A study on Chinese college students' acceptance of brain-computer interface technology and its underlying logic-an empirical analysis based on the extended UTAUT model.

OBJECTIVE: To examine the acceptance and influencing factors of brain-computer interface (BCI) technology among Chinese college students based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. METHODS: A questionnaire survey was administered to 800 students recruited from 10 universities across eastern, central, and western China using a convergent mixed-methods design. Additionally, 40 students were selected for semi-structured in-depth interviews. We analyzed the quantitative data using partial least squares structural equation modeling (PLS-SEM) and the qualitative data using thematic analysis. RESULTS: Teacher support (β = 0.337, p < 0.001), personal innovativeness (β = 0.219, p < 0.001), and effort expectancy (β = 0.156, p < 0.001) were positively associated with behavioral intention, while social influence showed a smaller, marginally significant association (β = 0.121, p = 0.049). Neither performance expectancy nor facilitating conditions turned out to be significant predictors, and neuroethical concerns did not reach significance either (β = -0.044). The model still accounted for a substantial 62.6% of the variance in behavioral intention (R2 = 0.626). On the qualitative side, four broad barrier categories emerged from the data: privacy anxiety, technological unfamiliarity, insufficient institutional readiness, and the absence of ethical review. CONCLUSION: The findings reveal that BCI technology acceptance follows a distinct psychological mechanism from traditional IT. Teacher support, rather than performance expectancy, emerges as the strongest predictor. Four main barriers were identified: privacy anxiety, technological unfamiliarity, insufficient institutional readiness, and the absence of ethical review. The study expands the UTAUT framework for neurotechnology applications in higher education and provides evidence-based recommendations for implementation.