From Enjoyment to Utility: Determinants of Digital Natives’ Adoption of Generative Artificial Intelligence

Abstrak

GenAI's growing popularity has altered the way digital natives approach their work and schoolwork. Since most people choose not to utilize GenAI, it is crucial to determine what makes people want to perform certain things. This study uses the Unified Theory of Acceptance and Use of Technology (UTAUT2) to investigate the elements that impact the intents of digital natives to use generative AI. Our quantitative cross-sectional research included 152 digital natives residing in Jakarta who had prior experience working with generative AI technologies. In order to analyze the collected data, Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used. The results show that effort and performance anticipation have a favorable effect on behavioral intention, which shows how important it is to have high hopes for performance improvements and simplicity of use to encourage adoption. Social influence has a statistically meaningful, although minor, effect on behavioral intention, in contrast to hedonic incentive, which does not. Overall, the model demonstrates moderate explanatory power, suggesting that generative AI adoption among digital natives is primarily driven by utilitarian considerations. These findings imply that generative artificial intelligence is mainly perceived as a functional, productivity-oriented tool rather than a source of enjoyment. The study offers practical implications for institutions and developers to prioritize usability and performance value when encouraging GenAI adoption in academic and professional context.