Professor of Strategy
Dispute Resolution; Community Governance; Problem Solving; Machine Learning; Inductive Theorizing;
Research summary: Resolving governance disputes is of vital importance for communities. Gathering data from GitHub communities, the authors employ hybrid inductive methods to study discussions around initiation and change of software licenses - a fundamental and potentially contentious governance issue.First, the authors apply machine learning algorithms to identify robust patterns in data: resolution is more likely in larger discussion groups and in projects without a license compared to those with a license.Second, the authors analyze textual data to explain the causal mechanisms underpinning these patterns.The resulting theory highlights the group process (reflective agency switches disputes from bargaining to problem solving) and group property (preference alignment over attributes) that are both necessary for the resolution of governance disputes, contributing to the literature on community governance.Managerial summaryOnline communities play an increasingly important role in how companies innovate across organizational boundaries and attract talent across geographic locations. However, online communities are no Utopia; disputes abound even (more) when we collaborate virtually. In particular, governance disputes can threaten the functioning and existence of online communities.The authors' study suggests that governance disputes in online communities either unfold as bargaining over which solution is better or searching for a satisfactory solution. The latter is more likely to reach a resolution, when there is common ground.Companies interested in leveraging the power of online communities should (a) identify or train certain participants to transform endless bargaining into collective problem solving and (b) foster shared knowledge and value basis among participants through recruitment and strong organizational culture.