Artificial Intelligence in Talent Acquisition: Procedural Justice, Organizational Attractiveness, and Perceived Competitive Positioning in Digital Talent Markets

This study examines AI-enabled talent acquisition from the perspective of prospective applicants and investigates how individual-level perceptions of AI-assisted selection are associated with anticipated procedural justice, organizational attractiveness, and perceived talent-market competitive positioning. Using a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, the proposed model explores the relationships among six key constructs: perceived AI expertise (AIEXP), trust in AI technology (TRUSTP), procedural justice (PJ), anxiety (ANX), organizational attractiveness (OA), and perceived talent-market competitive positioning (PTCP). Based on data collected from 202 respondents, predominantly aged 18–24, the results indicate significant positive associations of perceived AI expertise and trust in AI with anticipated procedural justice. Procedural justice exhibits the largest structural association with organizational attractiveness, which, in turn, is strongly associated with perceived talent-market competitive positioning. The association between anxiety and organizational attractiveness is negative but not statistically significant in the present sample. Beyond individual direct relationships, bootstrapped specific indirect effects support a sequential mechanism in which perceived AI expertise and trust in AI are associated with organizational attractiveness through anticipated procedural justice and, subsequently, with perceived talent-market competitive positioning through procedural justice and organizational attractiveness. The study contributes an integrative sequential perceptual evaluation framework that connects technology-related appraisals, anticipated process legitimacy, employer attractiveness, and perceived talent-market competitive positioning under the ex-ante condition of prospective applicant evaluations.