Proponent/Claimant
Liang Qunxi, Jereco Jims Agapito, Jude Alexes M. Ramas, Mary Joy Baltonado
Abstract
n a business context, organizations often face a significant challenge related to employee turnover intention which can negatively impacts its stability and performance. This study aims to identify factors that affect employee turnover and to develop a predictive model. Survey with 81 participants was conducted with online Likert-scale questionnaire. Positive correlations are found between employee engagement, organizational commitment, satisfaction, and employee turnover intention. However, it was discovered that gender and marital status as moderating variables do not moderate the relationship between employee engagement and employee turnover intention. Based on the research results, the paper constructs a model to predict turnover intention and puts forward the employee retention strategy of the organization. Based on the findings, recommendations include prioritizing employee engagement through communication, skill development, and work-life balance, and nurturing a supportive supervisor-employee relationship. Similarly, employee’s satisfaction can be increased by enhancing compensation, adopting non-monetary incentives, and instituting a comprehensive rewards system with gradually in line with the employee turnover intention. Research limitations include findings might not be appliable to other organization or industries as the research is focus on particular organization. Hence, future work includes conducting similar research in larger scale. Keywords: Employee engagement, employee turnover intention, moderate, prediction model, China.
Name of Research Journal
International Journal of Research and Innovation in Social Science
Volume and Issue No.
Volume 8, Issue No. 10
Date/Year of Publication
November 5, 2024
Citation
Liang Qunxi., Jereco Jims J. Agapito., Jude Alexes M. Ramas., Mary Joy Baltonado (2024). Employee Turnover Intention: Generation of Prediction Model. International Journal of Research and Innovation in Social Science (IJRISS), 8(10), 966-984. https://doi.org/https://dx.doi.org/10.47772/IJRISS.2024.8100080
