Exploring Students’ Intention to Use Generative Artificial Intelligence in Business Administration Education: An Integrated TOE-TAM Model with PLS-SEM and fsQCA

Business administration education relies on case analysis, strategic simulation, and complex decision-making, all of which generative artificial intelligence (GenAI) can substantially support. However, limited research has examined the mechanisms driving business students’ intention to use GenAI tools to support their learning. To address this gap, this study aims to investigate business administration students’ intention to use GenAI by drawing on an integrated Technology-Organization-Environment (TOE) framework and an extended Technology Acceptance Model (TAM) incorporating perceived trust and perceived risk. Using survey data from 385 Chinese business administration students, a hybrid analytical approach combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) is adopted to examine both the net effects and the configurational mechanisms underlying behavioral intention. The results show that perceived ease of use and perceived trust are the two strongest direct predictors of intention to use GenAI, jointly outweighing perceived usefulness, and that perceived trust serves as the sole significant mediator linking cognitive evaluations to intention to use. Perceived risk, in contrast, exhibits neither a direct nor a mediating effect, suggesting a risk tolerance pattern among business students. Among the TOE dimensions, environmental support (peer influence) emerges as the most potent contextual driver, whereas organizational support facilitates perceived ease of use but does not directly enhance perceived usefulness. The fsQCA findings further identify five typical pathways to high intention to use, namely TAM-driven, trust-compensated, environment-driven, organization-driven risk-tolerant, and comprehensive external-support configurations. These findings provide guidance for designing targeted GenAI-supported strategies and peer-driven interventions in higher business education.