Adoption of Generative AI Technologies: Insights From the UTAUT2 Model, Personality Characteristics, and Behavioural Factors

Objectives: This study investigates the adoption and integration of Generative AI (GAI) technologies in daily life, focusing on factors that influence user behavior and attitudes. Method: Using a mixed-methods approach, we combined quantitative, qualitative, and semi-experimental methodologies to capture the complexities of GAI tool usage. A total of 897 individuals completed an online survey with both closed and open-ended questions; additionally, 38 students participated in a semi-experimental stage to assess the impact of an online course, “GAI: From Theory to Practice.”. Results: Key findings underscore the role of personality traits such as openness and self-efficacy in promoting GAI usage, mediated by behavioural perceptions and digital engagement patterns. In line with the UTAUT2 model, Performance and Effort Expectancy were the strongest predictors of GAI usage. Social media engagement and information overload management also emerged as predictors, illustrating the interplay between digital behaviours and GAI adoption. Qualitative findings revealed a nuanced understanding of user perceptions, emotions, and societal needs, uncovering themes of GAI's revolutionary potential, emotional ambivalence, and the demand for regulatory and educational frameworks. The experimental results demonstrated significant improvements in students' perceptions of GAI utility, self-reported skill levels, and GAI usage across work, study, and personal contexts following the course. Conclusion: This research advances understanding of GAI's societal integration and highlights the need for targeted training and regulatory policies to support responsible adoption and enhance digital literacy. It offers practical recommendations for educators, policymakers, and designers of generative AI tools.