Academic journal article Journal of the Association for Information Systems

What Makes a Review Voted? an Empirical Investigation of Review Voting in Online Review Systems

Academic journal article Journal of the Association for Information Systems

What Makes a Review Voted? an Empirical Investigation of Review Voting in Online Review Systems

Article excerpt


Many online review systems adopt a voluntary voting mechanism to identify helpful reviews to support consumer purchase decisions. While several studies have looked at what makes an online review helpful (review helpfulness), little is known on what makes an online review receive votes (review voting). Drawing on information processing theories and the related literature, we investigated the effects of a select set of review characteristics, including review length and readability, review valence, review extremity, and reviewer credibility on two outcomes-review voting and review helpfulness. We examined and analyzed a large set of review data from Amazon with the sample selection model. Our results indicate that there are systematic differences between voted and non-voted reviews, suggesting that helpful reviews with certain characteristics are more likely to be observed and identified in an online review system than reviews without the characteristics. Furthermore, when review characteristics had opposite effects on the two outcomes (i.e. review voting and review helpfulness), ignoring the selection effects due to review voting would result in the effects on review helpfulness being over-estimated, which increases the risk of committing a type I error. Even when the effects on the two outcomes are in the same direction, ignoring the selection effects due to review voting would increase the risk of committing type II error that cannot be mitigated with a larger sample. We discuss the implications of the findings on research and practice.

Keywords: Online Review Systems, Review Voting, Review Flelpfulness, Review Length, Readability, Review Valence, Review Extremity, Reviewer Credibility, Vividness, Diagnosticity, Sample Selection Bias.

1. Introduction

Consumers increasingly rely on online product reviews in guiding purchases. According to eMarketer (2010), 92 percent of consumers read online product reviews before making purchases, and 89 percent said that their purchase decisions were affected (favorably or unfavorably) after reading the reviews. However, online consumer reviews are not limited to online retailers. Traditional retailers can also take advantage of online consumer reviews to add value to in-store shopping experience. In fact, 82 percent of consumers consider online consumer reviews to be better than researching in-store with a sales associate (ZDNet, 2008). As a result, it is not surprising that over 80 percent of retailers planned to use online consumer reviews by the end of 2010 (eMarketer, 2010). For example, Sephora, the leading beauty retailer with presence in 13 countries and over 500 stores in the US, launched a mobile service in 2009 that allows in-store shoppers to read product reviews online.

Among the challenges posed by online consumer reviews is their explosive growth in number. It is unlikely and impossible for consumers to read all reviews in detail1. To help consumers find helpful reviews among hundreds of reviews on a particular product, online review providers and retailers such as Amazon2 have implemented a voting mechanism whereby consumers can rate whether a review is helpful. For example, Figure 1 shows a list of movie DVDs from Amazon. Figure 2 shows that for each single item the number of reviews often exceed several hundreds. Figure 2 shows that, on clicking an item, one can see a list of specific reviews, the ratings given by the reviewers to the item, and the proportion of helpful votes that each review received. Many websites, including the online Apple Store, eBay,, and so on, have implemented a similar voting mechanism in their review systems.

The voluntary voting mechanism provides a practical way for someone to identify helpful reviews, provided that the reviews are voted on their helpfulness in the first place. Reviews that are supposed to be helpful but hasn't received votes cannot be identified by online review systems. Given the voluntary nature of the voting mechanism, reviews are not equally likely to receive votes, just as they are not considered equally helpful. …

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