Job demands, job resources, burnout, health and life satisfaction of support staff in a higher education institution

Abstract

Higher education institutions in South Africa are undergoing transformation because of increasing student numbers, government and the private sector relying on tertiary institutions to assist in solving problems in addition to the globalisation of knowledge. University staff is continuously faced major changes. Immense pressure is placed on academic institutions, including support staff. Support staff are constantly faced with increasing job demands and decreasing job resources. This imbalance and the increase of job stress over a prolonged period of time can lead to the devastating result of burnout. The objective of this study was to investigate the relationship between task characteristics, burnout, health and life satisfaction in a higher education institution in the North West Province. A cross-sectional design was used. The study population (N=334) consisted of support staff members of higher education institutions in the North West Province. The Job Demands-Resources Scale (JDRS), The Maslach Burnout Inventory - GS (MBI-GS), the Health subscale of the ASSET and The Satisfaction with Life Scale (SWLS) were used as measuring instruments. Descriptive statistics (for example, means, standard deviations and kurtosis) were used to analyse the data. Pearson correlations and multiple regression analyses were used to assess the relationships between job demands, job resources, burnout, physical health, psychological health and life satisfaction. The correlation coefficients indicated that exhaustion was positively related to psychological ill-health. Cynicism correlated negatively with growth opportunities. Multiple regression analysis showed that overload and growth opportunities predicted 26% of the variance for exhaustion and 29% of the variance in cynicism. Exhaustion predicted 24% of the variance for physical ill-health and 37% of the variance for psychological ill-health. Psychological ill-health predicted 16% of the variance of life satisfaction. Recommendations for future research and the organisation were made.