Application of distinct motivational types in shaping generative AI (GenAI) adoption behaviour

Differing from AI and GenAI adoption, research on traditional systems emphasised extrinsic factors like utility, social influence and innovativeness as predictors of user behaviour. The role of proximal psychological factors like motivation, however, has been overlooked in this context, which becomes essential with this shift towards AI. In the educational sector, the students’ use of AI shows the possibility of intrinsic factors like motivation in shaping adoption behaviour. This study uses Self-Determination Theory (SDT) and its Organismic Integration Theory (OIT) extension to propose a conceptual map that examines the role of distinct motivational types in shaping students’ GenAI adoption behaviour. The adoption behaviour of 348 Indian students pursuing higher education was collected through a cross-sectional survey and analysed using structural equation modelling. Findings indicated that autonomous motivation, including intrinsic, identified, and integrated motivation, significantly predicts students’ intentions to use GenAI tools. The study further examined the moderating role of perceived compatibility, revealing that alignment between users’ lifestyles and GenAI usage strengthens the impact of controlled motivations. When students feel that AI fits well with their needs and learning requirements, showing high compatibility, external motivators have a stronger effect on their decision to adopt it. This makes compatibility an important new finding and provides additional insights into the motivational types of GenAI adoption in academic contexts. This study extends the body of knowledge by moving beyond the binary treatment of motivation and empirically distinguishing between specific types of motivation. It emphasises the importance of self-determined motivation while showing how the correlations between various motivation types and GenAI usage intentions are conditioned by perceived compatibility. The study also offers practical insights based on the significant results.