Algorithms in personnel selection, applicants' attributions about organizations' intents and organizational attractiveness: An experimental study

Abstract

Machine-learning algorithms used in personnel selection are a promising avenue for several reasons. We shift the focus to applicants' attributions about the reasons why an organization uses algorithms. Combining the human resources attributions model, signaling theory, and existing literature on the perceptions of algorithmic decision-makers, we theorize that using algorithms affects internal attributions of intent and, in turn, organizational attractiveness. In two experiments (N = 259 and N = 342), including a concurrent double randomization design for causal mediation inferences, we test our hypotheses in the applicant screening stage. The results of our studies indicate that control-focused attributions about personnel selection (cost reduction and applicant exploitation) are much stronger when algorithms are used, whereas commitment-focused attributions (quality enhancement and applicant well-being) are much stronger when human experts make selection decisions. We further find that algorithms have a large negative effect on organizational attractiveness that can be partly explained by these attributions. Implications for practitioners and academics are discussed.