Kansei affinity cluster for affective product design

Abstract

In recent years, product emotion and affective design has received encouraging attention from the industry as well as academia all over the world. Several methods and tools exist and used to assist the process of evaluating users' emotional experience, and the proceeding associated procedure. Previous studies involving the assessment of emotion have seen different ways used to represent verbal description of the subjective emotion. Most of them set their basis on several keywords that somehow fit to describe the study domain. However, these have lead to many cases of poor semantic dimension, since a good reference for affinity of words does not exist. This research aimed to develop a full-range of emotional keywords and their affinity cluster by the use of KJ Method. As a result, a total of 820 words were derived and forty-three clusters were generated. The resulting cluster is developed into Kansei Affinity Cluster, which will be a good reference for all studies involving the assessment of emotion. It will benefit the industry as well as academia towards accessing users' subjective emotional experience with product design.