Unpacking the dual-path trust mechanism driving students' continuance intention toward campus GenAI: a hybrid SEM-machine learning approach within the S-O-R framework.

As generative artificial intelligence agents become embedded in higher education, understanding students' continuance intention (CUI) is critical. To address the underexplored mechanisms regarding how campus AI environments are associated with user trust, this study adopts the Stimulus-Organism-Response (S-O-R) framework to investigate a dual-path trust mechanism: AI System-like Trust (AST) and AI Human-like Trust (AHT). Through a hybrid methodology combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and non-parametric Random Forest classification, this study analyzes university students' CUI toward campus GenAI. The findings demonstrate that Information Quality (IQ) and Service Quality (SQ) are positively associated with both AST and AHT pathways. Conversely, Facilitating Conditions (FC) and Performance Expectancy (PE) do not exhibit independent linear associations within the simultaneous structural estimation. Rather than indicating an empirical absence of functional value, this configuration uncovers a holistic evaluation mechanism among digital natives, where basic functional affordances operate as baseline hygiene constraints whose variance is subsumed by quality attributes. Notably, while both AST and AHT exert concurrently significant linear driving forces on CUI within the structural path framework, the exploratory machine learning analysis clarifies that under recursive partitioning, AHT exhibits a higher relative predictive weight. Furthermore, a localized asymmetry operates within the linear mediation channels, where SQ selectively mobilizes AHT over AST to drive continuous usage.