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Wasko & Faraj/Social Capital & Knowledge Contribution

MIS Quarterly Vol. 29 No. 1, pp. 35-57/March 2005 35

SPECIAL ISSUE

WHY SHOULD I SHARE? EXAMINING SOCIALCAPITAL AND KNOWLEDGE CONTRIBUTIONIN ELECTRONIC NETWORKS OF PRACTICE1

By: Molly McLure WaskoDepartment of Management Information

SystemsFlorida State University Tallahassee, FL [email protected]

Samer FarajDepartment of Decision and Information

TechnologiesR. H. Smith School of BusinessUniversity of MarylandCollege Park, MD [email protected]

Abstract

Electronic networks of practice are computer-mediated discussion forums focused on problemsof practice that enable individuals to exchangeadvice and ideas with others based on commoninterests. However, why individuals help strangersin these electronic networks is not well under-stood: there is no immediate benefit to the contri-

1V. Sambamurthy and Mani Subramani were theaccepting senior editors for this paper.

butor, and free-riders are able to acquire the sameknowledge as everyone else. To understand thisparadox, we apply theories of collective action toexamine how individual motivations and socialcapital influence knowledge contribution in elect-ronic networks. This study reports on the activitiesof one electronic network supporting a professionallegal association. Using archival, network, survey,and content analysis data, we empirically test amodel of knowledge contribution. We find thatpeople contribute their knowledge when they per-ceive that it enhances their professional repu-tations, when they have the experience to share,and when they are structurally embedded in thenetwork. Surprisingly, contributions occur withoutregard to expectations of reciprocity from others orhigh levels of commitment to the network.

Keywords: Electronic networks of practice,knowledge management, online communities,social capital

Introduction

Knowledge has long been recognized as avaluable resource for organizational growth andsustained competitive advantage, especially fororganizations competing in uncertain environments(Miller and Shamsie 1996). Recently, some


36 MIS Quarterly Vol. 29 No. 1/March 2005

researchers have argued that knowledge is anorganization’s most valuable resource because itrepresents intangible assets, operational routines,and creative processes that are hard to imitate(Grant 1996; Liebeskind 1996). However, mostorganizations do not possess all required knowl-edge within their formal boundaries and must relyon linkages to outside organizations and individ-uals to acquire knowledge (Anand et al. 2002). Indynamic fields, organizational innovation derivesfrom knowledge exchange and learning fromnetwork connections that cross organizationalboundaries (Nooteboom 2000). Organizationalmembers benefit from external network connec-tions because they gain access to new infor-mation, expertise, and ideas not available locally,and can interact informally, free from the con-straints of hierarchy and local rules. Even thoughthe employing organizations may be direct com-petitors, informal and reciprocal knowledgeexchanges between individuals are valued andsustained over time because the sharing of knowl-edge is an important aspect of being a member ofa technological community (Bouty 2000).

One way to create linkages to external knowledgeresources is through electronic communicationnetworks. Electronic networks make it possible toshare information quickly, globally, and with largenumbers of individuals. Electronic networks thatfocus on knowledge exchange frequently emergein fields where the pace of technological changerequires access to knowledge unavailable withinany single organization (Powell et al. 1996). Elec-tronic networks have been found to supportorganizational knowledge flows between geo-graphically dispersed coworkers (Constant et al.1996) and distributed research and developmentefforts (Ahuja et al. 2003). These networks alsoassist cooperative open-source software develop-ment (Raymond 1999; von Hippel and von Krogh2003) and open congregation on the Internet forindividuals interested in a specific practice (Butler2001; Wasko and Faraj 2000).

However, as management in many organizationshas discovered, the availability of electronic com-munication technologies is no guarantee thatknowledge sharing will actually take place (Alavi

and Leidner 1999; Orlikowski 1996). One of theproblems with accessing knowledge from acquain-tances and unknown others is that it requiresdepending upon the “kindness of strangers”(Constant et al. 1996). Despite the growinginterest in online cooperation and virtual orga-nizing, there is surprisingly little empirical researchinto the communication and organization pro-cesses of electronic networks, and how partici-pation in these networks relates to sharingknowledge (Lin 2001; Monge et al. 1998). Thegoal of our research is to better understandknowledge flows by examining why peoplevoluntarily contribute knowledge and help othersthrough electronic networks.

This paper is organized as follows. First, weintroduce the concept of an electronic network ofpractice and discuss the key issues for under-standing knowledge contribution in these networks.Then, we apply theories of social capital todevelop a model for examining how individualmotivations and social capital foster knowledgecontribution. We test this model empiricallythrough survey and objective data collected fromone electronic network of practice focused on theexchange of legal advice between lawyers.Finally, we discuss how our empirical findingscontribute to theory development and improve ourunderstanding of how information technologiessupport cross-organization knowledge exchange.

Knowledge Contributionin Electronic Networksof Practice

Brown and Duguid (2001) suggest that knowledgeflows are best understood by examining how workis actually performed and thinking about knowl-edge and learning as an outcome of actualengagement in practice. When individuals have acommon practice, knowledge readily flows acrossthat practice, enabling individuals to create socialnetworks to support knowledge exchange (Brownand Duguid 2000). Brown and Duguid suggestthat there are two practice-related social networks


MIS Quarterly Vol. 29 No. 1/March 2005 37

that are essential for understanding learning, work,and the movement of knowledge: communities ofpractice and networks of practice. These re-searchers conclude that the key to competitiveadvantage is a firm’s ability to coordinate auton-omous communities of practice internally andleverage the knowledge that flows into thesecommunities from network connections (Brownand Duguid 2000, 2001).

A community of practice consists of a tightly knitgroup of members engaged in a shared practicewho know each other and work together, typicallymeet face-to-face, and continually negotiate, com-municate, and coordinate with each other directly.In a community of practice, joint sense-making andproblem solving enhances the formation of stronginterpersonal ties and creates norms of directreciprocity within a small community (Lave 1991;Lave and Wenger 1991; Wenger 1998). In con-trast, networks of practice consist of a larger,loosely knit, geographically distributed group ofindividuals engaged in a shared practice, but whomay not know each other nor necessarily expect tomeet face-to-face (Brown and Duguid 2001).Networks of practice often coordinate through thirdparties such as professional associations, orexchange knowledge through conferences andpublications such as specialized newsletters.Although individuals connected through a networkof practice may never know or meet each otherface to face, they are capable of sharing a greatdeal of knowledge (Brown and Duguid 2000).

With recent advances in computer mediatedcommunications, networks of practice are able toextend their reach using technologies such aswebsites, electronic bulletin boards, and e-maillistservs. Building upon Brown and Duguid’s(2000) general description of networks of practice,we define an electronic network of practice as aspecial case of the broader concept of networks ofpractice where the sharing of practice-relatedknowledge occurs primarily through computer-based communication technologies. While manynetworks of practice are increasingly using elec-tronic communication to supplement their tradi-tional activities, electronic networks of practicediffer from networks of practice due to the impact

of technology on communications, which mayresult in different dynamics (DeSanctis and Monge1999). More formally, we define an electronicnetwork of practice as a self-organizing, openactivity system focused on a shared practice thatexists primarily through computer-mediatedcommunication.

This definition highlights some key aspects of anelectronic network of practice. First, the network isgenerally self-organizing in that it is made up ofindividuals who voluntarily choose to participate.Second, the term open activity denotes thatparticipation is open to individuals interested in theshared practice, and who are willing to mutuallyengage with others to help solve problems com-mon to the practice. While many electronic net-works of practice reside outside organizations(e.g., on the Usenet or the Web), our definitionincludes networks that are sponsored by a specificorganization or professional association as long asthey exist primarily through computer-mediatedcommunication.

However, because participation is open andvoluntary, participants are typically strangers.Knowledge seekers have no control over whoresponds to their questions or the quality of theresponses. Knowledge contributors have noassurances that those they are helping will everreturn the favor, and lurkers may draw upon theknowledge of others without contributing anythingin return. This sharply contrasts with traditionalcommunities of practice and face-to-face knowl-edge exchanges where people typically know oneanother and interact over time, creating expec-tations of obligation and reciprocity that areenforceable through social sanctions. Priorstudies consistently find that knowledge sharing ispositively related to factors such as strong ties(Wellman and Wortley 1990), co-location (Allen1977; Kraut et al. 1990), demographic similarity(Pelled 1996), status similarity (Cohen and Zhou1991), and a history of prior relationship (Krack-hardt 1992), all factors that are not readily appa-rent in electronic networks of practice. This begsthe question: Why do people spend their valuabletime and effort contributing knowledge and helpingstrangers in electronic networks of practice? In



order to investigate this question, we turn totheories of collective action and social capital.

Collective Action, Social Capital,and Knowledge Contribution

Contributions of knowledge to electronic networksof practice seem paradoxical. Previous researchargues that giving away knowledge eventuallycauses the possessor to lose his or her uniquevalue relative to what others know (Thibaut andKelley 1959), and benefits all others except thecontributor (Thorn and Connolly 1987). Therefore,in the context of an electronic network of practice,it seems irrational that individuals voluntarilycontribute their time, effort, and knowledge towardthe collective benefit, when they can easily free-ride on the efforts of others. However, if everyonechose to free-ride, the electronic network of prac-tice would cease to exist. Theories of collectiveaction help explain why individuals in a collectivechoose not to free-ride, and suggest that individ-uals forego the tendency to free-ride due to theinfluence of social capital (Coleman 1990; Putnam1993, 1995a). Social capital is typically defined as“resources embedded in a social structure that areaccessed and/or mobilized in purposive action”(Lin 2001, p. 29). In recent years, social capitalconcepts have been offered as explanations for avariety of pro-social behaviors, including collectiveaction, community involvement, and differentialsocial achievements that the concept of individual-based capital (such as human or financial capital)is unable to explain (Coleman 1990). The keydifference between social capital and other formsof capital is that social capital is embedded in thesocial realm. While other forms of capital arebased on assets or individuals, social capitalresides in the fabric of relationships betweenindividuals and in individuals’ connections withtheir communities (Putnam 1995b).

Some researchers have suggested that socialcapital will have difficulty developing in or trans-ferring to electronic networks of practice becausesocial capital is more likely to develop in collec-

tives characterized by a shared history, high inter-dependence, frequent interaction, and closedstructures (Nahapiet and Ghoshal 1998; Nohriaand Eccles 1992). It has also been argued thatelectronic networks cannot support significantknowledge outcomes because knowledge is oftentacit and highly embedded, requiring high-band-width communication that is difficult to sustainthrough technology (Brown and Duguid 2000;Nonaka 1994). Thus, current theory and researchseems to suggest that significant levels of socialcapital and knowledge exchange will not developin electronic networks of practice. This studyattempts to address the question of why peoplenevertheless contribute knowledge to others inelectronic networks of practice. Based on thetheoretical model proposed by Nahapiet andGhoshal (1998), we develop a series of hypoth-eses to examine how individual motivations andthree forms of social capital (cognitive, structural,and relational) relate to knowledge contribution inelectronic networks of practice.

Hypotheses

Nahapiet and Ghoshal (1998) presented socialcapital as an integrative framework for under-standing the creation and sharing of knowledge inorganizations. They argued that organizationshave unique advantages for creating knowledgeover more open settings such as markets becauseorganizations provide an institutional environmentconducive to the development of social capital.They suggested that the combination and ex-change of knowledge is facilitated when (1) individ-uals are motivated to engage in its exchange,(2) there are structural links or connectionsbetween individuals (structural capital), (3) individ-uals have the cognitive capability to understandand apply the knowledge (cognitive capital), and(4) their relationships have strong, positive charac-teristics (relational capital). Each of these forms ofsocial capital constitutes an aspect of the socialstructure and facilitates the combination andexchange of knowledge between individuals withinthat structure.



Although Nahapiet and Ghoshal’s model focuseson group level social capital factors to explain thecreation of intellectual capital within organizations,we suggest that social capital is also relevant forexplaining individual-level knowledge contributionin electronic networks of practice.2 We proposethat electronic networks of practice are sources oflearning and innovation because mutual engage-ment and interaction in the network creates rela-tionships between individuals and the collective asa whole. These individual relationships are aprimary source for the generation of social capital,which influences how individuals behave in relationto others and promotes knowledge creation andcontribution within the network.

For instance, Nahapiet and Ghoshal refer tostructural capital at the organizational level, whichassesses the network density and centralization ofthe overall organization. We adapt this to theindividual level, suggesting that an individual’sposition in the network influences his or herwillingness to contribute knowledge to others.Similarly, the Nahapiet and Ghoshal frameworkexamines the cognitive capital of the organization,suggesting that organizations whose membersshare common understandings and language arebetter suited for the creation of new intellectualcapital. At the individual level, we examine how anindividual’s cognitive capital affects his or her levelof knowledge contribution to the network. We alsoadapt the concept of relational capital from theorganizational level to the individual level, exam-ining how an individual’s perception of relationalcapital influences his or her participation in thenetwork. Figure 1 presents the model of ourhypotheses. We describe each of the constructsand their relationships to knowledge contribution inthe following sections.

Individual Motivations

Knowledge contribution in an electronic network ofpractice primarily occurs when individuals aremotivated to access the network, review the ques-tions posted, choose those they are able andwilling to answer, and take the time and effort toformulate and post a response. Although knowl-edge contribution may take on a variety of forms,the focus here is on two key aspects: the volumeof knowledge contributed through the posting ofresponse messages, and the average helpfulnessof those responses in directly answering thequestions posed.

In order to contribute knowledge, individuals mustthink that their contribution to others will be worththe effort and that some new value will be created,with expectations of receiving some of that valuefor themselves (Nahapiet and Ghoshal 1998).These personal benefits or “private rewards” aremore likely to accrue to individuals who activelyparticipate and help others (von Hippel and vonKrogh 2003). Thus, the expectation of personalbenefits can motivate individuals to contributeknowledge to others in the absence of personalacquaintance, similarity, or the likelihood of directreciprocity (Constant et al. 1996).

Social exchange theory (Blau 1964) posits thatindividuals engage in social interaction based onan expectation that it will lead in some way tosocial rewards such as approval, status, andrespect. This suggests that one potential way anindividual can benefit from active participation isthe perception that participation enhances his orher personal reputation in the network. Reputationis an important asset that an individual canleverage to achieve and maintain status within acollective (Jones et al. 1997). Results from priorresearch on electronic networks of practice areconsistent with social exchange theory and provideevidence that building reputation is a strongmotivator for active participation (Donath 1999). Inan organizational electronic network, the chance toimprove one’s reputation provided an importantmotivation for offering useful advice to others(Constant et al. 1996), and in extra-organizationalelectronic networks, individuals perceived that they

2Social capital is widely recognized as exhibiting aduality: at the group level, it reflects the affective natureand quality of relationships, while on the individual, itfacilitates an actor’s actions and reflects their access tonetwork resources (see Coleman 1990; Lin 2001;Putnam, 2000).



Reputation

Enjoy Helping

Knowledge Contribution

Centrality

Self-rated Expertise

Commitment

Reciprocity


Structural Capital

Cognitive Capital

Relational Capital

H1

H2

H3

H4

H5

H6

Tenure in the Field

H7

Reputation

Enjoy Helping

Knowledge Contribution

Centrality

Self-rated Expertise

Commitment

Reciprocity


Structural Capital

Cognitive Capital

Relational Capital

H1

H2

H3

H4

H5

H6

Tenure in the Field

H7

Figure 1. Individual Motivations, Social Capital, and Knowledge Contribution

gained status by answering frequently and intelli-gently (Lakhani and von Hippel 2003). Moreover,there is some evidence that an individual’s repu-tation in online settings extends to one’s profes-sion (Stewart 2003). Thus, the perception thatcontributing knowledge will enhance one’s repu-tation and status in the profession may motivateindividuals to contribute their valuable, personalknowledge to others in the network. This leads tothe first set of hypotheses.

H1a: Individuals who perceive that partici-pation will enhance their reputations inthe profession will contribute more helpfulresponses to electronic networks ofpractice.

H1b: Individuals who perceive that participa-tion will enhance their reputations in theprofession will contribute more responsesto electronic networks of practice.

In addition to enhancing their reputations, individ-uals may also receive intrinsic benefits from contri-buting knowledge. Knowledge is deeply integratedin an individual’s personal character and identity.Self-evaluation based on competence and socialacceptance is an important source of intrinsicmotivation that drives engagement in activities forthe sake of the activity itself, rather than forexternal rewards (Bandura 1986). Thus, individ-uals may contribute knowledge in an electronicnetwork of practice because they perceive thathelping others with challenging problems isinteresting, and because it feels good to help otherpeople (Kollock 1999). Prior research in electronicnetworks suggests that individuals are motivatedintrinsically to contribute knowledge to othersbecause engaging in intellectual pursuits andsolving problems is challenging or fun, andbecause they enjoy helping others (Wasko andFaraj 2000). Therefore, the second set of hypoth-eses predicts the following:



H2a: Individuals who enjoy helping others willcontribute more helpful responses toelectronic networks of practice.

H2b: Individuals who enjoy helping others willcontribute more responses to electronicnetworks of practice.

Structural Capital

In addition to individual motivations, theories ofcollective action and social capital propose that theconnections between individuals, or the structurallinks created through the social interactionsbetween individuals in a network, are importantpredictors of collective action (Burt 1992; Putnam1995b). When networks are dense, consisting ofa large proportion of strong, direct ties betweenmembers, collective action is relatively easy toachieve (Krackhardt 1992). The more individualsare in regular contact with one another, the morelikely they are to develop a “habit of cooperation”and act collectively (Marwell and Oliver 1988).Therefore, collectives characterized by high levelsof structural capital (dense connections in thecollective) are more likely to sustain collectiveaction.

Structural capital is also relevant for examiningindividual actions, such as knowledge contribution,within a collective. Individuals who are centrallyembedded in a collective have a relatively highproportion of direct ties to other members, and arelikely to have developed this habit of cooperation.Furthermore, such individuals are more likely thanothers to understand and comply with group normsand expectations (Rogers and Kincaid 1981).Thus, an individual’s structural position in an elec-tronic network of practice should influence his orher willingness to contribute knowledge to others.

Prior research suggests that one way to measurean individual’s embeddedness in an electronicnetwork of practice is to determine the number ofsocial ties the individual has with others in thenetwork (Ahuja et al. 2003). Social interaction inthese networks is similar to a conversation that

occurs through the posting of messages. Postingand responding to messages creates a social tiebetween individuals. Therefore, a social tie orstructural link is created when one person re-sponds to another’s posting. How many such tiesany one individual creates determines his or hercentrality in the network, which leads us to thefollowing hypotheses:

H3a: Individuals with higher levels of networkcentrality will contribute more helpful re-sponses to electronic networks ofpractice.

H3b: Individuals with higher levels of networkcentrality will contribute more responsesto electronic networks of practice.

Cognitive Capital

Cognitive capital refers to those resources thatmake possible shared interpretations andmeanings within a collective. Engaging in a mean-ingful exchange of knowledge requires at leastsome level of shared understanding betweenparties, such as a shared language and vocabu-lary (Nahapiet and Ghoshal 1998). Language isthe means by which individuals engage incommunication. It provides a frame of referencefor interpreting the environment and its mastery istypically indicated by an individual’s level ofexpertise. Individuals must also understand thecontext in which their knowledge is relevant (Orr1996). An individual’s cognitive capital developsas he or she interacts over time with otherssharing the same practice and learns the skills,knowledge, specialized discourse, and norms ofthe practice. This understanding may be gainedeither through hands-on experience or throughnarratives told over time. These narratives, some-times called war stories or workarounds, provideinsights into how other members have faced andresolved problems (Brown and Duguid 1991). Inshort, cognitive capital consists of both individualexpertise, or mastery of the language within thepractice, as well as experience with applying theexpertise.



In an electronic network of practice, even if anindividual is motivated to contribute knowledge toothers within the network, contribution is stillunlikely unless he or she has the requisite cogni-tive capital—that is, unless he or she has knowl-edge to contribute. Researchers have found thatindividuals with higher levels of expertise are morelikely to provide useful advice on computer net-works (Constant et al. 1996). At the same time,individuals are less likely to contribute when theyfeel their expertise to be inadequate (Wasko andFaraj 2000). Therefore, individual expertise, or theskills and abilities possessed by an individual,should increase the likelihood he or she will con-tribute knowledge. Cognitive capital also consistsof mastering the application of expertise, whichtakes experience. Individuals with longer tenure inthe shared practice are likely to better understandhow their expertise is relevant, and are thus betterable to share knowledge with others. This leads tothe following hypotheses:

H4a: Individuals with higher levels of expertisein the shared practice will contributemore helpful responses to electronicnetworks of practice.

H4b: Individuals with higher levels of expertisein the shared practice will contributemore responses to electronic networks ofpractice.

H5a: Individuals with longer tenure in theshared practice will contribute morehelpful responses to electronic networksof practice.

H5b: Individuals with longer tenure in theshared practice will contribute moreresponses to electronic networks ofpractice.

Relational Capital

In addition to motivations, structural capital, andcognitive capital, knowledge contribution is alsofacilitated by the affective nature of the relation-ships within a collective, referred to as relational

capital (Nahapiet and Ghoshal 1998). Relationalcapital exists when members have a strong identi-fication with the collective (Lewicki and Bunker1996), trust others within the collective (Putnam1995b), perceive an obligation to participate in thecollective (Coleman 1990), and recognize andabide by its cooperative norms (Putnam 1995a).Coleman (1990) suggests that the main function ofthis relational aspect of social capital is to facilitateactions for individuals within the structure, and thatrelational capital is an important asset that benefitsboth the community and its members. Membersare willing to help other members, even strangers,simply because everyone is part of the collectiveand all have a collective goal orientation (Leanaand Van Buren 1999). We examine here twodimensions of relational capital that prior researchindicates may be relevant to electronic networks ofpractice: commitment and reciprocity.

Commitment represents a duty or obligation toengage in future action and arises from frequentinteraction (Coleman 1990). Although commitmentis often described as direct expectations devel-oped within particular personal relationships, it canalso accrue to a collective. Commitment to a col-lective, such as an electronic network of practice,conveys a sense of responsibility to help otherswithin the collective on the basis of sharedmembership. Prior research finds that in anorganizational electronic network, individualsposting valuable advice are motivated by a senseof obligation to the organization (Constant et al.1996). In addition, findings from extra-organiza-tional electronic networks suggest that individualsparticipate in networks due to a perceived moralobligation to pay back the network and theprofession as a whole (Wasko and Faraj 2000).Therefore, individuals participating in an electronicnetwork of practice who feel a strong sense ofcommitment to the network are more likely toconsider it a duty to assist other members andcontribute knowledge. This leads to the followinghypotheses:

H6a: Individuals who are committed to thenetwork will contribute more helpful re-sponses to electronic networks ofpractice.



H6b: Individuals who are committed to the net-work will contribute more responses toelectronic networks of practice.

In addition to commitment, many researcherssuggest that trust is a key aspect of relationalcapital and facilitator of collective action (Coleman1990; Fukuyama 1995). In general, trust developswhen a history of favorable past interactions leadsto expectations about positive future interactions.Trust is a complex phenomenon, and severaldimensions of trust operating at multiple levels ofanalysis exist in organizational settings (McAllister1995; McKnight et al. 1998; Ring and Van de Ven1994; Tsai and Ghoshal 1998). Trust has beenstudied in a variety of online settings, and resultsindicate that trust in others’ ability, benevolence,and integrity is related to the desire to give andreceive information (Ridings et al. 2002) andimproved performance in distributed groups(Jarvenpaa 1998). Another aspect of social trustthat has not been investigated relates to expec-tations that an individual’s collective efforts will bereciprocated (Putnam 1995b).

A basic norm of reciprocity is a sense of mutualindebtedness, so that individuals usually recip-rocate the benefits they receive from others,ensuring ongoing supportive exchanges(Shumaker and Brownell 1984). Even thoughexchanges in electronic networks of practice occurthrough weak ties between strangers, there isevidence of reciprocal supportiveness (Wellmanand Gulia 1999). Prior research indicates thatknowledge sharing in electronic networks ofpractice is facilitated by a strong sense ofreciprocity—favors given and received—along witha strong sense of fairness (Wasko and Faraj2000). Thus, when there is a strong norm ofreciprocity in the collective, individuals trust thattheir knowledge contribution efforts will bereciprocated, thereby rewarding individual effortsand ensuring ongoing contribution. This leads tothe final hypotheses:

H7a: Individuals guided by a norm ofreciprocity will contribute more helpfulresponses to electronic networks ofpractice.

H7b: Individuals guided by a norm of reci-procity will contribute more responses toelectronic networks of practice.

Method

Sample

Data were collected from members of a nationallegal professional association in the United States.This association sponsors and maintains an elec-tronic network of practice as part of its website. Allmembers (approximately 7,000) have access tothe electronic network of practice as part of theirmembership benefits and participation in thenetwork is voluntary. The electronic network ofpractice, referred to within the association as theMessage Boards, is supported by a Web-basedsystem similar to a bulletin board where ex-changes are visible to everyone and relatedmessages are structured into discussion threads.Participation in the electronic network of practice isnot anonymous, so knowledge contribution to theelectronic network could influence perceptions ofprofessional reputation. Participants have to loginto the system in order to participate, and the firstand last names of the participants are visible aspart of the message header.

The professional association sponsored this studyand provided access to the electronic network ofpractice. In addition, the association provideddemographic information about its members. Weobserved and collected all message postingsduring a four-month period (February through May2001). This time period was divided into twophases. In the first phase (February and March),messages were collected to examine an individ-ual’s centrality in the network. In the secondphase (April and May), messages were collectedand examined to identify survey participants anddetermine knowledge contribution. At the end ofthe second phase, we looked up each individualwho participated in the electronic network ofpractice in the association’s membership databaseto collect demographic data and postal addresses.Each individual was assigned a random number



identifier to ensure anonymity. We then sent eachindividual a paper survey with the random numberidentifier. Completed surveys were matched toindividual participation on the message boards anddemographic data from the membership database.Demographic data, survey data and the observedmessage postings to the electronic network ofpractice served as input for the data analysis.

Measures

The survey measures for the study were derivedfrom previously published studies. The scalesmeasuring the motivations of reputation and enjoyhelping others were adapted from Constant et al.(1996). Commitment was adapted from Mowdayet al. (1979). Reciprocity measures were adaptedfrom Constant et al. The actual items used in thesurvey are presented in Table 2 (see the “Results”section).

Structural capital was assessed by determiningeach individual’s degree of centrality to the net-work. In electronic networks of practice, a dyadiclink is created between two individuals when oneresponds to another’s posting (Ahuja et al. 2003).To determine individual centrality, these links wererecorded in a square social network matrix suchthat if there was a link (one or more messages)between two individuals, a 1 was placed in thatcell. A zero was placed in the cell if the twoindividuals were not linked. This measure ofcentrality assesses to how many unique individuals(alters) a focal individual (the ego) is connected,independent of the total number of messagesposted. For example, an individual whoexchanges 20 messages with 15 unique individ-uals has a high centrality (degree = 15), while anindividual exchanging 20 messages with only oneindividual has a low centrality (degree = 1). Onepossible threat to validity when measuring networkcentrality (derived from the pattern of messages)concurrently with knowledge contribution (derivedfrom the frequency and content of messages) istheir joint dependence on the same messages. Toremedy this potential threat, we derived networkcentrality from messages collected during the two

months prior to the period during which the contentof messages was analyzed to evaluate knowledgecontribution. This temporal separation betweenthe assessment of centrality and the dependentvariables guarantees independent measurementand allows a stronger claim of causality in ourmodel.

Centrality was calculated using the UCINET 6program (Borgatti et al. 1999). There were 3,000messages posted by 604 participants in thenetwork during this time frame, indicating a vibrant,active network. To reduce skewness, the variablewas transformed using a log transformation.3

Cognitive capital was assessed by self-ratedexpertise and tenure in the field (a proxy forexperience). Expertise was self-rated as part ofthe survey. The association domain covers one ofthe recognized federal legal specializations (e.g.,patent, environmental, or immigration law), and,according to the senior staff members of theprofessional association, there are nine relevantlegal subspecialties within the association’sspecialized domain. Survey respondents wereasked to indicate their level of expertise (fromnovice = 1 to expert = 5) in each of these nineareas. The self-rated expertise score wasassessed by taking the average for each individualacross the nine areas. Tenure in the field wastaken from the association’s member database,indicating the number of months an individual hasbeen a member of the professional association,representing how much experience he or she hasin the association’s legal specialty. These mea-sures of expertise and tenure were considered themost relevant for assessing cognitive capital at theindividual level, and were chosen over others,such as tenure as a lawyer and tenure in theelectronic network of practice. This is because notall of a lawyer’s skills and experience come fromeither a general understanding of law (required topass the bar exam) or solely through participation

3Of the 604 participants, 91 individuals (15%) had acentrality score of zero; 168 individuals (28%) had scoresof one; 108 individuals (18%) had scores of two; and 237individuals (39%) had scores greater than or equal tothree.



in the electronic network of practice. Although theelectronic network of practice may have developedsocial cognitive capital, such as a languagespecific to the network as a whole, this was not afocus of our study.

The dependent variable in this study is knowledgecontribution. To accurately assess this, weexamined two independently measured dependentvariables based on message postings: (1) thehelpfulness of contribution and (2) the volume ofcontribution. First, content analysis was performedon all of the messages to determine whether themessage was a question, a response to a ques-tion, or some other type of post (i.e., thank you,announcements, or spam). The “other” categorywas used to reduce the confounding of the contentanalysis, recognizing that some messages do notcontribute knowledge. For example, “thank you!”or “me too!” messages are primarily social innature compared to messages that provideanswers. As a result, we did not consider these torepresent a knowledge contribution, which wedefined as a response to a question. One impli-cation of this coding is that general announcementpostings were not considered knowledge contri-bution in this study.

Response messages were then reviewed toassess the extent to which the content actuallyaddressed and answered the posted questions.The responses were rated as very helpful, helpful,somewhat helpful, and not helpful, using thefollowing guidelines:

• Very Helpful (received a score of 4). Theresponse directly answered the questionposted, and also provided a knowledgesource or meta-knowledge for the seeker(pointers to the actual law, statute, website,etc.).

• Helpful (received a score of 3). The re-sponse directly answered the questionposted.

• Somewhat Helpful (received a score of 2).The response did not directly answer thequestion, but provided a valuable insight into

how the issue was resolved elsewhere,information relevant to the problem at hand, apartial answer, or meta-knowledge.

• Not Helpful (received a score of 1). Thisrating indicates that the response was nothelpful to the knowledge seeker.

One of the authors and a domain expert (a staffmember of the association with extensive legalbackground) independently coded a subset of 100messages. There was agreement on 92 of the100 messages. Intercoder reliability usingCohen’s kappa (Cohen 1960) was .84, indicatingadequate agreement. Message coding discrep-ancies were reviewed and given the rating by thedomain expert. Given the accuracy of the inter-coder reliability on the first 100 messages, onlyone of the authors continued coding the rest of themessages. Once the helpfulness of the messageswas assessed, an individual’s helpfulness scorewas calculated by taking the mean helpfulness oftheir response messages.

The second measure of knowledge contributionassessed the total volume of an individual’s knowl-edge contribution. This was the total number ofresponse messages (messages that addressed aquestion) posted by each individual during thestudy’s period.

Respondents

During the second phase (April and May, 2001),2,555 messages were posted to the network by597 unique individuals. Of these 2,555 messages,1,156 were seeds, 1,181 were responses ad-dressing questions, and the average thread lengthwas 2.21 messages. Of the 597 unique individualsposting messages, we identified 593 validaddresses, and sent each of these individuals apaper-based survey. We received 173 responses,for a response rate of 29 percent. In order toassess response bias, we compared the parti-cipation rates in the electronic network of practicefor respondents with the participation rates of non-respondents. The participation rate of individualswho responded to the survey was not significantly



different from that of non-respondents (F = .823,n.s.). The total of female respondents was 43percent (compared to 41 percent in the associa-tion), and the mean age of respondents was 41years (compared to 38 in the association).Respondents had an average of 11 years ofoverall legal experience (vs. 9.6 in the associa-tion), of which 8.5 years was spent on the legalspecialty of the professional association (vs. 6.9 inthe association as a whole). The total of respon-dents who worked for themselves as private practi-tioners (typically a one-lawyer firm) was 45percent, while the rest worked in larger law firms.Comparative information was not available fromthe association member database, but the asso-ciation director thought that the respondents’employment pattern was similar to that of theassociation members as a whole. Respondentswere, therefore, typical in terms of gender andemployment status, but they had a higher overalllevel of experience than average associationmembers. We also compared the centrality scoresbetween phase 1 and phase 2 to ensure that parti-cipation in the electronic network of practice wasstable over time. The correlation between cen-trality in phase 1 and centrality in phase 2 is .88.

Results

We chose partial least squares (PLS) structuralequation analysis to test the hypotheses. PLS isa structural equation modeling technique thatsimultaneously assesses the reliability and validityof the measures of theoretical constructs andestimates the relationships among these con-structs (Wold 1982). PLS can be used to analyzemeasurement and structural models with multi-item constructs, including direct, indirect, andinteraction effects, and is widely used in ISresearch (Ahuja et al. 2003; Chin and Todd 1995;Sambamurthy and Chin 1994). PLS requires asample size consisting of 10 times the number ofpredictors, using either the indicators of the mostcomplex formative construct or the largest numberof antecedent constructs leading to an endog-enous construct, whichever is greater. Althoughthe measurement and structural parameters are

estimated together, a PLS model is analyzed andinterpreted in two stages: the assessment of thereliability and validity of the measurement model,and the assessment of the structural model.

Measurement Model

The first step in PLS is to assess the convergentvalidity of the constructs by examining the averagevariance extracted (AVE). The AVE attempts tomeasure the amount of variance that a latentvariable component captures from its indicatorsrelative to the amount due to measurement error.AVE values should be greater than the generallyrecognized .50 cut-off, indicating that the majorityof the variance is accounted for by the construct.In addition, individual survey items that make up atheoretical construct must be assessed for inter-item reliability. In PLS, the internal consistency ofa given block of indicators can be calculated usingthe composite reliability (ICR) developed by Werts,Linn, and Joreskog (1973). Acceptable values ofan ICR for perceptual measures should exceed .70(Fornell and Larcker 1981) and should beinterpreted like a Cronbach’s coefficient. All ICRand AVE values meet the recommended thresholdvalues. Table 1 summarizes the measurementmodel results.

Discriminant validity indicates the extent to whicha given construct is different from other constructs.The measures of the constructs should be distinctand the indicators should load on the appropriateconstruct. One criterion for adequate discriminantvalidity is that the construct should share morevariance with its measures than with otherconstructs in the model (Barclay et al. 1995). Toevaluate discriminant validity, the AVE may becompared with the square of the correlationsamong the latent variables (Chin 1998). Thediagonal of Table 1 contains the square root of theAVE. All AVEs are greater than the off-diagonalelements in the corresponding rows and columns,demonstrating discriminant validity.

A second way to evaluate convergent and discrim-inant validity is to examine the factor loadings of

Table 1. Descriptive Statistics, Correlation of Constructs,a ICRs, and Square Root of AVE Valuesb

MeanStdDev Range VIF ICR 1 2 3 4 5 6 7 8 9

1 Reputation 2.60 1.02 1–5 1.22 .91 .88

2 Enjoy Helping 4.08 .77 1.7–5 1.45 .88 .33** .84

3 Centralityc 4.46 12.9 0–147 1.20 n/a .09 .28** 1.00

4 Self-rated Expertise 3.21 .94 1–5 1.27 n/a –.02 .01 .07 1.00

5 Tenure in Field – months 69.3 62.0 2–267 1.35 n/a .02 –.15 .01 .44** 1.00

6 Commitment 3.91 1.00 1–5 1.75 .90 .33** .36** .32** –.19* –.29** .86

7 Reciprocity 3.67 .90 1–5 1.62 .90 .26** .45** .12 –.16* –.23** .54** .90

8 Helpfulness of Contribution 2.43 1.27 0–4 n/a n/a .20** .21** .33** .11 .11 .004 .04 1.00

9 Volume of Contribution 2.17 8.05 0–92 n/a n/a .19* .13 .50** .15* .26** .11 –.12 .28** 1.00

aCorrelations > .15 significant at the .05 level and > .20 significant at the .01 level (two-tailed).bSquare root of the AVE are the bolded diagonal values.cDescriptive statistics of centrality are based on active participants (N = 600) in the two month period preceding the main data collection.

Table 2. Factor Analysis, Constructs, and Item Wording

ReputationEnjoy

Helping CentralitySelf-ratedExpertise Tenure

Commit-ment

Recip-rocity

Helpfulness ofContribution

Volume ofContribution

I earn respect from others byparticipating in the Message Boards 0.90 0.33 0.09 0.06 0.02 0.33 0.25 0.25 0.18

I feel that participation improves mystatus in the profession 0.90 0.26 0.06 –0.06 –0.03 0.31 0.25 0.16 0.15

I participate in the Message Boards toimprove my reputation in theprofession

0.83 0.27 0.08 –0.05 0.05 0.30 0.19 0.14 0.16

I like helping other people 0.20 0.79 0.15 0.00 –0.15 0.23 0.30 0.18 0.05It feels good to help others solve theirproblems 0.29 0.86 0.26 0.02 –0.14 0.31 0.42 0.16 0.13

I enjoy helping others in the MessageBoards 0.34 0.86 0.27 0.01 –0.09 0.37 0.39 0.19 0.13

Centrality 0.09 0.27 1.00 0.07 0.01 0.32 0.11 0.33 0.50Self-rated Expertise –0.01 0.01 0.07 1.00 0.44 0.18 –0.15 0.11 0.15Tenure in the Field – months 0.02 –0.15 0.01 0.44 1.00 0.27 –0.22 0.11 0.26I would feel a loss if the MessageBoards were no longer available 0.28 0.31 0.26 –0.22 –0.29 0.82 0.44 0.02 0.07

I really care about the fate of theMessage Boards 0.19 0.28 0.28 –0.12 –0.27 0.85 0.44 –0.05 0.07

I feel a great deal of loyalty to theMessage Boards 0.39 0.34 0.29 –0.15 –0.19 0.92 0.51 0.03 0.14

I know that other members will helpme, so it’s only fair to help othermembers

0.27 0.42 0.08 –0.13 –0.17 0.49 0.95 0.01 –0.14

I trust that someone would help me if Iwere in a similar situation 0.20 0.39 0.14 –0.15 –0.26 0.52 0.85 0.05 –0.07

Helpfulness of contribution (contentanalysis) 0.22 0.21 0.33 0.11 0.11 0.01 0.03 1.00 0.28

Volume of contribution (total numberof responses) 0.19 0.13 0.50 0.15 0.26 0.12 –0.12 0.28 1.00



each indicator. Each indicator should load higheron the construct of interest than on any otherfactor (Chin 1998). Factor loadings and cross-loadings for the multi-item measures werecalculated from the PLS output and are presentedin Table 2. Inspection of loadings and cross-loadings confirms that the observed indicatorsdemonstrate adequate discriminant and conver-gent validity.

Hypothesis and Model Testing

The theoretical model and hypothesized rela-tionships were estimated using 200 iterations ofthe bootstrapping technique in PLS Graph 2.91(Chin and Frye 1996). The explanatory power ofthe structural model is evaluated by looking at theR2 value in the final dependent construct.Because we measure knowledge contribution intwo ways, we present two sets of results, one foreach dependent variable. We first present resultsfor helpfulness of contribution (per content analysisof the messages). Next, we present results forvolume of contribution (the number of responsesposted by each individual). To examine the spe-cific hypotheses, we assessed the t-statistics forthe standardized path coefficients and calculatedp-values based on a two-tail test with a signi-ficance level of .05. Table 3 presents the resultsof the PLS analysis used to test the model.

Links to Helpfulness of Contribution

The R2 for the helpfulness of knowledge contri-bution model was .19. We proposed direct linksbetween perceptions of enhanced reputation(H1a), enjoy helping (H2a), and the helpfulness ofcontribution. Only the path between perceptions ofenhanced reputation and helpfulness was positiveand significant ($ = .21, p < .01). The pathbetween enjoy helping and helpfulness ap-proached significance ($ = .13, p < .10).Hypothesis 3a proposed a link between an individ-ual’s network centrality and the helpfulness ofcontribution. The path was positive and significant($ = .33, p < .001), suggesting that structuralcapital increases the likelihood of more helpful

contributions. Hypotheses 4a and 5a suggested alink between high levels of cognitive capital andthe helpfulness of contribution. The results indi-cated that neither self-rated expertise nor tenure inthe field were linked to providing helpful contri-butions. Finally, hypotheses 6a and 7a suggesteda link between the dimensions of relational capitaland the helpfulness of contribution. Contrary toH6a, the results show a negative and significantlink between commitment to the electronic networkof practice and helpfulness ($ = –.20, p < .05),while no link was found between expectations ofreciprocity and the helpfulness of contribution.

Links to Volume of Contribution

The R2 for the volume of contribution model was.37. We proposed direct links between percep-tions of enhanced reputation (H1b), enjoy helping(H2b), and volume of contribution. The path forreputation was significant ($ = .15, p < .05), whilethe path for enjoy helping was not. Hypothesis 3bproposed a link between an individual’s networkcentrality and the volume of his or her contr-ibutions. The path was positive and significant ($= .46, p < .001), supporting the contention thatstructural capital increases the likelihood of a highvolume of contribution. Hypotheses 4b and 5bsuggested a link between high levels of cognitivecapital and volume of contribution. The resultswere split, with no significant link between self-rated expertise and volume of contribution, whiletenure in the field was positively and significantlylinked to volume of contribution ($ = .23, p < .01).Contrary to the prediction of H7b, the resultsshowed a negative and significant link between anexpectation of reciprocity and volume of contri-bution ($ = -.24, p < .05), and no link was foundbetween commitment to the network and volumeof contribution.

Discussion

The aim of this study was to test a model of socialcapital to investigate why people contribute knowl-edge to others, primarily strangers, in electronicnetworks of practice. Our results provide support



Table 3. Individual Motivations, Social Capital, and Knowledge Contribution Results

Helpfulness ofContribution

Volume ofContribution

$ t-statistic $ t-statistic

H1 Reputation 0.21** 2.75 0.15* 2.12

H2 Enjoy Helping 0.13† 1.67 0.06 1.14

H3 Centrality 0.33*** 4.29 0.46*** 7.07

H4 Self-rated Expertise 0.02 0.24 0.00 0.00

H5 Tenure in Field - months 0.06 0.71 0.23** 2.84

H6 Commitment –0.2* 2.01 0.10 1.06

H7 Reciprocity 0.01 0.07 –0.24* 2.01†p < .10, *p < .05, ** p < .01, *** p < .001

for the theoretical model and qualified support formost of our hypothesized relationships. Theresults indicate that a significant predictor ofindividual knowledge contribution is the perceptionthat participation enhances one’s professionalreputation. These results are also consistent withprior research in online settings, providing addi-tional evidence that building reputation is a strongmotivator for active participation and knowledgecontribution (Donath 1999), and that reputations inonline settings extend to one’s profession (Stewart2003). The results from this study also provideweak evidence that individuals who enjoy helpingothers provide more helpful advice, as suggestedby prior research examining electronic networksopenly available on the Internet (Kollock and Smith1996). One potential explanation for the weakinfluence of intrinsic motivations may be due to thenon-anonymous nature of the network and theprofessional implications of participation in thenetwork. The results may indicate that when elec-tronic networks of practice are used to supportprofessional activities, the ability to leverageextrinsic rewards may become more salient thanintrinsic returns to motivate knowledge contribu-tion. Thus, an interesting area of further researchwould compare networks that directly supportprofessional activities with other types of electronicnetworks of practice, and the influence of anonym-ity, to see whether there are differences in

motivations for posting different types of content inthe different contexts.

In addition to individual motivations, our resultsprovide some evidence that social capital developsand plays an important role underlying knowledgeexchange, despite the media richness limitationsinherent in online communication. Most significantis the role of structural social capital. Consistentwith theories of collective action, individuals whoare central to the network and connected to a largenumber of others are more likely to sustain contri-butions to the collective (Burt 1992), indicating thatthe development of a critical mass of activeparticipants is important for sustaining electronicnetworks of practice (Marwell and Oliver 1993).

The results also provide some indication thatcognitive social capital plays a vital role underlyingknowledge contribution. Consistent with researchon communities of practice (Brown and Duguid1991; Orr 1996), an individual’s experience in thepractice is an important predictor of knowledgecontribution. However, although an individual’sself-rated expertise had a significant correlationwith the volume of knowledge contributed, self-rated expertise was not significant in the overallmodel. This result is at variance with prior studies,which found that individual expertise is animportant predictor of knowledge contribution and



the helpfulness of replies in electronic networks ofpractice in an organizational context (Constant etal. 1996) and in open networks on the Internet(Wasko and Faraj 2000). One potential explana-tion for the different results may be due to howexpertise was measured across the three studies.In the current study, expertise was measured byaveraging an individual’s general level of self-ratedexpertise across nine legal subspecialties. In theConstant et al. (1996) study, expertise was self-rated based on the content of a specific message,indicating how informed an individual was on thesubject matter of the question. In the Wasko andFaraj (2000) study, expertise was elicited throughopen-ended comments about why people partici-pate and help others in general. While we pre-dicted that cognitive capital consisted of both self-rated expertise as well as experience in the prac-tice, the results seem to indicate that masteringthe application of expertise and understanding howexpertise is relevant, which takes experience, maybe just as important in electronic networks of prac-tice focused on professional knowledge exchange.Thus, the importance of experience and expertisein the practice when considering the type ofknowledge exchanged, and how these constructsare measured, are additional areas in need offurther research.

Directly contrary to expectations, the results sug-gest that high levels of relational capital do notpredict knowledge contribution. This findingseems to provide support to the argument thatrelational capital may not develop in electronicnetworks due to a lack of shared history, highinterdependence, frequent interaction, and co-presence (Cohen and Prusak 2001; Nahapiet andGhoshal 1998; Nohria and Eccles 1992). Individ-uals contribute more knowledge in terms ofvolume, even though they expect that their helpwill not be reciprocated, and regardless of theirlevel of commitment to the network. Thesefindings directly contradict prior research in face-to-face settings, where it is consistently found thatreciprocity is critical for sustaining supportiverelationships and collective action (Putnam 1995b;Shumaker and Brownell 1984). One possibleexplanation is that network-based interactions maybe generalized rather than dyadic, and direct

reciprocity is not necessary for sustaining collec-tive action. In contrast to personal exchangesbetween two individuals where there is an expec-tation of direct reciprocity, reciprocity in electronicnetworks of practice may be generalized (Waskoand Teigland 2002). Generalized reciprocityoccurs when one’s giving is not reciprocated bythe recipient, but by a third party (Ekeh 1974). Ifexpectations of direct reciprocity are not key tosustaining knowledge contribution in electronicnetworks of practice, one potentially exciting areaof further research would be to apply socialnetwork analysis techniques to examine whetherpatterns of generalized exchange substitute fordirect reciprocity and how.

Another surprising result is the negative relation-ship between commitment and the helpfulness ofcontributions, even though these two variableswere not correlated. Examination of the varianceinflation factors suggests that multicollinearity isnot the cause of this significant relationship. Weperformed additional analyses, which indicatedthat commitment is acting as a suppressor vari-able. Suppressor variables explain residual vari-ance in the dependent variable after controlling forthe effects of other variables (Cohen 1988). Aclassical suppressor variable is a variable that hasa zero-order correlation with the dependentvariable, but is correlated with one or morepredictor variables and leads to improved predic-tion when included in multiple regression analysis(Pedhazur 1982). We investigated the suppressorimpact by removing variables from the model andchecking if the suppressor effect of commitmentstill remained. We found that reputation andcentrality must be present in the model to get thesuppressor effect,4 indicating that the semi-partialcorrelation between commitment and helpfulnessis greater than its zero-order correlation becausethe irrelevant variance shared with reputation andcentrality is suppressed, in effect purifying therelation between the commitment and the depen-

4Removing reputation results in a reduction of commit-ment $ from .20 to .13 (p = n.s.), removing centrality re-sults in a reduction of commitment $ from .20 to .07 (p =n.s.), and removing both reputation and centrality resultsin a reduction of commitment $ to .02 (p = n.s.).



dent variable. Thus, while commitment has aweak, positive correlation with the helpfulness ofknowledge contribution, once the impacts ofreputation and centrality are taken into account,higher levels of commitment predict lower levels ofhelpfulness. One potential explanation for thisfinding may be that after taking reputation andcentrality into account, it is the individuals that arereceiving knowledge, rather than contributing, thatare more committed to the network. This would bean interesting question to examine in futureresearch.

The results of this study have interesting impli-cations for practitioners interested in knowledgemanagement and how to leverage electronicnetworks of practice for competitive advantage.Organizations benefit from accessing externalknowledge through electronic networks of practicebecause valuable expertise flows into the organi-zation at relatively little cost. By participating in anelectronic network of practice, individuals gainreputation and become central to a larger networkof resources. Disallowing such participation maycut off valuable knowledge flows and reduceemployee efficacy (Anand et al. 2002).

Managers interested in developing and sustainingknowledge exchange through electronic networksof practice should focus attention on the creationand maintenance of a set of core, centralizedindividuals with experience in the practice by usingextrinsic motivators such as enhanced reputationto actively promote contributions to the network.Centralized individuals create a “critical mass” thatsustains the network and maintains the network’susefulness by contributing knowledge to others.To help generate a critical mass, managers shouldtarget individuals with longer tenure and moreexperience in the practice. Another method topromote individual participation in the critical massis to develop techniques that help build an individ-ual’s reputation in the profession. For example, itcould be helpful to assign status to individuals andmake this status apparent both within the elec-tronic network of practice and off-line as well.Individual reputations may become more salientwhen managers build bridges between physicaland virtual networks, finding ways to spread

reputations developed online to the profession asa whole.

Leveraging centrality and promoting individualreputations may also help signal the potentialquality of responses to novice participants andlurkers, making the knowledge more accessible toall participants in the network. As Smith (2002)suggests, techniques that identify an individual’scentrality can effectively support knowledgesharing by helping knowledge seekers assess thequality of responses to their questions. Gainingstatus and recognition in this way would motivateindividuals to participate more in electronic net-works of practice (von Hippel and von Krogh2003). Therefore, making centrality a part of anindividual’s identification may provide an additionalincentive for participants to respond frequently andwell to many different people.

We should note that there are several limitations tothis study, requiring further examination andadditional research. One limitation is that weexamined only one aspect of collective action:knowledge contribution. While it can be arguedthat knowledge contribution is key to sustainingonline networks, future research should alsoexamine how participation in electronic networks ofpractice affects individual learning and knowledgecreation. Another limitation of this study is itsfocus on active participants. We did not investi-gate individuals who read but do not post, ormembers who do not log onto the electronic net-work of practice at all. Why individuals choose toparticipate in an electronic network of practice oronline group is another area for future research.

Furthermore, the generalizability of our results maybe limited, as we examined only a single electronicnetwork of practice supporting a specialized knowl-edge practice. Future studies should examinewhether other electronic networks of practiceexhibit similar dynamics and compare individualmotivations and social capital across networks tosee if there are variations in the level of parti-cipation and knowledge outcomes similar to whatwe found. A related open question is whether thesocial capital model applies to different practicesthat are not strictly professional in nature such asthose focused on hobbies or diseases.



Finally, this study was cross-sectional (based onfour months of exchanges), so we cannotinvestigate the process by which social capitaldevelops or the ways in which network structurechanges over time. Because one of the indepen-dent variables and one of the dependent variablesexamined in this study were both assessed frommessage posting activity, the cross-sectionaldesign makes it difficult to examine the dynamicinteraction between knowledge contribution andthe resulting changes to network structure.Therefore, we relied on theory to position networkcentrality as an independent variable in the modeland used message postings from the two monthsprior to data collection for the dependent variableto test this relationship. However, network cen-trality could also be considered a dependentvariable, or outcome of knowledge contribution.For example, while we argue that network cen-trality is an important indicator of why individualschoose to contribute knowledge, centralitymeasures may also potentially be used to showthat individuals have in fact contributed, how oftenthey have contributed, and to whom. Thus, futurestudies should take this dynamic nature of networkstructuring into account, using longitudinal dataand additional measures of network centrality.Alternatively, future research might also benefitfrom examining different dependent variables thatare not based on message activity, such as per-ceptions of knowledge contribution and knowledgeacquisition at the individual level. Researcherscould also incorporate event-driven methods thatexamine perceptions at the message level, similarto the method used by Constant et al. (1996).

Conclusion

Despite the promise of knowledge managementtechnologies, organizations are struggling to turnelectronic networks into active discussion forums(Orlikowski 1996). Knowledge contribution in elec-tronic networks of practice is a socially complexprocess that involves a variety of actors withdifferent needs and goals. In electronic networks,individuals contribute knowledge and help others

despite the lack of a personal, face-to-face rela-tionship and the easy alternative of free-riding onthe efforts of others. So, why do individuals sharetheir valuable knowledge in electronic networks ofpractice? Individuals contribute knowledge toelectronic networks of practice when they perceivethat it enhances their professional reputations, andto some extent because it is enjoyable to helpothers. They contribute when they are structurallyembedded in the network, and when they haveexperience to share with others. Surprisingly, wefind that individuals who contribute knowledge donot seem to be more committed to the electronicnetwork of practice than noncontributors, nor dothey seem to expect help in return.

Acknowledgements

We would like to thank the editors of the specialissue, V. Sambamurthy and M. Subramani, as wellas our anonymous AE and reviewers for theirefforts in helping us develop this paper and for anexemplary review process as a whole. Specialthanks to Herbert R. McLure for his comments onearlier drafts of this work. We would also like toacknowledge the participation and feedback fromour colleagues at the MISRC Conference onKnowledge Management, 2003.

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About the Authors

Molly McLure Wasko is an assistant professor inthe department of Management Information Sys-tems at Florida State University where she teachesprimarily strategic information technologies. Shereceived her doctorate in MIS from the Universityof Maryland, College Park, and she holds an MBAfrom Averett University. Prior to getting her doc-torate, she spent eight years working in productionand operations management. Her researchinterests include technology and strategy, thedevelopment of online knowledge communities,and the strategic human resource management ofIT professionals. Her work has appeared in theJournal of Strategic Information Systems andDecision Science, and has been presented at theInternational Conference on Information Systems,

Academy of Management, and Americas Con-ference on Information Systems. She is a memberof the Academy of Management, Association forInformation Systems, and INFORMS.

Samer Faraj is an assistant professor in theDepartment of Decision and Information Tech-nologies at the University of Maryland, CollegePark. He received his doctorate in MIS fromBoston University’s School of Management andholds an M.S. in Technology and Policy from MIT.Prior to getting his doctorate, he spent a decadeworking in a variety of consulting and IS positions.His research interests include the coordination ofexpertise in knowledge teams in settings such assoftware development and trauma care, thedevelopment of online knowledge communities,and the impact of IT on organizations. His workhas appeared in journals such as InformationSystems Research, Management Science, Journalof Applied Psychology, the Journal of StrategicInformation Systems, and Information Technology& People. He serves on the editorial board ofOrganization Science and is an associate editor forInformation Systems Research.

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