Immersive technologies (IMTs) have significantly impacted the tourism sector by offering experiences that enhance engagement with destinations. Although previous research confirms that IMT affects travelers’ behavioral intentions, there is a lack of studies specifically focusing on the post-experience context of agritourism and the factors contributing to technological resistance. This study introduces a conceptual model that combines the Diffusion of Innovation framework, the technology acceptance model, and the psychological factors related to innovation resistance to examine the decision-making processes of IMT users in the post-experience context of agritourism. The research model is evaluated through partial least squares structural equation modeling (PLS-SEM) techniques involving 400 users who engaged with IMT for a duration not exceeding 3 months. The findings indicate that the amount of storytelling content, which enhances engagement in agritourism, significantly affects users’ perceptions of IMT and their intentions to revisit and continue using IMT. Additionally, factors related to compatibility, along with privacy and security risks, influence the reluctance or readiness to adopt IMT and the decision to revisit a destination. These findings contribute to the understanding necessary to develop content and apply IMT in the agritourism sector to promote long-term sustainability.

The extensive implementation of AI-driven chatbots in e-commerce has generated both acceptance and objection; however, previous studies have predominantly analyzed these responses separately. This study examines the mechanisms that drive dual customer responses to AI chatbots in the pre-purchase phase of the online shopping customer journey. The study integrates the Artificially Intelligent Device Use Acceptance (AIDUA) model with the Stimulus–Organism–Response (SOR) framework to explain how consumers develop both positive and negative behavioral responses to AI chatbot usage and to derive design-relevant implications for AI chatbot systems in online shopping. Data were collected from active or recent chatbot users in online shopping and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that social influence and chatbot anthropomorphism significantly affect consumers’ attitudes toward AI chatbot usage, whereas novelty value does not show a significant effect. Attitude toward using serves as a significant psychological mechanism that increases willingness to accept AI chatbots and purchase intention, while also being positively associated with objection to use among active or recent chatbot users. The findings extend understanding of AI-enabled interactive marketing and provide applied implications for AI chatbot system design, particularly with respect to interface anthropomorphism, transparent pre-purchase support, objection-sensitive escalation, and adaptive interaction strategies.