Subcoalition Cluster Analysis: A New Method for Modeling Conflict in Organizations
提出子联盟聚类分析（SCA）框架，用于从观测数据中识别组织中不可观测的子联盟结构，并应用于两个政治决策案例，展示其作为因变量和自变量的用途。
The renewed interest among managers and management researchers in stakeholder governance has also underscored the shortage of quantitative methods available for studying business firms as political systems. A critical problem for researchers applying coalition-based theories of organizational politics to observational data is that the units of analysis, coined “subcoalitions,” are often unobservable. This paper introduces subcoalition cluster analysis (SCA) as a new computational framework for analyzing intrafirm conflict that permits researchers to model groups of heterogeneous actors in terms of a smaller set of representative subcoalitions. The SCA approach to identifying latent fault-lines among groups of actors is based on widely held ideas in management research about the structure of intrafirm coalition politics, is computationally practicable in settings with many alternatives or actors, and can be straightforwardly applied to observational settings with incomplete data on actor preferences. We then apply SCA to two cases in which an organization characterized by multiple, partially inconsistent goals and stakeholders with heterogeneous preferences face a politically contested decision. In both cases, we first analyze preference data using SCA to identify the subcoalition structure that best characterizes the set of actors in the organization. We then use the SCA output as a dependent variable in an analysis of the predictors of subcoalition membership in the first case and as an independent variable in an analysis of the changing patterns of social influence in the second. This paper was accepted by Lamar Pierce, organizations. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2020.00013 .