Citation
Wan Abdullah, Wan Arnidawati and Hanib, Faidatul Nadiah
(2020)
Effects of perceived organisational support and emotional intelligence on turnover intention among logistics drivers.
Malaysian Journal of Social Sciences and Humanities, 5 (10).
258 - 266.
ISSN 2504-8562
Abstract
The turnover trends in the logistics industry are surprisingly high, regardless of the rapid growth of the industry. Turnover contributes to an impactful loss for the industry to tolerate the new recruitment costs, time consumption, performance disruption, and moral decline among employees due to the workload transfer. Previous literature presented various factors affecting turnover intention, and it could be concluded that factors within an organisation and individual play an essential role in turnover actions. Hence, this research focuses on perceived organisational support and emotional intelligence as factors to turnover intention among logistics drivers in Selangor. Eighty respondents from POS Logistics Berhad in Selangor participated in self-administered survey questions of Survey Perceived Organizational Support, Wong and Law Emotional Intelligence Scale, and Turnover Intention Scale. Results revealed that most of the respondents have a moderate level of POS (84%) and a high level of EI (91%). In comparison, slightly half have a low level of TOI (56%). Research also found that POS and EI have no significant relationship with turnover intention, which is contrary to previous literature. Similarly, three criteria of EI; self-emotion appraisal, uses of emotions, and others-emotions appraisal also have no significant relationship with turnover intention. Only regulations of emotions (r=0.024, p=0.031) has significant relationship with turnover intention. Since the current study location was restricted for a few branches of Pos Logistics in Selangor, it limits the result of the study due to misrepresent the whole community fairly. Future researchers are encouraged to extend or increase the sampling size to other companies and regions to make better generalisations.
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