Artificial Intelligence-Based Conversational Agents in the Indian Banking System: An Adoption and Integration Perspective

This research investigates the adoption of AI conversational agents within the Indian banking sector. Drawing upon the UTAUT-2 model, the study extends the framework to encompass trust, anthropomorphism, and perceived privacy risk. A cross-sectional design was employed collecting data from 384 actively engaged mobile banking customers in India. The study's findings reveal that performance expectancy, effort expectancy, hedonic gratification, trust, and human-like traits positively influence the intention to adopt AI conversational agents. In contrast, privacy and security concerns exert a negative impact on adoption intent. This research contributes a holistic understanding of AI agent adoption dynamics, addressing the multi-faceted factors that influence user behavior within the Indian banking context.