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Comparing Utility-based and Network-based Approaches in Modeling Customer Preferences for Engineering Design

Published online by Cambridge University Press:  26 July 2019

Zhenghui Sha
Affiliation:
University of Arkansas;
Youyi Bi
Affiliation:
Northwestern University;
Mingxian Wang
Affiliation:
Ford Motor Company
Amanda Stathopoulos
Affiliation:
Northwestern University;
Noshir Contractor
Affiliation:
Northwestern University;
Yan Fu
Affiliation:
Ford Motor Company
Wei Chen*
Affiliation:
Northwestern University;
*
Contact: Chen, Wei, Northwestern University, Mechanical Engineering, United States of America, [email protected]

Abstract

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Customer preference modeling provides quantitative assessment of the effects of engineering design attributes on customers’ choices. Utility-based approaches, such as discrete choice model (DCM), and network analysis approaches, such as exponential random graph model (ERGM), have been developed for customer preference modeling. However, no studies have compared these two approaches. Our objective is to identify the distinctions and connections between these two approaches based on both the theoretical foundation and the empirical evidence. Using the vehicle preference modeling as an example, our study shows that when network structure effects are not considered, results from ERGM are consistent with DCM in most of the test cases. However, in one case where customers have varying choice set with multiple alternatives, inconsistencies are observed, possibly due to the discrepancies of the two models in taking different information when calculating choice probabilities. The insights will lead to valuable guidance for choosing the technique for customer preference modeling and co- developing the two frameworks to support engineering design.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

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