Useful but Unjust? Examining how Perceived Usefulness and Willingness to Engage Shape Ethical Responses to Ai in Recruitment

Abstract Book of the 7th International Conference on Research in Human Resource Management

Year: 2025

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Useful but Unjust? Examining how Perceived Usefulness and Willingness to Engage Shape Ethical Responses to Ai in Recruitment

Muna Khoury, Abeer Al Shikh, Hadi Janem, Malak Tahboub

ABSTRACT:

This study investigates how job seekers evaluate the ethicality of AI-driven recruitment processes, focusing on the perceived fairness, transparency, and trustworthiness of algorithmic hiring systems, particularly in high-risk, underrepresented labor markets.
Anchored in the Unified Theory of Acceptance and Use of Technology (UTAUT) and organizational justice theory, we propose and test a conceptual model in which perceived AI-driven hiring exerts a negative influence on perceived ethicality, mediated by perceived usefulness and moderated by individuals’ willingness to engage with AI.
Utilizing a mixed-methods design, we surveyed over 500 job seekers in Palestine and conducted 20 in-depth interviews with HR professionals and AI developers.
Results reveal that AI’s perceived involvement undermines ethicality judgments, primarily by diminishing perceptions of usefulness. However, for individuals more open to engaging with AI, the negative effect is attenuated. Our findings contribute to emerging literature on algorithmic HRM by illuminating the complex mechanisms through which AI affects applicants’ trust and acceptance, especially in turbulent contexts. The results offer theoretical insights into the behavioral consequences of automation in recruitment. The paper also offers actionable insights for organizations aiming to design trustworthy and ethically aligned recruitment systems in volatile labor environments

Keywords: Algorithmic Hiring, Ethicality, Perceived Fairness, Perceived Transparency, Perceived Trust, Perceived Usefulness, Willingness to Engage