Collaboration Patterns Between Humans and Bots in an Online Knowledge Community: The Influence of Resources and Dependencies
研究了在线知识社区中人类与机器人如何基于资源和依赖关系形成协作模式，发现机器人也能主导任务流程，任务复杂度起调节作用，对管理人机协作有指导意义。
In online knowledge communities (OKCs), humans and bots collaborate at massive scale, yet how their distinct resources shape collaboration patterns remains insufficiently understood. In particular, it is unclear whether humans consistently direct collaborative processes or whether bots can assume leading roles. Grounded in coordination theory, this study applies gSpan and local process model mining to 200 Wikipedia articles to examine how flow, fit, and sharing dependencies shape human-leadoff and bot-leadoff collaboration patterns. The results show that bots can function as initiating actors, guiding task flows in ways comparable to humans. Fit and sharing dependencies are key pathways linking human and bot resources to collaboration patterns. Task complexity exerts a moderating influence, revealing boundary conditions for coordination in human–bot collaboration. This study extends coordination theory to open, human–bot collaboration contexts and provides actionable guidance for managing task dependencies and fostering effective collaboration between humans and bots in OKCs.