Understanding heterogeneity of human preferences for engineering design

Christopher Hoyle, Wei Chen*, Nanxin Wang, Gianna Gomez-Levi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

In today's competitive market, it is essential for producers to provide products which not only achieve high performance, but also appeal to the tastes of consumer. Therefore, a key element of design is an understanding of human preferences for products and features. In this work, a human appraisal experiment is conducted to understand preferences for automobile occupant package design. The experiment is conducted to build predictive parametric models of consumer preferences. An issue with this class of experiment is that the heterogeneity of the experimental respondents contributes to the response, and this heterogeneity must be understood to separate the influence of design factors from that of human factors. Latent class analysis is used to combine multiple responses of the human appraisal respondents to an appropriate set of measures. Cluster analysis and smoothing spline regression are used to gain an understanding of respondent rating styles and preference heterogeneity. These analyses allow estimation of ordered logit models for prediction of consumer occupant package preferences. Methods from machine learning are also investigated as an alternative to parametric modeling.

Original languageEnglish (US)
Title of host publicationDS 58-5
Subtitle of host publicationProceedings of ICED 09, the 17th International Conference on Engineering Design
Pages229-240
Number of pages12
Volume5
StatePublished - Dec 1 2009
Event17th International Conference on Engineering Design, ICED 09 - Palo Alto, CA, United States
Duration: Aug 24 2009Aug 27 2009

Other

Other17th International Conference on Engineering Design, ICED 09
CountryUnited States
CityPalo Alto, CA
Period8/24/098/27/09

Keywords

  • Heterogeneity
  • Human appraisal
  • Latent class analysis
  • Machine learning
  • Ordered logit
  • Smoothing spline regression

ASJC Scopus subject areas

  • Engineering (miscellaneous)
  • Modeling and Simulation

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