Impact of perceived quality of work life on job performance of teachers in Ampara district of Sri Lanka

Abstract

Quality of work life is one of the most important factors for human motivating and improving of job performance. The objective of the study was to determine the impact of quality of work life on job performance of teachers in government schools in the Ampara District in Sri Lanka. 562 teachers in government schools in 7 zones in the Ampara District of Sri Lanka participated in this study. The questionnaire administered had 26 questions of Quality of Work life measuring 8 variables and 45 questions on Job Performance measuring 7 variables. Data analysis was done using SPSS 16.0. The Descriptive Statistics showed based on the responses of the respondents that the level of Quality of Work Life and Job Performance were moderate. The Mean value was 2.79 and 2.57 respectively. The Pearson correlation coefficient and Multiple Regression were used to test the relationship between the variables. Overall, there was a significant and positive correlation between Quality of Work Life of teachers and their Job Performance with the Correlation Coefficient of 0.53. Regression analysis was done to test the level of impact of Quality of Work Life of teachers on their Job Performance. The results showed that quality of work life had a significant impact on job performance with the value of R2 0.581 (58.1%). .Managerial dimensions of quality of work life make a better predictor for job performance among teachers in government schools. There have been studies on the influence of motivation on job performance and on job satisfaction and job performance; however there is a lack of studies on the impact of Quality of Work Life on the job performance of teachers serving in government schools. This study seems to fill the gap. Future study is also suggested to be carried out to private schools, and other educational institutions. Meanwhile other dimensions of the quality of work life such as psychological dimension could be added to improve the model.