Consumers' Kansei Needs Clustering Method for Product Emotional Design Based on Numerical Design Structure Matrix and...

Area of Science:

• Product Design
• Human-Computer Interaction
• Computational Intelligence

Background:

• Consumer perception, or Kansei needs, is often described using numerous adjectives, complicating product design.
• Simplifying these complex Kansei needs is crucial for effective product positioning and design strategy.

Purpose of the Study:

• To develop and present a novel method for clustering Kansei adjectives.
• To reduce the dimensionality of consumer Kansei needs for practical design applications.

Main Methods:

• Parameterization of conventional Design Structure Matrix (DSM) into Numerical Design Structure Matrix (NDSM).
• Integration of genetic algorithms for optimizing Kansei clusters within the NDSM framework.
• Application of a four-point scale for assigning link weights between Kansei adjectives.

Main Results:

• The proposed method successfully clusters Kansei adjectives, reducing complexity.
• Demonstrated effectiveness through a case study involving electronic scooter Kansei needs.
• The NDSM and genetic algorithm approach provides an optimal clustering solution.

Conclusions:

• The developed method is effective and promising for clustering Kansei needs in product emotional design.
• Facilitates explicit product positioning and provides a basis for design work.
• Offers a quantitative approach to understanding subjective consumer perceptions.