From Chatbots to Checkout: How AI Shopping Assistants Influence Purchase Intention

Abstract

This study examines how AI shopping assistants influence consumers' purchase intentions by integrating technology adoption factors (perceived usefulness and perceived ease of use), hedonic motivations (enjoyment and passing time), and privacy concerns into a unified model. Grounded in the Technology Acceptance Model (TAM), the research develops a framework where trust serves as a key mediating variable. A quantitative survey (N = 235) of AI shopping assistants’ users was analyzed using partial least squares structural equation modeling (PLSSEM). The results indicate that perceived usefulness (β = 0.345) and enjoyment (β = 0.306) significantly enhance consumer trust in AI shopping assistants, while privacy concerns (β = – 0.290) exert a direct, negative influence on purchase intention. Trust fully mediates the effects of technology-related and hedonic factors on purchase intention (β = 0.526). Additionally, cultural (EU vs. non-EU) and product-type (hedonic vs. utilitarian) differences have impact on these relationships. The findings advance theoretical understanding of AI-driven consumer behavior and provide actionable insights for e-commerce platforms to optimize chatbot design—balancing functionality, engagement, and privacy assurances.