Applying Kansei Engineering and data mining to design door-to-door delivery service

Abstract

This study proposes a service design approach integrating Kansei Engineering and a data mining technique, in which Kansei Engineering is an ergonomic approach of customer-oriented product/service development and can translate the users' subjective perceptions into a design specifications. The integrated approach collects customers' relevant perceptual vocabulary and service properties based on the Kansei Engineering procedure. Subsequently, it quantifies the relationship among service properties, perceptual responses and usage intention through the data mining technique using a decision tree. A case of door-to-door delivery (D2DD) service is adopted to demonstrate that the proposed approach can incorporate the customers' feelings into the process of service design or improvement and illustrate that the decision tree is suitable to be integrated with Kansei Engineering. The analytical results show the influence of a combination of different service properties (resp. perceptual responses) on a perceptual response (resp. usage intention). It is found that the combinations of crucial perceptual responses result in positive (resp. negative) usage intention and the property combinations result in these crucial perceptual responses. Accordingly, the D2DD service provider can improve or create its service based on the research findings.