The Effects of Career Interest and Academic Self-Efficacy on Postdoctoral Job Satisfaction: The Mediating Role of Career Expectations and the Moderating Role of Generative Artificial Intelligence Use
Keywords:
Career interest, Academic self-efficacy, Postdoctoral job satisfaction, Career expectations, Generative AIAbstract
The postdoctoral community plays a critical role in scientific innovation and academic development worldwide. This study examines how career interest and academic self-efficacy influence postdoctoral job satisfaction through the mediating role of career expectations and the moderating role of generative artificial intelligence (GAI) use. The analysis was based on secondary data obtained from the Nature 2023 Global Postdoctoral Survey conducted by Springer Nature, covering postdoctoral researchers from 93 countries. After data screening, 948 valid samples were retained for analysis. Partial least squares structural equation modeling (PLS-SEM) was employed to test the proposed hypotheses. The results indicate that career interest (β = 0.498, p < 0.001), academic self-efficacy (β = 0.574, p < 0.001), and career prospect expectations (β = 0.278, p < 0.001) positively influence postdoctoral job satisfaction. Furthermore, academic self-efficacy and career prospect expectations jointly form a significant chain-mediating mechanism between career interest and postdoctoral job satisfaction. Although the direct effect of generative AI use on job satisfaction was not significant, GAI positively moderated the relationship between career interest and postdoctoral job satisfaction (β = 0.087, p < 0.05). The findings extend the literature on postdoctoral career development and provide practical implications for research institutions seeking to improve postdoctoral satisfaction in the AI era.
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