IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v30y2011i6p1098-1114.html
   My bibliography  Save this article

Understanding Responses to Contradictory Information About Products

Author

Listed:
  • Ajay Kalra

    (Jones Graduate School of Business, Rice University, Houston, Texas 77252)

  • Shibo Li

    (Kelley School of Business, Indiana University, Bloomington, Indiana 47405)

  • Wei Zhang

    (College of Business, Iowa State University, Ames, Iowa 50011)

Abstract

Although prior literature has examined reactions to drastic negative news, we examine the situation in which decision makers receive contradictory information about products and they have to decide whether to persist with or abandon product usage. We investigate physician reactions to conflicting information concerning the cardiovascular risk of Avandia, a diabetes drug. We examine how beliefs about both drug effectiveness and drug safety are updated and speculate that experience, expertise, and self-efficacy impact how such information is integrated with current quality beliefs. Unlike previous Bayesian learning models, we consider that some signals, such as positive and negative news releases and the firm's marketing effort, may be biased in that they provide an opinionated point of view. The results show interesting differences in how physician types (specialists, hospital-based primary care physicians, heavy and light prescribers) update their beliefs and the information sources they use to do so. We find evidence that safety issues about Avandia resulted in spillover concern to close competitor Actos. The results have implication for determining who should be targeted and what vehicles should be used if a firm is faced with a situation where consumers are in a quandary because of receiving conflicting messages.

Suggested Citation

  • Ajay Kalra & Shibo Li & Wei Zhang, 2011. "Understanding Responses to Contradictory Information About Products," Marketing Science, INFORMS, vol. 30(6), pages 1098-1114, November.
  • Handle: RePEc:inm:ormksc:v:30:y:2011:i:6:p:1098-1114
    DOI: 10.1287/mksc.1110.0671
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1110.0671
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.1110.0671?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. Sridhar Narayanan & Puneet Manchanda, 2009. "Heterogeneous Learning and the Targeting of Marketing Communication for New Products," Marketing Science, INFORMS, vol. 28(3), pages 424-441, 05-06.
    2. Pradeep Chintagunta & Renna Jiang & Ginger Jin, 2009. "Information, learning, and drug diffusion: The case of Cox-2 inhibitors," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 399-443, December.
    3. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    4. 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.
    5. Cleeren, K. & Dekimpe, M.G. & Helsen, K., 2008. "Weathering product-harm crises," Other publications TiSEM 283b51f8-dd35-4a10-930a-8, Tilburg University, School of Economics and Management.
    6. Ahluwalia, Rohini, 2002. "How Prevalent Is the Negativity Effect in Consumer Environments?," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 29(2), pages 270-279, September.
    7. 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.
    8. Harald Van Heerde & Kristiaan Helsen & Marnik G. Dekimpe, 2007. "The Impact of a Product-Harm Crisis on Marketing Effectiveness," Marketing Science, INFORMS, vol. 26(2), pages 230-245, 03-04.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang Gao & Wenjing Duan & Huaxia Rui, 2022. "Does Social Media Accelerate Product Recalls? Evidence from the Pharmaceutical Industry," Information Systems Research, INFORMS, vol. 33(3), pages 954-977, September.
    2. McKibbin, Rebecca, 2023. "The effect of RCTs on drug demand: Evidence from off-label cancer drugs," Journal of Health Economics, Elsevier, vol. 90(C).
    3. 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.
    4. Andrew T. Ching & Robert Clark & Ignatius Horstmann & Hyunwoo Lim, 2016. "The Effects of Publicity on Demand: The Case of Anti-Cholesterol Drugs," Marketing Science, INFORMS, vol. 35(1), pages 158-181, January.
    5. Jie Bai, 2016. "Melons as Lemons: Asymmetric Information, Consumer Learning and Seller Reputation," Natural Field Experiments 00540, The Field Experiments Website.
    6. 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.
    7. Katharina Elisabeth Blankart & Tom Stargardt, 2020. "The impact of drug quality ratings from health technology assessments on the adoption of new drugs by physicians in Germany," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 63-82, October.
    8. Jürgen Maurer & Katherine M. Harris, 2016. "Learning to Trust Flu Shots: Quasi‐Experimental Evidence from the 2009 Swine Flu Pandemic," Health Economics, John Wiley & Sons, Ltd., vol. 25(9), pages 1148-1162, September.
    9. Maurer, J. & Harris, K.M., 2015. "Learning to trust flu shots: quasi-experimental evidence on the role of learning in influenza vaccination decisions from the 2009 influenza A/H1N1 (swine flu) pandemic," Health, Econometrics and Data Group (HEDG) Working Papers 15/19, HEDG, c/o Department of Economics, University of York.

    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. Xu, Yan, 2017. "Essays on preference formation and home production," Other publications TiSEM b028fd7e-53ba-4ff6-97eb-4, Tilburg University, School of Economics and Management.
    2. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    3. 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.
    4. Nuno Camacho & Bas Donkers & Stefan Stremersch, 2011. "Predictably Non-Bayesian: Quantifying Salience Effects in Physician Learning About Drug Quality," Marketing Science, INFORMS, vol. 30(2), pages 305-320, 03-04.
    5. 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.
    6. 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.
    7. Andrew T. Ching & Masakazu Ishihara, 2012. "Measuring the Informative and Persuasive Roles of Detailing on Prescribing Decisions," Management Science, INFORMS, vol. 58(7), pages 1374-1387, July.
    8. Jie Bai, 2016. "Melons as Lemons: Asymmetric Information, Consumer Learning and Seller Reputation," Natural Field Experiments 00540, The Field Experiments Website.
    9. Tat Y. Chan & Jia Li & Lamar Pierce, 2014. "Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth," Marketing Science, INFORMS, vol. 33(4), pages 463-484, July.
    10. Yang Gao & Wenjing Duan & Huaxia Rui, 2022. "Does Social Media Accelerate Product Recalls? Evidence from the Pharmaceutical Industry," Information Systems Research, INFORMS, vol. 33(3), pages 954-977, September.
    11. 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.
    12. Sven Tischer, 2012. "Measuring the impact of critical incidents on brand personality," SFB 649 Discussion Papers SFB649DP2012-064, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    13. 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.
    14. Carey, Colleen & Lieber, Ethan M.J. & Miller, Sarah, 2021. "Drug firms’ payments and physicians’ prescribing behavior in Medicare Part D," Journal of Public Economics, Elsevier, vol. 197(C).
    15. van Ewijk, Bernadette J. & Gijsbrechts, Els & Steenkamp, Jan-Benedict E.M., 2022. "The dark side of innovation: How new SKUs affect brand choice in the presence of consumer uncertainty and learning," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 967-987.
    16. Olga Untilov & Stéphane Ganassali, 2020. "Product‐harm science communication: The halo effect and its moderators," Journal of Consumer Affairs, Wiley Blackwell, vol. 54(3), pages 1002-1027, September.
    17. Zhu, Z.;, 2023. "The Value of Patients: Heterogenous Physician Learning and Generic Drug Diffusion," Health, Econometrics and Data Group (HEDG) Working Papers 23/12, HEDG, c/o Department of Economics, University of York.
    18. Szymanowski, M.G., 2009. "Consumption-based learning about brand quality : Essays on how private labels share and borrow reputation," Other publications TiSEM b12825d8-5e21-4437-adda-b, Tilburg University, School of Economics and Management.
    19. Sven Tischer & Lutz Hildebrandt, 2012. "Brand equity – how is it affected by critical incidents and what moderates the effect," SFB 649 Discussion Papers SFB649DP2012-062, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    20. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.

    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:30:y:2011:i:6:p:1098-1114. 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.