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Design of Consumer Review Systems and Product Pricing

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  • Yabing Jiang

    ()
    (Lutgert College of Business, Florida Gulf Coast University)

  • Hong Guo

    ()
    (Mendoza College of Business, University of Notre Dame)

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    Abstract

    Consumer review systems have become an important marketing communication tool through which consumers share and learn product information. Although there is abundant evidence that consumer reviews have significant impact on consumer purchasing decisions, the design of consumer review systems and its impact on review outcomes and product sales have not yet been well examined. This paper analyzes firms’ review system design and product pricing strategies. We formally model two design features of consumer review systems – rating scale and disclosure of specific product attribute information. We show that firms’ optimal strategies critically depend on contextual characteristics such as product quality, product popularity, and consumer misfit cost. Our results suggest that firms should choose a low rating scale for niche products and a high rating scale for popular products. Firms should disclose specific product attribute information to attract the desired consumer segment when product quality is low relative to misfit cost, and the resulting optimal size of the targeted consumer market increases in product popularity and product quality. Different pricing strategies should be deployed during the initial sale period for different product types. For niche products, firms are advised to adopt lower-bound pricing for high-quality products to take advantage of the positive word of mouth. For popular products, firms are advised to adopt upper-bound pricing for high-quality products to enjoy the direct profit from the initial sale period, even after taking into account the negative impact of high price on consumer reviews.

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    File URL: http://www.NETinst.org/Jiang_12-10.pdf
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    Bibliographic Info

    Paper provided by NET Institute in its series Working Papers with number 12-10.

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    Length: 63 pages
    Date of creation: Sep 2012
    Date of revision:
    Handle: RePEc:net:wpaper:1210

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    Web page: http://www.NETinst.org/

    Related research

    Keywords: economic modeling; e-commerce; consumer reviews; online word of mouth; product uncertainty;

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    1. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    2. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    3. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    4. Dina Mayzlin, 2006. "Promotional Chat on the Internet," Marketing Science, INFORMS, vol. 25(2), pages 155-163, 03-04.
    5. Dmitri Kuksov & Ying Xie, 2010. "Pricing, Frills, and Customer Ratings," Marketing Science, INFORMS, vol. 29(5), pages 925-943, 09-10.
    6. Bakos, Yannis & Dellarocas, Chrysanthos, 2003. "Cooperation Without Enforcement? A comparative analysis of litigation and online reputation as quality assurance mechanisms," Working papers 4295-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    7. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    8. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    9. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    10. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    11. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
    12. Hansen, Flemming, 1976. " Psychological Theories of Consumer Choice," Journal of Consumer Research, University of Chicago Press, vol. 3(3), pages 117-42, December.
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