Understanding continuance intention of artificial intelligence (AI)-enabled mobile banking applications: an extension of AI characteristics to an expectation confirmation model

Artificial intelligence (AI) has been proven to be a disruptive financial technology in the context of mobile banking that can provide more practical value to users and banks. AI is a critical way of facilitating user acceptance and adoption of mobile banking applications (apps). Nevertheless, the ways in which AI features influence users' continuance intention towards AI-enabled mobile banking apps have not been investigated from the perspective of an expectation confirmation model (ECM). To address this research gap, this paper develops a research model by combining two constructs pertaining to AI characteristics, namely, perceived intelligence and perceived anthropomorphism, and by using the ECM to explore users' continuance intentions in this context. We employed a survey research method using a random sampling approach to collect 365 valid responses. A partial least squares approach was used to examine the model. The results show that both intelligence and anthropomorphism can increase user satisfaction via confirmation and perceived usefulness, which in turn fosters users' willingness to continue to engage in mobile banking. This paper offers theoretical advancements, discusses future directions for mobile banking research and provides practical guidance to app developers with respect to designing and developing proper mobile banking apps using AI technology.

Jung-Chieh Lee, Yuyin Tang, SiQi Jiang