邱兆民 資訊管理學系 中央大學

2007/4/19 1

Understanding Knowledge Sharing in Virtual Communities:

An Integration of Social Capital and Social Cognitive Theories

邱兆民資訊管理學系中央大學

2007/4/19 2

Introduction

Rapid growth of virtual communities.

Participate in virtual communities to seek knowledge to resolve problems at work.

42% of those involved in a virtual community say it is related to their profession.

Many organizations have begun to support the development and growth of CoPs to meet their business needs and objectives.

For example, Caterpillar Inc. launched its Knowledge Network in 1999

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Introduction

Content (i.e., knowledge) of virtual communities is the king .

The biggest challenge in fostering a virtual community is the supply of knowledge.

Identifying the motivations underlying the knowledge sharing behavior in virtual communities

Two complementary social theories are applied: Social Cognitive Theory and Social Capital Theory

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Introduction

Social Cognitive Theory (SCT) (Bandura, 1986 ) defines human behavior as a triadic, dynamic, and reciprocal interaction of personal factors, behavior, and the social network (system).

Behavior

EnvironmentPersonCognitive

and Personal Factors

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Introduction

Core of Social Cognitive Theory are self-efficacy and outcome expectations.

Self-efficacy is “a judgment of one’s ability to organize and execute given types of performances” .

Outcome expectation is “a judgment of the likely consequence such performances will produce” .

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Introduction

Virtual communities: are online social networks in which people with common interests, goals, or practices interact to share information and knowledge, and engage in social interactions.

It is the nature of social interactions and the set of resources embedded within the network that sustains virtual communities.

Studies on virtual communities address issues related to both personal cognition and social network .

Social Cognitive Theory is limited in addressing what components are within a social network and how they influence an individual’s behavior

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Introduction

Social Capital Theory : the network of relationships possessed by an individual or a social network and the set of resources embedded within it

strongly influences the extent to which interpersonal knowledge sharing occurs (Nahapiet and Ghoshal, 1998).

Nahapiet and Ghoshal define social capital with three distinct dimensions:

Structural: the overall pattern of connections between actors Relational: the kind of personal relationships people have developed

with each other through a history of interactions Cognitive: those resources providing shared representation,

interpretations, and systems of meaning among parties

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Research Model

Shared Vision H8b

H1b

Structural Dimension

H2b

H2a

H5a

H4b

H4a

H3b

H3a

H1a

Social InteractionTies

Community- Related Outcome

Expectations

Personal Outcome Expectations

Quantity of Knowledge

Sharing

Trust

Relational Dimension

Norm of Reciprocity

Identification

Shared Language

Cognitive Dimension

Knowledge Quality

H5b

H6a

H6b

H7a

H8aH7b

In a voluntary setting, individuals who have no confidence in their ability to share knowledge would be unlikely to perform the behavior. Therefore, the research model does not include self-efficacy.

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Social Cognitive Theory and Knowledge Sharing

People who come to a VC: seek knowledge and solving problem + meet other people, to seek support, friendship and a sense of belongingness.

According to the BUSINESS WEEK, 35% of those involved in a virtual community say their community is a social group.

Social Cognitive Theory: a person’s behavior is partially shaped and controlled by the influences of social network and the person's cognition.

Studies in the IS literature have demonstrated the importance of self-efficacy and outcome expectations for predicting and improving computer training performance, computer usage, and Internet behaviors.

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Social Cognitive Theory and Knowledge Sharing

Researchers interested in understanding the motivations prompting people to share knowledge or participate in virtual communities have shown the importance of social influences.

Strong community ties could provide important environmental

conditions for knowledge exchange (Wellman and Wortley, 1990) Trust : a key element in fostering the level of participation or

knowledge sharing in virtual communities (Ridings et al., 2002).

Bock et al. (2005): anticipated reciprocal relationships attitude; subjective norm intention.

Some studies found that a sense of community (Hars and Ou, 2002 ) and social identity (Dholakia et al., 2004) can enhance the likelihood of members’ contribution and participation in a virtual community.

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Social Cognitive Theory and Knowledge Sharing

Bock et al. (2005: MISQ)

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Social Cognitive Theory and Knowledge Sharing

Ridings et al. (2002: JSIS)

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Social Cognitive Theory and Knowledge Sharing

Hsu et al. (2007: IJHCS)

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Social Cognitive Theory and Knowledge Sharing

Prior studies drawing upon Social Cognitive Theory have ignored the importance of social network influence

Studies in the virtual community literature have paid less attention to the role of personal cognition, such as outcome expectations.

Why do individuals spend their valuable time and effort on sharing knowledge with members in virtual communities?

According to Social Cognitive Theory, the question should be addressed from the perspectives of both personal cognition and social network.

Consequently, Social Capital Theory is introduced to supplement Social Cognitive Theory to address our research question.

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Social Capital Theory and Knowledge Sharing

Social capital: “the sum of the actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual or social unit” (Nahapiet and Ghoshal, 1998).

Putnam (1995) suggested that social capital facilitates coordination and cooperation for mutual benefit.

Tsai and Ghoshal (1998) empirically justified how social capital facilitates resource exchange and production innovation within the organization.

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Social Capital Theory and Knowledge Sharing

Yli-Renko et al. (2001) examined the effects of social capital on knowledge acquisition and exploitation in young technology-based firms.

Virtual communities differ notably from organizational settings since interaction among community members is through online communication.

Consequently, whether the impact of social capital on resource exchange and knowledge management activities found in the organizational settings could be generalized to virtual communities is still unclear.

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Social Capital Theory and Knowledge Sharing

Wasko and Faraj (2005: MISQ)

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Hypotheses – H1

Personal outcome expectations refer to the knowledge contributor’s judgment of likely consequences that his or her knowledge sharing behavior will produce to him or herself.

According to Social Cognitive Theory, individuals are more likely to engage in the behavior that they expect to result in favorable consequences.

Several studies in IS research provided support for this contention.

One study found that performance-related outcome expectations had a significant effect on computer use (Compeau and Higgins, 1995 ).

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Hypotheses – H1

The expectations of enriching knowledge, seeking support, making friends, etc (Langerak et al., 2004).

The expectations of being seen as skilled, knowledgeable or respected (Butler et al., 2002).

H 1a: Members’ personal outcome expectations are positively associated with their quantity of knowledge sharing.

H 1b: Members’ personal outcome expectations are positively associated with the quality of knowledge shared by them.

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Hypotheses – H2

Community-related outcome expectations refer to a knowledge contributor’s judgment of likely consequences that his or her knowledge sharing behavior will produce to a virtual community.

Individuals share knowledge with the expectation of helping the virtual community to accumulate its knowledge, continue its operation, and grow (Bock and Kim, 2002; Kolekofski and Heminger, 2003)

H 2a: Members’ community-related outcome expectations are positively associated with their quantity of knowledge sharing.

H 2b: Members’ community-related outcome expectations are positively associated with the quality of knowledge shared by them.

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Hypotheses – H3

Social interaction ties represent the strength of the relationships, and the amount of time spent, and communication frequency among members of virtual communities.

Larson (1992) and Ring and Van de Ven (1994) noted that the more social interactions undertaken by exchange partners, the greater the intensity, frequency, and breadth of information exchanged. Knowledge is important in providing a basis for action but is costly to obtain.

Nahapiet and Ghoshal (1998) argued that “network ties influence both access to parties for combining and exchanging knowledge and anticipation of value through such exchange” .

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Hypotheses – H3

Recent studies have provided empirical support for the influence of social interaction ties on

interunit resource exchange and combination (Tsai and Ghoshal, 1998) knowledge sharing among units that compete with each other for

market shares (Tsai, 2002), and knowledge acquisition (Yli-Renko et al., 2001).

H 3a: Members’ social interaction ties are positively associated with their quantity of knowledge sharing.

H 3b: Members’ social interaction ties are positively associated with the quality of knowledge shared by them.

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Hypotheses – H4

Trust has been viewed as a set of specific beliefs dealing primarily with the integrity, benevolence, and ability of another party in the management literature.

This study focuses on integrity, which refers to an individual’s expectation that members in a virtual community will follow a generally accepted set of values, norms, and principles.

Nahapiet and Ghoshal (1988) suggested that when trust exists between the parties, they are more willing to engage in cooperative interaction.

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Hypotheses – H4

According to Blau (1964), trust creates and maintains exchange relationships, which in turn may lead to sharing knowledge of good quality.

H 4a: Trust is positively associated with the quantity of knowledge sharing.

H 4b: Trust is positively associated with the quality of knowledge shared by members.

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Hypotheses – H5

Norm of reciprocity refers to knowledge exchanges that are mutual and perceived by the parties as fair.

Social Exchange Theory (Thibaut and Kelly, 1959) suggests that participants in virtual communities expect mutual reciprocity that justifies their expense in terms of time and effort spent sharing their knowledge.

H 5a: Norm of reciprocity is positively associated with the quantity of knowledge sharing.

H 5b: Norm of reciprocity is positively associated with the quality of knowledge shared by members.

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Hypotheses – H6

Identification refers to an individual’s sense of belonging and positive feeling toward a virtual community.

Identification is similar to emotional identification proposed by Ellemers et al. (1999).

Emotional identification fosters loyalty and citizenship behaviors in the group setting (Bergami and Bagozzi, 2000 ).

Emotional identification is useful in explaining individuals’ willingness to maintain committed relationships with virtual communities (Bagozzi and Dholakia, 2002).

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Hypotheses – H6

Given that valuable knowledge is embedded in individuals and people usually tend to hoard the knowledge, one would not contribute his knowledge unless another person is recognized as his group-mate and the contribution is conducive to his welfare.

H 6a: Identification is positively associated with the quantity of knowledge sharing.

H 6b: Identification is positively associated with the quality of knowledge shared by members.

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Hypotheses – H7

Shared language goes beyond the language itself; it also addresses “the acronyms, subtleties, and underlying assumptions that are the staples of day-to-day interactions” (Lesser, J. Storck , 2001, p. 836).

Shared codes and language facilitate a common understanding of collective goals and the proper ways of acting in virtual communities (Tsai and Ghoshal, 1998).

Nahapiet and Ghoshal (1998) stated that shared language influences the conditions for the combination and exchange of intellectual capitals in several ways.

H 7a: Shared language is positively associated with the quantity of knowledge sharing.

H 7b: Shared language is positively associated with the quality of knowledge shared by members.

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Hypotheses – H8

Tsai and Ghoshal (1998) noted that a shared vision “embodies the collective goals and aspirations of the members of an organization” (p. 467).

Cohen and Prusak (2001) argued that shared values and goals bind the members of human networks and communities, make cooperative action possible, and finally benefit organizations, now to be mentioned -- better knowledge sharing in terms of quantity and quality.

H 8a: Shared vision is positively associated with the quantity of knowledge sharing. H 8b: Shared vision is positively associated with the quality of knowledge shared by

members.

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Research Methodology - Measurement Development

Measurement items were adapted from the literature wherever possible.

A pretest of the questionnaire: 6 experts in the IS area.

An online pilot study : two professors, three Ph D. students and 20 master students.

The dependent variables in this study are two characteristics of knowledge sharing:

the quantity of knowledge sharing

the quality of knowledge shared (knowledge quality)

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Research Methodology - Survey Administration

The research model was tested with data from members of one professional virtual community called BlueShop.

A banner with a hyperlink connecting to our Web survey was posted on the homepage of the BlueShop from July 11 to August 18, 2005.

Thirty randomly selected respondents were offered an incentive in the form of cash of $20.

The exclusion of 26 invalid questionnaires resulted in a total of 310 complete and valid ones for data.

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Research Methodology - Data analysis

Data analysis utilized a two-step approach as recommended by Anderson and Gerbing (1988):

Measurement model

Structural model

For a measurement model to have sufficiently good model fit: The chi-square value normalized by degrees of freedom (χ2/df) should

not exceed 3

Non-Normed Fit Index (NNFI) should exceed 0.9

Comparative Fit Index (CFI) should exceed 0.9

For the current CFA model, χ2/df was 1.96 (χ2=1194; df=610), NNFI was 0.93, and CFI was 0.94, suggesting adequate model fit.

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Research Methodology - Data analysis

Reliability was examined using the composite reliability values. The composite reliabilities of the constructs ranged between 0.82 and 0.93.

The convergent validity of the scales was verified by using two criteria suggested by Fornell and Larcker (1981):

(1) all indicator loadings should be significant and exceed 0.7

(2) average variance extracted (AVE) by each construct should exceed the variance due to measurement error for that construct (i.e., AVE should exceed 0.50).

For the current CFA model, all loadings were above the 0.7 threshold. AVE ranged from 0.61 to 1.00.

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Research Methodology - Data analysis

The discriminant validity of the scales was assessed using the guideline suggested by Fornell and Larcker:

the square root of the AVE from the construct should be greater than the correlation shared between the construct and other constructs in the model.

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Research Methodology - Data analysis

The structural model reflecting the assumed linear, causal relationships among the constructs was tested with the data collected from the validated measures.

The model fit indices were within accepted thresholds:

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Research Methodology - Data analysis

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Discussion and Implications - Summary of Results

The results indicate that community-related outcome expectations play an important role underlying knowledge sharing in terms of both quantity and quality.

Personal outcome expectations have a negative but insignificant effect on quantity of knowledge sharing:

It suggests that individuals contribute less knowledge, even though they expect that knowledge sharing will produce desirable consequences to them.

One possible explanation for this finding might be that when the impact of community-related outcome expectation is taken into account, knowledge contributors are more concerned about the successful functioning, survival, and growth of the virtual communities than the benefits that will produce to themselves.

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Discussion and Implications - Summary of Results

The study shows that social interaction ties, reciprocity, and identification increased individuals’ quantity of knowledge sharing but not knowledge quality.

Tsai and Ghoshal (1998) found that social interaction ties had a strong effect on trust in the context of resource exchange and production innovation within the organization.

According to Blau (1964), norm of reciprocity builds trust, which in turn is centrally important to social exchange relationships.

Accordingly, a possible explanation for the findings may be that social interaction ties, norm of reciprocity, and identification have indirect effects on knowledge quality via trust.

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Discussion and Implications - Summary of Results

Trust did not have a significant impact on quantity of knowledge sharing.

One possible explanation may be that individuals are willing to share their personal knowledge due to close and frequent interaction among members, fairness in exchanging knowledge, and strong feelings toward the virtual community, without necessarily trusting other members in the virtual community.

Another possible explanation is that trust is not crucial in less risky knowledge sharing relationships.

Coleman (1990) argued that only in risky situations do we need trust.

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Discussion and Implications - Summary of Results

Shared language did not have a significant impact on quantity of knowledge sharing

Shared vision had a negative and strong influence on quantity of knowledge sharing.

One plausible explanation is that with shared language and vision, contributors focus more on quality rather than quantity of contributions.

This implies that they may not contribute just for the sake of contribution but may be more concerned about their quality of contribution.

An avenue for future research is to examine why a negative relationship between shared vision and quantity of knowledge sharing exists in the virtual community settings.

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Discussion and Implications - Limitations

Whether our findings could be generalized to all types of professional virtual communities is unclear.

The results may have been impacted by self-selection bias. Our sample comprises only active participants.

This study examined only one aspect of knowledge exchange—knowledge sharing.

Fourth, the data presented are cross-sectional. The development of social capital leading to knowledge sharing is an ongoing phenomenon.

This study focused on the paths from six facets of social capital to knowledge sharing

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Discussion and Implications - Implications for Theory

Outcome expectations can contribute to knowledge sharing to some extent, but it is the social capital factors that lead to greater level of knowledge sharing in terms of quantity or quality.

Prior research suggests that a greater level of knowledge sharing may lead to better development of social interaction ties, mutual trust, identification, and shared vision.

Future research should look at changes in social capital and outcome expectations over time and the relationships of those changes to knowledge sharing.

Later studies should explore what factors influence the facets of social capital in the virtual community setting.

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Discussion and Implications - Implications for Theory

The results imply that individuals are less concerned about the desirable consequences that knowledge sharing will produce to them.

According to social exchange theory, however, individuals will behave according to rational self-interest.

Therefore, knowledge sharing will be stimulated when its rewards exceed its cost (Kankanhalli et al. 2005).

Thus, another direction for future research is to examine whether reward systems are useful in motivating an individual to share knowledge in the virtual community and what form of reward or incentive plays a significant role.

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Discussion and Implications - Implications for Practice

Social interaction ties were significant predictor of individuals’ knowledge sharing in terms of quantity.

Managers should develop strategies or mechanisms that encourage the interaction and the strength of the relationships among members.

Held face-to-face meetings or seminars.

Invited top knowledge contributors and professional instructors to share their knowledge and experience with members of the community.

Personal message boards and blogs.

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Discussion and Implications - Implications for Practice

Managers of virtual communities can encourage reciprocity by using extrinsic motivators such as rewards for sharing knowledge.

For example, the BlueShop community provides a mechanism that knowledge receivers can donate value-added points (VP) to knowledge contributors as a return of favors.

Earning VP by contributing knowledge can be considered as an approach to forcing an individual to reciprocate the benefits he or she received from others.

The VP may represent knowledge contributors’ status and reputation within the community and can also be changed into monetary rewards from the community.

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Discussion and Implications - Implications for Practice

Creating and maintaining a set of core and experienced individuals plays an important role in developing and sustaining a professional virtual community.

Raising these core knowledge contributors’ identification with the virtual community is one of the approaches.

BlueShop provides a list of top knowledge contributors for each week and month, enhancing the contributors’ identification and also their reputation.

BlueShop posts information about job opportunities and outsourcing cases and help top and well-recognized knowledge contributors get those job opportunities and outsourcing cases.

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Discussion and Implications - Implications for Practice

The results suggest that trust plays an important role in increasing the quality of knowledge shared within virtual communities.

Research suggests that there are various types of trust and the development of trust is multi-staged in online communities (Ba, 2001)

Like the development of trust, the development of knowledge sharing is also multi-staged.

Quantity of knowledge sharing may be the major concern at the early stage of a virtual community’s development

Knowledge quality may be the major concern when the community becomes more mature.

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Discussion and Implications - Implications for Practice

Community-related outcome expectation plays an important role in knowledge sharing.

The development and maintenance of virtual communities depend not only on members’ knowledge sharing but also managers’ strategies for running the virtual communities.

For example, BlueShop’s strategy is to become members of famous alliance programs,

receive online advertising cases, and

win awards of excellent virtual communities to enhance its reputation

meet members’ expectation of its sustenance and growth.

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The End!Thank You Very Much !