Study Of HR Practices & Process Of Performance Appraisal

Authors

  • 1Mr.M.Prasanna Kumar, 2Maddu Giri Durga Shanmukhan Author

DOI:

https://doi.org/10.64751/

Abstract

Human Resource (HR) practices and performance appraisal systems play a crucial role in improving employee efficiency, organizational productivity, and overall business performance. The present study, titled "Study of HR Practices and Process of Performance Appraisal," aims to examine the various HR practices adopted by organizations and analyze the effectiveness of their performance appraisal processes. The study focuses on key HR functions such as recruitment and selection, training and development, compensation management, employee welfare, performance evaluation, and employee engagement. The research adopts a descriptive approach and utilizes both primary and secondary data sources. Primary data are collected through structured questionnaires and interactions with employees, while secondary data are obtained from journals, books, company reports, and online resources. The findings indicate that effective HR practices contribute significantly to employee motivation, job satisfaction, and organizational commitment. Furthermore, a transparent and fair performance appraisal system helps employees understand their strengths and areas for improvement, thereby enhancing their productivity and career development. The study concludes that organizations should continuously improve their HR policies and performance appraisal methods by incorporating objective evaluation criteria, regular feedback mechanisms, and employee participation in the appraisal process. A well-structured performance appraisal system, supported by effective HR practices, serves as a strategic tool for achieving organizational goals and maintaining a competitive advantage in the dynamic business environment.

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Published

2026-07-15

How to Cite

1Mr.M.Prasanna Kumar, 2Maddu Giri Durga Shanmukhan. (2026). Study Of HR Practices & Process Of Performance Appraisal. International Journal of Data Science and IoT Management System, 5(3), 221-227. https://doi.org/10.64751/