Impact Impact of Artificial Intelligence on the Efficiency of Human Resource Management Practices: A Human-centered Approach
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The current study aimed to improve Human Resource Management (HRM) through the introduction of novel technologies. Furthermore, this study primarily focused to scrutinize the scientific studies carried out in recent years regarding the application of Artificial Intelligence (AI) in HRM. In order to enhance theoretical as well as empirical knowledge, the study also examined the evolution of conceptual, social, and intellectual structures within the field of HRM. To achieve the intended objectives, a bibliometric analysis was conducted with the help of VOS viewer and Excel for those articles and reviews indexed in the Scopus database. The study emphasized the analysis of research work carried out during the time period (1999-2024) that investigated AI utilization for HRM. The initial investigation found 761 such documents among which 141 were shortlisted using the PRISMA methodology. It was determined that during the time period (2017-2023), there has been a consistent rise in annual publications with the most notable surge (110.7%) in 2022. The most dominant themes uncovered in the HRM using keyword analysis were information management, resource allocation, and resource management. The main application of AI in HRM revolves around the themes of recruitment, talent management and ethical considerations. With a growing interest in consolidating the existing knowledge, review articles attracted significant attention. Three were the notable contributors with the latter being cited the highest despite authoring fewer publications. The surge in publications in the year 2022 shows that the aftermath of COVID-19 pandemic resulted in increased adoption of AI for efficient HR management. “Human Resource Management Review” was the top journal in this theme, whereas the journal titled “International Journal of Human Resource Management” had the maximum citations. The study had certain limitations caused by potential biases resulting from selection criteria, methodological choices, and dependence on a single Scopus database with possible inaccuracies. To address these issues, future studies should investigate publication frequencies in electronic HRM to understand observed differences and the influence of AI in HRM practices and results. It is pertinent to mention that the Scopus data sheet had various inaccuracies, such as misclassifying review articles as regular articles, labelling regular articles as reviews, and errors in publication years. Researchers are advised to verify datasets with original documents and encourage Scopus to enhance data accuracy in order to uphold the credibility of research findings.
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