This study examined the influence of performance management practices on employee performance at Uganda’s Ministry of Works and Transport (MoWT). Adopting a positivist research approach, a quantitative correlational design was used, and data were collected from 55 employees across senior, middle, and supervisory levels through structured questionnaires. The study focused on key performance management practices, including clarity of performance expectations, fairness and objectivity of appraisals, constructive feedback, and identification of training needs. Descriptive analysis revealed that employees generally agreed that performance expectations were clear (Mean = 3.91, SD = 0.823), appraisals were fair and objective (Mean = 3.64, SD = 1.112), feedback motivated improvement (Mean = 3.96, SD = 0.816), and training needs were appropriately addressed (Mean = 3.91, SD = 0.823). These findings highlight that structured performance management systems provide clarity, accountability, and support for professional development. Pearson correlation analysis showed a significant positive relationship between performance management practices and employee performance (r = 0.519, p < 0.01). Multiple regression analysis further confirmed the effect, with performance management practices explaining 62.8% of the variation in employee performance (R² = 0.628, Adjusted R² = 0.599). The regression model (Y = 0.678 + 0.330X + e) indicated that improvements in these practices were associated with measurable increases in employee productivity, work quality, and accountability (t = 4.608, p < 0.001). Overall, the study demonstrates that well-structured performance management practices are crucial for enhancing employee effectiveness in public sector institutions. The findings provide actionable insights for policymakers and human resource practitioners seeking to strengthen workforce performance and service delivery in Uganda.
Performance Management Practices, Employee Performance, Public Sector, Uganda, Ministry of Works and Transport, Regression Analysis, Employee Engagement, Human Resource Management.
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