Human Resource Metrics as Predictors of Organizational Productivity: A Systematic Review of Evidence from Service-Based Organizations

The growing demand for evidence-based human resource management (HRM) has positioned Human Resource (HR) metrics as central instruments for evaluating workforce contribution to organizational outcomes. Yet the literature linking specific HR metrics to productivity in service-based organizations remains fragmented, conceptually inconsistent, and dominated by sources whose methodological rigour has not been independently examined. This article addresses that gap through a systematic review of peer-reviewed and grey literature examining three widely used HR metrics – absenteeism rate, employee turnover rate, and training return on investment (ROI) – as predictors of organizational productivity in service contexts. Following a PRISMA-informed protocol, the review synthesises findings from sources published between 1995 and 2025, drawing principally on human capital theory and the resource-based view of the firm as interpretive frameworks. The synthesis indicates consistent evidence that absenteeism and turnover are negatively associated with productivity-related outcomes, primarily through workflow disruption, knowledge loss, and replacement cost, while training investment evaluated through ROI-based frameworks shows a generally positive but contextually variable association with performance. However, the review also identifies a persistent evidence gap: rigorous, peer-reviewed empirical validation of HR-metric–productivity relationships specifically within service organizations – as distinct from manufacturing or mixed-sector samples – remains scarce, and much of the practitioner literature lacks methodological transparency. The article concludes that HR metrics retain meaningful predictive value for organizational productivity but that their interpretation requires sectoral and contextual calibration rather than universal application. Implications for HR measurement practice, theoretical refinement, and a future empirical research agenda are discussed.