Mapping user gratifications in the age of LLM-based chatbots: An affordance perspective

Abstract

Despite the widespread use of large language model (LLM)-based chatbots, little is known about what specific gratifications users obtain from the unique affordances of these systems and how these affordance-driven gratifications shape user evaluations. To address this gap, the present study maps the gratification structure of LLM chatbot use and examines whether users’ primary purpose of chatbot use (information-, conversation-, or task-oriented) influences the gratifications they derive. A survey of 249 LLM chatbot users revealed nine distinct gratifications aligned with four affordance types: modality, agency, interactivity, and navigability. Purpose of use meaningfully shaped which gratifications were most salient. For example, conversational use heightened Immersive Realism and Fun, whereas information- and task-oriented use elevated Adaptive Responsiveness. In turn, these affordance-driven gratifications predicted key outcomes, including perceived expertise, perceived friendliness, satisfaction, attitudes, and behavioral intentions to continued use. Across outcomes, Adaptive Responsiveness consistently emerged as the strongest predictor, underscoring the pivotal role of contingent, high-quality dialogue in LLM-based human–AI interaction. These findings extend uses and gratifications theory and offer design implications for developing more engaging, responsive, and purpose-tailored chatbot experiences.