NONE 20260417090237681 naun/naun content-registration-form+ja@crossref.org content-registration-form International Journal of Education and Information Technologies 2074-1316 Aivars Spilbergs BA School of Business and Finance, University of Latvia, K.Valdemara 161, Riga, LV-1013 Latvia https://orcid.org/0000-0003-2537-8053 The rapid expansion of generative AI (GenAI) in higher education raises important questions about what drives its sustained and responsible use. This study applies an extended UTAUT2 framework to examine GenAI adoption among social science students and researchers. Using PLS SEM on responses from 569 participants across Baltic Sea region universities, the study evaluates how performance expectancy, effort expectancy, social norms, study value, facilitating conditions, hedonic motivation, and habit shape self-reported GenAI use in classroom tasks, homework, self-study, and thesis work. Habit emerges as the strongest predictor for both groups (students = 0.420; staff = 0.712), explaining substantial variance (R² = 0.479 and 0.558). Students exhibit modest positive effects from performance expectancy, effort expectancy, social norms, and study value. In contrast, study value is the sole additional predictor identified for staff. These findings underscore the importance of scaffolded GenAI activities that foster responsible habits, as well as institutional strategies that prioritize demonstrable academic value. 04 17 2026 04 17 2026 64 8 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.46300/9109.2026.20.8 https://npublications.com/journals/educationinformation/2026/a162008-008(2026).pdf C. A. Bail, “Can Generative AI improve social science?” Proc. Natl. Acad. Sci., vol. 121, no. 21, p. e2314021121, 2024, doi: 10.1073/pnas.2314021121. L. Hughes, T. Malik, S. Dettmer, A. S. Al-Busaidi, and Y. K. 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