Burnout As Predictor Towards Employee Turnover Intention: The Moderating Roles Of Emotional Intelligence

Abstract

Employees are vital stakeholders in any business. Employee turnover intention has become one of the most pressing issues in most organizations and turnover intention has garnered more attention than retention. Turnover rate has a significant influence on organizations due to the costs involved with hiring and retraining employees and can have a negative impact on an organization's performance. The purpose of this study was to examine if employee burnout (exhaustion & disengagement) predicted employee turnover intention, and to investigate the moderating roles of emotional intelligence towards the relationship between burnout and turnover intention among employees in Malaysia. This study was based on a quantitative research method and the data were collected using online questionnaires. Since the place of employment is not a limitation of the study, respondents in this research can be anywhere in Malaysia due to the nature of snowball sampling technique. Descriptive, correlational, multiple regression and hierarchical regression analyses were employed to analyze the data obtained and to answer the research questions.

Eighty-six employees responded to the questionnaire. The findings revealed that the majority of the respondents were female (86.4%), single (67.4%) and fairly young about 20-30 years old (38.4%). The findings of this study also revealed that there was a relationship between burnout and turnover intention among employees in Malaysia (r=0.609, p<0.01). Through multiple regression analysis, it was found that disengagement was the most influential component of burnout towards the turnover intention (β=0.620, p<0.05). In addition, hierarchical regression analysis discovered that emotional intelligence (intrapersonal, interpersonal, adaptability, stress management, general mood) burnout did not moderate the turnover intention among employees. For future research, it is recommended that an explicit sample size should be adopted so that the findings can be generalized to other similar population.