Pergamon
Journal of Management 2003 29(3) 427-442
Inter-Firm Networks and Entrepreneurial Behavior:
A Structural Embeddedness Perspective
Zeki Simsek∗
Management Department, School of Business Administration, University of Connecticut,
2100 Hillside Road Unit 1041, Storrs, CT 06269-1041, USA
Michael H. Lubatkin
University of Connecticut, 2100 Hillside Road Unit 1041, Storrs, CT 06269-1041, USA
Steven W. Floyd
University of Connecticut, 2100 Hillside Road Unit 1041, Storrs, CT 06269-1041, USA
We develop a theory of the effects of inter-organizational networks on both radical and incremental forms of firm-level entrepreneurial behavior (EB). The central argument is that
structural embeddedness, with its focus on the network as a whole, and its two consequences,
relational and cognitive embeddedness, individually and collectively influence incremental
and radical forms of EB. Relationships in our model are driven by reciprocal interactions
between intra- and inter-organizational sensemaking. This reasoning leads us to a dynamic,
co-evolutionary model of EB. © 2003 Elsevier Science Inc. All rights reserved.
Inter-firm networks are “a select, persistent, and structured set of autonomous firms (as
well as nonprofit agencies) engaged in creating products or services based on implicit
and open-ended contracts to adapt to environmental contingencies and to coordinate and
safeguard exchanges. These contracts are socially not legally-binding”1 (Jones, Hesterly &
Borgatti, 1997: 914). An insightful view for understanding these networks is Granovetter’s
(1985) structural embeddedness theory, which posits that firm behavior is embedded in, or
partially determined by ongoing systems of inter-firm relations. The word “partially” in the
description is important because it signals the view that firms’ economic behavior should
be seen as neither “undersocialized” nor “oversocialized.”2
We extend Granovetter’s theory to develop an explanation of firm-level entrepreneurial
behavior (EB), which we define as the sum of the firm’s innovation, venturing, and strategic
∗ Corresponding author. Tel.: +1-860-486-6423; fax: +1-860-486-6415.
E-mail addresses: simsek@business.uconn.edu (Z. Simsek), mike@business.uconn.edu (M.H. Lubatkin),
steven@business.uconn.edu (S.W. Floyd).
0149-2063/03/$ – see front matter © 2003 Elsevier Science Inc. All rights reserved.
doi:10.1016/S0149-2063(03)00018-7
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renewal activities (Guth & Ginsberg, 1990; Zahra, 1996). Our thesis is that the social relationships comprised by a firm’s inter-firm network represents a sensemaking community,
and thus are driven by recursive cycles of individual and shared sensemaking. As such, these
networks are likely to be in a continual state of adjustment, or punctuated disequilibriums,
as member firms attempt to innovate, take risks, and act proactively. Put differently, we
posit that EB, like other firm behaviors, is embedded in the structure of a firm’s ongoing
inter-firm relations. In so doing, we depart from the tradition of explaining EB with firmand managerial-level explanations, and instead select the inter-firm network as the primary
unit of analysis.
Three gaps in the literature motivate our paper. First, while a growing number of researchers recognize the important role that inter-firm networks play in promoting entrepreneurial activities (e.g., Powell, Koput & Smith-Doerr, 1996), their explanations make
only indirect reference to the nature of ties a firm may have in a network, how those ties
may fit together, and the effects of such ties on firm behavior. Second, the pervasiveness
of networks in entrepreneurially intensive industries has been observed in a number of
recent empirical studies, including biotechnology (Barley, Freeman & Hybels, 1992), fashion (Uzzi, 1997), film (Faulkner & Anderson, 1987), financial services (Podolny, 1994),
and semiconductors (Saxenian, 1990). This persistence has been explained as resulting
from a reduction in transaction costs (Gulati & Gargiulo, 1999), the inertia of personal
and structural attachments (Seabright, Levinthal & Fichman, 1992), and absorptive capacity considerations (Dyer & Singh, 1998). Surprisingly absent in these explanations have
been network effects on EB. Finally, a growing body of evidence suggests that networks
affect firm processes, including trust, collective sanctions, and reputation (Dacin, Ventresca
& Beal, 1999). This work has focused largely on transactional efficiency as an outcome,
however, ignoring the possible effects on EB.
Our paper attempts to respond to these gaps. We argue that structural embeddedness
theory, with its explicit focus on the network as whole, and a sensemaking perspective on
networks, provide the basis for a model of the multilevel and multidirectional interactions
that relate inter-firm network characteristics to firm-level EB. In the next section, we differentiate EB into its incremental and radical forms. Then, we provide an overview of the
network antecedents of EB, conceptualized as the relational and cognitive consequences of
structural embeddedness. This is followed by detailed arguments leading to a set of testable
research propositions. Finally, the discussion traces the implications of the model for future
research.
Theoretical Model
The principle virtue of a structural embeddedness perspective lies in its ability to describe
action vis-à-vis networks of relationships while acknowledging the motives and interests
of individual actors (Granovetter, 1985). The theory’s core idea is that network relationships cannot be validly decomposed into independent “bilateral monopolies” (Baker, 1990).
Should firm dyads be used as the basis for analyzing socially motivated firm behavior, the
problem of atomization (inappropriately shifting the unit of analysis) occurs because the
locus of explanation is transferred from the actor to the dyad (Granovetter, 1992). In short,
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429
“without reference to the nature of other ties in the network or how they fit together”
(Wellman, 1991), the analyst cannot adequately show how social relationships influence
firm behaviors such as EB.
In attempting to offer a theory of EB from an embeddedness perspective, it is important
to be clear about the meaning of firm-level EB. Miller (1983) defines entrepreneurship as
an orientation, or the degree to which a firm has a propensity to innovate, take risks, and act
proactively. Guth and Ginsberg (1990) and Zahra (1996) define it in terms of actual behaviors, that is, the sum of the firm’s innovation, venturing, and strategic renewal activities.
March (1991), Morris (1998), and Tushman and Anderson (1986) suggest a useful distinction between two types of EB, based on the degree of change in existing organizational
arrangements. IncrementalEB (and its synonyms, such as adaptive or means-seeking behavior) involves innovation, venturing and strategic renewal activity, but all within the bounds
of the established premises, policies, and customary views. Accordingly, these behaviors
are typically focused on improvements in existing products or processes and doing (or making) things better or more efficiently (Argyris & Schon, 1978; March, 1991). By contrast,
radical EB is intended to produce fundamental changes in organizational routines and/or
approaches to products, processes, and markets (Lant & Mezias, 1990). Rather than doing
the same things better, these behaviors are focused on doing new things.
This distinction allows us to explore the relationship between embeddedness and EB in
two forms, incremental and radical. This is important since the literature views these two
types of behavior as competing for scarce organizational resources and managerial attention.
As Levinthal and March note, “the basic problem confronting an organization is to engage
in sufficient exploitation to ensure its current viability, and at the same time, devote enough
energy to exploration to ensure its future” (1993: 105). Thus, EB may involve innovation,
venturing, or renewal activities that are totally new (radical) or that improve on existing
(incremental) products or processes. In both cases, the firm may gain entrepreneurial profits,
but it does so in very different ways. Incremental EB generates predictable returns, while the
returns from radical EB, though are much more uncertain, are “the only way to finish first”
(Levinthal & March, 1993, p. 106). For example, entry into a small niche in an existing
market (incremental EB behavior) is likely to generate more certain but relatively low
returns, while entry into a totally new, unrelated market (radical EB behavior) is likely to
generate more uncertain but probably higher returns.
With these observations in mind, we propose the model in Figure 1 as a way of thinking
about how structural embeddedness influences EB. At the model’s center, sensemaking
allows the reciprocal relationships among network characteristics and EB occur. Specifying such intervening processes is important in order to avoid creating a “black box”—a
tendency in prior research that Uzzi (1997) describes as the “theoretical indefiniteness” of
embeddedness theory. As Granovetter notes, “the assertion that economic action is embedded in networks of personal relations among actors ties into the classic question in social
theory of just how any behavior and institution are affected by social relations” (1992: 27).
We will argue that sensemaking drives the co-evolutionary processes by which structural
embeddedness affects EB.Weuse “sensemaking”inthe way Weick (1995, pp. 91–100) does,
as an individual and social process by which organization members form understandings
from what they experience as ambiguous situations, and then use these understandings to
guide behavior.
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Figure 1. Relationships between inter-firm embeddedness and EB.
Use of the term co-evolution may also require a brief explanation. Co-evolution processes
exist as complex systems of continual and reciprocal interactions, where one condition
influences and is influenced by another (Lewin & Volberda, 1999; Bosch, Volberda & Boer,
1999). Thus, co-evolutionary processes do not lend themselves to simple linear progressions
or to directional relationships, but instead to recursive relationships among variables within
the system.
In our model, structural embeddedness describes the network’s overall architecture, encompassing the properties of inter-firm ties as a whole (Dacin et al., 1999). Among the
important facets of structural embeddedness is the presence or absence of ties between
actors. Variables along these lines include closure, density, connectivity, and hierarchy
(Wasserman & Faust, 1994). Relational embeddedness refers to the quality of dyadic exchanges, including the degree to which parties consider one another’s needs and goals as
well as the behaviors that they exhibit toward one another, such as trust, norms, reputation, sanctions, and obligations (Coleman, 1990). Finally, cognitive embeddedness refers
to similarity in the representations, interpretations, and systems of meaning among firms
(Nahapiet & Ghoshal, 1998). While it is new to the network literature, we include cognitive
embeddedness in our model based on related work on industry-level macro-cognitive elements (e.g., Scott, 1995). Here, cognition is treated as a social phenomenon being shaped
by, and shaping, interactions between actors (Abrahamson & Fombrun, 1994).
Finally, our model also considers Powell (1990) and Miller (1996), who caution that
embeddedness constructs may be too broad to develop refutable propositions. To avoid
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this, we follow three decision rules in choosing how to conceptualize the components of
embeddedness. First, while the explanatory constructs in the model should emerge at the
network level of analysis, they should not exaggerate the effect of social context on firms
to a point of depicting them as “social dopes.” Second, the constructs should be sufficiently
developed in the literature to permit the identification of specific relationships and research
propositions. Third, wherever possible, the effects of the constructs should be profound, of
course, but also discriminate between radical and incremental forms of EB.
To represent structural embeddedness,we selected network closure; institutionalized reciprocity to represent relational embeddedness, and cognitive similarity to represent cognitive
embeddedness. As discussed below, we believe these specific facets of embeddedness meet
the above criteria and provide significant insight into EB, but we are not suggesting that
other facets should be ignored. As is in most theory building efforts, therefore, we frame
our arguments on the basis of “all else being equal” assumptions.
Structural and Relational Embeddedness
Structural embeddedness describes the network’s overall architecture. More specifically,
it is defined by the presence or absence of ties, and the boundaries of the network as a
whole can be conceptualized on the basis of network closure (Wasserman & Faust, 1994).
Following Coleman (1988), Krackhardt and Stern (1988), and Marsden (1990), we define
structural embeddedness as the relative proportion of internal and external ties, i.e., the
number of existing relationships to the total number of possible relationships among all
network members (if each network member were tied to every other member) and the
number of relationships that network members have to non-network members, respectively.
Theoretically, a totally closed network is one in which all organizations are directly tied
to each other and have no ties to organizations “outside” the network. This view suggests
that structural embeddedness increases when the number of external ties decreases or the
number of internal ties increases.
Research shows that networks in the same industry and across industries tend to have
varying levels of closure (e.g., Gulati, 1995; Uzzi, 1997). These differences are due to
organizations’ attempts to maximize their status (i.e., avoiding partners of lower status) and
to relational contracting, that is, working with fewer partners more often (Jones et al., 1997).
We argue that variability in this structural facet is a determinant of the level of relational
embeddedness in a network, or the quality of reciprocal exchange. Such quality, in turn,
may be defined along three dimensions: (1) the dependence of one actor’s behavior on
another’s; (2) equivalence of returns for reciprocating (i.e., good is repaid by good, and
harm by harm); and (3) the immediacy of such returns (i.e., the timing of reciprocation).
Thus, perfect reciprocity exists when exchanges between two organizations are maximally
contingent, functionally equivalent, and immediately reciprocated (Molm, 1994).
Consistent with structural embeddedness theory, the concept of reciprocity can be extended beyond the dyadic level of analysis to include cases where a network of actors is
involved. For example, Ekeh (1974) uses the concept of generalized norms of reciprocity
to describe situations in which reciprocation involves multiple actors (more than just two)
and where it creates indirect (rather than direct) benefits. Axelrod and Keohane (1985)
argue that reciprocity can become widespread and “institutionalized” because reciprocity
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is economically beneficial; member firms have incentives to institutionalize it so that they
will benefit from its wider use. Exchange theory (Blau, 1964) suggests that reciprocity
might promote the development of trust-like social mechanisms because there is inherently
greater risk in reciprocal transactions than discreet ones, and because reciprocity allows the
demonstration of trust and intentions.
A maximally embedded network, therefore, is one where all exchanges are guided by
generalized norms of reciprocity. Here, all member firms would undertake transactions confident that others will reciprocate. The efficiencies in such a “clan governance” arrangement
are believed to be significant (Ouchi, 1980).
Why then don’t all networks evolve into a state of maximum embeddedness? Reciprocity
is impeded by information asymmetries among network actors. For example, member firms
may intuit an incentive to withhold, filter, and misrepresent information in a particular
transaction. If opportunism results, it introduces ambiguities into network relationships that
cannot be resolved by means of rational decision-making. Instead, such ambiguities become
the focus of organizational and inter-organizational sensemaking (Weick, 1995), whereby
actors interpret individually and socially construct their understandings and potential responses to discrepant behaviors. Like that among individuals, sensemaking at the level of
inter-organizational networks occurs as the result of a set of ongoing series of replicated
(or not) interactions. Tying this notion to Axelrod and Keohane (1985) suggests that the
direction and pace of sensemaking about the extent of reciprocity in the network is impeded
by lack of information about the extent of conformance and the presence of defectors. Said
differently, an inter-organizational environment where defectors cannot be identified (or
interpreted as such) is less likely to produce norms of reciprocity among firms.
Structural embeddedness (network closure) can minimize this problem and thus engender
relational embeddedness (i.e., the institutionalization of reciprocity) within the network
structural embeddedness increases the likelihood of identifying defectors because of the
amount and speed with which information about actors circulates within an embedded
network. Evidence suggests that actors become aware of others’ activities and behaviors as
a by-product of direct and indirect relationships (e.g., Friedkin, 1982). As the proportion of
internal over external ties increases for a particular network, then, this is likely to produce
a higher level of awareness of discrepant cues, such as defection, among network actors.
In the presence of ambiguous norms of reciprocity, network closure makes high quality
information about defectors more accessible and facilitates the identification of defectors
(Uzzi, 1997).
Although identificationofdefectors maybethe trigger for sensemaking, the process might
also include the construction of actors’ responses, particularly the use of collective sanctions (Coleman, 1988). Collective sanctions “. . . involve group members punishing other
members who violate group norms, values, or goals and range from gossip and rumors to ostracism (exclusion from the network for short periods or indefinitely) and sabotage” (Jones
et al., 1997: 931). As a form of retaliation against defectors, collective sanctions define
and reinforce the parameters of acceptable behavior (Jones et al., 1997). In a closed such
information is more likely to travel faster and spread more widely among network members, thereby enforcing reciprocal behavior. Moreover, since information about defection is
diffused more widely within a closed network, the costs of defection in one relationship are
likely to carry over other relationships. In short, the costs on reputation and transactional
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integrity go up as closure increases, and with increasing closure, there is greater incentive
for actors to abide by norms of reciprocity and to impose sanctions on defectors. This has
the effect of increasing the institutionalization of reciprocity within the network. Formally
stated:
Proposition 1: Structural embeddedness is positively associated with relational embeddedness, such that as network closure increases, so too will the levels of institutionalized
reciprocity in the network.
Structural and Cognitive Embeddedness
Another mechanism through which structural embeddedness may influenceEB is through
its effects on cognitive embeddedness. Consistent with previous work on macro cognitive
elements in inter-organizational fields (Abrahamson & Fombrun, 1994), we define cognitive
embeddedness as the degree of similarity among network actors (i.e., individuals in network
member firms who are central in the intra-organizational network) concerning their beliefs
about the types of issues perceived to be important, how such issues are conceptualized and,
perhaps, alternative approaches for dealing with such issues.
Structural embeddedness in the form of network closure promotes cognitive similarity
among its members through partner selection and recruitment practices. Because organizations tend to be status maximizers and employ relational contracting strategies, a closed
network tends to limit new relationships to individuals who are similar in terms of their
socioeconomic backgrounds, educational levels, and industrial tenures. For example, in a
study of a large population of corporations and investment banks, Baker (1990) found
that about 30% of those corporations gave 100% of their investment banking business to
a single bank. Common experiences and socialization of this sort should generate similar
perspectives on intra- and inter-organizational issues (Hambrick & Mason, 1984).
Structural embeddedness also increases cognitive similarity through its effect on the ongoing sensemaking processes in the network. Common socialization promotes interaction,
and thus reinforces the process through which individuals share information among others
who are like themselves. Thus, closed networks are more likely to develop a common sense
of identity and shared mental framework, and these in turn are likely to influence their
actions and interpretation of future events. This argument for cognitive similarity resonates
with Burt’s (1987) concept of social cohesion whereby organizations influence the content
of one another’s cognitions. Like social cohesion, we would expect that cognitive similarity,
among actors within a network will be a function of the proximity and frequency of interactions, such that directly linked and frequently interacting managers are more likely to have
similar cognitive content than non-linked members. Since the proximity and frequency of interactions in a network increase with increasing closure, cognitive embeddedness increases
with network closure.
Cognitive embeddednessisalso positively associated with relational embeddedness. Simply put, as firms develop a closer agreement on core beliefs, assumptions, and expectations
within the network, norms of reciprocity are likely to become stronger. Since we identify
sensemaking as the mechanism underlying this relationship, however, the relationship between beliefs and actions is a two-way street. That is, as reciprocity becomes increasingly
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institutionalized, actors develop common behavior patterns, such as “tit for tat” routines,
that harmonize their behavior. These behavioral similarities, in turn, lead to the development
of similar beliefs, i.e., cognitive similarities. Hence:
Proposition 2a: Structural embeddedness is positively associated with cognitive embeddedness, such that as network closure increases, so too will cognitive similarity about
network issues among central actors within member firms.
Proposition 2b: A common set of network sensemaking processes produce and reinforce relationships between structural, relational and cognitive embeddedness, causing
such network characteristics to co-evolve, such that the levels of closure, institutionalized
reciprocity, and cognitive similarity are reciprocally and positively associated with one
another.
Relational Embeddedness and Entrepreneurial Behaviors
The existing literature on networks and the diffusion of innovations implies two opposing arguments as to the effects of relational embeddedness (specifically institutionalized
reciprocity) on network members’ EB. The first of these is the strength of weak ties perspective (Granovetter, 1973). According to the weak-tie theory (Granovetter, 1973), distant
and infrequent relationships (i.e., weak ties) are efficient for accessing novel information
because weak-tie bridge otherwise disconnected individuals and groups. Because they represent previously unconnected actors, however, bridging relationships are unlikely to have
been influenced by shared norms of any kind, including the norm of reciprocity. Hence, a
network characterized by weak relationships and, therefore, low levels of reciprocity, is
more conducive to entrepreneurship because actors can build relationships with multiple
disconnected clusters and use these connections to obtain novel information that may, in
turn, generate EB. For example, Uzzi (1997, p. 675) studied clothing firms and found that
as the number of strong ties in their network increased, so did the “sealing of firms in the
network from new and novel information or opportunities that exist outside the network.”
This suggests that low reciprocity promotes greater levels of EB because weak ties
broaden members’ awareness of new ideas and insights from outside the network. Said
simply, EB should be higher in networks that are rich in “structural holes” (Burt, 1992). A
second view, however, called the strength of strong ties perspective, suggests the opposite
line of reasoning: The more the strong ties in a network, the greater the level of EB. Proponents of this view (Krackhardt, 1992; Uzzi, 1997) argue that strong ties have several benefits
that facilitate entrepreneurship. Strong ties are more likely to promote in-depth communication as well as valuable and accurate information exchange. Granovetter (1985) argues,
for example, that the information acquired from reciprocal relationships is inexpensive and
accurate, while also being more detailed than commercially available information. Thus,
although strong ties may not generate new information, they tend to be more useful than
weak ties in helping network members to interpret external opportunities and threats and
formulate potential responses (Krackhardt, 1992; Uzzi, 1997).
The discrepancy between these competing arguments may be partly due to differences
in focus. The strength of weak ties perspective tends to focus on finding new information,
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435
detecting environmental changes, and discovering new entrepreneurial opportunities. The
strength of strong ties perspectives, on the other hand, concentrates on how strong ties
facilitate exchanges of high quality information, knowledge, and resources between organizations and, therefore, help network members discover new ways to exploit opportunities
that are already known or already existing within the network. We, therefore, suggest that the
extent to which reciprocity facilitates or impedes entrepreneurial behavior varies across the
two types of EB. In particular, weak reciprocity norms are more significant in relationship
to radical EB and strong reciprocity norms are more significant for incremental EB.
There are three reasons why weak reciprocity norms enhance radical EB within firms. The
first has already been mentioned: A network characterized by low reciprocity increases informational diversity because the penetrationofnovel information from different partsofthe
industry into the network is easier. As information diversity in the network increases, diversity within each organization and, therefore, the potential for EB also increases (DiMaggio
& Powell, 1983). Second, relationships low in reciprocity are less likely to constrain the
ability of firms to undertake radical change. Indeed, Uzzi (1997) suggests that tightly coupled relationships may negatively influence radical or divergent knowledge-sharing among
firms because the social aspects of exchange (the desire to preserve harmony) supersede the
economic imperative to learn. Finally, a reciprocally coupled network might interfere with
radical EB because the incentives to engage in this type of behavior will be “low-powered”
or “flat” in such a social context (Williamson, 1991). A network of strong relationships degrades the perceived importance of economic incentives in comparison to social legitimacy
and preservation of order within the network, however. Thus, a network without such strong
ties and with more structural holes is likelyto amplify the perceived importance of long-term
economic incentives relative to social concerns and hence encourage radical forms of EB.
On the other hand, a high level of reciprocity is more valuable in relation to incremental
EB. Reciprocity promotes the development of inter-firm knowledge-sharing routines (Dyer
& Singh, 1998). This suggests that as reciprocity becomes institutionalized in a network,
it endows each member firm with adaptive capabilities. Actors may activate the network
when seeking guidance about best practice, technical assistance or inspiration. Reciprocity
might also facilitate incremental EB through its effects on relation-specific investments.
Investments are specific to a relationship when they are highly specialized and thus have
little or no general-purpose use outside of the relationship (Williamson, 1991). Because
there is typically a maintenance cost associated with these investments, firms need to assess
whether they will obtain economic returns from such investments and be confident that their
partners will not act opportunistically. Therefore, absent reciprocity, firms are less likely
to make these investments. In turn, the reason why these investments are important for
incremental EB is that they allow firms to focus on and exploit their existing capabilities
(Williamson, 1985).
In sum, while some network theorists posit that strong ties promote EB and others posit
that weak ties promote EB, we deduce from the literature that both positions are too broad
to be refuted. Both schools ignore the different types of EB, and both have a tendency to
treat concepts like reciprocity as an either/or phenomenon. Finally, both schools assume
that the direction of influence is one-way, going from the network to the firm. In a later
section, we will posit a two-way pattern of influence. However, for present purposes, we
posit:
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Proposition 3: The level of relational embeddedness (the degree to which reciprocity is
institutionalized in a network) is positively associated with a firm’s incremental EBs and
negatively associated with its radical EBs.
Cognitive Embeddedness and Entrepreneurial Behaviors
A long held understanding in the literature is that the cognitions of a firm’s key actors influences the firm’s EB by affecting how the firm identifies environmental opportunities and
problems, considers organizational capabilities and constraints, and formulates and implements strategies (Gavetti&Levinthal, 2000; Hambrick&Mason, 1984; Walsh, 1995).While
most of the theory about cognition’s effects on communications pertains to teams within
an organization (e.g., Nahapiet & Ghoshal, 1998), in this paper, we extend this reasoning
to actors within inter-organizational networks. For example, from the within organization
studies of top management team consensus (e.g., Dess & Priem, 1995), communication frequency (e.g., Katz, 1982), communication effectiveness (e.g., Zenger & Lawrence, 1989),
we might deduce that alignment of cognitions within a network leads to more efficient
inter-organizational communication.
Like reciprocity, however, cognitive embeddedness is a two-sided endowment to the
firm, in that it promotes incremental entrepreneurial orientations and outcomes, but inhibits
the firm’s ability to sustain its viability through radical EB. On the one hand, cognitive
similarities can positively influence the rate of incremental EB by increasing efficiency of
inter-organizational communication and allowing executives across the network to quickly
acquire a common definition of the situation (Scott, 1995). By having similar cognitive
content, key actors across organizations are more likely to understand each other and more
likely to need a minimum of cognitive effort to communicate. Therefore, cognitive similarities may increase the rate of incremental EB by permitting communication economies to
be realized, allowing nuances to be signaled and received and thereby increase information
processing across the network. Confirming this, decades of research conducted in the realm
of diversity, organizational demography and upper-echelon theory finds that actors have
difficulties working and sharing knowledge in cognitively diverse contexts (e.g., Amason,
1996; Ancona & Caldwell, 1992).
On the other hand, cognitive differences are necessary for radical EB. Heterogeneity
of cognitive content stimulates discussion, decreases groupthink and leads to more
original ideas (Janis, 1972; Katz, 1982; Weick, 1976). Dutton and Duncan (1987) argue
that differentiation in belief structures enhances the search for information, increase the
perception that change is feasible, and generates momentum for change. The importance
of cognitive diversity is also a central feature in March’s (1991) discussion of the
exploration/exploitation trade-off, where diversity sustains the capacity for exploration
and for avoiding competency traps. High cognitive similarities also adversely affect the
rate of radical EB by compressing search times, resulting in a failure to consider multiple
alternatives and push for “one” best solution. In fact, at high levels of similarity,
consensus becomes so widely shared and articulated that it reduces the urge to communicate (Hambrick, 1994). Conversely, if key actors have more varied issue sets and
agendas, they are likely to exert more cognitive effort, attend to more aspects of the situation, and detect novel solutions. Holding constant the concurrent network influences of
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relational embeddedness, which were specified in the third proposition, we propose the
following:
Proposition 4: The level of cognitive embeddedness (the degree of inter-firm cognitive
similarities in a network) is positively associated with a firm’s incremental EB and negatively associated with its radical EB.
Entrepreneurial Behavior as a Co-Evolutionary Sensemaking Process
As we previously noted, our intent in this paper is to use Granovetter’s (1985) structural
embeddedness theory to explain how networks affect firm-level EBs. As such, our model is
novel in that it departs from the tradition of explaining EBs with firm- and managerial-level
explanations. Up to this point, however, our model is incomplete, for it implies a one-way
direction of influence, going from inter-firm networks to the firm, potentially producing an
overly deterministic setof effects, orinGranovetter’s terms,an“oversocialized” explanation
of EB.
In addition, without reciprocal effects between firm-level EB and network-level characteristics our model remains static and closed. This raises fundamental questions like,
“How did the network emerge into its current structure?” Our thesis in advancing the
model beyond an oversocialized, static description of EB is that the overall architecture of
an inter-firm network (i.e., its level of structural, relational and cognitive embeddedness) is
partly shaped by the each member firm’s entrepreneurial needs, which themselves are driven
by intra-organizational sensemaking. More simply put, just as the inter-firm network influences each member firm’s EBs, so too does each member firm’s EB influence the network.
At the core of this thesis is the recognition that within every firm resides a network of
decision makers. These can be seen as intra-firm sensemaking communities that are nested
within the inter-firm sensemaking community. While sensemaking occurs at both levels,
the contexts for sensemaking are different. As we have detailed in this paper, inter-firm
communities are concerned primarily with the challenge of attaining effective governance
over a set of autonomous firms based on open-ended social contracts that are not legally
binding (Jones et al., 1997). Here, we have defined effective governance largely in terms of
reciprocity norms and cognitive similarity.
In contrast, intra-firm communities are concerned primarily with their firm’s current and
future viability. The role of central actors within these networks, as the principal architects
of their firm’s strategy and administrative system, is to adjust firm-level attributes to suit
competitive realities, including imperfections in the firm’s resource and output markets and
information asymmetries in inter-firm networks. As such, central actors within a firm are
faced with the challenge of making sense of proposed inter-firm resource commitments
before the relationship between those commitments and their potential outcomes for firm
performance are fully understood. Such a context calls for the kind of innovative, risk taking,
proactive behavior that lies at the heart of EB.
As the result of sensemaking, then, some actors within an inter-firm network may conclude that the pool of resources and capabilities already embedded in the network are
sufficient for the firms’ entrepreneurial needs. They look within the network for the information and capabilities needed to sustain them and seek to strengthen inter-firm ties through
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Z. Simsek et al. /Journal of Management 2003 29(3) 427–442
more intimate and frequent interaction. These actors may also seek to form new reciprocal
ties, thereby bridging existing structural holes in the inter-firm network. By virtue of this
EB, which as we have argued is largely incremental, these actors increase network closure
and thus contribute to the level of structural embeddedness.
At the same time, other actors from the same inter-firm network may decide on a more
radical adaptive response, and, therefore, look outside the network for resources and capabilities to supplement those already available within the network. In the process, these
actors may distance themselves from other network partners (weaken existing strong ties)
while introducing new firms with new competencies into the network (bridging weak ties).
As with organizations pursuing incremental EB, these more radical behaviors disrupt the
network’s overall architecture. Rather than filling structural holes, however, radical EB on
the part ofnetwork members engenders new holes and more weak ties. This may enhance the
network’s potential to produce novel information and facilitate radical EBs in other firms.
We argued with our first four propositions that the embeddedness of inter-firm networks
affects the locus of firm-level EB. In this section, we are proposing that the direction of
influence flows both ways, or that firm EB and network embeddedness co-evolves. This
recognition completes our model’s development bysuggesting that the relationship between
inter-firm networks and firm-level EBs is both embedded and dynamic. Stated formally:
Proposition 5: The structural, relational, and cognitive embeddedness attributes of an
inter-firm network and the EBs of its member firms co-evolve as the result of inter- and
intra-organizational sensemaking processes.
Implications and Conclusion
Explanations of entrepreneurial firms have to date focused on either the entrepreneur
or the processes in an organization that promotes EBs, while giving short shrift to the
influence of inter-organizational networks. We argue that this oversight promotes a problem
of atomization, or prematurely shifting the unit of analysis from networks to individual firms
without first taking into the effects of the network. Our multilevel co-evolutionary model,
with structural embeddedness at its core, offers a more holistic view. Specifically, structural
embeddedness provides a theory to explain how ties in inter-organizational settings shape
firm behavior, something that the larger literature about embeddedness tends to be silent
about. Moreover, by distinguishing between incremental and radical EB, we provide a
richer description of network effects, including why the network structure simultaneously
constrains and enables entrepreneurial firm behavior.
Our model suggests several avenues for further research. For example, we limited our
argumentto a select set of network-level variables inthe attempt to theorizean “all else being
equal” link between structural embeddedness and EB and to advance refutable propositions.
By doing so, we overlooked other network variables that might enrich the linkage, such as
centralization. Not to be confused with “centrality,” which is an individual-level construct
that measures the relative position of actors within the network, centralization refers to the
variability in centrality scores in anetwork and captures the extent that network members are
heterogeneous in the number of ties each has (Marsden, 1990). The maximally centralized
Z. Simsek et al. /Journal of Management 2003 29(3) 427–442
439
“star” network concentrates all relationships in one central actor who communicates directly
with all other network members (Freeman, 1979). Consistent with Burt’s (1992) contention
that a central actors’ ties to outside network, particularly those characterized by weak ties
or structural holes (i.e., disconnections or non-equivalencies among players in an arena)
will increase the efficiency of new information diffusion throughout the network while
minimizing redundancy of central actors’ ties, we, therefore, speculate that the presence of
a centralized firm can enhance the network’s entrepreneurial ability to endow its member
firms with both incremental and radical EBs.
Important questions remain: Is there a particular level of structural embeddedness that
produces the optimal balance of incremental and radical EBs to ensure the member firm’s
long-term viability? Is that optimal mix contingent upon market effects like dynamism
and munificence? These questions and propositions advanced in this paper suggest the
fruitfulness of examining EB with a structural embedded theoretic lens.
The difficulty in testing models involving network embeddedness variables has to do
with collecting data on structural and compositional network constructs. However, studying inter-firm networks is not an impossible undertaking, as exemplified by studies on
takeover defenses, corporate acquisitions, total quality management, multidivisional and
matrix structures, and strategic alliances. Several other studies have examined how inter-firm
networks can directly affect organizational outcomes including organizational survival and
adaptation to changes (e.g., Uzzi, 1997). These investigations suggest that one or two principal investigators with relatively modest research funds might be sufficient to conduct
successful investigations of EB in inter-firm settings.
Finally, our model is managerially relevant. For example, by providing an understanding
about how specific aspects of the firm’s network influence EB, it offers managers insight
as to how they can position their firms within inter-firm networks to balance the need for
incremental forms of EB with the need for radical EB. According to our model, managers
might attain the former by positioning their firm in a network characterized by closure,
reciprocity, and cognitive similarity, while attain the latter by positioning their firm in a
network with weak ties and many structural holes.
Inconclusion,weextendfirm and managerial explanationsofEBbyfocusingoninter-firm
networks as the primary unit of analysis. Our core argument is that structural embeddedness,
with its focus on the network as a whole, and its two consequences, relational and cognitive
embeddedness, individually and collectively influence incremental and radical forms of EB.
The underlying mechanisms driving these relationships are intra- and inter-organizational
sensemaking. The ongoing reciprocal interaction between these two levels of analysis produces dynamic, co-evolutionary effects on EB. By defining the concepts and developing
specific propositions about these relationships, we hope that we have suggested an agenda
for future empirical research.
Notes
1. The term “select” indicates that network members are only a subset of the firms with
which a given organization has contact; “persistent” means that relationships are
characterized by repeated and recurrent transactions; “structured” indicates that these
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Z. Simsek et al. /Journal of Management 2003 29(3) 427–442
exchanges are neither random nor uniform; “autonomous firm” conveys the idea that
each network member is legally independent; and finally “implicit and open-ended
contracts” highlights the fact that network relationships and exchanges do not rely on
authority or legal contracts (Jones et al., 1997: 914–916). 2. The undersocialized view
refers to explanations in which economic action is assumed toberational and minimally
influencedbyrelationships and social context.Inthis view, actors arrive at goals
independently and are entirely self-interested (Granovetter, 1985; Coleman, 1988).
Whatever happens in the network can be described exhaustively in terms of the
individuals involved. TCE represents an undersocialized theory because it posits that
economic action is an amalgamation of discrete transactions and atomistic motives and
beliefs. By contrast, the oversocialized view portrays actors as completely socialized.
Their behavior is governed by the social context, which manifests itself in the form of
generalized norms, socialization rules, and obligations (Coleman, 1988; Granovetter,
1985). Institutional theory may represent an oversocialized perspective because it
tends to downplay actors’ strategic goals and self-interests.
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Zeki Simsek is an Assistant Professor of strategic management in the School of Business at
University of Connecticut. He received his Ph.D. from University of Connecticut. His current research interests include inter-firm networks,firm-level entrepreneurship, and strategic
management of organizations.
Michael H. Lubatkin is the Wolff Family Chair in Strategic Entrepreneurship in the School
of Business at the University of Connecticut and Professor at Ecole de Management de Lyon
(France). His current research interests include the modeling and testing of governance
problems that are unique to family firms, the debating of paradigmatic differences between
strategy and various sub-fields of economics, including financial economics, agency theory, and industrial organizations, and the problems of core competency transfer between
divisions and across national boundaries.
Steven W. Floyd is the Cizik Chair of Strategic Management, Technology and Manufacturing, and an Associate Professor of strategic management at the School of Business at
the University of Connecticut. His recent research focuses on the role of social context in
the development of strategic initiatives, the contributions of middle-level management to
strategy-making and the organizational processes associated with strategic renewal.