The impacts of machine learning on human resource management: a systematic literature review and bibliometric analysis

Abstract This paper aims to evaluate the role of machine learning (ML) in driving the impact of accelerating human resource management (HRM) functionalities. In line with the literature review on the influence of ML on HRM, the integration of ML into human resources (HR) sectors dictates structural shifts that allow for the possibility of enhanced HR services intelligence. This research seeks to assess the potential of the ML and HRM conceptual framework by defining research questions through conducting a systematic literature review and bibliometric analysis. All mentioned steps of the study are accomplished by analyzing the research through the bibliometric analysis created by investigating 100 relevant articles collected from the Scopus database, which would assist in answering the research questions. The selected articles and databases helped answer the research question about the relationship between ML and HRM. The findings of this research state that HRM is affected to a good extent by ML, bringing transformation in it by enhancing efficiency, predictive capabilities, and also by automating the processes, especially employee turnover prediction, training, and recruitment. In addition, the use of ML in HRM has issues such as ethical complexities, transparency, and use in situations of algorithmic decision-making, underlining the importance of having clear frameworks for ethical and sustainable use. The paper delves into the current systematic literature review and bibliometric analysis and, therefore, helps operationalize the informed, data-driven HR strategies via ML, enabling significant benefits to a given organization and, hence, can serve as a basis for further research.