Brain drain? An examination of stereotype threat effects during training on knowledge acquisition and organizational effectiveness


Stereotype threat describes a situation in which individuals are faced with the risk of upholding a negative stereotype about their subgroup based on their actions. Empirical work in this area has primarily examined the impact of negative stereotypes on performance for threatened individuals. However, this body of research seldom acknowledges that performance is a function of learning—which may also be impaired by pervasive group stereotypes. This study presents evidence from a 3-day self-guided training program demonstrating that stereotype threat impairs acquisition of cognitive learning outcomes for females facing a negative group stereotype. Using hierarchical Bayesian modeling, results revealed that stereotyped females demonstrated poorer declarative knowledge acquisition, spent less time reflecting on learning activities, and developed less efficiently organized knowledge structures compared with females in a control condition. Findings from a Bayesian mediation model also suggested that despite stereotyped individuals “working harder” to perform well, their underachievement was largely attributable to failures in learning to “work smarter.” Building upon these empirical results, a computational model and computer simulation is also presented to demonstrate the practical significance of stereotype-induced impairments to learning on the development of an organization’s human capital resources and capabilities. The simulation results show that even the presence of small effects of stereotype threat during learning/training have the potential to exert a significant negative impact on an organization’s performance potential. Implications for future research and practice examining stereotype threat during learning are discussed.

Journal of Applied Psychology, 102, 115-150
James A. Grand
James A. Grand
Associate Professor, Psychology

A scientist at heart, an organizational psychologist by training, and a lucky dad and husband all the time.