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Generative AI in Recruitment: Implications for Recruiter Fairness, Professional Identity and Talent Acquisition
Abstract
The impact of Generative Artificial Intelligence (GenAI) on recruitment is undeniable, as it quickly creates new opportunities and challenges for recruiters and the recruitment process; however, HRM research has only recently begun to explore the implications of this technology for recruiters and talent acquisition processes. The purpose of this study is to examine the design and organisational implications of a GenAI-based recruitment assistant designed to facilitate the analysis of curriculum vitae (CVs) and evaluate candidates. The paper is inspired by organisational justice theory, role identity theory, and contingency theory and proposes a theoretical framework that examines how OCRs shape the perceptions of the recruiter concerning the perceived fairness of recruitment systems, role adaptation and talent acquisition outcomes. The study investigates the development and pilot implementation of a recruitment assistant which unifies the automated analysis of CVs with conversational access to the information about the candidate in a large enterprise environment, using a design-science approach. Initial implementation evidence indicates that the system enhances the effectiveness and uniformity of screening processes and transforms the recruiters' job from information processing tasks to verification, interpretation and judgment-based ones. The results also showed that having AI outputs that are transparent, explainable and still under human control increases acceptance by recruiters. The study advances the ongoing HRM research on the use of GenAI in recruitment by showing how GenAI can support rather than supplant the role of recruiters and highlighting the factors that affect GenAI's effectiveness in recruitment. Some practical tips are provided for HR leaders looking to incorporate generative AI in talent acquisition while staying fair, accountable and professional.
Article information
Journal
Journal of Business and Management Studies
Volume (Issue)
8 (9)
Pages
184-195
Published
Copyright
Copyright (c) 2026 Journal of Business and Management Studies
Open access

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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