E-Commerce 추천 시스템이 소비자의 상품 선택 행동에 미치는 차별적 영향 분석: 아이트래커를 통한 시각적 반응 분석

This study examines the differential effects of consumer tendencies on two recommendation systems offered by e-commerce platforms ("Best Recommendation" vs. "AI Personalized Recommendation") in influencing consumer product selection behavior. The e-commerce industry has rapidly expanded with the widespread adoption of smartphones and the acceleration brought about by the COVID-19 pandemic, leading many consumers to rely on recommendation systems for optimal purchasing decisions. "Best Recommendation" suggests popular products based on the preferences of the majority, enabling quicker decision-making, while "AI Personalized Recommendation" provides tailored experiences by analyzing purchase histories and data from similar users. Employing mobile eye-tracking technology, this study quantifies consumers' visual responses, such as fixation frequency, to the two recommendation methods in a smartphone environment. Furthermore, it investigates how consumers' psychological traits - self-orientation and other-orientation - moderate the relationship between recommendation systems and purchase intention. This research identifies the influence of recommendation systems on visual attention and decision-making processes. The findings contribute to the development of refined personalized marketing strategies for e-commerce platforms, offering practical insights for companies aiming to enhance UX/UI design and customer experience.