The research employs partial least squares structural equation modelling, complemented by Importance–Performance Map Analysis and Necessary Condition Analysis as the main analyses to examine survey data from 230 mobile banking users across the Gulf Cooperation Council region.

The model employed explains 77.8% of the variance in intentions to change to AI-based Chatbots. It was found that the most powerful motivators were hedonic motivation, habit formation and trust. Hedonic motivation and habit directly affected intentions to change and also influenced ideas about the potential effects of performance and effort expectancies. Expectation of improved performance was an underlying assumption, but could not be used to anticipate changed behaviours, while the prospects of improved workload and security were present, but were not primary determinants, suggesting their role as bottlenecks in adoption. Findings also identified a connection between banking security, hedonic motivation, and habit, and indicated that the perceived banking security reinforced the association between hedonic motivation and habit, thereby increasing the possibility of long-term adoption. Together, the findings clarify that all factors are key and necessary in enabling more user preferences from menu-based mobile banking interfaces to voice and text-enabled AI Chatbots in performing banking chores.

The results provide useful guidelines for banks and financial institutions who want to motivate customers to adopt AI-based Chatbots. The findings of the study show that banks’ actions cannot only focus on performance potential, but also primacy must be given to ensuring trust, providing a pleasurable experience and cultivating habit formation, assuring that the latter two aspects are underpinned by a commitment to security. By fostering digital transformation in banking, these strategies align with the United Nations’ Sustainable Development Goal 8.

This study both builds on and addresses gaps in the existing scholarship by considering the important, but still undeveloped topic of why users decide to change their digital banking behaviours to the use of AI Chatbots. Previous studies have tended to focus on intrinsic factors which motivate this change; this research evaluates how these inherent factors mediate the impact of external factors on intentions to make the change to AI Chatbots. Furthermore, this research draws attention to the role of trust and banking security in this decision-making process. This is an area which is not fully addressed in the existing literature. Methodologically, the study extends UTAUT2 by integrating necessity logic and importance–performance perspectives. It is among the first to employ advanced Partial Least Squares Structural Equation Modelling analyses in this setting, thereby contributing novel theoretical and practical insights into mobile banking transformation.

https://www.emerald.com/ijicc/article-abstract/doi/10.1108/IJICC-11-2025-0771/1351755/Tapping-out-talking-in-what-drives-users-to-ditch?redirectedFrom=fulltext  

Dawood, H. M., Liew, C. Y., & Rajan, M. E. S. (2026). Tapping out, talking in: what drives users to ditch menu-based navigation and switch to voice and text AI-based chatbots in mobile banking? International Journal of Intelligent Computing and Cybernetics, 1-35. doi:10.1108/ijicc-11-2025-0771