Abstract
ChatGPT and other AI chat information retrieval and processing service systems can deal with many problems, which are crucial for users, yet current research lacks depth in user experience. And those studies predominantly focus on positive aspects of user intention, which have a large limitation. Against this backdrop, this study, framed by Behavioral Reasoning Theory, utilizes factor analysis and linear regression to create a comprehensive user intention evaluation scale and model. The eight-factor evaluation scale, shaped by user values, includes supporting reasons like usefulness, convenience, growth, and interactivity, and inhibiting factors such as inaccuracy, semantic rigidity, security risk, and cognitive limitation. Attitude serves as an intermediary, positively affected by supporting factors and negatively influenced by inhibiting ones. Core factors impacting user intention are convenience, cognitive limitation, and security risk. This research not only bridges existing gaps but also lays a theoretical foundation for related industries.
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