Abstract:
Employee welfare programs play a critical role in enhancing staff performance by motivating them, raising their productivity levels, aiding their financial burdens and helping to break the monotony of work. Non-implementation of employee welfare programs leads to increased incidences of employee illness and absence, low productivity, increased accidents, internal conflicts and unenviably high staff turnover rate. The main goal of the study was to evaluate how employee welfare programs affected the University of Nairobi's Faculty of Health Sciences staff members' work performance. The study's specific goal was to ascertain how staff performance at the University of Nairobi's Faculty of Health Sciences was affected by worker relaxation activities, career development initiatives, and safety and health initiatives. It also aimed to investigate the connection between employees' leisure time and productivity in the university's Faculty of Health Sciences. The study's driving hypotheses were the Function Theory of Labor Welfare, the Social Exchange Theory, as well as the Theory of Expectancy. The research utilized a descriptive survey methodology in order to clarify how staff welfare programs affect employees' productivity at work. The study's target population consisted of 975 employees, of which 275 were selected as a sample. Using a straightforward random sampling technique, the sample size was split into five strata: teaching (111), support workers (239), technical staff (102), project personnel (208), and Student Wellbeing Association (SWA) members (193). The data collected via a questionnaire was presented using tables, charts, and figures. Face validity was assessed by looking at what the research instrument was measuring, while criterion validity was checked for correlation during the pre-test. The split half approach was utilized to calculate reliability. Reliability was validated by at least a 0.7 Cronbach's alpha. The link and strength among the variables were examined using both inferential statistics, such as simple and multiple regression, and descriptive statistics, such as mean and standard deviation. The degree of correlation between the dependent and independent variables was examined using Pearson correlation analysis. Based on the study results, the investigator came to certain conclusions and suggestions.
