Abstract

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.

Original languageEnglish (US)
Pages (from-to)3831-3840
Number of pages10
JournalProceedings of the International Conference on Engineering Design, ICED
Volume2019-August
DOIs
StatePublished - 2019
Event22nd International Conference on Engineering Design, ICED 2019 - Delft, Netherlands
Duration: Aug 5 2019Aug 8 2019

Keywords

  • Big data
  • Customer preference modeling
  • Market implications
  • Network modeling
  • User centred design

ASJC Scopus subject areas

  • Engineering (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Modeling and Simulation

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