Network centrality and contributions to online public good - The case of Chinese Wikipedia
|Network centrality and contributions to online public good - The case of Chinese Wikipedia|
|Author(s)||Wang C., Zhang X.|
|Published in||Proceedings of the Annual Hawaii International Conference on System Sciences|
|Keyword(s)||Unknown (Extra: Centrality measures, Collaboration network, Collaborative technologies, Dynamic collaborations, Effort allocation, Internet technology, Knowledge production, Network economics, Online collaboration, Panel data, Public goods, Social network theory, Theoretical prediction, Virtual collaboration, Wikipedia, Systems science, Websites)|
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Network centrality and contributions to online public good - The case of Chinese Wikipedia is a 2011 conference paper written in English by Wang C., Zhang X. and published in Proceedings of the Annual Hawaii International Conference on System Sciences.
Internet technology enables virtual collaboration and plays an important role in knowledge production. However, collaborative technology will not function without conducive underlying social mechanisms. Previous research mostly investigates individual-level motivations of editors, with only a few exceptions examining the collaboration relationships. In this paper, we take a structural perspective and investigate the impact of positions (centralities) of editors in the collaboration networks on their efforts and effort allocations. To achieve this, we empirically reconstruct the dynamic collaboration network of Chinese Wikipedia for the period between 2002 and 2007. Based on a dynamic view of the network, we compose a panel data set that covers both the contribution behavior and network position characteristics of Wikipedia editors. We strengthen our causal interpretation by leveraging the exogenous block that prevented Wikipedia editors in Mainland China from accessing the website. We find distinctive effort allocation patterns that strongly correlate with network centrality measures. This confirms theoretical predictions derived in recent developments in network economics and social network theories. This research enhances our understanding about how collaboration network structure shapes individuals' behavior in online collaboration platforms.
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