Abstract
The purposes of this study were to investigate Chinese female consumers' shopping orientation and clothing shopping behaviors on the internet and to find the differences in internet shopping behaviors of consumer groups segmented by clothing shopping orientation. The subjects were 417 women in their 20s and 30s from the Gillim Province, China. The research method was a survey, and the questionnaire consisted of a clothing shopping orientation subscale, clothing, their shopping behaviors via the internet, and the subjects' demographic characteristics. For data analysis, a frequency analysis, a cross-tab analysis, a factor analysis, a cluster analysis, ANOVA, and Duncan's multiple range test were performed. The results of this study were as follows. The clothing shopping orientation was derived using five factors (trend pursuit, pleasure pursuit, brand pursuit, economic pursuit, and convenience pursuit). Chinese female consumers were classified into three groups (hedonic group, ambivalent group, and practical group) by clothing shopping orientation. These three groups showed many significant differences in their clothing shopping behaviors on the internet. The hedonic group preferred the specialty and cross-border shopping malls, and considered product quality and trend as their main purchase motives. The ambivalent group considered the convenience of the purchase and trend as important motives as compared to the other groups, and they use more various product selection criteria. The practical group considered low price and convenience and the search simplicity of various products as major purchase motives. In addition, the hedonic and ambivalent groups had a higher purchase satisfaction and purchase intention from internet shopping than the practical group. This study suggested that clothing shopping orientation is one of the useful segmentation variables and fashion marketers needed to establish differentiated marketing strategies for each consumer group that is segmented by clothing shopping orientation.