Abstract:
Purpose – The aim of this research is to investigate the impact of perceived usefulness of AI recruitment processes (PU-AIRP) on candidate perceptions of utility (PUT), fairness (PF), and privacy (PP), and how these factors influence organizational attractiveness (OA). It further examines the mediating roles of PF, PP, and PUT, and explores whether candidate experience with AI recruitment (E-AIRP) moderates the relationship between PU-AIRP and PUT.
Design/methodology/approach – A quantitative methodology was employed using an online survey targeting job seekers and employees with exposure to AI-based recruitment tools such as applicant tracking systems (ATS), chatbots, or automated screening. Data were collected in early 2025, yielding 221 valid responses from participants across Lebanon, the GCC, Europe, and the United States. Structural relationships were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4, assessing both direct and indirect effects, as well as the moderating role of experience. Findings – The results show that PU-AIRP positively influences candidates’ perceptions of fairness and PUT but negatively affects PP. Fairness and utility significantly contribute to OA, while privacy does not. Additionally, candidates with E-AIRP strengthens the positive relationship between PU-AIRP and PUT but has no direct effect. Mediation analysis confirms that fairness and utility serve as key intermediaries in shaping candidate perceptions, while privacy does not.
Research limitations/implications – Although the study is limited to a cross-sectional sample and self-reported data, it offers valuable insights into how candidates form ethical and practical evaluations of AI in recruitment. It supports organizations in designing AI hiring systems that balance efficiency with fairness, transparency, and candidate trust. Originality/value – This research contributes to the growing literature on AI ethics in HR by integrating both utilitarian and deontological perspectives into a single candidate-centered framework. It is among the first to model fairness, privacy, and utility as mediators in AI recruiting perceptions while testing the moderating influence of prior experience.
