Artificial intelligence-powered financial assistant adoption factors and their association with behavioural intention of banking customers: An extended UTAUT model

Technology and human expertise collaborate to improve consumer experiences, boost operational effectiveness, and provide creative financial solutions in this new era of banking. The current study aims to investigate the relationship between customer adoption characteristics and the Behavioural intention to use an AI-powered financial assistant, given that more customers are inclining towards tech-based services for increased productivity and efficiency. An adapted questionnaire has been used in the study that includes the adoption factors of AI-powered financial assistant services, including performance expectancy, effort expectancy, social influence, facilitating conditions, and personal innovativeness, as well as their relationship with the behavioural intention to use AI-powered financial assistants. The data has been collected from 204 banking customers of Haryana through a survey. Secondary Data has been gathered from magazines, books, journals, and websites to complement the primary data. Statistical tools such as Variance-Based Structural Equation Modelling (VB-SEM) using the software SMART-PLS 4.0, an advanced statistical technique has been used to analyse the proposed model. The result of the present study indicates that Personal Innovativeness, Effort Expectancy, Performance expectancy and Facilitating conditions are the significant adoption factors of Behavioural Intention to use the AI powered financial assistant while social influence is showing insignificant relationship with the behavioural intention to use the AI powered financial assistant. The R-squared value and the adjusted R-squared value demonstrate that the model explains a large amount of variance in the dependent variable i.e. behavioural intention to use the AI powered financial assistant which suggest that the proposed model has strong predictive potential. Additionally, because the model's SRMR value falls substantially inside the allowed range, it is understood to have a strong fit to the data. By looking at the important adoption factors of behavioural intention to use AI-powered financial assistants for banking services, the current study's findings will help banks and other financial institutions better understand their customers and better meet their expectations.