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

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Summary
This summary is machine-generated.

This study introduces a new method using numerical design structure matrices (NDSM) and genetic algorithms to simplify complex consumer Kansei needs. This approach effectively clusters adjectives for better product positioning and design.

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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.