Generative Artificial Intelligence in Human Resource Management
Abstract
Generative Artificial Intelligence (GAI) addresses a critical challenge in Human Resource Management (HRM) by optimising and personalising HR processes, thereby enhancing decision-making and operational efficiency. This study traces the academic and conceptual development of GAI in HRM using a bibliometric approach informed by Strategic Human Resource Management (SHRM) and the Technology Acceptance Model (TAM). The analysis covers 262 articles indexed in the Web of Science between 1991 and 2024. The field shows an annual growth rate of 15.31 percent, an average of 24.38 citations per article, contributions from 827 authors and 48.89 % international co-authorship. Research activity accelerates after 2020, with 2024 as the most prolific year, producing 110 articles. Co-occurrence and thematic mapping reveal central clusters around artificial intelligence, performance, technology, and HRM, while ethically oriented themes such as bias, explainability and privacy remain underexplored. Interpreted through SHRM, bibliometric patterns highlight the strategic alignment of human capital and decision augmentation, while the TAM perspective extends beyond usefulness and ease of use to include ethical trust, transparency and algorithmic neutrality. For researchers, future priorities include causal and mixed-methods studies on bias, privacy, and transparency. For practitioners, organisational readiness in governance, data protection and capability building is critical for responsible adoption.