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Trends in product development today indicate that customers will find it hard to distinguish between many products due to functional equivalency. Customers will, therefore, base their decisions on more subjective factors. Moreover, in the future, products will consist, to a higher grade, of a combination of a tangible and intangible part. Kansei Engineering is a tool translating customer's feelings into concrete product parameters and provides support for future product design. Presently, a total of six different types of Kansei Engineering are in use. The aim of this paper is to propose a framework in Kansei Engineering to facilitate the understanding of the different types of Kansei Engineering and to open Kansei Engineering for the integration of new tools. The new structure includes the choice of a product domain, which can be described from a physical and a semantic perspective as building a vector space in each. For the latter mentioned space, the Semantic Differential Method is used. In the next step, the two spaces are merged and a prediction model is built, connecting the Semantic Space and the Space of Product Properties together. The resulting prediction model has to be validated using different types of post-hoc tests.
Schütte et al. (Sat,) studied this question.