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The Impact of Online Product Reviews on Product Returns

Author

Listed:
  • Nachiketa Sahoo

    (Questrom School of Business, Boston University, Boston, Massachusetts 02215)

  • Chrysanthos Dellarocas

    (Questrom School of Business, Boston University, Boston, Massachusetts 02215)

  • Shuba Srinivasan

    (Questrom School of Business, Boston University, Boston, Massachusetts 02215)

Abstract

Although many researchers in information systems and marketing have studied the effect of product reviews on sales, few have looked at their effect on product returns. We hypothesize that, by reducing product uncertainty, product reviews affect the probability of product returns. We elaborate this hypothesis starting with an analytical model that examines how changes in valence and precision of information from product reviews influence the purchase and return probabilities of risk-averse, but rational, consumers. We then empirically test our hypotheses using a transaction-level data set from a multichannel, multibrand North American specialty retailer. Harnessing different consumers’ purchases and returns of the same products, but with varying sets of product reviews over two years, we show that the availability of more reviews and the presence of more “helpful” reviews, as voted by consumers, lead to fewer product returns—after controlling for customer, product, and other context-related factors. Analyzing the purchase behavior of the consumers, we find that when fewer product reviews are available, consumers buy more substitutes in conjunction with a product, potentially to mitigate their uncertainty. Purchase of substitutes, in turn, leads to more product returns. Finally, leveraging a discontinuity in the displayed average ratings, we find that when products are shown with an average rating that is higher than the true rating, they are returned more often. These results support the predictions of our theoretical model—unbiased online reviews indeed help consumers make better purchase decisions, leading to lower product returns; biasing reviews upward results in more returns. The presence of online reviews has important cost implications for the firm beyond the cost of reprocessing the returns; we observe that when consumers return products, they are more likely to write online reviews and that these reviews are more negative than reviews that follow a nonreturned purchase. The online appendix is available at https://doi.org/10.1287/isre.2017.0736 .

Suggested Citation

  • Nachiketa Sahoo & Chrysanthos Dellarocas & Shuba Srinivasan, 2018. "The Impact of Online Product Reviews on Product Returns," Information Systems Research, INFORMS, vol. 29(3), pages 723-738, September.
  • Handle: RePEc:inm:orisre:v:29:y:2018:i:3:p:723-738
    DOI: isre.2017.0736
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    References listed on IDEAS

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    1. Dina Mayzlin & Yaniv Dover & Judith Chevalier, 2014. "Promotional Reviews: An Empirical Investigation of Online Review Manipulation," American Economic Review, American Economic Association, vol. 104(8), pages 2421-2455, August.
    2. Young Kwark & Jianqing Chen & Srinivasan Raghunathan, 2014. "Online Product Reviews: Implications for Retailers and Competing Manufacturers," Information Systems Research, INFORMS, vol. 25(1), pages 93-110, March.
    3. Yili (Kevin) Hong & Paul A. Pavlou, 2014. "Product Fit Uncertainty in Online Markets: Nature, Effects, and Antecedents," Information Systems Research, INFORMS, vol. 25(2), pages 328-344, June.
    4. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    5. Michael Anderson & Jeremy Magruder, 2012. "Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database," Economic Journal, Royal Economic Society, vol. 122(563), pages 957-989, September.
    6. Elie Ofek & Zsolt Katona & Miklos Sarvary, 2011. ""Bricks and Clicks": The Impact of Product Returns on the Strategies of Multichannel Retailers," Marketing Science, INFORMS, vol. 30(1), pages 42-60, 01-02.
    7. 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.
    8. 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.
    9. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    10. Prabuddha De & Yu (Jeffrey) Hu & Mohammad S. Rahman, 2013. "Product-Oriented Web Technologies and Product Returns: An Exploratory Study," Information Systems Research, INFORMS, vol. 24(4), pages 998-1010, December.
    11. Eric T. Anderson & Karsten Hansen & Duncan Simester, 2009. "The Option Value of Returns: Theory and Empirical Evidence," Marketing Science, INFORMS, vol. 28(3), pages 405-423, 05-06.
    12. 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.
    13. Heckman, James, 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.
    14. 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.
    15. Chrysanthos Dellarocas & Charles A. Wood, 2008. "The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias," Management Science, INFORMS, vol. 54(3), pages 460-476, March.
    16. 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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