Fuzzy Set Representation of Kansei Texture and its Visualization for Online Shopping

Abstract

A fuzzy set representation method of Kansei Texture is proposed to express individual difference of Kansei Texture feelings for the purpose of online shopping. The method provides buyers with criteria whether a request to send samples is necessary according to the variance degree of individual differences, and it also offers sellers with information regarding the possibility of returned goods in case of significant individual differences with regard to expensive prices. The correlation coefficient of the degree of individual difference and sample demand is 0.78 (P<0.05, t-test), i.e., a directly proportional relationship is observed between the two degrees. There is a tendency for expensive goods, e.g., those with price greater than $50, to be returned in the case of a large individual difference degree, i.e., the individual difference degree of Kansei Texture with price information provides a useful strategy for estimating the possibility of returned goods. Moreover, the relationship between stress and individual difference is also shown. Further validity verification is planned in order to realize practical applications in the real market.