Generative AI adoption and innovative behavior among Gen Z students: a deep-learning based dual-stage PLS-SEM-ANN analysis

This study explores the impact of generative artificial intelligence (GAI) adoption on the innovative behavior of Generation Z students in higher education. Anchored in the UTAUT and Social Cognitive Theory, a conceptual model was developed to examine the determinants of GAI adoption and its subsequent effect on students’ innovative behavior. Data were gathered through an online survey in China, yielding 291 valid responses, and analyzed using PLS-SEM and Artificial Neural Network (ANN) approaches. The PLS-SEM results affirmed that performance expectancy, effort expectancy, social influence, and facilitating conditions positively influence GAI adoption, with actual GAI use significantly enhancing students’ innovative behavior. Furthermore, knowledge sharing and creative self-efficacy were identified as positive drivers of innovative behavior, mediating the relationship between GAI use and innovative behavior, thus enriching the understanding of these mediators in educational settings. Complementing these findings, the ANN analysis underscored the pivotal role of creative self-efficacy in fostering innovation. This study advances the burgeoning literature on GAI in education by highlighting its capacity to cultivate innovative behavior among students and offering actionable insights for its effective integration into learning environments.