What Is Below the Surface? Rejected Applicant Reactions Toward AI/HM-Based Hiring
Lingjun Zhou, Jin Song Li, Jie Cao · Academy of Management Proceedings · 2024
The increasing use of artificial intelligence (AI) has maintained the high disparity in the applicant-to-hire ratio, elevating rejected applicant reactions to a significant issue. However, little is known about how applicants react to AI-based rejection. Drawing on appraisal theory, we propose that applicants rejected by AI tend to appraise the rejection as other-responsibility, leading to heightened negative emotions and more adverse reactions against the organization. Using a person-centered approach and mix-method design in two studies, we identify four distinct profiles based on the combination of outward-focused negative emotions (OFNE) and inward-focused negative emotions (IFNE). Specially, we argue and test that introducing a human manager to review AI-based rejection can soothe rejected applicants. Our finding shows that involving human touch can shift applicants from toxic to harmless profiles, leading to better reactions towards the organization. Theoretical and practical implications for applicant reactions in the novel context of AI-based hiring processes are discussed.