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Examining the dark side of human resource analytics: an empirical investigation using the privacy calculus approach

Author

Listed:
  • Sheshadri Chatterjee
  • Ranjan Chaudhuri
  • Demetris Vrontis
  • Evangelia Siachou

Abstract

Purpose - The purpose of this study is to explore the negative consequences of human resource analytics applications using the privacy calculus approach. Design/methodology/approach - By using the existing literature and privacy calculus theory, a theoretical model has been developed. This model helps to examine the benefits and risks associated with HR analytics applications. The theoretical model was validated using the partial least square structural equation modeling (PLS-SEM) technique with 315 respondents from different organizations. Findings - HR analytics provides multiple benefits to employees and organizations. But employee privacy may be compromised due to unauthorized access to employee data. There are also security concerns about the uncontrolled use of these applications. Tracking employees without their consent increases the risk. The study suggests that appropriate regulation is necessary for using HR analytics. Research limitations/implications - This study is based on cross-sectional data from a specific region. A longitudinal study would have provided more comprehensive results. This study considers five predictors, including other boundary conditions that could enhance the model’s explanative power. Also, data from other countries could improve the proposed model. Practical implications - The proposed model is useful for HR practitioners and other policymakers in organizations. Appropriate regulations are important for HR analytics applications. The study also highlights various employee privacy and security-related issues emerging from HR analytics applications. The study also discusses the role of leadership support for the appropriate usage of HR analytics. Originality/value - Only a few research studies have explored the issues of HR analytics and its consequences. The proposed theoretical model is the first to consider the negative consequence of HR analytics through privacy calculus theory. In this perspective, the research is considered to be novel.

Suggested Citation

  • Sheshadri Chatterjee & Ranjan Chaudhuri & Demetris Vrontis & Evangelia Siachou, 2021. "Examining the dark side of human resource analytics: an empirical investigation using the privacy calculus approach," International Journal of Manpower, Emerald Group Publishing Limited, vol. 43(1), pages 52-74, June.
    • Handle: RePEc:eme:ijmpps:ijm-02-2021-0087
    DOI: 10.1108/IJM-02-2021-0087
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    Citations

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    Cited by:

    1. Roslyn Cameron & Heinz Herrmann & Alan Nankervis, 2024. "Mapping the evolution of algorithmic HRM (AHRM): a multidisciplinary synthesis," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    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. Li, Keyao & Griffin, Mark A., 2023. "Unpacking human systems in data science innovations: Key innovator perspectives," Technovation, Elsevier, vol. 128(C).

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