Using a Computational Model to Understand Possible Sources of Skews in Distributions of Job Performance
通过动态计算模型和蒙特卡洛模拟，研究运气、乘法组合、马太效应和反馈效应如何导致工作绩效分布出现正偏态，对理解绩效评估和人力资源管理有参考价值。
The typical assumption that performance is distributed normally has come under question in recent years (e.g., O'Boyle & Aguinis, 2012). This paper uses a dynamic, computational model of performance‐as‐results to examine possible sources of such distributions. That is, building off the classic model of job performance (Campbell & Pritchard, 1976), components of a dynamic model are examined in 4 separate experiments using Monte Carlo simulations. The experiments indicate that positively skewed distributions can arise from pure luck, multiplicative combinations of factors where 1 of those factors has a zero origin, Matthew effects associated with learning, and feedback effects of performance on resource allocation policies by external agents. The results are discussed in terms of explanations for positively skewed performance distributions and the use and expansion of the computational model for examining dynamic performance more generally.