THE IMPACT OF HUMAN RESOURCE MANAGEMENT PRACTICES ON THE EMPLOYEE’S TURNOVER INTENTION (SPECIAL REFERENCE TO HOUSEKEEPING EMPLOYEES IN DAMBULLA)

Abstract

The hospitality industry in Sri Lanka plays a crucial role in generating foreign revenue. This research aims to explore the impact of Human Resource Management (HRM) practices on the turnover intentions of housekeeping employees in hotels located in the Dambulla region. In line with existing literature, five HRM practices, namely training and development, working conditions, supervisory support, job security, and compensation, have been selected for investigation. The central research question revolves around the potential influence of these HRM practices on the turnover intentions of housekeeping staff working in hotels. The study was conducted in Sri Lanka, focusing on housekeeping employees in hotels. Using a convenience sampling method, an online survey was administered, and 48 employees from various hotels in Dambulla responded to a Google form. The collected data was analyzed using a 5-point Likert scale, involving descriptive assessment, correlation analysis, and regression analysis. The research formulated five hypotheses to determine the relationship between HRM practices and employee turnover intentions. The results of the study confirmed two hypotheses while rejecting the remaining three. Specifically, a significant negative correlation was identified between turnover intention and both training and development, as well as supervisory support. Consequently, it is recommended to extend the duration and scope of training programs and enhance supervisory support to reduce turnover intentions. These recommendations carry significant implications for addressing the research findings. Furthermore, the study suggests exploring additional strategies for improving HRM practices to mitigate turnover intentions. Future researchers interested in investigating employee turnover intentions are encouraged to replicate similar studies in broader regions of Sri Lanka and consider larger sample sizes for more generalized findings and comprehensive recommendations.