Resolving governance disputes in communities: A study of software license decisions

Abstract

Research summary Resolving governance disputes is of vital importance for communities. Gathering data from GitHub communities, we employ hybrid inductive methods to study discussions around initiation and change of software licenses—a fundamental and potentially contentious governance issue. First, we 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, we 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 summary Online 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. Our 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.

The Making of Convergence: Knowledge Reuse, Boundary Spanning, and the Formation of the ICT Industry

“…individual-level forces at play that initiate and drive the phenomenon at large (e.g., [14], [15]). Similarly, the literature on strategy and organization commonly calls for "grassroot analysis" of complex phenomena, for example, how individual-level factors or "microfoundations" impact organizations, and how this interaction may come to shape emergent, collective, and organizational-level outcomes (see [16]- [19]). Indeed, a core thesis of this current study is that decision makers need to consider microlevel activities in order to deal with macrolevel changes such as industry convergence.…”

Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?

“…Building theory from data, whether with large or small samples, requires pattern detection and pattern explanation. The procedure for conducting algorithm supported induction that we have outlined in this paper can help scholars identify robust patterns in data, which can then become inputs to abductive theory construction (He et al 2020). There are four key stages in this approach (Table 1): a split of the data into samples for pattern detection and hypothesis testing, the use of algorithms with tunable complexity to strike a well-considered balance between comprehension and prediction, the use of subsample replication to avoid overfitting even within the sample used for induction, and the imperative to create out-of-pattern tests in the hold-out sample.…”

On the strategic value of equifinal choice

“…This dire state can be abated by following developments in human-machine interaction, such as human-in-the-loop and active learning frameworks (Cohn et al 1996;Grønsund and Aanestad 2020). Effectively, these frameworks function as a double-loop learning organization (Argyris 1977;Rerup and Feldman 2011) in which the algorithms perform the dayto-day actions and managers handle exceptions (He et al 2020). In a double-loop learning organization, decisions are escalated to managers when strategic action is needed the most.…”

“…This allows, errors, and more importantly their minimization, to guide organization design (Shannon 1948;Tushman and Nadler 1978). A pursuit subsumed in the search for optimal solutions (i.e., optimization) to the problems of division of labor and integration of efforts (Puranam et al 2014), such as error minimization (Csaszar 2013), optimal preference aggregation (Csaszar and Eggers 2013), efficient problem solving (Glynn et al 2020), interdependency reduction (Puranam et al 2012), effective conflict resolution (He et al 2020), optimal policy search (Rivkin and Siggelkow 2003), among others (see Puranam 2018 for a review).…”