Performance Management Practices and Its Impact on Organizational Performance in Afghanistan

Abstract

Background – Performance management is a systematic approach that seeks to enhance the overall performance of an organisation. This methodology can improve the effectiveness of individuals and groups. Effective performance management methodologies can enhance the operational performance of organisations in Afghanistan. Effective implementation of practises is crucial for organisations. Alignment of practises with organisational culture and context, coupled with provision of sufficient training and resources to employees, is imperative for enhancing their performance.

Purpose – The objective of the current research is to evaluate the effectiveness of various performance management strategies that have been put into practice in Afghan organizations. The objective is to ascertain the obstacles and constraints that have arisen in the course of executing the implementation plan and to investigate the association between employee job satisfaction, motivation, and commitment and their influence on organizational performance.

Design/Methodology/Approach – In this study, the SMART paradigm approach has been implemented. The research design employed is descriptive, following established norms and recommendations. The primary data collection method involved using a questionnaire. The gathered data was then analyzed using various techniques, including T-test, regression analysis, and ANOVA, to derive meaningful insights and draw conclusions from the research findings.

Findings – The regression analysis found a minor positive association between variables. R-Square = 0.427 indicates that the model accounts for 42.7% of response variable variability. Modified co-determination is 0.388. The coefficient estimate's standard error is 0.116, showing correctness. Regression model fails ANOVA (p = 0.171). Thus, predictor-response may not be significant. The coefficient table shows predictor factors of statistical significance (p < 0.05). Model limitations and significance thresholds make these coefficients unreliable.