Beyond Acceptance: The Role of Attitudes and Social Identity in Generative AI Adoption Across Europe

As generative AI (GenAI) technologies such as ChatGPT and Copilot become increasingly integrated into professional and everyday life, understanding the factors driving their adoption is essential. This study moves beyond traditional technology acceptance models to explore how attitudes and social identity influence GenAI use. Drawing on a theoretical framework that combines the diffusion of innovations theory, the unified theory of acceptance and use of technology, and social identity theory, we examine GenAI adoption across diverse European populations. We utilize a mixed-method survey design, combining cross-national data from six countries—Finland, France, Germany, Ireland, Italy, and Poland—with longitudinal data from Finland. Our analyses reveal that GenAI adoption is significantly predicted by positive attitudes toward AI, prior use of intelligent technologies, and a strong self-identification as a user of new technology. While sociodemographic factors such as age, gender, and education also play important roles, our findings emphasize that psychological and social identity-based factors offer higher explanatory power. Longitudinal results demonstrate that changes in attitudes over time are associated with increased GenAI use, underlining the dynamic nature of technology engagement. This study contributes to theoretical discussions by demonstrating that GenAI adoption is shaped not only by utilitarian considerations but also by how individuals see themselves in relation to technology. These insights call for a reconceptualization of technology adoption frameworks to better incorporate identity-based and attitudinal dimensions. Moreover, the findings highlight emerging digital divides, suggesting that the growing diffusion of GenAI may benefit some social groups disproportionately. Addressing these divides requires an understanding of both individual-level dispositions and broader societal patterns, offering guidance for inclusive technology policy and future research on human–AI interaction.