Mapping a decade of digital transformation in HRM: trends, implications, and future research directions

The digital transformation of human resource management, referred to as HRM 4.0, is reshaping workforce strategies through artificial intelligence, big data, automation, and cloud computing. However, research is fragmented, focusing on specific technologies rather than a comprehensive understanding of HRM 4.0's evolution, applications, and implications. This study conducts a bibliometric analysis of 2,743 peer-reviewed publications and qualitative content analysis of key papers from 2014 to 2024 sourced from Web of Science, Scopus, IEEE Xplore, and PsycINFO. The study identifies key research themes, theoretical frameworks, global research trends, and sectoral differences in HRM 4.0 adoption using performance analysis, science mapping, and network visualization. The findings reveal a rapid increase in HRM 4.0 research post-2020, with AI-driven HRM, digital talent management, and automation emerging as dominant themes. Geographic disparities indicate that China, the USA, and India lead research production, whereas Europe exhibits stronger international collaborations. Sectoral variations highlight faster adoption in technology and finance, while healthcare and public sectors lag due to regulatory and ethical concerns. The study also uncovers biases in AI-based HRM processes, emphasizing the need for transparent and explainable AI models to mitigate algorithmic discrimination. This study contributes by offering a comprehensive, systematized analysis of HRM 4.0 and highlighting critical ethical, strategic, and implementation challenges. Unlike prior fragmented reviews, it integrates both bibliometric trends and qualitative insights, providing trends for future research. The findings offer practical implications for policymakers, HR professionals, and researchers navigating the complexities of digital workforce transformation.