Temporary deembedding buyer – supplier relationships: A complexity perspective

• DOI: http://doi.org/10.1002/joom.1008
• Published date: 01 March 2019
• Date: 01 March 2019

ORIGINAL ARTICLE

Temporary deembedding buyer –supplier relationships:

A complexity perspective

Fabian J. Sting

1,2

| Merieke Stevens

2

| Murat Tarakci

2

1

Faculty of Management, Economics and

Social Sciences, University of Cologne,

Cologne, Germany

2

Rotterdam School of Management,

Erasmus University, Rotterdam,

The Netherlands

Correspondence

Fabian J. Sting, University of Cologne,

Albertus-Magnus-Platz, Cologne 50923,

Germany.

Email: sting@wiso.uni-koeln.de

Handling Editors: Anand Nair and Felix

Reed-Tsochas

Research on buyer–supplier relationships has debated the advantages and disadvan-

tages of embedded relationships. We join this debate by developing theory on the per-

formance implications of relaxing embedded buyer–supplier relationships for a

limited period of time—a previously neglected phenomenon we refer to as temporary

deembedding. To capture this phenomenon's dynamic and complex nature, we use a

combined-method approach. First, we conducted a longitudinal case study of the rela-

tionship between Nissan and a strategic first-tier supplier. This case study suggests

that temporary deembedding reinvigorates search and leads to higher performance for

both the buyer and supplier. Second, we built a computational simulation model using

the search perspective from complexity theory to complement the theory grounded in

our case study. Our simulations confirm the case findings while s hedding additional

light on how frequency, duration, and intensity of deembedding affect supply chain

performance.

KEYWORDS

buyer–supplier relationships, complexity, embeddedness, combined methods, dyadic and longitudinal

study

1|INTRODUCTION

Supply chain management is inherently complex anddynamic

(Nair, Narasimhan, & Choi, 2009), because decisions made

by one member of the supply chain affect subsequent deci-

sions of other actors. In this dynamic and complex setting,

the existence of an “optimized”master plan proves elusive.

Instead, supply chain members engage in coevolutionary search

to advance and to innovate (Chandrasekaran, Linderman,

Sting, & Benner, 2015; Giannoccaro, 2011; Kim, Choi, &

Skilton, 2015; Levinthal, 1997; Sting & Loch, 2016), much

like BMW's ongoing “Industrie 4.0”initiative to digitalize

manufacturing processes and technologies. As part of this

initiative, BMW has scanned its entire Rolls Royce plant in

Goodwood, UK, within a two-millimeter tolerance. Supply

chain gains from such firm-level improvements, however,

critically depend on suppliers' compatible interfaces. BMW

COO Zispe reflects: “How will our suppliers connect with

these emerging systems?”(Mayer & Klein, 2015). This quote

illustrates how pivotal buyer–supplier relationships are for

supply chain innovation and performance (Chen & Paulraj,

2004; Choi & Kim, 2008; Kim et al., 2015; Terpend, Tyler,

Krause, & Handfield, 2008). Surprisingly, however, little is

known about how buyer–supplier relationships affect coevo-

lutionary search processes, let alone how these processes

subsequently drive supply chain performance—the main

motivation for this study.

Prior research has shown that closely embedded buyer–

supplier relationships foster joint problem-solving activities

and information exchange (Dyer & Chu, 2000; Dyer &

Singh, 1998; Gul ati & Sytch, 2007; U zzi, 1996, 1997), which,

in turn, boost buyer performance (e.g., Cachon & Lariviere,

2005; Choi & Kim, 2008; Chopra & Meindl, 2007; Kim

et al., 2015). Yet, embedded relationships can also trigger

DOI: 10.1002/joom.1008

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original

work is properly cited.

© 2019 The Authors. Journal of Operations Management published by Wiley Periodicals, Inc. on behalf of The Association for Supply Chain Management Inc.

114 wileyonlinelibrary.com/journal/joom J Oper Manag. 2019;65:114–135.

complacency, limit access to nonredundant information, and

lead to poor performance for both the buyer and supplier

(Swink & Zsidisin, 2006; Villena, Revilla, & Choi, 2011).

Consequently, prior research has often advised a constant,

moderate degree of embeddedness in buyer–supplier relation-

ships (Gargiulo & Benassi, 2000; Swink & Zsidisin, 2006;

Villena et al., 2011; Zhou, Zhang, Sheng, Xie, & Bao, 2014).

Despite these advances, current research has three shortcom-

ings.

1

First, the main focus has been on the buyer's perspec-

tive, thus forfeiting a more complete picture of relationship

performance for both parties (e.g., Carey, Lawson, & Krause,

2011; Cousins, Handfield, Lawson, & Petersen, 2006; Cous-

ins & Menguc, 2006; Gulati & Sytch, 2007; Lawson, Tyler, &

Cousins, 2008; Villena et al., 2011; Zhou et al., 2014). Sec-

ond, perhaps stemming from a strong reliance on cross-

sectional research designs, studies often overlook the fact that

a relationship's level of embeddedness can change over time

(for a few exceptions, see Azoulay, Repenning, & Zuckerman,

2010, Jap & Anderson, 2007, Saavedra, Reed-Tsochas, &

Uzzi, 2008). Such a static view of buy er–supplier relation-

ships limits our understanding of how these relati onships

evolve and how different periods in this evolution affect the

relationship's search and performance. Third, a prevalent

underlying assumption is that an unmalleable relationship

structure pegs performance outcomes—an approach that disre-

gards the critical agency aspects of relationships that are delib-

erately and strategically altered by either party.

Consider Nissan, which cut the embedded ties with its

keiretsu suppliers in 1999, many having been affiliated with

Nissan since the 1950s (Aoki & Lennerfors, 2013; Stevens,

MacDuffie, & Helper, 2015). This was a deliberate, strategic

choice as part of the so-called “Nissan Revival Plan”(NRP),

announced on October 18, 1999. Likewise, five years after

deembedding, Nissan decided to rebuild close relationships

with several of its suppliers. This illustrates that the static,

deterministic conceptualization of buyer–supplier relation-

ships in prior research neglects the fact that firms can strategi-

cally relax, and later reinstate, embedded ties—a phenomenon

we refer to as temporary deembedding. Developing a theory

for temporary deembedding is our goal.

To this end, we employ a combined-method research

design—a prerequisite for a broader understanding of complex

supply chain management phenomena (Boyer & Swink, 2008).

First, we collected longitudinal and dyadic data at Nissan and a

strategic first-tier supplier over a 12-year timespan. We exam-

ine how the relationship's embeddedness evolved over time

and how that evolution interacted with search and performance.

We observe that when the relationship was overembedded,

both firms were limited to only incremental improvement ini-

tiatives that proved insufficient in breaking the relationship's

deadlock that was freezing each firm's innovative potential.

Deembedding, in contrast, shifted priority to intrafirm goals

over interfirm goals, reinvigorating both Nissan's and the sup-

plier's independent search initiatives. Thus, deembedding

helped the supply chain to escape its sticking point. Notably,

five years after the abrupt commencement of its deembedding

effort, Nissan opted to reembed supply chain ties in order to

ensure compatibility among independent search initiatives.

Secondly, to generalize and augment the theory emerging

from the case study, we devised a computational model to

simulate deembedding in supply chains. The model uses the

search notion of complexity theory where a supply chain

gradually explores a rugged performance landscape in search

of improvement and innovation. Generalizing the case find-

ings, we show that temporarily deembedding supply chain

relationships enhances performance by promoting broader

search for improvements in complex environments. Never-

theless, deembedding can be a double-edged sword: an

intense cut reinvigorates search, but too frequent or pro-

longed cutting of ties leads to incompatible outcomes that

can hamper supply chain performance.

We offer sever al contributio ns to research o n buyer–

supplier relationships. First, we investigate the phenomenon

of temporary deembedding and develop theory on how it

affects supply chain performance. This is important because

prevailing research advises balanced degrees of embedded-

ness (e.g., Uzzi, 1996, 1997; Villena et al., 2011; Zhou et al.,

2014), while largely ignoring the question of how an ideal bal-

ance can be achieved. We address this issue by proposing a

dynamic balance. More specifically, we argue that the level of

embeddedness can be altered dynamically over time to reinvi-

gorate supply chain innovation and performance. Second, we

integrate supply chain embeddedness with complexity theory

and its fundamental notion of search. The search perspective

offers new theoretical insights on the outcomes of buyer–

supplier relationships that go beyond the current explanations

based on transaction cost economics (e.g., Williamson, 1985),

the relational view (e.g., Dyer & Singh, 1998), and social net-

work theory (e.g., Gulati & Sytch, 2007; Uzzi, 1996, 1997).

Third, we propose an agency view of buyer–supplier relation-

ships. This view qualifies extant approaches that consider

these relationships as either relatively stable (e.g., Villena

et al., 2011; Zhou et al., 2014) or as following a presaged

course (e.g., Jap & Anderson, 2007; Vanpoucke, Vereecke, &

Boyer, 2014). Instead, we explicitly recognize the agency of

firms in deliberately and strategically tuning their relation-

ships. Supply chain researchers can benefit from this fresh

agency viewpoint, because it sheds light on the pivotal role of

endogenous change s in buyer–supplier relationships.

2|LITERATURE REVIEW

Complexity theory, with its central notion of search (Cyert &

March, 1963), describes how organizations generate innovations

STING ET AL.115