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Abstract

While much research has focused on the initial adoption of new technology, surprisingly little is known about what keeps people engaged with it after the novelty effect wears off. This study uses a Model for Sustained Technology Use (MSTU) to fill this gap. This conceptual framework explains what motivates individuals to use generative AI after the initial trial. To investigate the behavioral intention for Sustained Technology Use (STU), the model employs three core factors: Satisfaction (ST), Perceived Usefulness (PU), and Habit (HB). This study empirically evaluates the framework and examines the significance of the constructs using structural equation modelling. Based on the analysis, the constructs are significantly associated with users' long-term intentions to use generative AI. Specifically, the sustained use of generative AI appears to be driven by user satisfaction with the technology (ST), its perceived usefulness (PU), and habitual engagement (HB). Thus, the findings highlight how positive experiences, tangible benefits, and consistent usage patterns contribute to a long-term commitment. This shifts the strategic focus from mere adoption to retention by developing AI into an agentic tool that integrates seamlessly into daily workflows. For developers, the message is clear. There is a need to prioritise user-centred design that cultivates satisfaction and seamless integration into daily routines; an environment where AI proactively anticipates needs and autonomously executes tasks, evolving from a mere tool into an accessible collaborative partner.