Ai-enabled chatbots and customer behavior: A comprehensive review  

The recent rise in artificial intelligence implementations across the realm of e-commerce has begun to question what the implications of LLM-based (large language model) chatbots mean for customer behavior. With e-commerce firms significantly investing in conversational AI, empirical findings concerning the true customer engagement and intention to purchase are more often rather fragmented and inconclusive. This literature review assesses the findings of many peer-reviewed publications to bridge knowledge about the chatbot phenomenon and its relationship with customer behavior and intentions. Three branches of research run through the literature review, including (1) the technological features of anthropomorphism, interactivity and intelligence; (2) the psychological impact of trust, emotion, and motivation and (3) situational considerations such as culture and use. While many findings suggest how the perceived human-like attributes of a chatbot can prompt more specific types of engagement, a gap is noted between engagement and actual intention to purchase. This gap seeks to undermine any suggestions of intention to purchase on a level that can be clearly understood, translating initial insights into anticipated outcomes mediated by trust and assessed further by potential moderators yet these conditions remain unexplored. Ultimately, this assessment argues for implications for future empirical research by creating an integrative framework that connects expectation-confirmation theory, technology acceptance models, and social response theory. The expected anticipated trust as a mediating variable, along with relevant moderators warrant systematic evaluation. Future studies should examine actual purchase behaviour rather than relying on stated intentions.