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Human Resources Analytics for Public Personnel Management: Concepts, Cases, and Caveats

Author

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  • Wonhyuk Cho

    (Wellington School of Business and Government, Victoria University of Wellington, Wellington 6011, New Zealand)

  • Seeyoung Choi

    (Ministry of Personnel Management, Sejong-si 30102, Republic of Korea)

  • Hemin Choi

    (Graduate School of Global Cooperation, Hallym University, Chuncheon-si 24252, Republic of Korea)

Abstract

The advancement of data technology such as machine learning and artificial intelligence has broadened the scope of human resources (HR) analytics, commonly referred to as “people analytics.” This field has seen significant growth in recent years as organizations increasingly rely on algorithm-based predictive tools for HR-related decision making. However, its application in the public sector is not yet fully understood. This study examined the concepts and practices of HR analytics through a thematic review, and proposed a five-step process (define, collect, analyze, share, and reflect) for implementation in the public sector—the process aims to assist with the integration of HR analytics in public personnel management practices. By analyzing cases in both the public and private sectors, this study identified key lessons for functional areas such as workforce planning, recruitment, HR development, and performance management. This research also identified the necessary conditions for introducing HR analytics in public organizations, including data management, staff capabilities, and acceptance, and discussed the potential challenges of privacy, integrity, algorithmic bias, and publicness.

Suggested Citation

  • Wonhyuk Cho & Seeyoung Choi & Hemin Choi, 2023. "Human Resources Analytics for Public Personnel Management: Concepts, Cases, and Caveats," Administrative Sciences, MDPI, vol. 13(2), pages 1-22, January.
    • Handle: RePEc:gam:jadmsc:v:13:y:2023:i:2:p:41-:d:1052480
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    References listed on IDEAS

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    Cited by:

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    3. Johannes Lehmann & Michael Beckmann, 2025. "Digital technologies and performance incentives: evidence from businesses in the Swiss economy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 161(1), pages 1-19, December.
    4. Kekez, Ivan & Lauwaert, Lode & Begičević Ređep, Nina, 2025. "Is artificial intelligence (AI) research biased and conceptually vague? A systematic review of research on bias and discrimination in the context of using AI in human resource management," Technology in Society, Elsevier, vol. 81(C).
    5. Wen-Chin Chen & An-Xuan Ngo & Hui-Pin Chang, 2024. "Enhancing Decision-Making Processes in the Complex Landscape of the Taiwanese Electronics Manufacturing Industry through a Fuzzy MCDM Approach," Mathematics, MDPI, vol. 12(13), pages 1-29, July.
    6. Md. Nazmus Sakib & Shah Ridwan Chowdhury & Mohammad Younus & Nehad Laila Sanju & Farhana Foysal Satata & Mahafuza Islam, 2024. "How HR analytics evolved over time: a bibliometric analysis on Scopus database," Future Business Journal, Springer, vol. 10(1), pages 1-22, December.

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