Presentation Schedule
Examining the Relations Between Pay, Job Satisfaction, and Turnover Intention (91970)
Session Chair: Sung-Chan Ku
Friday, 28 March 2025 14:05
Session: Session 3
Room: Room 703 (7F)
Presentation Type: Oral Presentation
Most employees are unlikely to continue working without compensation. However, the question of whether pay consistently benefits individuals’ work remains a subject of ongoing debate. Some studies have demonstrated that pay is significantly and positively associated with individuals’ internal states and negatively linked to maladaptive outcomes. Other studies have found no significant relations between pay and individuals’ work outcomes. This discrepancy highlights the complexity of the relations between pay and its correlates. Specifically, findings from meta-analyses have emphasized the importance of identifying moderators to obtain a clearer understanding of the role pay plays in organizational contexts. The present study leveraged a nationally representative sample of workers in Taiwan to investigate the interplay between pay, job satisfaction, and turnover intention. Using structural equation modeling, including additive effects and latent moderated models, the results revealed that after controlling for demographics such as gender, education, and vocation, pay significantly and negatively predicted turnover intention (logit = -0.27, p < 0.001) but did not significantly predict job satisfaction (b = -0.11, p = 0.75). Furthermore, there was a significant interaction effect (b = -0.092, p = 0.042) between pay and job satisfaction on turnover intention. The Johnson-Neyman procedure indicated that the relationship between pay and turnover intention was not statistically significant when job satisfaction was one standard deviation below the mean. However, above this threshold, higher job satisfaction strengthened the negative relation between pay and turnover intention. These findings suggest that turnover intention is minimized when both pay and job satisfaction are simultaneously high.
Authors:
Shonn Cheng, National Taipei University of Technology, Taiwan
Sung-Chan Ku, National Taipei University of Technology, Taiwan
Nurul Annisa, National Taipei University of Technology, Taiwan
About the Presenter(s)
Sung-Chan Ku is currently a doctoral student and a member of the META Lab at National Taipei University of Technology.
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