Social network analysis in international business research: An assessment of the current state of play and future research directions

Abstract

The theory of networks has attracted increasing attention in international business (IB) research. However, despite its growing theoretical utilization, the systematic description, modelling and analysis of network relationships has been scarce in IB. The field has taken a stand in favour of conventional methodological approaches, which hinders the close interaction between theoretical development and empirical reality in network-based IB research. This study seeks to contribute to IB research by incorporating social network analysis (SNA) as an innovative and promising research tool and aims to shed light on the richness of SNA in fostering IB research, through adding more nuanced network understanding. In particular, it calls attention to its potential applications through exemplifying around two fundamental IB phenomena: firm internationalization and multinational enterprises (MNEs). It concludes by suggesting future directions for utilising SNA in the IB field.

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Social network analysis in international business research:
An assessment of the current state of play and future
research directions
Yusuf Kurt • Mustafa Kurt
This is an Author’s Original Manuscript (AAM) of an article published by
Elsevier in International Business Review. Please cite the published
article:
Kurt, Yusuf and Mustafa Kurt (2020) "Social network analysis in international business research:
An assessment of the current state of play and future research directions" International
Business Review https://doi.org/10.1016/j.ibusrev.2019.101633
Abstract
 The theory of networks has attracted increasing attention in international business (IB)
research. However, despite its growing theoretical utilization, the systematic description,
modelling and analysis of network relationships has been scarce in IB. The field has taken a
stand in favour of conventional methodological approaches, which hinders the close
interaction between theoretical development and empirical reality in network-based IB
research. This study seeks to contribute to this body research by incorporating social network
analysis (SNA) as an innovative and promising research tool and aims to shed light on the
richness of SNA in fostering IB research, through adding more nuanced network
understanding. In particular, it calls attention to its potential applications through
exemplifying around two fundamental IB phenomena: firm internationalization and
multinational enterprises (MNEs). It concludes by suggesting future directions for utilising
SNA in the IB field.
Key Words
 Social network analysis; business networks; internationalization; multinational enterprises;
network structure; structural holes; network closure
Acknowledgements
 We gratefully acknowledge the comments and recommendations received from S. Tamer
Cavusgil, Rudolf R. Sinkovics, Mo Yamin, and Steven Yen-Hung Liu at the different stages
of article development. We also thank the IBR editor and two anonymous reviewers.

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Social network analysis in international business
research: An assessment of the current state of play
and future research directions
Abstract
The theory of networks has attracted increasing attention in international business (IB) research.
However, despite its growing theoretical utilization, the systematic description, modelling and
analysis of network relationships has been scarce in IB. The field has taken a stand in favour of
conventional methodological approaches, which hinders the close interaction between theoretical
development and empirical reality in network-based IB research. This study seeks to contribute to
IB research by incorporating social network analysis (SNA) as an innovative and promising
research tool and aims to shed light on the richness of SNA in fostering IB research, through
adding more nuanced network understanding. In particular, it calls attention to its potential
applications through exemplifying around two fundamental IB phenomena: firm
internationalization and multinational enterprises (MNEs). It concludes by suggesting future
directions for utilising SNA in the IB field.
Keywords: Social network analysis; business networks; internationalization; multinational
enterprises; network structure; structural holes; network closure.

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1. Introduction
Parkhe, Wasserman, and Ralston (2006, p. 560) note that ‘networks are reshaping the global
business architecture’, an idea that is also applicable to current international business (IB)
research. The network view has reshaped conventional theoretical understandings by elevating
research focus, from being firm-centric towards a more relational, contextual and systemic level
(cf. Forsgren and Johanson, 2014). Network thinking has been intensively integrated into various
IB phenomena, including internationalization (Coviello, 2006; Johanson and Vahlne, 2009;
Yamin and Kurt, 2018), multinational enterprises (Buckley, 2011; Dhanaraj, 2007; Ghoshal and
Bartlett, 1990), and foreign direct investment (Chen and Chen, 1998; Jean, Tan, and Sinkovics,
2011), to name a few.
However, despite its growing theoretical utilization, the systematic and empiric analysis
of networks has been scarce in IB (Yamin and Kurt, 2018). The field conceptualizes markets as
systems of networks and acknowledges that international business activities take place within the
context of network relationships (e.g. Forsgren and Johanson, 2014; Johanson and Vahlne, 2009,
2011). Nonetheless, the network view in IB research has been predominantly generic and
metaphorical, as structural and positional attributes of networks have not been effectively
integrated into empirical analyses. For instance, networks are mostly considered as general webs
of relationships, in which network outcomes are assumed to be evenly distributed among the
members. However, the structural attributes and configuration of a network in which a focal actor
is embedded define the level and type of network outcomes available to the actor. Hence, this
approach implies a static network view that treats actors as passive agents and overlooks their
conscious and deliberate actions in attempting to create network configurations that benefit them
(Ahuja, Soda, and Zaheer, 2012).

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Furthermore, despite the field’s increasing recognition of the importance of context in
determining opportunities for or constraints on actors (e.g. Michailova, 2011), the influence of
structural patterns of social context on actor-level outcomes has not been sufficiently established.
This is mainly because context has been treated as homogenous and focus has been largely on
actors rather than the patterns of interaction and interdependence among them. Hence, the field’s
efforts at understanding contexts are likely to be incomplete and potentially flawed without
appreciation of the underlying structural patterns of social context (Ahuja, et al., 2012, p. 434).
Therefore, for IB scholarship to progress, analytical tools are needed in the analysis of network
regularities within social structures, revealing the interdependence of IB activities and actor
behaviours and thus developing a more nuanced understanding of networks. This need was
identified by IB scholars, who pointed out that ‘social networks need to be investigated with
appropriate methodologies’ (Zucchella, Palamara, and Denicolai, 2007, p. 277).
One such methodology is social network analysis (SNA), which refers to a set of
analytical tools for mapping and measuring relationships among social entities, such as
individuals, organizations or any social units (Cross, Parker, and Sasson, 2003; Wasserman and
Faust, 1994). SNA’s essential premise is that the social world and actors within it are created and
shaped primarily by relationships and patterns formed by these relationships (Marin and
Wellman, 2011). Hence, it takes a fundamentally different perspective from individualist and
attribute-based methodologies in explaining actor behaviours and substantive outcomes. It sees
the social world in terms of interactions, rather than in an aggregation of actors who act
independently, and thus focuses on patterns of relationships as the unit of analysis (Marin and
Wellman, 2011).
From an SNA perspective, a notable shortcoming of network-based IB research is its
effort to understand actor behaviours and substantive outcomes by employing conventional

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individualist methods, which hinders the compatibility of theoretical development with empirical
reality. This incompatibility is primarily associated with the underlying assumption of the
independence of conventional statistical methods, which explain the attributes of an entity as a
function of other attributes of that same entity (Borgatti and Li, 2009). While IB research
theoretically recognizes that causation is located not only in individual attributes, but also in
networks within the social context (i.e. Forsgren and Johanson, 2014; Holm, Eriksson, and
Johanson, 1996), empirical research is not supported by appropriate methodological tools that can
reveal a network’s attributes within the social context.
In response to this shortcoming, the purpose of the present study is to propose SNA to IB
scholarship as a promising tool for systematically describing, modelling and analysing network
relationships. The key contribution of the study lies in demonstrating the rich potential of SNA in
revealing the interdependence of actor behaviours and IB activities within network relationships.
By suggesting a shift from an actor-centric to a relational focus in the unit of analysis, the study
presents how the use of SNA can lead to more nuanced understandings of existing network-based
theoretical frameworks and organizational conceptualizations.
The study is not primarily engaging in a technical analysis of network data, as there are
seminal sources (e.g. Borgatti, Everett, and Johnson, 2013; Wasserman and Faust, 1994) that can
guide IB scholars in SNA’s methodological application. Rather, it aims to draw the attention of
IB scholars to the untapped potential of SNA by exemplifying around two fundamental IB
phenomena: firm internationalization and multinational enterprises (MNEs). The utilization of
SNA in network-based IB research is far from exhausted. Hence, this is a timely and important
attempt, as building bridges with other disciplines and close integration between theoretical
development and empirical reality with appropriate analytical tools are considered to be exactly

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what IB research needs to proceed as a viable and progressive field (Buckley, Doh, and
Benischke, 2017; Cheng, Henisz, Roth, and Swaminathan, 2009).
The remainder of the paper is structured as follows. The next section introduces SNA with
its key concepts. It is followed by an extensive review of the IB literature to demonstrate how and
how far SNA has been utilized so far. The subsequent section develops the core arguments
around two fundamental IB domains in which SNA can advance our existing understanding. The
paper concludes with suggestions for future research directions in utilizing SNA.
2. Social network analysis
2.1. What is social network analysis?
A social network is defined as a finite set or sets of actors (e.g. people, organizations or other
social entities) and the relation or relations defined on them (Wasserman and Faust, 1994, p. 20).
Social networks play a fundamental role as a means of spreading information, ideas, resources,
and influence among members (Kempe, Kleinberg, and Tardos, 2003; Lea, Yu, Maguluru, and
Nichols, 2006). The basic assumptions at the heart of social network research include: that
exchange is embedded in social relations and complex social structures; that relationships do not
occur in isolation; and that relationships matter in terms of outcomes at both actor and group
levels (Kilduff and Brass, 2010).
SNA focuses on the analysis of relationships among social entities, and on the patterns
and implications of these relationships (Wasserman and Faust, 1994). It is not a formal theory,
but rather a set of analytical tools for investigating social structures (Marin and Wellman, 2011;
Otte and Rousseau, 2002). Based on the mathematical approaches of graph theory, it enables
relationships to be represented and described systematically and compactly (Hanneman and
Riddle, 2005; Scott, 2013). Where traditional social science methods have mainly relied on

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‘attribute data’ concerning the properties of particular actors, SNA deals with ‘relational data’
1
,
dealing mainly with the properties of network relationships among actors (Scott, 2013).
In contrast to traditional individualist methodological approaches that focus on actors as
the unit of analysis, SNA takes relationships among actors as the unit of analysis and asserts that
actors’ behaviours are influenced by the structural regularities of relationships surrounding them
(Borgatti and Li, 2009; Carter, Ellram, and Tate, 2007; Otte and Rousseau, 2002). Based on
Coleman (1958) earlier discussions, Fombrun (1982, p. 281) emphasized the distinction between
traditional methodologies and SNA:
In usual statistical analysis, [an] interview is regarded as independent of others…
[whereas in network analysis] an individual is seen as a part of some larger structure in
which the respondent finds himself…Thus, the individual is not treated independently. He
is seen embedded in a context that both constrains and liberates.
Thus, SNA offers the possibility of leveraging social and behavioural science research
through providing precise formal definitions of, and metrics for, aspects of the social context in
which actors are embedded (Wasserman and Faust, 1994, p. 3). In their seminal work,
Wasserman and Faust (1994, p. 4) identified four central principles of SNA that distinguish it
from other research approaches: (i) actors and their actions are considered to be interdependent
rather than independent; (ii) relationships between actors are seen as channels for tangible and
intangible resources; (iii) network patterns either facilitate or constrain individual actions; and
(iv) social structures are conceptualized as patterns of relations among social actors. In sum, the
relationships between actors, the properties of relationships and their influence on actors’
behaviour are among the prime concerns and the unit of analysis in SNA. Hence, SNA offers
1
Attributes and relational data are not the only types of data in social sciences: a third type of data is ideational data, which
directly describes the meanings, motives, definitions and typifications involved in actions (Scott, 2013, p.3).

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analytical tools distinct from the traditional methods of statistics and data analysis (Wasserman
and Faust, 1994).
2.2. Key concepts in social network analysis
This section introduces some of the key SNA concepts (i.e. open and closed networks,
actor centrality, and core and periphery structure), as core arguments in subsequent sections are
developed with reference to these SNA metrics. The concepts are mainly discussed with
reference to the concept of social capital, which is defined as ‘the sum of the actual and
potential resources embedded within, available through and derived from the network of
relationships possessed by an individual or social unit’ (Nahapiet and Ghoshal, 1998, p.
243).
Open network (structural holes)
An open network can be understood as a network structure in which structural holes exist.
Structural holes refer to links between otherwise disconnected groups (Burt, 1992, 2000). The
core proposition of structural holes is that network positions associated with the highest social
capital lie between rather than within dense regions of relationships (Walker, Kogut, and Shan,
1997, p. 112). The key benefit of open networks results from accessing complementary, novel,
and heterogeneous knowledge and resources (Reagans and McEvily, 2003). Additionally, firms
in open networks possess higher independence and greater latitude in their cooperative strategies
(Walker, et al., 1997). The IB literature has touched upon the concept of structural holes in
explaining the identification and exploitation of international business opportunities. For
example, international entrepreneurship is explained as a function of brokerage opportunities
relating to the position of structural holes (Ellis, 2011).

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Closed network (network closure)
Network closure refers to a network structure in which all actors are connected directly or
through third parties (Burt, 2001). This highlights the impact of cohesive networks on the
creation of social capital. The two fundamental outcomes of closed networks – more specifically,
networks of densely interconnected contacts – are access to information and facilitating sanctions
that make it less risky for people in the network to trust one another (Burt, 2001, p. 37; Coleman,
1988). The presence of common third parties facilitates trust and creates incentives to cooperate,
deriving from concern for one’s reputation and fear of group sanctions (Gargiulo, Ertug, and
Galunic, 2009). While network closure provides benefits in terms of social capital, it also
increases information redundancy, as cohesive and structurally equivalent contacts are likely to
have similar information (Burt, 1992, 2001; Gargiulo, et al., 2009). Additionally, a closed
network is likely to reduce actor autonomy in cooperative strategies with outsiders because of the
potential sanctions and expectations of others in the network (Gargiulo, et al., 2009).
Centrality
Centrality is a property of a node’s position in a network (Borgatti, et al., 2013). As a metric
measuring actor positioning, centrality metrics are widely applied as indices of the power,
prestige, influence, prominence and importance
2
of nodes within a network (Brass, 1984;
Sparrowe, Liden, Wayne, and Kraimer, 2001). Centrality is mainly associated with instrumental
outcomes, such as an individual’s access to network resources and social capital. Central actors
have more relationships through which to obtain resources and also are less dependent on any one
single actor (Sparrowe, et al., 2001). Centrality also provides an advantage in terms of the degree
of control an actor might exert over the relationships and flow/exchange of resources. SNA offers
2
The basic assumption here is that an actor is important if the ties of that actor make the actor visible to other actors in the
network (Wasserman and Faust, 1994: 172).

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various centrality measures for the identification of the most important actor(s) in a social
network, including degree centrality, closeness centrality, betweenness centrality and eigenvector
centrality (see Borgatti, et al., 2013)
Core and periphery structure
Core and periphery structure is ‘characterized by a cohesive subgroup of core actors and a set of
peripheral actors that are loosely connected to the core’ (Borgatti and Everett, 2000; Cattani and
Ferriani, 2008, p. 826). Actors located in the core or periphery are subject to different sets of
opportunities and constraints. While the core nodes enjoy the flow of tacit knowledge in dense,
close-knit networks in which they are embedded, the peripheral nodes have the advantage of
connections outside the network that facilitate novel information flows (Cattani and Ferriani, 2008).
Table 1 illustrates the SNA measures explained above.
Insert Table 1 around here
3. Applications of SNA in international business research
In order to determine the status quo of SNA in international business research, an extensive
literature review was conducted in leading IB journals, following the study of Tüselmann,
Sinkovics, and Pishchulov (2016), which provided a recent ranking of IB journals. Although this
does not cover the complete set of possible outlets publishing IB-related research, we believe that
the selected journals are a representative of the research trends in terms of both the phenomenon
of interest and the field in general. The journals included in the review are presented in Table 2.

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Insert Table 2 around here
The literature review, conducted through key academic publishing databases (i.e.
ABI/Inform, Elsevier ScienceDirect, Emerald Insight, and Google Scholar), revealed 20
academic articles that applied SNA in the IB field. The website of each journal was also searched
for articles employing SNA. The review assessed the main motivation for the application of
SNA, the type of network data used, and the software employed for social network analysis.
Table 3 lists all the identified articles that use SNA in IB journals.
Insert Table 3 around here
The review provides evidence of limited yet increasing utilization of SNA in IB research,
particularly in recent years, and shows that authors’ key motivation for using SNA was its ability
to generate empirical insights into structural and relational patterns among social actors.
Applying SNA leads to a better representation of the interdependence of actors and hence deeper
understanding of their behaviours within different network structures. The studies generally used
SNA measures such as network density, network range, degree, closeness, and betweenness
centrality. Through employing SNA, these articles aimed to develop existing understandings of
various IB topics, including knowledge/information flows, talent management, firm
internationalization, corporate social responsibility and organizational learning. The studies
largely used UCINET as analysis software, but other software packages (i.e. Gephi, Pajek,
Bibexcel) were also employed to reveal the structural and positional attributes of networks and

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actors. The key data sources for these studies are both quantitative primary data collected through
social network surveys, and secondary archival, co-citation and patent data. The next section
develops ideas of potential IB topics in which SNA can advance existing understanding.
4. Potential avenues for the application of SNA in the IB field
Although SNA can be applied to virtually any sub-domain of the IB field, the objective here is to
delineate the potential of SNA by showing how and how far it can advance existing knowledge.
Drawing upon recent discussions of IB scholars in tackling ‘big questions’ (Buckley, 2002;
Buckley, et al., 2017; Buckley and Ghauri, 2004; Peng, 2004), we identified two major areas that
have propelled the development of IB as a research field: namely, the internationalization of
firms and the MNE phenomenon. The rationale of focusing on these topics is twofold. First,
understanding the internationalization of firms has been a core pillar of IB research and central to
multiple theoretical approaches, including new international ventures and born-global
frameworks, the regionalization theory, and the process model (Riviere and Bass, 2018). Second
is the pre-eminence of the MNE as the global organization of interest within the IB field (Teegen,
Doh, and Vachani, 2004). MNEs are regarded as dominant actors in international business
activities and an ideal context for the development of IB theories (Roth and Kostova, 2003;
Rugman and Brain, 2003). Furthermore, through the inclusion of network thinking, these two
research bodies have witnessed a tremendous evolution in advancing both traditional theoretical
models and organizational conceptualizations, such as the revised Uppsala internationalization
model (Johanson and Vahlne, 2009) and the global factory (Buckley and Ghauri, 2004). Hence,
these two central tenets of IB research serve as ideal contexts to demonstrate the rich potential of
SNA for IB research.

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4.1. Revisiting internationalization research with SNA perspective
4.1.1. Internationalization and network theory
Internationalization has been one of the main research streams in IB (Buckley, 2002).
Internationalization research, particularly studies focusing on small and medium-sized enterprises
(SMEs), born-globals and international new ventures, has extensively employed a network
perspective (Cavusgil and Knight, 2009, 2015; Coviello, 2006; Hånell and Ghauri, 2016;
Kontinen and Ojala, 2011; Odlin and Benson-Rea, 2017; Sinkovics, Kurt, and Sinkovics, 2018).
Although this body of the literature recognizes networks as effective means of accessing external
resources, the fact that networks vary in terms of the extent to which they provide accessible
resources has been mostly overlooked. In other words, previous network-based
internationalization research has applied a static and generic network view and thus left
considerable variance between different network configurations and their subsequent network
outcomes unexplained.
While several theories have been developed to explain the internationalization of firms,
Johanson and Vahlne's (1977) Uppsala model has been one of the most influential in explaining
the process of firms’ gradual commitment to foreign markets. The model introduced the liability
of foreignness as the key impediment to internationalization and discussed the process through
which firms gradually increase their market knowledge and market commitment. This model has
received criticism for being deterministic and firm-centric, but later conceptualizations have
incorporated a network perspective in explaining the internationalization (Johanson and Vahlne,
1990, 2006, 2009; Vahlne and Johanson, 2013). The most dramatic shift was introduced in the
2009 revised model, in which the concept of the liability of foreignness was replaced with the
liability of outsidership (Johanson and Vahlne, 2009; Yamin and Kurt, 2018). The revised model
viewed markets as webs of network relationships and asserted that firms internationalize within

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networks of connected business relationships (Johanson and Vahlne, 2009). This model was a
full-blown application of network theory to internationalization research (Yamin and Kurt, 2018).
The revised model created room for new research, which probed the ‘network’ component
of the phenomenon more deeply, through employing the theoretical apparatus of SNA. In one of
these studies, Yamin and Kurt (2018) employed the SNA-related structural and positional
attributes of networks to theorize the relationship between the liabilities of foreignness and
outsidership. The paper argued that the structural characteristics of the target network affect the
motivations of both insider and outsider actors, in terms of overcoming the liability of
outsidership. Moreover, the perceived liability of foreignness of the outsider determines whether
the outsider should build its insidership within a closed or open network, considering the different
benefits of both in facilitating tacit knowledge-sharing as opposed to explicit information flows
(Kurt and Yamin, 2016; Yamin and Kurt, 2018). The recent studies utilizing SNA-related
concepts also shed light on the potential empirical application of SNA to internationalization
research.
4.1.2. Internationalization and SNA
Internationalization research has relied extensively on attribute data and traditional
methodological perspectives, which concern the focal actor explaining its internationalization in
terms of notions such as firm resources, managerial capabilities and international experience.
Data was primarily collected through surveys and interviews and analysed with traditional
statistical tools. The utilization of relational data in revealing the role of social structure in
influencing firm internationalization has been rare, with few exceptions (i.e. Coviello, 2006).
Through integrating relational data, SNA can contribute to this body of IB research in
various ways. Firstly, whereas Johanson and Vahlne (2009) argued that building insidership is a

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necessary condition for successful internationalization, they did not explicitly specify how and
how far the characteristics of the target network in which the outsider builds an insidership
position can facilitate/impede or expedite/decelerate the internationalization process (Yamin and
Kurt, 2018). They indicated that ‘anything that happens, happens within the context of a
relationship, and a firm that is well established in a relevant network or networks is an “insider”’
(Johanson and Vahlne, 2009, p. 1415). They employed a generic network view and did not
clearly articulate what ‘well established’ means in the network context. Different perspectives
have also emerged from disagreements over what ‘better connected’ means (Yamin and Kurt,
2018). The social network literature provides two fundamental competing views regarding the
‘well established’ (i.e. better-connected) dichotomy. Building on the concept of social capital,
Coleman (1988) argues that actors are better connected in dense networks, whereas advocates of
open networks argue that firms occupying the structural holes position are better connected and
more well-established (Burt, 2001). In other words, while network closure argues that social
capital is created in densely-connected networks (Coleman, 1988), the structural holes
perspective argues that social capital is an outcome of the brokerage role between otherwise
disconnected groups (Burt, 2000).
SNA can enable identification of the most valuable ‘well established’ status for an
internationalizing firm in a target network that provides the required social capital for entering
the market. For instance, building an insidership position in an open network for entering a
psychically close market might be a viable strategy, as the cost of building relationships in an
open network is likely to be relatively low. Further, the information benefits of an open network
in this context would be sufficient, considering the low level of the liability of foreignness and
psychic distance between home and host markets (Yamin and Kurt, 2018). On the other hand, a
firm seeking to enter a psychically distant market would benefit more from building an

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insidership position in a dense network structure, which can provide social network benefits to
overcome high liability of foreignness. However, the cost of building insidership in a dense
network is likely to be higher.
Secondly, building insidership is a two-sided process and both insider and outsider should
be motivated to overcome (or help to overcome) the outsidership. The motivational disposition of
the insider in helping the outsider is bounded by the network structure in which the insider is
embedded. Actors in open networks are likely to have more flexibility in opening doors for
outsiders, while actors in closed networks are likely to be restricted by dense network structures,
as adding a new actor eventually affects all relationships in a closed network (Yamin and Kurt,
2018). Therefore, SNA can reveal the structure of the foreign market network, in which the
optimal insider actor can be targeted to build the most effective insidership position.
Third, the positional features of the insider actor through which the outsider wishes to
build insidership also affect the internationalization process. SNA enables identification of the
key actors who can provide the necessary resources required to internationalize successfully. For
instance, linking to a central actor in the target network can provide reputation capital, legitimacy
and acceptance in the target market. Yet, it demands the investment of more resources and time
to convince the insider. On the other hand, building insidership through a peripheral actor is
likely to be faster and less costly. If an internationalizing firm needs only basic market
information, then it would be more feasible to connect to a peripheral actor for market entry. A
firm’s choice of whether a central or peripheral actor would be more beneficial is shaped by their
perceived liability of foreignness in the target market and the costs of building insidership.

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4.1.3. SNA and overcoming the liability of outsidership: An illustrative example
To give a visual representation of the role of network structure and actor positioning in accessing
different forms of social capital required for internationalization, a hypothetical network scenario
case, in which node 15 seeks to enter a host country through building an insider position in the
target network, is provided below (Figure 1).
Figure 1 around here
Starting with the home country network, previous network studies investigating the role
of home country networks in firm internationalization implicitly assume that all actors embedded
in a home country network are able to access the same network resources and social capital.
However, the structure of networks surrounding the focal actors and the actors’ position within
the network affect the type and level of social capital and resources available. For instance,
whereas node 15 enjoys the benefits and social capital created in its surrounding dense network
and high central position, node 22 does not have access to the same level and type of social
capital, due to its peripheral position in an open network (Burt, 2001; Coleman, 1988). Similarly,
node 18 lacks the social capital created in the dense network, while its structural holes position
enable it to access novel information from otherwise disconnected sub-groups (Burt, 2001).
Secondly, regarding overcoming the liability of outsidership and host country networks,
the structural attributes of the target network and the position of the insider within it are not a

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feature of Johanson and Vahlne's (2009) insidership analysis. Their theory argues that insidership
is built within target host country networks displaying trust and commitment, reflecting a dense
network structure, which entails trust and commitment, in the host country. However, as
indicated by Yamin and Kurt (2018, p. 3), ‘not all networks are the same nor [do] all network
relationships necessarily engender trust and commitment or to the same extent’. For instance,
open networks have relatively low levels of relational trust compared to closed networks. Based
on Johanson and Vahlne's (2009) insidership perspective, one would expect no difference, if node
15, as an internationalizing firm, is connected to either node 1 or node 8 within the target network
of the host country. However, from the SNA perspective, building an insidership position via
connecting to node 1 provides benefits in terms of social capital produced in dense networks (i.e.
trust, relational norms, and sensitive/tacit knowledge). On the other hand, building insidership in
the same target network of the host country via node 8 cannot provide the same type and level of
social capital. Node 8 can only provide social capital created in open networks, such as flows of
non-redundant and novel information.
On the other hand, the cost of building insidership via node 1 is likely to be higher than an
insidership position built via node 8, as insidership in closed networks requires more resources
and time than open networks. Decisions on whether a firm should build insidership in open or
closed networks in a host country depend on an analysis of the cost of insidership and the type of
social capital needed for successful internationalization. For instance, building insidership in an
open network for entering a psychically close market might be a viable strategy, for two reasons.
First, the cost of building relationships in an open network is likely to be lower than that of a
closed network. Moreover, the information and resources acquired via an open network would be
sufficient for internationalization, considering the low risk of foreignness and psychic distance
(Kurt and Yamin, 2016; Yamin and Kurt, 2018). Therefore, building insidership via node 8

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would be a more viable strategy for the internationalization of node 15 if the host country is
psychically close. On the contrary, internationalization to psychically distant markets requires
insidership in closed network structures, as the firm needs tacit and sensitive knowledge that can
be obtained via trust-based closed networks. The internationalizing firm is likely to invest in
building insidership in closed networks only if the expected benefits exceed the cost. Expected
benefits of social capital created in dense networks would be valued more highly if the firm has
high liability of foreignness in relation to the host country. Therefore, building insidership via
node 1 would be a viable strategy for the internationalization of node 15 if the host country is
psychically distant.
In sum, a deeper and more nuanced understanding of overcoming the liability of
outsidership requires paying closer attention to the structural characteristics of networks in which
internationalization takes place. Internationalization cannot be fully explained by the individual
attributes of internationalizing firms alone. It requires a broader perspective that considers the
attributes of networks and actor interdependence within the social structures in which
internationalizing firms are embedded, rather than seeing networks as similar entities. SNA can
be used as a significant methodological tool that reveals structural and positional attributes of
networks of both home and host countries, across which internationalization occurs. Hence, IB
scholars and managers of internationalizing firms could gain a richer understanding of
overcoming the liability of outsidership in networks.
4.2. Reconsidering multinational enterprises through the lens of social
network analysis
The research on the existence, organization and strategy of MNEs is a fundamental pillar of the
IB field (Buckley, 2002; Peng, 2004). Further, IB scholars agree that the distinctive

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characteristics of the MNE context (i.e. heterogeneity, complexity) hold promise in terms of
enriching existing theoretical models or developing new IB theories (Roth and Kostova, 2003).
However, as indicated by Roth and Kostova (2003, p. 894), this promise can only be fully
realised through more rigorous and systematic approaches. Similarly, Dhanaraj (2007, p. 1233),
in his review of the book by Forsgren, Holm, and Johanson (2005), suggested the use of recent
advances in network analysis for developing more nuanced understandings of the network
structures of multinationals. In this respect, SNA may reveal the complex intra- and inter-
organizational network structures of today’s MNEs in their heterogeneous and complex social
contexts. In turn, MNE behaviours, strategies, performance outcomes, and their interdependence
with social/network contexts could be better understood. In a similar vein, this study suggests the
use of SNA to complement the theoretical developments in the network conceptualization of
MNE with appropriate analysis of the empirical reality. The following sections discuss how this
body of research provides an ideal context for the utilization of SNA, which can then contribute
to a more nuanced understanding of networked-MNEs.
4.2.1. MNEs as networked organizations
Early IB research and theories aimed to understand the existence of MNEs (Buckley and Casson,
1976; Coase, 1937; Dunning, 1988; Hennart, 1982; Penrose, 1959). Where earlier studies
considered multinationals as closed systems vis-à-vis markets, later conceptualizations
incorporated a relational approach and extended the boundaries of MNEs into markets seen as
webs of network relationships (Buckley, 2009b; Forsgren, et al., 2005; Ghoshal and Bartlett,
1990). As indicated by Cantwell, Dunning, and Lundan (2010, p. 569), an MNE is now
understood ‘as a coordinated system or network of cross-border value-creating activities, some of
which are carried out within the hierarchy of the firm, and some of which are carried out through

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informal social ties or contractual relationships’. Similarly, Forsgren, et al. (2005) argue that
understanding MNEs requires consideration of their embeddedness in business networks in order
to generate more complete findings. The analysis carried out by Forsgren, et al. (2005) revealed
the importance of business networks in understanding MNEs as networked organizations. As
today’s MNEs are becoming a system of differentiated networks, Buckley and Ghauri (2004)
concept of ‘the global factory’ serves as the most appropriate and fruitful conceptualization of
MNEs for the integration of SNA into the MNE research context. Hence, the arguments in this
section will be developed with reference to the global factory.
4.2.2. Global factory as a networked MNE
Buckley and Ghauri (2004) introduced the concept of ‘the global factory’ as a new structure for
an MNE. From a Coasean ‘externalise or internalise’ point of view, the global factory reflects the
externalization of activities through outsourcing (Buckley, 2009a). This emphasizes the role of
the centre (HQ) as the orchestrator of complex and extensive networks of organizations, within or
beyond the legal boundaries of the focal firm (Buckley, 2009a, 2011; Buckley and Ghauri, 2004;
Yamin, 2011). The global factory model depicts a complex web of relationships among brand
owners, contractors and suppliers, which together denote different levels of activities (i.e. design,
R&D, manufacturing, distribution) under the control of the brand owner. Buckley's (2009b)
original discussion emphasizes the importance of the structural attributes of the network in
affecting the benefit/cost outcomes of those networks:
Global factories are both horizontal and vertical networks…The benefits of the horizontal
network arise from learning and the diffusion of knowledge. The benefits of the vertical
network arise from the coordination of activities…The degree to which benefits outweigh
costs depends on the extent to which the global factory’s networks are open and
transparent versus being closed and opaque. (Buckley, 2009b, pp. 229-230)

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As Buckley indicates above, the structural attributes of networks (i.e. open versus closed)
determine the outcomes of horizontal and vertical networks (i.e. learning, co-ordination) that
constitute the global factory. Buckley’s conceptualization of the global factory is also related to
the ‘better connected’ dichotomy in terms of whether open or closed networks are more
beneficial for the lead firm in orchestrating activities. Different enabling features of network
structures, such as facilitating the transfer of tacit knowledge in closed networks versus the flow
of novel information in open networks, can provide different benefits for the lead firm. On the
other hand, the costs of building and sustaining relationships in open or closed networks are
different. Therefore, firms’ decisions on developing networks in open or closed networks can be
understood as a result of their cost-benefit analysis.
For instance, developing closed networks around core functions (i.e. design, engineering
and R&D) can facilitate the flow of tacit knowledge among tightly-connected units. Closed
networks foster learning through increasing trust and the willingness of units to try to transfer
knowledge, curbing opportunism and creating cooperative norms via third-party ties (Cavusgil,
Calantone, and Zhao, 2003; Reagans and McEvily, 2003). However, developing and sustaining
dense networks comes with two fundamental costs. One is the high investment in terms of time
and resource required for forming and sustaining networks with network partners; and the other
relates to the ‘paradox of embeddedness’ (Uzzi, 1997). Actors embedded in dense networks may
lack access to novel and non-redundant information that exists beyond their network (Fritsch and
Kauffeld-Monz, 2008), which can impede their performance.
Conceptualization of the global factory as a networked organization consisting of vertical
and horizontal networks provides a rich context in the utilization of SNA. For instance, executing
an appropriate cost-benefit analysis of different network structures, as suggested by Buckley
(2011), requires one to reveal and clearly visualize open and closed network structures. The

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optimum network structure based on cost-benefit analysis, which provides competitive
advantages, can be achieved via effective network management through applying SNA.
4.2.3. Coordination and control in the global factory and SNA
A recent relevant discussion of the global factory revolved around who controls dispersed
activities and captures the rents earned in global value chains (GVCs) (Buckley and Strange,
2015). The positions of the actors (either lead firm or suppliers) and the structure of the network
in which value chain activities are carried out affect the distribution of power and rent in GVCs.
Forsgren, et al. (2005) note that MNE subsidiary power comes not only from unique resources,
but also from unique network position. For instance, external embeddedness of the subsidiary,
through which it can access heterogeneous resources via its ‘structural holes’ position,
contributes its power within the MNE. Regarding power relationships within the global factory,
Buckley (2011, p. 280) indicated that:
The position of the MNE’s headquarters in the global factory is important to its
power…power is a function of centrality. The more physical and knowledge links that the
headquarters possesses in the global factory, the more it might be argued to have power
over the global network.
Thus, we need to borrow credible measures from SNA (i.e. centrality) to reveal the
positional attributes of the actors and understand the power dynamics within the network
structure of the global factory. Centrality has been amongst the most fundamental concepts in
SNA and is highly associated with power (Borgatti, 2005). Central actors are those tied to a
greater number of other actors than those who are peripheral actors (Mizruchi and Potts, 1998).
Power is function of an actor’s ability to monopolize the dependence of other actors, which is
derived from its structural position in its surrounding networks (Cook, Emerson, Gillmore, and
Yamagishi, 1983; Mizruchi and Potts, 1998). In the global factory, the assumption is that the lead

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firm has the ability to monopolize the dependence of contractors, suppliers and other actors.
However, the power of the lead firm in the value chain activities can be better understood through
revealing its position within the network structure. Hence, SNA offers numerous measures,
including degree centrality, closeness, betweenness and eigenvector centrality (Borgatti, 2005),
which can reveal whether the focal actor’s position generates any power and control benefits.
Furthermore, Buckley (2011) also highlighted the need and value of employing SNA
metrics in understanding coordination of the global factory. Success in the global factory is
considered to be dependent on the effective orchestration and coordination of activities across
both ownership and non-ownership ties of globally interconnected units. Following Ghoshal and
Bartlett (1990), Buckley (2011, p. 280) further argued that the performance of the global factory
is dependent upon ‘(1) the density and (2) the cohesiveness of (a) the internal networks and (b)
the external networks of global factories’. Therefore, understanding the antecedents of the
performance of ‘the global factory’ as well as the ‘embedded multinational’ requires credible
SNA measures, such as density, coherence, and other metrics measuring network structures. Both
the internal networks of the global factory and its embeddedness within the external networks
affect its performance. Managing this complexity in networks requires appropriate analytical
tools to map the relationships. Hence, empirical application of SNA can hence develop a more
nuanced understanding of the structure, organization and performance of today’s networked
multinational organizations.
4.2.4. An overview of SNA’s potential utilization in the context of MNE research
Based on the above arguments positing the importance of network relationships in the
organization and strategy of the global factory, SNA can further contribute to the MNE research
context in various ways. Firstly, unlike earlier studies that conceptualized multinationals as

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closed systems vis-à-vis markets, the relevant contemporary literature agrees that MNEs are
networked organizations. In particular, the concept of the global factory reflects an absolute
network understanding of MNEs. The success, governance and control of the global factory are
associated with its capability to orchestrate a complex web of networks of globally
interconnected units. However, current studies only benefit theoretically from the notion of a
network to understand the global factory. Hence, empirical utilization of SNA can enable
scholars and practitioners to reveal and understand the network structure of globally
interconnected value chain activities organized by HQs, through which control, governance, rent
capturing and performance dimensions can be appropriately understood.
Secondly, Rugman and Verbeke (2004) introduced a dichotomy between ‘regional MNE’
and ‘global MNE’ through focusing mainly on downstream sales of MNEs. Despite a common
perception that multinationals pursue global strategies, their study showed that most of the
world’s largest companies are not global, but regional. They indicated that a strong discrepancy
between intra-regional and inter-regional sales is likely to have important implications for MNE
structure (Rugman and Verbeke, 2004, p. 14). Regional MNEs are treated as complex,
differentiated organizational systems vis-à-vis global MNEs. The success of regional MNEs is
associated with their ability to overcome the ‘regional effect’, which is conceptualized as a
liability of outsidership imposed on subsidiaries operating in the host region (Verbeke, Kano, and
Yuan, 2016). In other words, building insidership in the host region via subsidiaries and hence
exploiting location-bound, firm-specific advantages (FSAs) are key critical success factors for
regional MNEs. This requires clear empirical evidence of the network structure of host regions in
which optimal ‘insidership’ positions can be developed by considering the distinctive enabling
characteristics of different network structures and actor positions. In this respect, SNA can enable
us to visualise network structures in host regions, through which imperfections across national

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and regional factor markets can be optimally exploited, and production and coordination costs
reduced (ibid.). Similarly, SNA can help us to better understand the well-known Bartlett and
Ghoshal (1989) matrix through empirically substantiating the structural differences between
transnational, global and multi-domestic MNEs.
5. Discussion and suggestions for future research
Recent studies have criticised the state of the IB field and recommended ways in which IB
research can progress (Buckley, et al., 2017; Buckley and Lessard, 2005; Delios, 2017). The key
criticism has been the ‘inward-looking’ approaches of IB scholarship, which restricts
interdisciplinarity (Buckley, et al., 2017). The success of IB in its early decades was associated
with its interdisciplinary roots, which integrated concepts and tools from different disciplines in
seeking to answer the ‘big questions’ (ibid.). Similarly, a recent and important recommendation
for advancing IB research is to incorporate interdisciplinary perspectives and use state-of-art
methodological approaches to develop innovative research designs that acknowledge
interdependencies among social actors in the global environment (Buckley, et al., 2017).
Buckley (2002, p.370) argued that ‘these successes were achieved by identifying the key
empirical factors in the global economy which needed to be explained and then searching out a
tractable means of explication within a coherent theoretical framework’. Today, as markets are
seen as a web of networks where actors are interlinked via relational ties, the success of the field
very much depends on appropriate efforts to understand actor interdependence within the social
structures where IB activities take place. Hence, IB scholars need to be equipped with appropriate
analytical tools to be able to identify key empirical factors.
Moreover, a close relationship between theoretical development and empirical reality has
been considered the key strength of past epochs of IB research (Buckley, 2002). However, an

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interdisciplinary perspective and a close interaction between theory and empirical reality have not
been yet achieved in network-based IB research. A major impediment has been IB scholars’ stand
in favour of traditional methodologies and a reluctance to apply state-of-art methodological
approaches to network relationships. In this vein, SNA, as an analytical tool borrowed from
sociology, has tremendous potential in advancing fundamental understanding, particularly in the
IB field, as the notion of networks has been extremely rewarding in answering ‘big questions’.
In response to aforementioned recommendations for more interdisciplinary IB research
through the integration of ideas and methods from different disciplines, this study is a timely
attempt to suggest SNA to IB scholars as an analytical research tool. It contributes to the IB field
by demonstrating the potential of SNA through articulating its potential application to two tenets
of IB: firm internationalization and MNEs. However, the study’s implications are applicable to
any domain of IB.
Further, SNA’s previous successful applications in other disciplines (i.e. supply chain
management, economic geography, knowledge management and innovation) can also shed light
on its application in multiple sub-domains of IB research. Firstly, SNA offers great potential for
supply chain management (SCM) research in investigating how patterns of network relationships
translate into competitive advantage through the management of resource flows, diffusion of
information, social control of opportunism and coordination, amongst others (Borgatti and Li,
2009). While early SCM research focused on linear relationships between buyers and suppliers,
recent studies have benefited from SNA to capture the complexity of the larger supply network in
which the firm is embedded (Kim, Choi, Yan, and Dooley, 2011). SNA contributes to the SCM
literature through revealing vertical and horizontal network interdependencies between multiple
firms participating in a supply chain. Previous studies also pointed out the managerial importance
of revealing interdependencies in supply chain network structures by highlighting that firms that

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understand and deliberately reconfigure their supply networks can outperform competitors
(Carter, et al., 2007; Choi, Dooley, and Rungtusanatham, 2001). Furthermore, as noted by
Lazzarini, Chaddad, and Cook (2001), firms can design effective coordinating mechanisms by
considering different interdependencies emerging from strong or weak ties via employing SNA.
Building on existing developments in the integration of SNA into the SCM context, IB
scholars can extend this body of research by examining global supply chains in which supply
networks and buyer-seller relationships cross borders. Managing global supply chains effectively
is a prerequisite for remaining competitive for MNEs. Hence, IB scholars and practitioners can
benefit considerably from SNA as a means to analyse the complex and dynamic nature of supply
chains and the effect of potential perturbations of supply networks (Wu, Blackhurst, and O’grady,
2007). Revealing global supply chain network structures via SNA may have implications for
designing robust supply network configurations against possible supply disruptions (Kim, et al.,
2011). Moreover, today’s compound political, social and economic uncertainties, emerging from
anti-globalization sentiments and protectionist policies (Sinkovics, et al., 2018), are likely to have
a serious effect on global connectivity and the sustainability of global supply chain activities. In
this vein, SNA might be of profound importance in responding to potential disruptive challenges,
through examining supply chain networks and thus analysing resilient supply chain
configurations.
Secondly, in economic geography, SNA has been considered a promising tool for empirically
investigating the structures and evolution of inter-organization networks and knowledge flows
within and across regions (Ter Wal and Boschma, 2009, p. 739). The development of global
value chain (GVC) and global production networks (GPN) perspectives in this field has brought
valuable insights to the social and political relations between actors involved in the process of
value capture in the production of commodities (Cumbers, Nativel, and Routledge, 2008). As

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previously suggested, the GVC/GPN literature can contribute to key debates in IB research,
particularly those concerned with networked multinationals (Azmeh and Nadvi, 2014). Therefore,
building on SNA’s previous successful application in relational economic geography, IB research
can also utilize it for analysing complex and geographically dispersed webs of production
networks, which IB considers to be orchestrated by leading MNEs. Additionally, in her research
incorporating a relational perspective into GVC governance, Kano (2018) argued that
orchestrating firms can enhance sustainability and efficiency outcomes in GVCs through using
social mechanisms, including network relationships. Accordingly, SNA can foster this
understanding through revealing the network structure and interdependencies within the social
context in which GVCs are embedded, and thus allow the design of resilient value chain
configurations.
Finally, previous research investigating different network contexts needs to be revisited
through empirically integrating social network data and analysis into existing knowledge and
understanding. For instance, informal constraints constituted within interpersonal networks in
emerging markets (i.e. guanxi, blat, and chaebol) are accepted as effective safeguards against
opportunism, and facilitate transactions when formal market-supporting institutions are
underdeveloped (Peng, Sun, Pinkham, and Chen, 2009). However, social capital benefits such as
social norms curbing opportunism, cooperative behaviours and trust can emerge in the context of
closed network structures. Therefore, understanding the effectiveness of these social networks in
navigating business activities in institutionally weak environment requires systematic analysis of
their structural configurations, in which both densely- (closed network) and loosely-connected
(open network) regions within the overall network might exist. For example, building an
insidership position within a guanxi network might not simply translate into informal constraint
benefits if the focal actor builds networks in loosely-connected guanxi structures. SNA can

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empirically reveal structural configurations and attributes of these network systems and their
impact on international business activities.
A longitudinal approach can empirically reveal dynamic changes of network structures
and actor positions over time and the effect of these changes on international business activities.
We hope this study stimulates future research that utilizes SNA in developing more precise and
nuanced network understanding in IB research and thus contributes to the progressive
development of the field.

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6. Appendix – Figures and Tables
Table 1. Visual representation of SNA measures.
Table 2: IB journals reviewed for SNA application
Journal
Number of
articles
using SNA
ABS 2018
Rank
Publisher
Journal of International Business Studies (JIBS)
5
4*
Palgrave
Journal of World Business (JWB)
0
4
Elsevier
Global Strategy Journal (GSJ)
2
3
Wiley
Management and Organization Review (MOR)
0
3
Cambridge
Journals
International Business Review (IBR)
3
3
Elsevier
Management International Review (MIR)
1
3
Springer
Journal of International Management (JIM
0
3
Elsevier
Asia Pacific Journal of Management (APJM)
6
3
Springer
Asia Pacific Business Review (APBR)
0
2
Taylor & Francis
Thunderbird IB Review (TIBR)
1
2
Wiley
Critical Perspectives on International Business
(CPoIB)
0
2
Emerald
Transnational Corporations (TCs)
0
2
UNCTAD
Multinational Business Review (MBR)
0
2
Emerald
European Journal of International
Management (EJIM)
2
1
Inderscience
Journal of Asia Pacific Business (JAPB)
0
1
Taylor & Francis
Journal of East West Business (JEWB)
0
1
Taylor & Francis

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Table 3: Articles using SNA in international business research
Study/Journal
Motivation for Applying SNA
SNA Data
Software
Applied
Turkina and Van
Assche (2018)
JIBS
‘We then combine insights from international
business theory, economic geography, and
social network analysis to evaluate how a
cluster’s relative position in the global cluster
network relates to (1) the type of value chain
activities in which it specializes, and (2) the
type of international organization-based
linkages that disproportionately strengthens
local innovation.’ (p.707).
Longitudinal
dataset on formal
linkages between a
network of cluster
firms
Regression
analysis
Devinney and
Hohberger
(2017)
JIBS
‘We…visualized and analyzed the results via
social network analysis’ (p. 50)
‘Network methods [SNA] are generally
accurate and effective…and allow the display
of a larger number of documents in
meaningful ways’ (p. 61).
Coupling
proximity scores
GEPHI
Monaghan,
Gunnigle, and
Lavelle (2014)
JIBS
‘[U]sing quantitative social network analysis […]
allowed a greater visually generated empirical
exploration of the structural and relational
patterns under review’ (p.137)
‘Social network analysis was employed to further
substantiate the composition of this institutional
network and to explore the relative centrality of
actors more effectively’ (p.138).
Quantitative
data collected
through social
network
questionnaires
UCINET
Chua, Morris,
and Ingram
(2009)
JIBS
‘The current research used methods of social
network analysis to investigate trust as a
differentiating dimension between Chinese and
Western networks’ (p. 503).
Egocentric
network data
collected
through
network
surveys
Random
effects
regression
analysis
Coviello (2006)
JIBS
‘As changes in network structure are expected to
cause changes in the venture’s social capital
(Borgatti et al., 1998; Burt, 2000), analysis
focused on the key structural dimensions of the
network and patterns of structural change’ (p.
719). ‘UCINET 6 was used to compute network
range, network density, closeness centrality and
betweenness centrality of international new
ventures’ (pp.719-720).
Transforming
each firm’s
chronology
into network
matrices
UCINET
‘[W]e will explore whether the roles and the
positions of countries in the network are related
IMF dataset to
identify the
structure and
UCINET

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Bolívar,
Casanueva, and
Castro (2019)
IBR
to their trade expansion and development
performance’ (p.697)
the topology of
the Global FDI
Network
Castro and
Roldán (2013)
IBR
‘The study uses SNA in order to analyze the
structural dimension of inter-organizational
networks through focusing on centrality
measure[s]’ (p. 1041).
Secondary
relational data
was collected
from the Public
Works
Financing
database
UCINET
Mäkelä,
Andersson, and
Seppälä (2012)
IBR
To understand how structural boundaries
influence knowledge exchanges.
Network data
obtained
through name
generator
technique
Multilevel
analysis
using
general
linear
regression
García-Lillo,
Claver-Cortés,
Marco-Lajara,
and Úbeda-
García (2017)
MIR
‘The research developed here through the use of
citation and co-citation analysis and social
networks analysis allowed us to examine and
represent the structure or intellectual base of
research on the phenomenon of born global firms
or international new ventures (INVs)’ (p. 645)
Co-citation
matrix
BIBEXCEL
and PAJEK
Henisz (2013)
GSJ
I add insight from social network analysis
(Wasserman and Faust, 1997) to better represent
stakeholders as embedded in a network structure
that both enables and constrains their behavior’
(p. 341).
Stakeholder
data
Simulation
Analysis
Awate, Larsen,
and Mudambi
(2012)
GSJ
‘We then conduct a comparative network analysis
along the breadth and depth of the knowledge
bases to shed light on how Suzlon’s innovation
capabilities differ with respect to Vestas’ (p. 209).
Backward-
cited patents
data
UCINET
Wang, Dong, Si,
and Dou (2017)
APJM
‘In step two, social network analysis (UCINET;
Borgatti, Everett & Freeman, 2002) was used to
identify the key elements in each type based on
their degree centrality and betweenness
centrality’ (p. 467).
Collective
turnover events
data
UCINET
Li, Li, Shu, and
Zhou (2015)
APJM
‘Finally and most interestingly, we have applied
the method of network analysis to the
identification of the co-citation pattern among the
listed overseas Chinese scholars’ (p. 1086).
Co-citation
data
GEPHI and
ForceAtlas

Page 41 of 42
Frenkel, Sanders,
and Bednall
(2013)
APJM
‘In order to test H3, which posits HR–line
management relations as a moderator in the
relationship between extent of agreement between
SM and LM relations and the two employee
outcomes, we add HR–LM relations as measured
in the social network analysis and the interaction
between this variable and the extent of agreement
between SM and LM relations’ (p. 19).
Quantitative
data collected
through social
network
questionnaires
UCINET
Ho and Chiu
(2013)
APJM
‘Social network analysis and theory help us not
only delineate the individual actors in the
knowledge flow network but also to determine
network ties, relations, frequency, and density’
(p. 1266).
Patent data
from USPTO
(US Patent and
Trademark
Office) patent
database
UCINET
Tang and Xi
(2006)
APJM
‘[W]e compared network positions using
Stephenson and Zelen’s (1989) information
centrality measure, which calculates centrality
based on all possible paths and takes into
consideration the weight of the relation, using the
UCINET network analysis package’ (p. 196).
Symmetrized
data based on
the average of
the estimates
of interaction
by each
member in the
network
UCINET
Peng, Au, and
Wang (2001)
APJM
‘Exploring interlocking directorates allows
researchers to “map” out the interorganizational
network of corporate governance’ (p. 162).
Published
archival data
UCINET
Smith, Ryan, and
Collings (2012)
EJIM
‘[T]his research seeks to make a contribution
methodologically, by using social network
analysis and interview techniques to investigate
how external knowledge emerges with rapid
internationalising born globals’ (p. 574).
Semi-
structured
interviews
N/A
Whelan (2011)
EJIM
‘To aid in the identification and management of
talented employees in knowledge intensive
settings, this paper proposes an approach based
on social network analysis (SNA) – a
methodology that analyses the relationships of
actors, such as employees, in a network context’
(p. 485).
Quantitative
data collected
through social
network
questionnaires
UCINET
Vohra and
Thomas (2016)
TIBR
‘To understand the process through
which people acquire and disseminate
information based on social relationships, we use
Quantitative
data collected
through social
UCINET

Page 42 of 42
social network analysis as a method to study
organizational learning’ (p. 588).
network
questionnaires
Figure 1: SNA and internationalization: an illustrative example