparts (i.e.
tier one suppliers) based on the contact information provided by the focal firm’s sourcing
manager. These steps were repeated for the successive tiers of suppliers (i.e. tier two and tier
three suppliers) until end-tier suppliers were reached. To check the existence of duplicate
respondents (i.e. suppliers), surveys on the successive tiers of suppliers were started after
finalizing all the surveys on OEMs or higher tier suppliers. Since all the requested information
was extremely confidential and sensitive to both buyers and suppliers, it was promised that all

33

individual responses would be kept completely confidential, and all analyses would be
implemented and presented only at the aggregate level. To reassure respondents regarding the
confidentiality of their responses, all completed questionnaires were directly collected by the
author instead of being routed through buying firms. As a result of these efforts, a total of 153
component-level (89 electronics and 64 mechanical) networks consisting of 1,852 total network
members were collected.

2.4.3 Analysis
As there is no stand-alone software package built on the SNA indices introduced above, all four
socio-centric indices (i.e. betweenness centralization, in-degree centralization, out-degree
centralization, and global clustering coefficient) of individual directed valued supply network
were calculated by using Microsoft Excel, and the codes were verified by two faculty members
with experience in the area. UCINET has been one of the most widely accepted SNA tools for
conducting the network analysis, but it is not appropriate for analyzing the given valued directed
network dataset since it automatically “binarizes” the valued network (Wei et al. 2011). As all
network ties of the dataset were valued (i.e. measured on a five-point scale or in percentage),
each tie value corresponded to an average value of the item measures assigned to the tie
(Wasserman and Faust 1994; Sparrowe et al. 2001; Sykes et al. 2009).

34

2.5 Results and Interpretations
2.5.1 Descriptive Statistics
Table 2.4 contains basic descriptive statistics and one-way analysis of variance (ANOVA) results
for the calculated socio-centric SNA indices based on 153 component-level supply networks by
four different tie types. ANOVA implemented to compare the means of tie types with respect to
each SNA index showed that they differ statistically. Homogeneity of variances was tested using
Levene’s test, and planned comparisons etween the means assessed using Dunnett’s T statistic.
The mean values were also plotted by network tie type on a radar chart in Figure 2.2 for
comparison. A few descriptive observations could be made from the computed indices. First, for
all the four SNA indices, transactional network had lower means than contractual network. For
instance, the betweenness centralization in transactional network (0.3905) was not as high as that
in contractual network (0.5786), which indicates the monetary exchanges are relatively more
equally distributed among supply network members. Transactional network with a lower indegree centralization (0.3246) than contractual network (0.3846) also hinted that more complete
contract terms given to a fewer particular focal firms within the supply network were not always
associated with more percentage amount of their monetary exchanges. Further, as shown in outdegree values, particular focal firms fell short of generating corresponding percentage amount of
monetary exchanges (0.2883) even when they yielded more complete contractual terms for their
counterparts (0.4895). These observations collectively suggest the completeness of contract
terms is not necessarily associated with more or less monetary exchanges among supply network
members. Second, the contractual network showed the highest means for betweenness and outdegree centralizations (0.5786 and 0.4895) compared to those of the other three network types
(0.3905 and 0.2883 for transactional network; 0.4840 and 0.4041 for professional network;
35

0.4922 and 0.3404 for personal network), whereas no such notable difference could be seen in
in-degree centralization. This implies that: 1) there exists a relatively smaller group of focal
firms mediating other members in contractual networks, and 2) those particular focal firms grant
rather more complete (i.e. less favorable) contract terms to their supply network counterparts,
which supports the first observation on the lack of relatedness between the completeness of
contract terms and the amount of transactions. Another noteworthy point was that the contractual
and professional networks exhibited the same pattern in the rank order of indices (i.e. higher outdegree centralization than in-degree centralization), which was contrary to transactional and
personal networks following the same pattern (i.e. higher in-degree centralization than outdegree centralization). This suggests that each set (i.e. contractual-professional and transactionalpersonal) can move in the same general direction with different magnitudes, signifying that the
monetary exchanges among supply network members are more associated with their personal
ties rather than contractual or professional ties. Perhaps the most interesting observation shown
in Table 2.4 is the apparently high GCC in personal network (0.7976) compared to the low
values of the other three networks (0.1344, 0.0749, and 0.1709). This demonstrates the personal
network has a very loosely knitted architecture as a whole, while the other three are highly
clustered together with respect to contractual, transactional, and professional ties. This also
clearly shows that supply network members interact very actively with one another on non-workrelated matters – creating a lateral (i.e. more egalitarian) personal network architecture – even
when they mostly do not share any other network ties with their non-immediate members.

36

Table 2.4 Descriptive Statistics and ANOVA Results
Socio-Centric
SNA Index

Tie

Contractual (A)
Transactional ( )
etweenness
Centralization
Professional (C)
Personal (D)
Contractual (A)
Transactional ( )
In-degree
Centralization
Professional (C)
Personal (D)
Contractual (A)
Transactional ( )
Out-degree
Centralization
Professional (C)
Personal (D)
Contractual (A)
Transactional ( )
Glo al Clustering
Coefficient
Professional (C)
Personal (D)
Note: N=153 component-level networks
a **
: Significant at the 0.01 level (2-tailed)

Mean
.5786
. 9 5
.484
.49
. 846
. 46
. 4 4
.4 9
.4895
. 88
.4 41
. 4 4
.1 44
. 749
.17 9
.7976

Stdev
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

167
4 8
455
191
89
59
6
468
15
94
1
146
1 1
7

37

Min
.55 5
. 5 1
.41
.41
. 51
. 9 1
. 9 7
. 6 8
.45 4
. 115
. 8
. 91
.1
. 5
.15
.75 4

Max
.6 86
.4
.5599
.5698
.4185
. 599
. 894
.4498
.5 99
. 698
.4 99
. 898
.1699
. 999
.1895
.8497

F-Value

a

Dunnett T3

**

A > C, D >

**

D>A>C>

**

A>C>D>

**

D>C>A>

759.7 5

55.188

1, 15.549

45

9.1 4

Contractual
0.8

0
Personal

Transactional

Betweenness Centralization
Indegree Centralization
Outdegree Centralization
Global Clustering Coefficient
Professional
Figure 2.2: Means of Socio-Centric SNA Indices by Network Tie Type

38

2.5.2 Correlations
To find statistical support for the existence of supply network multiplexity, the bivariate
correlation matrix among all four different indices in four different relational dimensions is
presented in Table 2.5.

39

Table 2.5 Bivariate Correlation Matrix

Contractual

Transactional

Professional

Socio-Centric
a
SNA Index

(a)

(a)

Tie Type

1.

( )
(c)
(d)
(e)
(f)

**

.76
. 9
. 65
-.1 7

**

. 7
. 6
-.1 5
**

(j)

.177

.1 9

(k)

**

**

.781
-.765
-.759

(n)
(o)
(p)

.74
-.744
.7

**
**
**
**
**

Note: N = 153 component-level networks;

**

(d)

(e)

(f)

(g)

(h)

1.

(g)
(h)
(i)

(m)

a

(c)

.759
. 11
. 6
. 6

(l)

Personal

(b)

.7

. 45
.1 8
-. 7
.7

**

-.716

**

-.71

**

1.
-. 1
. 1

1.
-.1

. 4

.

-. 51

6

. 4
-.

*

.
. 61
. 4

1.
-. 68
. 45

1.
-.178

*

-. 76

-.1 7

**

.14

**
**

.14

-. 5
.1 9

-.771

. 88

-.717

-.

. 41
.

4

9

. 45
*

-.1

*

.114

*

-.17

*

.776

.

.

-. 9

8

-. 78
-.

-. 56

1.

. 4

. 57

-.7 6
.71

*

-.1
. 56
. 98
. 61

. 16

.

. 1

**

6

. 6
-.1 4
-. 69

-. 8
.1 7
-. 68

.764

**

1.

**

.7 7

**

-.744
.76

**

1

-. 91
4

. 59
-. 5
.1 7

-. 79
.

4

-. 8
. 67

: Significant at the .01 level; : Significant at the 0.05 level (2-tailed)

(a), (e), (i), (m) = Betweenness centralization; (b), (f), (j), (n) = In-degree centralization; (c), (g), (k), (o) = Out-degree centralization;
(d), (h), (l), (p) = Global clustering coefficient

40

Table 2.5 (cont’d)
Tie Type

Contractual

Transactional

Professional

Socio-Centric
a
SNA Index
(a)
( )
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
(m)

Personal

(n)
(o)
(p)

(i)

1.
.16
. 9
. 1
.

(l)

1.
.116

-.751

.68

7
5
**

(n)

(o)

(p)

1.

-.718

(m)

1.

-.1

. 71
-.

(k)

-.18

.

Note: N = 153 component-level networks;
a

(j)

. 7
-.1
.144

**
**
**

.776

**

**

-.7

**
**

1.
**

-.75

-.749

.7 4

.671

.7 4

-.7 6

-.717

**

**

**

*

**

1.
**

-.76
.781

**

1.
-.71

**

1.

: Significant at the .01 level; : Significant at the 0.05 level (2-tailed)

(a), (e), (i), (m) = Betweenness centralization; (b), (f), (j), (n) = In-degree centralization; (c), (g), (k), (o) = Out-degree centralization;
(d), (h), (l), (p) = Global clustering coefficient

41

2.5.3 Tests
Before comparing different SNA indices showing significant correlations in Table 2.5, the
Shapiro-Wilk test was implemented for normality in them. Seeing all the p-values were less than
0.01 as shown in Table 2.6, the hypothesis that all the indices across different tie types are
normally distributed was rejected, which indicates nonparametric tests are called for.
Table 2.6 Normality Test
Socio-Centric SNA Index

Statistic

(a) Contractual etweenness Centralization
( ) Contractual In-degree Centralization
(d) Contractual Glo al Clustering Coefficient
(e) Transactional etweenness Centralization
(f) Transactional In-degree Centralization
(j) Professional In-degree Centralization
(k) Professional Out-degree Centralization
(l) Professional Glo al Clustering Coefficient
(m) Personal etweenness Centralization
(n) Personal In-degree Centralization
(o) Personal Out-degree Centralization
(p) Personal Glo al Clustering Coefficient

.957
.947
.94
.967
.969
.956
.9
.9
.954
.947
.946
.965

df
15
15
15
15
15
15
15
15
15
15
15
15

Sig.
.
.
.
.
.
.
.
.
.
.
.
.

1
1

1

In this regard, Wilcoxon signed-rank test and sign test were used to statistically assess
whether there exists any discernible difference within eight sets of highly correlated socio-centric
indices. When the distribution is symmetric, the Wilcoxon signed-rank test is used; in other cases,
the less powerful sign test can be used when the distribution is highly skewed (Conover 1999).
For completeness, both tests were conducted. As shown in the following Table 2.7, the results
indicate that there are statistically significant differences between most pairs (except sets 22, 24,
and 29) of different socio-centric indices, which validate the first proposition concerning the
different architectural properties of the same supply network with regard to different types of
network ties (i.e. multiplexity).
42

Table 2.7 Statistical Pairwise Comparisons between Socio-Centric Indices
Inter-Index
a
Relationship Set
Set 1: ( ) – (a)

p-value
(WSR test)
.

Set : (f) – (a)

.

Set : (f) – ( )

.

Set 4: (f) – (e)

.

Set 5: (j) – (d)

.

Set 6: (j) – (f)
Set 7: (k) – (a)

.
.

Set 8: (k) – ( )

.

Set 9: (k) – (e)

.

Set 1 : (k) – (f)

.

Set 11: (l) – (a)

.

Set 1 : (l) – ( )

.

Set 1 : (l) – (f)

.

Set 14: (l) – (k)

.

Set 15: (m) – (a)

.

Set 16: (m) – ( )

.

Set 17: (m) – (f)

.

Set 18: (m) – (k)

.

Set 19: (m) – (l)

.

Set

: (n) – (a)

.

Set 1: (n) – ( )

.

a

**

p-value
(sign test)

**
**
**
**
**
**

**
**
**
**
**
**
**
**
**
**
**
**
**
**

Association (+/−)

2c

R-square (R )

.
.
.

+

.558

+
−

.511
. 9

**

.

.6 1

**

.

+

**

.

**
**

.

+

.

**

+

.

**

†
†

†

**

.

.584

−

.55

−

.561

−

.579

−

.56

+

.6

+

**

.

−

**

.

.5

**

.

−

**

.

.56

**

.

−

**

.

.6

**

.

−

**

.

.617

**

.

+

**

.

−

.567
. 6

**

.

+

**

.

.565

**

.

+

.54

+

.517

(a) Contractual Betweenness Centralization; (b) Contractual In-degree Centralization; (d)
Contractual Global Clustering Coefficient; (e) Transactional Betweenness Centralization; (f)
Transactional In-degree Centralization; (j) Professional In-degree Centralization; (k)
Professional Out-degree Centralization; (l) Professional Global Clustering Coefficient; (m)
Personal Betweenness Centralization; (n) Personal In-degree Centralization; (o) Personal Outdegree Centralization; (p) Personal Global Clustering Coefficient
b **
*
: Significant at the .01 level; : Significant at the 0.05 level (2-tailed)
c†

†

2

: Coefficient of determination (R ) ≥ 0.50

43

†
†
†
†
†
†
†
†
†
†
†
†
†

Table 2.7 (cont’d)
Inter-Index
a
Relationship Set
Set : (n) – (e)
Set

: (n) – (f)

p-value
(WSR test)
.
.

Set 4: (n) – (k)

**

.

Set 6: (n) – (m)

.

Set 7: (o) – (a)
Set 8: (o) – ( )

.
.

Set 9: (o) – (f)

.

Set

: (o) – (k)

.

Set 1: (o) – (l)

.

Set

: (o) – (m)

.

Set

: (o) – (n)

.

Set 4: (p) – (a)

.

Set 5: (p) – ( )

.

Set 6: (p) – (e)

.

Set 7: (p) – (f)

.

Set 8: (p)– (k)

.

Set 9: (p) – (l)

.

Set 4 : (p) – (m)

.

Set 41: (p) – (n)

.

Set 4 : (p) – (o)

.

**
**
**
**

**
**
**
**
**
**
**
**
**
**
**
**
**
**

Association (+/−)
−

2c

R-square (R )
.

+

.579

.518

+

**

.645

−

**

.646

−

**

.581

−

**

.5 9

−

.569

−
**

.544

−

**

.594

+

**

.6 8

+

**

.55

−

**

.549

+

**

.51

+

**

−

.567
. 4

**

+

**

.591

+

**

.575

−

**

.5

−

**

.595

+

**

.5 7

−

.59

.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.

(a) Contractual Betweenness Centralization; (b) Contractual In-degree Centralization; (d)
Contractual Global Clustering Coefficient; (e) Transactional Betweenness Centralization; (f)
Transactional In-degree Centralization; (j) Professional In-degree Centralization; (k)
Professional Out-degree Centralization; (l) Professional Global Clustering Coefficient; (m)
Personal Betweenness Centralization; (n) Personal In-degree Centralization; (o) Personal Outdegree Centralization; (p) Personal Global Clustering Coefficient
b **
*
: Significant at the .01 level; : Significant at the 0.05 level (2-tailed)
c†

6

**

.

. 6

Set 5: (n) – (l)

a

**

p-value
(sign test)
. 15

2

: Coefficient of determination (R ) ≥ 0.50

44

†
†
†
†
†
†
†
†
†
†
†
†
†
†
†
†
†
†
†

2.5.4 Comparisons
Previous supply network studies using SNA simply compared various SNA indices to interpret
the results (e.g. higher or lower). Taking these findings one step further, the present research
additionally explores the second proposition on how different socio-centric SNA indices in pairs
derived from a directed valued network dataset are related to each other by using scatter plot
diagrams with best fit line. The obtained coefficient of determination (

) and direction of

association of each pairwise set are provided in Table 2.7. A coefficient of determination (i.e.
squared correlation coefficient) greater than or equal to 0.50 was considered to indicate a
reasonably predictable relationship and the dependence of the paired SNA indices.
First, as shown in Sets 1-3, all three SNA indices (i.e. contractual betweenness
centralization, contractual in-degree centralization, and transactional in-degree centralization) are
positively associated with one another. These collectively indicate that there exists a particular
few firms with more complete contract terms, and specifically, those firms take more of the
outside incoming monetary flows (i.e. sales incurred from other network members) than their
supply network partners. This observation corresponds well with previous studies which have
investigated the effects of information asymmetry on bargaining power. Specifically, it supports
the conventional view that more complete contract terms in favor of fewer focal firms provides
them with greater leverage to derive more economic benefits from their partners. The next three
sets (7, 8, and 10) extend these findings by additionally considering another SNA index,
professional out-degree centralization, which measures the extent to which the particular focal
firms have more outgoing work-related interactions with other members of the supply network.
As commonly observed in all of those three sets, professional out-degree centralization
demonstrates positive associations with contractual betweenness, contractual in-degree, and

45

transactional in-degree centralizations. Taken together these show that the particular focal firms
with more complete contract terms and more sales sent out more work-related communications
to the rest of the supply network members.
Second, Sets 11-14 describe the interrelationships between the four SNA indices
investigated in first six sets (i.e. contractual betweenness, contractual in-degree, transactional indegree, and professional out-degree centralizations) and professional GCC which measures how
freely supply network members communicate with non-adjacent partners about work-related
matters. All of the four sets exhibit that there are negative correlations between professional
GCC and those four indices. Linking to the previous findings, this shows that the particular focal
firms with the more sales and complete contract terms are present only when the supply network
has rather hierarchical (i.e. less egalitarian) architecture of work-related interactions with their
supply network partners.
Sets 15-19 further extend previous findings by additionally considering another SNA
index, betweenness centralization, for non-work-related interactions within the supply network,
demonstrating the extent to which there exist particular focal firms with more of those
interactions than others. As shown in first four sets (Sets 15-18), the positive interrelationships
among contractual betweenness, contractual in-degree, transactional in-degree, and professional
out-degree centralizations decrease as the corresponding personal betweenness centralization
decreases. This signifies the particular focal firms with positive interplays among more complete
contract terms, sales, and outgoing work-related interactions cannot enjoy those synergies when
there are more firms which have similarly equal amount of non-work-related interactions with
their supply network counterparts. In addition, Set 19 shows that personal betweenness
centralization and professional GCC have a positive correlation. This illustrates there can still

46

exist particular focal firms with more non-work-related interactions even when the supply
network as a whole has more lateral (i.e. more egalitarian) architecture of work-related
interactions among members. Interestingly, this corroborates the first research proposition by
confirming that two invisible network ties (i.e. professional and personal interactions) can be
compatibly embedded within the same supply network while holding different network
properties.
The next five sets (Sets 20, 21, 23, 25, and 26) show the interrelationships between
personal in-degree centralization and the five SNA indices which demonstrated a statistical
significance (i.e. contractual betweenness, contractual in-degree, transactional in-degree,
personal betweenness centralizations, and contractual GCC). Sets 20, 21, and 23 show the
positive relatedness of contractual betweenness, contractual in-degree, transactional in-degree
centralizations still holds as personal in-degree centralization increases. This means that the
particular focal firms with more complete contract terms and sales also possess more incoming
non-work-related interactions than others. However, the more incoming non-work-related
interactions those firms have, the less professional GCC and professional betweenness
centralization scores they exhibit (See Sets 25 and 26), which collectively implies that: 1) focal
firms have more outside incoming non-work-related flows on non-work-related matters when
they have more indirect work-related communications with other network members (i.e. under
more hierarchical professional network architecture), and 2) focal firms’ high control level of
work-related interactions may actually discourage their network partners from sending out more
non-work-related interactions.
The next six paired sets (Sets 27, 28, 30, 31, 32, and 33) further extend previous findings
by considering another index, personal out-degree centralization, which demonstrates the extent

47

to which particular focal firms have more outgoing non-work-related flows. In line with our
previous findings, Sets 27, 28, 30, and 33 illustrate negative associations between personal outdegree centralization and four other SNA indices (i.e. contractual betweenness, contractual indegree, and professional out-degree, and personal in-degree centralizations). These additional
findings collectively signify that: 1) the particular focal firms which already have more complete
contract terms and more outgoing work-related interactions are less motivated to generate nonwork-related interactions for the rest of the supply network members, and 2) this declining
motivation for more outgoing non-work-related flows still holds even when focal firms have
more incoming non-work-related flows (i.e. no reciprocal exchange). Sets 31 and 32 do rather
stretch the second implication by showing that particular focal firms send out more non-workrelated flows when the overall supply network has a more lateral communications architecture of
work-related interactions.
Lastly, this study investigated the correlations between personal GCC and other network
indices with statistically significant predictabilities. The first four sets (Sets 34, 35, 37, and 38)
go on to show positive associations with personal GCC that describes the extent to which all the
supply network members freely communicate non-work-related matters across firm boundaries.
The sum of these findings highlights the double-edged effects of tightly knitted non-work-related
interactions within supply networks which result in that particular focal firms acquire more sales
as well as less favorable (i.e. more complete) contract terms as they create more work-related
communications. It alludes to an interesting aspect that contractual and transactional inter-firm
exchanges are more associated with personal ties between supply network partners, rather than
professional ties. This hint is further supported by Set 39 illustrating a negative correlation
between the personal and professional GCCs. The previous finding on non-reciprocities of non-

48

work-related interactions is also reaffirmed by Sets 41 and 42 which denote that the more
incoming non-work-related interactions particular focal firms have, the less of them are returned
to the remaining network members even when the overall supply network has a more lateral (i.e.
more egalitarian) architecture. This may reflect: 1) the non-work-related exchanges are being
regarded as one of the most valuable resources within supply networks, and hence 2) firms are
not willing to share them with others despite the network as a whole being characterized by
relatively limited variations of non-work-related interactions.

2.6 Discussion
2.6.1 Contributions
Previous SCM studies have mainly focused on dyads (i.e. buyer-supplier) or triads (i.e. buyersupplier-supplier) between supply chain partners. Although those approaches have enhanced our
understanding of the exchanges occurring between a buyer and its immediate supplier(s), they
have suffered from the restrictive scope to grasp the whole picture of supply network comprising
multiple tiers of supply chain partners (Choi and Kim 2008; Wilhelm 2011). A few recent works
have adopted a network perspective to the SCM context; but they have fallen within limited
domains such as conceptual frameworks and descriptive case studies without further empirical
substantiation. Drawing upon socio-centric SNA indices, this study collected and analyzed
primary data to examine component-level supply network architectures, and consequently it
makes important contributions from both theoretical and practical standpoints.
To the best of the author’s knowledge, the present work is the first empirical study which
discusses how the overall network architecture consisting of same supply chain partners vary
depending on different types of supply network ties considered (i.e. multiplex supply network
perspective). Adding to previously investigated visible interorganizational ties (i.e. contractual

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and transactional ties), this study incorporated the invisible dimensions (i.e. professional and
personal ties) into supply network analyses to fully explore the invisible dynamics between
supply chain partners. All previous supply network studies to this point have investigated the
aforementioned ties separately under the untested assumption of uniplex supply network.
Nonparametric tests show that there exist statistically significant differences among different
network tie types, which supports the multiplex properties of supply networks. Specifically, it is
found that more specific or complete contracts between supply chain partners are not necessarily
associated with more transactions. The more interesting finding is that interfirm network
transactions possess a higher association with personal network ties rather than contractual or
professional ties exhibiting the same pattern in the rank order of socio-centric SNA indices.
These results collectively confirm interorganizational networks between supply chain partners
are multiplex and thus call for a multidimensional (rather than uni- or bi-dimensional) approach
in trying to analyze and understand supply chain dyads, triads, and/or supply networks.
This essay also provides meaningful hints to the question of why even firms seemingly
have outstanding buyer-supplier relationships still (sometimes) fail to maximize the network
benefits from their supply chain partners or are vulnerable to external shocks. Social network
studies have suggested that multiplex networks are stronger and more durable than uniplex ones
as network actors have multiple bases of interaction (Morin and Seidman 1986; Wellman and
Wortley 1990). In most of the previous SCM literature, however, different types of interfirm
relational ties have either been investigated in separate research models or been lumped together
under the same research construct such as buyer-supplier relationship strength or engagement.
Either approach can prevent academics from recognizing the existence of network multiplexity
and consequently mislead practitioners into believing that they are doing great work in managing

50

their supply networks. As noted in the introduction, the examples of Japanese automakers after
the 2011 earthquake and tsunami offer additional evidence that existing measures of buyersupplier relationship focus only on a specific tie and thus cannot be directly applied for a supply
network which is essentially multiplex in nature. In a practical sense, the findings in this study
urge the SCM practitioners to view and manage their supply network not as a simple collection
of multiple buyer-supplier relationship but a more complex combination of multiplex interfirm
ties.
Managing multiplex networks is considerably more complex in that actors embedded in
the same network will perceive the overall network architecture differently (Kim et al. 2006;
Shipilov and Li 2012). In a supply network context, this perception discrepancy can give rise to
divergent incentives among network partners and consequently hinder the supply network itself
from achieving full cooperative outcomes. The current study thus explored the interrelationships
between different socio-centric SNA indices in an attempt to provide a foundation for future
research about multiplex supply network management. The results of pairwise comparisons
generated some interesting observations. For example, showing that particular focal firms can
have more incoming flows of professional supply network ties when they give more favorable
contract terms to their counterparts; at the same time, they do not have to build stronger
professional ties with their supply chain partners if the power across the network is not equally
distributed nor concentrated (See Section 2.5.4 for more detailed findings of pairwise
comparisons). The empirical findings confirm that no single universal indicator can fully
describe the multi-faceted supply network; rather, different network tie types work
interdependently in shaping different supply network architectures. This essay offers a draft set
of practical guidelines suggesting that SCM practitioners need to consider both the visible

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interorganizational and invisible personal exchanges encompassing non-immediate supply chain
partners to better understand and design supply network architectures. For instance, some notable
invisible network phenomena are brought to light, such as: 1) network members interact with one
another on non-work-related matters even when they mostly do not share any other network ties,
2) invisible network ties can be compatibly embedded within the same supply network while
holding different network properties, 3) focal firms send out more non-work-related flows when
the overall supply network has a more lateral communications architecture of work-related
interactions, and 4) non-work-related changes are the most valuable network resource in supply
network context.
This study also provides unique methodological contributions. To respond to repetitive
calls for applying SNA approach to the SCM context, two more realistic and rigorous network
approaches were adopted in the present work: directed valued network and whole network
approaches. First, while the widely-used binary network approach relies on counterintuitive
premises that all ties are completely homogeneous and symmetrical, directed valued network
approach has definite advantages in grasping network phenomena by considering both directions
and strengths of network ties (Wasserman and Faust 1994; Freeman 2004). However, the latest
developments in directed valued network indices have, to the author’s knowledge, not yet been
applied in existing supply network literature. To draw out the fullest grasp of the primary data
collected from a survey of 153 component-level supply networks, the present work analyzed and
compared socio-centric SNA indices defined for directed valued network. Second, the whole
network approach was also used to clarify potential problems related to arbitrary supply network
boundaries that depend on a focal firm’s perception. Lastly, the current study illustrated supply
network phenomena in a statistically testable form by using individual supply network as a unit

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of analysis. Existing interfirm network literature has highlighted that system-level analyses
employed in this study provide more grounded insights about network phenomena (Mizruchi and
Marquis 2006; Galaskiewicz 2011). Our findings show that those analyses can complement or
even substitute the previous findings of case-based or local-level supply network research.

2.6.2 Limitations and Directions for Future Research
Given the exploratory nature of empirical supply network research, a few limitations should be
noted in ways that represent opportunities for future work. First, four different types of network
ties examined in this study are not exhaustive. Although this does not curtail the contributions
related to the existence of supply network multiplexity, a few additional tie types can be taken
into consideration. One interesting example is knowledge exchange among supply network
partners. Based upon the tenets of resource-based view, firms gain access to complementary or
synergistic knowledge by forming and managing alliance networks (Inkpen and Tsang 2005;
Phelps 2010). Strategic alliance literatures have extensively characterized such knowledge as
independent versus systemic (Garud and Nayyar 1994), individual versus collective (Spender
1996), architectural versus component (Matusik and Hill 1998), experiential versus articulated
(Simonin 1999), etc. By additionally considering the acquisition or transfer (i.e. tie direction) and
effectiveness (i.e. tie strength) of interfirm knowledge, future studies could provide additional
evidence that a supply network possesses multiplex architectural properties. Further, it may be
valuable for future research to investigate interrelationships among various SNA indices by
incorporating different knowledge, resource, market, and/or technology types.
Second, the current study does not utilize all available socio-centric SNA indices in
describing network architectures. This limitation comes from the setting of this study: an
empirically substantive investigation of directed valued supply networks. For instance, network

53

density defined as the ratio of the actual number of ties out of the maximum number of ties
possible, is useful in describing the connectedness among network members, but at the same
time suffers from its dependence on network size (Scott 2000). In general, the bigger the supply
network (in which there are more suppliers), the lower the network density. The measure was
thus excluded from the analysis to insure rigorous statistical inference. In addition, despite the
surging need for directed valued network approach, an overwhelming majority of existing SNA
indices is solely defined for non-directional and binary networks. It therefore will be worthwhile
for researchers to incorporate future methodological developments in quantifying architectural
properties of directed valued supply networks.
Finally, future researchers may find it interesting to expand this perspective to examine
antecedents and performance implications of supply network multiplexity. In certain respects,
the field of sociology views the emergence of social networks – the collection of interpersonal
ties – as social and psychological phenomena occurring among non-predetermined individuals
through face-to-face conversations (Tienda and Rajman 2001). In contrast, corporate managers
behave as network architects by defining the objectives and designating member companies of
the network, and their ability to coordinate the complex network of multiple inter-organizational
ties is regarded as having a significant impact on the success of a firm (Hinterhuber 2002;
Pollock et al. 2004; Fjeldstad et al. 2012). A multiplex supply network, in this regard, can be
viewed as an intentionally and strategically designed outcome to enhance network actors’
performances. Although no unified framework for network-level performance measurement has
yet emerged, such an extension of the current study also could serve as a starting point for future
investigations on the role of network multiplexity in maximizing a focal firm’s network benefits

54

from its supply chain partners and for designing a robust network for dealing with supply
network risks.
In conclusion, the focus of this essay is to present a multiplex supply network perspective
and investigate the corresponding interrelationships between network architectural properties in
the way of empirical substantiation. Hopefully, the findings here provide the basics for verifying
whether the current knowledge on supply chain dyads/triads still holds in a supply network
setting, which will eventually advance understandings of multi-faceted supply network
phenomena.

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3 MOVING BEYOND SERENDIPITY: HOW SUPPLY NETWORK
ARCHITECTURE BECOMES STRATEGIC
3.1 Introduction
The first principle of architectural beauty is that the essential lines of a construction be
determined by a perfect appropriateness to its use. — Gustave Eiffel
A firm’s network consists of multiple interfirm relationships among two or more partners
that voluntarily provide, exchange, or share their value-adding activities or specialized resources
to co-develop products, technologies, or services (Gulati 1998; Podolny 2001). Faced with the
changes in a business environment where the coordination of complex global networks of a
firm’s activities is becoming a prime source of competitive advantage (Porter 1998), more supply
chain management (SCM) studies have paid increasing attention to the supply network which
involves simultaneous interactions among multiple supply chain entities rather than dyadic or
triadic ties between one OEM and its immediate supplier(s) (See Table 2.1 for more detail
regarding the development stages of supply network research). The term “network” is often
regarded as relatively new to SCM researchers, but it has long been associated with the concept
of SCM. Cooper and Ellram (1993) called SCM an “integrative philosophy”, and Mentzer et al.
(2001) acknowledged its objective is “to integrate and manage the sourcing, flow and control of
materials using a total systems perspective across multiple functions and multiple tiers of
suppliers.” Therefore, a supply chain is an excellent example of a multi-level complex system
which has a strict architecture in that it includes “coordination and colla oration with channel
partners, which can be suppliers, intermediaries, third-party service providers, and customers”
(Gibson et al. 2005).

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Network thinking and analysis were originally regarded as a subtype within the general
framework of structural sociology (Wellman 1988). In certain respects, the field of sociology
viewed the emergence of networks – the collection of interpersonal ties (e.g. kinship, friendship,
communication, co-membership, etc.) – as social and psychological phenomena occurring
among non-predetermined individuals through face-to-face conversations, and this view has been
widely adopted by other social science disciplines industrial relations. Corporate managers,
however, behave as network architects by defining the objectives and designating member
companies of the network (Hinterhuber 2002; Pollock et al. 2004; Fjeldstad et al. 2012). In this
vein, business academics have explored a quite different logic that firms intentionally engage in
multiple interfirm alliances, and subsequently formulate networks to achieve their strategic goals.
This research stream has been developed into the network strategy which investigates how a firm
can manage its portfolio of multiple simultaneous alliances (e.g. Parise and Casher 2003;
Hoffmann 2007; Wassmer 2010), yet an important question still remains unclear: “what
determines different network architectures” (Brass 2002); in other words, what are the strategic
antecedents of network properties. Anchoring on a strategic network perspective emphasizing the
importance of network design in achieving a firm’s strategic objectives (Gulati et al. 2000);
Doreian (2002) gives a hint towards the existence of antecedents of network architecture by
asserting that “networks have instrumental character for network members as these members
have structured goals and some goals are achieved through network choices” as the “first
principle” of network formulation.
In the above vein, SCM researchers and practitioners have also conjectured the existence
of antecedents for heterogeneous supply network architectures. For instance, facing a turbulent
business environment, firms need to build and maintain multiple supply bases (Lee 2002) which

57

are “the portion(s) of the (bigger) supply network that is within the managerial purview of the
focal company” (Choi and Krause 2006). While it is obvious that the strategic network
perspective should be considered as an integral component of theory in SCM in which
researchers should consider multiple entities commonly composed of large numbers of firms
from multiple interrelated industries, empirical SCM research has confined itself to simple
descriptions on supply network characteristics. The lack of such consideration – antecedents of
network formulation – may give misleading answers about how different supply networks across
various contexts should be managed. Goal conflicts are also more likely to arise in the supply
network setting essentially consisting of multiple tiers of legally separate profit-making
organizations with their own strategic goals; in other words, an OEM cannot attain supply chain
success without deliberately designing its entire supply network in accordance with different
strategic intents. In exploring supply network phenomena, therefore, it is too naï to rely upon
ve
the sociological viewpoint, which characterizes network in terms of spontaneous and informal
face-to-face conversations among non-predetermined individuals. Rather, a supply network
should be viewed as a systematic outcome which is intentionally and strategically designed,
implemented, and maintained in conformity with the OEM’s strategic intent(s).
In line with this argument, this essay attempts to address the theoretical and empirical gap
of supply network research by exploring the unknown strategic antecedents of different supply
network architectures. Specifically, it looks into the following questions: 1) Are an OEM’s
strategic intent choices associated with supply network architecture; and 2) If so, what
differential effects do those strategic intents have on what architectural properties of the supply
network. In doing so, Fisher (1997)’s supply chain design considerations (i.e. cost leadership and
market responsiveness) are taken into account as OEMs’ two major strategic intents. Drawing

58

upon a unique dataset which allows analyses of multiple directed valued supply networks, this
research sheds lights on the unresolved question of the supply network antecedents in a directed
valued network setting and, consequently, offers a strategic supply network perspective. The
remainder of this study is structured as follows. Section 2 provides the theoretical background
and develops the hypotheses. Section 3 reviews the data, measures, and research methods used to
test the proposed hypotheses. Section 4 provides the key results and interpretations. In section 5,
further field investigations were conducted to provide further insights to the quantitative and
qualitative findings from the previous section. Section 6 discusses the theoretical and
methodological contributions of this research, followed by section 7 which includes limitations
as well as directions for future research.

3.2 Theoretical Background and Hypotheses
3.2.1 Network Resource and Strategic Intent
Many sociologists traditionally viewed the emergence of social networks as outcomes of
spontaneous and informal face-to-face conversations among non-predetermined individuals
(Tienda and Rajman 2001). On the other hand, a stream of strategic alliance literature has
adopted a different view that firms utilize strategic alliance to access partners’ knowledge or
skills (Mowery et al. 1996), to hedge their performance risk (Das and Teng 1999), or to enter a
certain foreign market (Zahra et al. 2000) within interfirm dyad settings. This view has been
anchored in the network resource theory (also known as social resource theory), which is one of
the most popular theories in social network research. The theory, mainly developed by Lin et al.
(1981), argued that interpersonal contacts enable better access to and mobilization of resources
embedded within and outside one’s social network such as valuable information and prestigious

59

others (Bourdieu 1985; Coleman 1988). Combined with interfirm network settings and
environments, however, comparatively less is known about what specific motives drive network
actors to interact with one another when the consequences of building an interpersonal or
interfirm network are widely investigated.
The concept of strategic intent initially suggested by Hamel and Prahalad (1989) has been
useful throughout various business disciplines in accounting for managerial motives behind the
strategic alliance or joint venture formulation. While a vision is commonly developed and held
by top management teams, strategic intent is more than just a vision or ambitious target of top
management in that it is shared and implemented at multiple levels of the organization that are
similar to SCM settings (Hart 1992; Hamel and Prahalad 1994). For instance, Koza and Lewin
(1998) proposed a framework emphasizing a firm’s strategic partnership structure varied by its
strategic intent (exploitation or exploration). DiRomualdo and Gurbaxani (1998) also highlighted
the importance of alignment between the strategic intent and supplier relationships to achieve
outsourcing success. Ryall (2013) more recently espoused this view by addressing that an OEM
should utilize different strategic intent (competitive or persuasive) in garnering the resources and
capabilities possessed by non-immediate members of its value network. Extending the
aforementioned conceptual arguments to the SCM domain, the OEM’s strategic intents may
serve as pivotal reference points for managing its supplying partners across multiple tiers which
result in different architectural properties of the formed supply network. Very little empirical
research, however, has been done to test this conjecture. This essay investigates strategic
antecedents of different supply network architecture by incorporating Fisher’s (1997) supply
chain design considerations (i.e. cost leadership and market responsiveness) as shown in Figure
3.1, and as a result, aims to provide a strategic supply network perspective.

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Cost
Cost Leadership
Leadership

Cost-Driven Supply Network
Cost-Driven Supply Network

Mixed-Intent-Driven
Mixed-Intent-Driven
Supply Network
Supply Network

ResponsivenessResponsivenessDriven Supply
Driven
Supply Network
Network
Market Responsiveness
Market Responsiveness
Figure 3.1 Strategic Intents and Corresponding Supply Network Types

3.2.2 Supply Network Tie Types
Ties across interfirm network serve as conduits for network actors to access, transmit, or
exchange critical organizational resources (Ahuja 2000a; Adler and Kwon 2002; Zaheer and Bell
2005; Soda and Zaheer 2012). Interestingly, the same supply network can have multiple different
architectural properties with regard to types and attributes of network ties (i.e. network
resources), which is commonly referred as “multiplexity” (Verbrugge 1979; Burt 1980; Feld
1981; Ibarra 1992). Thus, in accounting for interfirm network phenomena such as supply
networks, it is essential to take a network multiplexity approach for finding “hidden” network
architectures. This study considers four different supply network tie types – contractual,
transactional, professional, and personal ties – which interlink across supply network partners.
The first two types represent visible network ties for exchanging tangible network resources such

61

as goods and services, whereas the other two capture invisible (and mostly intangible) network
resources exchanged between supply network partners.
Obviously, a supply network consists of visible ties such as a contract or delivery and
receipt of goods and services (Choi and Hong 2002; Kim et al. 2011). Contractual ties mostly
comprise detailed information on interfirm transactions by defining the guidelines for the
operational requirements, quality monitoring and control, warranty policies, penalties, and
expected service level. Another type of visible network ties considered is a transactional tie
reflecting the amount of monetary exchanges which have been regarded as a simple but clear
manifestation of the economic transactions occurring within interfirm networks. This tie
represents the economic interdependence between network members. In other words, a buying
firm gets more dependent on the supplier as the percentage of its total spend to a specific
supplier relative to other suppliers increases while the same occurs to the supplier when more
percentage of its total sales comes from a specific buying firm relative to others. As the most
fundamental element of economic exchanges between supply chain partners, a contractual tie (i.e.
a formal written contract between one supply network actor’s sourcing partner) has conflicting
natures which can foster or hinder commitment between buyers and suppliers (Dyer and Singh
1998; Cannon et al. 2000). For instance, a stronger contractual tie (i.e. more complete contract)
including explicit work-related provisions and prescriptions can protect buyers from
opportunistic behavior of their counterpart (Williamson 1985). On the contrary, from a supplier’s
standpoint, a strong contractual tie specifying more control and legal rules can serve as a threat
when buyers opportunistically utilize it by imposing terms and conditions that are unreasonably
difficult to comply with for the supplier (Ghoshal and Moran 1996; Woolthuis et al. 2005). In
this vein, a transactional tie (i.e. the actual exchange of goods and services) can be established

62

without a formal written contract when both parties share relational norms such as reciprocity,
solidarity and information sharing (Williamson 1993; Poppo and Zenger 2002; Lazzarini et al.
2004). This study thus regards the above two visible supply network ties (i.e. contractual and
transactional ties) as separate types in which a stronger contractual tie does not necessarily imply
more or less economic transactions and vice versa.
Prior network research has pointed out much of interorganizational commitment is often
formalized at a personal rather than organizational level and, hence, can offer exclusive access to
network resources (Brass and Krackhardt 1999; Gulati 2007; Kleinbaum and Stuart 2014).
However, interpersonal and thus invisible ties in supply networks has received relatively less
research attention whereas visible network ties representing economic exchange have been
actively discussed in the literature. This study thus additionally considers two invisible network
ties (i.e. professional and personal ties) bridging the supply chain personnel of partnering firms.
Professional ties are normally task-oriented and focus on achieving assigned objectives, while
personal ties deal more with the social/emotional side of non-work-related interactions and focus
on the interpersonal likeability (Lewicki and Wiethoff 2000; Grayson 2007). In an SCM context,
such invisible ties between purchasing and supply managers play a crucial role in facilitating
buyer-supplier cooperation, trust, reputation and image and subsequent organizational
performance (Zaheer et al. 1998; Stanley and Wisner 2001; Ireland and Webb 2007; Lindgreen
et al. 2013). When incorporated with social network analysis, this consideration further enables
the inter- and intra-comparisons of different tie types and comparable network indices and
consequently can provide invaluable insights concerning the underlying network architecture
(Borgatti et al. 2009; Borgatti and Li 2009). Table 2.2 provides conceptual definitions of the four

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supply network tie types under consideration and their measurement items used based on the
literature.

3.2.3 Indices for Network Characterization
To demonstrate different supply network architectures consisting of the four aforementioned
heterogeneous supply network ties (i.e. contractual, transactional, professional, and personal ties),
this study adopts social network analysis (SNA) which has long been used in analyzing any
social network as a set of interrelated actors and ties. The field of SCM has stressed the potential
applicability of SNA in a supply network context. For instance, Carter et al. (2007) proposed
SNA as a key approach to advance current knowledge on various relationships existing within
and beyond the supply chain by complementing traditional methodologies. This view was
echoed by Borgatti and Li (2009) who pointed out that supply chain settings are particularly
suitable to adopt SNA indices, which have been proven “highly portable” across other disciplines
from economics to physics. More recently, Galaskiewicz (2011) also noted that SCM theories
mostly captured at the local level (e.g. dyad or triad) can be tested by using a supply network as
the primary unit of analysis. The present study adopts the directed valued network approach
which considers both direction and strength (or magnitude) of the network tie between different
network actors (See section 2.3.2 for more details).
More specifically, this study focuses on four socio-centric network indices (i.e.
betweenness centralization, in-degree centralization, out-degree centralization, and global
clustering coefficient) which analyze the overall pattern of multiple actors within a single,
bounded network. While ego-centric indices such as centralities deal with a particular actor (i.e.
ego)’s position within the network, they provide a better understanding of the directed valued
network in that the network architecture from one ego’s viewpoint can be markedly different

64

from those of others linked directly or indirectly (Scott 2000; Marsden 2002). They also
perfectly fit with the purpose of this essay to explore the association between an OEM’s strategic
orientation and its supply network architecture corresponding to different types of supply
network ties. Table 2.3 proposes a new framework of supply network implications of the sociocentric SNA indices for directed valued networks used in this study for four types of supply
network ties. Please refer to the section 2.3.2 for more details on computing those indices.

3.2.4 Hypotheses
A firm has a power advantage when it is relatively less dependent upon the resources of its
counterpart(s), and it often leverages this power over others to achieve intended strategic goals
(Pfeffer and Salancik 1978; Dyer and Singh 1998). Social network studies have adopted
betweenness centrality to measure an individual actor’s power, and the extent to which it
controls the resource flows in its network. As a socio-centric measure indicating the variation of
the betweenness centralities of all network actors, betweenness centralization characterizes to
what extent the overall network is built around a particular group of actors serving as hubs
relative to the rest of the network (Freeman 1979; Scott 2000). A low betweenness centralization
score represents that the network resources running through various tie types are almost equally
distributed across the entire network, whereas a high score indicates that there exist particular
focal firms possessing more network resources in it. This measure can be differently interpreted
by different supply network tie types. For instance, an OEM pursuing a cost leadership will try to
make supply contracts as complete and detailed as possible to reduce any uncertainty, which may
translate into cost savings (Lacity and Willcocks 1998; Ghosh and John 2005) and as a result,
will induce the unequal completeness of contracts among supply network members. On the other
hand, the lack of predictability of market changes prevents OEMs in designing complete supply

65

contacts when they pursue market responsiveness, and this will result in cooperative but loose
contracts containing rather general information (Shrader 2001; Wathne and Heide 2004).
Regarding transactional ties, an OEM can exploit economies of scale by focusing on a relatively
small number of supply network members when it pursues a cost leadership, whereas it
diversifies its supply/purchasing sources as a means of promptly responding to unexpected
market changes. This difference is also reflected for the supply networks consisting of
professional and personal ties. In other words, the supply network personnel devoted to the lowcost focused strategy may tend to interact with a smaller range of counterparts, while the other
leads to professional and personal interactions among a broader array of them. Personal and
professional ties in a market responsiveness focused supply network, especially, might be
expected to lead to more frequent interactions involving a greater number of actors because it is
not a stable environment. Under such environment, information seeking and problem solving
behaviors can be expected to dominate leading to greater interactions with more number of
network partners. Based on this line of reasoning, the following set of hypotheses is proposed:
HYPOTHESIS 1A. An OEM’s strategic intent of pursuing cost leadership is positively
associated with the betweenness centralizations of its supply networks consisting of
contractual, transactional, professional and personal ties.
HYPOTHESIS 1B. An OEM’s strategic intent of pursuing market responsiveness is
negatively associated with the betweenness centralizations of its supply networks
consisting of contractual, transactional, professional and personal ties.
A firm with more power over their counterparts also can more easily draw and absorb
network resources from the rest of its network by exerting coercive or punitive pressure, and
consequently can achieve its strategic goals (Ahuja 2000b; Das and Teng 2000). In social

66

network research, this power of an individual network actor is commonly measured by in-degree
centrality, which represents the total number of ties pointing toward the actor. In-degree
centralization, derived by the variation in individual actor’s in-degree centrality at the network
level, indicates to which extent network resources are concentrated in particular actors
(Wasserman and Faust 1994). From a supply network perspective, an OEM trying to achieve
cost leadership characterized by pursuing economies of scale will have network architecture with
a relatively small group of members which brings in more transactional, professional and
personal inflows from the rest of the network. The one seeking market responsiveness, in
contrast, will try to hedge against unexpected market changes using diversification strategy, and
as a result will have supply network architecture demonstrating relatively equal distributions of
transactional, professional and personal inflows across network members. The supply network
in-degree centrality accounting for contractual ties may need more cautious interpretation
because complete contract terms can impose institutional constraints on interorganizational
transactions (Salamon 1987; Lutz 1995; Bassok and Anupindi 1997; Lawrence 1999). The more
inflows of complete contracts (i.e. high in-degree centrality) thus indicate that the network actor
pulls away the less favorable (or more restrictive) terms and conditions from its counterpart(s).
In this sense, OEMs which need tight cost controls may build supply networks where a few focal
firms take up more favorable (i.e. less complete) contracts showing low in-degree centralization,
whereas their strategic intent of achieving market responsiveness drives the opposite
consequence (i.e. supply network members have mutually favorable – that is, equally complete –
contracts with others). The preceding discussion leads to the following hypotheses:
HYPOTHESIS 2A. An OEM’s strategic intent of pursuing cost leadership is positively
associated with the in-degree centralizations of its supply networks consisting of

67

transactional, professional and personal ties, while being negatively associated with the
ones consisting of contractual ties.
HYPOTHESIS 2B. An OEM’s strategic intent of pursuing market responsiveness is
negatively associated with the in-degree centralizations of its supply networks consisting
of transactional, professional and personal ties, while being positively associated with
the ones consisting of contractual ties.
In addition, a firm may relax its own institutional constraints on other exchange partners
expecting reciprocal behavior, which eventually helps to achieve its strategic goals (Uzzi 1997;
Larsson et al. 1998). This is true especially when both parties have complementary resources to
each other or similar sources of uncertainty, and can provide more useful feedbacks to refine
their own efforts for their own benefits (Oliver 1991; McEvily et al. 2000). Companies such as
Dell and Whirlpool, for example, were transformed to “virtually integrated” organizations by
sharing their information and knowledge on inventory level and sales forecasting with other
supply network members. A network actor’s use of this kind of influence on its exchange
partners has been measured by out-degree centrality which denotes the number of network ties
originating from the actor. As a socio-centric measure indicating the variation of the out-degree
centralities of the entire network actors, out-degree centralization explains the extent to which
particular actors distribute transactional or relational network resources to others (Wasserman
and Faust 1994). In other words, a high out-degree centralization score indicates that a few
particular focal firms disseminate most of the transactional or relational network resources for
the rest of the members, whereas a low score represents that each member of the network has a
more equal amount of those resources. This measure would be differently interpreted by each
type of supply network tie. For instance, when OEMs seek cost leadership, their supply networks

68

will have the architecture which has small group of firms that send out more complete (i.e. less
favorable) contract terms and more amount of monetary exchanges for the rest of the network.
They will not be much interested in establishing reciprocal professional and personal ties since
those relationship-specific investments can increase switching cost as well as prevent their search
for lower cost suppliers (Celly et al. 1999). On the other hand, the ones pursuing market
responsiveness will be more willing to initiate more professional and personal interactions with
other network partners to detect potential market changes while maintaining a balanced approach
for contract completeness and transaction amount. This reasoning leads to the following two
hypotheses:
HYPOTHESIS 3A. An OEM’s strategic intent of pursuing cost leadership is positively
associated with the out-degree centralizations of its supply networks consisting of
contractual and transactional ties, while being negatively associated with the ones
consisting of professional and personal ties.
HYPOTHESIS 3B. An OEM’s strategic intent of pursuing market responsiveness is
negatively associated with the out-degree centralizations of its supply networks
consisting of contractual and transactional ties, while being positively associated with
the ones consisting of professional and personal ties.
Direct contacts and connections between a firm and its customers/suppliers also facilitate
the exchange and distribution of organizational resources, and subsequently contribute to the
strategic goals and competitive advantage of the involved actors (Porter 1990; Morgan and Hunt
1999). For instance, Japanese automobile manufacturers such as Toyota and Nissan have
endeavored to maintain direct connections with non-immediate suppliers by means of different
supplier associations and considerable owner interests in their suppliers (Cusumano 1985; Dyer

69

1996). Those efforts enabled them to overview the whole supply networks by supplementing the
potential shortcomings of hierarchical supply network, characterized by a reliance on a limited
number of first-tier suppliers. In social network research, this connectivity among network actors
has been measured by global clustering coefficient which means, in plain terms, the probability
that the friend of John’s friend is also John’s friend. More formally, this index indicates the
extent to which how cliquish (or tightly knit) a network is as a whole (Newman 2003; Schank
and Wagner 2005). A low global clustering coefficient represents that only a few network actors
are directly connected to one another which results in hierarchical (i.e. more cliquish)
architecture as a whole, whereas a high coefficient value indicates more actors have direct
connections with others collectively manifesting lateral (i.e. less cliquish) network architecture.
From a supply network perspective, an OEM’s intent to acquire cost leadership will drive itself
to build a hierarchical supply network which allows for easier and more thorough control on a
limited number of major suppliers. The one interested in achieving market responsiveness, on the
other hand, will try to establish direct connections as many down-tier suppliers as possible in
order to perceive and respond to changing market circumstances, which subsequently leads to
lateral supply network architecture. Accordingly, this essay investigates the following set of
hypotheses:
HYPOTHESIS 4A. An OEM’s strategic intent of pursuing cost leadership is negatively
associated with the global clustering coefficients of its supply networks consisting of
contractual, transactional, professional and personal ties.
HYPOTHESIS 4B. An OEM’s strategic intent of pursuing market responsiveness is
positively associated with the global clustering coefficients of its supply networks
consisting of contractual, transactional, professional and personal ties.

70

3.3 Methodology
3.3.1 Data
Given the interests of this study, a quantitative method of survey-based questionnaire was
employed to collect the data about an OEM’s component-level strategic intent and the direction
and strength of ties among all supply network partners involved to supply the selected
component. A single product is mostly built up by incorporating a mix of functional and
innovative components (Huang et al. 2002; Vonderembse et al. 2006), and on this account a few
but notable studies such as Huang et al. (2005) and Kim et al. (2011) have used the component(or module-) level supply network investigation. In collecting network data, the boundary of
each component-level supply network should be firstly specified in order to avoid potential
distortions in describing overall network architecture (Choi et al. 2001; Cross and Parker 2004;
Luke and Harris 2007). Initial OEM (i.e. tier-0) contacts mostly at the executive level, were thus
asked to select a strategically important component with manageable network sizes (i.e. no more
than 3 tiers and 5 suppliers per tier) and recommend the most knowledgeable sourcing manager
in charge of the selected component. This step also contributed to minimize key informant bias
(Kumar et al. 1993). Next, a combined sampling approach of fixed list and snowball selections
was adopted based on the selected components (Doreian and Woodard 1992; Stevenson and
Greenberg 2000; Borgatti and Li 2009). The recommended sourcing managers were asked to
evaluate their perceptions on different types of ties (i.e. contractual, transactional, professional,
and personal) with their major immediate suppliers mostly listed as the OEM’s preferred supplier.
The same questions were given to the OEM’s counterparts (i.e. tier one suppliers) based on the
contact information provided by the OEM’s sourcing manager, and this dyadic data collection
process was repeated for the successive tiers of suppliers (i.e. tier two and tier three suppliers)

71

until end-tier suppliers were reached. To check the existence of duplicate network partners,
surveys on lower-tier suppliers were started after finalizing all the surveys on their immediate
upper-tier buyers. The overall data collection process is illustrated in the flow chart in Figure 3.2.
As a result of these efforts, a total of 153 component-level networks of three major South Korean
automobile and consumer electronics manufacturers consisting of 1,852 total network members
were collected. Table 3.1 presents the demographics and descriptive statistics of the study
population.

72

Supply Network Tier

(1) Contact executive-level respondent(s)
(2) Ask them to select strategically important components with
manageable network sizes (i.e. no more than 3 tiers and 5
suppliers per tier)
(3) Ask to recommend the most knowledgeable sourcing
managers in charge of the selected components

OEM

(5)

(6)

Tier-1 Suppliers

(8)

(9)

Tier-2 Suppliers

(11)

Actions Conducted

(12)

Tier-3 Suppliers

(4) Ask the recommended sourcing managers to introduce their
major tier-1 suppliers and contacts
(5) Survey OEM sourcing managers about strengths of their
network ties with the introduced tier-1 suppliers and
contacts
(6) Survey the introduced tier-1 counterparts about their network
ties with OEM and contacts
(7) Ask the tier-1 counterparts to introduce their major tier-2
suppliers and contacts

(9) Survey the tier-1 suppliers and contacts about strengths of
their network ties with the introduced tier-2 suppliers and
contacts
(11)Survey the tier-2 counterparts about strengths of their network
ties with the tier-1 buyers and contacts
(12)Ask them to introduce their major tier-3 suppliers and contacts

(8) Survey the tier-2 counterparts about strengths of their network
ties with the introduced tier-3 buyers and contacts

(10)Survey the tier-3 counterparts about strengths of their network
ties with the tier-2 buyers and contacts

Figure 3.2 Data Collection Process

73

Table 3.1 Sample Demographics and Descriptive Statistics
Supply Network
Tier Level

Number of
a
Employees
N

Average
Sales
(Million
a
USD)

Less than 100
0
100-249
0
OEMs (i.e. Tier-0s)
250-499
0 78,726.50
(N=153)
500-999
0
1000 or more
153
Less than 100
0
100-249
45
Tier-1 Suppliers
250-499
219
143.13
(N=308)
500-999
44
1000 or more
2
Less than 100 188
100-249
239
Tier-2 Suppliers
250-499
56
67.26
(N=483)
500-999
0
1000 or more
0
Less than 100 597
100-249
311
Tier-3 Suppliers
250-499
0
13.90
(N=908)
500-999
0
1000 or more
0
Note: Total Number of Supply Network Members =1,852
a
All as of 2011 end

Respondent’s Year of Average Relationship Average Relationship
Length with
Length with
Experience Pertaining to
a Immediate Suppliers Immediate Buyers
the Selected Component
a
a
(Yrs.)
(Yrs.)
N
Less than 1 year
9
1-5 years
79
6-10 years
57
6.88
N/A
11-20 years
6
21 years or more
2
Less than 1 year
72
1-5 years
122
6-10 years
94
5.36
6.27
11-20 years
12
21 years or more
8
Less than 1 year
7
1-5 years
227
6-10 years
195
3.51
5.84
11-20 years
38
21 years or more
16
Less than 1 year
13
1-5 years
422
6-10 years
323
N/A
3.95
11-20 years
89
21 years or more
61

74

3.3.2 Measures
Capturing Strategic Intents
The measures for the component-level strategic intent were adopted from extant studies such as
Gunasekaran et al. (2004) and Li et al. (2005). OEM-level respondents, not knowing which item
is for what strategic intent, were asked to answer “yes” or “no” for each of eight measure items
shown in Table 3.2. A score of +1 was given to “yes” responses to the first four items
representing cost leadership, while those to the latter four items for market responsiveness
received -1. “No” responses were given a value of “0” for both intents. These response scores
were summed up, creating a 9-point scale ranging from -4 to +4, and pre-classified the collected
153 component-level supply networks into three groups based on strategic intent: cost leadership
(from +2 to +4; N=42), market responsiveness (from -4 to -2; N=36), and mixed (from -1 to +1;
N=75).

75

Table 3.2 Item Measures for Strategic Intents
Strategic Intent

Cost
Leadership

Market
Responsiveness

Item Measure
We predominantly implement value analyses with a view to reduce
costs.
We predominantly emphasize whether they offer prices as low or
lower than other suppliers.
We predominantly emphasize whether the supplier offers quantity
discounts.
We predominantly emphasize whether they have lower manufacturing
costs compared to other suppliers.
We predominantly emphasize whether they have short production lead
time.
We predominantly emphasize whether they can adapt to fast-changing
market/industry.
We predominantly emphasize the ability to scale up (or down) quickly
to changing market demands.
We predominantly emphasize whether they can rapidly incorporate
consumer preferences into the design process.

Validation of this pre-classified group membership was implemented by incorporating Kmeans clustering and SPSS’s Crosstabs procedure. This approach was previously used by
Frohlich and Westbrook (2001) to validate their classification of “arcs of integration.” As a
widely applied method of ordinary cluster analysis, the K-means clustering identifies relatively
homogeneous groups of cases for selected variables (Dillon and Goldstein 1984). It seeks
elements that have distances from the mean of the own group that are larger than those from
another group. The element is then shifted to this group. This process stops if all elements have
found ‘their’ groups. Group memberships for each of the three strategic intent groups were saved
and then compared using Crosstabs procedure. Crosstabs was used to count the number of cases
that were in common (and different) etween the two classifications and to calculate ivariate
statistics. The results of this validation approach are shown in Table 3.3. The Pearson’s
correlation between group membership for the two classification procedures was .46
76

(p <

0.000), which collectively confirms the validity of the employed classification method for
component-level strategic intents. Please refer to the section 2.3.2 for more detailed definitions
and calculations of SNA indices.
Table 3.3 Classification Results of K-Means Clustering and Accuracy
a

Group 1

+4

11 (7.2%)

( %)

( %)

11

+

15 (9.8%)

( %)

( %)

15

+

Actual Group

a

Predicted Group
Summed Score

16 (10.5%)

( %)

( %)

16

Group 1

Group

Group

Total

-4

6 (3.9%)

( %)

6

-

( %)

14 (9.2%)

( %)

14

-

( %)

16 (10.5%)

( %)

16

+1

( %)

( %)

21 (10.3%)

( %)

Group

( %)

( %)

30 (14.7%)

( %)

( %)

24 (11.8%)

4

6

75

153

Group
-1
Total
a

4

1

Group 1 = Cost Leadership; Group 2 = Market Responsiveness; Group 3 = Mixed

3.3.3 Methods
Multinomial logit (MNL) analysis was carried out to understand the association between
strategic intent groups and supply network tie types for each network index (i.e. Hypotheses 1-4),
while controlling for the component type dummy (0 for electronic; 1 for mechanical) of each
supply network. Table 3.4 presents descriptive statistics for all variables.

77

Table 3.4 Descriptive Statistics for Variable
a

Variable

Mean

Stdev

Min

Max

(a) C for Contractual Ties
.5786
. 167
.55 5
.6 86
( ) C for Transactional Ties
. 9 5
.
. 5 1
.4
(c) C for Professional Ties
.484
. 4 8
.41
.5599
(d) C for Personal Ties
.49
. 455
.41
.5698
(e) IDC for Contractual Ties
. 846
.
. 51
.4185
(f) IDC for Transactional Ties
. 46
. 191
. 9 1
. 599
(g) IDC for Professional Ties
. 4 4
. 89
. 9 7
. 894
(h) IDC for Personal Ties
.4 9
. 59
. 6 8
.4498
(i) ODC for Contractual Ties
.4895
.
6
.45 4
.5 99
(j) ODC for Transactional Ties
. 88
. 468
. 115
. 698
(k) ODC for Professional Ties
.4 41
. 15
. 8
.4 99
(l) ODC for Personal Ties
. 4 4
. 94
. 91
. 898
(m) GCC for Contractual Ties
.1 44
. 1
.1
.1699
(n) GCC for Transactional Ties
. 749
. 146
. 5
. 999
(o) GCC for Professional Ties
.17 9
. 1 1
.15
.1895
(p) GCC for Personal Ties
.7976
. 7
.75 4
.8497
(q) Component Type
.418
.4949
1
Note: N=153 component-level networks; Component type was used as a control variable.
a
BC = Betweenness Centralization; IDC = In-degree Centralization; ODC = Out-degree
Centralization; GCC = Global Clustering Coefficient
If X is the vector of network indices explaining the marginal effects of three strategic
intents (i.e. cost leadership, market responsiveness and mixed), the general form of the MNL
model are expressed as:
(

(

)

(

(

(

)+

)
)+

(

))

(

(

)

78

(

(

)+

)

(

)+

(

))

(

(

where

,

)

and

(

(

)+

(

)
)+

(

))

correspond to each strategic intent. For model identification, in

this case, the “mixed” strategic intent was chosen as a reference group (by putting

) in

that Fisher’s (1997) framework presented only two strategic considerations – cost leadership and
market responsiveness – in designing supply chains. This is also appropriate as the sampling
frame of this study consists of respondents in either electronics or automotive industry. The
electronics industry is characterized by rapid changes in technology and shorter lifecycles where
competition revolves around better market responsiveness. As a more mature industry, the
automotive industry is characterized by relatively slow technological evolution, longer product
lifecycles and hard price competition. In these regards, the usage of “mixed” strategic intent
group as a reference in the current MNL is a reasonable approach which enables finding a clearer
distinction between those two conflicting strategic intents. The above equations therefore can be
rephrased as:
(

(

)

(

(

)

)+

(

)+

)

(

(
(

)
)

(

(

)+

(

(
)+

(

)+

)
(

)+

)

)

As a result, it only calculates the relative probability of a strategic intent (either cost leadership –
coded as Group 1 – or market responsiveness – coded as Group 2) compared to the mixed intent
79

(coded as Group 3), not the absolute probability. In other words, the relative probabilities of
Groups 1 and 2 to the mixed intent are:

(

)
(

(

)

(

)

)
(

)

(

)

These ratios are referred to as the relative risk, and its ratio for one-unit change in a network
index (

) then becomes

(

)

or

(

)

. The exponential value of the estimated

coefficient is the relative risk for a one-unit change in the corresponding variable. In this sense,
the relative risk less (or greater) than one indicates a negative (or positive) association between
network index and strategic intent. Therefore, all the other estimated parameters can also be
interpreted as the marginal effect (i.e. change in the odds ratio) of a network index associated
with the strategic intent as opposed to mixed intent. The parameter vectors ( ) are estimated
using the maximum likelihood method.

3.4 Results and Interpretations
A series of MNL models were estimated to test the hypotheses. As noted above, analyses
focused on the likelihood that supply networks have either cost leadership (Group 1) or market
responsiveness (Group 2) intent other than the mixed (Group 3) intent. Table 3.5 summarizes the
empirical results on the associations between supply network characteristics and strategic intent.
The likelihood ratio (LR) chi-square test statistics with 10 degrees of freedom indicate that all
models are significant at the 1% level (94.89 for Model 1; 99.17 for Model 2; 83.19 for Model 3;

80

103.33 for Model 4). This provides preliminary support for the presented hypotheses by showing
that the hypothesized network characteristics account for significant variability in strategic intent.
In terms of the control variable, it was found that mechanical components are significantly
associated with cost leadership intent, which is generally consistent with previous findings on the
impact of product types on the choice of supply chain strategy (e.g. Fisher 1997; Fine 1998;
Lamming et al. 2000; McCardle et al. 2007). For the sake of brevity, the estimated coefficients
and their significance levels will be mainly used for model interpretations, while other estimates
(e.g. standard errors, relative risk, etc.) are also included in Table 3.5 for completeness.
Besides the theoretical and statistical considerations, field investigations were also
deemed necessary to interpret supply network phenomena from a more realistic perspective, and
consequently, to further support the contention that an OEM’s specific strategic intents play a
central role in designing supply networks with differential characteristics. For verifications of the
empirical findings and preliminary interpretations of the models, several component-level supply
networks of the sample were revisited and went through follow-up interviews asking about: 1)
what supply network management practices are being implemented, 2) how long they have been
using those practices, and 3) why and how those practices did (or did not, if applicable) work for
achieving specific strategic intents and lead to the revealed network properties. This would
provide a more comprehensive and real-world perspective of the association between strategic
intent and corresponding network architecture. The exact names of companies, products,
components and technologies are replaced with alphabets for anonymity.

81

Table 3.5 Results of Multinomial Logit Estimation
Cost Leadership (Group 1) Versus Mixed (Group 3)
Variables

a

b,c

c

B

SE

2

c

Market Responsiveness (Group 2) Versus Mixed
b,c

RRR [95% CI]

c

B

c

SE

RRR [95% CI]

2

Model 1 (H1A and H1B) (LR χ (10) = 94.89; Probability > χ = .000)
(a)
(e)
(i)
(m)
Component
Intercept

***

76.710

*

21.530

.446
**

-20.594

*

1.006

**

-45.002

23.904

2.06e+33 [9.29e+12, 4.59e+53]

-21.634

20.100

4.02e-10 [3.13e-27, 5.18e+07]

12.451

2.24e+09 [.0564938, 8.89e+19]

.703

9.692

2.019 [1.14e-08, 3.59e+08]

6.103

1.562 [9.98e-06, 244581.9]

-3.681

5.082

.025 [1.19e-06,533.6256]

7.866

3.97e+07 [8.006318,1.97e+14]

**

17.498

8.668

1.14e-09 [4.77e-17, .0271508]

.519

2.735 [.9886056, 7.567924]

-.108

.451

.898 [.3710203, 2.171726]

18.181

2.86e-20 [9.56e-36, .0000855]

4.102

14.464

60.452 [2.95e-11, 1.24e+14]

-3.517

19.845

0.023 [3.81e-19, 2.31e+15]

21.472

1.01e-16 [5.33e-35, 190.9992]

8.073

19.70147 [2.65e-06, 1.47e+08]

15.227

6.21e-19 [6.79e-32, 5.67e-06]

.475

0.841 [.3311284, 2.133791]

8.384

8.43e+11 [61622.66, 1.15e+19]

2

2

Model 2 (H2A and H2B) (LR χ (10) = 99.17; Probability > χ = .000)
(b)
(f)

10.033
*

37.792

(j)

-0.938

(n)

**

Component
Intercept
a

41.345

**

1.063

***

-34.559

21.109

22761.83 [2.45e-14, 2.11e+22]

21.402

2.59e+16 [.015697, 4.27e+34]

8.629

0.391312 [1.77e-08, 8667815]

17.086

*

-36.833

2.981
***

9.04e+17 [2586.658, 3.16e+32] -41.923

.504

2.893625 [1.077254, 7.772601]

7.591

9.80e-16 [3.38e-22, 2.84e-09]

-.174
***

27.460

(a) Contractual Betweenness Centralization; (b) Contractual In-degree Centralization; (d) Contractual Global Clustering Coefficient;
(e) Transactional Betweenness Centralization; (f) Transactional In-degree Centralization; (j) Professional In-degree Centralization;
(k) Professional Out-degree Centralization; (l) Professional Global Clustering Coefficient; (m) Personal Betweenness Centralization;
(n) Personal In-degree Centralization; (o) Personal Out-degree Centralization; (p) Personal Global Clustering Coefficient
b ***
**
*
: Significant at p < .01 level; : Significant at p < .05 level; : Significant at p < .10 level (2-tailed)
c

B = Estimated Coefficient; SE = Standard Errors; RRR = Relative Risk Ratio; CI = Confidence Interval

82

Table 3.5 (cont’d)
Cost Leadership (Group 1) Versus Mixed (Group 3)
Variables

a

b,c

c

B

SE

2

c

Market Responsiveness (Group 2) Versus Mixed
b,c

RRR [95% CI]

c

B

c

SE

RRR [95% CI]

2

Model 3 (H3A and H3B) (LR χ (10) = 83.19; Probability > χ = .000)
(c)

-12.753

9.824

2.89e-06 [1.26e-14, 666.7517]

-1.879

9.075

.153 [2.88e-09, 8094740]

(g)

-2.017

4.925

.133 [8.54e-06, 2071.658]

-.787

5.143

.455 [.0000191, 10858.15]

(k)

***

26.823

1.03e+32 [1.51e+09, 6.97e+54]

-42.428

26.820

3.75e-19 [5.56e-42, 25285.06]

12.362

9.47e+11 [28.39945, 3.16e+22]

(o)

73.708

**

-27.289

**

Component

1.076

Intercept

-15.353

**

27.576

13.628

1.41e-12 [3.53e-24, .5607792]

.494

2.934 [1.115116, 7.721501]

.172

.456

1.187 [.4853584, 2.904532]

14.600

2.15e-07 [8.03e-20, 575571.5]

7.305

13.738

1488.243 [3.01e-09, 7.35e+14]

2

2

Model 4 (H4A and H4B) (LR χ (10) = 103.33; Probability > χ = .000)
(d)

***

45.204

(h)

-5.136

(l)

***

(p)
Component
Intercept
a

-99.201

***

41.407

***

1.468

-24.175

14.005

4.29e+19 [5.14e+07, 3.57e+31]

-4.344

11.244

.013 [3.49e-12, 4.84e+07]

17.674

.006 [5.31e-18, 6.51e+12]

-15.016

17.233

3.01e-07 [6.45e-22, 1.40e+08]

34.835

8.27e-44 [1.85e-73, 3.70e-14]

38.369

30.095

4.61e+16 [1.11e-09, 1.91e+42]

13.917

5.51e-18 [7.85e-30, 3.86e-06]

.478

.619 [.2428988, 1.579997]

13.943

1.25e+11 [.1686685, 9.21e+22]

15.493

***

9.61e+17 [62361.18, 1.48e+31] -39.741

.555

4.341 [1.46346, 12.87402]

16.119

3.17e-11 [6.03e-25, 1665.899]

-.479
*

25.549

(a) Contractual Betweenness Centralization; (b) Contractual In-degree Centralization; (d) Contractual Global Clustering Coefficient;
(e) Transactional Betweenness Centralization; (f) Transactional In-degree Centralization; (j) Professional In-degree Centralization;
(k) Professional Out-degree Centralization; (l) Professional Global Clustering Coefficient; (m) Personal Betweenness Centralization;
(n) Personal In-degree Centralization; (o) Personal Out-degree Centralization; (p) Personal Global Clustering Coefficient
b ***
**
*
: Significant at p < .01 level; : Significant at p < .05 level; : Significant at p < .10 level (2-tailed)
c

B = Estimated Coefficient; SE = Standard Errors; RRR = Relative Risk Ratio; CI = Confidence Interval

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3.4.1 Model 1: Strategic Intent and Betweenness Centralization
Model 1 testing the associations between strategic intents (either cost leadership or market
responsiveness) and betweenness centralizations of different tie types provides four significant
results altogether. Specifically, compared to OEMs focusing on mixed intent, the ones pursuing
cost leadership are more likely to have supply networks characterized by: 1) unequal
completeness of contract terms among network members (B = 76.710, p < .01), and 2)
concentrated transactions (i.e. large proportions with few network members) (B = 21.530, p
< .10). Based on the follow-up interviews, it was confirmed that those observations are based on
the logic of cost savings via complete contracts and economies of scale as expected in hypothesis
H1A. For instance, one sourcing manager at a large automobile OEM noted: “It can vary case by
case, but the baseline strategy for cost and quality control is continuing bulk purchase from a few
select vendors who can accommodate every single detail of our proposed terms and conditions.”
Interestingly, as opposed to hypothesized, survey results show that supply network
personnel of Group 1 are more inclined to have a broader array of personal interactions in
relatively equal portions (B = -20.594, p < .05) while the ones pursuing market responsiveness
interact with a smaller range of them in an unequal manner (B = 17.498, p < .05). These
observations can be interpreted as evidence that supply network personnel may be utilizing their
personal contacts for control purpose, that is, by monitoring whether their counterparts offer the
best (lowest) possible cost. One interviewee, the founder and CEO of a tier-1 supplier for an
automobile OEM confirmed this conjecture by stating: “Information about cost structure is
extremely sensitive for all down-tier suppliers since the buying firm can easily calculate our
profit margins, which is not good for us. They (buying firm) already keep asking us to cut down
price while maintaining the same quality level as their products are under fierce price

84

competition. Unfortunately, but inevitably, we are expecting and asking the same thing from and
to our suppliers (tier-2).” One senior-level purchasing manager at an OEM commented on this
situation: “We already know and understand that they (down-tier suppliers) do not like to share
all the information about cost structure. I guess the cost information shared upon our request is 12-year-old one or rough numbers only. We cannot simply switch to other alternatives because it
raises issues of uncertainty in terms of quality, communication, searching cost, etc. Based on my
experience, a quick check whether their profit margins are reasonable is meeting multiple lowerlevel employees and ask them about how their boss is doing during the casual conversation. If he
recently bought a new car or traveled abroad, for instance, it tends to signify there exists a room
for additional cuts.”
In the market responsiveness versus mixed setting, the only significant result shows that
the aforementioned monitoring practice via personal interactions stands in contrast for OEMs
focusing on market responsiveness. One of our Group 2 respondents at a large consumer
electronics OEM provided the following hint concerning this observation: “I admit that utilizing
personal relationships is a key in coping with technological and environmental market
uncertainties. At the same time, however, it does not look reasonable putting too much weight on
building and maintaining personal relationships with all the existing down-tier suppliers.
Although our market environment changes rapidly, a meteoric rise rarely happens in this
business. For instance, the current key technology “A” for component “B” was introduced by
one of our few but long-time (more than 8-10 years) tier-1 suppliers who have accumulated
enough experience and resource to search for the next big thing. We have maintained a short list
of the strongest candidates instead of completely predicting what changes will be occurred. This
rule has been worked well so far.”

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3.4.2 Model 2: Strategic Intent and In-degree Centralization
Model 2 yielded four significant associations in hypothesized directions which collectively
support the associations between strategic intent choices and out-degree centralizations. The first
two results in cost leadership (Group 1) versus mixed intent (Group 3) comparison demonstrate
that the supply network in-degree centralizations based on its transactional (B = 37.792, p < .10)
and personal ties (B = 41.345, p < .05) are higher than the ones focusing on mixed intent if an
OEM pursues cost leadership. In other words, compared to Group 3, an OEM’s intent to tackle
costs seems to be associated with supply networks characterized by fewer particular focal firms
which have more incoming amount of transaction percentages and non-work-related interactions
than the other network members. Follow-up interviews provided more managerial backgrounds
to understand these findings. On the positive coefficient of transactional in-degree centralization
under cost leadership, one executive-level respondent of an automobile OEM confirmed the
hypothesized logic by stating: “Yes, we try to achieve economies of scale in purchasing to
reduce costs. We further encourage our immediate (tier-1) suppliers to keep searching for downtier suppliers who can provide lower cost for them because that also matters to us. As a result,
there must be a relatively small number of firms which draw in more transactions than others.
Why don’t we simply replace them with Chinese suppliers who can possibly offer lower deals?
Considering issues such as quality, security, communication, wage, etc., they can increase total
costs while reducing manufacturing costs.” Interestingly, the tier-1 counterpart of the above
OEM was seeing this business in a very different way which provides hints to explain the
positive personal in-degree centralization under cost leadership intent. He replied as follows:
“Well, I cannot agree with his argument at all. Who will provide the expected price under cost
competition with other OEMs? It is OEM. As they make a bulk purchase for economies of scale,

86

small and medium-sized suppliers like us are heavily relying on them. By utilizing this
bargaining power, they continuously press us to drop our markups by alluding that they can
always switch to Chinese alternatives. We also keep searching for cheaper sub-suppliers around
the world, but below a certain price, we should often compromise or sacrifice the current quality
level which will eventually backfire on ourselves. As a result, we pay special attention to
establish the personal relationship to gain their trust. For example, we regularly hire their retiring
executives or managers to build and maintain close personal relationships with the OEM.”
The next two results in the setting of market responsiveness (Group 2) versus Group 3
also showed predicted patterns for the in-degree centralizations of supply networks based on
their transactional (B = -36.833, p < .10) and personal (B = -41.923, p < .01) ties. This means
that, when an OEM pursues market responsiveness, its supply network is more likely to have
network properties that each member has relatively equal amounts of incoming transactional and
non-work-related flows from others compared to Group 3. One purchasing manager at a
consumer electronics OEM explained about the transactional inflows by stating that: “We build a
diversified sourcing portfolio for our component “C” with a short life cycle and uncertain
demand. Not surprisingly, most of our immediate (tier-1) suppliers are not dedicated to us either
to realize the maximum benefits from the current generation of the component, and they even
supply to other OEMs including our competitors. Their sub-suppliers (tier-3s) think and act
alike.” Regarding the relatively equal inflows of non-work-related (i.e. personal) ties, one tier-1
counterpart personnel of the above OEM noted: “I admit that we have a pretty high level of
autonomy, but they (OEMs) still have a position advantage in sensing and responding to market
uncertainties such as consumer demand, price and end-consumers’ tastes. Unfortunately, OEMs
are reluctant to share that information with us because we are doing business with other OEMs

87

including their suppliers, and thus the opportunities of information sharing through professional
seminars, workshops or training are lacking. We try to obtain such information via personal
communication. Seeing your result that every network member has almost equal amount of
incoming personal ties, it seems our downstream (tier-2 or tier-3) suppliers must be doing the
same to our sourcing managers. Well, it is interesting to see everyone got on the same
bandwagon.”

3.4.3 Model 3: Strategic Intent and Out-degree Centralization
Model 3 tested the associations between strategic intents and out-degree centralizations of four
supply network tie types. The results reported partial supports for both hypotheses H3A and H3B.
First, in a Group 1 versus Group 3 comparison, it was shown that OEM’s cost leadership intent
seemed to be positively associated with the out-degree centralization of its supply network
composed of professional ties (B = 73.708, p < .01) whereas it showed a negative association for
the same index of personal ties (B = -27.289, p < .05). The result statistically indicates, compared
to Group 3, that the OEMs pursuing cost leadership tend to have a supply network architecture
characterized by a few particular focal firms that send out most of the work-related interactions
to the rest of the supply network members, and every supply network member has a relatively
equal amount of non-work-related interactions with others. On these phenomena, the purchasing
manager at a consumer electronics OEM commented as follows: “We emphasize the importance
of persistent searching for possible supplier alternatives to achieve our price goal. Regular and
frequent interactions such as business meetings, conference calls and other formats of online
communication have been useful to ensure our suppliers (tier-1) invest enough efforts to
progress.” He also added, “Meanwhile, too close personal relationships with suppliers can put us

88

in awkward situation when we have to lay off the ones that fall short of our expectations. We
thus always try to keep personal distance.”
On the contrary, in the setting of Group 2 versus Group 3 comparisons, the out-degree
centralizations of personal ties showed a positive association with market responsiveness intent
(B = 27.576, p < .05). This observation shows that a fewer particular group of supply network
members who initiate more non-work-related interactions with others when the OEM focuses on
market responsiveness intent, as conjectured in H3B. One executive-level respondent at a tier-2
automobile component supplier explained this finding as follows: “Our customers (tier-1
suppliers) are closely working with their buyers (OEMs). Considering the technology level of
sub-components, unfortunately, they have a relatively wider range of options for finding subsuppliers like us – they can simply switch to one of our competitors. In order to avoid or at least
delay this situation, we try to build and strengthen a close personal relationship with them, and
sometimes hire their retiring executives with no manufacturing background for this. This also has
been very effective in preventing the retirees with plenty of ‘friends’ in this business from
starting their own, which can be a threat to us.”

3.4.4 Model 4: Strategic Intent and Global Clustering Coefficient
The Model 4 demonstrated partial support for hypotheses H4A and H4B examining the
associations between strategic intent choices and global clustering coefficients. When an OEM
pursues cost leadership, its supply network seemed to be positively related to the global
clustering coefficients of contractual (B = 45.204, p < .01) and personal (B = 41.407, p < .01)
ties on the contrary to expectations. In contrast, the OEM’s supply network showed a negative
association with the global clustering coefficient based on professional ties under the same intent
(B = -99.201, p < .01). These results collectively indicate that, compared to mixed intent, OEMs

89

focusing on cost leadership for its component tend to have supply networks where: 1) more
network members are connected to one another with complete contract terms and non-workrelated interactions, and 2) less of them are connected with work-related interactions. A followup interview with one purchasing manager of an automobile OEM provided the rationale to
interpret the positive association between cost leadership and global clustering coefficient of
contractual ties. The respondent noted: “When we source cost leadership-focused component, the
rule of business is quite simple: the supplier who can offer a lower price with reasonable quality
wins the deal. Cost part is easy, but it is hard to be sure whether the supplier can meet our
expectations on quality. Thus, when discussing and developing contract terms with tier-1
suppliers, we mostly ask them to provide a list of current or potential lower-tier suppliers and to
include terms and conditions outlining the quality requirements for sub-components.” Regarding
the next two findings (i.e. the positive and negative estimated coefficients for personal and
professional interactions), he stated: “These two are unexpected, but wholly make sense.
Although we try to check sub-suppliers’ quality conformance, it is still officially tier-1 supplier’s
right and responsibility, and hence we cannot directly communicate with lower-tier suppliers on
work matters which might lead to the hierarchical network structure of professional interactions.
However, we still can indirectly do our part via extensive personal communications, while not
directly asking about work-related issues. There are hundreds of possible ways to do this. We
also can inquire into the reputation of our original contractor’s sub-supplier through another tier1 supplier’s sub-supplier, while not violating the original contract.”
Lastly, it was shown that an OEM’s market responsiveness intent seemed to be
negatively associated with the global clustering coefficient of its supply network composed of
personal ties (B = -39.741, p < .01) as opposed to hypothesized direction. This observation

90

indicates that, compared to mixed intent, supply network personnel pursuing market
responsiveness are more inclined to have a disconnected network of non-work-related
interactions. The founder and CEO of a tier-1 supplier for a consumer electronics OEM
explained this phenomenon as follows: “Several government, quasi-governmental, or private
organizations hold exhibitions and conferences to help the business entities in dealing with this
fast changing market. We regularly participate to those events for socializing. Although
participants do not explicitly share sensitive work-related information, those events have been
regarded as “must go” events not to be isolated from other supply network members. Good
personal relationships do not always bring in new business opportunities, but people may be
hesitant to work with a total stranger.”

3.5 Further Investigations
The current study utilizes samples from multiple industries and companies. When investigating
the supply network design effects of strategic intent, it can be misleading to analyze network data
from a single industry or company because of their intrinsic characteristics. Further industry or
company wise case comparisons were thus deemed necessary to enrich research findings and
yield richer insights. By dividing the sample by industry and firm, this study additionally
explored how the supply network properties corresponding to each strategic intent (either cost
leadership or market responsiveness) are differently manifested by: 1) industries (consumer
electronics and automotive) and 2) different OEMs within the same industry (i.e. consumer
electronics OEMs, A and B). Figure 3.3 illustrates this scheme and the sample size for each
quadrant. Follow-up interviews on the findings were additionally conducted to offer more
practical and meaningful interpretations.

91

Panel Industry to Industry Comparison by Strategic Intent
Panel 1:1: Industryto Industry Comparison by Strategic Intent
Automotive
Consumer Electronics
Automotive
Consumer Electronics

Cost
Cost
Leadership
Leadership

N = 27
N = 27

N = 15
N = 15

Market
Market
Responsiveness
Responsiveness

N = 16
N = 16

N = 20
N = 20

Panel 2: Consumer Electronics Firm to Firm Comparison by Strategic Intent
Panel 2: Consumer Electronics Firm Firm Comparison by Strategic Intent
CompanyA
Company
Company A
Company BB
Cost
Cost
Leadership
Leadership

N = 99
N=

N=6
N=6

Market
Market
Responsiveness
Responsiveness

N = 11
N = 11

N=
N = 99

Figure 3.3 Framework for Further Investigations

3.5.1 Industry to Industry Comparison
T-test was employed to analyze each of the four comparison sets in Figure 3.3 for any significant
mean differences with respect to supply network properties. One OEM per each industry was
also contacted and asked to share their views and experiences which can provide hints for
interpreting the test results. As shown in Table 3.6, three SNA indices indicated significant mean
differences between industries. Specifically, consumer electronics OEMs showed higher values
in professional ODC under cost leadership (F = .188, p < .05) and in contractual IDC under

92

market responsiveness (F = .247, p < .05). These findings indicate that, compared to the
automotive industry, consumer electronics OEMs have supply networks where there exist fewer
particular focal firms that: 1) send out most of the work-related interactions to others (under cost
leadership intent), and 2) have more complete contract terms than others (under market
responsiveness intent). One purchasing manager of a consumer electronics OEM explained these
findings: “I believe the supplier base plays a role here. Compared to automobile manufacturers,
consumer electronics OEMs typically have a broader supplier base; thus we also can more easily
probe for potential suppliers who can offer lower prices than the existing ones. Our suppliers
may enjoy a similar situation because there are more OEMs in our industry than in the
automotive

industry.

As

such,

consumer

electronics

suppliers

sometimes

behave

opportunistically. For instance, one of our former suppliers boasted that they could beat the
existing supplier’s price and won the deal. They kept their word … by sacrificing quality! Buyers
mostly get what they pay for. In this regard, we keep tight controls on the quality of our subcomponents while maintaining lower cost by, as found in your results, initiating more workrelated interactions with downstream suppliers. We follow a similar logic when sourcing market
responsiveness-focused components. We do not know how responsive our suppliers are likely to
be until actual market changes take place on the ground, and thus they can be opportunistic. To
deal with this concern, we always try to develop extremely complete contacts specifying every
single possible market-related contingency. I believe our suppliers may apply the same policy to
their suppliers.”
Table 3.6 also shows that automotive industry OEMs have a higher value of personal
ODC than consumer electronics when they pursue cost leadership (F = .005, p < .05). In a supply
network context, this means that the supply networks of automotive OEMs have fewer particular

93

focal firms which create more non-work-related interactions for others. In explaining this
observation, an executive-level respondent of an automotive OEM also counted the supplier base
as the main source of this difference. He noted: “I agree with his view – the supplier base makes
a big difference. Let me tell you the reason. Of course, we also try to find a supplier which offers
the lowest price when we source cost leadership-focused components. One big difference here is
the unit price difference between consumer electronics and automobiles; in other words, this
industry has a much more severe consequence for quality problems. This mostly restricts our
supplier choice to a shortlist of potential suppliers who previously met our quality requirements
or the ones with certain level of quality reputation. Another big difference, I believe, is our
understanding on the supply market. We produced most of the components ourselves till about
thirty years ago, and our industry change is relatively stable. We therefore do not need to control
our suppliers as tightly as consumer electronics OEMs do; rather we try to develop strong
personal ties among supply network members. I personally think Toyota has proven that this
approach is quite effective for securing cost leadership while maintaining quality.”

3.5.2 Firm to Firm Comparison
This study also tested the mean differences of network properties between two consumer
electronics OEMs. Pseudonyms (“Company A” and “Company “B”) were used for
confidentiality. Both companies have well-established brand names and compete neck to neck in
the global consumer electronics market. Company A has a strong market position in Latin
American, Middle East and South-East Asian consumer electronics markets, while the primary
markets of Company B are North American and European countries. The results in Table 3.7
provide some interesting observations on those two companies’ differences in supply network
properties with respect to strategic intent. Specifically, Company A showed higher values of

94

contractual IDC (F = 2.304, p < .10) and professional BC (F = .155, p < .05) when it pursues cost
leadership intent. It also had a higher personal ODC value (F = .198, p < .10) under the pursuit of
market responsiveness. These findings collectively suggest that, compared to Company B,
Company A’s supply network has these characteristics: 1) a particular group of firms possess
less favorable contract terms, 2) those particular firms serve as “hubs” for work-related
interactions among network members (both under cost leadership), and 3) particular focal firms
initiate more non-work-related interactions for others members (under market responsiveness).
One executive-level respondent of Company A provided the following interesting explanation:
“Compared to Company B, we are well known for our product localization which provides
various product variants in style and features. We have been doing this by slightly modifying the
basic product platform. This approach has been quite successful in the fast growing markets of
developing countries where there are few domestic alternatives for us but consumers are
sensitive to price at the same time. Consumers in those countries expect to get more than they
pay for and, thus, are very demanding on our product quality. When we source cost leadershipfocused components used for the basic product platform, we should devise complete contracts to
perfectly control at less cost than the suppliers’ opportunistic behaviors relevant to price and
quality (which may lead to our higher contractual IDC). Further, to control whether they abide
by those contract terms, we also should become the hub of work-related communications among
our supply network members (and that may affect the higher professional BC). Our market
responsiveness-focused components are used for localization purposes. As we have a better
understanding of the local markets, in this case, we should keep informing our suppliers of
potential local market changes to get them prepared for accommodating those changes. I believe
this may lead to the finding high professional ODC of us.”

95

Also shown in Table 3.7 is that Company B has significantly higher values of contractual
GCC (F = 2.869, p < .05) under cost leadership intent and professional ODC (F = .022, p < .10)
under market responsiveness intent. These results collectively indicate that: 1) more of its supply
network members are directly connected by contract relations when it pursues cost leadership,
while 2) there exist particular focal firms who send out most of the work-related interactions to
others under the pursuit of market responsiveness. One executive of the company explained these
observations by stating that: “Being different from Company A, we mostly launch ‘global
products’ using leading-edge technology, and thus rarely localize our products. As a result, we
have had a great success in North America and European continent which are the major
battlefields of the global consumer electronics industry. We should gain and protect our
technological leadership to keep surviving in those competitive markets because our consumers
are ready to pay more for a superior product compared to the ones at emerging markets. I believe
all these circumstances may contribute to make the network architectural differences between us
and Company A. Even when we source cost leadership-focused components, we focus more on
quality-for-money rather than lowest cost per se. Therefore, we should maintain extensive
controls via more complete contracts specifying the required quality level over our nonimmediate sub-suppliers as well as tier-1 suppliers. This may be leading to our higher contractual
GCC. The ones focusing on market responsiveness are the key components with short lifecycles
for our final products. When we source those components, we should quickly transfer the
technologies we developed to our tier-1 suppliers before they get outdated while preventing any
potential leakage or spillover of them. It inevitably creates more of our technical interventions
and surveillance activities on tier-1 suppliers, which may be leading to our high professional
ODC.”

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Table 3.6 Panel 1 Comparison Results
Cost Leadership

Market Responsiveness
Automotive

Consumer
b
Electronics

(a)

.565 (.010)

.564 (.008)

1.146

(b)

.366 (.009)

.374 (.009)

.491 (.027)

.028

(c)

.487 (.029)

.493 (.021)

2.752

.144 (.021)

.137 (.021)

.184

(d)

.133 (.020)

.130 (.021)

.005

(e)

.386 (.022)

.395 (.018)

1.889

(e)

.392 (.024)

.387 (.020)

1.164

(f)

.341 (.009)

.339 (.012)

4.105

(f)

.308 (.008)

.308 (.010)

1.950

(g)

.296 (.049)

.294 (.049)

.024

(g)

.289 (.038)

.284 (.042)

.461

(h)

.075 (.013)

.073 (.017)

2.876

(h)

.071 (.014)

.075 (.015)

.023

(i)

.486 (.038)

.482 (.039)

.008

(i)

.472 (.043)

.483 (.043)

.113

(j)

.345 (.027)

.347 (.030)

.480

(j)

**

.350 (.029)

.336 (.028)

.135

(k)

.391 (.008)

.395 (.007)

.180

Automotive

Consumer
Electronics

(a)

.595 (.009)

.599 (.008)

.508

(b)

.401 (.010)

.403 (.012)

(c)

.489 (.025)

(d)

SNA Index

a

b

c

F-Value

SNA Index

a

b

c

F-Value

.865

**

.247

(k)

.414 (.008)

.420 (.008)

(l)

.162 (.006)

.159 (.006)

.751

(l)

.178 (.007)

.180 (.005)

2.926

(m)

.445 (.026)

.458 (.023)

.908

(m)

.522 (.026)

.536 (.026)

.004

(n)

.431 (.012)

.427 (.013)

.000

(o)

.323 (.013)

.315 (.012)

(p)

.823 (.016)

.819 (.012)

a

.188

(n)

**

.377 (.012)

.381 (.014)

.821

(o)

.371 (.015)

.364 (.014)

.970

4.622

(p)

.777 (.012)

.775 (.017)

6.256

.005

(a) = Contractual BC; (b) = Contractual IDC; (c) = Contractual ODC; (d) Contractual GCC; (e) Transactional BC; (f) Transactional
IDC; (g) Transactional ODC; (h) Transactional GCC; (i) Professional BC; (j) Professional IDC; (k) Professional ODC; (l) Professional
GCC; (m) Personal BC; (n) Personal IDC; (o) Personal ODC; (p) Personal GCC
b
Mean (Standard Deviation)
c ***

: Significant at the 0.01 level;

**

*

: Significant at the 0.05 level; : Significant at the 0.10 level (2-tailed)

97

Table 3.7 Panel 2 Comparison Results
Cost Leadership
SNA Index
(a)

a

Market Responsiveness
b

Company A

Company B

.597 (.009)

.602 (.005)

(b)

.407 (.009)

.396 (.013)

(c)

.488 (.030)

.494 (.024)

(d)

.128 (.020)

.151 (.013)

(e)

.400 (.012)

(f)

b

c

F-Value

8.069

SNA Index
(a)

a

b

b

c

Company A

Company B

F-Value

.562 (.007)

.567 (.009)

.097

*

(b)

.374 (.009)

.373 (.009)

.065

.774

2.304

(c)

**

.494 (.024)

.492 (.019)

.561

(d)

.131 (.020)

.128 (.025)

1.678

.387 (.023)

3.419

(e)

.389 (.021)

.385 (.019)

.839

.337 (.011)

.342 (.013)

3.062

(f)

.309 (.011)

.306 (.010)

.176

(g)

.307 (.046)

.273 (.051)

.052

(g)

.292 (.047)

.275 (.035)

4.106

(h)

.075 (.017)

.069 (.018)

.031

(i)

.464 (.035)

.510 (.028)

(j)

.350 (.032)

(k)

2.869

.071 (.014)

.079 (.015)

.291

(i)

.470 (.041)

.499 (.044)

.515

.342 (.028)

1.024

(j)

.334 (.025)

.338 (.033)

.720

.422 (.007)

.416 (.009)

.995

(k)

.392 (.007)

.398 (.007)

(l)

.158 (.007)

.159 (.007)

.019

(l)

.181 (.005)

.179 (.005)

.087

(m)

.462 (.018)

.451 (.030)

2.837

(m)

.538 (.020)

.533 (.033)

8.309

(n)

.430 (.012)

.422 (.014)

.122

(n)

.383 (.014)

.379 (.013)

.146

(o)

.315 (.012)

.314 (.013)

.090

(o)

.369 (.012)

.358 (.015)

(p)
a

(h)

**

.817 (.013)

.822 (.011)

.011

(p)

.775 (.017)

.774 (.018)

.155

*

.022

*

.198

.007

(a) = Contractual BC; (b) = Contractual IDC; (c) = Contractual ODC; (d) Contractual GCC; (e) Transactional BC; (f) Transactional
IDC; (g) Transactional ODC; (h) Transactional GCC; (i) Professional BC; (j) Professional IDC; (k) Professional ODC; (l) Professional
GCC; (m) Personal BC; (n) Personal IDC; (o) Personal ODC; (p) Personal GCC
b
Mean (Standard Deviation)
c ***

: Significant at the 0.01 level;

**

*

: Significant at the 0.05 level; : Significant at the 0.10 level (2-tailed)

98

3.6 Discussion
3.6.1 Contributions
This study makes unique theoretical and methodological contributions to the study of supply
networks. While there have been significant conceptual developments, there are only handful of
empirical works investigating a multi-tiered supply network setting (e.g. Choi and Hong 2002;
Kim et al. 2011; Mena et al. 2013). Even those studies have focused on describing the
complexity of supply network while remaining in the domain of case-based investigations that
require further empirical substantiation. By analyzing the dataset of 153 supply networks
consisting of 1,852 total network members, this study attempted to shed light on the question,
“what determines different supply network architectures,” which has been also recognized by
scholars of other disciplines (e.g. Brass 2002; Borgatti and Li 2009; Galaskiewicz 2011). To
address this important unresolved question, the current study adopted the strategic intent
perspective of organizational formation which has been widely used in strategic supply chain
design studies (e.g. Fisher 1997; Huang et al. 2002) that mainly argued different products require
different supply chains. The results from social network analyses and MNL models provided
significant empirical evidence that supply networks have discernible architectural properties
according to a specific strategic intent (either cost leadership or market responsiveness). Further
field investigations were conducted to support and interpret the preceding statistical findings.
The in-depth follow-up interviews confirmed that supply network members consistently
implement certain practices based on their own rational judgments, which consequently lead to
the supply network properties found in the study. Taken together, this study suggests that a
supply network should be viewed as a systematic outcome which is intentionally and

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strategically designed, implemented, and maintained in conformity with the OEM’s strategic
intent(s).
This essay also further develops as well as contradicts the theory of supply network as a
complex adaptive system (CAS). In their seminal paper on CAS theory, Choi et al. (2001)
conceptually regarded a supply network as an “emerging” form in that: 1) one single network
actor cannot completely control the entire supply network, and 2) too much control rather
deteriorates innovation and flexibility outcomes. In this vein, the authors defined CAS as “a
system that emerges over time into a coherent form, and adapts and organizes itself without any
singular entity deliberately managing or controlling it” (Choi et al. 2001, p.352). The current
study empirically shows what the “coherent” supply network properties had emerged under the
OEM’s consideration of a specific strategic intent instead of arguing a deterministic view of
supply network architecture. Acknowledging the cross-sectional nature of the data, however, the
empirical findings here also cast doubt on the conceptual propositions of CAS theory. As clearly
seen in the responses from follow-up interviews, each supply network actor reacted to their
immediate customers and suppliers by relying on their own rational assessment of potential costs
and benefits of accepting or imposing a strategic intent on their counterparts. The empirical
findings of this study thus can be regarded as the architectural outcomes of supply networks
resulting from each network actor’s continued self-centered perception and behavior reinforced
by their counterparts’ intents, which is maintained over time. From a managerial standpoint, the
current study also provides useful guidance for understanding direct or indirect relationships
across multiple tiers in the supply network. Based on the found network properties, supply
network managers can infer: 1) how their immediate and non-immediate partners work together
(or often against one another) in pursuing common (or sometimes incompatible) interests, and 2)

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whether those concordance (or discordance) among network members would help or hinder to
achieve the strategic intent of the sourced component. It also can enable open collaboration
across firm boundaries by allowing them to freely discuss how much and what kind of network
ties should be: 1) orchestrated by hub firms (with betweenness centralization), 2) gained from
and disseminated to others (with in-degree and out-degree centralizations), and 3) connecting
with one another (with global clustering coefficient) to achieve different strategic intents. All the
findings of this study were further corroborated and extended by in-depth follow-up field
investigations and interviews with the supply chain professionals.
In addition, this essay is one of few attempts to adopt a concept of network multiplexity
by considering multiple types of network ties to examine supply network phenomena. Adding to
the traditional visible ties (contractual and transactional ties), invisible dimensions (professional
and personal ties) were additionally incorporated in demonstrating supply network architecture.
This enabled a more thorough description of supply network architecture, which is essential for
drawing more meaningful conclusions about the association between strategic intents and supply
network properties. A series of social network analyses confirmed the multiplex traits of supply
networks by showing that a given supply network having the same set of firms can be perceived
differently based on different tie types with different directions and strengths. This can provide a
theoretical foundation to reexamine and confirm that the previous buyer-supplier relationship
literature based on uniplex perspective still hold for other types of interfirm ties.
Lastly, the current study adopted directed valued network and whole network approaches.
Directed valued network approach has definite advantages in grasping network phenomena by
considering both directions and strengths of network ties, whereas the widely-used binary
network approach relies on counterintuitive premises that all ties are completely homogeneous

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and symmetrical (Wasserman and Faust 1994; Freeman 2004). In addition, for the sake of saving
the time and efforts for network data collection, many social network studies have taken an ego
network approach which considers one single network actor and a set of directly linked
neighbors. This approach, however, has serious methodological limitations for analyzing supply
networks in that: 1) it considers a focal network actor’s perceptions only (Mehra et al. 2001;
Marsden 2005), and 2) a clear determination of supply network boundary is almost impossible
(Choi et al. 2001; Borgatti and Halgin 2011). Therefore, this study adopted a whole network
approach collecting bidirectional responses stretching from one network actor to its raw
materials suppliers, which has been repeatedly recommended as the most desirable approach to
investigate supply networks comprising all network entities involved to sourcing activities
(Ketchen and Hult 2007; Borgatti and Li 2009). Taken together, this study tried to draw out the
fullest grasp of supply network phenomena by adopting more realistic and rigorous network
approaches which have not yet been used in existing supply network literature.

3.6.2 Limitations and Future Directions
This essay is not without limitations. First, the strategic intents and supply network tie types
considered in this research are not exhaustive. Although Fisher’s (1997) considerations are one
of the most highly cited paper when discussing supply chain design, future studies may
incorporate more component-level strategic intents not represented by Fisher’s framework.
Innovation-focused components, for instance, can be taken into consideration because it
occasionally focuses on both lower cost (in case of incremental innovation) and responsiveness
(in case of radical innovation) intents. In this vein, knowledge sharing often proxied by
customer- and supplier-specific R&D investments can be considered as an additional supply
network tie type.

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Second, this study used cross-sectional network data in describing supply network
properties because of the non-availability of longitudinal dataset. An access to the secondary data
sources could be helpful to provide more generalizable findings on the associations between
strategic intents and network properties even though it will still sacrifice sample size to obtain
the whole network data. A potential remedy for this challenge is carrying out the same data
collection procedure to the original respondents after a certain time period, and conducting an
event study analysis on abnormal changes of network properties. It can be another option to
targeting industries such as information technology, software or fashion with relatively shorter
industry clockspeeds and checking whether the changed network properties remain changed or
get restored to the original states.
Lastly, future researchers may find it interesting to examine performance implications of
supply network architecture. Prior research in organization theory and strategic management
have argued that the interfirm networks consisting of direct and indirect relations with other
firms systematically affect a firm’s performance such as innovation and financial performance,
theoretically termed as “network competence.” Depending on a key premise underlying network
strategy that a firm’s inimitable and non-substitutable resources lie outside its boundaries (Ring
and Van de Ven 1994; Mowery et al. 1996; Gulati 1999), it will be an interesting extension to
test to see if the existing theories still hold in a supply network context.
In conclusion, this essay provides a strategic supply network perspective by investigating
the associations between strategic intents and supply network properties. Hopefully, the findings
here provide the basics for developing novel theories of supply network management by testing
the applicability of existing knowledge on interfirm networks to a supply network setting, which
will eventually advance our understandings on multi-faceted supply network phenomena.

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4 SUPPLY NETWORK ARCHITECTURE AND PERFORMANCE: A
CONTINGENCY PERSPECTIVE
4.1 Introduction
Individual businesses no longer compete as standalone entities, but rather as collaborative
networks. We are now entering the era of ‘network competition’ where the prizes will go to those
organizations who can better structure, coordinate and manage relationships with their partners
in a network committed to creating customer and consumer value through collaboration. —
Martin Christopher, Logistics and Supply Chain Management: Creating Value-Adding Networks
(2011, p.104)
A supply chain is a multi-level complex system with a strict architecture consisting of a
wide range of organizations, people, and activities that are all linked via exchanges of materials,
information, and resources in creating a product and then delivering it to the end customer
(Cooper et al. 1997; Mentzer et al. 2001; Christopher and Towill 2002; Ketchen et al. 2008). It is
also viewed as a set of value-adding activities of separate but well-aligned entities within and
across tiers, providing more benefits than the sum of its individual participants could generate,
and consequently making those benefits available to all the participants (Lee 2004; Moyaux et al.
2006; Ryall 2013). These notions collectively suggest that a supply chain should be analyzed as a
network that comprises multiple tiers of network entities such as raw material suppliers,
manufacturers, distributors, wholesalers, retailers, and customers.
Although supply chain management (SCM) research has long recognized the importance
of building interfirm relationships and their impacts on firm performance, much of this work has
been restricted to the investigation of dyadic or triadic relationships between one OEM and its

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immediate supplier(s) (Galaskiewicz 2011). In this setting, a buying firm can create influence
over its supplier(s) by demonstrating its willingness to jointly improve the earnings and decrease
the costs of both parties or by making relationship-specific investments (Hoetker et al. 2007; Ho
2013). While previous dyadic and triadic approaches were useful for investigating interfirm
exchanges between a focal firm and its immediate supply chain partners, in the era of “network
competition” (Christopher 2011), they fall short of grasping the whole picture of a complicated
supply network (Parkhe et al. 2006; Choi and Kim 2008; Wassmer et al. 2010; Wilhelm 2011).
Frohlich and Westbrook (2001) also acknowledged the pitfalls of those local-level investigations
by noting that manufacturers cannot fully utilize their performance potential when they focus on
only one side of their supply chains. A few recent works have viewed and explored supply
chains as a form of network (See Table 2.1 for more detail regarding the development stages of
supply network research); but they still have fallen within limited domains such as conceptual
frameworks and descriptive case studies without further empirical substantiation on the existence
of network competence in the SCM context (Borgatti and Li 2009).
Recent literature, mainly in the domains of organizational behavior and strategic
management, has indicated that interfirm networks consisting of multiple direct and indirect
relationships play important roles in a firm’s competitive strength and performance. As a step to
further the resource-based view, those studies utilized various network measures and
demonstrated that interfirm networks systematically affect a firm’s performance such as
organizational learning (Powell et al. 1996; Dyer and Nobeoka 2000), innovation (Hargadon and
Sutton 1997; Ahuja 2000a; Tsai 2001), new venture survival (Lee et al. 2001; Hager et al. 2004),
team creativity (Uzzi and Spiro 2005), and financial performance (Granovetter 2005; Shipilov
2006). This causality has been termed network competence and is defined as the ability of a firm

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to develop and utilize interfirm relationships in striving for better performance (Gemünden and
Ritter 1997; Ritter 1999). The prevalence of network competence perspective raises the
following important questions to supply network researchers. Does the supply network
architecture of a firm affect its supply chain performance? If so, what architectural properties of
the supply network will enhance or deteriorate which supply chain performance outcomes?
Finally, how does an OEM’s efforts to exert its influence on its suppliers’ sourcing decisions
interplay with such causalities?
To address these questions, this study examines the impact of key indices quantifying
supply network architecture on supply chain performance measures with consideration of the
contingent effects of OEM intervention in selecting non-immediate suppliers. Drawing upon a
unique dataset which allows the analyses of directed valued supply networks, this research aims
to shed light on the study of supply network, and consequently, to offer a supply network
competence perspective. The remainder of this piece begins by introducing previous research
streams on network competence that will set a stage for developing hypotheses about supply
network competence. Section 3 reviews the data, measures, and research methods used to test the
proposed hypotheses. Section 4 provides the key results and interpretations. Section 5 presents
the results of field investigations to provide further insights to the quantitative and qualitative
findings from the previous section. Section 6 discusses the theoretical and methodological
contributions, followed by the final section on limitations and directions for future research.

4.2 Theoretical Background and Hypotheses
4.2.1 Network Competence Perspective
Among the studies used to identify the source of network competence, two main research
streams can be highlighted: one has tried to find the determinant of network competence at the

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ego-centric level focusing on a particular firm’s position within the network while another has
concentrated on the socio-centric level which analyzes the overall pattern of multiple firms
(Kilduff and Tsai 2003; Provan et al. 2007). First, a group of researchers has posited that a firm’s
position in interorganizational network shapes network competence and its performance.
Podolny (1993), for instance, argued that a bank’s network position in terms of centrality is
positively associated with its reputation and economic advantages. In the context of the chemical
industry, Ahuja (2000a) showed that positional aspects of a firm’s ego network – direct/indirect
ties and structural holes – influence its subsequent innovation output. More recently, Koka and
Prescott (2008) also found the firms in an entrepreneurial position with multiple structural holes
are superior than the ones with high centrality in their productivity performance under
environmental changes in the steel industry. These representative studies commonly argue that a
firm occupying a certain advantageous position within the network may be better able to access
and assimilate more resources, and thereby gains better performance. The existing SCM
literature examining dyadic or triadic supply chain relationships agrees with this ego-centric
view in which it mainly focuses on how a focal firm’s interactions with immediate supply chain
partner(s) influence its actions and outcomes.
An alternative stream of literature, labeled the socio-centric view, extends the search for a
firm’s network competence from its position to the overall architectural properties and
characteristics of the network where it is embedded. This view regards the interfirm network as
a governance system which has definite impacts on performance under the presumption that it
involves multiple independent entities collaborating toward a common goal to optimize the
overall performance of the system (O'Toole 1997; Gulati 1998; Powell et al. 2005). By
employing the network as a unit of analysis, it enables researchers to grasp the whole picture of a

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complicated interlinked network rather than the individual firms that comprise the network. In
adopting the network competence perspective to the SCM context, such view aligns well with
the previously mentioned definition of a supply chain as invoked by some pioneering studies (e.g.
Cox et al. 2001; Choi and Kim 2008; Srai and Gregory 2008; Kim et al. 2011), and thus is more
appropriate to analyze the architecture of supply networks per se and their supply chain
performance outcomes. Despite those repeated calls and evident benefits, much of socio-centric
network competence research in the field of management as well as SCM has primarily fallen
within limited domains such as concept development and descriptive case studies that lack
generalizability (e.g. Kogut 2000; Dhanaraj and Parkhe 2006; Koka et al. 2006). The main
reason for this dearth of further empirical substantiations is because the data collection is very
costly and time-consuming (Scott 2000; Provan et al. 2007; Knoke and Yang 2008). One rare
exception is a study by Gibbons (2004) who examined the differential influences of six
prototypical inter-regional network architectures on innovation diffusion via the simulation
method, but this one example is not sufficient. The current study thus adopts the socio-centric
view in examining the performance consequences of supply network properties to further the
research on network competence perspective. To the best of the author’s knowledge, there has
been no other large-scale empirical attempt to explore the associations between an OEM’s sociocentric supply network competence and its performance consequences.

4.2.2 Supply Network Tie Types and Characterization Indices
Sociologists illustrated a network as a single closed set of actors (or nodes) and one or more
types of ties (or edges) such as friendship, kinship, communication, and co-membership between
them, and social network research has sought to understand observed dynamics of multiple
network entities (i.e. persons). In business settings, network can be viewed as a complex

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organizational outcome of multiple strategic alliances that could have governance and
performance implications (Webster 1992; Heide 1994); in this regard, an extensive literatures on
corporate governance has addressed that interfirm networks composed of interorganizational and
interpersonal ties serve as a firm’s inimita le and non-substitutable resources and capabilities
(Wiewel and Hunter 1985; Gulati et al. 2000; Håkansson and Ford 2002; Lavie 2006). One
aspect that has been neglected in previous studies is the multiplex nature of the
interorganizational and interpersonal ties embedded in interfirm network, which refers to the
multiple different architectural properties with regard to types and attributes of network ties that
can be observed from the same network (Verbrugge 1979; Burt 1980; Feld 1981; Ibarra 1992).
Considering the various dimensions of supply network ties, it is essential to incorporate this
multiplexity approach as a platform for finding the “hidden” properties of supply network
architecture. This research thus considers four different supply network tie types – contractual,
transactional, professional, and personal ties – which run across multiple partners nested within
the supply network. The first two types represent visible network ties for exchanging tangible
network resources such as goods and services, whereas the other two capture invisible (and
mostly intangible) resource exchanges taking place among supply network partners. The
conceptual definitions of the four supply network tie types under consideration and their
measurement items used based on the literature are presented in Table 2.2.
Social network analysis (SNA) indices, which have been widely used by other network
researchers in fields of organizational behavior and strategic management, were adopted to
quantify the architectural properties of supply networks formed by different tie types. More
specifically, this study focuses on four socio-centric network indices (betweenness centralization,
in-degree centralization, out-degree centralization, and global clustering coefficient) which

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characterize the overall pattern of multiple network actors. Further, unlike the majority of the
existing network studies that presumed all ties are completely homogeneous and symmetrical,
this research also takes the directed valued network approach which considers both direction and
strength (or magnitude) of the network tie, which is more useful and realistic approach for
exploring network phenomena. Table 2.3 provides a new framework of supply network
implications of the socio-centric SNA indices for directed valued networks used in this study for
four types of supply network ties.

4.2.3 Hypotheses
The proliferation of network views in interfirm relationship research has raised discussions about
how the performance of an interfirm network should be measured (e.g. Dyer 1996; Soda et al.
2004; Villarroel and Taylor 2006; Miller et al. 2013). Thus far, however, they have lacked a
consensus as to which measures are appropriate and preferable for describing the outcomes of
interfirm networks. This fragmentation arises in that those measures depend upon research
context and level of analysis (Park 1996). Supply network research is almost free from this
concern in that each supply chain, a basic building block of supply networks, is strategically
designed to achieve a set of system-wide objectives through functional differentiation and
business process interdependence among its members in offering finished products to end
customers (Mentzer et al. 2001; Ketchen and Hult 2007; Chopra and Meindl 2009). A supply
network thus is intrinsically built, managed, and coordinated by incorporating micro- and macrolevel planning, control, and adjustment that function as integrating mechanisms (Lee and
Billington 1995; Chapman et al. 2002; Gibson et al. 2005; Christopher 2011). In this vein, most
of the existing literatures dealing with and within a supply network context (or a quasi-supplynetwork context comprising more than two supply chain members and tiers) have predominantly

110

viewed an OEM’s supply chain performance measures as collective system outcomes of the
OEM (or the one closest to end customers) itself and all the rest of the members embedded in its
supply network (Beamon 1999; Pathak et al. 2007; Choi and Kim 2008; Schoenherr and Swink
2012). The five most frequently discussed measures of supply network outcomes are: cost (e.g.
Lamming et al. 2000; Rusinko 2007), quality (e.g. Taylor 2005; Wang and Du 2007; Choi and
Kim 2008), delivery (e.g. Guimaraes et al. 2002; Hallikas et al. 2002; Vachon and Klassen 2002),
and flexibility (e.g. Bertrand 2003; Lin 2004; Krajewski et al. 2005), and more recently,
innovation (e.g. Rong et al. 2010; Arlbjørn et al. 2011; Corsaro et al. 2012).
Even with the theoretical and empirical underpinnings on the usage of conventional
supply chain performance measures in network settings, one distinction should be noted between
the cost and the others (i.e. quality, delivery, flexibility, and innovation) due to their different
dynamics leading to the OEM’s network-wide performance outcomes. More specifically, an
OEM’s total supply chain cost mostly equals to the sum of those of all supply chain participants
(Bowersox and Closs 1996; Lau et al. 2002). From the OEM’s standpoint, the benefits of cost
reduction generated by direct or indirect partners can be readily transferred to and manifested in
its network-wide cost performance outcome (Anderson and Parker 2002; Bajaj et al. 2004;
Sarmah et al. 2006). This non path-dependent nature of cost performance can be represented as
an additive function of utility gains (i.e. local level cost performance) made by each of its supply
network members (i.e. (

)

( ) + ( ) + ( ) where “ (

)” denotes an OEM’s cost

performance) in a network setting. In other words, the OEM that is closest to end-customers can
enjoy at least some of the benefits of one upstream supply network member’s improved cost
performance even when the others do not show comparable outcomes (e.g.
( )

, ( )

, and ( )

(

)

when

). On the other hand, the other four performance measures (i.e.

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quality, delivery, flexibility, and innovation) of an OEM are intrinsically path-dependent, and
thus system-wide alignment and integration efforts among supply chain members become
essential for an individual member’s performance improvement to lead to an OEM’s (or other
downstream members’) outcome(s) (Frohlich and Westbrook 2002; Narasimhan and Kim 2002;
Flynn et al. 2010; Jin et al. 2013). This collective and systemic nature of the four measures
emerges as a multiplicative function of all individual supply network members’ performance (i.e.
(

)

( )

( )

( ) where “ (

) ” denotes an OEM’s performance). That is, in

network settings, one or more supply network members’ superior quality, delivery, flexibility,
and innovation performance does not always lead to an OEM’s network-wide performance but
rather is limited by a bottleneck not prepared to keep up with such improvement (e.g. (
when ( )

, ( )

, and ( )

)

). This study tests the performance consequences of key

supply network architecture indices based upon the distinction of supply chain performance
measures presented in the preceding chapters.
Social network studies have adopted betweenness centrality to measure an individual
actor’s power, and the extent to which it controls the resource flows in its network. As a sociocentric measure indicating the variation of the betweenness centralities of all network actors,
betweenness centralization characterizes to what extent the overall network is built around a
particular group of actors serving as hubs relative to the rest of the network (Freeman 1979; Scott
2000). A low betweenness centralization score represents that the network resources running
through various tie types are almost equally distributed across the entire network, whereas a high
score indicates that there exist particular firms possessing more or less network resources in it.
This measure can differently impact on an OEM’s path-dependent (i.e. cost) and non-pathdependent performance measures. When supply network entities are linked through a particular

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group of focal firms, those firms will be able to achieve higher cost outcomes by securing more
network resources in terms of contract terms, transaction amounts, professional and personal
relations, and the OEM can enjoy those benefits achieved by its upstream supply network
partner(s). From the OEM’s standpoint, in contrast, its quality, delivery, flexibility, and
innovation performance outcomes will be realized and observed when network resources are
more equally distributed across its supply network entities – collectively characterized as low
betweenness centralization – because of their nature as path-dependence. In this setting, an OEM
can enjoy a broader range of resource sharing that enables combining its network members’
problem solving insights, physical assets, and news of technological breakthroughs (Ahuja
2000a). Based on this line of reasoning, the following set of hypotheses is proposed:
HYPOTHESIS 1A. The betweenness centralizations of an OEM’s supply network
consisting of contractual, transactional, professional and personal ties are positively
associated with its cost performance.
HYPOTHESIS 1B. The betweenness centralizations of an OEM’s supply network
consisting of contractual, transactional, professional and personal ties are negatively
associated with its quality, delivery, flexibility, and innovation performance.
In network terms, in-degree centrality represents the total number of ties pointing toward
one specific network actor. In-degree centralization, a socio-centric version of in-degree
centrality, is derived by the variation in individual actor’s in-degree centrality at the network
level, and this indicates to which extent network resources are converged on particular actors
(Wasserman and Faust 1994). A network entity which has more amounts of incoming network
resources indicates that the other entities are more dependent upon it. When a firm which is
relatively less dependent upon the resources of its counterpart(s), according to Pfeffer and

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Salancik (1978) and Dyer and Singh (1998), it often leverages the resource dominance over
others to achieve its intended goals. For cost-side effects, the more inflows of network resources
towards particular focal firms can enable them to more easily realize cost economies to be
reflected in the OEM’s cost performance measure. Similarly, when there exists a particular group
of network entities possessing more network resources, those entities also can have enough
power to serve as coordinators and catalysts in aligning and driving higher levels of networklevel objectives to subsequently maximize its own path-dependent performance outcomes. This
reasoning leads to the following hypotheses:
HYPOTHESIS 2. The in-degree centralizations of an OEM’s supply network consisting
of contractual, transactional, professional and personal ties are positively associated
with its cost, quality, delivery, flexibility, and innovation performance.
As a socio-centric measure indicating the variation of the out-degree centralities of the
entire network actors, out-degree centralization explains the extent to which particular actors
disseminate network resources to others (Wasserman and Faust 1994). In other words, a high
out-degree centralization score indicates that a few particular focal network actors disseminate
most of the network interactions for the rest of the members, whereas a low score represents that
each actor of the network shares a more equal amount of interactions. In an interfirm network
context, high out-degree centralization can be interpreted as that a particular group of focal firms
empowers other network entities by sharing its network resources. The majority of the rest of the
network receiving network resources from a small group of firms will not be able to, nor seek to,
take initiative for aligning and coordinating the network-level objectives. For cost-side effects,
the small groups firms disseminating network resources to others themselves will also suffer
from the lack of needed resources to achieve their own cost benefits. Based on this reverse logic

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from the hypothesized associations between in-degree centralizations and supply chain
performance above, the following hypothesis is proposed:
HYPOTHESIS 3. The out-degree centralizations of an OEM’s supply network consisting
of contractual, transactional, professional and personal ties are negatively associated
with its cost, quality, delivery, flexibility, and innovation performance.
According to interfirm network literature, direct contacts and connections between a firm
and its counterpart (e.g. customer or supplier) facilitate the exchange and distribution of
organizational resources, and subsequently contribute to the competitive advantage of involved
actors (Porter 1990; Morgan and Hunt 1999). In social network research, this connectivity
among network actors has been measured by a global clustering coefficient which indicates the
extent to which how cliquish (or tightly knit) a network is as a whole (Newman 2003; Schank
and Wagner 2005). A low global clustering coefficient represents that only a few network actors
are directly connected to one another which results in hierarchical (i.e. more cliquish)
architecture as a whole, whereas a high coefficient value indicates more actors have direct
connections with others collectively manifesting lateral (i.e. less cliquish) network architecture.
In the context of supply chain performance outcome, more direct connections to one another (i.e.
high global clustering coefficient) will be improving path-dependent measures in that those
established connections can serve as conduits for better alignment and coordination. From an
OEM’s standpoint, however, this high connectivity will raise unnecessary costs and efforts
which will subsequently deteriorate cost performance because the OEM could enjoy cost benefits
without that many direct connections with its network partners. Accordingly, this essay
investigates the following set of hypotheses:

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HYPOTHESIS 4A. The global clustering coefficients of an OEM’s supply network
consisting of contractual, transactional, professional and personal ties are negatively
associated with its cost performance.
HYPOTHESIS 4B. The global clustering coefficients of an OEM’s supply network
consisting of contractual, transactional, professional and personal ties are positively
associated with its quality, delivery, flexibility, and innovation performance.
As supply networks usually involve complex interfirm dependencies that require proper
coordination, network researchers can improve the completeness, accuracy, and predictability of
their findings (Borgatti and Halgin 2011). Considering that supply networks consist of multiple
independent entities pursuing their own interests, especially (i.e. not taking account of their
reciprocal behaviors), supply network members can behave opportunistically to maximize their
own local-level performance (Liu et al. 2009; Danese and Romano 2012; Mahapatra et al. 2012).
From an OEM’s standpoint, a growing number of outsourcing and offshoring projects have
resulted in the increase of indirect supply network members that are invisible and difficult to be
monitored. An OEM can prevent the opportunistic behaviors of its indirect upstream supply
network members by imposing a strong influence on its immediate (i.e. tier-1) suppliers’
sourcing decisions (Williamson 1985). For cost-side effects, this influence will enable the OEM
to transfer more non-path-dependent benefits (i.e. cost performance) generated by its indirect as
well as immediate supply network members. As discussed in Chapter 3 of this dissertation
research, a supply network is viewed as a systematic outcome which is intentionally and
strategically designed, implemented, and maintained in conformity with an OEM’s strategic
intent(s). Therefore, an OEM’s influence on its immediate suppliers’ sourcing decisions will also
be helpful in better defining and spreading its network-level objectives on path-dependent

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performance for other supply network members and, subsequently, may amplify the positive
performance effects of network resources on its performance. The preceding discussion thus
leads to the following hypothesis:
HYPOTHESIS 5. An OEM’s influence on its immediate suppliers’ sourcing decisions
positively moderates the associations between the architectural properties of supply
network and its supply chain performance.

4.3 Methodology
4.3.1 Data
Survey is the most common method to collect social network data. Given the interests of this
study, a quantitative method of survey-based questionnaire was employed to collect the data
about an OEM’s component-level strategic intent and the direction and strength of ties among all
supply network partners involved to supply the selected component. Especially, the current study
takes a whole network approach, which collects bidirectional responses stretching from a focal
actor to its raw materials suppliers (Wellman 1988; Kilduff and Tsai 2003; Provan et al. 2007),
which differentiates this study from prior studies. This approach has been repeatedly
recommended as the most desirable approach for researchers to investigate the holistic and
systemic architecture of a supply network, but according to the exhaustive literature reviews of
Provan et al. (2007) on academic articles published between 1985 and 2005 discussing interfirm
networks, only 26 articles dealt with interfirm network outcomes based on a whole network
approach because of its cost-intensive and time-consuming nature.
To lessen the burden of data collection, three global South Korean automobile and
consumer electronics manufacturers were contacted, and then a combined sampling approach of

117

fixed list and snowball selections was adopted (Doreian and Woodard 1992; Stevenson and
Greenberg 2000; Borgatti and Li 2009). First, to keep the whole network perspective in data
collection, initial contacts mostly at the executive level were asked to select a strategically
important component with manageable network sizes (i.e. no more than 3 tiers and 5 suppliers
per tier) and recommend the most knowledgeable sourcing manager in charge of the selected
component. This step also contributed to minimize key informant bias (Kumar et al. 1993).
Secondly, sourcing managers were asked to evaluate their perceptions on different types of ties
(i.e. contractual, transactional, professional, and personal) with their major immediate suppliers
mostly listed as the OEM’s preferred supplier. Contractual, professional, and personal ties were
evaluated using a five-point scale, anchored by “1” (strongly disagree), “3” (neither disagree not
agree), and “5” (strongly agree), and the transactional tie was assessed by percentages of total
spend (or sales) for each supplier (or buyer) for the selected component. Next, the same
questions were given to the OEM’s counterparts (i.e. tier one suppliers) based on the contact
information provided by the focal firm’s sourcing manager. These steps were repeated for the
successive tiers of suppliers (i.e. tier two and tier three suppliers) until end-tier suppliers were
reached. The overall data collection process is summarized in the flow chart in Figure 3.2.
To check the existence of duplicate respondents (i.e. suppliers), surveys on the successive
tiers of suppliers were started after finalizing all the surveys on OEMs or higher tier suppliers.
Since all the requested information was extremely confidential and sensitive to both buyers and
suppliers, it was promised that all individual responses would be kept completely confidential,
and all analyses would be implemented and presented only at the aggregate level. To reassure
respondents regarding the confidentiality of their responses, all completed questionnaires were
directly collected by the author instead of being routed through buying firms. As a result of these

118

efforts, a unique dataset of 153 component-level (89 electronics and 64 mechanical) whole
networks with 1,852 total network members was collected. Table 3.1 provides the demographics
and descriptive statistics of the study population.

4.3.2 Variables and Measures
Network Indices
Item measures for each type of supply network ties are shown in Table 2.2. Based on the
collected data about the directions and strengths of supply network tie types, the methods in
section 2.3.2 were adopted in calculating network indices that characterize overall patterns of
relationships among supply network actors (i.e. supply network architecture) in a directed valued
network setting. Table 3.4 provides descriptive statistics for all network indices used in this study.
Performance Measures and OEM’s Influence on Sourcing Decisions
As discussed earlier, the OEM’s performance outcome from its component-level supply network
architecture in accordance with the level of its influence on immediate supplier(s) is a main
interest of this study. Thus, well-defined and validated scales from the existing literature were
adopted for five conventional supply chain performance and one contextual variable (i.e. OEM’s
influence) measures. All multi-item measures had a five-point Likert scale, anchored by “1”
(significantly worse), “3” (neither better nor worse), and “5” (significantly better) for cost,
quality, delivery, and flexibility performance, and “1” (strongly disagree), “3” (neither agree nor
disagree), and “5” (strongly agree) for innovation performance and OEM’s influence. Table 4.1
contains a list of all performance and the OEM’s influence measures and corresponding
measurement items.
Confirmatory factor analysis (CFA) is used to assess the internal and external consistency
of all constructs measured with multi-item reflective indicators. Table 4.1 presents the factor

119

loadings demonstrating significant relationships with their underlying theoretical constructs and
the average variance extracted (AVE) values are all well above the criterion of 0.50 (Fornell and
Larcker 1981). These results collectively provide evidence of convergent validity. Next,
discriminant validity is assessed by comparing the squared correlation between two constructs to
their respective AVE (Fornell and Larcker 1981; Chau 1997). None of the squared correlations
are equal to or higher than the AVE for each individual construct. Hence, it can be concluded
that there is discriminant validity among the theoretical constructs. In addition, reliability by
Cronbach’s coefficient alpha and composite reliabilities (CR) are assessed. As shown in Table
4.1, all measurement models have high reliabilities with Cron ach’s alpha values exceeding 0.80
(Nunnally 1978) and CR values equal to 0.70 or higher (Bagozzi and Yi 1988). Taken together,
these results indicate that the theoretical constructs exhibit good psychometric properties.

120

Table 4.1 Confirmatory Factor Analysis Results
Factor
Loadings

Composite
Reliability

Cronbach’s
Alpha

0.776

0.932

0.792

0.876

0.951

0.648

0.892

0.813

0.752

Cost Performance
Acquisition costs
Cost reduction performance
Designing cost out of the component
Ability to meet target costs
Supplier’s a ility to engage in strategic cost modeling
Quality Performance
Technical capability
Conformance quality
Internal process quality
Component durability
Component reliability
Delivery Performance
On-time delivery
Manufacturing lead time
Customer lead time
Shipping accuracy
Flexibility Performance
Volume flexibility
Delivery flexibility
Design flexibility
Launch flexibility
Note: N=153 component-level networks

AVE
0.734

Construct and Measurement Items

0.924

0.891

0.782
0.911
0.886
0.894
0.795
0.831
0.903
0.925
0.907
0.894
0.731
0.875
0.802
0.543
0.854
0.906
0.868
0.840

121

Table 4.1 (cont’d)
Factor
Loadings

Construct and Measurement Items
Innovation Performance
By sourcing this component, our firm could significantly increase the
number of new products to the market.
By sourcing this component, our firm could add much more new
features to existing product(s).
By sourcing this component, our firm could add unique features to
existing product(s).
By sourcing this component, our firm could have significantly higher
new product success rate.
By sourcing this component, our firm could develop new product or
features much faster.
OEM’s Influence
Our firm maintains active communication of our sourcing strategy with
all supply network partners.
Our firm and immediate (i.e. tier-1) suppliers always make joint
decisions on selecting tier-2 or 3 suppliers.
Our immediate (i.e. tier-1) suppliers must get our firm’s approval on
their selection of tier-2 or 3 suppliers.
Our firm puts significant efforts to align suppliers across the whole
supply network to our sourcing strategy.
Our firm has well-established guidelines to support our immediate (i.e.
tier-1) suppliers’ selection of suppliers.
Note: N=153 component-level networks

AVE

Composite
Reliability

Cronbach’s
Alpha

0.506

0.754

0.807

0.847

0.965

0.960

0.796
0.759
0.667
0.704
0.679

0.924
0.981
0.976
0.746
0.973

Cronbach’s alpha ≥ 0.80; Average variance extracted [AVE] ≥ 0.50; Composite reliability ≥ 0.70; Factor loading ≥ 0.50

122

4.3.3 Methods
Hierarchical multiple regression analyses were adopted to test the main effect and interactional
models (i.e. H1-H5) while controlling for the component type and network size of each
component-level supply network. Before running the regressions, the responses on OEM’s
supply chain performance were converted into new weighted scores weighted by factor loadings
generated by factor analyses. This was because responses based on rankings (e.g. 5-point Likert
scale) often cannot provide meaningful statistical results. For example, on such a scale, let 50%
of the total responses show marked as “1” while the other half signed “5” for a particular
question. The mean value of the item is represented as “3” in interpretation even though none of
the respondents chose that score. The response scores on supply chain performance thus were
converted into new weighted scores weighted by factor loadings generated by factor analyses. In
other words, for
supply networks);
item,

, where n is the number of respondents (i.e. component-level
, m is the number of factors, and

is the factor loading of th

, k is the number of items included in the th factor, where

respondent’s score for th item, the score for each factor (

+

+ +

is the th

) is:

∑

By following the procedures described by Cohen et al. (2003), next, only control variables were
entered in the first step. In the second step, the main indices characterizing supply network
architecture were added to the regression. In the third and final step, the interaction terms, the
cross-products of supply network indices and OEM’s influence on its immediate suppliers’
2

sourcing decisions (OFI) were introduced into the regression. The change in R from step 2 to

123

step 3 provides a test of whether the interaction term makes a statistically significant contribution
to the equation. This three-step procedure was repeated in 20 separate regressions, 4 with
network indices (i.e. betweenness centralization, in-degree centralization, out-degree
centralization, and global clustering coefficient) and 5 with supply chain performance (i.e. cost,
quality, delivery, flexibility, and innovation) as the dependent variable. The regression equation
of this approach using 10 variables (including 2 control variables) per each performance measure
is represented as follows:
Performance = b0 + b1×BC + b2×IDC + b3×ODC + b4×GCC + b5×BC×OFI + b6×IDC×OFI +
b7×ODC×OFI + b8×GCC×OFI + e
The key variables were mean-centered to simplify the interpretation of the results and to control
the multicollinearity in moderated multiple regressions with continuous variables (Cronbach
1987; Aiken and West 1991; Hox 2010). Variance inflation factors (VIF) were also checked to
see if there was a multicollinearity problem. Based on the all the regression models, the highest
VIF was 3.173, which suggests no strong multicollinearity concern.

4.4 Results and Interpretations
A series of hierarchical multiple regression models were estimated to test the hypotheses. The
empirical results on the associations between architectural characteristics of component-level
supply networks and the OEM’s supply chain performance are summarized in Tables 4.2
(Hypotheses 1A and 1B), 4.3 (Hypothesis 2), 4.4 (Hypothesis 3), and 4.5 (Hypotheses 4A and
4B). Moderating effects of an OEM’s influence on its tier-1 suppliers’ sourcing decisions (H5)
were tested in each of the previous four hypotheses. For the sake of brevity, the estimated
coefficients (B) and their significance levels will be mainly used for model interpretations, while

124

2

other estimates such as F and R values are reported in those tables for completeness. In
interpreting the results, field investigations were deemed necessary to interpret supply network
phenomena from a more realistic perspective. Several component-level supply networks of the
sample were thus revisited for verifications of the empirical findings and their preliminary
interpretations. This provided a more complete and real-world perspective of the associations
between an OEM’s supply network architecture and its performance consequences. The exact
names of companies, products, components were replaced with alphabets for anonymity.

125

Table 4.2 Results of Hierarchical Regression Analyses: Performance Effects of Betweenness Centralizations
Dependent Variables
Cost
a

B

F

Quality
2

R

a

B

F

Delivery
2

R

a

B

F

Flexibility
2

R

a

B

F

Innovation
2

a

2

R

B

F

R

.004

-.074

.688

.009

1.867

.071

Step 1: Controls (Comp = Component Type; Size = Network Size)
Compo
Size

-.020

.004

.012

Step 2: Main Effects
Compo

.276

-.019

.009

.050

.001

.008

.016

.000

-.002

.005

.045

.286

.004

.001

b

.182

.007

.011

.861

.034

.019

2.79

.103

.039

.629

.025

-.082

.011

.005

-.002

.004

.001

(a)

1.347

1.339

1.662

-.994

-2.358

(e)

-.420

-3.048

.364

1.305

-2.068

(i)

-.138

.234

-1.223

.817

.671

Size

.132
-2.090
-1.036
.747
(m)
.211
Note: N=153 component-level networks
Bold: Significant at p < .01 level; Underlined and Italic: Significant at p < .05 level; Underlined Only: Significant at p < .10 level
a
B = Unstandardized Coefficient
b

(a) Contractual Betweenness Centralization; (e) Transactional Betweenness Centralization; (i) Professional Betweenness
Centralization; (m) Personal Betweenness Centralization

126

Table 4.2 (cont’d)
Dependent Variables
Cost
a

B

F

Quality
2

R

a

B

F

Delivery
2

R

a

B

F

Flexibility
2

R

a

B

F

Innovation
2

a

R

B

.048

-.088

F

2

R

b

Step 3: Interactions (OFI = OEM’s Influence on Tier-1 Suppliers’ Sourcing Decisions)
Compo

-.017

.375

.026

.006

1.01

.066

.024

2.33

.141

.039

.723

.010

.005

-.004

.004

-.001

(a)

1.496

2.228

.752

-.148

-2.515

(e)

-.705

-3.359

.621

1.225

-2.163

(i)

.034

.262

-1.064

.818

.709

(m)

.315

.427

-2.312

-.811

.493

(a) × OFI

-6.606

-7.926

9.437

-1.374

4.756

(e) × OFI

-.434

4.945

-4.352

6.653

.024

(i) × OFI

.875

-1.103

-.917

-.136

1.801

-4.674

Size

.505
-.530
2.803
-5.612
(m) × OFI
-1.932
Note: N=153 component-level networks
Bold: Significant at p < .01 level; Underlined and Italic: Significant at p < .05 level; Underlined Only: Significant at p < .10 level
a
B = Unstandardized Coefficient
b

(a) Contractual Betweenness Centralization; (e) Transactional Betweenness Centralization; (i) Professional Betweenness
Centralization; (m) Personal Betweenness Centralization

127

.113

Table 4.3 Results of Hierarchical Regression Analyses: Performance Effects of In-degree Centralizations
Dependent Variables
Cost
a

B

F

Quality
2

R

a

B

F

Delivery
2

R

a

B

Flexibility
2

a

Innovation
2

a

F

R

B

F

R

B

.016

.000

.045

.286

.004

.012

F

2

R

Step 1: Controls (Comp = Component Type; Size = Network Size)
Compo
Size

-.020

.004

.012

Step 2: Main Effects
Compo

.276

-.022

.009

.050

.001

.005

.008
-.002

.004

.688

.009

1.558

.060

.009

b

.457

.018

.004

.222

.009

.009

3.093

.113

.025

1.681

.065

-.084

Size

.017

.001

-.009

.004

.003

(b)

2.858

-2.110

.758

-1.693

-2.456

(f)

.852

1.462

5.793

8.142

.558

(j)

-1.117

-.411

1.804

-.836

-.461

-1.879
1.294
.085
-3.044
-1.800
(n)
Note: N=153 component-level networks
Bold: Significant at p < .01 level; Underlined and Italic: Significant at p < .05 level; Underlined Only: Significant at p < .10 level
a
B = Unstandardized Coefficient
b

(b) Contractual In-degree Centralization; (f) Transactional In-degree Centralization; (j) Professional In-degree Centralization; (n)
Personal In-degree Centralization

128

Table 4.3 (cont’d)
Dependent Variables
Cost
a

B

F

Quality
2

R

a

B

F

Delivery
2

R

a

B

F

Flexibility
2

a

R

B

F

Innovation
2

a

R

B

.110

-.087

F

2

R

b

Step 3: Interactions (OFI = OEM’s Influence on Tier-1 Suppliers’ Sourcing Decisions)
Compo

-.034

.637

.043

-.006

.384

.026

.005

1.934

.120

.012

1.758

Size

.022

.004

-.009

.009

.001

(b)

3.411

-1.492

1.089

-.651

-1.788

(f)

.177

.686

5.261

7.214

-.263

(j)

-1.320

-.437

2.015

-.740

-.052

(n)

-1.988

1.338

.298

-2.862

-1.513

(b) × OFI

4.332

.223

-5.014

-3.313

-.743

(f) × OFI

-11.225

-6.449

1.421

1.888

7.701

(j) × OFI

-2.222

-3.235

-2.270

-7.379

1.387

-3.371

1.599
1.900
1.102
3.819
-7.623
(n) × OFI
Note: N=153 component-level networks
Bold: Significant at p < .01 level; Underlined and Italic: Significant at p < .05 level; Underlined Only: Significant at p < .10 level
a
B = Unstandardized Coefficient
b

(b) Contractual In-degree Centralization; (f) Transactional In-degree Centralization; (j) Professional In-degree Centralization; (n)
Personal In-degree Centralization

129

.089

Table 4.4 Results of Hierarchical Regression Analyses: Performance Effects of Out-degree Centralizations
Dependent Variables
Cost
a

B

F

Quality
2

R

a

B

F

Delivery
2

R

a

B

Flexibility
2

F

R

.016

.000

a

B

F

Innovation
2

a

2

R

B

F

R

.004

-.074

.668

.009

1.374

.053

Step 1: Controls (Comp = Component Type; Size = Network Size)
Compo
Size

-.020

.004

.012

Step 2: Main Effects
Compo

.276

-.013

.009

.050

.001

.005

.008
-.002

.045

.286

.004

.001

b

.986

.039

.007

.622

.025

.008

2.331

.087

.045

.361

.015

-.071

.016

.000

-.009

.003

.005

(c)

-.311

.785

1.813

-.995

-.607

(g)

-1.312

-.011

-.743

-.228

.059

(k)

5.566

-2.963

2.427

-1.889

-3.853

Size

2.455
-2.854
-2.797
-1.680
1.073
(o)
Note: N=153 component-level networks
Bold: Significant at p < .01 level; Underlined and Italic: Significant at p < .05 level; Underlined Only: Significant at p < .10 level
a
B = Unstandardized Coefficient
b

(c) Contractual Out-degree Centralization; (g) Transactional Out-degree Centralization; (k) Professional Out-degree Centralization; (o)
Personal Out-degree Centralization

130

Table 4.4 (cont’d)
Dependent Variables
Cost
a

B

F

Quality
2

R

a

B

F

Delivery
2

R

a

B

F

Flexibility
2

R

a

B

F

Innovation
2

a

R

B

.046

-.051

F

2

R

b

Step 3: Interactions (OFI = OEM’s Influence on Tier-1 Suppliers’ Sourcing Decisions)
Compo

-.027

1.258

.081

.010

1.297

.084

-.004

1.656

.104

.046

.685

.014

-.003

-.008

.002

-.251

1.023

1.680

-.887

-.428

(g)

-1.399

-.051

-.771

-.283

.092

(k)

5.906

-2.636

2.422

-1.870

-3.974

(o)

2.863

-2.327

-2.861

-1.747

1.130

(c) × OFI

-.586

4.002

-3.982

4.619

4.218

(g) × OFI

.259

-2.023

1.177

2.701

-1.654

(k) × OFI

10.74

14.26

-6.200

7.350

.081

.004

(c)

1.248

-9.250

Size

-1.529
2.201
-2.968
10.78
11.80
(o) × OFI
Note: N=153 component-level networks
Bold: Significant at p < .01 level; Underlined and Italic: Significant at p < .05 level; Underlined Only: Significant at p < .10 level
a
B = Unstandardized Coefficient
b

(c) Contractual Out-degree Centralization; (g) Transactional Out-degree Centralization; (k) Professional Out-degree Centralization; (o)
Personal Out-degree Centralization

131

Table 4.5 Results of Hierarchical Regression Analyses: Performance Effects of Global Clustering Coefficients
Dependent Variables
Cost
a

B

F

Quality
2

R

a

B

F

Delivery
2

R

a

B

Flexibility
2

F

R

.016

.000

a

B

F

Innovation
2

a

2

R

B

F

R

.004

-.074

.688

.009

2.524

.094

Step 1: Controls (Comp = Component Type; Size = Network Size)
Compo
Size

-.020

.004

.012

Step 2: Main Effects
Compo

.276

-.027

.009

.050

.001

.005

.008
-.002

.045

.286

.004

.001

b

.716

.029

.015

.291

.012

.033

1.298

.051

.047

.397

.016

-.100

.006

.000

-.006

-.001

.012

(d)

-2.655

-.525

.306

-.158

1.144

(h)

-1.900

.327

3.202

-.551

-3.606

(l)

1.924

1.468

-3.544

3.847

-1.945

Size

1.572
1.980
1.631
2.427
-4.261
(p)
Note: N=153 component-level networks
Bold: Significant at p < .01 level; Underlined and Italic: Significant at p < .05 level; Underlined Only: Significant at p < .10 level
a
B = Unstandardized Coefficient
b

(d) Contractual Global Clustering Coefficient; (h) Transactional Global Clustering Coefficient; (l) Professional Global Clustering
Coefficient; (p) Personal Global Clustering Coefficient

132

Table 4.5 (cont’d)
Dependent Variables
Cost
a

B

F

Quality
2

R

a

B

F

Delivery
2

R

a

B

F

Flexibility
2

R

a

B

F

Innovation
2

a

R

B

.039

-.104

F

2

R

b

Step 3: Interactions (OFI = OEM’s Influence on Tier-1 Suppliers’ Sourcing Decisions)
Compo

-.033

1.009

.066

.012

.292

.020

.036

1.097

.072

.049

.573

.002

-.002

-.003

.000

.007

(d)

-3.341

-.832

.091

-.685

.992

(h)

-2.232

.140

3.457

-.502

-3.721

(l)

2.773

1.863

-3.774

3.948

-.932

(p)

1.652

1.999

1.711

2.641

-4.028

(c) × OFI

1.207

.092

5.216

6.336

-1.579

(g) × OFI

-5.441

-2.091

-6.366

-2.165

2.691

(k) × OFI

-1.987

-.713

1.927

-2.295

1.897

-9.968

Size

-6.713
-3.203
2.024
-3.465
-6.888
(o) × OFI
Note: N=153 component-level networks
Bold: Significant at p < .01 level; Underlined and Italic: Significant at p < .05 level; Underlined Only: Significant at p < .10 level
a
B = Unstandardized Coefficient
b

(d) Contractual Global Clustering Coefficient; (h) Transactional Global Clustering Coefficient; (l) Professional Global Clustering
Coefficient; (p) Personal Global Clustering Coefficient

133

.118

4.4.1 Cost Performance Effects of Supply Network Architecture
First, no significant result was found regarding the cost performance consequences of all supply
network characteristics; that is, an OEM’s supply network architecture in terms of contractual,
transactional, professional, personal ties do not affect its cost performance in hypothesized
directions. Some significant coefficients were found. For instance, transactional out-degree
centralization showed a negative impact on the OEM’s cost performance, which is in the
hypothesized direction. However, interpretations were not warranted as the overall model was
not statistically significant. Those results collectively suggest network-wide cost benefits are not
conveyed via the interfirm ties among supply network members in terms of contractual,
transactional, professional, or personal interactions. This finding contradicts previous literatures
(e.g. Anderson and Parker 2002; Bajaj et al. 2004; Sarmah et al. 2006) which argued the
transferability of cost benefits across supply network. Going a step further, this might also imply
the possibility that cost performance is intrinsically local, and thus can be shared within a
narrower scope such as supply chain dyads or triads. One senior-level purchasing manager at an
automobile OEM commented: “They (cost benefits generated by downstream suppliers) should
be theoretically transferable. In an automobile industry, based on my experience so far, most of
the cost benefits are coming from manufacturing processes rationalization, capacity management,
and/or manpower coordination (e.g. efficient work shifts) which are very internal. Further,
suppliers will never want to announce it to their counterparts to keep all those benefits inside
their own. We (i.e. an OEM) thus cannot realize what cost improvements were made (or not) by
our suppliers, and this invisibility gets worse when dealing with non-immediate suppliers. This is
one of main reasons why we set up cost reduction goals every 2-3 years and often offer
incentives to encourage them achieving those goals.”

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4.4.2 Quality Performance Effects of Supply Network Architecture
Similar to cost performance, no model on the association between quality performance supply
network index in terms of contractual, transactional, professional, personal ties was found
statistically significant. Some significant coefficients supported the hypothesized direction. The
interaction terms of an OEM’s influence with professional and personal network ties among
supply network members especially showed significantly positive impacts on quality
performance; however, the overall model was not statistically significant. These results were
hard to interpret and lack of intuitive meaning considering that numerous studies on total quality
management have highlighted the positive impacts of close interfirm and interpersonal
relationships. One purchasing manager of a consumer electronics OEM said: “I believe it is
coming from the measure, quality. The qualities of sourced components are continuously traced
and tested along the entire supply chain, from raw material supplier to our tier-1 suppliers.
Therefore, you would not be able to find any notable quality increase or decrease within sourced
components if you measure our (OEM’s) performance only – those aspects will be more visible
at a more downstream level. Most of our quality problems rather occur in assembly lines where
all components are gathered.”

4.4.3 Delivery Performance Effects of Supply Network Architecture
The results demonstrated partial support for hypotheses H1B and H2 examining the associations
between delivery performance and supply network architecture in terms of betweenness
centralization and in-degree centralization. First, the betweenness centralization regarding supply
network entities’ personal ties showed a negative coefficient (B = -2.312, p < 0.05) on an OEM’s
delivery performance. This indicates that an OEM’s delivery performance gets worse as fewer
focal firms serve as powerful and dominating hubs for exchanging non-work-related interactions
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among supply chain personnel within the supply network. From a practitioner’s perspective,
however, personal relationships are invisible and thus are hard to control by the management.
The significant negative interaction coefficient (B = -5.612, p < 0.05) gives a useful hint for
mitigating the risk of personal interactions overly dominated by a few supply network entities.
The result shows that the detrimental effects of personal ties on delivery performance get weaker
when an OEM has a strong influence on its suppliers’ su -supplier selections. In other words, an
OEM may mitigate the negative impact of high betweenness centralization of personal ties by
increasing the influence on its counterpart’s (i.e. first-tier supplier’s) selection of su -suppliers.
Although there is only one significant dependent variable, it is a meaningful result in that it
shows the role of personal ties among supply chain personnel in managing a traditional supply
chain performance measure. Second, the in-degree centralization of transactional ties among
supply network entities positively affects the focal firm’s delivery performance (B = 5.261, p <
0.05). This shows that an OEM’s delivery performance was improved when there are only a few
particular focal firms which take up more percentage amounts of monetary exchanges within its
supply network. This can be interpreted as: 1) other supply network entities are more dependent
on fewer focal firms with regard to their sales, 2) those focal firms can consolidate and
coordinate orders from multiple tiers of suppliers, and 3) this consequently improves the delivery
performance of an OEM who is the end-customer of the supply network. Further, it implies that
OEMs can reduce the upstream amplification (or bullwhip) effects in their supply networks by
encouraging a few key down-tier suppliers to purchase a larger amount from the rest of the
supply network. Third, the in-degree and out-degree centralizations regarding supply network
entities’ professional ties (B = 2.015 and 2.422 respectively, p < 0.1) are shown positive and
significant. This collectively means that an OEM’s delivery performance gets improved as the

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more work-related interactions are focused on (in-degree) or disseminated by (out-degree)
particular focal firms. This fits with the well-accepted delivery performance benefits of more
centralized supply chain control (e.g., van der Vaart and van Donk 2004; Lockamy 2008). These
results collectively suggest that an OEM can improve its delivery performance by encouraging a
particular group of suppliers to collect and/or send out work-related interactions from and/or to
the rest of the suppliers.

4.4.4 Flexibility Performance Effects of Supply Network Architecture
It was also found that the out-degree centralization of transactional ties among supply network
entities positively affects the OEM’s delivery performance (B = 7.214, p < 0.01), which provides
partial support for H2. This illustrates that an OEM’s flexibility performance in terms of volume,
delivery, design, and launch is improved as fewer focal firms account for more percentage
amount of monetary exchanges than the others within the supply network. Taken together with
the positive association between in-degree centralization of transactional ties and delivery
performance, this can be interpreted as evidence that: 1) those focal firms may be utilizing the
financial resources assured by more incoming monetary transactions, and 2) the generated
benefits are conveyed to the OEM’s improved flexibility outcomes and possibly to other supply
network members.

4.4.5 Innovation Performance Effects of Supply Network Architecture
The first notable finding is the detrimental effects of in-degree centralization of transactional ties
among supply network entities on OEM’s innovation performance. This SNA measure showed a
positive effect on an OEM’s delivery performance above. This collectively indicates that an
OEM’s over-empowerment for its down-tier suppliers’ consolidated purchasing may hurt its

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innovation performance. This also might indicate that relying on a few key suppliers is not
enough for innovation performance since knowledge assets/resources are much more dispersed
in today’s supply networks (e.g. crowd sourcing). At the same time, however, the positive impact
of the betweenness centralization with regard to supply network entities’ transactional ties on an
OEM’s innovation performance highlights that there should be particular focal firms which have
more percentage amount of monetary exchanges than others to gain better innovation
performance. This implies that a small group of supply network entities with more transactional
interests (in both sales and purchasing) may take the initiative to improve their end-customer’s
(i.e. OEM’s) innovation performance. In other words, it is a situation in which “the more you
have, the more you worry.” This conjecture can be partially verified by the positive impact of the
in-degree centralization with regard to network members’ transactional ties on the OEM’s
innovation performance. Lastly, the negative coefficient of the global clustering coefficient with
regard to network members’ transactional ties indicates that the OEM’s innovation performance
deteriorates as its supply network as a whole has a more ‘lateral’ architecture of monetary
exchanges among supply network members. It also supports the above observation (i.e.
innovation by a small group of firms) by showing that the OEM’s innovation performance
becomes worse as more supply network entities have direct transactional connections with others
on the supply network. Interestingly, this result contradicts those of Ahuja (2000a) who found
the positive impact of an OEM’s direct ties on its innovation performance. This might imply that
an OEM’s direct tie benefits such as knowledge transfer, knowledge spill-overs, and information
sharing could be offset or even outweighed by transaction costs incurred in establishing and
maintaining more direct interfirm ties. Given that Ahuja’s (2000a) study utilized binary and egocentric SNA index (i.e. degree centrality), furthermore, this finding also shows the usefulness

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directed valued and socio-centric network analyses adopted by this study in addressing interfirm
network architecture questions that pertain to its performance consequences.

4.5 Discussion
What makes one supply chain more successful than another under the era of supply-chain-tosupply-chain competition? A promising body of research claims that the answer lies within the
orchestration of interfirm relationships, but it has suffered from the restrictive scope to grasp the
whole picture of supply network comprising multiple tiers of supply chain partners (Choi and
Kim 2008; Wilhelm 2011). A few recent works have adopted a network perspective to the SCM
context; but they have fallen within limited domains such as conceptual frameworks and
descriptive case studies without further empirical substantiation. This study draws on an
innovative setting and data to overcome the limitations of previous dyadic/triadic studies. By
analyzing the dataset of 153 supply networks consisting of 1,852 total network members, this
study attempted to shed light on the question, “what architectural properties of supply networks
drive the OEM’s performance?” To address this question, this essay adopted the network
competence perspective which has been widely used in organization behavior and strategic
management literatures. The results from social network analyses and hierarchical regression
analyses provided significant empirical evidence that certain architectural properties of supply
networks improve or sometimes deteriorate OEMs’ performance consequences in accordance
with its influence on immediate suppliers’ sourcing decisions. Further field investigations were
conducted to support and extend the preceding statistical findings.
To the best of the author’s knowledge, the present work is the first empirical study which
discusses how the overall network architecture consisting of same supply chain partners vary
depending on different types of supply network ties considered (i.e. multiplex supply network

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perspective). Adding to previously investigated visible inter-organizational ties (i.e. contractual
and transactional ties), this study incorporated the invisible dimensions (i.e. professional and
personal ties) into supply network analyses to fully explore the invisible dynamics between
supply chain partners. Most other previous supply network studies to this point have investigated
the aforementioned ties separately under the untested assumption of uniplex supply network. A
series of social network analyses confirmed the multiplex traits of supply networks by showing
that a given supply network having the same set of firms can be perceived differently based on
different tie types with different directions and strengths. This can provide a theoretical
foundation to reexamine and confirm that the previous buyer-supplier relationship literature
based on uniplex perspective still hold for other types of interfirm ties.
Given the exploratory nature of empirical supply network research, a few limitations
should be noted in ways that represent opportunities for future work. First, four different types of
network ties examined in this study are not exhaustive. Second, the current study does not utilize
all available socio-centric SNA indices in describing network architectures. This limitation
comes from the setting of this study: as an empirically substantive investigation of directed
valued supply networks. Second, this study used cross-sectional network data in describing
supply network properties because of the non-availability of longitudinal dataset. An access to
the secondary data sources could be helpful to provide more generalizable findings on the
associations between strategic intents and network properties even though it will still sacrifice
sample size to obtain the whole network data. A potential remedy for this challenge is carrying
out the same data collection procedure to the original respondents after a certain time period, and
conducting an event study analysis on abnormal changes of network properties. It can be another
option to targeting industries such as information technology, software or fashion with relatively

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shorter industry clockspeeds and checking whether the changed network properties remain
changed or are restored to the original states. Lastly, this study also utilized OEMs’ supply chain
performance as a proxy of its overall network performance. Although anchored by theoretical
and empirical foundations on the usage of supply chain performance, it will be an interesting
extension to compare the performance consequences of two different scopes (i.e. existing OEMlevel measures vs. new measures) for the future research.

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5 CONCLUSIONS
This dissertation is a theory-building endeavor and has resulted in many useful insights in
understanding supply network phenomena. The overarching objective of this dissertation
research is to shed light on supply network management by delving into the internal architecture
of supply networks and its strategic antecedents and performance consequence. Given the
shortcomings and limitations in extant SCM literature, this dissertation asks the following
meaningful but understudied questions: 1) how can the multidimensional and complex
architecture of whole supply network be collectively described; 2) what antecedent forces drive
firms to adopt certain types of supply network architecture; 3) how does an OEM’s supply
network architectures affect its ensuing supply chain performance. More specifically, the first
essay investigated how different directed valued network ties across multiple tiers of supply
chain partners shape different supply network architectures. Managing multiplex networks is
considerably more complex in that actors embedded in the same network will perceive the
overall network architecture differently (Kim et al. 2006; Shipilov and Li 2012). In a supply
network context, this perception discrepancy can give rise to divergent incentives among
network partners and consequently hinder the supply network itself from achieving full
cooperative outcomes. Drawing upon social network analysis, the primary survey data from 153
component-level supply networks was analyzed to explore the “hidden” dynamics between 12
network-level social network analysis indices for characterizing different supply network
architectures. This research offered, to the best of the author’s knowledge, the first quantitative
and statistically significant evidence on the existence of supply network multiplexity and its
effects on supply network architecture.

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Anchored by the multiplex nature of supply networks found in the previous study, the
second essay attempted to shed light on the question, “what determines different supply network
architectures,” which has been also recognized by scholars of other disciplines (e.g. Brass 2002;
Borgatti and Li 2009; Galaskiewicz 2011). To tackle this important but unresolved question, I
adopted the strategic intent perspective of organizational formation which has been widely used
in strategic supply chain design studies (e.g. Fisher 1997; Huang et al. 2002) that mainly argued
different products require different supply chains. The results from social network analyses and
multinomial logit models provided significant empirical evidence that supply networks have
discernible architectural properties according to the strategic intent that is pursued. Further field
investigations were conducted to support and extend the preceding statistical findings. The indepth follow-up interviews confirmed that supply network members consistently implement
certain practices based on their own rational judgments, which impact supply network properties.
Taken together, this study suggests that a supply network should be viewed as a systematic
outcome which is intentionally and strategically designed, implemented, and maintained in
conformity with the OEM’s strategic intent(s). From a managerial standpoint, the findings from
this essay also provided useful guidance for understanding direct or indirect relationships across
multiple tiers in the supply network. Based on the found network properties, supply network
managers can infer: 1) how their immediate and non-immediate partners work together (or often
against one another) in pursuing common (or sometimes incompatible) interests, and 2) whether
the concordance (or discordance) among network members would help or hinder the
achievement of the strategic intent of the sourced component. It also can enable open
collaboration across firm boundaries by allowing them to freely discuss how much and what kind
of network ties should be: 1) orchestrated by hub firms (with betweenness centralization), 2)

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gained from and disseminated to others (with in-degree and out-degree centralizations), and 3)
connecting with one another (with global clustering coefficient) to achieve different strategic
intents. All the findings of this study were further corroborated and extended by in-depth followup field investigations and interviews with the supply chain professionals.
The third essay examined the performance implications of supply network architecture.
Prior research in organization theory and strategic management have argued that the interfirm
networks consisting of direct and indirect relations with other firms systematically affect a firm’s
performance such as innovation and financial performance, theoretically termed as “network
competence.” Depending on a key premise underlying network strategy that a firm’s inimitable
and non-substitutable resources lie outside its boundaries (Ring and Van de Ven 1994; Mowery
et al. 1996; Gulati 1999), this research aimed to shed light on the study of supply network, and
consequently, to offer a supply network competence perspective. By analyzing the unique dataset
consisting of 1,852 total network members with social network analyses and hierarchical
regression analyses, this study provided significant empirical evidence that certain architectural
properties of supply networks improve or sometimes deteriorate OEMs’ performance
consequences in accordance with its influence on immediate suppliers’ sourcing decisions.
Further field investigations were conducted to support and extend the preceding statistical
findings.
To conclude, the primary objective of this research was to provide a deeper
understanding of how interfirm network architecture can be structured, viewed, and affect firms
in the context of supply chain management. This dissertation contributes to theory development
in supply chain management in several ways. The first contribution of this dissertation is that it
extends the theory of buyer-supplier relationship beyond the models found in organizational

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behavior and strategic management literature. Specifically, an investigation of network
multiplexity provides a setting for re-evaluating existing SCM practices and theories pertaining
to supply chain configuration. This conceptualization aids future theory-building activities in
supply network management to consider a multi-faceted framework incorporating multiple types
of interfirm ties (i.e., contractual, transactional, professional, and personal ties). The second
contribution is that this research reveals the importance of an OEM’s strategic initiative by
investigating the differential impacts of cost leadership and market responsiveness intents, which
poses a doubt to the extant propositional view on the emergence of supply networks. In order to
support the empirical findings, further field investigations (industry-to-industry and firm-to-firm
comparisons) were conducted. As a result, this research asserts that the competitive strategy
based on a focal firm’s intent could potentially permeate into its supply network architecture.
The existing supply network literature has not conceptually or empirically considered the
implications of cost leadership and market responsiveness focused competitive strategies in a
collaborative supply network context. The findings of this research allow a deeper understanding
of the implications of strategic intent which has been regarded as important determinants in
many supply chain strategy studies. The third contribution of this dissertation is that it presents
the competitive role of supply network architecture comprising multiple network entities across
multiple tiers. This study provides reasoning for building and managing supply networks
characterized in terms of various architectural traits (i.e., betweenness centralization, in-degree
centralization, out-degree centralization, and global clustering coefficient) in drawing supply
chain performance. The performance incentive for an OEM to strategically maneuver its overall
supply network architecture is highlighted. Finally, from a methodological standpoint, this
dissertation provides motivation to use the directed valued and socio-centric SNA approach to

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address some of the interesting questions about supply network phenomena. By employing upto-date techniques to analyze a large-scale network-level dataset, this research enhances the
existing methodological foundations for supply network studies. By theorizing, analyzing, and
synthesizing the pursued research questions and data, the author endeavors to extend our
knowledge of supply networks and to provide a clear roadmap for scholars exploring the links
between network studies and supply chain management.

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APPENDIX

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APPENDIX

Determinants of Supply Network Architecture and Ensuing Performance
PART 1
June 4, 2012
To Whom It May Concern,
We would like to invite your participation in a research being conducted by researchers at the Eli
Broad Graduate School of Management, Michigan State University (MSU). I, a project
investigator, Ram Narasimhan is serving as a John H. McConnel Chaired Professor and
University Distinguished Professor here at MSU. MSU has been one of the top-notched research
universities in accordance with the U.S. News’ Best Business Schools rankings in Supply Chain
Management/Logistics specification over last decade. I have been impressed by Korean
manufacturers’ supply chain management capabilities whilst teaching MBA classes at Seoul
National University Business School as a part of the Global Scholars Invitation Fellowship
Program and having a featured interview with Chosun Ilbo
(http://news.chosun.com/site/data/html_dir/2008/04/04/200804040099 3.html).
Given the highly interdependent and closely interlinked business environment in recent years,
individual businesses no longer compete as standalone entities, but rather as collaborative
networks. In this era of ‘network-based competition,’ the purpose of this study is to investigate
how firms’ supply network architectures are formed via various types of interactions with their
buyers and suppliers. We hypothesize that differences in strategic intents of firms for individual
component will induce different supply network architectures and affect the ensuing
performance, depending on the contextual features in their buyer-supplier relationships.
Knowledge from this study will help supply chain managers in different situations design an
appropriate supply network architecture which coordinates multiple supply chain entities
involved in global sourcing. The survey will take approximately 30-40 minutes.
All of your answers will be kept strictly confidential. Your firm name will NOT appear in any
future reports without your permission, and your individual responses will NOT be disclosed to
any outside agencies. Only summary data and aggregate results from statistical analysis of the
data will be published. We request you to answer all questions to the best of your ability and
knowledge, as incomplete surveys can create serious problems during data analysis and reduce
148

the sample size available for meaningful analysis of data. If you are not sure of an answer to a
question, please provide your best estimate.
Upon your request, a copy of the research findings will be sent to you, allowing you to
benchmark your supply network management practices with those of other companies. Simply
indicate your interest at the end of this survey and provide your contact information. In the report,
individual responses will be kept confidential and only the summary data and aggregate results
will be used.
If you have any questions about your rights as a participant in this research, or if you feel you
have een placed at risk, you can contact the MSU’s Research Integrity Officer through the MSU
Social Science/Behavioral/ Education Institutional Review Board (SIRB) at 1-517-355-2180.
Return of the questionnaire will be considered your consent to participate.
Thank you very much for helping with this important study. We will be happy to answer any
questions or concerns you may have on this study. Please contact Myung Kyo Kim.

Sincerely,

Ram Narasimhan, Ph.D.
Professor of Supply Chain Management
University Distinguished Professor
John H. McConnel Endowed Chair of Business Administration
narasimh@bus.msu.edu
1-517-432-6426
Myung Kyo Kim
Ph.D. Candidate of Supply Chain Management
myungkyo@bus.msu.edu
1-517-432-6446

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Determinants of Supply Network Architecture and Ensuing Performance
PART 2
June 4, 2012
To Whom It May Concerned,
We deeply appreciate your participation in this research. This executive brief is intended to help
you understand the concepts on which your response is sought in this survey and to provide you
several important guidelines before starting to answer the questions in the survey.
To pursue the research o jectives, we need to collect detailed data on a whole ‘supply network’
consisting of multiple suppliers in multiple tiers. The simplified supply network of a focal firm
(FF) illustrated below consists of 7 suppliers in 2 tiers. Supply chain management academics and
practitioners call firms supplying components to your firm (FF) such as S11, S1 , and S1 , ‘tier1 suppliers’ and firms SS21, SS22, SS23, and SS24 supplying sub-components to tier-1 suppliers,
‘tier- suppliers.’ The lead dictionary1 definition of component refers to ‘a part of a mechanical
or electrical system.’ By following this definition, in the setting of this survey, the ‘components’
mean subsystems that go into your finished (i.e. final) product to be sold to customers. Also, in
Figure 1 below, the depth of a focal firm’s supply network is 2 (= the number of tiers to reach
end-item suppliers) and the width (= the greatest number of suppliers existing in the same tier) of
the focal firm’s network is 4.

1

http://dictionary.reference.com/browse/component

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FF

Network
Depth = 2

S12

S11

S21

: Focal Firm

S13

S22

S23

: Tier-1 Suppliers

S24

: Tier-2
Suppliers

Network Width = 4
Supply Network

Figure 5.1 Simplified Example of the Supply Network

Questions in this survey will ask you only about your (i.e. focal firm’s) finished (i.e. final)
product(s), one of its components, and relationships with major immediate (i.e. tier-1) suppliers
of the component (not about your tier-2 suppliers). Based on your responses, we will: 1) contact
your tier-1 suppliers, 2) give them almost the same questions relative to their position in the
supply network, 3) contact their immediate suppliers (i.e. your tier-2 suppliers), and continue this
procedure until we reach the end-tier supplier(s) of your whole supply network.
However, to successfully complete these data collection processes, we need your help to set up
the ‘boundaries’ of your supply network. As you might have already noticed, our data collection
processes can be excessively time-consuming and labor intensive without manageable depth and
width of a network. For instance, too big network size (e.g. more than 5 suppliers per each tier or
more than 4 tiers in a network) or a complex network can significantly delay data collection
processes and completion of our research. In contrast, too small (e.g. only 2 tiers per network) or
simplistic (e.g. only 1 supplier per tier) can diminish theoretical/practical contributions of this
research. Therefore, we request you to select a strategically important component with a
“manageable” network sizes (i.e. ‘no more than 3 tiers and 5 suppliers per tier’) efore starting
this survey on the next page.
If you have any questions about this executive brief, please contact M.K. Kim at
myungkyo@bus.msu.edu or 1-517-432-6446. Again, thank you very much for your participation.

151

Sincerely,

Ram Narasimhan, Ph.D.
Professor of Supply Chain Management
University Distinguished Professor
John H. McConnel Endowed Chair of Business Administration
narasimh@bus.msu.edu
1-517-432-6426
Myung Kyo Kim
Ph.D. Candidate of Supply Chain Management
myungkyo@bus.msu.edu
1-517-432-6446

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Determinants of Supply Network Architecture and Ensuing Performance
PART 3

Section 1: General Information

Please answer ALL questions.

Your organization’s name:
Your position or title in your organization:
How many years has it been since you started to work in this organization?
Less than 1 year
1-5 years
6-10 years
11-20 years
21 or more years
Your organization’s approximate number of employees:
Less than 100
100-249
250-499
500-999
1000 or more
Section 2: Component and Supplier Information Please answer ALL questions.
In answering the questions below, please consider a specific component with strategic
importance of your product and its major immediate suppliers (may be listed in your firm’s
preferred supplier list).
Name of component:
Function of component:
Finished (i.e. final) product in which this component is used:

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For how many years have you been involved in decisions (e.g. design, development, quality,
procurement, etc.) pertaining to this component?
Less than 1 year
1-5 years
6-10 years
11-20 years
21 or more years
On each of the following statements, please check (√) only ONE answer (either “Yes” or “No”)
which best describes your firm’s emphasis in selecting immediate (i.e. tier-1) suppliers of the
specific component you selected. Please answer questions to the best of your ability and
knowledge.
When our firm selects an immediate (i.e. tier-1) supplier(s) for this component:
Y
1

We predominantly implement value analyses with a view to reduce
costs.

2

We predominantly emphasize whether they offer prices as low or lower
than other suppliers.

3

We predominantly emphasize whether the supplier offers quantity
discounts.

4

We predominantly emphasize whether they have lower manufacturing
costs compared to other suppliers.

5

We predominantly emphasize whether they have short production lead
time.

6

We predominantly emphasize whether they can adapt to fast-changing
market/industry.

7

We predominantly emphasize the ability to scale up (or down) quickly
to changing market demands.

8

We predominantly emphasize whether they can rapidly incorporate
consumer preferences into the design process.

154

N

This question pertains to your operational information sharing with immediate (i.e. tier-1)
suppliers of the specific component you selected. Please indicate the extent to which you agree
or disagree with the following statements by checking (√) ONE answer based on a scale of 5
where 1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

Our firm provides immediate
suppliers with demand
forecast information.

1

2

3

4

5

2

Our firm and immediate
suppliers share capacity
planning information.

1

2

3

4

5

3

Our firm shares its inventory
level information with
immediate suppliers.

1

2

3

4

5

4

Our firm shares its
production plans with
immediate suppliers.

1

2

3

4

5

5

Our firm can easily monitor
the status of its orders.

1

2

3

4

5

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This question pertains to your influence on supply network partners related to the specific
component you selected. Please indicate the extent to which you agree or disagree with the
following statements by checking (√) ONE answer based on a scale of 5 where 1 represents
“Strongly disagree” and 5 represents “Strongly agree.”
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

Our firm maintains active
communication of our
sourcing strategy with all
supply network partners.

1

2

3

4

5

2

Our firm and immediate (i.e.
tier-1) suppliers always make
1
joint decisions on selecting
tier-2 or 3 suppliers.

2

3

4

5

3

Our immediate (i.e. tier-1)
suppliers must get our firm’s
approval on their selection of
tier-2 or 3 suppliers.

2

3

4

5

2

3

4

5

2

3

4

5

4

5

1

Our firm puts significant
efforts to align suppliers
across the whole supply
1
network to our sourcing
strategy.
Our firm has well-established
guidelines to support our
immediate (i.e. tier-1)
1
suppliers’ selection of
suppliers.

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Please name ALL major immediate (i.e. tier-1) suppliers of the component, percentage of total
spend for this component’s supplier, and contact information. We will contact them and send a
survey. They will not see your responses. The information you provide below will not be
identifiable in any published results generated from this study.
Supplier 1 (Name):
Length of relationship (yrs):
Percentages of total spend for this component’s supplier (%):
Contact person name:
Phone number:
E-mail:
____________________________________________________
Supplier 2 (Name):
Length of relationship (yrs):
Percentages of total spend for this component’s supplier (%):
Contact person name:
Phone number:
E-mail:
____________________________________________________
Supplier 3 (Name):
Length of relationship (yrs):
Percentages of total spend for this component’s supplier (%):
Contact person name:
Phone number:
E-mail:
____________________________________________________
Supplier 4 (Name):
Length of relationship (yrs):
Percentages of total spend for this component’s supplier (%):
Contact person name:
Phone number:
E-mail:
____________________________________________________
Supplier 5 (Name):
Length of relationship (yrs):
Percentages of total spend for this component’s supplier (%):
Contact person name:
Phone number:
E-mail:

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Section 3: Supplier Relationship & Contextual Information
questions.

Please answer ALL

In this section, you will be asked about your relationship with EACH of the above suppliers
listed in Q8 of the previous section. Specifically, this section seeks details about your personal
and your firm’s contractual, professional, personal, and contextual aspects of your relationship
with individual immediate supplier regarding the specific component selected in the previous
section.
Thus, this section of the survey should be completed multiple times for EACH supplier you
listed in Q8 of the previous section. If you listed three suppliers there, for instance, please answer
the questions for ALL those three suppliers ONE BY ONE). We understand that it may be time
consuming to provide answers to this section. However, your input is vital to our analyses to
figure out your firm’s overall supply network architecture uilt through various types of
interactions with all of your major suppliers. Thank you again for your participation.
1. Your Relationship with Supplier 1
Do you have a formal contractual relationship with Supplier 1?
If “Yes,” please indicate the extent to which you agree or disagree with the following statements
y checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree” for the following questions. If “No” please proceed to Q .
Contractual Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We have a formal written
contract(s) detailing the
operational requirements.

1

2

3

4

5

2

We have a formal written
contract(s) that detail(s) how
performance will be
monitored.

1

2

3

4

5

3

We have a formal written
contract(s) detailing warranty 1
policies.

2

3

4

5

158

4

5

We have a formal written
contract(s) detailing how to
handle complaints and
disputes (e.g. penalties for
contract violations).
We have a formal written
contract(s) detailing the level
of service expected from this
supplier.

1

2

3

4

5

1

2

3

4

5

The following question (Q ) pertains to your firm’s usiness (or professional) relationship with
Supplier 1’s counterparts. Please indicate the extent to which you agree or disagree with the
following statements y checking (√) ONE answer ased on a scale of 5 where 1 represents
“Strongly disagree” and 5 represents “Strongly agree.”
Business/Professional Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We regularly communicate
(via face-to-face, conference
calls, e-mails, etc.) on work
matters.

1

2

3

4

5

2

We widely share and
welcome each other’s ideas
or initiatives via open
communication (e.g. joint
workshops, etc.).

1

2

3

4

5

3

The communication between
us occurs at different levels
of management and crossfunctional areas.

1

2

3

4

5

4

I (or our executives) receive
periodic feedback (via faceto-face, conference calls, email, etc.) on progress,
problems, and plans from
this supplier’s counterparts.

1

2

3

4

5

159

5

I (or our executives) do
periodic on-site visits to this
supplier’s plants.

1

2

3

4

5

The following question (Q ) pertains to your or other executives’ personal relationship (or
friendship) with Supplier 1’s counterparts. Please indicate the extent to which you agree or
disagree with the following statements y checking (√) ONE answer ased on a scale of 5 where
1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Personal Relationship/Friendship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We always invite each other
to participate in various
activities to socialize.

1

2

3

4

5

2

We do personal favors for
each other.

1

2

3

4

5

2

3

4

5

2

3

4

5

2

3

4

5

3

4

5

We voluntarily exchange
something of a personal
nature to each other on
appropriate occasions (e.g.
1
birthday cards,
congratulations, condolences,
etc.).
We often communicate (via
face-to-face, phone calls, emails, social network
1
services, etc.) during nonworking time.
We often communicate (via
face-to-face, phone calls, emails, social network
1
services, etc.) outside work
places.

160

2. Your Relationship with Supplier 2
Do you have a formal contractual relationship with Supplier 2?
If “Yes,” please indicate the extent to which you agree or disagree with the following statements
y checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree” for the following questions. If “No” please proceed to Q .
Contractual Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We have a formal written
contract(s) detailing the
operational requirements.

1

2

3

4

5

2

We have a formal written
contract(s) that detail(s) how
performance will be
monitored.

1

2

3

4

5

3

We have a formal written
contract(s) detailing warranty 1
policies.

2

3

4

5

1

2

3

4

5

1

2

3

4

5

4

5

We have a formal written
contract(s) detailing how to
handle complaints and
disputes (e.g. penalties for
contract violations).
We have a formal written
contract(s) detailing the level
of service expected from this
supplier.

161

The following question (Q ) pertains to your firm’s usiness (or professional) relationship with
Supplier ’s counterparts. Please indicate the extent to which you agree or disagree with the
following statements y checking (√) ONE answer ased on a scale of 5 where 1 represents
“Strongly disagree” and 5 represents “Strongly agree.”
Business/Professional Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We regularly communicate
(via face-to-face, conference
calls, e-mails, etc.) on work
matters.

1

2

3

4

5

2

We widely share and
welcome each other’s ideas
or initiatives via open
communication (e.g. joint
workshops, etc.).

1

2

3

4

5

3

The communication between
us occurs at different levels
of management and crossfunctional areas.

1

2

3

4

5

4

I (or our executives) receive
periodic feedback (via faceto-face, conference calls, email, etc.) on progress,
problems, and plans from
this supplier’s counterparts.

1

2

3

4

5

5

I (or our executives) do
periodic on-site visits to this
supplier’s plants.

1

2

3

4

5

162

The following question (Q ) pertains to your or other executives’ personal relationship (or
friendship) with Supplier ’s counterparts. Please indicate the extent to which you agree or
disagree with the following statements y checking (√) ONE answer based on a scale of 5 where
1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Personal Relationship/Friendship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We always invite each other
to participate in various
activities to socialize.

1

2

3

4

5

2

We do personal favors for
each other.

1

2

3

4

5

2

3

4

5

2

3

4

5

2

3

4

5

3

4

5

We voluntarily exchange
something of a personal
nature to each other on
appropriate occasions (e.g.
1
birthday cards,
congratulations, condolences,
etc.).
We often communicate (via
face-to-face, phone calls, emails, social network
1
services, etc.) during nonworking time.
We often communicate (via
face-to-face, phone calls, emails, social network
1
services, etc.) outside work
places.

163

3. Your Relationship with Supplier 3
Do you have a formal contractual relationship with Supplier 3?
If “Yes,” please indicate the extent to which you agree or disagree with the following statements
y checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree” for the following questions. If “No” please proceed to Q .

Contractual Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We have a formal written
contract(s) detailing the
operational requirements.

1

2

3

4

5

2

We have a formal written
contract(s) that detail(s) how
performance will be
monitored.

1

2

3

4

5

3

We have a formal written
contract(s) detailing warranty 1
policies.

2

3

4

5

1

2

3

4

5

1

2

3

4

5

4

5

We have a formal written
contract(s) detailing how to
handle complaints and
disputes (e.g. penalties for
contract violations).
We have a formal written
contract(s) detailing the level
of service expected from this
supplier.

164

The following question (Q ) pertains to your firm’s usiness (or professional) relationship with
Supplier ’s counterparts. Please indicate the extent to which you agree or disagree with the
following statements y checking (√) ONE answer ased on a scale of 5 where 1 represents
“Strongly disagree” and 5 represents “Strongly agree.”
Business/Professional Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We regularly communicate
(via face-to-face, conference
calls, e-mails, etc.) on work
matters.

1

2

3

4

5

2

We widely share and
welcome each other’s ideas
or initiatives via open
communication (e.g. joint
workshops, etc.).

1

2

3

4

5

3

The communication between
us occurs at different levels
of management and crossfunctional areas.

1

2

3

4

5

4

I (or our executives) receive
periodic feedback (via faceto-face, conference calls, email, etc.) on progress,
problems, and plans from
this supplier’s counterparts.

1

2

3

4

5

5

I (or our executives) do
periodic on-site visits to this
supplier’s plants.

1

2

3

4

5

165

The following question (Q ) pertains to your or other executives’ personal relationship (or
friendship) with Supplier ’s counterparts. Please indicate the extent to which you agree or
disagree with the following statements y checking (√) ONE answer based on a scale of 5 where
1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Personal Relationship/Friendship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We always invite each other
to participate in various
activities to socialize.

1

2

3

4

5

2

We do personal favors for
each other.

1

2

3

4

5

2

3

4

5

2

3

4

5

2

3

4

5

3

4

5

We voluntarily exchange
something of a personal
nature to each other on
appropriate occasions (e.g.
1
birthday cards,
congratulations, condolences,
etc.).
We often communicate (via
face-to-face, phone calls, emails, social network
1
services, etc.) during nonworking time.
We often communicate (via
face-to-face, phone calls, emails, social network
1
services, etc.) outside work
places.

166

4. Your Relationship with Supplier 4
Do you have a formal contractual relationship with Supplier 4?
If “Yes,” please indicate the extent to which you agree or disagree with the following statements
y checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree” for the following questions. If “No” please proceed to Q2.
Contractual Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We have a formal written
contract(s) detailing the
operational requirements.

1

2

3

4

5

2

We have a formal written
contract(s) that detail(s) how
performance will be
monitored.

1

2

3

4

5

3

We have a formal written
contract(s) detailing warranty 1
policies.

2

3

4

5

1

2

3

4

5

1

2

3

4

5

4

5

We have a formal written
contract(s) detailing how to
handle complaints and
disputes (e.g. penalties for
contract violations).
We have a formal written
contract(s) detailing the level
of service expected from this
supplier.

167

The following question (Q ) pertains to your firm’s usiness (or professional) relationship with
Supplier 4’s counterparts. Please indicate the extent to which you agree or disagree with the
following statements y checking (√) ONE answer ased on a scale of 5 where 1 represents
“Strongly disagree” and 5 represents “Strongly agree.”
Business/Professional Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We regularly communicate
(via face-to-face, conference
calls, e-mails, etc.) on work
matters.

1

2

3

4

5

2

We widely share and
welcome each other’s ideas
or initiatives via open
communication (e.g. joint
workshops, etc.).

1

2

3

4

5

3

The communication between
us occurs at different levels
of management and crossfunctional areas.

1

2

3

4

5

4

I (or our executives) receive
periodic feedback (via faceto-face, conference calls, email, etc.) on progress,
problems, and plans from
this supplier’s counterparts.

1

2

3

4

5

5

I (or our executives) do
periodic on-site visits to this
supplier’s plants.

1

2

3

4

5

168

The following question (Q ) pertains to your or other executives’ personal relationship (or
friendship) with Supplier 4’s counterparts. Please indicate the extent to which you agree or
disagree with the following statements y checking (√) ONE answer ased on a scale of 5 where
1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Personal Relationship/Friendship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We always invite each other
to participate in various
activities to socialize.

1

2

3

4

5

2

We do personal favors for
each other.

1

2

3

4

5

2

3

4

5

2

3

4

5

2

3

4

5

3

4

5

We voluntarily exchange
something of a personal
nature to each other on
appropriate occasions (e.g.
1
birthday cards,
congratulations, condolences,
etc.).
We often communicate (via
face-to-face, phone calls, emails, social network
1
services, etc.) during nonworking time.
We often communicate (via
face-to-face, phone calls, emails, social network
1
services, etc.) outside work
places.

169

4. Your Relationship with Supplier 5
Do you have a formal contractual relationship with Supplier 5?
If “Yes,” please indicate the extent to which you agree or disagree with the following statements
y checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree” for the following questions. If “No” please proceed to Q .
Contractual Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We have a formal written
contract(s) detailing the
operational requirements.

1

2

3

4

5

2

We have a formal written
contract(s) that detail(s) how
performance will be
monitored.

1

2

3

4

5

3

We have a formal written
contract(s) detailing warranty 1
policies.

2

3

4

5

1

2

3

4

5

1

2

3

4

5

4

5

We have a formal written
contract(s) detailing how to
handle complaints and
disputes (e.g. penalties for
contract violations).
We have a formal written
contract(s) detailing the level
of service expected from this
supplier.

170

The following question (Q ) pertains to your firm’s usiness (or professional) relationship with
Supplier 5’s counterparts. Please indicate the extent to which you agree or disagree with the
following statements y checking (√) ONE answer ased on a scale of 5 where 1 represents
“Strongly disagree” and 5 represents “Strongly agree.”
Business/Professional Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We regularly communicate
(via face-to-face, conference
calls, e-mails, etc.) on work
matters.

1

2

3

4

5

2

We widely share and
welcome each other’s ideas
or initiatives via open
communication (e.g. joint
workshops, etc.).

1

2

3

4

5

3

The communication between
us occurs at different levels
of management and crossfunctional areas.

1

2

3

4

5

4

I (or our executives) receive
periodic feedback (via faceto-face, conference calls, email, etc.) on progress,
problems, and plans from
this supplier’s counterparts.

1

2

3

4

5

5

I (or our executives) do
periodic on-site visits to this
supplier’s plants.

1

2

3

4

5

171

The following question (Q ) pertains to your or other executives’ personal relationship (or
friendship) with Supplier 5’s counterparts. Please indicate the extent to which you agree or
disagree with the following statements y checking (√) ONE answer ased on a scale of 5 where
1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Personal Relationship/Friendship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We always invite each other
to participate in various
activities to socialize.

1

2

3

4

5

2

We do personal favors for
each other.

1

2

3

4

5

2

3

4

5

2

3

4

5

2

3

4

5

3

4

5

We voluntarily exchange
something of a personal
nature to each other on
appropriate occasions (e.g.
1
birthday cards,
congratulations, condolences,
etc.).
We often communicate (via
face-to-face, phone calls, emails, social network
1
services, etc.) during nonworking time.
We often communicate (via
face-to-face, phone calls, emails, social network
1
services, etc.) outside work
places.

172

Section 4: Component-Level Supply Chain Performance Information Please answer ALL
questions.
In this section, you will e asked a out your firm’s supply chain performance for the specific
component you selected in Section 2. All of your responses should refer to the relative
performance compared to the industry average or major competitors. Please make check only
ONE answer based on a scale of 5.
How would you judge your QUALITY performance for the COMPONENT compared to
industry average (or your major competitors)?
Neither
better
Significantly
nor
Significantly
worse
Worse worse
Better better

1

2

3

4

Technical capability:
Ability of component
capable of performing
desired functions
Conformance quality:
Ability to meet targets
for component quality
and/or to conform to
given component
specifications and
perceptions of quality
Internal process quality:
Ability to produce highquality (or low-defectrate) finished products
(e.g. in-plant
defect/fallout rate,
finished-product firstpass rate, etc.)
Component durability:
Ability to maximize the
amount of time before
the component
deteriorates or needs to
be replaced

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

173

5

Component reliability:
Ability to maximize the
amount of time before
component’s failure or
malfunction

1

2

3

4

5

How would you judge your FLEXIBILITY performance for the COMPONENT compared to
industry average (or your major competitors)?
Neither
better
Significantly
nor
Significantly
worse
Worse worse
Better better

1

2

3

4

Volume flexibility:
Ability to change the
level of production
volumes for the
component in order to
accommodate demand
variations
Delivery flexibility:
Ability to change
planned delivery dates
and/or to respond to
changes in delivery
requirements
Design flexibility:
Ability to change
product mix (i.e. variety
of products) or
customize components
to meet customers’
requests
Launch flexibility:
Ability to quickly
introduce new versions
of components

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

174

How would you judge your DELIVERY performance for the COMPONENT compared to
industry average (or your major competitors)?
Neither
better
Significantly
nor
Significantly
worse
Worse worse
Better better

1

2

3

4

On-time delivery:
Ability to meet quoted
or anticipated delivery
dates on a consistent
basis
Manufacturing lead
time:
Ability to reduce the
time which elapses
between the start of
production and the
completion of the
products
Customer lead time:
Ability to reduce the
time which elapses
between the receipt of
our firm’s order and the
delivery of the
components
Shipping accuracy:
Ability to deliver the
right amount of the right
component to the right
place on a consistent
basis

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

175

How would you judge your suppliers’ COST performance with regard to the COMPONENT
compared to industry average (or your major competitors)?
Neither
better
Significantly
nor
Significantly
worse
Worse worse
Better better

1

Acquisition costs

1

2

3

4

5

2

Cost reduction
performance

1

2

3

4

5

3

Designing cost out of
the component

1

2

3

4

5

4

Ability to meet target
costs

1

2

3

4

5

5

Supplier’s ability to
engage in strategic cost
modeling

1

2

3

4

5

176

How would you judge the INNOVATION performance of your firm’s PRODUCT that uses this
component compared to industry average (or your major competitors) over the last 3 years?
Please indicate the extent to which you agree or disagree with the following statements by
checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree.”
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree

1

By sourcing this component,
our firm could significantly
increase the number of new
products to the market.

1

2

3

4

5

2

By sourcing this component,
our firm could add much
1
more new features to existing
product(s).

2

3

4

5

3

By sourcing this component,
our firm could add unique
features to existing
product(s).

1

2

3

4

5

4

By sourcing this component,
our firm could have
significantly higher new
product success rate.

1

2

3

4

5

5

By sourcing this component,
our firm could develop new
product or features much
faster.

1

2

3

4

5

177

Summary
Thank you very much for your help with this research study. Your participation will contribute to
a better understanding of the increasingly complex supply network architectures and their
performance outcomes. Are there any important issues that you feel have been left out? If so,
please comment in the space provided on this page.
Please let us know if there are other areas that we should consider for future study.
Comments and additional remarks:

Please return the completed questionnaire by June 30, 2012
If you want us to send you a summary of our findings, please provide your mailing address or
contact information. Your personal contact information will be kept strictly confidential.
Name
Job Title
Business Unit (if applicable)
Company
Postal Address
Phone
E-mail

THANK YOU FOR YOUR TIME AND PARTICIPATION!

178

Determinants of Supply Network Architecture and Ensuing Performance (for TIER-1
SUPPLIERS)
PART 1
June 4, 2012
To Whom It May Concern,
In collaboration with O O O, we would like to invite your participation in a research being
conducted by researchers at the Eli Broad Graduate School of Management, Michigan State
University (MSU). I, a project investigator, Ram Narasimhan is serving as a John H. McConnel
Chaired Professor and University Distinguished Professor here at MSU. MSU has been one of
the top-notched research universities in accordance with the U.S. News’ est usiness Schools
rankings in Supply Chain Management/Logistics specification over last decade. I have been
impressed y Korean manufacturers’ supply chain management capa ilities whilst teaching
MBA classes at Seoul National University Business School as a part of the Global Scholars
Invitation Fellowship Program and having a featured interview with Chosun Ilbo
(http://news.chosun.com/site/data/html _dir/2008/04/04/200804040099 3.html).
Given the highly interdependent and closely interlinked business environment in recent years,
individual businesses no longer compete as standalone entities, but rather as collaborative
networks. In this era of ‘network- ased competition,’ the purpose of this study is to investigate
how firms’ supply network architectures are formed via various types of interactions with their
buyers and suppliers. We hypothesize that differences in strategic intents of firms for individual
component will induce different supply network architectures and affect the ensuing
performance, depending on the contextual features in their buyer-supplier relationships.
Knowledge from this study will help supply chain managers in different situations design an
appropriate supply network architecture which coordinates multiple supply chain entities
involved in global sourcing. The survey will take approximately 25-30 minutes.
All of your answers will be kept strictly confidential. Your firm name will NOT appear in any
future reports without your permission, and your individual responses will NOT be disclosed to
any outside agencies. Only summary data and aggregate results from statistical analysis of the
data will be published. We request you to answer all questions to the best of your ability and
knowledge, as incomplete surveys can create serious problems during data analysis and reduce

179

the sample size available for meaningful analysis of data. If you are not sure of an answer to a
question, please provide your best estimate.
Upon your request, a copy of the research findings will be sent to you, allowing you to
benchmark your supply network management practices with those of other companies. Simply
indicate your interest at the end of this survey and provide your contact information. In the report,
individual responses will be kept confidential and only the summary data and aggregate results
will be used.
If you have any questions about your rights as a participant in this research, or if you feel you
have een placed at risk, you can contact the MSU’s Research Integrity Officer through the MSU
Social Science/Behavioral/ Education Institutional Review Board (SIRB) at 1-517-355-2180.
Return of the questionnaire will be considered your consent to participate.
Thank you very much for helping with this important study. We will be happy to answer any
questions or concerns you may have on this study. Please contact Myung Kyo Kim.
Sincerely,

Ram Narasimhan, Ph.D.
Professor of Supply Chain Management
University Distinguished Professor
John H. McConnel Endowed Chair of Business Administration
narasimh@bus.msu.edu
1-517-432-6426
Myung Kyo Kim
Ph.D. Candidate of Supply Chain Management
myungkyo@bus.msu.edu
1-517-432-6446

180

Determinants of Supply Network Architecture and Ensuing Performance (for TIER-1
SUPPLIERS)
PART 2
Section 1: General Information

Please answer ALL questions.

Your organization’s name:
Your position or title in your organization:
How many years has it been since you started to work in this organization?
-5 years
-10 years
-20 years
Your organization’s approximate num er of employees:
-249
-499
-999

181

Section 2: Component and Supplier Information Please answer ALL questions.
In answering the questions below, please consider the component your firm supplies to (focal
firm’s name), all of its su -components, and their major immediate suppliers (may be listed in
your firm’s preferred supplier list).
Questions about the component your firm supplies to O O O:
For how many years have you been involved in decisions (e.g. design, development, quality,
procurement, etc.) pertaining to the component?
-5 years
-10 years
-20 years
This component share (in percentage) of total sales (i.e. O O O) (%) (= sales of component to O
O O /your firm’s total sales):
Please indicate the production technology used for manufacturing the component your firm
supplies to O O O.

Other (Please specify:

)

182

The next set of questions pertains to the changes of the component your firm supplies to O O O.
Please indicate the extent to which you agree or disagree with the following statements by
checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree” for the following questions.
Compared with other components manufactured by our firm:
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

The production technology
used in manufacturing this
component changes much
more rapidly.

1

2

3

4

5

2

It is much more important for
this component to adapt to
1
changing technological
trends.

2

3

4

5

3

New versions of this
component are introduced
much more frequently.

1

2

3

4

5

1

2

3

4

5

1

2

3

4

5

4

5

Technological changes
provide much bigger
opportunities for this
component.
It is much more difficult to
forecast where the
technology used in this
component will be in the
next 2-3 years.

183

Questions about the sub-components your firm sources from immediate suppliers to produce the
component your firm supplies to O O O:
Names of sub-components:
Functions of each sub-component:
Please name ALL major immediate suppliers of the component, percentage of total spend for this
component’s supplier, and contact information per each su -component. We will contact them
and send a survey. They will not see your responses. The information you provide below will not
be identifiable in any published results generated from this study.
(Sub-component 1)
Supplier 1 (Name):
Length of relationship (yrs):
Percentages of total spend for this component’s supplier (%):
Contact person name:
Phone number:
E-mail:
____________________________________________________
Supplier 2 (Name):
Length of relationship (yrs):
Percentages of total spend for this component’s supplier (%):
Contact person name:
Phone number:
E-mail:
(Sub-component 2)
Supplier 1 (Name):
Length of relationship (yrs):
Percentages of total spend for this component’s supplier (%):
Contact person name:
Phone number:
E-mail:
____________________________________________________
Supplier 2 (Name):
Length of relationship (yrs):
Percentages of total spend for this component’s supplier (%):
Contact person name:
Phone number:
E-mail:

184

Section 3: Buyer/Supplier Relationship Information

Please answer ALL questions.

In this section, you will be asked about your relationship with your immediate buyer and EACH
of the above immediate suppliers listed in Q7 of the previous section. Specifically, this section
seeks details a out your personal and your firm’s contractual, professional, and personal
relationships with (focal firm) and individual immediate supplier regarding the each subcomponent selected at Q5 of the previous section.
Thus, this section of the survey should be completed multiple times for (O O O) and EACH
supplier of EACH sub-component you listed at Q7 of the previous section. If you listed two
suppliers per each three sub-components there, for instance, please answer ALL those seven (= O
O O + 2*3 suppliers) firms ONE BY ONE. We understand that this may prove difficult and time
consuming to provide answers to this section. However, your input is vital to our analyses to
figure out a firm’s overall supply network architecture uilt through various types of interactions
with your focal firm (i.e. O O O) and all of your major suppliers. Thank you again for your
participation.
1. Your Relationship with O O O
Do you have a formal contractual relationship with O O O?
If “Yes,” please indicate the extent to which you agree or disagree with the following statements
y checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree” for the following questions.
Contractual Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We have a formal written
contract(s) detailing the
operational requirements.

1

2

3

4

5

2

We have a formal written
contract(s) that detail(s) how
our performance will be
monitored.

1

2

3

4

5

3

We have a formal written
contract(s) detailing our
warranty policies.

1

2

3

4

5

185

4

5

We have a formal written
contract(s) detailing how we
will handle their complaints
and disputes (e.g. penalties
for contract violations).
We have a formal written
contract(s) detailing the level
of their service expected
from us.

1

2

3

4

5

1

2

3

4

5

The following question (Q ) pertains to your or executives’ usiness (or professional)
relationship with (O O O)’s counterparts. Please indicate the extent to which you agree or
disagree with the following statements y checking (√) ONE answer ased on a scale of 5 where
1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Business/Professional Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We regularly communicate
(via face-to-face, conference
calls, e-mails, etc.) on work
matters.

1

2

3

4

5

2

We widely share and
welcome each other’s ideas
or initiatives via open
communication (e.g. joint
workshops, etc.).

1

2

3

4

5

3

The communication between
us occurs at different levels
of management and crossfunctional areas.

1

2

3

4

5

4

I (or our executives) give
periodic feedback (via faceto-face, conference calls, email, etc.) on our progress,
problems, and plans to O O
O’s counterparts.

1

2

3

4

5

186

5

O O O’s executives (or other
employees) do periodic onsite visits to our plants.

1

2

3

4

5

The following question (Q ) pertains to your or executives’ personal relationship (or friendship)
with O O O’s counterparts. Please indicate the extent to which you agree or disagree with the
following statements y checking (√) ONE answer ased on a scale of 5 where 1 represents
“Strongly disagree” and 5 represents “Strongly agree.”
Personal Relationship/Friendship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We always invite each other
to participate in various
activities for socialization.

1

2

3

4

5

2

We do personal favors to
each other.

1

2

3

4

5

2

3

4

5

2

3

4

5

2

3

4

5

3

4

5

We voluntarily exchange
something of a personal
nature to each other on
appropriate occasions (e.g.
1
birthday cards,
congratulations, condolences,
etc.).
We often communicate (via
face-to-face, phone calls, emails, social network
1
services, etc.) during nonworking time.
We often communicate (via
face-to-face, phone calls, emails, social network
1
services, etc.) outside work
places.

187

2. Your Relationship with Supplier 1 of Sub-Component 1
Do you have a formal contractual relationship with Supplier 1 of Sub-Component 1?

If “Yes,” please indicate the extent to which you agree or disagree with the following statements
y checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree” for the following questions.
Contractual Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We have a formal written
contract(s) detailing the
operational requirements.

1

2

3

4

5

2

We have a formal written
contract(s) that detail(s) how
performance will be
monitored.

1

2

3

4

5

3

We have a formal written
contract(s) detailing warranty 1
policies.

2

3

4

5

1

2

3

4

5

1

2

3

4

5

4

5

We have a formal written
contract(s) detailing how to
handle complaints and
disputes (e.g. penalties for
contract violations).
We have a formal written
contract(s) detailing the level
of service expected from this
supplier.

188

The following question (Q ) pertains to your or executives’ usiness (or professional)
relationship with Supplier 1 of Sub-Component 1’s counterparts. Please indicate the extent to
which you agree or disagree with the following statements y checking (√) ONE answer based
on a scale of 5 where 1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Business/Professional Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We regularly communicate
(via face-to-face, conference
calls, e-mails, etc.) on work
matters.

1

2

3

4

5

2

We widely share and
welcome each other’s ideas
or initiatives via open
communication (e.g. joint
workshops, etc.).

1

2

3

4

5

3

The communication between
us occurs at different levels
of management and crossfunctional areas.

1

2

3

4

5

4

I (or our executives) receive
periodic feedback (via faceto-face, conference calls, email, etc.) on progress,
problems, and plans from
(Supplier 1 of SubComponent 1)’s
counterparts.

1

2

3

4

5

5

I (or our executives) do
periodic on-site visits to
(Supplier 1 of SubComponent 1)’s plants.

1

2

3

4

5

189

The following question (Q ) pertains to your or executives’ personal relationship (or friendship)
with Supplier 1 of Sub-Component 1’s counterparts. Please indicate the extent to which you
agree or disagree with the following statements y checking (√) ONE answer ased on a scale of
5 where 1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Personal Relationship/Friendship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We always invite each other
to participate in various
activities for socialization.

1

2

3

4

5

2

We do personal favors to
each other.

1

2

3

4

5

3

We voluntarily exchange
something of a personal
nature to each other on
appropriate occasions (e.g.
1
birthday cards,
congratulations, condolences,
etc.).

2

3

4

5

4

We often communicate (via
face-to-face, phone calls, emails, etc.) during nonworking time.

1

2

3

4

5

5

We often communicate (via
face-to-face, phone calls, emails, etc.) outside work
places.

1

2

3

4

5

190

3. Your Relationship with Supplier 2 of Sub-Component 1
Do you have a formal contractual relationship with Supplier 2 of Sub-Component 1?

If “Yes,” please indicate the extent to which you agree or disagree with the following statements
y checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree” for the following questions.
Contractual Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We have a formal written
contract(s) detailing the
operational requirements.

1

2

3

4

5

2

We have a formal written
contract(s) that detail(s) how
performance will be
monitored.

1

2

3

4

5

3

We have a formal written
contract(s) detailing warranty 1
policies.

2

3

4

5

1

2

3

4

5

1

2

3

4

5

4

5

We have a formal written
contract(s) detailing how to
handle complaints and
disputes (e.g. penalties for
contract violations).
We have a formal written
contract(s) detailing the level
of service expected from this
supplier.

191

The following question (Q ) pertains to your or executives’ usiness (or professional)
relationship with Supplier 2 of Sub-Component 1’s counterparts. Please indicate the extent to
which you agree or disagree with the following statements y checking (√) ONE answer based
on a scale of 5 where 1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Business/Professional Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We regularly communicate
(via face-to-face, conference
calls, e-mails, etc.) on work
matters.

1

2

3

4

5

2

We widely share and
welcome each other’s ideas
or initiatives via open
communication (e.g. joint
workshops, etc.).

1

2

3

4

5

3

The communication between
us occurs at different levels
of management and crossfunctional areas.

1

2

3

4

5

4

I (or our executives) receive
periodic feedback (via faceto-face, conference calls, email, etc.) on progress,
problems, and plans from
(Supplier 2 of SubComponent 1)’s
counterparts.

1

2

3

4

5

5

I (or our executives) do
periodic on-site visits to
(Supplier 2 of SubComponent 1)’s plants.

1

2

3

4

5

192

The following question (Q ) pertains to your or executives’ personal relationship (or friendship)
with Supplier 2 of Sub-Component 1’s counterparts. Please indicate the extent to which you
agree or disagree with the following statements y checking (√) ONE answer ased on a scale of
5 where 1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Personal Relationship/Friendship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We always invite each other
to participate in various
activities for socialization.

1

2

3

4

5

2

We do personal favors to
each other.

1

2

3

4

5

3

We voluntarily exchange
something of a personal
nature to each other on
appropriate occasions (e.g.
1
birthday cards,
congratulations, condolences,
etc.).

2

3

4

5

4

We often communicate (via
face-to-face, phone calls, emails, etc.) during nonworking time.

1

2

3

4

5

5

We often communicate (via
face-to-face, phone calls, emails, etc.) outside work
places.

1

2

3

4

5

193

4. Your Relationship with Supplier 1 of Sub-Component 2
Do you have a formal contractual relationship with Supplier 1 of Sub-Component 2?

If “Yes,” please indicate the extent to which you agree or disagree with the following statements
y checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree” for the following questions.
Contractual Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We have a formal written
contract(s) detailing the
operational requirements.

1

2

3

4

5

2

We have a formal written
contract(s) that detail(s) how
performance will be
monitored.

1

2

3

4

5

3

We have a formal written
contract(s) detailing warranty 1
policies.

2

3

4

5

1

2

3

4

5

1

2

3

4

5

4

5

We have a formal written
contract(s) detailing how to
handle complaints and
disputes (e.g. penalties for
contract violations).
We have a formal written
contract(s) detailing the level
of service expected from this
supplier.

194

The following question (Q ) pertains to your or executives’ usiness (or professional)
relationship with Supplier 1 of Sub-Component ’s counterparts. Please indicate the extent to
which you agree or disagree with the following statements y checking (√) ONE answer based
on a scale of 5 where 1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Business/Professional Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We regularly communicate
(via face-to-face, conference
calls, e-mails, etc.) on work
matters.

1

2

3

4

5

2

We widely share and
welcome each other’s ideas
or initiatives via open
communication (e.g. joint
workshops, etc.).

1

2

3

4

5

3

The communication between
us occurs at different levels
of management and crossfunctional areas.

1

2

3

4

5

4

I (or our executives) receive
periodic feedback (via faceto-face, conference calls, email, etc.) on progress,
problems, and plans from
(Supplier 1 of SubComponent )’s
counterparts.

1

2

3

4

5

5

I (or our executives) do
periodic on-site visits to
(Supplier 1 of SubComponent )’s plants.

1

2

3

4

5

195

The following question (Q ) pertains to your or executives’ personal relationship (or friendship)
with Supplier 1 of Sub-Component ’s counterparts. Please indicate the extent to which you
agree or disagree with the following statements y checking (√) ONE answer ased on a scale of
5 where 1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Personal Relationship/Friendship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We always invite each other
to participate in various
activities for socialization.

1

2

3

4

5

2

We do personal favors to
each other.

1

2

3

4

5

3

We voluntarily exchange
something of a personal
nature to each other on
appropriate occasions (e.g.
1
birthday cards,
congratulations, condolences,
etc.).

2

3

4

5

4

We often communicate (via
face-to-face, phone calls, emails, etc.) during nonworking time.

1

2

3

4

5

5

We often communicate (via
face-to-face, phone calls, emails, etc.) outside work
places.

1

2

3

4

5

196

5. Your Relationship with Supplier 2 of Sub-Component 2
Do you have a formal contractual relationship with Supplier 2 of Sub-Component 2?

If “Yes,” please indicate the extent to which you agree or disagree with the following statements
y checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree” for the following questions.
Contractual Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We have a formal written
contract(s) detailing the
operational requirements.

1

2

3

4

5

2

We have a formal written
contract(s) that detail(s) how
performance will be
monitored.

1

2

3

4

5

3

We have a formal written
contract(s) detailing warranty 1
policies.

2

3

4

5

1

2

3

4

5

1

2

3

4

5

4

5

We have a formal written
contract(s) detailing how to
handle complaints and
disputes (e.g. penalties for
contract violations).
We have a formal written
contract(s) detailing the level
of service expected from this
supplier.

197

The following question (Q ) pertains to your or executives’ usiness (or professional)
relationship with Supplier 2 of Sub-Component ’s counterparts. Please indicate the extent to
which you agree or disagree with the following statements by checking (√) ONE answer ased
on a scale of 5 where 1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Business/Professional Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We regularly communicate
(via face-to-face, conference
calls, e-mails, etc.) on work
matters.

1

2

3

4

5

2

We widely share and
welcome each other’s ideas
or initiatives via open
communication (e.g. joint
workshops, etc.).

1

2

3

4

5

3

The communication between
us occurs at different levels
of management and crossfunctional areas.

1

2

3

4

5

4

I (or our executives) receive
periodic feedback (via faceto-face, conference calls, email, etc.) on progress,
problems, and plans from
(Supplier 2 of SubComponent )’s
counterparts.

1

2

3

4

5

5

I (or our executives) do
periodic on-site visits to
(Supplier 2 of SubComponent )’s plants.

1

2

3

4

5

198

The following question (Q ) pertains to your or executives’ personal relationship (or friendship)
with Supplier 2 of Sub-Component ’s counterparts. Please indicate the extent to which you
agree or disagree with the following statements y checking (√) ONE answer ased on a scale of
5 where 1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Personal Relationship/Friendship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We always invite each other
to participate in various
activities for socialization.

1

2

3

4

5

2

We do personal favors to
each other.

1

2

3

4

5

3

We voluntarily exchange
something of a personal
nature to each other on
appropriate occasions (e.g.
1
birthday cards,
congratulations, condolences,
etc.).

2

3

4

5

4

We often communicate (via
face-to-face, phone calls, emails, etc.) during nonworking time.

1

2

3

4

5

5

We often communicate (via
face-to-face, phone calls, emails, etc.) outside work
places.

1

2

3

4

5

199

Summary
Thank you very much for your help with this research study. Your participation will contribute to
a better understanding of the increasingly complex supply network architectures and their
performance outcomes. Are there any important issues that you feel have been left out? If so,
please comment in the space provided on this page.
Please let us know if there are other areas that we should consider for future study.
Comments and additional remarks:

Please return the completed questionnaire by June 30, 2012
If you want us to send you a summary of our findings, please provide your mailing address or
contact information. Your personal contact information will be kept strictly confidential.
Name
Job Title
Business Unit (if applicable)
Company
Postal Address
Phone
E-mail

THANK YOU FOR YOUR TIME AND PARTICIPATION!

200

Determinants of Supply Network Architecture and Ensuing Performance (for SECONDTIER SUPPLIERS)
PART 1
June 4, 2012
To Whom It May Concern,
In collaboration with O O O and ◇ ◇ ◇, we would like to invite your participation in a
research being conducted by researchers at the Eli Broad Graduate School of Management,
Michigan State University (MSU). I, a project investigator, Ram Narasimhan is serving as a John
H. McConnel Chaired Professor and University Distinguished Professor here at MSU. MSU has
been one of the top-notched research universities in accordance with the U.S. News’ est
Business Schools rankings in Supply Chain Management/Logistics specification over last decade.
I have een impressed y Korean manufacturers’ supply chain management capa ilities whilst
teaching MBA classes at Seoul National University Business School as a part of the Global
Scholars Invitation Fellowship Program and having a featured interview with Chosun Ilbo
(http://news.chosun.com/site/data/html _dir/2008/04/04/200804040099 3.html).
Given the highly interdependent and closely interlinked business environment in recent years,
individual businesses no longer compete as standalone entities, but rather as collaborative
networks. In this era of ‘network- ased competition,’ the purpose of this study is to investigate
how firms’ supply network architectures are formed via various types of interactions with their
buyers and suppliers. We hypothesize that differences in strategic intents of firms for individual
component will induce different supply network architectures and affect the ensuing
performance, depending on the contextual features in their buyer-supplier relationships.
Knowledge from this study will help supply chain managers in different situations design an
appropriate supply network architecture which coordinates multiple supply chain entities
involved in global sourcing. The survey will take approximately 10 minutes.
All of your answers will be kept strictly confidential. Your firm name will NOT appear in any
future reports without your permission, and your individual responses will NOT be disclosed to
any outside agencies. Only summary data and aggregate results from statistical analysis of the
data will be published. We request you to answer all questions to the best of your ability and
knowledge, as incomplete surveys can create serious problems during data analysis and reduce
201

the sample size available for meaningful analysis of data. If you are not sure of an answer to a
question, please provide your best estimate.
Upon your request, a copy of the research findings will be sent to you, allowing you to
benchmark your supply network management practices with those of other companies. Simply
indicate your interest at the end of this survey and provide your contact information. In the report,
individual responses will be kept confidential and only the summary data and aggregate results
will be used.
If you have any questions about your rights as a participant in this research, or if you feel you
have een placed at risk, you can contact the MSU’s Research Integrity Officer through the MSU
Social Science/Behavioral/ Education Institutional Review Board (SIRB) at 1-517-355-2180.
Return of the questionnaire will be considered your consent to participate.
Thank you very much for helping with this important study. We will be happy to answer any
questions or concerns you may have on this study. Please contact Myung Kyo Kim.

Sincerely,

Ram Narasimhan, Ph.D.
Professor of Supply Chain Management
University Distinguished Professor
John H. McConnel Endowed Chair of Business Administration
narasimh@bus.msu.edu
1-517-432-6426
Myung Kyo Kim
Ph.D. Candidate of Supply Chain Management
myungkyo@bus.msu.edu
1-517-432-6446

202

Determinants of Supply Network Architecture and Ensuing Performance (for SECONDTIER SUPPLIERS)
PART 2
Section 1: General Information

Please answer ALL questions.

Your organization’s name:
Your position or title in your organization:
How many years has it been since you started to work in this organization?
Less than 1 year
-5 years
-10 years
-20 years
Your organization’s approximate num er of employees:
-249
-499
-999

203

Section 2: Component Information Please answer ALL questions.
In answering the questions below, please consider the component your organization supplies to
◇ ◇ ◇.
For how many years have you been involved in decisions (e.g. design, development, quality,
procurement, etc.) pertaining to the component?
-5 years
-10 years
-20 years
This component share (in percentage) of total sales (i.e. ◇ ◇ ◇) (%) (= sales of component to
◇ ◇ ◇/your firm’s total sales):

204

Section 3: Buyer Relationship Information

Please answer ALL questions.

In this section, you will be asked about your relationship with your immediate buying firm (i.e.
◇ ◇ ◇). Specifically, this section seeks details a out your personal and your firm’s contractual,
professional, and interpersonal relationships with ◇ ◇ ◇.
Do you have a formal contractual relationship with ◇ ◇ ◇?
If “Yes,” please indicate the extent to which you agree or disagree with the following statements
y checking (√) ONE answer ased on a scale of 5 where 1 represents “Strongly disagree” and 5
represents “Strongly agree” for the following questions.
Contractual Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
1

We have a formal written
contract(s) detailing the
operational requirements.

1

2

3

4

5

2

We have a formal written
contract(s) that detail(s) how
our performance will be
monitored.

1

2

3

4

5

3

We have a formal written
contract(s) detailing our
warranty policies.

1

2

3

4

5

4

We have a formal written
contract(s) detailing how we
will handle their complaints
and disputes (e.g. penalties
for contract violations).

1

2

3

4

5

1

2

3

4

5

5

We have a formal written
contract(s) detailing the level
of their service expected
from us.

205

The following question (Q ) pertains to your or executives’ usiness (or professional)
relationship with ◇ ◇ ◇’s counterparts. Please indicate the extent to which you agree or
disagree with the following statements y checking (√) ONE answer ased on a scale of 5 where
1 represents “Strongly disagree” and 5 represents “Strongly agree.”
Business/Professional Relationship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree

1

We regularly communicate
(via face-to-face, conference
calls, e-mails, etc.) on work
matters.

1

2

3

4

5

2

We widely share and
welcome each other’s ideas
or initiatives via open
communication (e.g. joint
workshops, etc.).

1

2

3

4

5

3

The communication between
us occurs at different levels
of management and crossfunctional areas.

1

2

3

4

5

4

I (or our executives) give
periodic feedback (via faceto-face, conference calls, email, etc.) on our progress,
problems, and plans to this
◇ ◇ ◇’s counterparts.

1

2

3

4

5

1

2

3

4

5

◇ ◇ ◇’s executives (or
5

other employees) do periodic
on-site visits to our plants.

206

The following question (Q ) pertains to your or executives’ personal relationship (or friendship)
with ◇ ◇ ◇’s counterparts. Please indicate the extent to which you agree or disagree with the
following statements y checking (√) ONE answer based on a scale of 5 where 1 represents
“Strongly disagree” and 5 represents “Strongly agree.”
Personal Relationship/Friendship
Neither
disagree
Strongly
nor
Strongly
disagree Disagree agree
Agree agree
We always invite each other
1

2

to participate in various
activities for socialization.
We do personal favors to
each other.

1

2

3

4

5

1

2

3

4

5

2

3

4

5

We voluntarily exchange

3

something of a personal
nature to each other on
appropriate occasions (e.g.
1
birthday cards,
congratulations, condolences,
etc.).

4

We often communicate (via
face-to-face, phone calls, emails, social network
services, etc.) during nonworking time.

1

2

3

4

5

5

We often communicate (via
face-to-face, phone calls, emails, social network
services, etc.) outside work
places.

1

2

3

4

5

207

Summary
Thank you very much for your help with this research study. Your participation will contribute to
a better understanding of the increasingly complex supply network architectures and their
performance outcomes. Are there any important issues that you feel have been left out? If so,
please comment in the space provided on this page.
Please let us know if there are other areas that we should consider for future study.
Comments and additional remarks:

Please return the completed questionnaire by June 30, 2012
If you want us to send you a summary of our findings, please provide your mailing address or
contact information. Your personal contact information will be kept strictly confidential.
Name
Job Title
Business Unit (if applicable)
Company
Postal Address
Phone
E-mail
THANK YOU FOR YOUR TIME AND PARTICIPATION!

208

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