Factors Influencing Consumer Buying Intention Toward AI-Powered Shopping: An Extended UTAUT Approach

Abstract

Artificial intelligence (AI) has changed e-commerce by making personalized recommendations, virtual assistants, and automated decisions possible, which has reshaped how people shop online. Despite these advances, we still do not fully understand what drives consumers to buy through AI-powered shopping. This study looks at the factors that influence consumer buying intention by expanding the Unified Theory of Acceptance and Use of Technology (UTAUT) to include Perceived Ease of Use, Attitude, Perceived Risk, and Trust in AI. We surveyed 432 people and analyzed the results using covariance-based Structural Equation Modeling (CB-SEM). The findings show that Performance Expectancy (β = 0.318, p < 0.001), Social Influence (β = 0.238, p < 0.001), Hedonic Motivation (β = 0.306, p < 0.001), and Perceived Ease of Use (β = 0.294, p < 0.001) all have a significant positive effect on Behavioral Intention. Behavioral Intention, in turn, strongly increases Consumer Buying Intention (β = 0.535, p < 0.001). On the other hand, Effort Expectancy, Attitude, and Perceived Risk did not have a significant impact, while Facilitating Conditions and Trust in AI were linked to lower Behavioral Intention. These results expand the UTAUT framework by adding AI-specific factors and offer practical advice for retailers and e-commerce platforms that want to encourage more consumers to use AI-powered shopping by improving usability, features, and the overall user experience.