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Investigating Endogeneity Bias in Marketing

Author

Listed:
  • Qing Liu

    (Department of Marketing, University of Wisconsin–Madison, Madison, Wisconsin 53706)

  • Thomas Otter

    (Faculty of Business and Economics, Johann Wolfgang Goethe University, Frankfurt, Germany)

  • Greg M. Allenby

    (Fisher College of Business, Ohio State University, 2100 Neil Avenue, Columbus, Ohio 43210)

Abstract

The use of adaptive designs in conjoint analysis has been shown to lead to an endogeneity bias in part-worth estimates using sampling experiments. In this paper, we re-examine the endogeneity issue in light of the likelihood principle. The likelihood principle asserts that all relevant information in the data about model parameters is contained in the likelihood function. We show that, once the data are collected, adhering to the likelihood principle leads to analysis where endogeneity becomes ignorable for estimation. The likelihood principle is implicit to Bayesian analysis, and discussion is offered for detecting and dealing with endogeneity bias in marketing.

Suggested Citation

  • Qing Liu & Thomas Otter & Greg M. Allenby, 2007. "Investigating Endogeneity Bias in Marketing," Marketing Science, INFORMS, vol. 26(5), pages 642-650, 09-10.
    • Handle: RePEc:inm:ormksc:v:26:y:2007:i:5:p:642-650
    DOI: 10.1287/mksc.1060.0256
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    References listed on IDEAS

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    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    2. Elaine Zanutto & Eric Bradlow, 2006. "Data pruning in consumer choice models," Quantitative Marketing and Economics (QME), Springer, vol. 4(3), pages 267-287, September.
    3. 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.
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    Cited by:

    1. Theodoros Evgeniou & Massimiliano Pontil & Olivier Toubia, 2007. "A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation," Marketing Science, INFORMS, vol. 26(6), pages 805-818, 11-12.
    2. Christian Schlereth & Bernd Skiera, 2017. "Two New Features in Discrete Choice Experiments to Improve Willingness-to-Pay Estimation That Result in SDR and SADR: Separated (Adaptive) Dual Response," Management Science, INFORMS, vol. 63(3), pages 829-842, March.
    3. Ioannis Politis & Ioannis Fyrogenis & Efthymis Papadopoulos & Anastasia Nikolaidou & Eleni Verani, 2020. "Shifting to Shared Wheels: Factors Affecting Dockless Bike-Sharing Choice for Short and Long Trips," Sustainability, MDPI, vol. 12(19), pages 1-25, October.
    4. Gensler, Sonja & Hinz, Oliver & Skiera, Bernd & Theysohn, Sven, 2012. "Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs," European Journal of Operational Research, Elsevier, vol. 219(2), pages 368-378.
    5. Tetyana Kosyakova & Thomas Otter & Sanjog Misra & Christian Neuerburg, 2020. "Exact MCMC for Choices from Menus—Measuring Substitution and Complementarity Among Menu Items," Marketing Science, INFORMS, vol. 39(2), pages 427-447, March.
    6. Srinivasan, V. Seenu & Netzer, Oded, 2007. "Adaptive Self-Explication of Multi-attribute Preferences," Research Papers 1979, Stanford University, Graduate School of Business.
    7. Nobuhiko Terui & Masataka Ban & Greg M. Allenby, 2011. "The Effect of Media Advertising on Brand Consideration and Choice," Marketing Science, INFORMS, vol. 30(1), pages 74-91, 01-02.
    8. Daniel Zantedeschi & Eleanor McDonnell Feit & Eric T. Bradlow, 2017. "Measuring Multichannel Advertising Response," Management Science, INFORMS, vol. 63(8), pages 2706-2728, August.
    9. Hongshuang (Alice) Li, 2022. "Converting free users to paid subscribers in the SaaS context: The impact of marketing touchpoints, message content, and usage," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2185-2203, May.
    10. Danaf, Mazen & Guevara, Angelo & Atasoy, Bilge & Ben-Akiva, Moshe, 2020. "Endogeneity in adaptive choice contexts: Choice-based recommender systems and adaptive stated preferences surveys," Journal of choice modelling, Elsevier, vol. 34(C).
    11. John R. Hauser & Guilherme (Gui) Liberali & Glen L. Urban, 2014. "Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph," Management Science, INFORMS, vol. 60(6), pages 1594-1616, June.
    12. Vardit Landsman & Moshe Givon, 2010. "The diffusion of a new service: Combining service consideration and brand choice," Quantitative Marketing and Economics (QME), Springer, vol. 8(1), pages 91-121, March.
    13. Olivier Toubia & John Hauser & Rosanna Garcia, 2007. "Probabilistic Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis: Theory and Application," Marketing Science, INFORMS, vol. 26(5), pages 596-610, 09-10.
    14. Mingyu Joo & Michael L. Thompson & Greg M. Allenby6, 2019. "Optimal Product Design by Sequential Experiments in High Dimensions," Management Science, INFORMS, vol. 65(7), pages 3235-3254, July.

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