Interorganizational networks as value creating systems
Understanding a firm’s sources of competitive advantage has become a major field of interest in strategy research (e.g. Barney
1991; Porter
2004b; Rumelt
1984; Teece et al.
1997). The concept of sustained competitive advantage is often associated with a firm’s value creating strategy, whose benefits cannot easily be duplicated by others in the present or future (Barney
1991, p. 102). Traditional models focus on firm-internal processes, resources or dynamic capabilities, while value creation in the digital economy results from the collaboration of several economic actors. In order to pursue value co-creating activities, organizations have started to open up their value creation structures and processes by collaborating with other firms in various forms (Fjeldstad et al.
2012, p. 734; Snow
2015, p. 7; see also Baldwin and von Hippel
2011; Bollingtoft et al.
2012; Chesbrough
2003). Instead of maintaining large, vertically integrated firms, organizations establish
multi-firm networks and
community-based structures to “built on a strategy of persistent exploration of an expanding set of complementary markets whose participants continuously adapt technologies to new uses” (Miles et al.
2009, p. 65). Accordingly, the locus of value creation as well as the shape of organizational forms has shifted from individual firms towards interorganizational networks (Miles et al.
2010, pp. 96; see Miles et al.
2009; Powell et al.
1996).
Firms have become increasingly embedded in interorganizational networks with respect to social, professional, and exchange relationships (see Granovetter
1985; Gulati
1998; Parkhe et al.
2006; Snow
2015; Snow et al.
2016). Digitization has become an accelerator for this development to the extent that firms have changed their modus operandi from competition to cooperation to collaboration due to social and technological conditions (Snow
2015, p. 1). Interorganizational networks encompass a firm’s relationships to suppliers, customers, competitors, or other entities across boundaries of industries or countries. Thereby, they can take different forms such as strategic alliances, joint ventures, franchising, long-term marketing and licensing contracts, reciprocal trade agreements, R&D partnerships, buyer-supplier relationships, director interlocks, investment bank ties, personnel movement links or cross-patent citation ties (Gulati et al.
2000, p. 203; Zaheer et al.
2010, p. 62). Interorganizational networks are constituted of organizations which are connected through a wide range of social and economic relationships. In general, a network is considered to be “a set of nodes and the set of ties representing some relationship, or lack of relationship, between nodes” (Brass et al.
2004, p. 795). Nodes are referred to actors (e.g. persons, teams, units, organizations) which are connected by ties to a set of binary social relations. Ties can have varying contents, strengths, and directions, “limited only by a researcher's imagination” (Brass et al.
2004, p. 795). The pattern of ties forms a specific structure in a network whereas actors occupy positions within this structure. Nodes in interorganizational networks are referred to as firms connected by ties which represent a set of relationships such as exchange, power, or solidarity. Organization scholars have developed a huge and diverse network research generating broad insights about the fragmented field of interorganizational networks (Baker and Faulkner
2002, p. 520; Zaheer et al.
2010, p. 64). Drawing on different theoretical approaches, network-related research has gained insights on how firms may intentionally affect structure and ties of networks to generate superior outcomes by accessing valuable and inimitable resources and capabilities (e.g. Dyer and Singh
1998; Gulati et al.
2000; Gulati
1999; Powell et al.
1996), gaining power and control (e.g. Burt
1992; Cook
1977; Santos and Eisenhardt
2009), establishing trust (e.g. Beamish and Lupton
2009; Coleman
1988; Gulati
1995; Zaheer et al.
1998) and signaling status (e.g. Baum et al.
2000; Higgins and Gulati
2003; Podolny
2005, 1993).
According to Normann and Ramirez, “the only true source of competitive advantage is the ability to conceive the entire value creating system and make it work” (Normann and Ramírez
1993, p. 69). From this perspective, understanding a firm’s interorganizational network and knowing how to enact it is crucial for achieving and sustaining competitive advantage. Therefore, there is need for a theoretical framework to systematize the vast network-related literature and make sense of network effects on firm performance.
Network theory
The network approach has become more popular in the last decades for providing an explanation for organizational phenomena (e.g. Borgatti and Foster
2003; Borgatti and Halgin
2011; Snow and Fjeldstad
2015; Zaheer et al.
2010) since it shifts the focus from attributes of single actors to relationships among systems of dependent actors (e.g. Bergenholtz and Waldstrøm
2011; Borgatti et al.
2014; Gulati et al
2000; Parkhe et al.
2006). Accordingly, firms’ behavior is interpreted “in terms of structural constraints on activity, rather than in terms of inner forces within units” (Wellman
1988, p. 20). Thereby, network theory provides a holistic view, since outcomes are not only explained by actors’ characteristics but are also attributed to actors’ network environments (Borgatti et al.
2014, p. 4). Organizational research has taken the network perspective in order to understand an array of outcomes such as individual, group, and organizational performance, power, turnover, job satisfaction, promotion, stakeholder relations, innovation, leadership, creativity, inter-firm collaboration, unethical behavior, and so on (Borgatti and Foster
2003; Brass et al.
2004; Kilduff and Brass
2010). Likewise, network analyses have become popular as prescriptive tools in management consulting (e.g. Anklam
2007). Some have criticized network research for wavering between metaphor and methodology and lacking theory (e.g. Salancik
1995; Knoke
2001). In order to address these critics, a number of literature reviews have tried to make sense of network research by outlining the theoretical foundations of network theory (e.g. Borgatti et al.
2014; Borgatti and Halgin
2011; Borgatti and Foster
2003; Parkhe et al.
2006; Moliterno and Mahony
2011).
Granovetter’s theory of the strength of weak ties (Granovetter
1973) and Burt’s structural holes theory (Burt
1992) are central to network theory. The former states that the diffusion of ideas or information tends to have a bigger impact if networks consist of weaker ties. Granovetter assumes that strong ties are rather established between actors who are similar to each other with respect to their social environment. Strong ties are likely to describe relationships between actors of the same third parties. Weaker ties, however, emerged between actors having not that much in common. Weaker ties, connecting actors who do not share similar social environments, can bridge ties since they connect different networks of similar actors. Granovetter considers bridging ties to be the source of new ideas and information because of the exclusive connection between actors. Thus, bridging ties facilitate the diffusion of new ideas and information. Therefore, weak ties have the best potential for achieving competitive advantages.
The second fundamental network theory is Burt’s structural holes theory (Burt
1992). Burt argues that actors will outperform others with the same amount of ties in similar strength if the actors’ network exhibits more structural holes. Structural holes are ties, which connect an actor with other cohesive networks. Whereas the information within one network is considered to be redundant, structural holes provide actors with new information and, thereby, with a competitive advantage. Burt’s theory offers a rather strategic view on networks as opposed to Granovetter’s notion of the random emergence of networks. However, both network theories emphasize the value of new information provided by structural holes respectively bridging ties. Burt’s theory of structural holes gives a theoretical explanation for Granovetter’s observation that weaker ties are more likely to bridge cohesive networks. As Burt states, tie weakness is a correlate rather than a cause of the value deriving from bridging ties. Therefore, both theories are strongly related to each other (Borgatti and Halgin
2011, p. 1171).
Fundamentally, network theory is based on two explanatory concepts. First, it focuses on structure and position as key characteristics to predict organizational outcomes. In accordance with Burt (
1992;
2000;
2001) and Granovetter (
1973), a network’s structure and an actor’s position in it are determinants for network as well as actor outcomes. Actors’ attributes are taken into account by relating them to structural aspects of networks. However, attributes play only a subordinate role, whereas the focus remains on structure. Secondly, networks are based on the pipe or flow model, which means that they function as distributors of information (Borgatti and Halgin
2011, p. 1172; Borgatti and Foster
2003, p. 1003; see also Burt
1992). The flow model implicates that the position and the distance between nodes have an impact on the length and frequency of flows, which, in turn, are related to more general outcomes. The flow model suggests that the point of time when nodes receive the flow, the degree of certainty as well as redundancy of flows are of major importance to understand organizational phenomena (Borgatti and Halgin
2011, pp. 1172). Thus, “network theory consists of elaborating how a given network structure interacts with a given process (such as information flow) to generate outcomes for the nodes or the network as a whole” (Borgatti and Halgin
2011, pp. 1172). Nodes taking up a central position may have an advantage since they are more likely to receive the flow earlier than others. The content of ties is of little importance with respect to flows, whereas the patterns of interaction have a huge impact on which and when flows are received. Actors having a central position gain advantages since they can more easily access resources controlled by their alters.
Borgatti and Halgin propose another fundamental model of network theory. The bond or coordination model of networks suggests that networks give nodes the opportunity to align and collaborate (Borgatti and Halgin
2011, p. 1174). Structure has also an impact on power relations between nodes. However, as opposed to the flow model the mechanisms behind it are different. Power in networks can be expressed by dependency relationships (see Cook and Yamagishi
1992; Cook and Emerson
1978). The nodes’ positions in networks matter not because one position is more likely to receive flows than others, but because positions determine the dependencies among nodes. Furthermore, network power is related to virtual amalgamation where ties of solidarity exist among dependent nodes, which may lead to unionization of nodes (Uzzi
1996, p. 676). Solidarity ties and exchange ties may be intertwined like in so-called network organizations (e.g. Powell
1990) where independent actors seem to act as one entity (Borgatti and Halgin
2011, p. 1174). Hence, the bond model considers network ties as bonds, which align nodes with each other and coordinate their actions. Actors gain advantage if their positions prevent them from being excluded from exchange deals.
Structure of interorganizational networks: a field of tension
Although network theory provides a valid theoretical foundation, the mechanisms behind network effects on firm performance are complex to understand and prevailing research provides only limited insights. A recent study by Baum et al. (
2014) casts doubt on the validity of empirical results regarding the impact of network effects on firm performance and, thereby, challenges derived strategic prescriptions regarding network positions. Network research provides explanations for organizational behavior and choices (Borgatti and Foster
2003, p. 1003; e.g. see Davis
1991) as well as performance (e.g. see Burt
1992; Lin
2001) based (most often implicitly) on either the pipe or the bond model. However, in order to understand the underlying complexities of network structure and to explain the contradictions in network research it is necessary to consider both models as two complementing perspectives. Zaheer et al. (
2010, pp. 64) have categorized the vast body of interorganizational network research into four mechanisms underlying the different theoretical considerations used in the literature. These mechanisms can be understood as the trigger for network operations and, as a matter of fact, are also implicitly considered in the flow or bond model of networks.
The first mechanism considers the interorganizational network as source of
resources and
capabilities. The notion of organizations accessing resources and capabilities through networks is especially elaborated in social capital theory (e.g. Coleman
1988; Uzzi
1996). According to Bourdieu and Wacquant (
1992, p. 119), “social capital is the sum of the resources, actual or virtual, that accrue to an individual or a group by virtue of possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition". Drawing mostly on the flow model, many studies discuss how ties or networks should be shaped in order to facilitate the flow of resources (e.g. information) finding expression in the dichotomy of cohesive networks and structural holes (Burt
2001; Coleman
1988; Gargiulo and Benassi
2000).
However, social capital may also cause lock-in effects in maladaptive situations. Organizations attempting to adopt their social capital due to environmental changes or endogenous forces are limited in their actions by previous established ties (e.g. Gargiulo and Benassi
1999; Sorensen and Waguespack
2006; Rowley et al.
2000). Ties with alters – especially when they have been considered as successful in the past – have been proven difficult to untighten since actors may feel a social obligation to preserve them (Coleman
1988, p. 98; Uzzi
1997, p. 36). Furthermore, firms tend to be inert in realizing that social capital may have lost value since strong bonds lead to a cognitive lock-in from developments outside the network (e.g. Kim et al.
2006). Strong interorganizational ties are characterized by relation-specific assets, e.g. institutionalized assets and human capital, which have initially been intended to induce competitive advantages (Dyer and Singh
1998). Dissolving or replacing these ties is therefore perceived as costly, which makes them extremely resilient despite losses in social capital.
The majority of social capital research implicitly relies on the flow model of networks. Here, networks function as conduit for a flow of resources (especially information) in which network structure and position determine flow outcomes (time of reception; share of non-redundant information). However, drawing on the bond model, strong interorganizational ties increase the tendency to preserving unprofitable relationships due to higher need for fulfilling social obligations and higher susceptibility for network inertia.
Secondly, networks may be used as a tool for
power and control. According to Burt (
2000, pp. 348; 2001, 1992), organizations bridging structural holes benefit from a position, where they are capable to influence and control the behavior of their alters. Due to an absence of connections between alters, actors may filter and maneuver information in order to exercise control over the network structure. Organizations occupying broker roles may exploit opportunities by excluding others from these opportunities, especially when “ambiguous or distorted information is strategically moved between contacts by the tertius” (Burt
2000, p. 355). However, network structures with structural holes invoke two major challenges. First, structural holes may cause an action problem, since brokers have more difficulties to coordinate or mobilize their dispersed and unconnected alters than in dense networks (Obstfeld
2005). Moreover, dense networks are characterized by close bonds facilitating collaboration and decreasing tendencies for opportunism (Adler and Kwon
2002, pp. 28). Therefore, strong ties are better for contexts, which require trust as basis for gaining benefits such as the transfer of tacit knowledge (Uzzi and Lancaster
2003, p. 385). Secondly, actors may benefit from structural hole positions only as long as opportunistic behavior does not negatively affect collaboration in the future. Brokers’ power-oriented behavior can be detrimental for group functioning and climate (Bizzi
2013). Although beneficial on an individual level, structural hole positions negatively affect individual outcomes on an aggregate level (Bizzi
2013, p. 1555). This results in a shared perception of potential opportunistic behaviors, which leads to increasing monitoring efforts (Bizzi
2013, p. 1558), a logic of calculation and personal gain (Buskens and van de Rijt
2008, p. 372), learning races within interorganizational networks (Gulati et al.
2000, p. 211), or even network failure (Schrank and Whitford
2011, p. 168).
The emphasis on brokers’ power-oriented behavior does not explicitly address flows since the focus is rather on the patterns of interconnections than on the content of ties. However, forming and exploiting ties in order to achieve objectives may have repercussions on the resource flow provided by the alters. Opportunistic behavior may influence the quality and quantity of resources since actors in a network are seldom independent but affiliated to others.
Thirdly, networks are seen as a source of
trust. Traditionally, trust is considered to be the premise for cooperation and the consequence of established cohesive social ties and, thus, network closure (e.g. Coleman
1988). Trust decreases transaction costs in interorganizational relationships since it facilitates collaboration between actors and increases performance by reducing uncertainty (e.g. Beamish and Lupton
2009; Herz et al.
2016). Furthermore, adding third parties to a relationship even amplifies trust and diminishes the risk of opportunism affecting cooperative relationships (Granovetter
1985, p. 490).
However, trust in networks entails also a dark side (for an overview see Gargiulo and Ertug
2006). Strong ties between network partners may filter external information and ignore new ideas causing a cognitive lock-in (Uzzi
1997, pp. 57). Therefore, actors tend to disregard declining network performance or other warning signals. As a consequence, the social capital of these cohesive networks becomes a liability leading to a decrease in performance. Moreover, high trust between actors eventually leads to unnecessary social obligations resulting in actors becoming “overembedded” in a network and, thus, causing a number of suboptimal exchanges (Uzzi
1997, p. 59). Finally, trust impairs actors’ ability to detect opportunistic behavior and to control the negative consequences due to reduced monitoring and safeguards (Granovetter
1985, p. 49). Although high network density enables organizations to share information faster and facilitates collaboration, it also poses a threat for protecting intellectual property (2010, p. 72).
Most of the network studies regarding trust contain flow-based explanations for achievement. Strong ties and cohesive networks facilitate the building of trust and collaboration and provide, therefore, access to resources. However, these studies may neglect bonding aspects of trust. Relationships, which are characterized by a high degree of trust, also form ties of solidarity, which potentially lead to unnecessary obligations.
The fourth underlying mechanism triggering network operations is the
signaling mechanism. Organizations, which affiliate with high-status partners or have been recognized by high-status peers, provide signals about the quality of the organizations to other actors (Baum et al.
2000; Podolny
1993;
2005; Stuart et al.
1999). Reducing uncertainty, this kind of signals may increase visibility, prominence, and supposedly organizations’ access to resources as well as (potential) network partners.
However, affiliations with high-status partners contain some negative consequences. Prominent partners often pose a thread of misappropriation (e.g. Katila et al.
2008). Especially, new entrepreneurial firms may be exposed by corporate sharks and not be able to protect valuable resources (e.g. knowledge) from their prominent partners (Katila et al.
2008, p. 296). Moreover, negative consequences for new firms are implicitly addressed when researchers argue that established firms “manage threats from market entry by selectively providing and withholding entering firms’ opportunities to collaborate with incumbent firms” (Jensen
2008, p. 723). Furthermore, poorly embedded firms need to accept rather unfavorable terms in negotiations since highly embedded partner may exploit their bargaining power in relationship agreements. Thus, firms attempting to endorse their quality and position by shaping networks may constrain further expansions (Ahuja et al.
2009, pp. 945).
Studies dealing with signaling aspects of networks are mostly based on bond-based explanations since network ties are considered to provide clues to audiences in order to signal quality of an actor. However, these studies may neglect the flow perspective that characterizes relationships by asymmetric ties since actors differ in terms of network position and centrality. Hence, better-embedded actors may use network relationships in order to get access to re-sources and capabilities.
The ambivalent effects of the four mechanisms reveal the bi-functional character of ties in an interorganizational context. Ties between firms simultaneously serve as pipes as well as bonds with both having consequences on outcomes.
4 This turns the structure of an interorganizational network into a
field of tension, in which firm performance can either positively or negatively be influenced by network position and structure.
Strategic implications: balancing the network
A common catchphrase in network research is that network structure and position provides actors with opportunities and constraints. Although agency has played a rather minor role in network research, the opportunities-and-constraints-perspective indicates that actors need to take actions in order to exploit opportunities and to deal with the constraints (Borgatti et al.
2014). Digital technology has dissolved previously stable industries and linear value chains in favor of dynamically emerging networks increasing the necessity to discuss structural effects on firm performance. Firms need to find strategies helping them to achieve and sustain competitive advantage under new circumstances which request digital technology to be an integral part of strategy formulations (Bharadwaj et al.
2013, p. 472; Yoo et al
2010a, p. 730). Given the high uncertainty in digital ecosystems, firms may attempt to shape their digital ecosystems instead of predicting developments or adapting to rapid changes. Strategic decisions concerning the control of digital platforms or the application and integration of resources into platforms owned by others influence the shape and the structure of networks around firms. In turn, the structure of networks and how firms are embedded in them have an impact on firms’ performance (Gnyawali and Madhavan
2001). Network structure and individual agency are intertwined: structure determines actions, which subsequently shape the structure (Bourdieu
1977; Gulati and Gargiulo
1999). Therefore, firms embedded in interorganizational networks seek to achieve beneficial positions and to form valuable ties in order to explore business opportunities and gain competitive advantage (Baum et al.
2014, p. 653; Gulati et al.
2000, p. 207; Zaheer and Bell
2005).
Strategic research concerning networks pays attention, especially, to the identification of factors and conditions under which firms may benefit from either open or closed network positions (e.g. Ahuja
2000; Baum et al.
2012; Rowley et al.
2000). Most studies are particularly concerned with contingent explanations for beneficial network effects, thereby neglecting the complexity of network effects on firm performance as well as how firms exert agency and intentionally alter their networks.
The notion of the interorganizational network as a field of tension shows that pitfall of networks may emerge if negative repercussions of presumptive beneficial network strategies are neglected. For example, the control benefit notion in structural hole theory focuses on the power of actors provided by structural hole positions, however, neglecting the potential negative effects on future resource flows with dependent alters. This example illustrates that actions that benefit certain aspects of interorganizational networks may have negative consequences on others. Zaheer et al. (
2010) have identified network mechanisms according to which firms form networks. In order to access resources, to establish trust, to gain power, or to signal quality to other firms, organizations may pursue different strategies. The proposed strategies most often rely implicitly on one of the underlying network models – flow or bond – which consider ties between nodes as conduits that facilitate the flow of something or as bonds that align and coordinate action respectively. The double-sided character of ties between actors serving as pipes and bonds often leads to contradicting effects on organizational outcomes. Hence, strategic decisions need to balance the opportunities and constraints of certain network ties and structures and consider the trade-offs.
According to the model of constrained agency firms actively create, perpetuate, and modify network structure by acquiring, activating, altering, and adjusting relationships (see Gulati and Srivastava
2014). Firms take actions through the choices they make regarding given interorganizational ties. Network positions determine firms’ resources at hand and shape their motivations to employ these resources. This constrained agency is expressed by network actions, which subsequently can alter firms’ initial position in interorganizational networks. The question of forming new ties or dropping old ones becomes therefore a strategic matter with respect to the design of digital platforms (e.g. Evans and Schmalensee
2016; Gawer
2009). Digital platforms connect actors by facilitating their value co-creating activities that shape the structure of certain digital ecosystems. A platform creates value by attracting third parties (e.g. customers, business partners, research companies etc.) to use the platform for applying and integrating their resources. Platform owners govern relationships and control with whom to establish ties by deploying technical boundary resources (Eaton et al.
2015; Ghazawneh and Henfridsson
2013). Third party participation enhances functionality and network effects and, thereby, allows owners to increase the value of their platforms (Gawer and Cusumano
2002, p. 6). Firms take network actions by leveraging their resources through the platform in order to increase their own performance (Ceccagnoli et al.
2012, p. 267). Thus, the structure of digital ecosystems enables firms to acquire, activate, alter, and adjust ties. Simultaneously, the structure shapes motivation to do so. As a consequence, the ecosystem structure itself tends to constantly change.
Due to the prevalent interdependence of structure and agency in digital ecosystems, the only remaining relevant source of sustained competitive advantage is the interorganizational network structure. Accordingly, firms effectuate digital ecosystems through digital ecosystems by balancing the different effects of network structure to their favor.