Yu-Kai Huang
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.
- Henn na Hotel 2019. Brand Concept. https://www.h-n-h.jp/en/concept.Google Scholar
- Koo, D.M. and Ju, S.H. 2010. The interactional effects of atmospherics and perceptual curiosity on emotions and online shopping intention. Comput. Hum. Behav. 26, 3 (May 2010), 377--388. DOI=https://doi.org/10.1016/j.chb.2009.11.009.Google ScholarDigital Library
- Chiu, C.M., Lin, H.Y., Sun, S.Y, and Hsu, M.H. 2009. Understanding customers' loyalty intentions towards online shopping: an integration of technology acceptance model and fairness theory. Behav. Inform. Technol. 28, 4 (Jun. 2009), 347--360. DOI= https://doi.org/10.1080/01449290801892492.Google ScholarCross Ref
- Kunst-Wilson, W.R. and Zajonc, R.B. 1980. Affective discrimination of stimuli that cannot be recognized. Sci. 207, 4430 (Feb. 1980), 557--558. DOI= https://doi.org/10.1126/science.7352271.Google ScholarCross Ref
Index Terms
- Preliminary Study of Factors Affecting the Spread and Resistance of Consumers' Use of AI Customer Service
Recommendations
The Influence of Online Personal Consumer Credit Products on Consumers' Impulse Purchasing Intention: A case study of Ant Credit Pay
ICEBT '19: Proceedings of the 2019 3rd International Conference on E-Education, E-Business and E-TechnologyE-commerce has developed rapidly in China recent years, and various e-commerce platforms have launched personal consumer credit products. Impulsive purchases often occur in online consumer shopping. This paper is based on the theoretical model of ...
Antecedents of Buying Behavior Patterns of E-Consumers: Lessons Learnt From the COVID-19 Pandemic
In the new digitalized era, seller and buyer interact at the market space whereby the market space is provided on a different digital platform. Gradually, consumers are transforming into e-consumers. The COVID-19 pandemic has changed consumers' ...
Research on the influence of artificial intelligence customer service on consumers' purchase process under the background of Internet
ICEME '22: Proceedings of the 2022 13th International Conference on E-business, Management and EconomicsThis paper aims to understand the inner state of users using intelligent customer service and their psychological expectations for intelligent customer service under the era boom of the development of mobile Internet and big data, and analyze the ...
Comments