Treffer: Exploring College Students' Utilization of Generative AI for Career Information Seeking: An Integrated Model with PLS-SEM and FsQCA Approach

This research delves into the factors that shape Chinese college students' engagement with Generative AI (GenAI) for career exploration purposes, employing the lenses of the Comprehensive Model of Information Seeking (CMIS) and the Technology Acceptance Model (TAM). Utilizing a mixed-methods design, which integrates partial least squares structural equation modelling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA), this study gathered empirical data from 502 participants through a paper-based questionnaire. The results revealed that college students' utilization of GenAI for career information seeking is influenced by both individual factors (work-relevant knowledge & experience, technological readiness, and efficacy beliefs) and perceptions of the information carrier (perceived usefulness, perceived output quality, and perceived value). Through fsQCA, four configurations influencing students' career information seeking behavior were identified. This highlights that such behavior is not driven by a single factor but rather by a combination of these factors, underscoring the complexity of information-seeking behaviors. The findings provide theoretical support for CMIS as a viable framework beyond health information seeking and identifying practical applications and opportunities for future research on career information seeking. Moreover, the results highlight the necessity of balancing GenAI integration in career guidance education. Future studies should explore how to combine GenAI with other educational strategies to further enhance career preparation and address the limitations of GenAI in educational settings.

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