Do Generative Chatbot Persuade Users? An Elaboration Likelihood Model Investigation with Cognitive and Emotional Trust

Abstract

Generative chatbots are increasingly used as consumer information sources, yet their effects on how consumers process and adopt information remain insufficiently understood. It is unclear how the persuasive communication cues used by these chatbots influence a user’s perception of information usefulness and the likelihood of adopting that information. Existing studies in generative chatbots persuasion and information adoption have rarely integrated their theoretical perspectives in this context, focusing mostly on technology acceptance rather than information processing, leaving a gap in understanding how persuasive processes influence in conversations with such systems. Addressing this gap, the study applies an extended Information Adoption Model (IAM), a framework rooted in a dual process theory of persuasion, specifically the Elaboration Likelihood Model (ELM), to generative chatbots dialogue. It investigates how specific persuasive cues in generative chatbots influence users' cognitive and emotional trust, subsequently affecting their perceived information usefulness and adoption decisions. By extending the IAM with a multidimensional view of trust mechanisms (cognitive and emotional), this research contributes to the persuasion process of artificially intelligent human interactions by demonstrating how persuasive mechanisms participate into perceived information usefulness, the critical mediator preceding information adoption in generative chatbots contexts. To investigate these relationships, this research employs a positivist quantitative, theory-testing design. Data from 438 experienced chatbot users were collected, processed for analysis and the proposed model was tested using PLS-SEM. Analysis of the survey data supports the integrated model and reveals that both central and peripheral cues play significant roles via distinct pathways. Central cues, such as high information quality and relevance in chatbots responses, significantly impact cognitive trust and directly enhance consumers’ perceptions of information usefulness. Peripheral cues, including the chatbots human-like characteristics, primarily influence emotional trust. In turn, both cognitive and emotional trust emerge as strong positive predictors of perceived information usefulness and of consumers’ willingness to adopt chatbots information. Interestingly, opposite to the ELM contention of central dominance over peripheral cues, emotional trust exerted stronger influence toward information usefulness. This study makes both theoretical and practical contributions. Theoretically, it demonstrates the value of bridging persuasion and information adoption theories in the field of human–AI communication. By presenting how cognitive and peripheral cues respectively foster cognitive versus emotional trust, the research provides a nuanced understanding of how cognitive and superficial cues through which -provided information becomes persuasive and valuable to consumers. It also extends information adoption models by introducing a multidimensional view of trust perspective and specific chatbots constructs to highlighting that rational credibility and emotional rapport are both critical for consumers’ acceptance of information. In practical terms, the findings offer guidance for designing more effective generative chatbot systems.