A Dynamic Systems Theory of Intrateam Conflict Contagion

• 成果类型: Article
• 署名作者: Somaraju, Ajay V.; Griffin, Daniel J.; Olenick, Jeffrey; Chang, Chu-Hsiang (Daisy); Kozlowski, Steve W. J.
• 署名单位: Michigan State University; Old Dominion University; State University System of Florida; University of South Florida; Michigan State University
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/apl0001172
• 发表日期: 2024
• 页码: 871-896
• 关键词: conflict contagion; team conflict; TEAM PROCESSES; computational modeling; Conflict resolution

摘要

Recognizing the challenges that conflict poses, organizational researchers have invested considerable energy toward investigating the processes by which conflict occurs and spreads within a team. However, current theoretical frameworks of conflict contagion posit a static growth trajectory in which members become engaged in conflict and stay in conflict. While this trajectory is certainly possible, the broader conflict literature outside of the organizational sciences has shown evidence for a more varied set of potential trajectories of conflict contagion. To advance theory on team conflict, we integrate conflict research from micro-level (interpersonal) to macro-level (interstate) perspectives into a formal theory of intrateam conflict contagion. Drawing from conflict stage and social contagion theory, we theorize that team members move through three stages of conflict (disengaged, at-risk, engaged) at rates determined by four process mechanisms (faultlines, forgiveness, frustration, integration) such that disengaged individuals become at-risk of engaging in conflict, engage in conflict, then disengage, only to potentially become at risk of reengaging at a later point in time. Using computational modeling, we demonstrate the generative sufficiency of our theory to account for conflict trajectories observed in the broader conflict literature. To facilitate the interpretation of such trajectories, we present a typology of contagion trajectories, discuss the dynamic properties of these trajectories (e.g., stability, bifurcations), and provide implications for future theory building and practice.