Abstract:
During the last decades, big data has been recognized as a salient enabler of human resource management performance. Although increasing academic attention on big data in human resource management has been generated, there is still a void in this research domain. Due to the increasing academic interest in big data in HRM, a thorough bibliometric analysis of the structure and development of this research topic is required. Correspondingly, classic narrative literature evaluations provide substantial contributions, notwithstanding inadequate to give an exhaustive overview of a particular research area. Consequently, scientific mapping, which garners bibliometric techniques to structure and develop a specific area graphically, is gaining salience. Thereby, the overriding aim of this research is to examine Scopus publications related to big data and human resource management. Bibliometric analysis was performed to explore the growing trends, global distribution, thematic evolution, influential articles, researchers, keywords, and dominating countries in big data and HRM. The cluster analysis results highlight the most important topics for current and future academics in the fields of HRM and AI. Emerged clusters include Cluster1: adoption of HR analytics; Cluster2: decision support systems; Cluster 3: dynamic capabilities; Cluster4: digital innovation; Cluster5: organizational ambidexterity; Cluster6: internet of things; Cluster 7: cloud computing. The result is intended to show academics and practitioners a state-of-the-art and comprehensive view of the diverse and multidimensional phenomena of big data and HRM research.
