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Intensity Measures of Consumer Preference

Author

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  • John R. Hauser

    (Northwestern University, Evanston, Illinois)

  • Steven M. Shugan

    (University of Chicago, Chicago, Illinois)

Abstract

To design successful new products and services, managers need to measure consumer preferences relative to product attributes. Many existing methods use ordinal measures. Intensity measures have the potential to provide more information per question, thus allowing more accurate models or fewer consumer questions (lower survey cost, less consumer wearout). To exploit this potential, researchers must be able to identify how consumers react to these questions and must be able to estimate intensity-based preference functions. This paper provides a general structure for using intensity measures for estimating consumer preference functions. Within the structure: (1) alternative measurement theories are reviewed, (2) axioms for developing testable implications of each theory are provided, (3) statistical tests to test these implications and distinguish which theory describes how consumers are using the intensity measures are developed, (4) functional forms appropriate for the preference functions implied by each theory are derived, and (5) procedures to estimate the parameters of these preference functions are provided. Based on these results, a practical procedure, implemented by an interactive computer package, to measure preference functions in a market research environment is developed. An empirical case illustrates how the statistical tests and estimation procedures are used to aid in the design of new telecommunications devices. Empirical results suggest the majority of consumers can provide intensity judgments. The intensity-based estimation procedures do better on several criteria than ordinal estimation.

Suggested Citation

  • John R. Hauser & Steven M. Shugan, 1980. "Intensity Measures of Consumer Preference," Operations Research, INFORMS, vol. 28(2), pages 278-320, April.
  • Handle: RePEc:inm:oropre:v:28:y:1980:i:2:p:278-320
    DOI: 10.1287/opre.28.2.278
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    Citations

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    Cited by:

    1. Nicole Wiebach & Lutz Hildebrandt, 2010. "Context Effects as Customer Reaction on Delisting of Brands," SFB 649 Discussion Papers SFB649DP2010-056, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Minardi, Stefania & Savochkin, Andrei, 2015. "Preferences with grades of indecisiveness," Journal of Economic Theory, Elsevier, vol. 155(C), pages 300-331.
    3. Collewet, Marion & Koster, Paul, 2023. "Preference estimation from point allocation experiments," Journal of choice modelling, Elsevier, vol. 48(C).
    4. Rulleau, Bénédicte & Dachary-Bernard, Jeanne, 2012. "Preferences, rational choices and economic valuation: Some empirical tests," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 41(2), pages 198-206.
    5. Olivier Toubia & Duncan I. Simester & John R. Hauser & Ely Dahan, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Marketing Science, INFORMS, vol. 22(3), pages 273-303.
    6. Steven M. Shugan, 2006. "Editorial: Errors in the Variables, Unobserved Heterogeneity, and Other Ways of Hiding Statistical Error," Marketing Science, INFORMS, vol. 25(3), pages 203-216, 05-06.
    7. Tarján, Tamás & Veres, Zoltán, 2018. "Szekvenciális fogyasztói termékválasztás döntési kontinuuma [The decision-making continuum of sequential consumer-product choices]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 525-550.
    8. Hauser, John R. & Urban, Glen L., 1982. "Prelaunch forecasting of new consumer durables : ideas on a consumer value - priority model," Working papers 1270-82., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    9. Dan Horsky & Paul Nelson, 2006. "Testing the Statistical Significance of Linear Programming Estimators," Management Science, INFORMS, vol. 52(1), pages 128-135, January.
    10. Wiebach, Nicole & Hildebrandt, Lutz, 2012. "Explaining customers' switching patterns to brand delisting," Journal of Retailing and Consumer Services, Elsevier, vol. 19(1), pages 1-10.
    11. Joachim Lammert & Christoph Watrin & Stefan Zeisberger, 2010. "Management Guidance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 21(3), pages 349-364, November.
    12. John R. Hauser & Olivier Toubia, 2005. "The Impact of Utility Balance and Endogeneity in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(3), pages 498-507, August.
    13. Marion Collewet & Paul Koster, 2023. "Preference estimation from point allocation experiments," Tinbergen Institute Discussion Papers 23-0012/VIII, Tinbergen Institute.
    14. Trieste, Leopoldo & Geisler, Elie & Turchetti, Giuseppe, 2022. "Columbus' egg and the engineer's effect in forecasting solutions adoption," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    15. Rajeev Kohli & Kamel Jedidi, 2007. "Representation and Inference of Lexicographic Preference Models and Their Variants," Marketing Science, INFORMS, vol. 26(3), pages 380-399, 05-06.

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