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Examining the determinants of successful adoption of data analytics in human resource management – A framework for implications

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  • Shet, Sateesh.V.
  • Poddar, Tanuj
  • Wamba Samuel, Fosso
  • Dwivedi, Yogesh K.

Abstract

Data analytics has gained importance in human resource management (HRM) for its ability to provide insights based on data-driven decision-making processes. However, integrating an analytics-based approach in HRM is a complex process, and hence, many organizations are unable to adopt HR Analytics (HRA). Using a framework synthesis approach, we first identify the challenges that hinder the practice of HRA and then develop a framework to explain the different factors that impact the adoption of HRA within organizations. This study identifies the key aspects related to the technological, organizational, environmental, data governance, and individual factors that influence the adoption of HRA. In addition, this paper determines 23 sub-dimensions of these five factors as the crucial aspects for successfully implementing and practicing HRA within organizations. We also discuss the implications of the framework for HR leaders, HR Managers, CEOs, IT Managers and consulting practitioners for effective adoption of HRA in organization.

Suggested Citation

  • Shet, Sateesh.V. & Poddar, Tanuj & Wamba Samuel, Fosso & Dwivedi, Yogesh K., 2021. "Examining the determinants of successful adoption of data analytics in human resource management – A framework for implications," Journal of Business Research, Elsevier, vol. 131(C), pages 311-326.
    • Handle: RePEc:eee:jbrese:v:131:y:2021:i:c:p:311-326
    DOI: 10.1016/j.jbusres.2021.03.054
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    Cited by:

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    2. Wang, Lijun & Zhou, Yu & Sanders, Karin & Marler, Janet H. & Zou, Yunqing, 2024. "Determinants of effective HR analytics Implementation: An In-Depth review and a dynamic framework for future research," Journal of Business Research, Elsevier, vol. 170(C).
    3. Luqman, Adeel & Wang, Liangyu & Katiyar, Gagan & Agarwal, Reeti & Mohapatra, Amiya Kumar, 2024. "Unpacking associations between positive-negative valence and ambidexterity of big data. Implications for firm performance," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    4. Amit Kumar Kushwaha & Ruchika Pharswan & Prashant Kumar & Arpan Kumar Kar, 2023. "How Do Users Feel When They Use Artificial Intelligence for Decision Making? A Framework for Assessing Users’ Perception," Information Systems Frontiers, Springer, vol. 25(3), pages 1241-1260, June.
    5. Dehghani, Milad & William Kennedy, Ryan & Mashatan, Atefeh & Rese, Alexandra & Karavidas, Dionysios, 2022. "High interest, low adoption. A mixed-method investigation into the factors influencing organisational adoption of blockchain technology," Journal of Business Research, Elsevier, vol. 149(C), pages 393-411.
    6. Chong, Woon Kian & Chang, Chiachi, 2024. "Information exploitation of human resource data with persistent homology," Journal of Business Research, Elsevier, vol. 172(C).
    7. Shet, Sateesh V. & Pereira, Vijay, 2021. "Proposed managerial competencies for Industry 4.0 – Implications for social sustainability," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

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