Kansei Engineering with Online Content Mining for Cross-Border Logistics Service Design

Abstract

A satisfactory cross-border logistics service (CBLS) can help promote business activities in cross-border e-commerce. Kansei engineering (KE) is an approach to design the elements which satisfy customers\' affective and emotional perceptions into services and products. In this study, the KE approach is applied to derive ideas for the development of CBLS. For this purpose, Partial Least Squares (PLS) is used to analyze the relationships between the feelings of customers and service elements of CBLS. Moreover, this study demonstrates the applications of text mining techniques to analyze the online contents regarding CBLS. Online content mining assists in identifying the service elements and Kansei words for CBLS. Importantly, the relationship between the feelings of customers and service elements of CBLS obtained by online content mining provides complementary results for CBLS design.