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A Normative Methodology for Modeling Consumer Response to Innovation

Author

Listed:
  • John R. Hauser

    (Northwestern University, Evanston, Illinois)

  • Glen L. Urban

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

Abstract

Consumer response determines the success or failure of new products and services. This paper proposes a methodology that integrates knowledge in the fields of psychometrics, utility theory, and stochastic choice theory to improve the design of new products and services. The methodology consists of a consumer response and a managerial design process. The design process is one of idea generation, evaluation, and refinement, while the consumer response is based on consumer measurement, models of the individual choice process, and aggregation of predictions of individual choices. The individual response model processes the consumer measures by first reducing them to an underlying set of perceptual dimensions. Then the measures of perception are combined to produce a scalar goodness measure for each choice alternative through a process called “compaction.” Next, homogeneous segments are defined based on similar preferences. The goodness measures for each consumer or segment are linked to probability of choice for the new products and services and for competing products and services. In each step theoretical, empirical, and statistical issues are identified. Various techniques are introduced and described for each phase. Selected techniques are demonstrated based on the survey data collected at MIT to support the design of a health maintenance organization (HMO) and in the consumer market to evaluate a new deodorant.

Suggested Citation

  • John R. Hauser & Glen L. Urban, 1977. "A Normative Methodology for Modeling Consumer Response to Innovation," Operations Research, INFORMS, vol. 25(4), pages 579-619, August.
    • Handle: RePEc:inm:oropre:v:25:y:1977:i:4:p:579-619
    DOI: 10.1287/opre.25.4.579
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    Citations

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    Cited by:

    1. De Bruyn, Arnaud & Lilien, Gary L., 2008. "A multi-stage model of word-of-mouth influence through viral marketing," International Journal of Research in Marketing, Elsevier, vol. 25(3), pages 151-163.
    2. Ana Oliveira-Brochado & Francisco Vitorino Martins, 2008. "Aspectos Metodológicos da Segmentação de Mercado: Base de Segmentação e Métodos de Classificação," FEP Working Papers 261, Universidade do Porto, Faculdade de Economia do Porto.
    3. Hauser, John R. & Urban, Glen L., 1976. "Direct assessment of consumer utility functions : von Neumann-Morgenstern utility theory applied to marketing," Working papers 843-76A., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    4. Marina Kholod & Nikita Mokrenko, 2023. "Development of Choice Model for Brand Evaluation," Papers 2312.16927, arXiv.org.
    5. Matsatsinis, Nikolaos F. & Siskos, Yannis, 1999. "MARKEX: An intelligent decision support system for product development decisions," European Journal of Operational Research, Elsevier, vol. 113(2), pages 336-354, March.
    6. Reinartz, Werner & Dellaert, Benedict & Krafft, Manfred & Kumar, V. & Varadarajan, Rajan, 2011. "Retailing Innovations in a Globalizing Retail Market Environment," Journal of Retailing, Elsevier, vol. 87(S1), pages 53-66.
    7. Sheldon, Tamara L. & Dua, Rubal, 2020. "Effectiveness of China's plug-in electric vehicle subsidy," Energy Economics, Elsevier, vol. 88(C).
    8. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
    9. John R. Hauser & Steven Shugan, 1978. "Intensity Measures of Consumer Preferences," Discussion Papers 291, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    10. Manhas, Parikshat S., 2010. "Strategic Brand Positioning Analysis Through Comparison Of Cognitive And Conative Perceptions," Journal of Economics, Finance and Administrative Science, Universidad ESAN, vol. 15(29), pages 15-33.
    11. John R. Hauser, 1977. "Consumer Preference Axioms: Behavioral Postulates for Describing and Predicting Stochastic Choice," Discussion Papers 287, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    12. Hauser, John R. & Koppelman, Frank S., 1977. "Designing Transportation Services: A Marketing Apporach," Transportation Research Forum Proceedings 1970s 318674, Transportation Research Forum.
    13. Chaab, Jafar & Salhab, Rabih & Zaccour, Georges, 2022. "Dynamic pricing and advertising in the presence of strategic consumers and social contagion: A mean-field game approach," Omega, Elsevier, vol. 109(C).
    14. Yan Huang & Param Vir Singh & Kannan Srinivasan, 2014. "Crowdsourcing New Product Ideas Under Consumer Learning," Management Science, INFORMS, vol. 60(9), pages 2138-2159, September.
    15. Steven M. Shugan, 2005. "Brand Loyalty Programs: Are They Shams?," Marketing Science, INFORMS, vol. 24(2), pages 185-193.
    16. Alain De Beuckelaer & Jarl Kampen & J. Van Trijp, 2013. "An empirical assessment of the cross-national measurement validity of graded paired comparisons," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 1063-1076, February.
    17. Spears, Steven & Houston, Douglas & Boarnet, Marlon G., 2013. "Illuminating the unseen in transit use: A framework for examining the effect of attitudes and perceptions on travel behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 58(C), pages 40-53.
    18. Marina Kholod & Nikita Mokrenko & Alberto Celani & Valentina Puglisi, 2023. "Choice Modeling of Laundry Detergent Data for Sustainable Consumption," Sustainability, MDPI, vol. 15(24), pages 1-16, December.
    19. Abbie Griffin & John R. Hauser, 1993. "The Voice of the Customer," Marketing Science, INFORMS, vol. 12(1), pages 1-27.

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