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Information Retrieval and Survey Design for Two-Stage Customer Preference Modeling

Published online by Cambridge University Press:  26 May 2022

Y. Xiao ,
Y. Cui ,
N. Raut ,
J. H. Januar ,
J. Koskinen ,
N. Contractor ,
W. Chen  and
Z. Sha
Y. Xiao
Affiliation:
The University of Texas at Austin, United States of America
Y. Cui
Affiliation:
Northwestern University, United States of America
N. Raut
Affiliation:
Amazon, United States of America
J. H. Januar
Affiliation:
The University of Melbourne, Australia
J. Koskinen
Affiliation:
The University of Melbourne, Australia
N. Contractor
Affiliation:
Northwestern University, United States of America
W. Chen
Affiliation:
Northwestern University, United States of America
Z. Sha*
Affiliation:
The University of Texas at Austin, United States of America
*
zsha@austin.utexas.edu

Abstract

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Customer survey data is critical to supporting customer preference modeling in engineering design. We present a framework of information retrieval and survey design to ensure the collection of quality customer survey data for analyzing customers’ preferences in their consideration-then-choice decision-making and the related social impact. The utility of our approach is demonstrated through the survey design for customers in the vacuum cleaner market. Based on the data, we performed descriptive analysis and network-based modeling to understand customers’ preferences in consideration and choice.

Keywords

data-driven designcustomer integration methodsinformation retrievalcustomer preference analysis
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 811 - 820
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), 2022.

References

Anon, J. (2013), “Announcement: Reducing our irreproducibility”, Nature, Vol. 496 No. April, p. 398.Google Scholar
Aral, S. and Walker, D. (2010), “Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks”, SSRN Electronic Journal, No. October 2021, available at:10.2139/ssrn.1564856. DOI:10.2139/ssrn.1564856.CrossRefGoogle Scholar
Argo, J.J. (2020), “A contemporary review of three types of social influence in consumer psychology”, Consumer Psychology Review, Vol. 3 No. 1, pp. 126–140. doi:10.1002/arcp.1059.Google Scholar
Bao, Q., Sinitskaya, E., Gomez, K.J., MacDonald, E.F. and Yang, M.C. (2020), “A human-centered design approach to evaluating factors in residential solar PV adoption: A survey of homeowners in California and Massachusetts”, Renewable Energy, Elsevier Ltd, Vol. 151, pp. 503–513. doi:10.1016/j.renene.2019.11.047.CrossRefGoogle Scholar
Barnard, A.S., Louviere, J.J., Wei, E. and Zadorin, L. (2016), “Using hypothetical product configurators to measure consumer preferences for nanoparticle size and concentration in sunscreens”, Design Science, Vol. 2, p. e12. doi:10.1017/dsj.2016.12.Google Scholar
Bernard, H.R. (2013), Methods in Human Geography, edited by Flowerdew, R. and Martin, D.M., Routledge, available at:10.4324/9781315837277. DOI:10.4324/9781315837277.Google Scholar
Bi, Y., Qiu, Y., Sha, Z., Wang, M., Fu, Y., Contractor, N. and Chen, W. (2021), “Modeling Multi-Year Customers’ Considerations and Choices in China's Auto Market Using Two-Stage Bipartite Network Analysis”, Networks and Spatial Economics, Networks and Spatial Economics, Vol. 21 No. 2, pp. 365–385. doi:10.1007/s11067-021-09526-9.CrossRefGoogle Scholar
Campbell, K.E. and Lee, B.A. (1991), “Name generators in surveys of personal networks”, Social Networks, Vol. 13 No. 3, pp. 203–221. doi:10.1016/0378-8733(91)90006-F.Google Scholar
Cui, Y., Ahmed, F., Sha, Z., Wang, L., Fu, Y. and Chen, W. (2020), “A Weighted Network Modeling Approach for Analyzing Product Competition”, Volume 11A: 46th Design Automation Conference (DAC), Vol. 11A-2020, American Society of Mechanical Engineers, pp. 1–13. DOI:10.1115/DETC2020-22591.Google Scholar
Harmer, L., Cooper, T., Fisher, T., Salvia, G. and Barr, C. (2019), “Design, Dirt and Disposal: Influences on the maintenance of vacuum cleaners”, Journal of Cleaner Production, Elsevier Ltd, Vol. 228, pp. 1176–1186. doi:10.1016/j.jclepro.2019.04.101.Google Scholar
Lee, T.Y. and Bradlow, E.T. (2011), “Automated Marketing Research Using Online Customer Reviews”, Journal of Marketing Research, Vol. 48 No. 5, pp. 881–894. doi:10.1509/jmkr.48.5.881.Google Scholar
Narayan, V., Rao, V.R. and Saunders, C. (2011), “How Peer Influence Affects Attribute Preferences: A Bayesian Updating Mechanism”, Marketing Science, Vol. 30 No. 2, pp. 368–384. doi:10.1287/mksc.1100.0618.CrossRefGoogle Scholar
Pescher, C. and Spann, M. (2014), “Relevance of actors in bridging positions for product-related information diffusion”, Journal of Business Research, Elsevier Inc., Vol. 67 No. 8, pp. 1630–1637. doi:10.1016/j.jbusres.2013.09.005.Google Scholar
Rai, R. (2012), “Identifying Key Product Attributes and Their Importance Levels From Online Customer Reviews”, Volume 3: 38th Design Automation Conference, Parts A and B, Vol. 3, American Society of Mechanical Engineers, pp. 533–540. DOI:10.1115/DETC2012-70493.Google Scholar
Rana, T.A. and Cheah, Y.-N. (2017), “A two-fold rule-based model for aspect extraction”, Expert Systems with Applications, Elsevier Ltd, Vol. 89, pp. 273–285. doi:10.1016/j.eswa.2017.07.047.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. 3, pp. 2017-01–0243. DOI:10.4271/2017-01-0243.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. doi:10.1007/BF02404071.Google Scholar
Stankevich, A. (2017), “Explaining the Consumer Decision-Making Process: Critical Literature Review”, JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING, Vol. 2 No. 6, pp. 7–14. doi:10.18775/jibrm.1849-8558.2015.26.3001.CrossRefGoogle Scholar
Sudman, S. (1966), “Probability Sampling with Quotas”, Journal of the American Statistical Association, Vol. 61 No. 315, pp. 749–771. doi:10.1080/01621459.1966.10480903.Google Scholar
Tuarob, S. and Tucker, C.S. (2015), “Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data”, Journal of Computing and Information Science in Engineering, Vol. 15 No. 3, pp. 1–12. doi:10.1115/1.4029562.Google Scholar
Wang, M., Chen, W., Fu, Y. and Yang, Y. (2015), “Analyzing and Predicting Heterogeneous Customer Preferences in China's Auto Market Using Choice Modeling and Network Analysis”, SAE International Journal of Materials and Manufacturing, Vol. 8 No. 3, pp. 2015-01–0468. DOI:10.4271/2015-01-0468.CrossRefGoogle Scholar
Wang, M., Chen, W., Huang, Y., Contractor, N.S. and Fu, Y. (2016), “Modeling customer preferences using multidimensional network analysis in engineering design”, Design Science, Vol. 2, p. e11. doi:10.1017/dsj.2016.11.CrossRefGoogle Scholar