AI 추천시스템 기반 패션 구독 서비스 특성과 사용 의도와의 관계에 관한 연구

Abstract

The AI-recommended system-based fashion subscription services are becoming a shopping model that provides new shopping methods through convergence with deep learning AI technology to meet consumers" personalization, fashion styling, and shopping needs. However, previous studies related to the fashion business mainly focused on developing AI recommendation systems or studying AI application cases, but there is a limit to understanding the behavioral patterns of fashion consumers. Therefore, it is meaningful to analyze consumers" purchasing intention to use these new technology-based subscription services. This study attempted to identify the main causal variables that affect the intention to use the fashion subscription service. The proposed research model and hypothesis were verified through a Partial Least Squares Structural Equation Modeling based on a survey of 430 consumers. It examined the relationship between these factors affecting the intention to use through perceived value based on the value-based adoption model. This research found that service convenience has a major influence on perceived usefulness in relation to benefits, and recency on perceived enjoyment. On the contrary, it was found that special preferential benefits for privacy concern and recency for subscription costs had the greatest positive and negative effects, respectively. In the case of AI-recommended system-based fashion subscription services, this study also suggested it is important to provide customers with a convenient, easy-to-use system and up-to-date products. It is also theoretically meaningful to apply the value-based acceptance model in the relationship between he characteristics of AI recommendation-based subscription services and intention to use.