Academic journal article Journal of Theoretical and Applied Electronic Commerce Research

Exploratory Study on Anchoring: Fake Vote Counts in Consumer Reviews Affect Judgments of Information Quality

Academic journal article Journal of Theoretical and Applied Electronic Commerce Research

Exploratory Study on Anchoring: Fake Vote Counts in Consumer Reviews Affect Judgments of Information Quality

Article excerpt

1 Introduction

Online consumer reviews (online reviews or reviews hereafter) offer great value to customers [52]. About 70% of Americans consult reviews before making a purchase decision [4] and 95% of U.K. consumers always or sometimes consult reviews before purchasing online or in stores [66]. These statistics show that online reviews are critical in consumers' purchase process.

Because reviews are important, review manipulation is becoming popular. A recent Gartner report [77] noted that 2% to 6% of ratings and reviews are deceptive and that an industry of fake reviewers is emerging with a price range of $1 to $200 per review depending on the products. Other algorithms and techniques have estimated that about 2% to 10.3% online reviews are fake [33], [45].

While assessing the content of a review may tell us if it is fake, detecting fake review ratings is much more challenging because a rating does not leave any traces of author authenticity. Because a manipulated rating gives a false signal to the information quality (IQ) of a review, it is important to understand the extent of influences by rating manipulations.

Some websites give users the ability to rate the quality of reviews. Amazon.com (Site 1), for instance, allows consumers to vote on whether a review is helpful or not (e.g., 12 of 20 people found the following review helpful) as shown in Figure 1. These helpfulness votes (H-Votes hereafter) and their ratios (the total YES votes divided by the total votes) are used to sort reviews in order of review helpfulness. In addition, Amazon.com (Site 1) prominently displays the two most helpful positive and negative reviews for each product.

Previous studies on consumer reviews focused on the relationship between content characteristics and content authenticity (e.g., [32], [53]) or between content characteristics and H-Vote ratios (e.g., [41], [52]). Yet, these studies regard H-Votes as authentic. Some studies assumed consumers used their own judgment to assess the review information quality (IQ) regardless of the number of H-Votes. However, the anchoring effect [80] can affect how consumers interpret existing H-Votes. That is, consumers may be more likely to rate a review as helpful when they see a higher H-Vote ratio than a lower one. If so, the existing H-Vote ratio would influence subsequent votes in a cascading manner. Thus, our first research question is to what extent does the anchoring effect exists in H-Vote ratios?

Anchoring may not only influence the moment of judging review IQ. It may also affect the product learning process that consumers typically go through before making a purchase decision. In this paper, we refer to product learning as the exploration of product functionality and other aspects, equivalent to the intelligence and design phases in Simon's decision-making process [70]. In contrast, purchase decision-making refers to the choice selection and purchase-making phases of his model. Thus, reviews may provide value in long-term learning and in short-term decision-making. For instance, one study found that the average consumer spent 6 hours and 21 minutes learning about a product online, even though they need just 6 minutes to shop at retailer websites [66]. A recent survey notes, "one in two respondents spent 75% of their overall shopping time researching product (sic) as compared to just 21% in 2010" [64]. Thus, our second research question is to what extent does anchoring affect learning and purchase decision-making?

Assessing how anchoring affects H-Vote ratios is important for four reasons. First, previous studies rarely examined the anchoring effects on H-Vote ratios. Second, if anchoring is confirmed on H-Vote ratios, how does it influence consumers helpfulness voting behavior? Third, such anchoring may depend on the type of good and review. Fourth, anchoring should be examined for both short- and long-term effects because consumers use reviews to purchase at some later time. …

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