Team Performance: Nature and Antecedents of Nonnormal Distributions

• 成果类型: Article
• 署名作者: Bradley, Kyle J.; Aguinis, Herman
• 署名单位: Kansas State University; George Washington University
• 刊物名称: ORGANIZATION SCIENCE
• ISSN/ISSBN: 1047-7039
• DOI: 10.1287/orsc.2022.1619
• 发表日期: 2023
• 页码: 1266-1286
• 关键词: TEAM PERFORMANCE; incremental differentiation; performance distribution

摘要

Team research typically assumes that team performance is normally distributed: teams cluster around average performance, performance variability is not substantial, and few teams inhabit the upper range of the distribution. Ironically, although most team research and methodological practices rely on the normality assumption, many theories actually imply non -normality (e.g., performance spirals, team composition, team learning, punctuated equilibrium). Accordingly, we investigated the nature and antecedents of team performance distributions by relying on 274 performance distributions including 200,825 teams (e.g., sports, politics, fire-fighters, information technology, customer service) and more than 500,000 workers. First, regard-ing their overall nature, only 11% of the distributions were normal, star teams are much more prevalent than predicted by normality, the power law with an exponential cutoff is the most dominant distribution among nonnormal distributions (i.e., 73%), and incremental differentia-tion (i.e., differential performance trajectories across teams) is the best explanation for the emer-gence of these distributions. Second, this conclusion remained unchanged after examining theory-based boundary conditions (i.e., tournament versus nontournament contexts, perform-ance as aggregation of individual-level performance versus performance as a team-level con-struct, performance assessed with versus without a hard left-tail zero, and more versus less sample homogeneity). Third, we used the team learning curve literature as a conceptual frame-work to test hypotheses and found that authority differentiation and lower temporal stability are associated with distributions with larger performance variability (i.e., a greater proportion of star teams). We discuss implications for existing theory, future research directions, and methodologi-cal practices (e.g., need to check for nonnormality, Bayesian analysis, outlier management).