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Big data and human resource management research: An integrative review and new directions for future research

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  • Zhang, Yucheng
  • Xu, Shan
  • Zhang, Long
  • Yang, Mengxi

Abstract

The lack of sufficient big data-based approaches impedes the development of human resource management (HRM) research and practices. Although scholars have realized the importance of applying a big data approach to HRM research, clear guidance is lacking regarding how to integrate the two. Using a clustering algorithm based on the big data research paradigm, we first conduct a bibliometric review to quantitatively assess and scientifically map the evolution of the current big data HRM literature. Based on this systematic review, we propose a general theoretical framework described as “Inductive (Prediction paradigm: Data mining/Theory building) vs. Deductive (Explanation paradigm: Theory testing)”. In this framework, we discuss potential research questions, their corresponding levels of analysis, relevant methods, data sources and software. We then summarize the general procedures for conducting big data research within HRM research. Finally, we propose a future agenda for applying big data approaches to HRM research and identify five promising HRM research topics at the micro, meso and macro levels along with three challenges and limitations that HRM scholars may face in the era of big data.

Suggested Citation

  • Zhang, Yucheng & Xu, Shan & Zhang, Long & Yang, Mengxi, 2021. "Big data and human resource management research: An integrative review and new directions for future research," Journal of Business Research, Elsevier, vol. 133(C), pages 34-50.
    • Handle: RePEc:eee:jbrese:v:133:y:2021:i:c:p:34-50
    DOI: 10.1016/j.jbusres.2021.04.019
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    2. Arab ul Mateen & Qasim Ali Nisar & Samia Jamshed & Sumaira Rehman & Muhammad Ali, 2025. "HRM Effectiveness as an Outcome of Big Data: The Role of Big Data–Driven HR Practices and Electronic HRM," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(3), pages 11339-11373, September.
    3. Chen, Yiran & Li, Jiaye & Tong, Yan & Jin, Laiqun, 2025. "Can big data inhibit earnings management in corporations? — An analysis based on national big data comprehensive pilot zones," Research in International Business and Finance, Elsevier, vol. 78(C).
    4. Dr. Zahra Ishtiaq Paul & Hafiz Muhammad Sohail Khan, 2024. "Reshaping the future of HR: Human Resource Analytics and Talent Management," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(2), pages 332-340.
    5. Xu, Ning & Xu, Longchao & Yan, Xiang-Wu, 2025. "Data factor marketization empowering enterprise innovation quality: New evidence from Chinese patent citations," International Review of Economics & Finance, Elsevier, vol. 103(C).
    6. Stefania Capogna, 2023. "Sociology between big data and research frontiers, a challenge for educational policies and skills," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 193-212, February.
    7. Mohd Mustafa Alfariz & Ariff Md Ab Malik & Anitawati Mohd Lokman, 2024. "Why Does It Fail? A Systematic Review of End-Users’ Challenges in Business Intelligence," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(11), pages 304-315, November.
    8. Chong, Woon Kian & Chang, Chiachi, 2024. "Information exploitation of human resource data with persistent homology," Journal of Business Research, Elsevier, vol. 172(C).
    9. Zhang, Wenyao & Zhang, Wei & Daim, Tugrul U, 2023. "The voluntary green behavior in green technology innovation: The dual effects of green human resource management system and leader green traits," Journal of Business Research, Elsevier, vol. 165(C).

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