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SOCIAL CAPITAL, NETWORKS, ANDKNOWLEDGE 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 ofliterature 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, absorptivecapacity, 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.

Academy of Management Review

2005, Vol. 30, No. 1, 146–165.

146



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 distinguishamong 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 advancethe 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 tiesthat 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 commonnetwork 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 allorganizational 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.

2005 147Inkpen and Tsang



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.

Strategic 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

FIGURE 1

A Typology of Network Types

148 January Academy of Management Review



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 multiplefirms, 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 thesame 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 ofanother (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 transfercan 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 (Arino & de la Torre, 1998; Doz, 1996).

Third, firms learn with an alliance partner when

the partners jointly enter a new business areaand 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-howthat 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 systematicanalysis 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 socialcapital 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 ofthose 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 TYPESKnowledge 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).




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 dealwith 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, suchsocial 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 socialcapital.

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.

TABLE 1

Social Capital Dimensions Across Network Types

Social Capital

Dimensions Intracorporate Network Strategic Alliance Industrial District

Structural

Network ties Fuzzy distinction between

intramember and

intermember ties

Intermember ties

determining social ties

within an alliance

Social ties as a foundation

for intermember ties

Network

configuration

Hierarchical, easy to

establish connectivity

between network

members

Nonhierarchical, possibility

of exploiting structural

hole positions

Nonhierarchical and dense

networks in a

geographical region

Network stability Stable membership High rate of instability Dynamic, with members

joining and leaving the

district

Cognitive

Shared goals Members working toward

a common goal set by

headquarters

Compatible goals but rarely

common goals

Neither shared nor

compatible goals

Shared culture Overarching corporateculture Culturalcompromise/conflict

among members

Industry recipe

Relational: Trust Little risk of opportunism,

institutional-based trust

Significant risk of

opportunism, behavioral-

based trust

Process-based personal

trust




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 partnersarrive 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 oftenformed 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 byfirms 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 partyturns 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 thanwithin 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.




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 aproposition, 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 interactionsfor 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

TABLE 2

Conditions Facilitating Knowledge Transfer

Social Capital

Dimensions Intracorporate Network Strategic Alliance Industrial District

Structural

Network ties Personnel transfer

between network

members

Strong ties through repeated

exchanges

Proximity to other members

Network

configuration

Decentralization of

authority by

headquarters

Multiple knowledge connections

between partners

Weak ties and boundary

spanners to maintain

relationships with

various cliques

Network stability Low personnel turnover

organization wide

Noncompetitive approach to

knowledge transfer

Stable personal

relationships

Cognitive

Shared goals Shared vision and

collective goals

Goal clarity Interaction logic derived

from cooperation

Shared culture Accommodation for local

or national cultures

Cultural diversity Norms and rules to govern

informal knowledge

tradingRelational: Trust Clear and transparent

reward criteria to

reduce mistrust among

network members

Shadow of the future Commercial transactions

embedded in social ties




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-termperspectives. 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 whichknowledge 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 throughwhich 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 participatingin 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 alliancestability (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 partnerno 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 importantchannels 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 howthey 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) foundthat 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 problemspromotes 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 culturalconflicts 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-orderlearning 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 normsand 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 structureof 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 riskof 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. Forexample, 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 andindividual 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 similarresource 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 theopposite 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 addressKostova 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 yetbeen 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 contingencyapproach 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 thatwhen concepts are broadened in order to extendtheir range of applications, they may be sobroadly defined (or, stretched) that they verge onbeing too all-embracing to be meaningful in therealm of empirical observation and professionalpractice (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 thesocial 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 toemerge 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 competitorsoutside 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 researchscholars 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 inline 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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