Risk Perception as a Moderating Variable in Generative AI Adoption Among Business School Students

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.