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 language | English (US) |
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Title of host publication | DS 58-5 |
Subtitle of host publication | Proceedings of ICED 09, the 17th International Conference on Engineering Design |
Pages | 229-240 |
Number of pages | 12 |
Volume | 5 |
State | Published - Dec 1 2009 |
Event | 17th International Conference on Engineering Design, ICED 09 - Palo Alto, CA, United States Duration: Aug 24 2009 → Aug 27 2009 |
Other
Other | 17th International Conference on Engineering Design, ICED 09 |
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Country/Territory | United States |
City | Palo Alto, CA |
Period | 8/24/09 → 8/27/09 |
Keywords
- Heterogeneity
- Human appraisal
- Latent class analysis
- Machine learning
- Ordered logit
- Smoothing spline regression
ASJC Scopus subject areas
- Engineering (miscellaneous)
- Modeling and Simulation