Revolutionizing banking services with ChatGPT: an integrated framework for user adoption

Abstract This study aims to understand the implementation of artificial intelligence (AI)-enabled technology in banking from a user perspective. For this purpose, we examine customer intention to adopt ChatGPT (Generative Pretrained Transformer) in performing banking services. To assess the study objectives, this research integrates two well-established technology models, namely, the Task-Technology Fit (TTF) Model and the Technology Acceptance Model (TAM). This study employed a quantitative research approach and utilized a survey-based questionnaire to collect primary data from banking customers. We analyzed a total of 424 valid responses via the partial least squares structural equation modeling (PLS-SEM) approach for the results and estimations. The results of this study indicate that the TTF and TAM have a positive and significant effect on customers’ intention to adopt ChatGPT in banking services. Additionally, this research revealed that ChatGPT knowledge plays a significant role as a moderator in predicting user intentions toward ChatGPT adoption. Our findings provide unique insights into the contemporary concept of AI-enabled technology adoption in the banking sector. This study highlights various aspects of the theoretical and managerial implications for managers and academicians. Theoretically, this study provides an in-depth analysis by integrating the TTF with an extended TAM. Practically, the findings of this study enhance strategic planning and informed decisions in the banking sector regarding the use of ChatGPT. This study also assumes that this is the first attempt in the Pakistani banking sector to analyze customer intentions to adopt ChatGPT in their banking transactions. Finally, our study limitations provide a discussion forum for subsequent studies that adapt or imitate our methodology in other demographics or countries. This will encourage the development of AI technologies in financial companies.