AI Chatbots for Humanized Service Touch and Customer Satisfaction Towards Promoting AI-Enabled Banking Interactions

Abstract

This study examines the effects of AI chatbot communication techniques on customer satisfaction and intention to use AI enabled banking in the banking sector. Drawing on Expectancy Disconfirmation Theory (EDT) and Privacy Calculus Theory (PCT), the study investigates the effects of socially focused conversational styles and text-based communication mode on bank customers' perceptions of humanness, happiness, and privacy concerns. A structured survey was completed by 135 Gen Z students, and Smart PLS software were used for analysis. The results demonstrate that both text-based and social-oriented communication styles significantly boost perceived humanness, which positively affects user satisfaction and intention to use. Additionally, it was demonstrated that privacy concerns moderated the relationship between pleasure and intention. The study fills in theoretical gaps and provides helpful information for chatbot designers and banking managers by applying the dual frameworks (EDT and PCT) in the context of AI-driven banking interactions. Enhancing user-centric chatbot capabilities while addressing privacy concerns may help to improve the customer experience and encourage broader AI use in digital banking.

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