Abstract: Despite the fact that Knowledge Sharing (KS) is very important, we found only little discussion about the reasons why people have the willingness to share knowledge at such platform even though there is no immediate benefit to the persons who contribute knowledge in it. The aim of this study is to develop an integrative understanding of the factors that support or inhibit individuals- knowledge sharing intentions in virtual communities and to find whether habit would generate people-s willingness to be involved. We apply Social Capital Theory (SCT), and we also add two dimensions for discussion: member incentive and habitual domain (HD). This research assembles the questionnaire from individuals who have experienced knowledge sharing in virtual communities, and applies survey and Structural Equation Model (SEM) to analyze the results from the questionnaires. Finally, results confirm that individuals are willing to share knowledge in virtual communities: (1) if they consider reciprocity, centrality, and have longer tenure in their field, and enjoy helping. (2) if they have the habit of sharing knowledge. This study is useful for the developers of virtual communities to insight into knowledge sharing in cyberspace.
Abstract: This study aims to examine the factors affecting
knowledge sharing behavior in knowledge-based electronic communities (e-communities) because quantity and quality of
knowledge shared among the members play a critical role in the community-s sustainability. Past research has suggested three
perspectives that may affect the quantity and quality of knowledge
shared: economics, social psychology, and social ecology. In this
study, we strongly believe that an economic perspective may be suitable to validate factors influencing newly registered members-
knowledge contribution at the beginning of relationship development.
Accordingly, this study proposes a model to validate the factors influencing members- knowledge sharing based on Transaction Cost
Theory. By doing so, we may empirically test our hypotheses in various types of e-communities to determine the generalizability of our research models.