A hybrid SEM-ANN approach to predicting generative AI use in higher education through knowledge management

Abstract

This study investigated the impact of “knowledge management” (KM) practices on the integration of Generative Artificial Intelligence (Gen-AI) in higher education institutions (HEIs). The research model extended the “Theory of Planned Behavior” (TPB) with KM factors and was validated using a mixed SEM-ANN approach based on data from 584 undergraduate students. PLS-SEM results revealed that attitude, subjective norms, and perceived behavioral control significantly predicted continuance intention. Moreover, KM factors, including “knowledge acquisition,” “knowledge application,” and “knowledge sharing,” significantly predicted students’ attitudes. Furthermore, ANN findings revealed that knowledge application was the most important factor in predicting attitudes. The findings provide important theoretical contributions to the existing literature and practical implications that can help understand the critical role of KM in the integration of Gen-AI in higher education institutions.

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