Applicant Perceptions of Hiring Algorithms - Uniqueness and Discrimination Experiences as Moderators
Chris Kaibel, Irmela Koch‐Bayram, Torsten Biemann, Max Mühlenbock · Academy of Management Proceedings · 2019
Organizations make increasingly use of algorithms for selection decisions. While previous research generated important insights on algorithms’ predictive power in this setting, not much research exists on applicants’ reactions to algorithm-based hiring decisions and the role of individual differences for this relationship. Building on a framework of procedural justice and applicant attribution-reaction theory, we study whether algorithm-based hiring decisions help organizations attracting and recruiting a diverse workforce with unique talents. In two experimental studies, we examine the effects of algorithm vs. human decision-makers on applicants’ perceptions of the selection process and organizational attractiveness. We find a negative effect of algorithm-based decisions on organizational attractiveness and personableness of the selection process. Furthermore, we find some support for moderating effects of discrimination experiences and applicants’ perceived uniqueness. While applicants that have made discrimination experiences tend to view algorithm-based decisions more positively, applicants with perceived unique career experiences and beliefs tend to view algorithm-based decisions more negatively. Implications and future research directions are discussed.