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

Cheng Ta Yeh*, Mu-Chen Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish
Pages (from-to)401-417
Number of pages17
JournalComputers and Industrial Engineering
StatePublished - 1 Jun 2018


  • Decision tree
  • Door-to-door delivery service
  • Kansei Engineering
  • Service design

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