Will they stay or will they go? How network properties of WebICs predict dropout rates of valuable Wikipedians

This paper contributes to our understanding of an increasingly prevalent work system, web-based internet communities (WebICs). We are particularly interested in how WebICs are governed given the fact how different they are compared to more classical forms of organization. We study the governance of a WebIC by studying the structure and dynamics of their edit network. Given the fact that the edit network is a relational structure, social network analysis is key to understanding these work systems. We demonstrate that characteristics of the edit network contribute to predicting the dropout hazard of valuable WebIC members. Since WebICs exist only thanks to the activity of their contributors, predicting drop-outs becomes crucial. The results show that reputation and controversy have different effects for different types of Wikipedians; i.e., an actor’s reputation decreases the dropout hazard of active Wikipedians, while participation on controversial pages decreases the dropout hazard of highly active Wikipedians.