A MULTIVARIATE REGRESSION MODEL FOR EMPLOYEE PERFORMANCE: FINDINGS FROM AN ECONOMETRIC AND STATISTICAL ANALYSIS

Abstract

This study investigates the determinants of employee performance using a multivariate regression model based on a dataset of 450 academic staff members. The analysis identifies digital skills, research productivity, organizational activity, and professional experience as key positive predictors of the KPI performance index, while excessive teaching workload demonstrates a negative association with employee outcomes. The model exhibits strong explanatory power, and the high correlation between actual and predicted KPI values (R² ≈ 0.997) confirms its robustness and predictive accuracy. The findings highlight the importance of strengthening digital competencies, optimizing academic workload, and integrating data-driven analytical tools into performance evaluation systems. The results provide practical guidance for higher education institutions aiming to modernize KPI frameworks and enhance evidence-based decision-making.