Generative AI and digital literacy: Unravelling user intentions through PLS-SEM and machine learning

The purpose of this study is to explore the factors influencing individuals’ intentions to adopt generative AI, particularly within the context of digital literacy. It examines the impact of human capabilities and the relationship with technology from a transhumanist perspective. The study utilises an integrated framework combining the Unified Theory of Acceptance and Use of Technology (UTAUT) and the information quality dimension of the Information Acceptance Model (IAM). The moderating effect of attitudes towards AI is also analysed. Survey data from 362 participants in Turkey were collected and analysed using partial least squares structural equation modelling (PLS-SEM), supported by multi-group analysis based on digital literacy levels determined through clustering. The findings indicate that information quality, performance expectancy, social influence and attitudes towards AI significantly influence AI adoption intention. However, effort expectancy does not have a significant effect. Multi-group analysis reveals that higher digital literacy strengthens the impact of attitudes towards AI on adoption, and the effect of social influence on attitudes towards AI. Conversely, lower digital literacy strengthens the effect of information quality on adoption intention. This research contributes to the understanding of the role of digital literacy in generative AI acceptance. It provides insights for improving digital competence to facilitate AI adoption, especially in the context of diverse digital literacy levels, highlighting that the moderating role of digital literacy varies across different predictor–outcome relationships, which has not been extensively explored in existing literature.