Abstract: Generative artificial intelligence (AI) tools are spreading fast through higher education, reshaping how students learn. Yet what actually drives students to adopt these tools - especially in business schools, where ethical sensitivity and interpretive judgment matter as much as technical skill - is still not well understood. This study looks at how risk perception shapes generative AI adoption, extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model to do so. Survey data collected from 441 business students in the Baltic Sea region were analyzed using partial least squares structural equation modeling (PLS-SEM) to evaluate the effects of performance expectancy, effort expectancy, social influence, study value, habit, and risk perception on AI usage. The model demonstrates substantial explanatory power (R² = 0.654), with habit identified as the most significant predictor, followed by risk perception and social influence. Students rated usefulness, ease of use, and study value highly - yet these factors mattered less than behavioral ones in predicting actual use. The findings point to a clear need: ethical awareness and AI literacy should be built directly into technology-adoption frameworks in business education, not treated as an afterthought.

Keywords: Business education, Generative AI, Moderating impact, PLS-SEM, Risk perception, UTAUT2

Pages: 1709-1727