Understanding Knowledge Sharing in Virtual Communities: An Integration of Social Capital and Social

1
Understanding Knowledge Sharing in Virtual
Communities An Integration of Social Capital
and Social Cognitive Theories

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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

3
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

4
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).

5
Introduction

• Core of Social Cognitive Theory are self-efficacy
  and outcome expectations.
• Self-efficacy is a judgment of ones ability to
  organize and execute given types of performances
  .
• Outcome expectation is a judgment of the likely
  consequence such performances will produce .

6
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 individuals behavior

7
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
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 persons 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 contributors 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 contributors 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 individuals 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 individuals 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 (?2/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, ?2/df was 1.96
  (?21194 df610), 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
37
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
  exchangeknowledge 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
  communitys 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, BlueShops 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 !