Chatbot Trust Dynamics in E-Commerce: Understanding Consumer Perceptions and Behavioral Intentions
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Abstract
This research paper explores the pivotal role of trust in shaping consumer interactions with AI-powered chatbots in the rapidly growing e-commerce sector, with a specific focus on urban regions of Nashik and Mumbai. Drawing upon established theoretical models such as the Technology Acceptance Model (TAM), Trust Theory in Human-Computer Interaction (HCI), and the Computers Are Social Actors (CASA) paradigm, the study examines how user perceptions of chatbot features influence trust formation. A structured quantitative survey of 210 e-commerce users was conducted, analyzing critical factors such as perceived accuracy, anthropomorphism, responsiveness, and privacy concerns. Findings from correlation and multiple regression analysis reveal that perceived accuracy and human-like characteristics significantly enhance user trust, which in turn positively influences purchase intention and consumer satisfaction. The study contributes to both academic literature and practical applications by recommending actionable strategies for enhancing chatbot design and fostering trust in digital customer service systems. These insights are particularly relevant for businesses aiming to improve customer experience, engagement, and conversion rates through intelligent conversational agents.
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