인터넷 쇼핑에서 의류제품 반품행동 결정요인

Abstract

With concerns for consumers` return behaviors affecting internet shopping malls` profits and product management in the internet clothing market, this study is designed to investigate determinants affecting return and path models for return behaviors. For an empirical study, questionnaires are prepared and respondents in their 20s and 30s with internet clothing purchase experience are selected using the convenience sampling. A total of 517 questionnaires are used for the final analysis. Data are analyzed by using SPSS 12.0 software and descriptive statistics, -test, discriminant analysis, regression analysis, and path analysis is conducted. The results are as follows. First, ones who have returned after purchasing clothing items in internet shopping reached 63.4% of the total consumers. Respondents returned items with price at 50 thousand won or less stood at 67.2%, and the most frequent return shopping malls are open markets with their return rate at 51.1%. Second, variables such as risk perception, information search, impulse buying, buying experience, and age have a positive effect on return experience. Impulse buying and buying experience turn out to have a significant effect on the degree of return, but risk perception, information search, age, and gender to have an insignificant effect. Return intention is significantly affected by risk perception, gender, and age. Third, the analysis of path model for return experience shows that perceived risk has a positively effect, and information search has a direct effect as well as an indirect effect through buying experience or impulse buying. The analysis of path model for the degree of return shows that risk perception does not have effect, but information search has indirect effect through buying experience or impulse buying. This study is thought to find consumers` return behavior characteristics in online shopping, and help businesses operating online shopping malls to efficiently manage returns and set up strategies against returns.