In ChatGPT We Trust? Unveiling the Dynamics of Reuse Intention and Trust Towards Generative AI Chatbots among Iranians

This study delves into the factors influencing reuse intention and trust toward generative artificial intelligence (AI) chatbots, with a particular focus on the Iranian context—a region where the adoption of such technologies is rapidly evolving yet remains underexplored. Utilizing a framework grounded in the Technology Acceptance Model and the Diffusion of Innovation theory, this research complements those theories with construct privacy concerns. Data were collected via a questionnaire distributed to ۵۶۷ Iranian ChatGPT users and analyzed using Partial Least Squares Structural Equation Modelling. The findings reveal that perceived usefulness, perceived ease of use, and compatibility significantly enhance trust toward ChatGPT among Iranian users. The findings also reveal that compatibility, perceived usefulness, and trust enhance ChatGPT users’ reuse intention. Interestingly, privacy concerns negatively impact trust but not reuse intention, highlighting a privacy paradox. This study expands upon existing literature by highlighting the intricate dynamics of trust and adoption, especially in an understudied context, Iran, offering practical insights for developers to enhance user experience through human-centric design. The results underscore the necessity for usability, compatibility with user values, and robust privacy measures to foster sustained user engagement and trust in the context of generative AI chatbots.

Keywords:

ChatGPT , generative AI , trust , reuse intention , Technology Acceptance Model , Diffusion of Innovation theory