Selecting representative affective dimensions using Procrustes analysis: an application to mobile phone design.

Area of Science:

• Design Research
• Psychometrics
• Human-Computer Interaction

Background:

• Understanding consumer affective responses (ARs) is vital for creating appealing products.
• Adjectives are commonly used to capture subjective feelings about product designs.
• Existing methods for selecting affective dimensions can be refined for greater accuracy.

Purpose of the Study:

• To propose a novel Kansei engineering (KE) approach for selecting representative affective dimensions.
• To utilize factor analysis (FA) and Procrustes analysis (PA) for adjective selection.
• To compare the proposed method with existing techniques, such as cluster analysis (CA).

Main Methods:

• A semantic differential (SD) experiment was conducted to collect consumer ARs.
• Factor analysis (FA) was employed to extract latent factors from affective dimensions.
• Procrustes analysis (PA) with a backward elimination process determined adjective significance based on RSSDs.

Main Results:

• The proposed KE approach successfully ranked adjectives based on their significance in describing affective responses.
• The method provided a systematic way to select representative affective dimensions for product design.
• A case study on mobile phone design illustrated the practical application and effectiveness of the approach.

Conclusions:

• The combined FA and PA method offers an effective strategy for selecting key affective dimensions in product design.
• This approach enhances the ability of designers to understand and quantify consumer subjective feelings.
• The findings contribute to more objective and data-driven product development processes.