HUMAN RESOURCE ANALYTICS AND EMPLOYEE PERFORMANCE: A DATA-DRIVEN MANAGEMENT APPROACH

Abstract

Background: HR analytics is useful when it improves a decision, not when it merely produces more indicators. Employee performance is affected by job design, resources, leadership and team conditions, so a data-driven approach needs context as much as it needs measurement. Aims: This article examines the mechanisms that connect the topic to organizational or policy performance and identifies the conditions that make those mechanisms stronger or weaker. Research Method: A structured narrative review integrates peer-reviewed research with authoritative policy, statistical, and professional sources, including Barrero et al. (2023); ILO (2025). Sources are coded by outcome, mechanism, boundary condition, and practical implication. Results and Conclusion: The synthesis indicates that outcomes are heterogeneous. The most measurable behavior is not always the most valuable behavior. If analytics rewards activity that is easy to count, employees can learn to optimize the metric rather than the work the organization actually needs. Six recurring themes show that implementation quality, information, capability, and institutional context frequently matter as much as the headline policy or technology. Contribution: The article offers an evidence-based framework for HR teams and line managers that translates the literature into decision principles without claiming primary data that were not collected.