Performance expectancy of AI and disclosure consequences: A psychological paradox

This study examines how AI-assisted hiring systems shape job seekers' motivational cognitions and how AI disclosure influences evaluative judgments. Specifically, it investigates how applicants' perceived performance expectancy of AI-assisted hiring systems influences their own performance expectancy of Generative AI tools and their subsequent intention to use them in crafting job applications, and hence how the disclosure of Generative AI assistance affects hiring managers' perceptions of applicants' pre-hire outcomes i.e. likeability and hireability, within AI-mediated selection contexts. Drawing on a cognitive-motivational lens, two experimental studies were conducted. Study 1 demonstrates a spillover effect whereby favorable perceptions of AI-assisted hiring enhance applicants' performance expectancy of Generative AI, thereby increasing their intention to adopt such tools. Study 2 reveals a psychological paradox: while disclosure of Generative AI use signals transparency, it simultaneously alters evaluators' social and competence-based judgments, producing differentiated effects on likeability and hireability compared to non-assisted applicants. Although experimental designs ensure internal validity, they may not fully capture the complexity of real-world hiring dynamics; future research could examine longitudinal consequences of AI disclosure norms and their implications for trust and psychological contracts in AI-enabled organizations. By highlighting the tension between technological efficiency and social evaluation, this study contributes to understanding the psychological mechanisms underlying AI adoption and assessment, offering practical insights for designing fair and psychologically sustainable AI-mediated recruitment systems.