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| Knowledge Transfer Model of a Network Q&A Community: An Empirical Analysis Based on the AskMe Portion of MetaFilter Data |
| Xia Lixin, Yang Jinqing, Ye Guanghui, Cheng Xiufeng |
| School of Information Management, Central China Normal University, Wuhan 430079 |
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Abstract To explore the influencing factors and mechanisms of user knowledge transfer in Q&A community networks, this study proposes a knowledge transfer model for Q&A communities by introducing social network analysis (SNA). This study first analyzes the interpersonal knowledge network structure of the MetaFilter Q&A community while considering net density, structural holes, central potential, average shortest path, and the clustering coefficient of SNA. The study then inspects the structures of the following four types of networks and their effects on knowledge transfer in the Q&A community: strong-and-sparse ties, weak and tight ties, strong and tight ties, and weak and sparse ties. The results of the study indicate the following. (1) Both strong-and-sparse and weak-and-tight ties networks contribute to knowledge transfer in a Q&A community. (2) When the restrictiveness of structural holes is lower and the network is sparser, users can more easily gain access to heterogeneity knowledge sources, and the average shortest path is shorter. In addition, the small-world effect is more obvious, and users can more effectively exchange knowledge. The clustering coefficient has a two-part effect in which it is neither higher nor lower. When it approximates the results of the entire network, it contributes to knowledge transfer. Furthermore, a higher central potential indicates that the relations of users are very close, and thus it has a greater influence on knowledge transfer. (3) Although the SNA index has a decisive role in knowledge transfer, some indices have a joint effect. An efficient network may still have several low indices. In summary, this study explores the influence of different network structures on knowledge transfer to enable network structures to be adjusted based on index coefficients, thereby improving the efficiency of knowledge transfer on Q&A communities.
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Received: 25 September 2018
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