Abstract
Abstract
This study explores the factors that make online customers select which reviews to read among the various ones on the Web. While most of literature on online consumer reviews has conveniently assumed that more helpful reviews would be read by more customers, no empirical study has tested whether the helpfulness assessment actually increases readership. Hence, this study explores various factors affecting consumer review readership and proposes that although helpfulness assessment promotes the readership of a review, the most dominant factor contributing to readership is the time of posting. A review posted late loses a significant chance of being read by consumers even if it is assessed as helpful by other readers. The hypotheses are tested using the data collected from
Introduction
To assist those consumers, major online shopping malls have adopted a number of review display policies, such as helpfulness voting systems. Online shopping malls encourage customers to assess the helpfulness of online reviews, and then the reviews are displayed based on assessment results. This process is a democratic evaluation method of product information quality that brings new value to online shopping malls. For example, an additional revenue of US$2.7 billion was generated from the helpfulness voting system of
However, the question remains as to whether reviews assessed as helpful are actually being read by more consumers. Do reviews assessed as helpful attract more customers? Do customers read reviews based on the results of the voting system? Do customers significantly benefit from this voting system? Studies on review helpfulness are significantly increasing. 2 However, most of these studies focus on the contextual features of helpful reviews 3 and not on the effectiveness of helpfulness voting systems on customer behavior.
The present study, therefore, examines the impact of review helpfulness on review readership. Specifically, this study (a) explores factors that influence consumers to read a specific review, and (b) compares the impact of review helpfulness on review readership with that of other factors, such as review rating and posting time. This study begins with a literature review on review helpfulness. Then, factors that influence reading of reviews are proposed. Based on data collected from
Literature Review
Previous studies on review helpfulness mainly focused on the quality of contents or on contextual patterns. 4 For example, Ghose and Ipeirotis 5 analyzed various aspects of review text, such as subjectivity levels, informativeness, measures of readability, and extent of spelling errors, to examine the influences of these text-based features on the perceived usefulness of the review. Mudambi and Schuff 6 also identified factors that contribute to review helpfulness, such as review extremity and review depth. Cao et al. 3 investigated basic, stylistic, and semantic characteristics of online user reviews, and examined their influence on the level of helpfulness. In this previous study, reviews that provided extreme opinions obtained more helpfulness votes than reviews with mixed or neutral opinions.
The approaches used in these studies were explorative, thus methodologies employed were data driven, such as data mining and econometric analysis. Large scale data of real reviews collected from major Web sites, such as
Despite the relatively short history of the field, studies on review helpfulness are significantly increasing.7,8 However, most of these studies have assumed that customers prefer reading more helpful reviews, and that review helpfulness voting systems encourage customers to read reviews with the highest vote, without verifying its content. Therefore, the present study establishes hypotheses to evaluate the aforementioned fundamental assumptions by identifying factors that influence customers to read reviews.
Hypotheses Development
Review helpfulness is viewed as the perceived value measure or an information quality assessment of a certain review.4,9 In several cases, review helpfulness is perceived as the truthfulness or readability of reviews, which largely embodies subjective judgment on the assessment of customers. 5 In spite of all these opinions and views, classifying which kinds of review are perceived as more helpful remains complicated, probably because of the highly context-dependent nature of the perceived diagnosticity level. 10
As such, this study adapts a technical method for defining review helpfulness, that is, the proportion of the consumers who vote that a particular review was helpful for their purchase decision making. For example, if 72 out of 90 consumers agree and vote that a certain review was helpful, the helpfulness of that review would be measured as 0.8. This technical definition is effective in quantifying review helpfulness 11 because, nowadays, most major online shopping malls are implementing and practicing this review voting system.
With this system, when a review is assessed as helpful, it will influence customers to make purchase decisions quickly and confidently. A review voted as helpful is acknowledged as having quality information and is likely to be read by many customers. Reading a helpful review first can reduce the time and effort exerted by a consumer when shopping. As such, the following hypothesis is formulated:
Another factor that influences consumers to read online reviews is criticism of a particular product. When a consumer posts a review, he/she also rates the product, using, for example, a certain number of stars on
Reading critical reviews is important in consumer decision making because obtaining opinions from the minority broadens perspectives and increases variety of information.
12
Critical reviews describe product features that most reviewers might overlook.
13
Reviews with negative ratings often contain rare information and show perspectives that differ from those of the majority of reviewers. For example, as of October 2012, among the 288 reviewers of the Canon EOS Rebel T3 digital SLR camera on
Therefore, the unit value of information in critical reviews can be considered higher in terms of criticality and rarity. Numerous previous studies14,15 have also emphasized the importance of negative reviews in providing different views and in increasing information variety, thereby enriching content.
If a customer posts his/her review earlier than other reviewers, his/her review will have a higher chance of being read by consumers because of its longer exposure. While major online shopping malls have their own preferred display rules, such as recency and content quality, a review posted earlier still apparently has a higher chance of being read than those posted later. This study captures this advantageous aspect of early posted reviews and calls it “posting time.” Specifically, posting time is defined as the number of months that the review has been posted online. It plays an important role in online review readership.
Posting time of a review would exhibit a stronger influence on the readership of the review than helpfulness or negativity because of the absoluteness of its advantage. Helpfulness and negativity of a review are relative values. As more customers vote, the classification of helpful or negative changes over time. A review can be most helpful at one point in time but might not be the most helpful at the next moment.
A similar logic can be applied to the conceptualization of review negativity. When there is a slightly negative review and if all other reviews are strongly positive, the slightly negative review will be considered a negative review. However, if all other reviews are strongly negative, the slightly negative review might be considered rather positive. In other words, the negativity of a certain review is influenced by other reviews, which implies that it changes as more reviews are posted.
However, posting time (i.e., the fact that a review is posted earlier than others) does not change despite more reviews being posted, but instead becomes more robust in its advantage. Hence, posting timing is expected to have a relatively stronger impact on review readership than helpfulness or negativity. Based on this assumption, the following hypothesis is proposed:
Data Analysis
Data collection
Data were collected from the world's largest online shopping mall,
One line contains approximately 22 words.
Hypotheses tests and results
A regression equation for hypotheses testing was formulated as follows:
The y value was assigned a rank, thus increase in readership (i.e., the number of votes) results in the decrease of y. For example, if the number of votes increases from 10 to 30, and vote ranking rises from 10th to 2nd, then the actual value of y decreases from 10 to 2 as a result. The detailed parameterization process of all variables is shown in Table 2.

A review from
Because of the inverted normalization used in the variable y parameterization process, the ±signs of β1, β2, and β3 in Table 3 are reversed compared with the tone of the hypotheses. For example, when the positive impacts of review helpfulness (H1) and posting time (H3) were hypothesized, negative signs for β1 and β3 were found. By contrast, when the negative impact of rating (H2) is hypothesized, a positive sign for β2 was presented. These reversed signs of the β coefficients support the hypotheses proposed.
Bold indicates significance at 0.1 level; Underline indicates cases are supported.
Table 4 summarizes the hypotheses test results. H1 is supported in 5 out of 10 cases. H2 is supported in 6 out of 10 cases. H3 is supported in 7 out of 10 cases, which is validated by the higher value of β3 than those of β1 or β2. Although not all hypotheses are supported, most of the cases clearly show the consistent impacts of review helpfulness, negativity, and posting time, as hypothesized.
A post hoc analysis of review concentration rate
The concentration rate of review readership is further examined to support the implications of the study. This parameter investigates how many readers have been attracted by the highest ranking reviews. It also reflects the current environment of online consumer review readership from a relative perspective. For example, if a high concentration level is observed, it will advise practitioners to discuss the balanced use of the review assessment systems, and the possibility of reducing the concentration level in the future.
Table 5 shows the high level of overall concentration in most products. In Carson and Angry Birds, one top review (i.e., CR1) captures more than 40% of the total readership, whereas the top four reviews (i.e., CR4) possess more than half of the total readership. Figure 2 shows the rapidly decreasing concentration rate after the first review. For example, in camera categories, the second highest ranking review captures less than half of the total readership compared with the highest ranking review (i.e., if the most read review is read 100 times, then the second or third most read review is read fewer than 50 times). In the toy category, the third highest ranking review has less than half of the total readership compared with the second highest ranking review. Such a high concentration level shows that most people only stop reading after reading several reviews. This result highlights the implication of the present study that displaying helpful reviews in an appropriate manner must be an important concern for current practitioners.

Vote distribution among highly voted reviews.
Discussion
Summary of findings
The following discussion points are derived from the results. First, a strong impact of posting time on review readership is observed, as in the H3 result. This finding implies that the most helpful review is actually the most helpful only among early posted reviews. Under the current display systems, if a review is posted late, then the review will hardly be recognized as helpful by customers because of the robust impact of posting time. To build more balanced and reasonable helpfulness voting systems, a method to reduce the strong impact of posting time should be discussed.
Second, negativity of a review shows a stronger impact than helpfulness (β2>β1) in most cases. This finding implies that consumers may be more influenced when a review contains negative or different opinions than when it only shows favorable but general opinions. The reason is that the helpfulness of a review is a value awarded by the majority of consumers, whereas negative reviews represent minority opinions that can offer readers new information in a different manner.
Third, in search goods such as cameras, variables show more consistent and robust associations than in experience goods such as toys. As shown in Table 3, 11 out of 15 associations are found to be significant in camera categories, whereas only eight are significant in toy categories. Furthermore, cases where all three hypotheses were supported are all found in camera categories (i.e., Canon 18 MP, Canon Powershot, and Canon 12.2 MP), whereas no product in the toy category demonstrates such a case. This is because the opinions of other consumers are more influential when buying cameras than when buying toys. 18 Measures reflecting the opinions of others, such as rating and helpfulness, are found to be less important in experience goods than in search goods, according to our study.
Academic contribution
This study raises the question of whether more helpful reviews are read by more customers as an outcome of review voting systems. While most previous helpfulness studies have analyzed content, style, and wording of helpful reviews, few have discussed the effectiveness of the helpfulness voting systems, even though it is a fundamental question that should be answered with justification.5,19 This study deepens understanding on the effectiveness of review helpfulness voting systems by measuring and comparing how much customers are influenced by signals such as helpfulness and negativity in these reviews when they decide which reviews to read.
This study also extends the scope of IS researchers from the review assessment to overall online review readership. While the scope of previous online review studies has often been limited to the content of reviews, the present study provides an integrative view on online review readership and its assessment system by exploring and comparing the impact of helpfulness and other factors, such as posting time and negative ratings, on review readership.
Practical implication
This study calls the attention of practitioners to the fact that the current review display policy can be improved for effective utilization of the review assessment system. The display of online consumer reviews has always been of interest to practitioners because consumer perception and behavior are significantly influenced by how reviews are displayed and presented. 9 Hence, most major online shopping malls now implement their own review display rules, such as sorting by recency or quality of the reviews 20 so that customers can easily find the reviews they are looking for. However, the result of the present project is alarming because, under the current review display system, posting timing, not helpfulness, remains the most influential factor on review readership, Practitioners are therefore advised to use helpfulness assessment systems in a manner that prioritizes the impact of helpfulness, not timing.
Another practical implication of the study is that it describes the influence of current review assessment systems through data collected from
Limitations and future study
Several limitations in this study can be resolved in future research. For example, the number of products could be increased to more than 10. Factors that affect consumer review readership, aside from timing and rating, could be explored more thoroughly. In an online review system, a helpfulness voting system applied in various topics would be valuable in future studies when explored from different perspectives.
Footnotes
Author Disclosure Statement
No competing financial interests exist.
