The Influence of Personal and Social Resources on Career Adaptability among Chinese Engineering Undergraduates
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Career adaptability is increasingly important for university students navigating rapid labor-market and technological change. This study examined the predictive relationships of self-leadership, self-efficacy, cognitive flexibility, AI literacy, and social support with career adaptability among Chinese engineering undergraduates. Drawing on Career Construction Theory and Social Cognitive Theory, the study adopted a cross-sectional quantitative design. Using stratified random sampling, self-reported questionnaire data were collected from 416 junior and senior students in the College of Civil Engineering at a comprehensive university in Guangxi, China. Pearson correlation analysis and multiple regression analysis were employed to examine bivariate associations and the unique contribution of each predictor. All five resources were positively correlated with career adaptability (p < .001). In the five-predictor regression model, self-leadership (β = .394) and cognitive flexibility (β = .276) showed comparatively stronger predictive relationships, followed by self-efficacy (β = .130), AI literacy (β = .126), and social support (β = .072). The model explained 75.9% of the variance in career adaptability (adjusted R² = .756). These findings suggest that self-regulatory, cognitive, technological, and relational resources each made a unique statistical contribution within the present sample. The comparatively larger coefficients for self-leadership and cognitive flexibility may help universities calibrate the emphasis of career-development programs while continuing to support students' efficacy beliefs, AI literacy, and social networks. Educational initiatives may therefore combine goal setting, self-monitoring, flexible problem solving, critical and ethical use of AI tools, and accessible mentoring or peer support. The study also extends career-adaptability research to engineering undergraduates in inland China. Given the cross-sectional and self-reported nature of the data, the findings should be interpreted as predictive associations rather than causal effects, and the high explained variance warrants cautious interpretation.
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