Preliminary Study of Factors Affecting the Spread and Resistance of Consumers' Use of AI Customer Service

Abstract

Based on the stimulus-organism-response model, this paper constructs an extended version of the technology acceptance model and is targeted at university students. In addition to analyzing the factors which affect consumers' use of AI customer service, the factor analysis technique is also used to explore and understand the factors that play a key role in consumer behaviors under different shopping situations. Research has found that consumers use AI customer service mainly because they can save waiting time and solve problems effectively. Therefore, it is suggested that the dealers can use the two-way interactive conversation system, which covers natural language understanding, in-depth learning and emotion recognition technology to truly understand users' communicated semantics. Furthermore, they should use feedback data to improve the accuracy and service effectiveness of AI customer service responses. Through the human-machine cooperation approach and the integrated platform that provides multi-channel communication, this will further solve the problem of AI customer service not able to respond effectively to consumers. In addition, the dealer must provide different assistance according to the different attributes of these innovative adapters, for example, using the customer's browsing history, behavioral habits, and other required materials in order to conduct in-depth learning. This way they can accurately establish user portrait graph spectrum so as to provide more personalized services.