From Lurkers to Workers: Predicting Voluntary Contribution and Community Welfare

• 成果类型: Article
• 署名作者: Kokkodis, Marios; Lappas, Theodoros; Ransbotham, Sam
• 署名单位: Boston College; Stevens Institute of Technology
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2019.0905
• 发表日期: 2020
• 页码: 607-626
• 关键词: generated content evidence; user content generation; ONLINE COMMUNITIES; VIRTUAL COMMUNITIES; Social media; PARTICIPATION; BEHAVIOR; success; internet; determinants

摘要

In an online community, users can interact with fellow community members by voluntarily contributing to existing discussion threads or by starting new threads. In practice, however, the vast majority of a community's users (approximate to 90%) remain inactive (lurk), simply observing contributions made by intermittent (approximate to 9%) and heavy (approximate to 1%) contributors. Our research examines increases and decreases of types of user engagement in online communities using hidden Markov models. These models characterize latent states of user engagement from trace user activity or lack of activity. The resulting framework then differentiates lurkers who can later become workers (i.e., engaged in the community) from those who will not. Differentiating lurkers who can be engaged from those who cannot enables managers to anticipate and proactively direct their resources toward the users who are most likely to become or remain workers (i.e., heavy contributors), thereby promoting community welfare. Analysis of 533,714 posts from an online diabetes community shows that incorporating latent user engagement variables can significantly improve the accuracy of welfare prediction models and guide managerial interventions. Application of our framework to five additional communities of various contexts demonstrates its generalizability.