Is Fairness Enough? the Impact of Expectation Disconfirmation on Job Pursuit Intention in AI-Enabled Recruitment

Engineering and technology intensive organizations increasingly adopt AI-enabled recruitment tools to improve selection efficiency, but challenges related to algorithmic fairness and trust influence applicant responses. Despite the use of objective and fair algorithms, applicants may still perceive unfairness, but empirical research on this phenomenon remains limited. Based on expectation disconfirmation (ED) theory, this study defines the gap between applicants' self-assessed qualifications and AI-generated person-job fit scores as person-job fit ED and estimates how it affects job pursuit intention through perceptions of procedural justice. To ensure research rigor, we employed a two-stage online scenario-based experiment, implemented attention checks, and conducted exploratory and confirmatory factor analyses to validate construct reliability and validity. Multiple procedural and statistical remedies were also applied to address potential common method bias. Empirical results show that ED significantly reduces job pursuit intention. Besides, procedural justice partially mediates this relationship, suggesting that weakened fairness perceptions serve as a critical mechanism underlying the negative effect of expectancy disconfirmation. Furthermore, trust in AI exacerbates the negative impact of ED in some cases. These insights deepen the understanding of how AI-generated evaluations shape individuals' behaviors and perceptions. This study extends the applicability of ED theory to algorithmic recruitment contexts and offers novel theoretical and practical implications.