IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v27y2008i6p1111-1125.html
   My bibliography  Save this article

A Dynamic Model of Brand Choice When Price and Advertising Signal Product Quality

Author

Listed:
  • Tülin Erdem

    (Stern School of Business, New York University, New York, New York 10012)

  • Michael P. Keane

    (University of Technology Sydney, Sydney, Australia 2006, and Arizona State University, Tempe, Arizona 85287)

  • Baohong Sun

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

In this paper, we develop a structural model of household behavior in an environment where there is uncertainty about brand attributes and both prices and advertising signal brand quality. Four quality signaling mechanisms are at work: (1) price signals quality, (2) advertising frequency signals quality, (3) advertising content provides direct (but noisy) information about quality, and (4) use experience provides direct (but noisy) information about quality. We estimate our proposed model using scanner panel data on ketchup. If price is important as a signal of brand quality, then frequent price promotion may have the unintended consequence of reducing brand equity. We use our estimated model to measure the importance of such effects. Our results imply that price is an important quality-signaling mechanism and that frequent price cuts can have significant adverse effects on brand equity. The role of advertising frequency in signaling quality is also significant, but it is less quantitatively important than price. In the printed version of , Vol. 27, No. 6, Erdem et al. (2008) was mistakenly identified as a Research Note. It is a regular article and has been corrected here and in the online table of contents.

Suggested Citation

  • Tülin Erdem & Michael P. Keane & Baohong Sun, 2008. "A Dynamic Model of Brand Choice When Price and Advertising Signal Product Quality," Marketing Science, INFORMS, vol. 27(6), pages 1111-1125, 11-12.
  • Handle: RePEc:inm:ormksc:v:27:y:2008:i:6:p:1111-1125
    DOI: 10.1287/mksc.1080.0362
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1080.0362
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.1080.0362?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kihlstrom, Richard E & Riordan, Michael H, 1984. "Advertising as a Signal," Journal of Political Economy, University of Chicago Press, vol. 92(3), pages 427-450, June.
    2. Igal Hendel & Aviv Nevo, 2006. "Measuring the Implications of Sales and Consumer Inventory Behavior," Econometrica, Econometric Society, vol. 74(6), pages 1637-1673, November.
    3. Kamel Jedidi & Carl F. Mela & Sunil Gupta, 1999. "Managing Advertising and Promotion for Long-Run Profitability," Marketing Science, INFORMS, vol. 18(1), pages 1-22.
    4. Tülin Erdem & Susumu Imai & Michael Keane, 2003. "Brand and Quantity Choice Dynamics Under Price Uncertainty," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 5-64, March.
    5. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    6. Hong, Pilky & McAfee, R. Preston & Nayyar, Ashish, 2002. "Equilibrium Price Dispersion with Consumer Inventories," Journal of Economic Theory, Elsevier, vol. 105(2), pages 503-517, August.
    7. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    8. Nitin Mehta & Surendra Rajiv & Kannan Srinivasan, 2003. "Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation," Marketing Science, INFORMS, vol. 22(1), pages 58-84, June.
    9. Daniel A. Ackerberg, 2003. "Advertising, learning, and consumer choice in experience good markets: an empirical examination," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(3), pages 1007-1040, August.
    10. Keane, Michael, 1997. "Current Issues in Discrete Choice Modeling," MPRA Paper 52515, University Library of Munich, Germany.
    11. Tülin Erdem & Michael Keane & Baohong Sun, 2008. "The impact of advertising on consumer price sensitivity in experience goods markets," Quantitative Marketing and Economics (QME), Springer, vol. 6(2), pages 139-176, June.
    12. Ching, Andrew T., 2010. "Consumer learning and heterogeneity: Dynamics of demand for prescription drugs after patent expiration," International Journal of Industrial Organization, Elsevier, vol. 28(6), pages 619-638, November.
    13. Luc Wathieu & Marco Bertini, 2007. "Price as a Stimulus to Think: The Case for Willful Overpricing," Marketing Science, INFORMS, vol. 26(1), pages 118-129, 01-02.
    14. Frank M. Bass & Norris Bruce & Sumit Majumdar & B. P. S. Murthi, 2007. "Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship," Marketing Science, INFORMS, vol. 26(2), pages 179-195, 03-04.
    15. Winer, Russell S, 1986. "A Reference Price Model of Brand Choice for Frequently Purchased Products," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(2), pages 250-256, September.
    16. Nelson, Phillip, 1970. "Information and Consumer Behavior," Journal of Political Economy, University of Chicago Press, vol. 78(2), pages 311-329, March-Apr.
    17. Caves, Richard E. & Greene, David P., 1996. "Brands' quality levels, prices, and advertising outlays: empirical evidence on signals and information costs," International Journal of Industrial Organization, Elsevier, vol. 14(1), pages 29-52.
    18. Robert C. Blattberg & Kenneth J. Wisniewski, 1989. "Price-Induced Patterns of Competition," Marketing Science, INFORMS, vol. 8(4), pages 291-309.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    2. Pradeep Chintagunta & Tülin Erdem & Peter E. Rossi & Michel Wedel, 2006. "Structural Modeling in Marketing: Review and Assessment," Marketing Science, INFORMS, vol. 25(6), pages 604-616, 11-12.
    3. Michael P. Keane, 2013. "Panel data discrete choice models of consumer demand," Economics Papers 2013-W08, Economics Group, Nuffield College, University of Oxford.
    4. Jie Bai, 2016. "Melons as Lemons: Asymmetric Information, Consumer Learning and Seller Reputation," Natural Field Experiments 00540, The Field Experiments Website.
    5. Chan, Tat Y. & Narasimhan, Chakravarthi & Yoon, Yeujun, 2017. "Advertising and price competition in a manufacturer-retailer channel," International Journal of Research in Marketing, Elsevier, vol. 34(3), pages 694-716.
    6. Ching, Andrew T. & Erdem, Tülin & Keane, Michael P., 2014. "A simple method to estimate the roles of learning, inventories and category consideration in consumer choice," Journal of choice modelling, Elsevier, vol. 13(C), pages 60-72.
    7. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    8. Jean‐Pierre Dubé & Günter J. Hitsch & Peter E. Rossi, 2010. "State dependence and alternative explanations for consumer inertia," RAND Journal of Economics, RAND Corporation, vol. 41(3), pages 417-445, September.
    9. Grunewald, Andreas & Kräkel, Matthias, 2017. "Advertising as signal jamming," International Journal of Industrial Organization, Elsevier, vol. 55(C), pages 91-113.
    10. Szymanowski, Maciej & Gijsbrechts, Els, 2013. "Patterns in consumption-based learning about brand quality for consumer packaged goods," International Journal of Research in Marketing, Elsevier, vol. 30(3), pages 219-235.
    11. Günter J. Hitsch, 2006. "An Empirical Model of Optimal Dynamic Product Launch and Exit Under Demand Uncertainty," Marketing Science, INFORMS, vol. 25(1), pages 25-50, 01-02.
    12. Nitin Mehta & Xinlei (Jack) Chen & Om Narasimhan, 2008. "Informing, Transforming, and Persuading: Disentangling the Multiple Effects of Advertising on Brand Choice Decisions," Marketing Science, INFORMS, vol. 27(3), pages 334-355, 05-06.
    13. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 151-196, June.
    14. Czajkowski, Mikolaj & Hanley, Nicholas & LaRiviere, Jacob, 2012. "The Effects of Experience on Preference Uncertainty: Theory and Empirics for Public and Quasi-Public Goods," Stirling Economics Discussion Papers 2012-17, University of Stirling, Division of Economics.
    15. Victor Aguirregabiria, 2023. "Dynamic demand for differentiated products with fixed-effects unobserved heterogeneity," The Econometrics Journal, Royal Economic Society, vol. 26(1), pages 1-25.
    16. Guofang Huang & Matthew Shum & Wei Tan, 2019. "Is pharmaceutical detailing informative? Evidence from contraindicated drug prescriptions," Quantitative Marketing and Economics (QME), Springer, vol. 17(2), pages 135-160, June.
    17. Simon P. Anderson & Federico Ciliberto & Jura Liaukonyte & Régis Renault, 2016. "Push-me pull-you: comparative advertising in the OTC analgesics industry," RAND Journal of Economics, RAND Corporation, vol. 47(4), pages 1029-1056, November.
    18. Hu, Yingyao, 2017. "The Econometrics of Unobservables -- Latent Variable and Measurement Error Models and Their Applications in Empirical Industrial Organization and Labor Economics [The Econometrics of Unobservables]," Economics Working Paper Archive 64578, The Johns Hopkins University,Department of Economics, revised 2021.
    19. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2020. "How much do consumers know about the quality of products? Evidence from the diaper market," The Japanese Economic Review, Springer, vol. 71(4), pages 541-569, October.
    20. Hintermann, Beat & Lange, Andreas, 2013. "Learning abatement costs: On the dynamics of the optimal regulation of experience goods," Journal of Environmental Economics and Management, Elsevier, vol. 66(3), pages 625-638.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:27:y:2008:i:6:p:1111-1125. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.