Generative AI Characteristics, User Motivations, and Usage Intention

This study analyzed the impact of Generative AI (GAI) characteristics (personalization, privacy loss, anthropomorphism) and user motivations on the intention to use free and paid services through trust mediation. The research model combined the extended Technology Acceptance Model (TAM) and Uses and Gratifications Theory (U&G) to capture both organizational and individual usage contexts. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze survey data from 400 GAI users, and showed that trust significantly mediates service adoption intentions, with a stronger effect on paid services. All factors except Perceived Usefulness (PU) had significant effects on trust formation. Although personalization and anthropomorphism enhanced trust, privacy loss concerns diminished it. To increase user acceptance, services must build trust through personalized content and emotional connections while strengthening privacy protection. These findings advance the understanding of technology acceptance and user motivation theories while providing important practical implications for user-centric service design.