Evaluating The Performance of an AI-Powered Chatbot in Providing Personal Loan Advisory Services in Sri Lanka

Abstract

The research is aimed at examining how AI-based chatbots can perform the duties of loan advisors in Sri Lanka. To attain this, it applies the Elaboration Likelihood Model (ELM) and the Need for Cognition (NC) synthesis to conceptualize how the Message Level (ML), Source Level (SL), and the argument Quality Level (QL) influence the user Intention (UI) to adopt this service. This survey was employed in the study based on a sample of 156 respondents of Generation Z and millennials in Colombo. The respondents were provided the opportunity to use this prototyped chatbot service, named “FinPal” and then respond to the online survey questionnaire. The data was analyzed using SPSS and SmartPLS 4 software. The findings indicated a highly positive relationship between Message Type (MT) and persuasiveness at the ML and SL, as well as QL. However, the Need for Cognition (NC) does not significantly moderate these connections, which implies that the cognitive engagement does not significantly contribute to determining the willingness of users to use AI chatbots to obtain advice on loans. The results imply that the willingness to borrow with the help of AI chatbots improves when two-sided messages imply high quality, credibility, and well-designed communication. The resilience of AI technologies provides an opportunity to fill in the space that emerges in the standards, such as in Sri Lanka.

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