The Impact of AI Recommendation Features on Consumer Purchasing Decisions in E-Commerce Environments

Abstract

Artificial intelligence recommendation features such as visual search, real-time offers, chatbot interactions, and generative recommendations have become central to e-commerce. A key gap remains because most studies focus on purchase intention instead of actual decisions and examine these AI features separately. This study addresses this gap by asking: What is the impact of AI recommendation features on consumer purchasing decisions in e-commerce environments? Using a sequential mixed-methods design in the Saudi Arabian e-commerce market, the quantitative phase surveyed 343 online shoppers analyzed with PLS-SEM, while the qualitative phase involved 15 interviews analyzed thematically with ATLAS.ti. The conceptual model was informed by the Technology Acceptance Model and Stimulus-Organism-Response framework. Results showed that trust was the only significant predictor of emotional reactions toward AI assistant tools use, while perceived intelligence, usefulness, and ease of use were not significant. Emotional reactions toward AI tools significantly influenced purchase decisions. The consideration set had a direct effect on purchase decisions but did not moderate the relationship. Qualitative findings revealed that AI does not simply assist at isolated points but actively shapes the entire buying journey from initial search to final decision. The significance appears in two areas. Theoretically, the study advances understanding of how AI-enabled systems shape consumer decision-making in digital platforms by focusing on purchase decisions rather than behavioral intentions and integrating quantitative and qualitative insights. Practically, the findings guide e-commerce businesses to design AI features that build consumer trust and support consumers throughout different stages of the buying journey.