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The Matthew Effect in social commerce

The case of online review helpfulness

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Abstract

Online reviews as a major form of user-generated contents play a strategic role in eliminating information asymmetry and facilitating transactions in electronic markets. To improve market efficiency, many online retailers use helpfulness voting as a crowdsourcing strategy to identify and rank online reviews. In this study, we analysed a data set of 2187 reviews as well as more than two months’ review ranking data collected from six best-selling products on amazon.com. We found that a disproportionately higher percentage of votes went to early-posted lengthy reviews due to the Matthew effect. We also found that early reviews, once identified as most helpful, could maintain their top ranking status throughout the product life cycle because of the Ratchet effect. The implications of these findings were discussed and strategies for how to mitigate negative impacts of such effects by online retailers were suggested.

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Notes

  1. O2O, or online to offline, is an ecommerce model popular in China since 2010. It essentially means attracting users online and directing them to a physical store to complete the purchase. It was initially started as Tuan Gou, or Group Buy, in China. Later such model caught on in North America and led to popular startups like Groupon and LivingSocial.

  2. The ranking also makes adjustment according to the base number of votes. For example, for a review that has more than 15 votes, the helpful/total ratio is adjusted to be more than 0.5 higher than reviews with a slightly better ratio but fewer than 15 total votes.

  3. The early amazon review system uses “newest first” ranking criteria. That is, when a new review is posted, it appears at the very beginning of the review page and earlier reviews move down accordingly. Thus, for a popular product, many early-posted helpful reviews are quickly sent to older pages without being noticed by shoppers. To overcome this disadvantage, amazon switched to the current “most helpful first” method. There have been a few insightful discussions about the change of practice by amazon on review management from chronological to ranking by votes. An influential discussion, “The Magic Behind Amazon’s 2.7 Billion Dollar Question”, can be found at http://www.uie.com/articles/magicbehindamazon/

  4. For example, if a review is voted as most helpful by many previous visitors, a new shopper will read this review first and then most likely vote it as helpful too though, at the same time, many subsequent reviews may be equally or even more helpful, providing different product perspectives, but not be noticed or appreciated by the shopper.

  5. We re-examined the ranking status in March 2014 and found all of the reviews were still ranked as the most helpful reviews for their respective underlying products. To check a review on amazon.com, use URL http://www.amazon.com/review/(add review id here).

  6. As recently as March 24, 2014, this most helpful favourable review (ID: R1KEPVFNKQHP5W) was still ranked as most helpful on the amazon.com website.

  7. Amazon.com founder Jeff Bezos mentioned in an HBS interview that his peers initially rejected his idea of allowing consumers to post product reviews on the amazon website because negative reviews would dampen sales (Kirby and Stewart 2007). Though Bezos chose to side with consumers and encourage customers to post reviews, his team may still have concerns about negative comments. Thus, Amazon may adopt the above strategy to mitigate the potential negative impact created by critical reviews.

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Acknowledgments

This paper is a revised and expanded version of a conference paper entitled “The Matthew effect in online review helpfulness” presented at the 15th International Conference on Electronic Commerce in Turku, Finland, 13–15.8.2013 and published in conference proceedings, “Co-created Effective, Agile, and Trusted eServices,” Lecture Notes in Business Information Processing Volume 155, 2013, pp. 38–49. The author is grateful for the suggestions and comments from conference participants, two anonymous reviewers, one senior editor of EM, and associate editor Harry Bouwman that greatly improved the manuscript.

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  1. University of Houston - Victoria, 14000 University Blvd, 77478, Sugar Land, TX, USA

    Yun Wan

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Correspondence to Yun Wan.

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Wan, Y. The Matthew Effect in social commerce. Electron Markets 25, 313–324 (2015). https://doi.org/10.1007/s12525-015-0186-x

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