The Role of Ego Networks in Manufacturing Joint Venture Formations

• Author: Sengun Yeniyurt,Steven Carnovale
• Date: 01 April 2014
• Published date: 01 April 2014
• DOI: http://doi.org/10.1111/jscm.12015

THE ROLE OF EGO NETWORKS IN MANUFACTURING

JOINT VENTURE FORMATIONS

STEVEN CARNOVALE AND SENGUN YENIYURT

Rutgers University

This article develops a network theory–based framework of manufacturing

joint venture formations and provides an empirical test in the context of

the automotive industry. Hypotheses are developed regarding the implica-

tions of the network structure for a firm’s partner selection in manufactur-

ing joint ventures. The roles of network theory constructs such as ego

network size, ego network density, and ego network betweenness central-

ity on new manufacturing joint venture formations are explored using a

dynamic framework. A comprehensive time series panel dataset with

3,247,124 observations containing the joint venture information of 1,158

automotive firms collectively engaging in 589 manufacturing joint

ventures over 19 years is utilized to test the hypotheses. Results provide

strong empirical support for the role of network structure in equity-based

partnership formation. Specifically, ego network size and ego network

betweenness centrality of both the focal manufacturer and potential part-

ner have significant effects on new manufacturing joint venture forma-

tions. Findings regarding the role of ego network density are mixed.

Keywords: joint ventures; partnerships; social networks

INTRODUCTION

As supply chains transcend traditional company

boundaries, the complexity associated with interorga-

nizational networks presents significant challenges to

managers. The complexity arises not just from the

great number of companies involved in a typical sup-

ply chain, but also from the myriad of interorganiza-

tional ties among them. This large number of players

and relationships results in increasingly complex net-

works where each company constantly aims to

identify and engage in new partnerships while main-

taining the existing collaborations and pruning poor

ones. New partnerships further increase the complex-

ity of the network by increasing the number of ties

among the members. Therefore, a dynamic interplay

exists between new partnership formations and the

network structure, and this phenomenon should be

studied on a longitudinal and not static basis (Gala-

skiewicz, 2011).

This article contributes to the extant supply chain

management literature by examining the dynamic

process of new manufacturing joint venture (JV)

formations over time by developing and testing a the-

oretical framework regarding the effect of network

structure on new JV formations. The primary contribu-

tions are to the domains of supply chain networks

and supply chain collaborative partnerships. Interorga-

nizational partnerships can be nonequity based or

equity based (e.g., Kogut, 1988). We concentrate on

equity-based manufacturing collaborations, that is,

manufacturing joint ventures, where two companies

make equity investments and develop a long-term col-

laborative venture and a new entity is established with

the purpose of manufacturing a specific component or

sets of components. Furthermore, we specifically focus

on the partner selection process from the perspective

of an original equipment manufacturer (OEM) seek-

ing to form a new manufacturing JV with a potential

partner. The potential JV partner can be another OEM

or a components supplier (i.e., not an OEM). The

manufacturing JV is formed to manufacture compo-

nents that are subsequently utilized in the production

process of the focal OEM.

Acknowledgments: The authors would like to thank the Institute for

Supply Management (ISM) for their generous support to the authors

throughout the duration of the research process; the feedback received

at each stage was invaluable. The authors would also like to thank

Myles D. Garvey for his vital help during the data coding, processing,

and analysis phase of the research. Finally, the authors would like to

thank Rutgers Business School’s Center for Supply Chain Manage-

ment for its unfettered commitment to and support for this research.

April 2014 1

One of the most fundamental building blocks of

network structure is the ego network of a particular

actor. The study of ego networks begins with an ego

or a specific social unit (i.e., a firm), that unit’s imme-

diate ties, and the ties among the actors to which the

ego is connected (Borgatti & Halgin, 2011; Burt,

1980; Freeman, 1982). As a unit of analysis, ego net-

works are fundamental to understanding and

interpreting the pattern and structure of the overall

network. Ego networks have been applied to various

contexts such as job acquisition (Granovetter, 1973),

power and influence (Burt, 1992), innovation adop-

tion (Ahuja, 2000), as well as knowledge sharing and

knowledge networks (Hansen, 2002; Hansen, Mors, &

LØv



As, 2005). We extend this stream of literature by

looking at the effect of ego networks on new supply

chain joint venture formations.

A large number of studies have investigated the

process of collaborative venture formations from a

variety of perspectives such as real options perspectives

(e.g., Kogut, 1988, 1991), information asymmetry

(Reuer & Koza, 2000), and entry mode decisions (Sea-

Jin & Rosenzweig, 2001). It is generally accepted that

the main role of collaborative ventures is providing

additional value and enhancing the market potential

of each partner (Adler, 1966; Varadarajan & Rajarat-

nam, 1986). Despite the large number of studies in

this domain and the general acceptance of the impor-

tance of collaborative ventures, the supply chain con-

siderations of collaborative venture formations have

been largely overlooked. In particular, there is a large

gap in the literature regarding studies investigating sup-

ply chain management practices and manufacturing

collaborations from a social network perspective (see

Borgatti & Li, 2009; Choi & Wu, 2009; Galaskiewicz,

2011).

Collaborative ventures enable a manufacturer to

access a wider set of resources, generating a signifi-

cant competitive advantage (Fang & Zou, 2009). Yet,

there are a lot of challenges facing the firm undertak-

ing the process of collaborative venture formation,

and these challenges propagate themselves principally

around the lack of information and uncertainty that

a firm faces (Mosakowski, 1997). Uncertainty is

defined as the difficulty firms have in predicting the

future, which comes from a lack of information

(Beckman, Haunschild, & Phillips, 2004). It has been

indicated that companies utilize collaborative venture

networks to access superior resources (Burt, 1992)

and that the network structure characteristics play a

role in interfirm collaborations (Powell, Koput,

Smith-Doerr, & Owen-Smith, 1999). We add to this

literature stream by focusing on the effects of net-

work structure on new supply chain JV formations.

Specifically, we investigate the following research

questions:

●What is the role of network structure when select-

ing a manufacturing JV partner?

●Which characteristics of a focal manufacturer’s

network are important?

●Which characteristics of a potential partner’s

network are important?

This article is organized in the following manner:

First, the extant literature is reviewed, and the theoret-

ically derived hypotheses are presented. Second, the

dataset, variables, and the empirical model are

explained. The results are presented, and a discussion

of the findings concludes the article.

LITERATURE REVIEW AND HYPOTHESIS

DEVELOPMENT

This presents the theoretical background and a con-

cise review of the literature for the relevant streams of

research which we will use to develop our hypotheses.

We use the tenets of network theory to develop spe-

cific hypotheses with respect to a manufacturer’s part-

ner selection in manufacturing joint ventures. After

having highlighted the critical facets from the extant

literature that are relevant to partner selection in man-

ufacturing joint ventures, we develop our propositions

using specific variables derived from social network

theory.

Social Network Theory

The literature in social networks is quite multidisci-

plinary. Some empirical research streams take a graph

theoretic approach to analyzing networks (e.g., Watts,

1999, 2004). Other research streams that are particu-

lar to the business literature include intergroup con-

flict and social capital (Labianca, Brass, & Gray, 1998;

Oh, Myung-Ho, & Labianca, 2004), learning (Borgatti

& Cross, 2003), supplier embeddedness (Choi & Kim,

2008), complexity and trust in strategic alliances

(Robson, Katsikeas, & Bello, 2008), and social com-

merce networks (Stephen & Toubia, 2010). Social net-

working’s most widely recognized stream is within the

confines of sociology (e.g., Bonacich, 1987; Granovet-

ter, 1973; Watts, 1999, 2004).

Social network analysis began with sociologists

attempting to understand the interactions among indi-

vidual actors in groups (Choi & Kim, 2008). The

study of these interactions is known as sociometry

(Granovetter, 1973). The end result of a sociometric

study is the formal construction of the network of the

interactions between actors. The goal is to understand

why one actor interacts with another based on the

empirical observations that have been collected over a

period of time. Later, these repeated interactions

between actors became known as ties. Perhaps, the

most well-known and well-cited work on social ties is

Volume 50, Number 2

Journal of Supply Chain Management

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