AI Robo-Advisory Acceptance in Emerging Markets: A Dual Analytical Approach Using PLS-SEM and NCA

This study aimed to determine the factors influencing the acceptance of AI robo-advisory services for financial decision-making among bank fintech app users in an emerging economy such as Bangladesh. The study also explored the moderating effect of digital financial literacy. This study employed a modified version of the Use of Technology and User Acceptance Theory 2 (UTAUT2) model. A survey was conducted using Google Forms. A total of 309 fintech app users in Bangladesh from selected districts participated in the survey. The study utilized partial least square structural equation (PLS-SEM) modeling to examine the correlations between the variables using SMARTPLS software. Moreover, to complement the sufficiency logic assessed with PLS-SEM, Necessary Condition Analysis was applied to identify antecedents that function as non-compensatory prerequisites for actual use (AU) of robo-advisors. Combining NCA with PLS-SEM reveals that behavioral intention is both necessary and sufficient for AI robo-advisor adoption, while digital financial literacy, facilitating conditions, and hedonic motivation serve as critical thresholds for achieving higher-intensity usage, especially in emerging markets. Digital financial literacy also moderates the relationship between behavioral intention and the actual usage of AI robo-advisor for financial decision-making. The outcome offers a new viewpoint on how users perceive utilizing robo-advisors, which will benefit the technical, customer care, and financial service delivery channels. Additionally, by linking the use of robo-advisors in the banking industry, the study adds to the body of knowledge on the UTAUT2 paradigm.