Applicant Perceptions of AI Recruitment and Organizational Attractiveness: The Person-Environment Fit Mechanism from an Algorithm Aversion Perspective

Abstract

The increasing adoption of artificial intelligence (AI) in recruitment has raised important questions about its unintended consequences for talent attraction. While prior research has emphasized the efficiency and accuracy benefits of AI-driven hiring, limited attention has been given to how such practices are interpreted by job applicants and how these interpretations shape organizational evaluations. Drawing on signaling theory and person–environment fit theory, this study conceptualizes AI recruitment as an ambivalent organizational signal that simultaneously conveys efficiency and impersonality. It proposes that applicant perceptions of AI recruitment influence organizational attractiveness through perceived person–environment fit, which functions as a key interpretive mechanism linking technological signals to organizational evaluations. Furthermore, this process is theorized to be contingent upon both individual and situational factors, such that technology anxiety amplifies negative interpretations, whereas perceived job technicity attenuates them. A between-subjects experimental design with 300 job seekers provides empirical support for the proposed model. Results indicate that perceived AI recruitment is negatively associated with organizational attractiveness and that this relationship is partially mediated by person–environment fit. In addition, the indirect effect is stronger among individuals with higher levels of technology anxiety and weaker when the job is perceived as highly technical. This study makes three contributions. First, it shifts the focus of AI recruitment research from organizational outcomes to applicant-centered interpretive processes. Second, it advances theory by identifying person–environment fit as a central mechanism through which ambivalent technological signals are translated into organizational evaluations. Third, it extends research on algorithm aversion by demonstrating that responses to AI in recruitment are systematically shaped by both individual differences and task characteristics. Overall, the findings highlight a critical paradox: technologies adopted to enhance efficiency and objectivity may simultaneously undermine organizational attractiveness. These insights contribute to a more nuanced understanding of AI adoption in recruitment and its implications for talent acquisition.