Factors determining user attitude and experience in artificial intelligence based retail banking applications: The case of academic staff

With the acceleration of digitalization, banks have increasingly adopted artificial intelligence-based virtual assistants in retail banking services to enhance customer experience, optimize costs, and make service processes more efficient. These technologies offer significant advantages in providing customer support, offering financial advice, and facilitating various banking transactions. However, scientific studies on factors such as users approach to artificial intelligence technologies, the adoption process, and actual usage remain limited. Therefore, the purpose of this study is to identify the factors that determine user attitudes and experiences in artificial intelligence-based retail banking applications. In this context, within the framework of the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the attitudes, usage intentions, and experiences of customers using artificial intelligence-based banking applications are analyzed. The study evaluates the effects of nine independent variables - perceived usefulness, perceived information quality, perceived empathy, anthropomorphism, trust, perceived time risk, technological anxiety, openness to experience and stickiness - on user attitude and actual usage. Through a survey form consisting of scales representing dependent and independent variables along with demographic variables, it was administered exclusively to academics across Turkey, yielding 489 valid observations. The findings indicate that cognitive and emotional factors are more influential in the adoption of artificial intelligence-based individual banking applications, while personality traits and aesthetic elements have relatively less impact. The functionality, reliability, and potential to offer benefits of these systems emerge as the most decisive factors shaping user attitudes. Meanwhile, concerns about technology and behavior patterns rooted in past experiences may hinder the development of these attitudes and their transformation into actual usage. This suggests that in technological transformation processes, it is not only essential to persuade users towards the new technology, but also to overcome emotional and behavioral barriers. Based on the findings of this study, various recommendations have been developed for future research and the banking sector. In conclusion, this study represents a significant step toward understanding the adoption process of artificial intelligence technologies in the retail banking sector.