Examining employee performance among academic staff at Kyambogo University

Abstract

This study investigated employee performance among academic staff at Kyambogo University, focusing on three objectives: assessing performance levels, examining evaluation methods, and identifying strategies for improvement. Guided by Fredrick Herzberg’s two-factor theory, a descriptive research design was employed with a sample of 191 participants from various Schools and Faculties. The study revealed a demographic profile of predominantly young, married academic staff with high educational qualifications (Bachelor's, Master's, and PhD). Most staff had tenure of less than 5 years, indicating a relatively young workforce with significant turnover. Performance among academic staff showed strong organizational commitment through high compliance with timetables, yet challenges in time management and task completion. Performance evaluation methods varied, including student evaluations and peer reviews, though issues of standardization and feedback consistency were noted. Strategies to improve performance emphasized motivational factors such as role alignment and supportive strategies, alongside knowledge management and structured development programs. These findings suggested opportunities for enhancing organizational effectiveness and employee satisfaction through improved performance management practices. In conclusion, this study provided insights into academic staff performance at Kyambogo University, highlighting the importance of organizational commitment and effective performance management strategies. Recommendations included refining evaluation processes and implementing tailored development initiatives to support continuous improvement and enhance overall organizational outcomes. Despite its contributions, this study faced several limitations that warrant consideration for future research. Firstly, the sample size and scope of the study, limited to Kyambogo University, restricted the generalizability of findings to other institutions or contexts. Future research could employ mixed-method approaches or triangulation techniques to validate results and mitigate bias.