






































17 

Betweenness Centrality and Supplier 
Performance: The Missing Link? 

Dong-Young Kim 
University of North Florida 

Although researchers have paid increasing attention to network centrality in the literature of supply 
chain management, little is known about the role of betweenness centrality in enhancing supplier 
performance. This study reviews prior research that conceptualizes betweenness centrality. We take 
the social network perspective and investigate whether betweenness centrality can be a source of 
external information and competitive advantage. This study contributes to the literature of social 
network and supply chain management by synthesizing conceptual views of betweenness centrality 
and highlighting the way in which betweenness centrality can play a role as a knowledge broker between 
isolated firms. 

Keywords: Centrality; Betweenness; Social network; Supplier; Performance 

Introduction 
Scholars in supply chain management have become increasingly interested in how the central 

position occupied by a firm in supply networks influences performance outcomes (Carnovale & 
Yeniyurt, 2014; Gokpinar et al., 2010; Schilling, & Phelps, 2007). As firms’ own knowledge base alone 
is not sufficient to obtain diversified knowledge, it is important for firms to capture, interpret, and 
implement knowledge resources from external networks (Gao et al., 2015). A firm’s position is 
essential for achieving goals and succeeding in a competitive business environment (Hanneman & 
Riddle, 2005; Hallikas et al., 2008; Mazzola et al., 2015). Position influences a firm’s decisions around 
investments, acquisition partners, and alliance formations (Ranganathan & Rosenkopf, 2014). Extant 
research on network positions has focused on how network configurations enable the channeling of 
scarce and valuable resources through various network connections. Researchers, for example, have 
discussed how to bridge ties in a web of relationships or how to build strong ties between network 
participants (Peng & Mu, 2011). However, establishing the network position also involves some costs 
to firms. Perry-Smith and Shalley (2003) argue, for example, that a firm’s position is not always 
beneficial for creativity. It is also difficult for individual firms to identify appropriate partners and 
resources in a network because information about new market opportunities is dispersed in supply 
networks (Soh & Roberts, 2005). 

Researchers have paid increasing attention to network centrality in the literature of supply 
chain management. Organizational collaboration in supply chains leads to the development of social 
ties and partner-specific knowledge that influence the variance in firms’ performance (Kim & Zhu, 
2018). However, the development of theories on network centrality is still at the early stages in the 

http://journals.sfu.ca/abr ADVANCES IN BUSINESS RESEARCH 
2019, Volume 9, pages 17-25 



18 

 

field of operations and supply chain management (Borgatti & Li, 2009). Limited attention has been 
directed to betweenness centrality. Betweenness centrality indicates a firm’s ability to manage direct 
and indirect ties and to potentially gain access to expertise (Cross & Cummings, 2004). We will begin 
by considering the insights of indirect ties, because the indirect ties are a critical source of information 
and innovation and serve as information intermediaries that enhance the information base of 
companies, and thus strengthen their ability to absorb and utilize external knowledge (Cross & 
Cummings, 2004; Lechner et al., 2010; Mehra et al., 2001; Sasovova et al., 2010). Few studies have 
explicitly accounted for the effect of this dimension on supply chain performance (e.g., Carnovale & 
Yeniyurt, 2014; Fox et al., 2013; Schilling & Phelps, 2007). Researchers have therefore called for more 
research in order to better understand the nature of betweenness centrality (Carnovale & Yeniyurt, 
2014; Easton & Rosenzweig, 2015; Fox et al., 2013; Wincent et al., 2010). 

The purpose of this study is to review prior research that has proposed definitions of 
betweenness centrality. This study will also discuss how and why a supplier firm’s betweenness 
centrality influences financial performance. We take the supplier perspective and explore whether 
betweenness centrality can be a source of external information and competitive advantage in supplier 
firms. Social network theory suggests that we examine the impacts of both direct and indirect ties on 
performance because firms are interconnected with one another and are embedded in various external 
social networks (Gao et al., 2015). Our argument is that although the number of a firm’s direct ties 
indicates an opportunity to have access to external information, the firm also requires the capability to 
bridge unconnected ties and leverage the unique information that is available in supply chains. We 
draw on insights from social network theory to explain why betweenness centrality can lead to access 
to diverse information and knowledge. 

The remainder of the paper is organized as follows. First, definitions of betweenness centrality 
in the literature are reviewed. Second, we discuss the measures of betweenness centrality. Third, we 
introduce a proposition for how betweenness centrality influences supplier performance. Finally, we 
summarize the key findings and contributions of this study. 
 

Definition 
Given the broad definitions of betweenness centrality that have been proposed, researchers 

have underlined one of the two dimensions: bridging structural holes and the number of shortest 
paths (see Figure 1). 
 

Figure 1: Classification of Definitions 
 

 
 
In terms of the bridging structural holes, researchers highlight that a firm’s position encourages them 
to bridge structural holes among unconnected partners (Davis & Mizruchi, 1999; Fox et al., 2013; 
Mehra et al., 2001; Sasovova et al., 2010; Van Wijk et al., 2013; Wincent et al., 2010; Yan et al., 2015). 



19  

The structural hole refers to an absence of ties between a firm’s partners, that provides non-redundant 
information from various disconnected network clusters (Andrevski et al., 2016). The structural hole 
also represents missing relationships that inhibit information flow between business partners (Burt, 
2007). In this dimension, betweenness centrality indicates the amount of brokerage each firm has 
between all other firms in a network (Borgatti et al., 2013). High betweenness indicates that a central 
firm has the power to threaten a network with operational disruption (Borgatti et al., 2013). For 
example, a firm (A) serves as a gatekeeper when the firm lies between two firms (B and C) that are not 
directly connected. The central firm (A) controls the flow of information and products by serving as a 
liaison between the isolated firms (Wincent et al, 2010; Carter et al., 2007). 

Van Wijk et al. (2013) and Wincent et al. (2010) define betweenness centrality as the extent to 
which a firm is bridging structural holes by connecting two or more partners that are not directly 
connected with one another (Wincent et al., 2010). Sasovova et al. (2010) view a firm with 
betweenness centrality as a broker in a network where the broker directly connects other firms that are 
not directly connected. Firms spanning many structural holes tend to be exposed to diverse 
information, which motivates the firms to discover new productive resource combinations and 
manage the pool of unused combinations of productive resources (Andrevski et al., 2016). Fox et al. 
(2013) describe betweenness centrality as the extent to which strategic suppliers or customers 
communicate with each other outside of a focal firm. Mehra et al. (2001) suggest that betweenness 
centrality is the extent to which a firm occupies a structurally advantageous position, connecting 
otherwise unconnected others in networks (Mehra et al., 2001). Davis and Mizruchi (1999) and Yan 
et al. (2015) argue that a firm with betweenness centrality plays the role of a gatekeeper who evaluates 
and imports external information and resources. 

The second dimension of betweenness centrality is the number of shortest paths that a firm 
occupies in a network. According to this dimension, a central firm should manage the shortest 
paths in a network in a way that enables it to gain the benefits of its network position. Researchers 
have attempted to quantify how a firm is positioned on the geodesic path connecting two other firms. 
Easton and Rosenzweig (2015), for instance, argue that betweenness centrality refers to the number of 
shortest paths that go through the node. Carter et al. (2007) describe betweenness centrality as the 
number of paths that pass through a central firm on the shortest paths connecting two other firms. 
Borgatti and Li (2009) define betweenness centrality as the extent to which a firm lies along many of 
the shortest paths between pairs of others. Carnovale and Yeniyurt (2014) and Schilling and Phelps 
(2007) suggest that betweenness centrality describes the extent to which a firm is located on the 
shortest path between any two other firms in its supply networks. These definitions highlight the 
position of a firm on the geodesic path that may explain why and how firms interact with one another 
and facilitate or constrain interactions (Cannella & McFadyen, 2016). Table 1 shows example 
definitions of betweenness centrality. 
 

Table 1: Example Definitions of Betweenness Centrality 
 

Category Definition 

Bridging holes • The extent to which partners partner and communicate with each other 
outside of a focal firm (Fox et al., 2013) 

• The extent to which a firm is bridging structural holes by connecting two or 
more partners that are not directly connected with one another (Wincent et 
al., 2010). 

• The extent to which each firm occupies a structurally advantageous position, 
connecting other firms that are unconnected in a network (Mehra et al., 2001) 

• The extent to which a firm plays the role of a gatekeeper in a network (Yan 
et al., 2015) 



20 

 

Category Definition 

 • The extent to which a firm in a network is on the shortest path among many 
network partners (Davis and Mizruchi, 1999) 

• The extent to which a firm plays the role of a broker who bridges structural 
holes (Van Wijk et al., 2013) 

• A broker in a network that represents the frequency with which a node 
directly connects pairs of nodes that are not directly connected (Sasovova et 
al., 2010) 

Shortest paths • The number of these shortest paths that pass through the node (Easton and 
Rosenzweig, 2015). 

• The number of paths that go through a firm on the shortest paths connecting 
two other firms (Carter et al., 2007) 

• The extent to which a firm lies along many shortest paths between pairs of 
other firms (Borgatti and Li, 2009) 

• The metric’s ability to assess the impact of ties beyond one remove from a 
person (Cross and Cummings, 2004) 

• The extent to which a firm is located on the shortest path between any two 
firms in its network (Schilling and Phelps, 2007) 

• The extent to which a network actor is located strictly between other actors 
(Carnovale and Yeniyurt, 2014) 

 

 

Measurement 
Betweenness centrality can be measured as the frequency with which company i is located along the 
shortest path between two other firms (Freeman, 1979) in the network. The measure was calculated as 
follows: 

 

 
 

where gjkt is the shortest path between companies j and k in year t and gjikt is the shortest path between 
companies j and k that contains company i in year t, and N, if the total number of firms in the network. 
The network analysis software UCINET 6 can be used to calculate the level of betweenness centrality. 
 

Role of Betweenness Centrality in Improving Supplier Financial Performance 
In this section, we draw on the social network theory to clarify the relationship between a supplier 

firm’s betweenness centrality and its financial performance. Supplier financial performance refers to the 
extent to which a supplier firm improves financial performance, measured as return on investment and 
sales growth rate. Social network theory suggests that a comprehensive understanding of supply networks 
requires a more complete consideration of network structure, network content, and the surrounding 
conditions (Gao et al., 2015). 

Betweenness centrality is likely to influence supplier financial performance for three reasons. 



21  

First, a supplier firm that acts as a bridge to new opportunities can connect other firms separated by 
structural holes, which may enable the firm to access novel information and improve firm performance 
(Tiwana, 2008). It is important to consider the extent to which a supplier firm engages in boundary 
stretching practices (Markóczy et al., 2013). This is because bridging a tie refers to a firm’s ability to span 
a structural hole (Regans et al., 2004). The structural position serves as a basis for the exercise of power 
(Brass and Burkhardt, 1993). Obstfeld (2005) argue that a firm’s effort to develop a relationship with 
disconnected partners is central to the combinative activity at the root of innovation. The structural 
characteristics are characterized by how the sources of information and knowledge are structurally 
connected in supply networks, and how effectively a firm can access the different sources of information 
and knowledge assets in the network (Bellamy et al., 2014; Brass and Burkhardt, 1993). Similarly, 
betweenness centrality as an intermediary role has been shown to enhance communication frequency in 
supply chains. Carnovale and Yeniyurt (2014), in an empirical study, found that a high level of 
betweenness centrality signals a supplier firm’s legitimacy and credibility, facilitating interaction and 
communication among network partners. Ahuja et al. (2003) suggest that centrality enables a firm to 
actively communicate social norms and expectations, which helps to align the interests of each partner 
with a mutual goal. Gokpinar et al. (2013) argue that the misalignment of communication patterns and 
product architecture is associated with a decrease in the quality of the final product. Cross and Cummings 
(2004) suggest that when a firm is positioned in the intermediary location, it can absorb knowledge and 
respond appropriately to a changing environment. Thus, a supplier firm with betweenness centrality is 
likely to have an opportunity to gain access to valuable knowledge and to enhance financial performance. 

Second, betweenness centrality offers a means of not only controlling opportunistic behaviors of 
supply partners but also collecting valuable knowledge (Fox et al., 2013). Management scholars have 
stressed the importance of network ties as a driver of a firm’s behavior (Brass et al., 2004). Uzzi (1996) 
argues that social ties and network structures influence economic behavior. Acting as a knowledge broker 
allows a supplier firm to have decision-making authority and to identify non-redundant information 
concerning resources and opportunities (Mehra et al., 2001). Moreover, a supplier firm with a high 
betweenness centrality can determine who deserves to access valuable external knowledge resources (Liu 
et al., 2005), which may influence firm performance. Although a supplier firm needs to maintain a variety 
of ties and work closely with other firms, obtaining unique and non-redundant information is important 
to improving financial performance. Betweenness centrality concerns the extent to which a firm serves 
as a bridge between two or more actors that are indirectly connected (Burt, 1992). This kind of centrality 
indicates how effectively a supplier firm controls its unconnected firms and accesses a flow of non-
redundant information (Wincent et al., 2010; Easton and Rosenzweig, 2015). The more a supplier firm 
acts as a knowledge broker between isolated firms in supply chains, the greater will be the likelihood of 
financial performance improvement. 

Third, a supplier firm’s structural hole position is the location of its brokerage between a firm 
and other disconnected firms in a network, representing an ability to monitor the flow of information 
and enhance performance (Burt, 1992; Lin et al., 2009). A broker that connects unknown partners can 
span distinct social circles where diverse information circulates (Wong and Boh, 2014). The structural 
position of a firm indicates different motivations for using knowledge and differential access to the 
knowledge circulated in a network (Paruchuri and Awate, 2017). This position functions as a signal of the 
prestige granted, indicating its prior performance and position in a social structure (Jensen, 2008). A firm’s 
structural position in a network indicates that the firm can cooperate with other firms in transferring 
information and resources (Peng et al., 2010). Such a network structure is the pattern of connections 
between two firms and is associated with the impersonal configuration of linkages within supply networks 
(Villena et al., 2011). The network structure is also related to a firm’s position relative to direct and indirect 
ties surrounding the firm (Lechner et al., 2010). Understanding the network structure is a process that 
requires identifying the position a supplier holds in an extended network, with the aim of evaluating the 



22 

 

informational values of that position (Kim, 2014). The structure of a supply network also represents 
channels of information and consists of mechanisms to search and monitor network partners’ strategies 
and actions (Lin et al., 2009). These arguments suggest the following hypothesis: 
 
Proposition: The network centrality of a supplier firm who acts as a bridge between unconnected 
partners influences its financial performance. 

 
Conclusion 

The goal of this study is to synthesize the definitions of betweenness centrality and discuss the 
effects of a supplier firm’s betweenness centrality on performance. Drawing on social network theory, we 
develop a proposition as an interpretation of the key idea of the theory. According to social capital theory, 
firms whose network connections bridge across holes or gaps in supply chains tend to have a greater 
capacity for knowledge sharing and generate more innovations than other firms whose network 
relationships feed into dense networks (Tortoriello, 2015). We synthesize insights from prior studies that 
proposed different definitions. Our review shows that researchers have defined betweenness centrality 
from two perspectives: bridging structural holes and the number of shortest paths. We found that 
researchers argue that firms with betweenness centrality leverage their shortest paths in networks and 
bridge unconnected ties in order to leverage unique information available in supply chains. 

This research makes three theoretical contributions to the literature on social network and supply 
chain management. First, this study advances our understanding of the role of a supplier firm’s 
betweenness centrality in enhancing financial performance. We applied the insights of the supply chain 
theory to the network literature. Researchers have mainly discussed the relationship between a focal firm’s 
degree centrality and performance (Arya and Lin, 2007; Carnovale and Yeniyurt, 2014; Reinholt et al., 
2011; Tsai et al., 2011). Second, this study highlights betweenness centrality as a way for supplier firms to 
act as knowledge brokers between isolated firms and thus improve their financial performance. From a 
theoretical perspective, this study suggests that a supplier firm that lies between other pairs of partners 
accrue advantages from their network position (Fox et al., 2013; Hanneman and Riddle, 2005; Yan et al., 
2015). Third, this study attempts to synthesize the conceptual views of betweenness centrality. Although 
extant research has highlighted the importance of betweenness centrality, comparative analysis of existing 
definitions of centrality has rarely been provided in the literature. We hope our findings will stimulate 
new avenues of future research. 
 

References 
Ahuja, M.K., Galletta, D.F., & Carley, K.M. (2003). Individual centrality and performance in virtual 

R&D groups: An empirical study. Management Science, 49(1), 21–38. 
Andrevski, G., Brass, D.J., & Ferrier, W.J. (2016). Alliance portfolio configurations and competitive 

action frequency. Journal of Management, 42(4), 811–837. 
Arya, B., & Lin, Z. (2007). Understanding collaboration outcomes from an extended resource-based 

view perspective: The roles of organizational characteristics, partner attributes, and network 
structures. Journal of Management 33(5), 697–723. 

Bellamy, M.A., Ghosh, S., & Hora, M. (2014). The influence of supply network structure on firm 
innovation. Journal of Operations Management, 32(6), 357–373. 

Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. SAGE 
Publications Limited, 1st edition. 

Borgatti, S.P., & Li, X. (2009). On social network analysis in a supply chain context. Journal of Supply 
Chain Management, 45(2), 5–22.  

Brass, D. J., & Burkhardt, M. E. (1993). Potential power and power use: An investigation of structure and 

behavior. Academy of Management Journal, 36(3), 441‐470. 



23  

Brass, D. J., Galaskiewicz, J., Greve, H. R., & Tsai, W. (2004). Taking stock of networks and 

organizations: A multilevel perspective. Academy of management journal, 47(6), 795‐ 817. 
Burt, R.S. (1992). Structural holes. Harvard University Press: Cambridge, MA. 
Burt, R.S. (2007). Secondhand brokerage: Evidence on the importance of local structure for 

managers, bankers, and analysts. Academy of Management Journal, 50(1), 119–148. 
Cannella Jr, A.A., & McFadyen, M.A. (2016). Changing the exchange: The dynamics of knowledge 

worker ego networks. Journal of Management, 42(4), 1005–1029. 
Carnovale, S., & Yeniyurt, S. (2014). The role of ego networks in manufacturing joint venture 

formations. Journal of Supply Chain Management, 50(2), 1–17. 
Carter, C.R., Ellram, L.M., & Tate, W. (2007). The use of social network analysis in logistics research. 

Journal of Business Logistics, 28(1), 137–168. 
Cross, R., & Cummings, J.N. (2004). Tie and network correlates of individual performance in 

knowledge-intensive work. Academy of Management Journal, 47(6), 928–937. 
Davis, G.F., & Mizruchi, M.S. (1999). The money center cannot hold: Commercial banks in the US 

system of corporate governance. Administrative Science Quarterly, 44(2), 215–239. 
Easton, G.S., & Rosenzweig, E.D. (2015). Team leader experience in improvement teams: A social 

networks perspective. Journal of Operations Management, 37, 13–30. 
Fox, G.L., Smith, J.S., Cronin Jr, J.J., & Brusco, M. (2013). Weaving webs of innovation. International 

Journal of Operations & Production Management, 33(1), 5–24.  
Gao, G. Y., Xie, E., & Zhou, K.Z. (2015). How does technological diversity in supplier network drive 

buyer innovation? Relational process and contingencies. Journal of Operations Management, 36, 
165–177. 

Gokpinar, B., Hopp, W.J., & Iravani, S.M. (2010). The impact of misalignment of organizational 
structure and product architecture on quality in complex product development. Management 
Science, 56(3), 468–484. 

Gokpinar, B., Hopp, W.J., & Iravani, S.M. (2013). In-house globalization: The role of globally 
distributed design and product architecture on product development performance. 

Production and Operations Management, 22(6), 1509–1523. 
Hallikas, J., Varis, J., Sissonen, H., & Virolainen, V.M. (2008). The evolution of the network structure 

in the ICT sector. International Journal of Production Economics, 115(2), 296– 304. 
Hanneman, R.A. & Riddle, M. (2005). Introduction to social network methods. Riverside, CA: University of 

California, Riverside (published in digital form at http://faculty.ucr.edu/~hanneman/) 
Jensen, M. (2008). The use of relational discrimination to manage market entry: When do social status 

and structural holes work against you? Academy of Management Journal, 51(4), 723–743. 
Kim, D.-Y. (2014). Understanding supplier structural embeddedness: a social network perspective. 

Journal of Operations Management, 32(5), 219–231. 
Kim, D.-Y., & Zhu, P. (2018). Supplier dependence and R&D Intensity: The moderating role of 

network centrality and interconnectedness. Journal of Operations Management, 64(1), 7- 18. 
Lechner, C., Frankenberger, K., & Floyd, S.W. (2010). Task contingencies in the curvilinear 

relationships between intergroup networks and initiative performance. Academy of Management 
Journal, 53(4), 865–889. 

Lin, Z.J., Peng, M.W., Yang, H., & Sun, S.L. (2009). How do networks and learning drive 
M&As? An institutional comparison between China and the United States. Strategic 
Management Journal, 30(10), 1113–1132. 

Liu, B. S., Madhavan, R., & Sudharshan, D. (2005). DiffuNET: The impact of network structure on 
diffusion of innovation. European Journal of Innovation Management, 8(2), 240-262.  

Markóczy, L., Li Sun, S., Peng, M. W., Shi, W., & Ren, B. (2013). Social network contingency, symbolic 
management, and boundary stretching. Strategic Management Journal, 34(11), 1367–1387. 

http://faculty.ucr.edu/~hanneman/)


24 

Mazzola, E., Perrone, G., & Kamuriwo, D.S. (2015). Network embeddedness and new product 
development in the biopharmaceutical industry: The moderating role of open innovation 
flow. International Journal of Production Economics, 160, 106–119. 

Mehra, A., Kilduff, M., & Brass, D. J. (2001). The social networks of high and low self- monitors: 
Implications for workplace performance. Administrative Science Quarterly, 46(1), 121–146. 

Obstfeld, D. (2005). Social networks, the tertius iungens orientation, and involvement in 

innovation. Administrative Science Quarterly, 50(1), 100‐130. 
Paruchuri, S., & Awate, S. (2017). Organizational knowledge networks and local search: The role 

of intra-organizational inventor networks. Strategic Management Journal, 38(3), 657–675. 
Peng, G., & Mu, J. (2011). Network Structures and Online Technology Adoption. IEEE 

Transactions on Engineering Management, 58(2), 323-333. 
Peng, T.-J. A., Lin, N.-J., Martinez, V., & Yu, C.-M. J. (2010). Managing triads in a military avionics 

service maintenance network in Taiwan. International Journal of Operations & Production 
Management, 30(4), 398–422. 

Perry-Smith, J.E., & Shalley, C.E. (2003). The social side of creativity: A static and dynamic 
social network perspective. Academy of Management Review, 28, 89–106. 

Ranganathan, R., & Rosenkopf, L. (2014). Do ties really bind? The effect of knowledge and 
commercialization networks on opposition to standards. Academy of Management Journal, 
57(2), 515–540. 

Regans, R., Zuckerman, E., & McEvily, B. (2004). How to make the team: social networks vs. 
demography as criteria for designing effective teams. Administrative Science Quarterly 49(1), 
101–133. 

Reinholt, M., Pedersen, T., & Foss, N.J. (2011). Why a central network position isn’t enough: The 
role of motivation and ability for knowledge sharing in employee networks. 
Academy of Management Journal 54(6), 1277–1297. 

Sasovova, Z., Mehra, A., Borgatti, S.P., & Schippers, M.C. (2010). Network churn: The effects of 
self-monitoring personality on brokerage dynamics. Administrative Science Quarterly, 55(4), 
639–670. 

Schilling, M.A., & Phelps, C.C. (2007). Interfirm collaboration networks: The impact of large- scale 
network structure on firm innovation. Management Science, 53(7), 1113–1126.  

Soh, P.H., & Roberts, E.B. (2005). Technology alliances and networks: an external link to research 
capability. IEEE Transactions on Engineering Management, 52(4), 419–428. 

Tiwana, A. (2008). Do bridging ties complement strong ties? An empirical examination of 
alliance ambidexterity. Strategic Management Journal, 29(3), 251–272. 

Tortoriello, M. (2015). The social underpinnings of absorptive capacity: The moderating effects of 
structural holes on innovation generation based on external knowledge. Strategic Management 
Journal, 36(4), 586–597. 

Tsai, W., Su, K., & Chen, M.J. (2011). Seeing through the eyes of a rival: Competitor acumen 
based on rival-centric perceptions. Academy of Management Journal 54(4), 761–778. 

Uzzi, B. (1996). The sources and consequences of embeddedness for the economic performance of 

organizations: The network effect. American Sociological Review, 61(4), 674‐698. 
Van Wijk, J., Stam, W., Elfring, T., Zietsma, C., & Den Hond, F. (2013). Activists and incumbents 

structuring change: The interplay of agency, culture, and networks in field evolution. 
Academy of Management Journal, 56(2), 358–386. 

Villena, V.H., Revilla, E., & Choi, T.Y. (2011). The dark side of buyer–supplier relationships: A 
social capital perspective. Journal of Operations Management, 29(6), 561–576. 

Wincent, J., Anokhin, S., Örtqvist, D., & Autio, E. (2010). Quality meets structure: Generalized 



25 

reciprocity and firm-level advantage in strategic networks. Journal of Management Studies, 
47(4), 597–624. 

Wong, S.S., & Boh, W.F. (2014). The contingent effects of social network sparseness and 
centrality on managerial innovativeness. Journal of Management Studies, 51(7), 1180– 1203. 

Yan, T., Choi, T.Y., Kim, Y., & Yang, Y. (2015). A theory of the nexus supplier: A critical supplier 
from a network perspective. Journal of Supply Chain Management, 51(1), 52– 66. 


