Understanding User Acceptance of AI-Driven Chatbots in China’s E-Commerce: The Roles of Perceived Authenticity, Usefulness, and Risk - Rob Kim Marjerison; Hang Dong; Jong-Min Kim; Hanyi Zheng; Youran Zhang; George Kuan (Systems)
This study examines users’ perceptions of Chatbots in China, with a particular focus on the factors influencing their acceptance and usage. Grounded in the Technology Acceptance Model (TAM), we analyze data from 542 online responses to explore the roles of Perceived Authenticity, usefulness, and risk in shaping user behavior toward AI-driven Chatbots. Using linear regression and mediation analyses, our findings indicate that both Perceived Authenticity and Perceived Usefulness positively impact users’ behavioral intentions, while Perceived Risk has a negative influence. Notably, Perceived Usefulness serves as a mediator between behavioral intentions and both Perceived Authenticity and Perceived Risk. These results contribute to the growing body of research on AI and e-commerce by providing empirical evidence of the key factors affecting Chatbot adoption. The study offers valuable implications for developers and marketers, suggesting that enhancing Perceived Authenticity and usefulness while addressing Perceived Risks can improve user acceptance. These insights are particularly pertinent for AI practitioners aiming to refine Chatbot technology and expand its application across various sectors.