Mapping the impacts of neural networks on human resource management research: a bibliometric analysis

Abstract Neural network has emerged as a transformative force reshaping various domains in response to the rapidly evolving technological landscape. This study aims to address the literature gap, delving into the current state of development, identifying key contributors, influential countries, and journals, and understanding publication trends. This bibliometrics literature review analysis comprehensively explores the cooperation between neural networks and human resource management (HRM). Through a bibliometric examination of 86 relevant articles from the Scopus database, this study employs bibliometric methodologies, network analysis, and content analysis to reveal research clusters and knowledge gaps though the use of R studio, Vosviewer, biblioshiney. The findings of this bibliometric analysis suggest that neural networks are a vital concept for HRM in recent years, with a large number of articles produced in the last 5 years, totaling 62 articles. This topic is a global concern, as contributions have come from countries across Europe, America, Asia, and Africa. The citation impact analysis and country collaboration analysis highlight the significant role played by Chinese and Indian researchers and institutions in advancing this research area. Thematic evaluation over time reveals the evolution of research themes, shifting from convolutional neural networks and forecasting to machine learning and artificial intelligence in the field of HRM. By bridging the gap between theory and practice, this research contributes to advancing HRM scholarship and facilitating the adoption of innovative HRM practices in organizations worldwide. These findings underscore the dynamic nature of the neural networks and HRM field and its potential for further scientific enrichment.