Abstract
With the popularity of financial technology (fintech) chatbots equipped with artificial intelligence, understanding the user’s response mechanism can help bankers formulate precise marketing strategies, which is a crucial issue in the social science field. Nevertheless, the user’s response mechanism towards financial technology chatbots has been relatively under-investigated. To fill these literature gaps, latent growth curve modeling was adopted by the present research to survey Taiwanese users of fintech chatbots. The present study proposed a customer continuance model to predict continuance intention for fintech chatbots and that cognitive and emotional dimensions positively influence the growth in a user’s attitude toward fintech chatbots, which in turn, positively influences continuance intention over time. In total, 401 customers of fintech chatbots were surveyed through three time points to examine the relationship between these variables over six months. The results support the theoretical model of this research and can advance the literature of fintech chatbots and the information technology adoption model.
Highlights
IntroductionBankers should make a strategy to attract more customers [1] and to gain commercial benefits [2]
To reach sustainable development, bankers should make a strategy to attract more customers [1] and to gain commercial benefits [2]
The reliability and validity were tested by the analysis technique of confirmatory factor (CA)
Summary
Bankers should make a strategy to attract more customers [1] and to gain commercial benefits [2]. Financial technology (fintech) services, such as fintech chatbots, have been confirmed as innovative services to improve customers’. The fintech chatbot is an online service that responds to consumers by an artificial intelligence algorithm [4,5]. Previous studies have found that the driving factors of continuous intention in online banking services are based on utilitarian orientation, and few studies have focused on emotional orientation [7]. The literature on adopting intention in the information system environment is almost based on the traditional information technology adoption model [8,9,10]. It is important to incorporate the emotional dimension into traditional adoption models to open the black box of customers’ behavior mechanisms
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