Determinants of continuance intention to use GAI in academic research among graduate students:Findings from PLS-SEM and fsQCA

The rapid advancement of generative artificial intelligence (GAI) has transformed academic research, especially for graduate students, who are progressively depending on GAI for diverse research activities. However, despite its potential to revolutionize research practices, a critical gap remains in understanding the factors that influence graduate students' continued use of GAI. This study addresses this gap by investigating the determinants of GAI continuance intention among graduate students, employing an integrated framework combining the expectation confirmation model (ECM) and task technology fit (TTF) model, incorporating IT self-efficacy and facilitating conditions. We collected data from 315 graduate students at a university in Jiangsu Province, China. A mixed method of partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) was used for data analysis. The results showed that task-technology fit, facilitating conditions, perceived usefulness, and satisfaction significantly impacted continuance intention, while IT self-efficacy did not. The fsQCA analysis revealed multiple configurations leading to high or low continuance intention, highlighting user heterogeneity. The research enhances the comprehension of GAI's ongoing use in academic settings, offering crucial knowledge for decision-makers, teachers, and administrators.