Algorithms in Personnel Selection, Applicants’ Attributions and Organizational Attractiveness

Abstract

Machine-learning algorithms used in personnel selection procedures seem to be a promising avenue for companies for several reasons. In our manuscript, we investigate the reasons (prospective) employees attribute regarding why an organization uses algorithms in the employee selection process. Based on the HR attributions framework, signaling theory, and the scant literature that exists on the perceptions of algorithmic and human decision-makers, we theorize that using algorithms affects the four different internal HR attributions of intent and, in turn, organizational attractiveness. In two experimental studies, we test our hypotheses in the initial applicant screening stage. The results of our experimental studies indicate that control-focused attributions such as cost saving and applicant exploitation are stronger when algorithms are used, whereas commitment-focused attributions such as quality enhancement and applicant well-being are stronger when human experts make selection decisions in the applicant screening process. We also find that algorithms have a negative effect on organizational attractiveness that can be partly explained by these attributions. Our findings have far reaching implications for practitioners and academics.