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Online Product Opinions: Incidence, Evaluation, and Evolution

Author

Listed:
  • Wendy W. Moe

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20472)

  • David A. Schweidel

    (Wisconsin School of Business, University of Wisconsin-Madison, Madison, Wisconsin 53706)

Abstract

Whereas recent research has demonstrated the impact of online product ratings and reviews on product sales, we still have a limited understanding of the individual's decision to contribute these opinions. In this research, we empirically model the individual's decision to provide a product rating and investigate factors that influence this decision. Specifically, we consider how previously posted ratings may affect an individual's posting behavior in terms of whether to contribute (incidence) and what to contribute (evaluation), and we identify selection effects that influence the incidence decision and adjustment effects that influence the evaluation decision. Across individuals, our results show that positive ratings environments increase posting incidence, whereas negative ratings environments discourage posting. Our results also indicate important differences across individuals in how they respond to previously posted ratings, with less frequent posters exhibiting bandwagon behavior and more active customers revealing differentiation behavior. These dynamics affect the evolution of online product opinions. Through simulations, we illustrate how the evolution of posted product opinions is shaped by the underlying customer base and show that customer bases with the same median opinion may evolve in substantially different ways because of the presence of a core group of "activists" posting increasingly negative opinions.

Suggested Citation

  • Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    • Handle: RePEc:inm:ormksc:v:31:y:2012:i:3:p:372-386
    DOI: 10.1287/mksc.1110.0662
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    References listed on IDEAS

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    1. Dmitri Kuksov & Ying Xie, 2010. "Pricing, Frills, and Customer Ratings," Marketing Science, INFORMS, vol. 29(5), pages 925-943, 09-10.
    2. James Heckman, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    3. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
    4. Manfred Gartner, 1976. "Endogenous bandwagon and underdog effects in a rational choice model," Public Choice, Springer, vol. 25(1), pages 83-89, March.
    5. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    6. Ann E. Schlosser, 2005. "Posting versus Lurking: Communicating in a Multiple Audience Context," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 32(2), pages 260-265, September.
    7. David Godes & José C. Silva, 2012. "Sequential and Temporal Dynamics of Online Opinion," Marketing Science, INFORMS, vol. 31(3), pages 448-473, May.
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