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The Effect of Individual Online Reviews on Purchase Likelihood

Author

Listed:
  • Prasad Vana

    (Tuck School of Business, Hanover, New Hampshire 03755)

  • Anja Lambrecht

    (London Business School, London NW1 4SA, United Kingdom)

Abstract

Online product reviews constitute a powerful source of information for consumers. Past research has studied the effect of aggregate measures of reviews (such as average product rating and number of reviews) on consumer behavior. In this study, we investigate how individual reviews displayed on a product web page affect consumers’ purchase likelihood. Identifying this effect is challenging because retailers are free to select which reviews to display on the product page and in what order, making the display of reviews in particular positions potentially endogenous. We address this challenge by utilizing an empirical context in which the retailer displays reviews by recency and exploit the variation in review positions generated as newer reviews are added on top of older ones. We find that individual reviews have a strong effect on consumer purchase decisions even after accounting for a product’s average rating. These effects are particularly pronounced when individual reviews help consumers resolve uncertainty about the product or contrast with the aggregate information that is instantly available on the product page.

Suggested Citation

  • Prasad Vana & Anja Lambrecht, 2021. "The Effect of Individual Online Reviews on Purchase Likelihood," Marketing Science, INFORMS, vol. 40(4), pages 708-730, July.
    • Handle: RePEc:inm:ormksc:v:40:y:2021:i:4:p:708-730
    DOI: 10.1287/mksc.2020.1278
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    References listed on IDEAS

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    1. Chunhua Wu & Hai Che & Tat Y. Chan & Xianghua Lu, 2015. "The Economic Value of Online Reviews," Marketing Science, INFORMS, vol. 34(5), pages 739-754, September.
    2. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
    3. Sridhar Narayanan & Kirthi Kalyanam, 2015. "Position Effects in Search Advertising and their Moderators: A Regression Discontinuity Approach," Marketing Science, INFORMS, vol. 34(3), pages 388-407, May.
    4. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    5. Babur De los Santos & Sergei Koulayev, 2017. "Optimizing Click-Through in Online Rankings with Endogenous Search Refinement," Marketing Science, INFORMS, vol. 36(4), pages 542-564, July.
    6. 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.
    7. 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.
    8. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    9. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
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