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Sequential and Temporal Dynamics of Online Opinion

Author

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  • David Godes

    () (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • José C. Silva

    () (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

Abstract

We investigate the evolution of online ratings over time and sequence. We first establish that there exist two distinct dynamic processes, one as a function of the amount of time a book has been available for review and another as a function of the sequence of reviews themselves. We find that, once we control for calendar date, the residual average temporal pattern is increasing. This is counter to existing findings that suggest that without this calendar-date control, the pattern is decreasing. With respect to sequential dynamics, we find that ratings decrease: the n th rating is, on average, lower than the n -1th when controlling for time, reviewer effects, and book effects. We test and find some support for existing theories for this decline based on motivation. We then offer two additional explanations for this "order effect." We find support for the idea that one's ability to assess the diagnosticity of previous reviews decreases: when previous reviewers are very different, more reviews may thus lead to more purchase errors and lower ratings.

Suggested Citation

  • David Godes & José C. Silva, 2012. "Sequential and Temporal Dynamics of Online Opinion," Marketing Science, INFORMS, vol. 31(3), pages 448-473, May.
  • Handle: RePEc:inm:ormksc:v:31:y:2012:i:3:p:448-473
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    File URL: http://dx.doi.org/10.1287/mksc.1110.0653
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Jacobsen, Grant D., 2015. "Consumers, experts, and online product evaluations: Evidence from the brewing industry," Journal of Public Economics, Elsevier, vol. 126(C), pages 114-123.
    2. Wendy W. Moe & David A. Schweidel, 2012. "Online Product Opinions: Incidence, Evaluation, and Evolution," Marketing Science, INFORMS, vol. 31(3), pages 372-386, May.
    3. S. Cicognani & P. Figini & M. Magnani, 2016. "Social Influence Bias in Online Ratings: A Field Experiment," Working Papers wp1060, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. repec:eee:touman:v:55:y:2016:i:c:p:15-24 is not listed on IDEAS
    5. Herbert Dawid & Reinhold Decker & Thomas Hermann & Hermann Jahnke & Wilhelm Klat & Rolf König & Christian Stummer, 2017. "Management science in the era of smart consumer products: challenges and research perspectives," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(1), pages 203-230, March.
    6. Filieri, Raffaele, 2015. "What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM," Journal of Business Research, Elsevier, vol. 68(6), pages 1261-1270.
    7. Michael Luca & Georgios Zervas, 2013. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud," Harvard Business School Working Papers 14-006, Harvard Business School, revised May 2015.
    8. 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.
    9. Leeflang, Peter S.H. & Verhoef, Peter C. & Dahlström, Peter & Freundt, Tjark, 2014. "Challenges and solutions for marketing in a digital era," European Management Journal, Elsevier, vol. 32(1), pages 1-12.

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