As artificial intelligence (AI) systems are increasingly adopted in recruitment practices, applicants’ responses to AI-mediated interviews have become an important issue for organizations. Understanding how applicants interpret these systems is relevant for organizational attractiveness and employer branding. Drawing on social exchange theory and signaling theory, this study examines the role of AI interview explainability in shaping applicants’ evaluations of organizations. It proposes that explainability influences organizational attractiveness through two parallel mechanisms: perceived organizational support and perceived innovativeness. Survey data were collected from 196 job applicants with experience in AI-based interviews. The results show that higher perceived explainability of AI interviews is associated with stronger perceptions of organizational support and organizational innovativeness. Both perceptions are positively related to organizational attractiveness. These findings support a dual-mediation model and suggest that explainable AI interview systems communicate both supportive intentions and technological capability to applicants. By focusing on applicants’ perceptions, this study contributes to the growing literature on AI use in human resource management. It highlights the importance of explainable system design in shaping early applicant reactions. The findings also provide practical implications for organizations seeking to implement AI-based recruitment tools that are transparent, credible, and attractive to potential applicants.
Zhou et al. (Mon,) studied this question.