A systematic review (SPAR-4) of human resource analytics’ review paper: Performance trends, intellectual structure, and TCCM analysis

This systematic review integrates the scientific procedure SPAR-4 methodology to analyse 268 peer-reviewed human resource analytics (HRA) papers published over 14 years. A set of data from “Scopus database” was used to visualize the research landscape with the utilization of VOSviewer software. Then this study used theories, contexts, methods, and characteristics (TCCM) framework for content analysis of HRA literature based on journal indexed in the Social Sciences Citation Index (SSCI), Emerging Sciences Citation Index (ESCI), and Australian Board of Deans Council (ABDC)-indexed journals. The study evaluates performance trends in terms of publication and intellectual structure in terms of co-occurrence keywords, citation networks, bibliographic coupling the documents, and citations cited authors. The analysis reveals that growth since the early 2010s has accelerated since the pandemic. Bibliographic coupling identifies key documents and authors, whereas keyword co-occurrence analysis reveals five knowledge clusters showing the progression of technology adoption The content analysis reveals the main theories including technology organization environment (TOE), diffusion of innovation (DOI), unified theory of acceptance and usage of technology (UTAUT), adaptive structuration theory (AST), and expectancy theory (ET), which focus on organizational performance. The highlighted methods are artificial neural network (ANN), regression, structural equation modelling (SEM), and structural topic modelling approaches in the field of HRA. Research has examined HRA applications in manufacturing, small medium enterprises (SMEs), information technology (IT) firms, public and private organizations, and hospitals, whereas education sector, corporate firms, transportation, and onsite applications remain understudied. Future research directions include blockchain, metaverse, machine learning, artificial intelligence (AI), and sustainable AI integration. Researchers should focus on smart technologies to develop human resource (HR) policies, strategies, employee satisfaction, and organizational growth while addressing financial and regulatory challenges.