Reducing Adverse Impact by Hiring on Vocational Interests: A Pareto-Optimal Approach

• 成果类型: Article; Early Access
• 署名作者: Wee, Serena; Newman, Daniel A.; Song, Q. Chelsea; Tang, Chen
• 署名单位: University of Western Australia; University of Illinois System; University of Illinois Urbana-Champaign; Indiana University System; IU Kelley School of Business; Indiana University Bloomington; American University
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/apl0001317
• 发表日期: 2025
• 关键词: adverse impact; Pareto-optimal weighting; personnel selection; vocational interests; vocational disinterests

摘要

In the study of personnel selection to enhance organizational diversity, Pareto-optimal predictor weights are designed to simultaneously optimize the diversity and job performance of new hires. One aspiration for this approach is to access stronger combinations of diversity and performance outcomes by shifting the diversity-validity trade-off curve outward. The current work examines the role of a particular set of predictors-vocational interests-for their capacity to shift the Pareto trade-off curve outward, creating superior diversity-validity outcome pairings. Empirical results based on meta-analytic estimates suggest that novel diversity benefits (at no loss in terms of validity) can be observed in two sets of scenarios: (a) when selecting on high levels of social or conventional vocational interests (i.e., when individuals enjoy social or conventional tasks) specifically when such interests are relevant to the job, and (b) when selecting on high levels of realistic, investigative, or artistic disinterests (i.e., when individuals find realistic, investigative, or artistic tasks aversive) specifically when such disinterests are relevant to the job. Implications for improving diversity through hiring on vocational interests and vocational disinterests, while simultaneously optimizing on job performance, are discussed.