Human Resource Analytics and EmployeePerformance: A Data-Driven Approach to TalentManagement
Keywords:
Workforce Analytics, Performance Prediction, HRMetrics, Organizational Performance, Regression AnalysisAbstract
The study explores the predictive capability of Human Resource Analytics (HR analytics) on employee performance through a data-driven approachto human resource management. The study uses a data set of 500employee records to investigate the associations between demographic,job-related, and behavioral factors with employee performance, asmeasured by performance ratings. The study used a quantitative andexploratory research approach, which involved descriptive statistics,correlation analysis and multiplelinear regression to determine thesignificant predictors and the effectiveness of the model. Results show thatemployee performance is centred at moderate levels, with low correlationsfound between performance and variables like work-life balance, salary,overtime, and age. The regression model showed weak predictive ability,with low explanatory power and negative R² values, suggesting that theHR variables considered are not sufficient to predict employeeperformance. These findings suggest the multifaceted nature ofperformance and the impact of other psychological, organisational andcontextual factors not included in the data. Notwithstanding theselimitations, the research highlights the potential of HR analytics to supporttalent management decisions. It shows how analytics can offer insightsinto workforce trends and inform HR strategies. The research adds to thebody of knowledge on HR analytics by showcasing its use and limitationsin predicting performance. It also highlights the importance of richer dataand sophisticated analytical methods for enhancing performanceprediction.
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