People Analytics To Mitigate Managerial Decision Bias In Modern Organizations
DOI:
https://doi.org/10.51903/vdb28v29Keywords:
People Analytics, Managerial Decision Bias, Decision-Making, General Management, Human ResourcesAbstract
The increasing complexity of contemporary organizations creates greater possibilities for cognitive bias to affect executive decision processes. The People Analytics method provides organizations with a data-based solution that improves their ability to make unbiased decisions. The research study investigates how People Analytics technology helps reduce inaccuracies in managerial decision-making. The research uses a quantitative explanatory method to investigate middle and upper management personnel who use human resource information, using a survey. The researchers employed linear regression analysis to examine their data. The results of the study show that People Analytics usage leads to a significant reduction in executive decision-making biases, helping organizations make more unbiased and consistent decisions. The research study establishes its value through the combination of People Analytics and decision bias theory within a universal management framework. The research findings demonstrate that People Analytics serves as a strategic tool that enhances both the quality and accountability of executive decisions.
References
Aida et al. (2025). Emotional Intelligence in Managerial Economic Decision- Making Article history : 4(2).
Aini & Nugroho. (2026). Monte Carlo Simulation for Assessing Operational Efficiency and Environmental Impact in the Green Manufacturing Industry Article history : 5(1), 20–28.
Al-tameemi et al. (2024). Insights into determinants of health-seeking behavior : a cross-sectional investigation in the Iraqi context, (June), 1–8. https://doi.org/10.3389/fpubh.2024.1367088
Bahuguna et al. (2023). Human resources analytics : where do we go from here ? (Ml). https://doi.org/10.1108/BIJ-06-2022-0401
Bonilla et al. (2024). The effect of innovation performance on the adoption of human resources analytics in business organizations, 32(October 2023), 1126–1144. https://doi.org/10.3934/era.2024054
Bottesch et al. (2025). Driving business value through people analytics : Literature review and research agenda from an information systems perspective. Electronic Markets (Vol. 35). Springer Berlin Heidelberg. https://doi.org/10.1007/s12525-025-00842-3
Carter, L., & Liu, D. (2025). International Journal of Information Management: How was my performance ? Exploring the role of anchoring bias in AI-assisted decision making. International Journal of Information Management, 82(February), 102875. https://doi.org/10.1016/j.ijinfomgt.2025.102875
Chacon, A., & Kaufmann, E. (2025). An overview of the effects of algorithm use on judgmental biases affecting forecasting. International Journal of Forecasting, 41(2), 424–439. https://doi.org/10.1016/j.ijforecast.2024.09.007
Danielle. (2025). Research in Business & Social Science Leveraging the power of human resource analytics for enhanced decision making : opportunities and challenges, 14(6), 0–2.
Gerber et al. (2024). HR analytics between ambition and reality, psychology, 225–236. https://doi.org/10.1007/s11612-024-00743-7
Hamid, R., Meetna, S., Varghese, D. T., O, P. K., Chaturvedi, S., Pujar, R., … Pujar, R. (2025). HR Analytics for Predictive Talent Management : A Framework for Data-Driven Decision-Making, 2(4), 2883–2893.
Hristov et al. (2026). Cognitive biases in implementing a performance management system : behavioral strategy for supporting managers ’ decision-making processes, (February). https://doi.org/10.1108/MRR-11-2021-0777
Huynh, M. (2025). Individual Data-Driven Mindset and Decision-Making Performance : The Mediating Roles of Effort and Persistence, 2511–2538.
Korherr et al. (2022). From intuitive to data-driven decision-making in digital transformation : A framework of prevalent managerial archetypes. Digital Business, 2(2), 100045. https://doi.org/10.1016/j.digbus.2022.100045
Madhuri, A., & Kumar, B. R. (2025). HR Analytics and Decision-Making : A Data-Driven Approach to Employee Performance Management, 14(7), 409–423.
McCartney, S. (2026). Bridging the gap : why, how, and when HR analytics can impact organizational performance, 60(13), 25–47. https://doi.org/10.1108/MD-12-2020-1581
Midtgard, K., & Selart, M. (2025). Cognitive Biases in Strategic Decision-Making.
Orjatsalo et al. (2026). Business analytics in managerial decision-making : top management perceptions, 29(1), 1–17. https://doi.org/10.1108/MBE-09-2023-0130
Pavithran, M. S., & Vadivel, S. M. (2026). Explainable attrition risk scoring for managerial retention decisions in human resource analytics, (January), 1–19. https://doi.org/10.3389/fdata.2025.1699561
Polzer, J. T. (2023). The rise of people analytics and the future of organizational research. Research in Organizational Behavior, 42(2022), 100181. https://doi.org/10.1016/j.riob.2023.100181
Sabrina et al. (2025). Adoption of Generative AI in Business Decision-Making : Opportunities, Risks, and Strategic Readiness in Emerging Markets, 4(2), 75–87.
Siddiqua et al. (2023). ORGANIZATIONAL PERFORMANCE AND HUMAN RESOURCES, 28(2), 0–2. https://doi.org/10.17512/pjms.2023.28.2.16
Tessema et al. (2025). The Effect of Human Resource Analytics on Organizational Performance : Insights from Ethiopia.
Theodorakopoulos et al. (2025). Cognitive Bias Mitigation in Executive Decision-Making : A Data-Driven Approach Integrating Big Data Analytics, AI, and Explainable Systems, 1–52.
Wenting et al. (2024). Analyzing the Impact on Talent Acquisition and Performance Management : HR and Data Analysis, 36(1), 1–30. https://doi.org/10.4018/JOEUC.342603
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