From expectations to satisfaction : a comparative study of customer interactions with AI chatbots vs human agents in e-commerce settings

Abstract

The purpose of this study is to explore the comparative impact of customer interactions with AI chatbots versus human agents in e-commerce settings, focusing on how expectations shape satisfaction. Using Expectation Confirmation Theory (ECT) as the theoretical framework, the research aims to examine the specific expectations customers hold when engaging with AI chatbots compared to human live-chat agents and how these expectations influence their overall satisfaction with both agents. Primary research was collected using a qualitative research method by conducting semi-structured interviews with a typical case sample of 27 Generation Z participants, analysed through the Gioia Method to identify recurring themes. The findings indicate that while AI chatbots offer efficiency and availability, customers still prioritise empathy, adaptability, and problem-solving skills from human agents, particularly in complex or emotionally charged situations. The study contributes to the existing literature by identifying key gaps in chatbot performance and providing actionable recommendations for balancing automation with human interaction in e-commerce customer service. Limitations and suggestions for future research are also discussed.