| Abstract |
| As artificial intelligence (AI) becomes increasingly integrated into online shopping environments, AI-driven recommendations are emerging as a standard practice. While prior research has primarily compared AI and human recommendations, relatively little attention has been paid to hybrid approaches that combine both. Addressing this gap, this study examines how consumers perceive and respond to AI+human recommendations in the context of fashion shopping. Using a between-subjects experimental design, participants were assigned to one of three conditions (AI-only, human-only, or AI+human recommendation), and data were collected from 299 female consumers aged 20-39 in the United States. The results show that AI+human recommendations lead to higher perceived recommendation quality, which in turn enhances consumer attitude toward the recommendation and purchase intention. Furthermore, the findings reveal that the effectiveness of hybrid recommendations varies depending on consumers’ fashion decision difficulty, such that the effect is stronger for those with lower decision difficulty. By identifying perceived recommendation quality as a key psychological mechanism, this study contributes to the literature on AI-based recommendations. The findings also provide practical implications for fashion firms by suggesting that integrating AI and human input and tailoring recommendation strategies to different consumer segments can enhance recommendation effectiveness. |
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| Key Words |
| AI-Based Recommendation, Hybrid Recommendation (AI+Human), Perceived Recommendation Quality, Consumer Response, Fashion Decision Difficulty |
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