<p>This paper focuses on developing and testing a conceptual model that examines how AI chatbot stimuli-control, responsiveness, personalization, and information quality-shape consumers’ cognitive and affective organism (perceived usefulness, perceived ease of use, and attitude), further enhancing perceived trust and, in turn, influencing their behavioral responses. Specifically, it investigates how AI chatbot evoked trust influences approach behaviors (reliance, and purchase intention) and avoidance behavior (resistance). A quantitative cross-sectional field survey was conducted to 366 Egyptian consumers who had prior experience with AI chatbots. The acquired data set was subjected to rigorous analysis using SmartPLS3 to test the proposed hypotheses. The findings reflect that AI chatbot stimuli-control, responsiveness, personalization, and information quality significantly and positively influence both perceived usefulness and perceived ease of use. Furthermore, perceived usefulness and perceived ease of use in turn, positively impact consumers’ attitude towards using chatbots, while perceived usefulness also boosts perceived trust. Moreover, consumers’ attitude towards using chatbots significantly drives purchase intention. In addition, consumers’ perceived trust influences both approach behaviors (reliance and purchase intention) and avoidance behavior (resistance). This study presents a significant theoretical contribution by examining chatbot interactivity from the consumer’s perspective, and its effect on behavior, addressing a gap in the literature. It offers a novel “TAM-SOR” model integration, specifically calibrated to capture AI chatbots stimuli-human-computer interaction, and its influence on cognitive and affective organism states and in turn their behavioral responses. It extends prior chatbot research in two keyways: by theorizing trust as an organism-level mediator that activates functional reliance and by introducing resistance as an avoidance response pathway alongside reliance, and purchase intention. The findings reveal important boundary conditions for TAM-derived trust relationships in emerging digital markets. The conceptual research model has been tested by using a convenient sample. The study focused on relational factors as determinants of using chatbots. Future studies could extend the model by adding other variables such as customer engagement, experience, and perceived risk, in addition to exploring the disadvantages of artificial intelligence chatbots. This study provides insights to policymakers and practitioners in AI-chatbots stimuli factors that influence consumers’ attitude and trust to boost the development of high-quality relationships with consumers</p>