The effect of artificial intelligence on the intention to use bank mobile applications (case study: private banks)


1 Full Professor University of Isfahan & visiting Professor University of Illinois at Chicago

2 Full Professor University of Illinois at Chicago

3 PhD Candidate in Business Management of IAU& ITM Research Group University of Isfahan


Purpose – The development of mobile technology has changed the traditional financial industry and banking sector. While traditional banks have adopted artificial intelligence (AI) techniques to deepen the development of mobile banking applications (apps), the current literature lacks research on the use of AI-based constructs to explore users’ mobile banking app adoption intentions. To fill this gap, based on stimulus-organism-response (SOR) theory, two AI feature constructs as stimuli are considered, namely, perceived intelligence and anthropomorphism. This study then develops a research model to investigate how intelligence and anthropomorphism affect tasktechnology fit (TTF), perceived cost, perceived risk and trust (organism), which in turn influence users’ AI mobile banking app adoption
(response). Design/methodology/approach – This study used a convenience nonprobability sampling approach; a total of 451 responses were collected to examine the model. The partial least squares technique was utilized for data analysis.
Findings – The results show that intelligence and anthropomorphism increase users’ willingness to adopt mobile banking apps through TTF and trust. However, higher levels of anthropomorphism enhance users’ perceived cost. In addition, both intelligence and anthropomorphism have insignificant effects on perceived risk. The results provide
theoretical contributions for AI-based mobile banking app adoption and offer practical guidance for bank planning to use AI to retain users. Originality/value – Based on SOR theory, this study reveals that as features, AI-enabled intelligence and anthropomorphism help us further understand users’ perceptions regarding cost, risk, TTF and trust in the context of AI-enabled app adoption intentions.