Effectiveness of Key Knowledge Spreader Identification in Online Communities of Practice: A Simulation Study from Network Perspective

Abstract

With the rapid development of online communities of practice (CoPs), how to identify key knowledge spreader (KKS) in online CoPs has grown up to be a hot issue. In this paper, we construct a network with variable clustering based on Holme-Kim model to represent CoPs, a simple dynamics of knowledge sharing is considered. Kendall's Tau coefficient is used to investigate the effectiveness of four typical KKS indicators from a network perspective. We also examine the relationship between knowledge acceptance rate (KAR) of members in CoPs and the effectiveness of indicators. The results conclude that variable clustering will lead to unstable fluctuation of the effectiveness of KKS indicators, and the change of KAR during knowledge sharing will lead to effectiveness of KKS indicators fluctuating with a U-shape. The conclusion suggests that indicators should be used under suitable conditions in practice to improve the efficiency of knowledge sharing.