The Impact of Personalized Recommendation Services in Online Fashion Commerce on User Satisfaction and Purchase Intention

As the shopping environment continues to shift from desktop to mobile, mobile shopping has emerged as a dominant force in online commerce. This growing trend underscores the need for more in-depth research into mobile consumer behavior. This study explores the characteristics of personalized recommendation systems in fashion commerce that cater to individual user tastes and examines their impact on consumer satisfaction and purchase intention. An online survey was conducted from April 15 to 22, 2025, using a panel from a professional internet research firm. The survey targeted individuals with experience purchasing fashion and beauty products (apparel, bags, shoes, accessories, and other fashion goods) via mobile or wireless internet. A total of 312 valid samples were collected and analyzed. Based on the analysis, first, the key characteristics of fashion commerce personalized recommendation systems were identified as diversity, novelty, convenience, reliability, and usefulness. Second, novelty, reliability, and usefulness were found to have a significant positive impact on consumer satisfaction. Third, novelty and convenience had a significant positive impact on purchase intention. This study enhances the understanding of how users evaluate personalized fashion recommendation services and reveals that these systems are being differentiated based on user behavior data to better match individual tastes. It was also confirmed that the services are evolving to enable users to quickly search for desired products, receive advanced recommendations tailored to size and fit, and ultimately leverage AI to combine fashion trends with personal fashion needs to suggest new, fashion styles.