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Individual knowledge sharing behavior in dynamic virtual communities: the perspectives of network effects and status competition
Authors:Shenglei Pi  Weining Cai
Affiliation:1.School of Management, Guagnzhou University,Guangzhou,China;2.Great Wall Strategy Consultants,Beijing,China
Abstract:

Purpose

While most literature concerning knowledge sharing examines it as an organizational method for innovation and value creation, this paper considers online knowledge sharing as an individual behavior decision embedded in a virtual community. We attempt to explore which sharing behavior can help individual participants gain a better position in an online community, improving social status, reputation, and other social networking interests.

Design/methodology/approach

We collected and measured the knowledge sharing activities and discussion from a Chinese online expertise knowledge network in Business Management Consulting. We tested the mediating effects of the sharing behavior of the major members of the online knowledge network on members’ status (network centrality) in different time units (days).

Findings

In a dynamic virtual community, the direct result of knowledge sharing behavior is reflected in the individual status position (the degree of node centrality). At the same time, individual knowledge sharing behavior has an “inertia effect”: individual prior status (the degree of node centrality) affects current knowledge sharing behavior, while current knowledge sharing behavior affects current status in the knowledge network, forming an inertial circuit between personal behavior and network status.

Originality/value

We expound the theory of individual knowledge sharing in the context of an inter-person dynamic virtual community; we provide action “strategies” for individual knowledge sharing behavior choice, for better understanding the nature of individual knowledge behavior, and we also propose and test the “inertia effect” of knowledge sharing behavior and the knowledge network, and demonstrate the theory of network effects from an individual perspective.
Keywords:
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