Social Capital, Networks, and Knowledge · PDF fileSocial Capital, Networks, and Knowledge Transfer Author(s): Andrew C. Inkpen and Eric W. K. Tsang Reviewed work(s): Source: The Academy

Social Capital, Networks, and Knowledge TransferAuthor(s): Andrew C. Inkpen and Eric W. K. TsangReviewed work(s):Source: The Academy of Management Review, Vol. 30, No. 1 (Jan., 2005), pp. 146-165Published by: Academy of ManagementStable URL: http://www.jstor.org/stable/20159100 .Accessed: 03/02/2012 03:34

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? Academy o? Management Review 2005, Vol. 30, No. 1, 146-165.

SOCIAL CAPITAL NETWORKS. AND KNOWLEDGE TRANSFER

ANDREW C. INKPEN Thunderbird and Nanyang Business School

ERIC W. K. TSANG Wayne State University

We examine how social capital dimensions of networks affect the transfer of knowl

edge between network members. We distinguish among three common network types:

intracorporate networks, strategic alliances, and industrial districts. Using a social

capital framework, we identify structural, cognitive, and relational dimensions for the

three network types. We then link these social capital dimensions to the conditions

that facilitate knowledge transfer. In doing so, we propose a set of conditions that

promote knowledge transfer for the different network types.

Networks provide firms with access to knowl

edge, resources, markets, or technologies. In this

article we focus on networks and how firms ac

quire knowledge through their positions within networks. Various scholars interested in net

work relationships have recognized the knowl

edge dimension of networks and its link with

competitive success (e.g., Baum, Calabrese, &

Silverman, 2000; Dyer & Nobeoka, 2000; Gupta &

Govindarajan, 2000; Nishiguchi, 1994). A key ar

gument is that, through membership in a net

work and the resulting repeated and enduring

exchange relationships, the potential for knowl

edge acquisition by the network members is cre

ated. Our interest is in knowledge acquisition, how knowledge transfer between network mem

bers occurs, and what role social capital plays in the transfer.

The primary motivator for us was a theoretical

gap in the research where the key concepts of

networks, social capital, and organizational knowledge transfer intersect. This gap is the

result of four interconnected theoretical re

search threads operating at an organizational level. First, there is a well-established body of

literature underscoring important relationships between knowledge and networks. Second, in

the network area there is increasing interest in

understanding how the social context in which

firms are embedded influences their behavior

and performance (Gulati, Nohria, & Zaheer, 2000; Uzzi & Gillespie, 2002). Third, social capital has

been identified as a concept that can add value to the study of network social processes (Lee, Lee, & Pennings, 2001; Leenders & Gabbay, 1999). Fourth, in various academic (e.g., Adler & Kwon,

2002; Gargiulo & Benassi, 2000; Nahapiet & Ghoshal, 1998) and practitioner-oriented publi cations (e.g., Anand, Glick, & Manz, 2002; Baker,

2000), researchers recently have argued that ac

cess to new sources of knowledge is one of the

most important direct benefits of social capital. Moreover, there is evidence suggesting that

knowledge transfer is facilitated by intensive

social interactions of organizational actors

(Lane & Lubatkin, 1998; Yli-Renko, Autio, & Sa

pienza, 2000; Zahra, Ireland, & Hitt, 2000). In examining these four conceptual threads, it

became apparent to us that a systematic theo

retical analysis of social capital and the transfer

of knowledge between network members did not

exist. Although various variables that affect net

work knowledge exchange and transfer have

been posited (such as firm intent, absorptive

capacity, and control systems), there are few

studies that examine how the social capital di

mensions of networks affect an organization's

ability to acquire new knowledge from the net

work and facilitate the transfer of knowledge among network members. The literature on so

cial capital identifies knowledge access as a

key benefit but does not address the manageri

ally relevant question of how social capital ac

We thank Richard Osborn, Arvind Parkhe, Henry Yeung, and the anonymous AMR reviewers for their valuable com

ments. Both authors contributed equally to the article.

146

2005 Inkpen and Tsang 147

tually affects knowledge transfer between net

work actors.

By explicitly linking social capital, networks,

and knowledge transfer, we strive to achieve

several objectives. The primary objective is to

examine how the social capital dimensions of

networks affect an organization's ability to ac

quire new knowledge from the network and fa

cilitate the transfer of knowledge among net

work members. A key premise is that networks

create access to knowledge for the network ac

tors. Since access is a necessary but not suffi

cient condition leading to transfer,1 we are in

terested in conditions that facilitate knowledge transfer.

A second objective is to integrate the diverse

literature on networks and knowledge transfer.

Because the network concept is broad and mul

tidimensional, we have chosen to distinguish among three common network types: intracorpo rate networks, strategic alliances, and indus

trial districts. Using a social capital framework

derived from Nahapiet and Ghoshal (1998), we

identify structural, cognitive, and relational di

mensions for the three network types. We then

link these social capital dimensions to the con

ditions that facilitate knowledge transfer. In do

ing so, we propose a set of conditions related to

knowledge transfer for the different network

types. Finally, a third objective is to help advance

the study of social capital beyond that of an

umbrella concept (Adler & Kwon, 2002) to a use

ful and valid concept with the potential for un

derstanding network processes. We organize the paper as follows. In the first

three sections we discuss the three core con

cepts?namely, network types, knowledge transfer, and social capital. We then identify how the social capital dimensions are embed ded in each network type. This is followed by a

section in which we examine how the social

capital dimensions influence knowledge trans

fer for the network types. Finally, we discuss

implications and conclusions.

NETWORK TYPES

In this article we focus on strategic networks, which are composed of interorganizational ties

that are enduring and of strategic significance for the firms entering them (Gulati et al., 2000). A

key characteristic of networks is repeated and

enduring exchange relationships between the

actors in the network (Podolny & Page, 1998). This definition of networks includes a wide

range of forms, including intracorporate busi ness units, strategic alliances, franchises, R&D

consortia, buyer-supplier relationships, busi ness groups, trade associations, government

sponsored technology programs, and so on.

Figure 1 shows a typology of some common

network types along two dimensions.2 The ver

tical-horizontal dimension represents the extent

to which network members occupy different po sitions along the network's value chain. The

structured-unstructured dimension represents the extent to which network governance is struc

tured. In a structured network, members' roles

and relationships are clearly defined, and mem

bers are well organized to achieve certain goals. The reverse is true for an unstructured network.

A challenge in studying networks is ade

quately specifying the boundaries of the net

works (Gulati, 1995). The three network types defined below are not intended to be exhaustive

in coverage. Doing so is beyond the scope of this

paper. Our intent is to cover a spectrum of hor izontal and vertical relationships that go from the single-node divisionalized firm (the intracor

porate network) to interfirm relationships (the alliance) to an unstructured collection of firms

(industrial district). As indicated by Figure 1, the

three network types cover both ends of each of

the two dimensions. Moreover, they are among the most researched and discussed network

types. Although it is not feasible to examine all

organizational network types, our discussion of

multiple types raises key issues to help under

stand the relationships between knowledge and

network types not specifically discussed.

1 Inkpen and Beamish (1997) examine the issue of knowl

edge access versus knowledge transfer in detail. With a

focus on alliances, the authors examine the conditions under

which knowledge is transferred from one partner to another

and the resulting impact on alliance stability. The authors

argue that the formation of the alliance creates knowledge access for the partners, but transfer only occurs under cer

tain conditions.

2 We thank an anonymous reviewer for suggesting the

typology of networks. Note that the location and shape of

each network type in the figure are approximations only.

148 Academy of Management Review January

FIGURE 1 A Typology of Network Types

Vertical

Horizontal

Structured Unstructured

Intracorporate Network

An intracorporate network consists of a group of organizations operating under a unified cor

porate identity, with the headquarters of the net work having controlling ownership interest in its subsidiaries. Following Ghoshal and Bartlett

(1990), we conceptualize an intracorporate net work as an interorganizational grouping, rather than a unitary organization, because valuable

insights on the internal structures and opera tions of such an entity can be gained from net

work-related concepts used for investigating in

terorganizational phenomena. There is a clear linkage between ownership

and hierarchical power in an intracorporate net

work. Nevertheless, the strength of the link var

ies greatly along several dimensions, such as

the extent of decentralizing decision-making au

thorities to subsidiaries, the nature of the indus

try concerned, and the physical and cultural dis tances between headquarters and subsidiaries.

Strategie Alliance

A strategic alliance is a group of firms enter

ing into voluntary arrangements that involve

exchange, sharing, or codevelopment of prod ucts, technologies, or services (Gulati, 1998). The last two decades have witnessed a proliferation of strategic alliances among firms as a result of

technological development and globalization. An alliance can be formed by firms located in different positions or in the same position of the value chain. In the latter case, the firms con

cerned may produce similar products and com

pete in similar geographical markets (see Hamel, Doz, & Prahalad, 1989).

It is common for firms to enter into multiple alliances with a number of partners?a phenom enon that has been called an "alliance network"

(Koka & Prescott, 2002). For the sake of clear and concise exposition, we base our discussion on

the context of a strategic alliance rather than a

collection of alliances. That said, the issue of


knowledge transfer is conceptually the same, whether there is one alliance or multiple alli

ances, because it still involves knowledge mov

ing between alliance partners. Also for clarity, we note that this context includes the complex form of alliance known as an alliance constella

tion, which is an alliance involving multiple firms, such as the code-sharing alliances among airlines. Although these alliances often involve

quite complex design and governance, they have the same value creation logic as bilateral alliances (Das & Teng, 2002).

Industrial District

An industrial district is "a network comprising independent firms operating in the same or re

lated market segment and a shared geographic locality, benefiting from external economies of scale and scope from agglomeration" (Brown &

Hendry, 1998: 133). Some famous examples in clude Silicon Valley, Route 128, the Third Italy, and the City of London. An industrial district consists of a network of producers, supporting organizations, and a local labor market (Scott, 1992). There may or may not be a vertical divi sion of labor among the producers. For instance,

Storper (1993) found that clusters of firms in northeast central Italy exhibited a marked divi sion of labor, whereas other Italian clusters

comprised groups of firms doing more or less the same thing. There are usually major universities located inside or close to industrial districts. The universities train skilled personnel and provide technical and research support to firms in the districts.

KNOWLEDGE TRANSFER

We are interested in conditions that facilitate

knowledge transfer in networks. Knowledge transfer is the process through which one net

work member is affected by the experience of another (Argote & Ingram, 2000). Knowledge transfer manifests itself through changes in

knowledge or performance of the recipient unit. In a growing body of research, scholars argue

that organizations able to transfer knowledge effectively from one organizational unit to an

other are more productive than organizations that are less capable of knowledge transfer (e.g.,

Almeida & Kogut, 1999; Argote, Beckman, &

Epple, 1990; Baum & Ingram, 1998; Hansen, 2002;

Kostova, 1999). New knowledge, especially

knowledge from outside the firm, can be an im

portant stimulus for change and organizational

improvement. Related to the network context more specifically, Kotabe, Martin, and Domoto

(2003) found that organizational benefits can

arise from knowledge transfer between network

firms.

Gupta and Govindarajan (1991) argue that the

MNC can be regarded as a network of capital,

product, and knowledge transactions among units operating in different countries and that "the primary reason why MNCs exist is because

of their ability to transfer and exploit knowledge more effectively and efficiently in the intracor

porate context than through external market mechanisms" (Gupta & Govindarajan, 2000: 473). For each type of transaction, subsidiaries can

differ in the extent to which they engage in

intracorporate transactions and whether they are receivers or providers of what is being trans

acted. Gupta and Govindarajan (2000) further maintain that MNCs exist primarily because of their superior ability to transfer knowledge in

ternally, relative to the ability of markets. In this article we focus on conditions that facilitate in

tracorporate knowledge flows among subsidiar ies.

In a strategic alliance, knowledge transfer can be viewed from several perspectives. First, firms may acquire knowledge useful in the de

sign and management of other alliances (Lyles, 1988). This collaborative know-how may be ap

plied to the management of future alliances.

Second, firms may acquire knowledge about an

alliance partner that supports the firm's ability to manage the collaborative task. The knowl

edge obtained can be central to the evolution of the alliance (Ari?o & de la Torre, 1998; Doz, 1996). Third, firms learn with an alliance partner when the partners jointly enter a new business area

and develop new capabilities. Last, firms ac

quire knowledge from an alliance partner by gaining access to the skills and competencies the partner brings to the alliance (Baum et al., 2000; Kogut, 1988). In this context, alliances pro vide opportunities to create redeployable knowledge (or private benefits), such as techni cal knowledge or market knowledge. For the

purpose of our discussion, we focus on the last


two perspectives, which concern knowledge flows between alliance partners.3

A large number of recent studies of industrial

districts have emphasized the capacity of dis tricts to support processes of knowledge acqui sition and innovation as the basis for creating competitive advantage (for a review, see Mac

Kinnon, Cumbers, & Chapman, 2002). Firms in an industrial district have various opportunities to tap into a larger knowledge resource base. The knowledge of primary interest is usually highly tacit, difficult to replicate, and not easily purchased. Helmsing, citing Lawson (1999), de scribes industrial district learning as the "emer

gence of basic shared knowledge and proce dures amongst a group of (geographically close) firms which facilitates co-operation and prob lem-solving" (2001: 289). Geographic proximity facilitates knowledge flows and technical ex

changes among firms (Marshall, 1920). Keeble and Wilkinson (1999) identify three ma

jor mechanisms for the spatial transfer of knowl

edge within the boundaries of an industrial dis trict: (1) interfirm mobility of the labor force

within the district; (2) interactions between sup

pliers and customers and the makers and users

of capital equipment; and (3) spin-off of new

firms from existing firms, universities, and pub lic sector research laboratories. The knowledge transfer processes taking place in an industrial

district give rise to cumulative local know-how

that goes beyond the boundaries of the firm but that remains within the spatial boundaries of the district (Capello, 1999).

SOCIAL CAPITAL

Those studying interfirm relationships in

creasingly focus on how firms are socially em

bedded in networks of relationships that incor

porate a diverse set of organizational actors.

Social capital is gaining prominence as a con

cept that provides a foundation for describing

and characterizing a firm's set of relationships. However, although the concept of social capital has found widespread acceptance, there re

mains widespread uncertainty about its mean

ing and effects (Koka & Prescott, 2002). In his review, Portes (1998) identifies Pierre

Bourdieu's (1986) analysis as the first systematic analysis of social capital. Bourdieu defined the

concept as "the aggregate of the actual or po tential resources which are linked to possession of a durable network of more or less institution alized relationships of mutual acquaintance or

recognition" (1986: 248). As the concept evolved,

through work by Coleman (1988), Burt (1992), and others, a consensus emerged that social capital represents the ability of actors to secure benefits

by virtue of membership in social networks or

other social structures (Portes, 1998). At an orga nizational level, benefits include privileged ac cess to knowledge and information, preferential opportunities for new business, reputation, in

fluence, and enhanced understanding of net

work norms.

Although Adler and Kwon's (2002) comprehen sive review identifies many different ap

proaches used in studying social capital, two

patterns emerge from the various definitions

(Leana & Van Buren, 1999). The first is derived from social network theorists (e.g., Belliveau,

O'Reilly, & Wade, 1996; Burt, 1997; Useem &

Karabel, 1986), who emphasize personal bene

fits, such as career advancement, that actors

gain directly from their social capital. Propo nents of this perspective consider social capital a private good possessed by individuals. Other scholars conceptualize social capital as a public good (e.g., Bourdieu, 1986; Coleman, 1988; Put

nam, 1993). They regard social capital as an

attribute of a social unit, rather than an individ ual. As a public good, social capital is available to and benefits not only those who create it but

also group members at large (Kostova & Roth,

2003). For this paper we adopt a definition of social

capital similar to that offered by Nahapiet and

Ghoshal (1998) and used by Bolino, Turnley, and

Bloodgood (2002).4 We define social capital as

3 We do not assume the existence of a dominant partner in

the strategic alliance. Power dynamics in a strategic alli

ance are complicated and beyond the scope of this paper. For instance, a joint venture partner with minority equity can

use various ways to control the venture (Hamel, 1991;

Schaan, 1988). Moreover, bargaining power may shift from

one partner to another, depending on their relative learning

speeds (Inkpen & Beamish, 1997). As can be seen from our

discussions, virtually all of our arguments apply to alliances

with or without dominant partners.

4 In their footnote 1 Bolino et al. (2002) acknowledge that

alternative social capital frameworks exist. Their rationale

for using Nahapiet and Ghoshal's (1998) framework is four

fold: (1) it integrates many of the social capital facets dis

cussed in previous work, (2) it is useful for examining social


the aggregate of resources embedded within, available through, and derived from the network of relationships possessed by an individual or

organization?a definition that accommodates both the private and public good perspectives of social capital. The central proposition in this view of social capital is that networks of rela

tionships are a valuable resource (i.e., capital) for the individual or organization. The logic of this view can be seen in the example of a firm that establishes a network tie with another firm, such as a supply contract. This network tie be comes a social capital resource of the two firms. As time passes, trust between the firms may

develop, and such trust, in addition to the formal tie between the firms, will also constitute a so

cial capital resource. The social capital of the firms is thus enhanced. From the social capital, various benefits, such as preferential knowl

edge access, may flow to the firms. Individual social capital originating from an

individual's network of relationships can be dis

tinguished from organizational social capital derived from an organization's network of rela

tionships.5 The former has the property of a pri vate good, whereas the latter takes on the na

ture of a public good. With social capital as a

public good, members of an organization can

tap into the resources derived from the organi zation's network of relationships without neces

sarily having participated in the development of those relationships (Kostova & Roth, 2003). These two levels of social capital are often interre lated. For example, a manager, through his or

her own social relationships and personal con

nections, can help his or her company set up a

joint venture with another company. In this case,

organizational social capital is created on the basis of individual social capital.

For a systematic analysis of organizational social capital across multiple network types, we need to distinguish among (1) the possessors of social capital, (2) the dimensions of social capi tal, (3) the benefits of social capital, and (4) the

factors that operate as determinants of social

capital benefits. In this article the possessors of

organizational social capital are the members of the three network types identified above. The social capital benefit examined is the opportu nity to acquire knowledge from other network members. As discussed in the following section, the dimensions of social capital refer to the clus ters of the main facets of social capital (Na

hapiet & Ghoshal, 1998). After the discussion of these dimensions, our attention shifts to the de terminants of knowledge transfer, which is the social capital benefit examined in this paper.

We propose conditions that facilitate knowledge transfer. Our fundamental argument is that, de

pending on the network type, different condi tions will affect how the social capital dimen sions influence knowledge transfer. This section links the social capital dimensions with network

types and provides a set of theoretical relation

ships. Although we focus on analyzing organi zational social capital across network types, we

also incorporate individual social capital in our

discussion, because the interplay between the two affects knowledge transfer between net

work members.

DIMENSIONS OF SOCIAL CAPITAL AND NETWORK TYPES

Knowledge acquisition has been identified as a direct benefit of social capital (Adler & Kwon, 2002; Nahapiet & Ghoshal, 1998). In this paper we seek to understand how knowledge moves within networks and how social capital affects the knowledge movement. To achieve this objec tive, we adopt Nahapiet and Ghoshal's (1998) three dimensions of social capital: structural,

cognitive, and relational. Table 1 shows the three network types and the

three social capital dimensions. We draw from the literature to illustrate how these social cap ital dimensions are embedded in each network

type. Depending on the network type, the nature of social capital varies. We note that within each network type there is substantial variance, in that there are different forms of intracorporate networks, strategic alliances, and so on. The characteristics of social capital in Table 1 are associated with the more typical forms of each network type.

capital at the organizational level, (3) it incorporates a cog nitive dimension, and (4) it establishes a relationship be

tween social capital and intellectual capital (i.e., organiza tional knowledge).

5 Our conceptualization of organizational social capital is

different from that of Leana and Van Buren, who define it as

"a resource reflecting the character of social relations within

the firm" (1999: 538).


TABLE 1 Social Capital Dimensions Across Network Types

Social Capital Dimensions Intracorporate Network Strategic Alliance Industrial District

Structural

Network ties

Network

configuration

Network stability

Cognitive Shared goals

Shared culture

Relational: Trust

Fuzzy distinction between

intramember and

intermember ties

Hierarchical, easy to

establish connectivity between network

members

Stable membership

Members working toward

a common goal set by

headquarters

Overarching corporate culture

Little risk of opportunism, institutional-based trust

Intermember ties

determining social ties

within an alliance

Nonhierarchical, possibility of exploiting structural

hole positions

High rate of instability

Compatible goals but rarely common goals

Cultural

compromise/conflict

among members

Significant risk of

opportunism, behavioral

based trust

Social ties as a foundation

for intermember ties

Nonhierarchical and dense

networks in a

geographical region

Dynamic, with members

joining and leaving the

district

Neither shared nor

compatible goals

Industry recipe

Process-based personal trust

Structural Dimension

The structural dimension of social capital in

volves the pattern of relationships between the network actors and can be analyzed from the

perspective of network ties, network configura tion, and network stability.6 Network ties deal

with the specific ways the actors are related. Ties are a fundamental aspect of social capital, because an actor's network of social ties creates

opportunities for social capital transactions

(Adler & Kwon, 2002). A key feature of intracor

porate networks is that members of a network

belong to the same corporation. As such, ties within a member, such as interdepartmental and interpersonal relationships, may not be

very different in nature from those between members. In other words, boundaries of network

members are more porous than those of other network types. The nature of ties between alli ance partners will impact the social ties be tween managers who are assigned to the alli ance by the partners. For instance, in an

alliance formed between keen competitors, such social ties will likely be cautious and tense, because each partner is wary of divulging valu able knowledge to other partners. That is, the nature of organizational social capital sets the tone for individual social capital. An important characteristic of network ties between members in an industrial district is that, more often than

not, they are established as a result of interper sonal relationships developed from informal so

cial gatherings and meetings (Brown & Hendry, 1998; Paniccia, 1998). As such, individual social

capital forms the basis of organizational social

capital. The configuration of a network structure de

termines the pattern of linkages among network

members. Such elements of configuration as hi

erarchy, density, and connectivity affect the

flexibility and ease of knowledge exchange

through their impact on the extent of contact and

accessibility among network members (Krack hardt, 1992). Intracorporate networks are often

6 The facets of each social capital dimension discussed in

this paper are by no means exhaustive. Owing to space

limitations, we focus on facets that are most related to

knowledge transfer between network members. For in

stance, we replace the facet "appropriable organization," which Nahapiet and Ghoshal (1998) include in their struc

tural dimension, with "network stability." Our rationale is

that stability varies greatly across network types and has

serious implications for knowledge transfer. These points are explained in detail in the following discussion.


arranged in a hierarchical way, with headquar ters being at the top of the hierarchy. Depending on the overall corporate structure, some mem

bers of the network may not be connected to

other members. Connectivity can be easily es

tablished either through the headquarters or on

the members' own initiatives. Although alli ances such as equity joint ventures are broader and deeper in interactions than alliances such as technology licenses, the structure of a strate

gic alliance is nonhierarchical. Connectivity is

less straightforward than the case of intracorpo rate networks, since it needs to be established across firm boundaries. Thus, some members

may span the structural holes of the network and enjoy the associated informational advan

tages (Burt, 1992). Connectivity between network members in an industrial district is usually es

tablished through informal interpersonal rela tions. While a general pattern, in terms of net

work density, for intracorporate networks and alliances would be unusual, a characteristic of an industrial district is dense, nonhierarchical networks of firms located within the district, with some of them forming cliques.

Network stability is defined as change of

membership in a network. A highly unstable network may limit opportunities for the creation of social capital, because when an actor leaves the network, ties disappear. While stability is not a major issue in intracorporate networks un

less there are frequent corporate restructuring activities, it is a much studied concept in the alliance area, perhaps because of the high in

stability rate usually attributed to this particu lar network form (Inkpen & Beamish, 1997; Yan &

Zeng, 1999). Last, membership in an industrial district is often unstable, with firms joining and

leaving the district continuously.

Cognitive Dimension

The cognitive dimension represents the re sources providing shared meaning and under

standing between the network members (Na

hapiet & Ghoshal, 1998). The two facets of the dimension we address are shared goals and shared culture among network members. Shared goals represent the degree to which net work members share a common understanding and approach to the achievement of network tasks and outcomes. Depending on the network

type, the tasks and outcomes may vary in clarity

and definition. Members of an intracorporate network usually work toward a common goal set

by headquarters, although they may have to fulfill certain secondary goals related to their own products and markets. Partner firms often have different goals in mind when they enter a

strategic alliance. Negotiation helps partners arrive at goals that are acceptable to most, if not

all, of them. In an industrial district there are

likely to be few shared or even compatible goals, owing to the complexity of the network ties.

Shared culture refers to the degree to which norms of behavior govern relationships. This facet is similar to tie modality, which is "the set of institutionalized rules and norms that govern

appropriate behavior in the network. While these are sometimes spelled out in formal con

tracts, most often they are simply understand

ings that evolve within the dyad and the net work" (Gulati et al., 2000: 205). In some cases

shared norms may create excessive expecta

tions of obligatory behavior and may possibly result in problems of free riding and unwilling ness to experiment beyond the network. Mem bers of an intracorporate network work under an

overarching corporate culture. For instance, all the operations of Johnson & Johnson worldwide subscribe to the strong ethical culture set by the

headquarters. Since partner firms usually have distinct cultures, strategic alliances are often formed on the basis of cultural compromise among the partners concerned. Cultural conflict will arise if certain partners rigidly push for ward their own ways of doing things. Although firms in an industrial district may have various distinct cultures, they tend to share an industry recipe. As organizational ecologists (e.g., Han nan & Freeman, 1977) argue, firms in the same

line of business experience substantial pressure to adopt similar policies. Similarly, Spender's (1989) study indicates that, in the face of uncer

tainty, managerial recipes specific to task envi ronments gradually evolve and are adopted by firms operating in the industries concerned.

Relational Dimension

The relational dimension focuses on the role of direct ties between actors and the relational, as opposed to structural, outcomes of interac tions. Among the facets of this dimension, such as trust, norms, and identification, we focus on


trust, both because of space limitations and be cause trust is a critical factor affecting interfirm

knowledge transfer and creation (Dodgson, 1993; Doz, 1996). Trust is based on social judgments (e.g., assessment of the other party's benevo

lence, competence, etc.), together with assess ment of the costs (i.e., risk) if the other party turns out to be untrustworthy (Rousseau, Sitkin, Burt, & Camerer, 1998). Under a risky condition, a

party's trust is signified by a decision to take action that puts its fate in the hands of the other

party. Trust plays a key role in the willingness of

network actors to share knowledge. A lack of trust may lead to competitive confusion about

whether or not a network firm is an ally (Powell,

Koput, & Smith-Doerr, 1996). Conversely, an at

mosphere of trust should contribute to the free

exchange of knowledge between committed ex

change partners, because decision makers should not feel that they have to protect them selves from others' opportunistic behavior (Blau, 1964; Jarillo, 1988). As trust develops over time,

opportunities for knowledge transfer between network members should increase. With the de

velopment of a pattern of interactions, organiza tions will decrease their efforts to protect their

knowledge and skills. Trust in an intracorporate network is institu

tional based: the fact that an organization is a

member of the network signifies to other mem

bers that the former should be trustworthy. While risk of opportunism is normally not a con cern for intracorporate networks, it is a serious concern for strategic alliances. Unlike intracor

porate networks, trust in strategic alliances is

behaviorial based. A partner firm needs to sig nify its trustworthiness through the way it be haves in the alliance. For industrial districts,

interpersonal trust plays a critical role, since, as

mentioned earlier, individual social capital drives the development of organizational social

capital. Moreover, trust is process based, in the sense that firms regularly test each other's in

tegrity, moving from small, discrete exchanges of limited risk to more open-ended deals that

subject the parties to substantial risk (Lazerson & Lorenzoni, 1999).

NETWORKS AND KNOWLEDGE TRANSFER

In this section we examine social capital de terminants of network knowledge transfer.

Through the various ties that firms have with other firms, network members are exposed to various types of knowledge that are potentially valuable. As Powell states, "The most useful information is rarely that which flows down the formal chain of command in an organization, or

that which can be inferred from price signals. Rather, it is that which is obtained from some one you have dealt with in the past and found to be reliable" (1990: 304).

From a network perspective, Podolny and

Page (1998) identify two types of learning.7 First, networks can facilitate learning via the transfer of knowledge from one firm to another. In other

words, the network acts as a conduit for process

ing and moving knowledge; learning from an

alliance partner is this type of learning. Second, networks may become the locus of novel knowl

edge creation at the network level, rather than within the nodes?firms?of the network. Al

though our focus is primarily on the network as a conduit for knowledge transfer, the two forms of network learning are not always easy to dis

tinguish in practice. The dependent variable in this discussion is

knowledge transfer between network members. Based on the key argument that social capital plays a critical role in the transfer and exchange of network knowledge, we propose a set of conditions that facilitate knowledge transfer in networks. These facilitating conditions, as sum marized in Table 2, are factors specifically as

sociated with the respective facets of the three social capital dimensions. Our objective in this section is to identify specific relationships be tween social capital and knowledge transfer. Because the facilitating conditions that influ ence knowledge transfer differ across network

types, we show that developing an understand

ing of social capital and networks requires an

analysis of the specific features of the different network types.

7 In integrating various bodies of literature, we were con

fronted with differences in how such terms as knowledge transfer and learning have been used. The social capital literature usually discusses knowledge transfer (and access) rather than learning. The network literature, however, as the

Podolny and Page (1998) reference indicates, uses the term

learning in reference to the knowledge acquisition process. We have tried to be as consistent as possible in the use of

terminology.


TABLE 2 Conditions Facilitating Knowledge Transfer

Social Capital Dimensions Intracorporate Network Strategic Alliance Industrial District

Structural

Network ties

Network

configuration

Network stability

Cognitive Shared goals

Shared culture

Relational: Trust

Personnel transfer

between network

members

Decentralization of

authority by headquarters

Low personnel turnover

organization wide

Shared vision and

collective goals Accommodation for local

or national cultures

Clear and transparent reward criteria to

reduce mistrust among network members

Strong ties through repeated

exchanges

Multiple knowledge connections

between partners

Noncompetitive approach to

knowledge transfer

Goal clarity

Cultural diversity

Shadow of the future

Proximity to other members

Weak ties and boundary

spanners to maintain

relationships with

various cliques Stable personal

relationships

Interaction logic derived

from cooperation Norms and rules to govern

informal knowledge

trading Commercial transactions

embedded in social ties

The focus in Table 2 is on organizations within the network, rather than the network itself. We discuss knowledge acquired by network mem

bers through participation in the network's

knowledge-sharing activities. The hypothesized conditions, elaborated in the following section, can be readily converted into testable proposi tions and provide suggested directions for fu ture research.

Before getting to the detailed discussion, we

illustrate the logic of the table. One of the struc tural facets is network configuration. At a broad level, network configuration affects the flexibil

ity and ease of knowledge exchange between network members. For example, for intracorpo rate networks, a facilitating condition (for net

work configuration) is headquarter's decentrali zation of authority to network members such that the development of lateral network ties and

knowledge transfer is enhanced. Expressed as a

proposition, the greater headquarter's decen tralization of authority to intracorporate network

members, the more likely ties between the mem

bers will develop that lead to knowledge trans fer. As another example, having boundary span ners maintain weak ties with various cliques for

exploration purposes is an important facilitat

ing condition for firms operating in an industrial district. Expressed as a proposition, the greater

the presence of boundary spanners with weak ties to various cliques, the more likely a pattern of linkages among network members will de

velop that lead to knowledge transfer.

Structural Dimension

Network ties. Since the boundaries between

intracorporate network members are more po rous than those between members of other net

work types, personnel transfer between mem bers should take place more readily. Such transfers establish social network ties on top of the more formal intermember ties; the latter, in turn, are strengthened by the existence of the former. The social network ties facilitate inter

member social interactions and provide chan nels for knowledge exchange. Ghoshal, Korine, and Szulanski's (1994) research on MNCs docu ments the importance of such social interactions for diffusing new ideas within the corporations. In an in-depth case study of a Dutch multina tional software company, Orlikowski (2002) found that expatriates brought with them skills and techniques to share with local engineers.

For knowledge transfer to occur in alliances,

strong ties between the partners are necessary (Inkpen & Dinur, 1998). Factors supporting the

development of strong ties include prior partner


relationships and repeated transactions (Gulati, 1995). In the absence of strong ties, especially in

alliances between competitors, partners may not develop the necessary relationships that al

low managers to share knowledge willingly. Larson (1992) has shown that strong ties promote and enhance trust, reciprocity, and long-term perspectives. Kale, Singh, and Perlmutter (2000) found a positive relationship between the

strength of ties and the degree of learning in

alliances.

For industrial districts, Camagni (1995) identi fies spatial proximity as a key characteristic of

what he calls "a local network." From the per

spective of an individual firm, it is beneficial to be located physically close to other firms in the district. Proximity helps the formation of net

work ties and facilitates interfirm and espe

cially interpersonal interactions through which

knowledge is exchanged. The more tacit the

knowledge involved, the more important spatial proximity is between the parties taking part in

the exchange (Maskell & Malmberg, 1999). This

implies that firms occupying more central loca tions of the district have the edge over those located at the periphery.

Network configuration. As argued by Grant, "Once firms are viewed as institutions for inte

grating knowledge, a major part of which is tacit

and can be exercised only by those who possess it, then hierarchical coordination fails" (1996: 118). Thus, the headquarters of an intracorporate

network must decentralize authority to members of the network so that they can determine how to make the best use of the knowledge they pos sess. Moreover, decentralization enables mem

bers to establish lateral ties on their own initia

tive, without first seeking approval from

headquarters. Decentralization can facilitate

timely knowledge sharing among the members.

Tsai's (2002) study of a large, multiunit company confirms that centralization is negatively asso

ciated with intracorporate knowledge sharing. Based on the notion of connections through

which alliance managers can share their obser vations and experiences (Von Krogh, Roos, &

Slocum, 1994), Inkpen and Dinur (1998) identify four types of alliance structural ties that can

lead to knowledge sharing: technology link

ages, alliance-parent interaction, personnel transfers, and strategic integration. The four

processes share a conceptual underpinning in

that each creates the potential for individuals to

share their observations and experiences. Each of the four processes provides an avenue for

managers to gain exposure to knowledge and ideas outside their traditional organizational boundaries, and each creates a connection for individual managers to communicate their alli ance experiences to others.

In industrial districts, cliques of firms with

strong ties may be formed. For instance, in the

City of London, Japanese banks may form one

clique and American banks another. While there are intense knowledge exchanges within a

clique, there may be little between cliques. From the perspective of an individual firm oper ating in the district, it is crucial to have bound

ary spanners who maintain weak network ties with various cliques for exploration purposes

(Rowley, Behrens, & Krackhardt, 2000). One sim

ple way to achieve this is through participating in the activities of professional associations. In the case of the City of London, one such associ ation is the Chartered Institute of Bankers, which is a British association of banking profes sionals.

Network stability. Although the membership of an intracorporate network is usually more

stable than that of other network types, this sta

bility may not help knowledge transfer if there is a high personnel turnover rate. Organiza tional learning depends, at least partially, on

memories of individuals and their learning abil ities (Carley, 1992). Individuals leaving a net

work take with them knowledge that may be crucial for organizational success. In addition,

personnel turnover affects intracorporate knowl

edge sharing, which often takes place through formal or informal exchanges on an individual basis. Such exchanges are facilitated by estab lished rapport and friendship. Maintaining a

stable pool of personnel within a network can

help individuals develop long-lasting interper sonal relationships.

Learning from an alliance partner is a key determinant of bargaining power and alliance

stability (Hamel, 1991; Inkpen & Beamish, 1997; Yan, 1998). Hamel (1991) proposes that the most

important determinant of partner bargaining power in alliances is the ability to learn. Firms that can learn quickly are able to acquire part ner skills, reducing their dependence and in

creasing their bargaining power. Inkpen and Beamish (1997) used these arguments to develop a framework of instability and international


joint ventures. In an alliance, dependence can

be a source of power for the firm controlling the

key resources. When knowledge acquisition shifts the dependency relationship, the cooper ative basis for the alliance may erode, leading to instability. If one partner acquires knowledge faster than the other, the faster-learning partner no longer has the same need, which can lead to a situation of partner asymmetry (Makhija &

Ganesh, 1997). Thus, if partner firms regard their alliance as a learning race field, the partner learning faster will be motivated to leave the alliance. If the attitude toward learning is non

competitive, however, destabilizing forces will be less likely and greater symmetric learning may occur.

Industrial districts are characterized by the constant entry and exit of firms. From the per

spective of firms remaining in the district, out

going firms take with them not only knowledge but also important personal contacts (unless the

employees concerned choose to stay behind).

Boundary spanners of the firms that remain may try to establish more intimate and stable per sonal relationships with their counterparts in the exiting firms. In this way, personal contacts can be maintained and may continue to serve as

useful sources of industrial information for firms that continue to stay in the district. In other

words, these firms' networks extend beyond the district. Such external contacts are important channels for obtaining fresh ideas, especially when managers within the district become more

homogeneous in their mental models over time

(Pouder & St. John, 1996).

Cognitive Dimension

Shared goals. We follow Tsai and Ghoshal

(1998) in using the term shared vision, which embodies the collective goals and aspirations of the members of an intracorporate network.

When a shared vision is present in the network, members have similar perceptions as to how

they should interact with one another. This can

promote mutual understandings and exchanges of ideas and resources. Thus, a shared vision can be viewed as a bonding mechanism that

helps different parts of a network integrate knowledge. When partner firms bring contradicting or in

consistent goals into their strategic alliance, in

terpartner conflict may arise. Conflict among

parties in an interfirm collaboration tends to result in frustration and dissatisfaction (Ander son, 1990). Such a negative atmosphere is not

conducive to the flow of knowledge between the

partners and the alliance. In studying intra- and

interdepartmental conflict within a large utility company, Schnake and Cochran (1985) found that lower levels of goal clarity increased both

types of conflict. For strategic alliances, we also

expect that goal clarity reduces interpartner conflict by facilitating the negotiation and es

tablishment of common goals. When the objec tives and strategies of an alliance are clearly stated, a foundation of common understanding and the means to achieve the collaborative pur pose is established among the partners (Das &

Teng, 1998). For firms in an industrial district to willingly

share knowledge, they must recognize that co

operation and knowledge sharing can enhance their competitive position. In addition, firms

must recognize that combining the economic, cultural, and technological resources of the in dustrial district can lead to joint knowledge cre ation. Thus, firms will share knowledge when an interaction logic is shared across network members (Helmsing, 2001). This logic is derived from the belief that value can be created

through cooperation and knowledge sharing. Joint problem-solving arrangements enrich the

network, because working through problems promotes innovation (Uzzi, 1997).

Shared culture. Although the headquarters of an intracorporate network may try to impose its

corporate culture in all worldwide operations, each operation is geographically embedded in local or national culture (Ghoshal & Bartlett, 1990). For instance, a corporate culture empha sizing participative decision making may not work well in a high power distance culture. The local or national culture needs to be understood and accommodated so that when knowledge is transferred from one member to another, the transfer process will not be hindered by cultural conflicts between concerned members (see Bha

gat, Kedia, Harveston, & Triandis, 2002).

Arguments for and against partner cultural

diversity as an antecedent for alliance learning have been made. Although Parkhe (1991) has

proposed that diversity between the partners in international strategic alliances could lead to

learning, Pitts and Lei (1997) have argued that alliances designed to learn and absorb tacit


knowledge are harder to manage among part ners that come from different cultural contexts

than partners from a similar cultural context.

Phan and Peridis (2000) have proposed that dif ferences between partners support the learning process. The authors' rationale is that attempts to eliminate differences can block second-order

learning processes. We maintain that the over

all effect of cultural diversity should be benefi cial to knowledge transfer.

A major barrier to informal exchange of

knowledge in an industrial district is the risk that the receiver of such knowledge may use it

against the interest of the sender. This risk can

be reduced and, hence, the exchange facilitated if there are implicit industrial norms and rules in the district governing informal know-how

trading among firms such that opportunism is

subject to severe social sanctions. The norms

and rules include a common language for talk

ing about organization and cultural problems and accepted but tacit codes of conduct between

firms (Helmsing, 2001). In an interesting study of

the extensive exchange of proprietary know

how by informal networks of process engineers in the U.S. steel minimill industry, Von Hippel (1987) found such norms.

Relational Dimension: Trust

An intracorporate network is a social structure of coopetition (Tsai, 2002). While intermember

cooperation is encouraged so as to realize econ

omies of scale, intermember competition can

also achieve efficiency (Hill, Hitt, & Hoskisson,

1992). When members compete against one an

other for resources and markets, suspicion may

replace trust in their relationship and, conse

quently, knowledge sharing may be sacrificed.8 It is important that headquarters establish clear

and transparent reward criteria so that the

members concerned will not suspect any under

the-table transactions or favoritism. Clear and

transparent reward criteria will reduce mistrust

among the members. Parkhe's (1993) study of strategic alliance

structuring suggests that opportunism is con

strained and cooperation promoted by the shadow of the future, which can be lengthened by long time horizons, frequent partner interac

tions, and high behavioral transparency. As the fear of opportunism by alliance partners fades because of the development of mutual trust,

partners will be more willing to move forward, even though uncertainty in the relationship may remain (Nooteboom, Berger, & Noorderhaven,

1997). When trust is high, firms may be more

likely to invest resources in learning because of the willingness of their partners to refrain from

instituting specific controls over knowledge spillovers.

In an industrial district, many of the ex

changes between network members are com

mercial transactions. Uzzi and Gillespie (2002)

argue that, in contrast to purely market-based

transactions, commercial transactions embed ded in social ties instill into future exchanges expectations of trust and reciprocity. In turn, re

lationships based on trust and reciprocity are

likely to promote the transfer of distinctive

knowledge and resources. When the relation

ships between industrial network members are

embedded with trust, firms may be more willing to share valuable knowledge and accept the risk

of spillover to competitors (Dyer & Singh, 1998).

Interplay Between Individual and

Organizational Social Capital

We noted earlier that social capital can be created at individual and organizational levels.9 For interorganizational knowledge transfer to

take place in a network, either or both levels of

social capital must be present. The discussion based on Table 1 indicates that the three net

work types display different characteristics with

respect to the two levels of social capital. For

example, for the network ties facet, interper sonal relationships developed from informal so

cial gatherings and meetings are an important characteristic of ties between members in an

8 We do not expect that the suspicion will lead to rampant

opportunism, because the penalties imposed on managers who are caught acting opportunistically may be severe. In

contrast, in strategic alliances, if opportunism is punished, the punishment will usually be at the organizational rather

than individual level. Managers who act opportunistically in

the alliance are normally under the direction of the partner

employing them.

9 In recent years organizational researchers have increas

ingly tried to examine multilevel theoretical perspectives for

concepts such as creativity (Drazin, 1999), learning (Kim,

1993), and trust (Inkpen & Currall, 2002).


industrial district. In contrast, for alliances, the nature of organizational-level ties has a signif icant impact on interpersonal ties. Based on the

mix of individual and organizational character

istics, the facilitating conditions summarized in

Table 2 can be examined more closely.

Compared with strategic alliances and indus

trial districts, in intracorporate networks orga nizational social capital is readily available between members. Intracorporate network mem

bers are connected within a corporate organiza tional structure that is relatively stable (com

pared to an alliance network or industrial

district). Also, relative to the other network

forms, intracorporate network members are

more likely to work toward a common corporate

goal, share an overarching corporate culture, and trust one another. As a result, some knowl

edge access and transfer will occur by default.

By default, we mean that, by virtue of being part of the network, subsidiary units are entitled to

obtain certain organizational knowledge from the headquarters or other subsidiaries. While there may be personal reasons for in

tracorporate managers not to share knowledge, there will not be the same type of competitive and firm-level barriers to knowledge sharing that exist in alliance networks and industrial districts. However, establishing individual so

cial capital will strengthen the knowledge flow. For instance, suppose an engineer of a subsid

iary is sent to headquarters to learn a new tech

nology. The public good nature of the organiza tional social capital between the subsidiary and

headquarters enables the engineer to access the new technology. Nevertheless, the development of positive interpersonal relationships between the engineer and those teaching the technology should allow for a faster and broader dissemi nation of knowledge. Thus, some of the facilitat

ing conditions in Table 2 are concerned with

developing individual social capital. For in

stance, personnel transfer and low managerial turnover can help individuals establish close,

long-lasting social ties that expedite knowledge transfer between network members.

Unlike an intracorporate network, a strategic alliance is an interfirm relationship with legal and organizational boundaries between firms. Also unlike an intracorporate network, the exis tence of an alliance does not guarantee a flow of

knowledge between partners, who often have a

competitive-collaborative relationship (see

Hamel, 1991). Knowledge may flow very slowly or not at all.10

Properly managing this relationship is critical if the partners seek access to each other's

knowledge. As discussed earlier, the nature of

organizational social capital often sets the tone for individual social capital in an alliance set

ting. Hence, organizational social capital will

likely be the dominant form of social capital necessary for knowledge to flow between alli ance partners. For example, it is not uncommon

for managers who are assigned to alliances to make comments such as "Joe, my counterpart from the alliance partner, is my good friend and I trust him. However, there are things we cannot share without our parents' authorization." Look

ing at Table 2 shows that the facilitating condi tions focus on building up organizational social

capital between alliance partners. In an industrial district, knowledge flows start

on a personal level, because there may not be formal interfirm relationships. When there are

formal relationships, they will tend to be com

mercial transactions, as opposed to the more

structured nature of an alliance relationship. Thus, individual social capital is critical, drives the development of organizational social capi tal, and becomes the focus of the facilitating conditions in Table 2.

In summary, the three network types involve different dynamics between organizational and individual social capital. Such dynamics have

important implications for knowledge transfer between network members and impact the pro

posed facilitating conditions. Organizational so cial capital, for example, takes priority over in dividual social capital in strategic alliances, whereas the reverse is true in industrial dis

10 The existence of an intracorporate network means that

some organizational social capital will exist (by default) and

some knowledge will flow between the members. When an

alliance is formed, there is at least some social capital, because a network tie has been formed. However, the basic

structural tie between the partners (in the form of an alliance

agreement) in the absence of additional social capital means that a large-quantity knowledge flow is unlikely. An

interesting and challenging empirical question involves the

minimum level of organizational and individual social cap ital necessary to effect knowledge transfer. Even more com

plicated is the comparative question, "How much social cap ital is necessary to effect knowledge transfer in an

intracorporate network versus an alliance network versus

an industrial district?"


tricts. For intracorporate networks, we have sug

gested that social capital that impacts knowl

edge transfer will begin at an organizational level and then be enhanced by social capital developments at the individual level.

Network Boundary Conditions

Our analysis helps identify boundary condi tions associated with each network type, with

respect to intermember knowledge transfer. For

example, in his study of knowledge sharing within an intracorporate network, Tsai (2002) maintains that social interaction among organi zational units facilitates intraorganizational knowledge sharing. He argues further that

"knowledge sharing among competing units within the same organization carries synergistic benefits because these units deal with similar resource constraints and market situations"

(2002: 182). Therefore, Tsai proposes that social interaction is more positively associated with

knowledge sharing among units that compete with each other than among units that do not

(i.e., his Hypothesis 4). Tsai's argument is valid in the case of intra

corporate networks, where organizational social

capital is readily available between network members and the development of individual so

cial capital will help knowledge transfer or

sharing. Although network members may com

pete with each other, the risk of knowledge leak

age from one unit to another is not a major concern, because these units work under the same corporate roof.

However, the situation will be different in al liance networks. When alliance partners are

keen competitors, interpartner learning may be come a race (Hamel, 1991). Partners avoid inad

vertently transferring knowledge beyond what is stated in the alliance agreement. Managers

who are appointed to the alliance will be well aware of this risk and more cautious in their social interaction. Hence, we expect exactly the

opposite of Tsai's hypothesis to happen?that is, social interaction will be more positively asso

ciated with knowledge sharing among alliance

partners that do not compete with each other than among partners that do. In fact, when alli ance partners are keen competitors and manag ers strictly follow the rules set by their respec tive parent companies, social interaction may not have any impact on knowledge sharing.

DISCUSSION

In examining various social capital dimen

sions, we address an issue raised by Uzzi and

Gillespie (2002): structural approaches to net works that ignore social qualities inadequately specify how networks function. We also address Kostova and Roth's (2003) call for more research on the outcomes of social capital. This paper shows that each network type has distinct social

capital dimensions. By linking these dimensions to knowledge transfer in networks, a social cap ital outcome, we show that the facilitating con

ditions vary across networks. For effective and efficient knowledge transfer to occur, firms may have to manage and build social capital proac

tively. The conditions identified can be viewed as predictive conditions and provide guidance for firms seeking to exploit network knowledge opportunities.

Implications for Future Research

An examination of the conditions facilitating learning and knowledge transfer (Table 2) re

veals some implications for future research. First and obviously, the sheer number of rela

tionships illustrates the complexity of this area.

The introduction of social capital variables into the analysis of networks and knowledge trans fer adds a level of complexity that has not yet been examined empirically.

Second, virtually all the existing theoretical and empirical studies of interorganizational knowledge transfer are based on a single net work type, without any reference to the bound

ary conditions. The question of how far the re sults of these studies can be generalized from one network type to another rarely has been examined. The distinct facilitating conditions across network types listed in Table 2, and the

preceding discussion of social capital levels,

suggest that generalizability across network

types may be limited and that a contingency approach is appropriate. In other words, pro cesses of interorganizational knowledge trans

fer are affected by the nature of the network type in which the organizations are embedded.

Third, although facilitating conditions are dis tinct across network types, there is value to be

gained by integration and synthesis. The litera ture we examined crossed a diverse terrain, from intracorporate networks to industrial dis


tricts. In examining the literature streams, which at present are not well integrated or

linked, we realized that all networks are, at their core, about social relationships, and, therefore, social capital dimensions have applicability, re

gardless of the network type. In reviewing the literature on knowledge transfer in organiza tions, Argote and Ingram (2000) argue that social networks play an important role in knowledge transfer and yet related research is inadequate. In this article we address this deficiency and

provide an agenda for future research. Gulati et al. (2000) suggest that incorporating

networks into strategic analysis leads to a more

comprehensive view of the strategic behavior of firms. We agree but would argue that, to truly understand network behavior, researchers should move beyond one-size-fits-all analyses of networks. Osigweh comments that

when concepts are broadened in order to extend

their range of applications, they may be so

broadly defined (or, stretched) that they verge on

being too all-embracing to be meaningful in the realm of empirical observation and professional practice (1989: 582).

The concept of network is one that suffers from

being overstretched. As we have shown, the dy namics of knowledge transfer vary across net work types. We illustrate that social capital di mensions are not uniform in their effects on

knowledge transfer. Rather, they vary across dif ferent types of networks. Network theories that fail to distinguish between network types will be unable to capture the complex variety of fac tors associated with network knowledge pro cesses. These theories need to develop beyond the early, broad theoretical discussions that

were based on a generic type of network (e.g., Jarillo, 1988; Thorelli, 1986) and to examine in de tail the characteristics of different network types.

Social capital is still in the "emerging excite ment" stage of the life cycle typical of an um brella concept and will face validity challenges in its next stage of development (Hirsch & Levin, 1999). The social capital concept has been used

extensively by scholars in discussing interper sonal or interorganizational relationships of a certain type. Yet the concept seldom has been

applied to compare and contrast different types of relationships. By addressing this deficiency, we have shown that the concept, in the form of both individual and organizational social capi tal, is useful for distinguishing between differ

ent network types and that each social capital facet gives rise to different knowledge transfer

facilitating conditions across network types. Through such a compare-and-contrast analysis, we have not only clarified the meanings of the dimensions and facets of social capital but also demonstrated the value added provided by the social capital concept. Further theoretical anal

yses like ours will help bring the concept to the next stage of its life cycle.

In addition to the specific hypotheses sug

gested in Table 2, which provide substantial

scope for new inquiry, several research direc tions can be identified. As we indicated earlier, the locus of network knowledge processes can

be the network or actors, or both. Although we

focused on knowledge transfer by actors that resulted from their participation in the network, there are various interesting questions involv

ing knowledge creation by the network. For ex

ample, if the network enhances its knowledge base, how do individual actors share in the en

hancement, and which social capital factors are most critical in disseminating network knowl

edge? In an industrial district, the network with the most fluid boundaries, how does the network

knowledge base get impacted by the entry and exit of network members? In alliances, the term common benefits is synonymous with network

knowledge acquisition (Khanna, Gulati, & No

hria, 1998). Are common benefits more likely to

emerge under certain network configurations or

conditions? Another important research question involves

the risks of social capital. Although our main thesis is that social capital has an important positive influence on knowledge transfer by net work actors, it must be noted that social capital is not without risks. Uzzi (1997) argues that over embeddedness could inhibit knowledge flow into the network. For example, when firms

within an industrial district establish intense network ties, they tend to pay little attention to the strategies and capabilities of competitors outside the district, resulting in a blind spot situation (Pouder & St. John, 1996). Hansen's

(2002) research on knowledge sharing in intra

corporate networks shows that a focal unit's di rect ties in a knowledge network had pros and cons. The ties provided immediate access to other business units that possessed related

knowledge. However, the ties were costly to maintain. Hansen argues that direct ties are


most effective when they help units deal with dif ficult transfer situations, which probably involve noncodifiable knowledge. When the transfer is not

difficult, the ties are likely to be harmful for unit effectiveness because of their maintenance costs.

We have discussed the three dimensions of social capital independently. In future research scholars should also examine the interaction effects among these dimensions. For instance, network stability (a facet of the structural di

mension) may help develop trust (a facet of the relational dimension). A facilitating condition

may have effects on more than one dimension. Personnel transfer between members of an in

tracorporate network, for example, may contrib ute to establishing social network ties (a facet of the structural dimension), shared culture (a facet of the cognitive dimension), and trust (a facet of the relational dimension).

Finally, although space limitations preclude a

discussion of knowledge types, other research has shown that different knowledge types have different effects on organizational processes

(e.g., Nonaka, 1994). For instance, for effective transfer of tacit knowledge between network

members, individual social capital must be de

veloped, because the transfer normally requires intimate personal interactions. In future re

search scholars may examine how social capi tal dimensions affect the transfer of different

knowledge types.

Limitations

This paper is not without limitations. First, we

discuss only three major network types. Other

important networks and interesting social capi tal relationships exist. Second, our discussion of

major network types is limited, in that it applies to the more typical members of each network

type. There are inevitably exceptions. Third, there are other factors besides social capital factors that impact network knowledge transfer.

Finally, there are multiple facilitating condi tions for each facet of the social capital dimen

sions; we identified what we view as the most

critical ones. For example, a condition we were

unable to explore for industrial districts and

knowledge transfer is institutional thickness in

the form of an interlocking and integrated web of supportive organizations, including firms, lo

cal authorities, development agencies, financial

institutions, local chambers of commerce, trade

associations, and so on (Amin & Thrift, 1995). Thickness involves collective representation and common purpose (Keeble, Lawson, Moore, &

Wilkinson, 1999) and helps industrial district firms develop a common vision.

Conclusion

We believe that network researchers must consider the potential conceptual differences across various network types. In this article we

partially integrate the voluminous network and

organizational knowledge literature and pro vide a common predictive basis for comparing knowledge transfer determinants across differ ent types of networks. When studying network

behaviors, it is thus important to first examine the nature of the network type concerned and how it differs from other types. This practice is in line with the recent call for contextualizing or

ganizational research (Rousseau & Fried, 2001). Contextualization makes theoretical models more accurate and interpretation of empirical results more robust.

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Andrew C. Inkpen is professor of management at Thunderbird, the Garvin School of International Management. He was a visiting professor at Nanyang Business School,

Nanyang Technological University, when this article was written. He received his Ph.D. from The University of Western Ontario. His research interests are in interna tional strategy, strategic alliances and joint ventures, organizational trust, and knowl

edge management.

Eric W. K. Tsang is an assistant professor in the School of Business Administration at

Wayne State University. He received his Ph.D. from the University of Cambridge. His current research interests include organizational learning, strategic alliance, super stitious decision making, and research methods.

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