Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-28T20:57:37.988Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Part of: User-Centred Design

Published online by Cambridge University Press:  26 July 2019

Zhenghui Sha ,
Youyi Bi ,
Mingxian Wang ,
Amanda Stathopoulos ,
Noshir Contractor ,
Yan Fu  and
Wei Chen
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, weichen@northwestern.edu

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

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.

Keywords

Market implicationsUser centred designBig dataNetwork modelingCustomer preference modeling
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 3831 - 3840
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

References

Ben-Akiva, M.E. and Lerman, S.R. (1985), Discrete Choice Analysis: Theory and Application to Travel Demand, Vol. 9, MIT press.Google Scholar
Bi, Y., Xie, J., Sha, Z., Wang, M., Fu, Y. and Chen, W. (2018), “Modeling Spatiotemporal Heterogeneity of Customer Preferences in Engineering Design”, ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Quebec City, Quebec, Canada.Google Scholar
Chang, D. and Chen, C.-H. (2014), “Understanding the influence of customers on product innovation”, International Journal of Agile Systems and Management 20, Vol. 7 No. 3–4, pp. 348–364, Inderscience Publishers Ltd.Google Scholar
Chen, C.-H., Khoo, L.P. and Chen, N.-F. (2015), “Consumer Goods”, Concurrent Engineering in the 21st Century, pp. 701–733, Springer.Google Scholar
Chen, W., Hoyle, C. and Wassenaar, H.J. (2013), Decision-Based Design: Integrating Consumer Preferences into Engineering Design, Decision-Based Design: Integrating Consumer Preferences into Engineering Design, Springer Science and Business Media.Google Scholar
Cranmer, S.J. and Desmarais, B.A. (2011), “Inferential network analysis with exponential random graph models”, Political Analysis, Vol. 19 No. 1, pp. 66–86, Cambridge University Press.Google Scholar
Frischknecht, B.D., Whitefoot, K. and Papalambros, P.Y. (2010), “On the Suitability of Econometric Demand Models in Design for Market Systems”, J. Mech. Des, Vol. 132 No. 12, p. 121007.Google Scholar
Fu, J.S., Sha, Z., Huang, Y., Wang, M., Fu, Y. and Chen, W. (2017), “Modeling Customer Choice Preferences in Engineering Design using Bipartite Network Analysis”, Proceedings of the ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Cleveland, Ohio.Google Scholar
Galili, T. (2013), “Log Transformations for Skewed and Wide Distributions”, R-Statistics Blog, available at: https://www.r-statistics.com/2013/05/log-transformations-for-skewed-and-wide-distributions-from-practical-data-science-with-r/.Google Scholar
Gorsuch, R.L. (1983), Factor Analysis, Lawrence Erlbaum Associates, Hillsdale.Google Scholar
Green, P.E. (1970), Multidimensional Scaling and Related Techniques in Marketing Analysis, Allyn and Bacon.Google Scholar
Handcock, M.S. and Hunter, D.R. (2018), “Reference manual of package ergm”, available at: https://cran.r-project.org/web/packages/ergm/ergm.pdf.Google Scholar
Hauser, J.R. and Wernerfelt, B. (1990), “An evaluation cost model of consideration sets”, Journal of Consumer Research, Vol. 16 No. 4, pp. 393–408, The University of Chicago Press.Google Scholar
He, L., Chen, W. and Conzelmann, G. (2012), “Impact of vehicle usage on consumer choice of hybrid electric vehicles”, Transportation Research Part D: Transport and Environment, Vol. 17 No. 3, pp. 208–214, Elsevier.Google Scholar
He, L., Wang, M., Chen, W. and Conzelmann, G. (2014), “Incorporating social impact on new product adoption in choice modeling: A case study in green vehicles”, Transportation Research Part D: Transport and Environment, Vol. 32, pp. 421–434, Elsevier.Google Scholar
Hoyle, C., Chen, W., Wang, N. and Koppelman, F.S. (2010), “Integrated Bayesian hierarchical choice modeling to capture heterogeneous consumer preferences in engineering design”, Journal of Mechanical Design, Vol. 132 No. 12, pp. 121010, American Society of Mechanical Engineers.Google Scholar
Hoyle, C.J. and Chen, W. (2009), “Product attribute function deployment (PAFD) for decision-based conceptual design”, IEEE Transactions on Engineering Management, Vol. 56 No. 2, pp. 271–284.Google Scholar
Hunter, D.R., Handcock, M.S., Butts, C.T., Goodreau, S.M. and Morris, M. (2008), “ergm: A package to fit, simulate and diagnose exponential-family models for networks”, Journal of Statistical Software, Vol. 24 No. 3, pp. nihpa54860, NIH Public Access.Google Scholar
Johnson, R. (2011), Multiple Discriminant Analysis: Marketing Research Applications, Marketing Classics Press.Google Scholar
Kaul, A. and Rao, V.R. (1995), “Research for product positioning and design decisions: An integrative review”, International Journal of Research in Marketing, Vol. 12 No. 4, pp. 293–320, Elsevier.Google Scholar
Lusher, D., Koskinen, J. and Robins, G. (2012), Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications, Cambridge University Press.Google Scholar
MacDonald, E.F., Gonzalez, R. and Papalambros, P.Y. (2009), “Preference Inconsistency in Multidisciplinary Design Decision Making”, J. Mech. Des., Vol. 131 No. 3, p. 031009.Google Scholar
McFadden, D. (1978), “Modeling the choice of residential location”, Transportation Research Record, No. 673.Google Scholar
Morris, M., Handcock, M.S. and Hunter, D.R. (2008), “Specification of exponential-family random graph models: terms and computational aspects”, J. Stat. Softw., Vol. 24 No. 4, p. 1548.Google Scholar
Rahman, M.H., Gashler, M., Xie, C. and Sha, Z. (2018), “Automatic Clustering of Sequential Design Behaviors”, ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Quebec City, Canada, Paper #: DETC2018-86300.Google Scholar
Sha, Z., Huang, Y., Fu, S., Wang, M., Fu, Y., Contractor, N. and Chen, W. (2018), “A Network-Based Approach to Modeling and Predicting Product Co-Consideration Relations”, Complexity, Vol. 2018.Google Scholar
Sha, Z., Moolchandani, K., Panchal, J.H. and DeLaurentis, D.A. (2016), “Modeling Airlines’ Decisions on City-Pair Route Selection Using Discrete Choice Models”, Journal of Air Transportation, American Institute of Aeronautics and Astronautics.Google Scholar
Sha, Z., Saeger, V., Wang, M., Fu, Y. and Chen, W. (2017), “Analyzing Customer Preference to Product Optional Features in Supporting Product Configuration”, SAE International Journal of Materials and Manufacturing, Vol. 10 No. 2017-1–243.Google Scholar
Shocker, A.D., Ben-Akiva, M., Boccara, B. and Nedungadi, P. (1991), “Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions”, Marketing Letters, Vol. 2 No. 3, pp. 181–197, Springer.Google Scholar
Snijders, T.A.B., Pattison, P.E., Robins, G.L. and Handcock, M.S. (2006), “New specifications for exponential random graph models”, Sociological Methodology, Vol. 36 No. 1, pp. 99–153, SAGE Publications Sage CA: Los Angeles, CA.Google Scholar
Tovares, N., Cagan, J. and Boatwright, P. (2013), “Capturing Consumer Preference Through Experiential Conjoint Analysis”, ASME Paper No. DETC2013-12549.Google Scholar
Train, K. (1986), Qualitative Choice Analysis: Theory, Econometrics, and an Application to Automobile Demand, MIT press, Vol. 10.Google Scholar
Wang, M., Chen, W., Huang, Y., Contractor, N.S. and Fu, Y. (2016a), “Modeling customer preferences using multidimensional network analysis in engineering design”, Design Science, Cambridge University Press, Vol. 2.Google Scholar
Wang, M., Sha, Z., Huang, Y., Contractor, N., Fu, Y. and Chen, W. (2016b), “Forecasting Technological Impacts on Customers’ Co-Consideration Behaviors: A Data-Driven Network Analysis Approach”, ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Charlotte, NC, USA, August 21-24, 2016.Google Scholar
Wang, M., Sha, Z., Huang, Y., Contractor, N., Fu, Y. and Chen, W. (2018), “Predicting Products’ Co-Considerations and Market Competitions for Technology-Driven Product Design: A Network-Based Approach”, Design Science Journal, Vol. 4.Google Scholar