Abstract

The development of AI-driven technologies offers unique opportunities for retailers to meet evolving customer needs and gain a competitive edge by providing personalized services to customers, but it also demands a radical evolution of company-customer interaction. AI in online retail includes the use of conversational technologies to improve customer engagement in one-to-one interaction with consumers. Despite empirical research on conversational technologies has seen substantial growth in recent years, there is a need to understand how user experience and online interaction habits influence AI agents’ usage and word-of-mouth intentions. To address this gap, based on the Technology Acceptance Model, this study investigates whether perceived ease of use, perceived usefulness, and perceived enjoyment impact the intentions toward AI e-retail agents in terms of electronic Word of Mouth (e-WOM) and usage intentions, and the moderating role of chat interaction frequency with human employees. Results from a sample of 208 users, analyzed with Structural Equation Modeling, indicate that ease of use impacts e-WOM rather than usage intentions. Perceived usefulness and perceived enjoyment influence both usage intentions and e-WOM. Interestingly, the relationship between perceived enjoyment and e-WOM is moderated by the chat interaction frequency with employees. The findings shed light on the complex interplay between user experience, human-agent interactions, and their impact on online retail dynamics, offering valuable insights for businesses aiming to optimize AI-driven retailing strategies to enhance system usage and encourage positive referrals.