Using a two-stage method to understand the critical factors influencing customers’ intention to switch from traditional to artificial intelligence based banking services: A perspective based on the push–pull–mooring model

Abstract

Purpose

Generative artificial intelligence (GenAI) enables banks to enhance customer service experiences. However, limited research has been conducted on factors influencing customers’ intention to switch from traditional to artificial intelligence (AI) based banking services. Therefore, the present study explored the key antecedents of the aforementioned intention.

Methods

This two-stage study was based on the push–pull–mooring model. In Stage 1, in-depth semistructured interviews were conducted with stakeholders related to AI-based banking services to identify critical factors influencing customers’ intention to switch from traditional to AI-based customer services. In Stage 2, the results obtained in Stage 1 were combined with results from the literature to create a second-order model and direct-effect model (Models 1 and 2, respectively). Quantitative survey data were then collected to validate these models.

Results

Model 1 indicated that among pull, push, and mooring factors, pull factors had the strongest effect on the aforementioned intention, followed by mooring factors and then push factors. The explanatory power (R²) of this model was 70 %. Furthermore, Model 2 indicated that the attractiveness of the alternative (a pull factor) and the need for interpersonal interaction and inertia (mooring factors) were the key factors influencing switching intention. The explanatory power (R²) of this model was 76 %.

Conclusion

In summary, this study identified and validated critical factors affecting customers’ intention to switch to AI-based banking services. The findings enrich the understanding the social interaction and user behavior of AI, offering valuable insights for promoting AI-driven services and applications.