Towards gaining a competitive advantage : the relationship between burnout, job satisfaction, social support and mindfulness

The prevalence of burnout has increased in the past 30 years. A review of the literature suggested that \nburnout could be prevented through the application of interpersonal as well as intrapersonal strategies. \nInterpersonal strategies consist of employees having access to social support systems and human resources \nmanagement’s ability that may have a positive influence on job satisfaction. Intrapersonal strategies take the form \nof training individuals to become mindful, thus being aware of their physical as well as psychological states. Little \nresearch has been conducted on the successfulness of such strategies and the need was identified to explore the \nrelationship between burnout, job satisfaction, social support and mindfulness among employees in a South \nAfrican corporate organisation. The purpose of this study was to determine the relationship between burnout, job \nsatisfaction, social support and mindfulness within a South African corporate organisation. The study was a \nquantitative study and a correlational research design was used. Systematic random sampling was used to \ncompile the sample. The sample consisted of 209 employees working in a financial corporate environment in \nJohannesburg. Pearson correlation coefficients were computed for burnout, job satisfaction, social support and \nmindfulness. Moderate to strong inverse correlations were discovered among the constructs under investigation. \nThereafter, a multiple regression analysis was deemed necessary to determine which of the independent \nvariables (mindfulness, job satisfaction and social support) contributed significantly to explaining the variance in \nburnout scores. All the constructs (job satisfaction, mindfulness and social support) appear to be significant \npredictors of burnout. Job satisfaction displayed the highest beta value whilst mindfulness scored the second \nhighest beta value in the multiple regression analysis.