Affective response dimension selection for product design: A comparison of cluster analysis and procrustes analysis

Abstract

In recent years, the relationship between consumers' affective responses (CARs) and product form features (PFFs) has been an important issue in the industrial design field. Responding to consumers' feelings towards a product's appearance, CARs are usually presented in the form of a choice of adjectives. Based on the Kansei Engineering (KE) concept, this study conducted Clustering Analysis (CA) and Procrustes Analysis (PA) to find the CARs of a product's shape, and compared the results of CA and PA. In the initial stage of the study, 75 samples of mobile phones were collected from the Taiwan market place. Twenty-two pairs of adjectives describing the cell phones were used for a Semantic Differential (SD) experiment. Two-stage clustering was implemented to find the clustering segmentations of the affective responses according to the factor loading from the Factor Analysis (FA), and to obtain representative pairs of adjectives within the clustering segmentations. PA was also used to decide adjective priorities according to the sorting rule. The KJ (Kawakita Jiro) method was used to verify both CA and PA. Finally, these two methods were analyzed and compared.