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This study enhances product design reliability by improving consumer opinion consistency. A consensus model with particle swarm optimization (PSO) minimizes opinion adjustments for better Kansei evaluation.

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Area of Science:

  • Human-Computer Interaction
  • Product Design
  • Consumer Behavior

Background:

  • Kansei evaluation, using subjective adjectives, is key to understanding consumer preferences for product designs.
  • Ensuring the reliability of Kansei evaluation is crucial for successful product development and reducing design uncertainty.

Purpose of the Study:

  • To develop a method for identifying and enhancing the reliability of consumer Kansei evaluation opinions on product design alternatives.
  • To address the subjectivity and perceptual nature of consumer opinions in design evaluation.

Main Methods:

  • A consensus model was employed to measure the consistency of consumer opinions.
  • An advanced particle swarm optimization (PSO) algorithm, incorporating a Linearly Decreasing Inertia Weight (LDW) method, was proposed for consensus reaching.
  • The method focuses on minimizing opinion adjustments among consumers.

Main Results:

  • The proposed consensus model and PSO-LDW algorithm effectively reached consensus among consumer opinions.
  • The method demonstrated an improvement in the reliability of Kansei evaluation opinions for design alternatives.
  • A case study involving electronic scooter design evaluation validated the proposed approach.

Conclusions:

  • The developed method offers a promising approach to achieving consensus in consumer evaluations.
  • By optimizing opinion adjustments, the method enhances the reliability of Kansei indexes for design decision-making.
  • This research contributes to more robust and data-driven product design processes.