Exploring extremity and negativity biases in online reviews: Evidence from Yelp.com

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Minjung Roh
Sung-Byung Yang
Cite this article:  Roh, M., & Yang, S. (2021). Exploring extremity and negativity biases in online reviews: Evidence from Yelp.com. Social Behavior and Personality: An international journal, 49(11), e10825.


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While some online reviews explicitly praise or criticize a product, others reveal a neutral evaluation. We predicted that extreme reviews would be considered more useful than moderate ones, and that negative reviews would be considered more useful than positive ones. To test these predictions, this study collected a dataset comprising 951,178 reviews of New York restaurants made by 142,286 reviewers on Yelp.com. By combining these two datasets, we incorporated each reviewer’s unique reference point into a model and showed that extremely positive or negative reviews were considered more useful than moderate ones and that negative reviews were considered more useful than positive ones. This dominance of negative over positive reviews was also more pronounced in the conditions of larger variance and lower average ratings for restaurants. Overall, these results support the presence and influence of extremity and negativity biases, particularly in the context of high preference heterogeneity.

While advertising and brands have in the past served as the primary means to convey the creative ideas conceived by professional marketers (Keller, 2003; Roy & Banerjee, 2014), the public opinion of lay customers about products and services is exerting increasingly greater effects on marketing (Boerman et al., 2017; Y. Chen & Xie, 2008; Bughin et al., 2010). Individual reviews based on actual experience of product usage have driven the onset and development of the stream of public opinion (Y. Chen & Xie, 2008; H. Zhang & Choi, 2018). The most prominent example is the 5-star review (Vana & Lambrecht, 2021), which has been widely used across various industries, such as restaurants (e.g., OpenTable.com, Yelp.com, Zomato.com), hotels (e.g., Agoda.com, Booking.com, TripAdvisor.com), films (e.g., RottenTomatoes.com), games (e.g., GameSpot.com), and even medical services (e.g., Healthgrades.com).

Magnifying this trend, a growing body of marketing and other academic research has informed the topic of review ratings (e.g., Y. Liu & Hu, 2021; Sparks & Browning, 2011; Wei et al., 2013; L. Zhang et al., 2013). The key antecedent factors that merit closer consideration in this context are the distribution and valence of review ratings. Inconsistent results have been obtained regarding whether extreme, compared to moderate, ratings are perceived as more helpful for consumers’ decision making (i.e., extremity bias) and whether negative, compared to positive, ratings are perceived as more useful (i.e., negativity bias). Some empirical studies have provided support for the presence of extremity bias (Choi & Leon, 2020; Filieri et al., 2018; Y. Liu & Hu, 2021; Park & Nicolau, 2015; Purnawirawan et al., 2012), whereas others have found evidence against the existence of such bias (Danescu-Niculescu-Mizil et al., 2009; Mudambi & Schuff, 2010). Similarly, several studies have found evidence of negativity bias (Lee et al., 2008; Papathanassis & Knolle, 2011; Sparks & Browning, 2011; J. Yang & Mai, 2010; L. Zhang et al., 2013), whereas others have found no support for this effect (Bi et al., 2019; Wei et al., 2013).

We sought to clarify whether extremity and negativity biases exist, using reviews on Yelp.com, which is reputable for building the largest dataset of online restaurant reviews (Salehi-Esfahani & Kang, 2019). Several studies have used Yelp.com to explore the factors that determine review usefulness (Z. Liu & Park, 2015; S. B. Yang et al., 2017; H. Zhang et al., 2017) and review website adoption behavior (Salehi-Esfahani & Kang, 2019). There have been attempts to develop a model that predicts a star rating using textual reviews (Qiu et al., 2018) and estimates the effect of such a rating on restaurant choice behavior (Schaffner, 2016). In line with this literature, we investigated 951,178 reviews of 953 restaurants in New York City on Yelp.com and sought to reveal how the prior predictions of extremity and negativity biases would manifest in relation to restaurants. Because intangible elements, such as the quality of the service provided by staff or the venue atmosphere, exert an enormous effect on the individual consumption experience, restaurant reviews posted by previous visitors may have a greater influence on decisions made by prospective customers than reviews in other consumption contexts (S. B. Yang et al., 2017). As such, this study focused specifically on restaurant reviews posted on Yelp.com during the data collection period.

Finally, we incorporated individually diverse reference points in the measurement and analysis of review ratings. To illustrate, a rating of 4 stars on a 5-star scale can be interpreted as a neutral signal if given by a generous reviewer who usually gives ratings of 4 or 5 stars, whereas it can be considered strongly positive if given by a less generous reviewer who mostly gives ratings of only 2 or 3 stars. To capture this diversity we calculated the average ratings per reviewer to form their unique reference point, and subtracted this from the raw star ratings (1 through 5 stars), using the resultant numbers as the input values for analysis. We believe this is the first such attempt to consider individual reviewers’ unique reference points while evaluating actual review data, which may provide a foundation for more theoretically informed approaches (Z. Chen & Lurie, 2013; Hu et al., 2009; Nguyen et al., 2021; Wang et al., 2019).

Theoretical Background and Development of Hypotheses

Extremity Bias and Negativity Bias in Online Reviews

Consumers assign more value to information that helps them make decisions. The more a piece of information contributes to improving the quality of consumers’ decision making, the greater is the value of assigned diagnosticity. The question then emerges, “What valence of review ratings would be considered to hold more diagnostic and helpful information?” Particularly, out of review ratings that lie (a) at the extremes of either the lowest or highest ratings, or (b) somewhere in the middle of these extremes, which would be evaluated to be more diagnostic and helpful? Further, when choosing between extreme and moderate ratings, which would be considered more useful and, therefore, be given greater weight in decision making?

Study findings regarding these questions have been inconsistent. While some researchers have reported that extreme ratings grasp greater attention from consumers and, thereby, receive a greater weight in their decision making (extremity bias), others found that consumers react adversely to extreme ratings; thus, moderate ratings are assigned a greater weight. For instance, previous studies of restaurant reviews on Yelp.com (Y. Liu & Hu, 2021; Z. Liu & Park, 2015), French hotel reviews on TripAdvisor.com (Filieri et al., 2018), and Amazon.com reviews of 24 product categories such as books, cellphones, and electronics (Choi & Leon, 2020) have found that extreme (vs. moderate) ratings count as more helpful for decision making and, thus, appear as more useful. In contrast, studies of Amazon.com reviews of MP3 players, music CDs, and computer games (Mudambi & Schuff, 2010) and the reviews of Boston and Honolulu hotels on TripAdvisor.com (Deng & Ravichandran, 2018) have found that moderate ratings count as more useful for decision making compared to extreme ratings. While some findings support the prediction of extremity bias, giving rise to a U-shaped curve, others have revealed that moderate ratings constitute the most useful information, yielding an inverted U-shaped curve.

To resolve these incongruent findings, in this study we relied on the concept of information diagnosticity, which indicates the degree to which information contributes to improving the quality of decision making (Chu et al., 2015). Such diagnosticity can be regarded as high when it prompts consumers to reduce from several candidates to a subset of final options (i.e., consideration sets) that better fits their focal goals. When confronted with various products that may appear similar, diagnostic information can help consumers make a clearer distinction between favorable and unfavorable alternatives, according to the extent of goal fit (Clemons et al., 2006).

However, one difficulty with consumer review adoption is that most Internet reviews are written and posted by strangers (M. Chen et al., 2018). For prospective users who have yet to make a purchase decision, reviews posted by anonymous users represent the opinions of strangers with unclear preferences or tastes; thus, they will likely experience uncertainty in accepting these reviewers’ opinions at face value. Such uncertainty is likely to be aggravated in consumption contexts where preference heterogeneity is high (Price et al., 1989). In the case of products or services (e.g., films, music, wine, or restaurants) where tastes and preferences vary among consumers and different ideal styles are pursued, even an identical alternative may be evaluated in various ways. In such contexts it would be unwise to adopt someone else’s evaluation (Chu et al., 2015); instead, reviews that clearly state the subjective criteria upon which the ratings are based will be perceived as more diagnostic. Thus, when there is a clear indication of which preferences shape the reviewer’s evaluation, a prospective user can assess the gap between their own unique tastes and those of the review poster, thereby arriving at a more realistic estimation of the alleged quality of some products or services (Duan et al., 2008).

Therefore, under the context of high preference heterogeneity, it can be expected that extremely favorable or unfavorable review ratings will be more likely to reflect reviewers’ underlying preference systems. When a reviewer is vigorously supportive or dismissive of some products or services, the preference system that underlies their specific tastes is more likely to be explicitly revealed. In terms of review valence, extremely high or extremely low ratings should appear as more clearly indicative of reviewers’ unique tastes and preference systems. Therefore, in the context of high preference heterogeneity, reviews with extreme ratings that better capture the reviewer’s characteristic preference system would be perceived as being more useful for a prospective user’s decision making compared to reviews with moderate ratings (Herr et al., 1991; Park & Nicolau, 2015; Purnawirawan et al., 2012). Thus, we formed the following hypothesis:
Hypothesis 1: When preference heterogeneity is perceived as high, Yelp.com reviews with extremely positive or negative ratings will be considered more useful than will reviews with moderate ratings, displaying an extremity bias.

Given this prediction, which of the ratings at either extreme—highest or lowest—will be considered more useful for decision making? Will negativity bias dominate as per the conventional wisdom, or will positivity bias be shown to be more influential?

While the empirical findings regarding this issue show conflict, overall, more studies have found support for negativity bias. Studies of book reviews on Amazon.com and Barnesandnoble.com (Chevalier & Mayzlin, 2006), video game reviews on Gamespot.com (J. Yang & Mai, 2010), and film reviews in Variety Magazine and on Baseline.hollywood.com (Basuroy et al., 2003) have reported finding a negativity bias in terms of sales. Experimental studies based on hotel reviews (Lo & Yao, 2019) and online consumer reviews (Lee et al., 2008) have further shown that negative reviews have a stronger word-of-mouth effect than do positive reviews. Finally, a qualitative study based on online holiday reviews (Papathanassis & Knolle, 2011) found evidence of negative reviews’ dominance. However, other studies have identified support for positivity bias. An experimental study of hotel reviews on TripAdvisor.com (Wei et al., 2013) and a survey study of video reviews on YouTube.com (Bi et al., 2019) revealed that, compared to negative reviews, positive reviews were considered more dependable and helpful for decision making.

Drawing upon these previous studies, a unique feature that characterizes the consumption experience at restaurants should be considered: Dining out can elicit hedonic pleasure, such as gastronomic delight or recreation as customers experience the restaurant. Accordingly, the process of review writing may enable customers to revisit and savor the enjoyable experiences they had at the restaurant, thereby providing a chance to redouble their positive emotions through recollection. Therefore, for restaurants, positive reviews should outnumber negative reviews, driven by people’s tendency to encourage positive emotions while suppressing negative emotions (Z. Cao et al., 2019; Gable et al., 2004; Kim & Hamann, 2007). In the case of restaurants, a negative review should then be more likely to be considered as useful for decision making compared to a positive review, due to its relative scarcity. Thus, we formed the following hypothesis:
Hypothesis 2: Yelp.com reviews with negative ratings will be considered more useful than will reviews with positive ratings, displaying a negativity bias.

The Moderating Roles of Variance and Average of Product Reviews

Researchers have considered how the distribution and average of product ratings affect the sales and evaluation of those products. Studies of the distribution of product ratings have examined how greater rating variance affects sales. For instance, studies of hotel review data on Elong.com (Qi et al., 2012) and of bath, fragrance, and beauty product reviews at U.S. national retailers (Moe & Trusov, 2011) have shown that higher variance in ratings pertains to higher product sales. Likewise, review data of craft beers on Ratebeer.com show that growth in rating variance pertains to sales growth (Clemons et al., 2006). Conversely, studies of hotel review data collected by China’s Ctrip.com indicate that higher variance has either no effect (Ye et al., 2011) or a negative effect (Ye et al., 2009) on sales growth.

Additional studies have considered moderating variables to produce a clearer explanation for conflicting findings over the variance of ratings. A study of reviews on Amazon.com and Barnesandnoble.com found that, in cases where the average rating is below 4 stars, larger variance has a positive effect on sales (Sun, 2012). A laboratory experiment found that larger variance can positively affect product evaluations when hedonic motives are emphasized in the focal consumption context (Chu et al., 2015). Likewise, a study of reviews of cameras, televisions, notebooks, and computer science books on Amazon.com found that while rating variance and sales are negatively related in the case of numerically expressed review ratings, the opposite is true in the case of reviews that are written in text (Z. Zhang & Li, 2010).

We examined if the variance of ratings affects the evaluation of the perceived usefulness of individual reviews. An increase in rating variance at the product level indicates that both positive and negative opinions emerge in equal measure over the same product, so that opinions of the product diverge rather than converge, implying it is controversial. Because cases such as these present further complexity and uncertainty in terms of information processing, customers are more likely to process information in a conservative manner. The exacerbation of uncertainty due to divergent opinions will likely lead people to process information in a more conservative and uncertainty-averse manner, thereby strengthening their dependence on negative ratings during decision making. Consequently, negativity bias, which signifies a higher dependence on negative ratings, should be more amplified with larger variance at the product level. Thus, we formed the following hypothesis:
Hypothesis 3: The evaluation of usefulness of reviews on Yelp.com with negative (vs. positive) ratings will be enhanced when product variance is high (vs. low).

In this study we also sought to clarify how average ratings affect the evaluation of usefulness of each individual review. Higher average ratings at the product level indicate that the general opinion surrounding a focal product is favorable, so when a certain restaurant receives overall high average ratings, customers with no prior information may use these good ratings as the baseline to shape their predecisional preference (Wei et al., 2013). Thus, high average ratings are likely to raise the expectation level of customers, driving more positive prior attitudes toward the restaurant. In this sense, at the level of an individual review, a positive rating would be accepted as a better match to prior positive attitudes of prospective customers, whereas a negative rating would run counter to prior attitudes. Thus, higher average ratings would lead to lower dependence on negative reviews, weakening the negativity bias of users (see Figure 1).
Hypothesis 4: A higher evaluation of usefulness of Yelp.com reviews with negative (vs. positive) ratings will more likely be reduced when the average rating is higher.

Table/Figure

Figure 1. The Proposed Effect of Review Ratings on Perceived Usefulness

Method

Dataset and Procedure

The complete set of reviews of restaurants in New York City posted on Yelp.com constituted the focal dataset for empirical verification. This consisted of 951,178 reviews composed by 142,286 reviewers concerning 953 restaurants, as of November 2018. Our web crawling focused on these reviews due to the diversity of culinary preferences of people from different cultural backgrounds and New York City residents’ vitality in review posting and evaluation activity. Yelp.com has also received attention for its sampling representativeness as it hosts the largest number of posted reviews concerning restaurants from major cities (Salehi-Esfahani & Kang, 2019; Yelp, Inc., 2019). At the review level, we recorded the star rating, word count, posting date, number of usefulness votes, and whether there were any accompanying images; at the restaurant level, we recorded number of occurrences of each star rating (1 to 5 stars); and at the reviewer level we recorded the number of votes for each star rating (1 to 5 stars).

Measures

Independent Variables
Raw star rating and reviewer reference rating served as the primary indicators to calculate the reference-based star rating. Raw star rating indicates the typical rating out of 5 stars that accompanies each text review, and reviewer reference rating refers to the average of all the star ratings given by a reviewer. Yelp.com hosts a profile webpage for each reviewer, where all their posting activities are summarized, including text reviews and numeric ratings, along with a rating distribution that illustrates the frequency of their use of each star rating. The reference-based star rating, one of the key predictors of the proposed model, was calculated by subtracting the reviewer’s reference rating from their raw star rating:

Reference-based star rating [Refstar] = Raw star rating − Reviewer reference rating

Next, variance captures how much variability there is among the star ratings posted for a particular restaurant. The greater the variance, the lower is the consistency of ratings among Yelp reviewers. Finally, average indicates the average value across all the star ratings posted for a particular restaurant. A higher average rating implies that most Yelp reviewers exhibit favorable opinions toward the restaurant.

Dependent Variable
Review usefulness served as the focal dependent measure. We operationalized review usefulness as the number of useful votes that a review received from users.

Control Variables
To avoid confounding the effects of the key predictors, we controlled for the review-specific variables of whether a review had accompanying images (1 = has images, 0 = otherwise) and review length (total word count). Review age also served as a control variable, calculated as the number of days from review posting to data collection (November 7, 2018). Table 1 illustrates the operationalization of the variables used for hypothesis testing.

Table 1. Operationalization of Variables

Table/Figure

Results

Specification

Few reviews received useful votes. As the dependent variable of usefulness is a count variable for which the variance exceeds its mean (M = 0.92, variance = 6.00), we used negative binomial regression to specify a nonnegative integer variable (Greene, 2008):

Table/Figure

where i indexes the review, j indexes the restaurant, Xi is the vector of review-specific controls, and εij is the idiosyncratic error.

Equation I captures the extremity bias proposed in Hypothesis 1 and negativity bias in Hypothesis 2, whereas Equations II and III capture the moderating effects of variance and average proposed in Hypotheses 3 and 4, respectively. We followed a three-step procedure to assess the fit of these specified models to the data: (1) enter the control variables (the presence/absence of review images, review length, and review age), (2) enter the main effect terms (reference-based star rating and variance), and (3) enter the squared (reference-based star rating2) and interaction (reference-based star rating × variance or reference-based star rating × average) terms.

The multicollinearity check preceded a hypothesis testing procedure to examine the possibility of linear dependency between the independent variables. The variance inflation factor test results indicate that there were no multicollinearity concerns (values ≤ 2.60). Overall, the analysis strategy of using negative binomial regression seems to be an appropriate approach for the focal dataset (Cohen et al., 2003).

Hypothesis Testing

Table 2 summarizes the frequency of raw star ratings in the dataset. Positive ratings were far more prevalent than were negative ratings, and the mean of raw star ratings was 3.88, reaching far beyond the midpoint of 3.00.

Table 2. Frequencies of Raw Star Ratings

Table/Figure

Next, Table 3 illustrates the results of empirical analysis of Equation I. Step 1 first involved evaluating the effect of control variables. The results indicate that review image inclusion, review length, and review age all positively affected the evaluation of review usefulness. This finding is consistent with previous reports derived from Yelp.com datasets (Z. Chen & Lurie, 2013; Hlee et al., 2019).

In Steps 2 and 3 we assessed potential negativity bias and extremity bias. Step 2 investigated the primary effect of reference-based star ratings, and Step 3 examined the quadratic effect. Table 3 summarizes the results, indicating that higher reference-based star ratings led to a lower usefulness evaluation, confirming the existence of negativity bias. Further, the quadratic effect proved to be positive, supporting the presence of extremity bias. Together, these results support the existence of a U-shaped relationship between star ratings and usefulness evaluation, which connotes that extremely negative or positive ratings count as more useful than do moderate ratings (Hypothesis 1), and that of these two extreme ratings, negative ratings are considered more useful than positive ratings (Hypothesis 2).

Table 3. Perceived Usefulness of Yelp Reviews as a Function of Reference-Based Star Rating (Equation I)

Table/Figure

Note. N = 951,178.
* p < .001.

We further analyzed the moderating roles of variance and average, to assess negativity bias. The data fit assessment results for Equation II reveal that negativity bias was aggravated when there was greater variance (see Table 4). That is, the tendency to place a greater weight on negative versus positive ratings became more pronounced as the variance of review ratings increased (Hypothesis 3).

Finally, the Equation III analysis results reveal that negativity bias was attenuated by a higher average rating (see Table 5). That is, as the average of review ratings increased, the tendency to place a greater weight on negative versus positive ratings was reduced (Hypothesis 4). To sum up, these results support the notion that negativity bias is amplified by greater variance but is weakened by higher average ratings.

Table 4. Perceived Usefulness of Yelp Reviews as a Function of Reference-Based Star Rating and Variance (Equation II)

Table/Figure

Note. N = 951,178.
* p < .001.

Table 5. Perceived Usefulness of Yelp Reviews as a Function of Reference-Based Star Rating and Average (Equation III)

Table/Figure

Note. N = 951,178.
* p < .001.

Discussion

In this study we examined a dataset comprising 951,178 reviews of New York restaurants on Yelp.com to determine whether reviews are more useful for decision making when they are (a) rated at either the positive or negative extremes, or (b) rated at some level between these two extremes. The results of negative binomial regression analysis demonstrate that reviews with extreme ratings were considered more useful for decision making compared to reviews with moderate ratings (i.e., extremity bias). Further, negative ratings were more useful compared to positive ones (i.e., negativity bias). These results confirm that, in the case of reviews of New York City restaurants posted on Yelp.com, negative reviews were viewed as more useful than positive reviews, as were extreme ratings compared to moderate ratings. Furthermore, negativity bias became more pronounced when the variance of ratings was large and when average ratings were low.

Theoretical Implications

The theoretical implications of this study are as follows: First, we introduced the concept of preference heterogeneity to provide a more consistent explanation for the conflicting findings concerning extremity bias. For instance, the reviews analyzed in a study of Amazon.com book reviews (Danescu-Niculescu-Mizil et al., 2009) featured a mix of genres with low preference heterogeneity (e.g., practical texts, study guides) and high preference heterogeneity (e.g., novels), which may have led to a confounding effect due to the inherent differences across the various genres of books. Similarly, in a study of hotel reviews on TripAdvisor.com (Deng & Ravichandran, 2018), the reviews by business travelers and leisure travelers were combined and analyzed together, which may have had a confounding effect due to differences in travel goals between the two traveler groups. A prior study of MP3 players, music CDs, and computer games on Amazon.com (Mudambi & Schuff, 2010) also had a limited scope of data collection—only certain brands (Apple iPod MP3 player) or a particular album by an artist (“Loose” by Nelly Furtado) were included—which may have limited the study’s generalizability. In this regard, our introduction of the concept of preference heterogeneity could provide a more plausible explanation for the previous conflicting empirical results on extremity bias.

Second, our results support the dominance of negativity bias over positivity bias in the context of online review information processing. By confirming that negative ratings were considered more helpful than positive ratings, this study adds evidence to the findings of previous works indicating the presence of negativity bias (e.g., Basuroy et al., 2003; Chevalier & Mayzlin, 2006; Sparks & Browning, 2011; J. Yang & Mai, 2010; L. Zhang et al., 2013). Notably, several studies that support the presence of negativity bias have included field studies based on observational data (Basuroy et al., 2003; Chevalier & Mayzlin, 2006; L. Zhang et al., 2013), whereas those that support positivity bias have been primarily based on empirical findings obtained from experimental studies (e.g., Bi et al., 2019; Wei et al., 2013). Our own results are based on observational data.

In addition, we used a measurement method that reflects the various reference points of individual reviewers. Contrary to previous studies that regarded 3 as the midpoint of 5-star ratings, such that ratings of 1 and 2 were seen to be negative and ratings of 4 and 5 were seen to be positive (Z. Chen & Lurie, 2013; Hu et al., 2009; Nguyen et al., 2021; Wang et al., 2019), we calculated the average of the past ratings given by a certain reviewer and used this as the reference point. By subtracting this reference point from the raw star rating (spanning 1–5 stars), we used continuous variable values as inputs for the analysis. This approach to measuring the valence of review ratings is of particular importance in that it reflects the differences in reference points across reviewers. For instance, a rating of 4 stars from a cynical reviewer who usually gives 2 or 3 ratings and rarely gives 4 or 5 ratings can be readily interpreted as a truly positive signal. On the other hand, a 4-star rating from a lenient reviewer who mostly gives ratings of 4 or 5 ratings may be regarded as a neutral signal rather than a truly positive one. In this context, this study—to the best of our knowledge—is the first to reflect each reviewer’s unique reference points when dealing with actual review data on restaurants, thereby distinguishing itself from previous studies that have merely incorporated the midpoint 3-star rating as the uniformly equal reference point across different reviewers (Z. Chen & Lurie, 2013; Hu et al., 2009; Nguyen et al., 2021; Wang et al., 2019). In this sense, this study holds significance in that it lays the foundation for more theoretically precise measurements of the valence of ratings for future field studies.

Practical Implications

The practical implications of this study are as follows: First, it is necessary to build user interfaces that more clearly reveal the contrast between review ratings at the positive or negative extremes. For instance, Amazon.com displays book reviews by placing both the “top positive reviews” alongside the “top critical reviews” at the top of the page, thereby presenting a clear comparison of the contrasting views reviewers had for a book, which lets prospective customers gauge the preference system of review posters. This measure resonates with the findings of this study, which support the dominance of extreme ratings over moderate ratings. In this sense, this study provides evidence that by adopting a format that clearly contrasts a product’s pros and cons in presenting customer reviews, online businesses can encourage visitors to their website to better address the uncertainty associated with information use.

Online businesses can also attempt to add criteria that are indicative of a review’s informational diagnosticity or usefulness to the options by which reviews are sorted, thereby prompting more effective review processing procedures among users. For instance, websites such as Amazon.com or Yelp.com provide sorting options such as by date (newest–oldest) or by rating (top rated–lowest rated) but do not provide sorting options such as by helpful votes or by useful votes. Adding these sorting options will provide users with the opportunity to sort reviews by significant criteria that will aid in their decision making.

Limitations and Future Research Directions

There are limitations to this study. First, this empirical examination offers an exploratory view into how preference heterogeneity affects the manifestation of extremity bias; however, we were unable to derive a formal outcome regarding the causal relationships between the examined variables. Future researchers could conduct experimental studies employing the construct of preference heterogeneity as another antecedent. Second, we recommend broadening the scope of future empirical examinations to include other types of tourism industries. Extremity bias, which we examined only within the restaurant context, could also be verified in wider fields of tourism. For instance, tourism websites such as Airbnb, TripAdvisor, and Expedia display text reviews composed by previous users, along with 5-point numerical ratings, which may help provide more convincing evidence concerning how extreme ratings and negative ratings interactively affect the processing procedure of review information. Future studies aimed at verifying extremity or negativity biases may gain a broader understanding of the dynamics of review interpretation by using such diverse data.

Conclusion

Although numerous studies have examined the effect of review ratings, the empirical evidence on extremity and negativity biases has been mixed: Some have found that extreme ratings are more useful than moderate ratings (Filieri et al., 2018; Y. Liu & Hu, 2021; Z. Liu & Park, 2015), whereas others have reported the opposite (Deng & Ravichandran, 2018; Mudambi & Schuff, 2010). Similarly, in some studies negative ratings have been reported to be viewed as more useful than positive ratings (Chevalier & Mayzlin, 2006; J. Yang & Mai, 2010), whereas others have established the opposite (Bi et al., 2019). To resolve these inconsistencies, we collected and analyzed a large review dataset of New York restaurants from Yelp.com. Negative binomial regression analysis results show that extreme ratings were perceived as more useful than moderate ratings. Moreover, between the two extremes of negative and positive ratings, the former were perceived as more useful than the latter. Overall, these results support the presence of extremity and negativity biases.

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https://doi.org/10.1016/j.chb.2018.05.042

Gable, S. L., Reis, H. T., Impett, E. A., & Asher, E. R. (2004). What do you do when things go right? The intrapersonal and interpersonal benefits of sharing positive events. Journal of Personality and Social Psychology, 87(2), 228–245.
https://doi.org/10.1037/0022-3514.87.2.228

Greene, W. H. (2008). Econometric analysis (6th ed.). Pearson/Prentice Hall.

Herr, P. M., Kardes, F. R., & Kim, J. (1991). Effects of word-of-mouth and product-attribute information on persuasion: An accessibility-diagnosticity perspective. Journal of Consumer Research, 17(4), 454–462.
https://doi.org/10.1086/208570

Hlee, S., Lee, J., Yang, S.-B., & Koo, C. (2019). The moderating effect of restaurant type on hedonic versus utilitarian review evaluations. International Journal of Hospitality Management, 77, 195–206.
https://doi.org/10.1016/j.ijhm.2018.06.030

Hu, N., Pavlou, P. A., & Zhang, J. (2009). Why do online product reviews have a J-shaped distribution? Overcoming biases in online word-of-mouth communication. Communications of the ACM, 52(10), 144–147.
https://doi.org/10.2139/ssrn.2380298

Keller, K. L. (2003). Brand synthesis: The multidimensionality of brand knowledge. Journal of Consumer Research, 29(4), 595–600.
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Kim, S. H., & Hamann, S. (2007). Neural correlates of positive and negative emotion regulation. Journal of Cognitive Neuroscience, 19(5), 776–798.
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Lee, J., Park, D.-H., & Han, I. (2008). The effect of negative online consumer reviews on product attitude: An information processing view. Electronic Commerce Research and Applications, 7(3), 341–352.
https://doi.org/10.1016/j.elerap.2007.05.004

Liu, Y., & Hu, H.-F. (2021). Online review helpfulness: The moderating effects of review comprehensiveness. International Journal of Contemporary Hospitality Management, 33(2), 534–556.
https://doi.org/10.1108/IJCHM-08-2020-0856

Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140–151.
https://doi.org/10.1016/j.tourman.2014.09.020

Lo, A. S., & Yao, S. S. (2019). What makes hotel online reviews credible? An investigation of the roles of reviewer expertise, review rating consistency and review valence. International Journal of Contemporary Hospitality Management, 31(2), 41–60.
https://doi.org/10.1108/IJCHM-10-2017-0671

Moe, W. W., & Trusov, M. (2011). The value of social dynamics in online product ratings forums. Journal of Marketing Research, 48(3), 444–456.
https://doi.org/10.1509/jmkr.48.3.444

Mudambi, S. M., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 34(1), 185–200.
https://doi.org/10.2307/20721420

Nguyen, P., Wang, X. S., Li, X., & Cotte, J. (2021). Reviewing experts’ restraint from extremes and its impact on service providers. Journal of Consumer Research, 47(5), 654–674.
https://doi.org/10.1093/jcr/ucaa037

Papathanassis, A., & Knolle, F. (2011). Exploring the adoption and processing of online holiday reviews: A grounded theory approach. Tourism Management, 32(2), 215–224.
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Park, S., & Nicolau, J. L. (2015). Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, 67–83.
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Price, L. L., Feick, L. F., & Higie, R. A. (1989). Preference heterogeneity and coorientation as determinants of perceived informational influence. Journal of Business Research, 19(3), 227–242.
https://doi.org/10.1016/0148-2963(89)90021-0

Purnawirawan, N., De Pelsmacker, P., & Dens, N. (2012). Balance and sequence in online reviews: How perceived usefulness affects attitudes and intentions. Journal of Interactive Marketing, 26(4), 244–255.
https://doi.org/10.1016/j.intmar.2012.04.002

Qi, L., Lei, X., & Qiang, Y. (2012, September 20–22). Investigating the impact of online word-of mouth on hotel sales with panel data [Paper presentation]. 19th Annual International Conference on Management Science and Engineering, Dallas, TX, USA.
https://doi.org/10.1109/ICMSE.2012.6414153

Qiu, J., Liu, C., Li, Y., & Lin, Z. (2018). Leveraging sentiment analysis at the aspects level to predict ratings of reviews. Information Sciences, 451, 295–309.
https://doi.org/10.1016/j.ins.2018.04.009

Racherla, P., & Friske, W. (2012). Perceived ‘usefulness’ of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications, 11(6), 548–559.
https://doi.org/10.1016/j.elerap.2012.06.003

Roy, D., & Banerjee, S. (2014). Identification and measurement of brand identity and image gap: A quantitative approach. Journal of Product & Brand Management, 23(3), 207–219.
https://doi.org/10.1108/JPBM-01-2014-0478

Salehi-Esfahani, S., & Kang, J. (2019). Why do you use Yelp? Analysis of factors influencing customers’ website adoption and dining behavior. International Journal of Hospitality Management, 78, 179–188.
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Schaffner, F. (2016, July 18). Information transmission in high dimensional choice problems: The value of online ratings in the restaurant market [Paper presentation]. 2016 German Economic Association VfS Annual Conference, Hamburg, Germany. https://bit.ly/3iAI9PI

Sparks, B. A., & Browning, V. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 32(6), 1310–1323.
https://doi.org/10.1016/j.tourman.2010.12.011

Sun, M. (2012). How does the variance of product ratings matter? Management Science, 58(4), 696–707.
https://doi.org/10.1287/mnsc.1110.1458

Vana, P., & Lambrecht, A. (2021). The effect of individual online reviews on purchase likelihood. Marketing Science, 40(4), 708–730.
https://doi.org/10.1287/mksc.2020.1278

Wang, Y., Wang, Z., Zhang, D., & Zhang, R. (2019). Discovering cultural differences in online consumer product reviews. Journal of Electronic Commerce Research, 20(3), 169–183. https://bit.ly/3lOVMNa

Wei, W., Miao, L., & Huang, Z. J. (2013). Customer engagement behaviors and hotel responses. International Journal of Hospitality Management, 33, 316–330.
https://doi.org/10.1016/j.ijhm.2012.10.002

Yang, J., & Mai, E. S. (2010). Experiential goods with network externalities effects: An empirical study of online rating system. Journal of Business Research, 63(9–10), 1050–1057.
https://doi.org/10.1016/j.jbusres.2009.04.029

Yang, S.-B., Hlee, S., Lee, J., & Koo, C. (2017). An empirical examination of online restaurant reviews on Yelp.com: A dual coding theory perspective. International Journal of Contemporary Hospitality Management, 29(2), 817–839.
https://doi.org/10.1108/IJCHM-11-2015-0643

Ye, Q., Law, R., & Gu, B. (2009). The impact of online user reviews on hotel room sales. International Journal of Hospitality Management, 28(1), 180–182.
https://doi.org/10.1016/j.ijhm.2008.06.011

Ye, Q., Law, R., Gu, B., & Chen, W. (2011). The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings. Computers in Human Behavior, 27(2), 634–639.
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Zhang, H., & Choi, Y. K. (2018). Preannouncement messages: Impetus for electronic word-of-mouth. International Journal of Advertising, 37(1), 54–70.
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Zhang, H., Liu, X., & Ying, K. (2017, March). Reviews usefulness prediction for Yelp dataset [Working paper]. University of California, San Diego, CA, USA.

Zhang, L., Ma, B., & Cartwright, D. K. (2013). The impact of online user reviews on cameras sales. European Journal of Marketing, 47(7), 1115–1128.
https://doi.org/10.1108/03090561311324237

Zhang, Z., & Li, X. (2010). Controversy is marketing: Mining sentiments in social media. In R. H. Sprague, Jr. (Ed.), Proceedings of the 43rd Hawaii International Conference on System Sciences (pp. 1–10). IEEE.
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https://doi.org/10.1016/j.chb.2018.05.042

Gable, S. L., Reis, H. T., Impett, E. A., & Asher, E. R. (2004). What do you do when things go right? The intrapersonal and interpersonal benefits of sharing positive events. Journal of Personality and Social Psychology, 87(2), 228–245.
https://doi.org/10.1037/0022-3514.87.2.228

Greene, W. H. (2008). Econometric analysis (6th ed.). Pearson/Prentice Hall.

Herr, P. M., Kardes, F. R., & Kim, J. (1991). Effects of word-of-mouth and product-attribute information on persuasion: An accessibility-diagnosticity perspective. Journal of Consumer Research, 17(4), 454–462.
https://doi.org/10.1086/208570

Hlee, S., Lee, J., Yang, S.-B., & Koo, C. (2019). The moderating effect of restaurant type on hedonic versus utilitarian review evaluations. International Journal of Hospitality Management, 77, 195–206.
https://doi.org/10.1016/j.ijhm.2018.06.030

Hu, N., Pavlou, P. A., & Zhang, J. (2009). Why do online product reviews have a J-shaped distribution? Overcoming biases in online word-of-mouth communication. Communications of the ACM, 52(10), 144–147.
https://doi.org/10.2139/ssrn.2380298

Keller, K. L. (2003). Brand synthesis: The multidimensionality of brand knowledge. Journal of Consumer Research, 29(4), 595–600.
https://doi.org/10.1086/346254

Kim, S. H., & Hamann, S. (2007). Neural correlates of positive and negative emotion regulation. Journal of Cognitive Neuroscience, 19(5), 776–798.
https://doi.org/10.1162/jocn.2007.19.5.776

Lee, J., Park, D.-H., & Han, I. (2008). The effect of negative online consumer reviews on product attitude: An information processing view. Electronic Commerce Research and Applications, 7(3), 341–352.
https://doi.org/10.1016/j.elerap.2007.05.004

Liu, Y., & Hu, H.-F. (2021). Online review helpfulness: The moderating effects of review comprehensiveness. International Journal of Contemporary Hospitality Management, 33(2), 534–556.
https://doi.org/10.1108/IJCHM-08-2020-0856

Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140–151.
https://doi.org/10.1016/j.tourman.2014.09.020

Lo, A. S., & Yao, S. S. (2019). What makes hotel online reviews credible? An investigation of the roles of reviewer expertise, review rating consistency and review valence. International Journal of Contemporary Hospitality Management, 31(2), 41–60.
https://doi.org/10.1108/IJCHM-10-2017-0671

Moe, W. W., & Trusov, M. (2011). The value of social dynamics in online product ratings forums. Journal of Marketing Research, 48(3), 444–456.
https://doi.org/10.1509/jmkr.48.3.444

Mudambi, S. M., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 34(1), 185–200.
https://doi.org/10.2307/20721420

Nguyen, P., Wang, X. S., Li, X., & Cotte, J. (2021). Reviewing experts’ restraint from extremes and its impact on service providers. Journal of Consumer Research, 47(5), 654–674.
https://doi.org/10.1093/jcr/ucaa037

Papathanassis, A., & Knolle, F. (2011). Exploring the adoption and processing of online holiday reviews: A grounded theory approach. Tourism Management, 32(2), 215–224.
https://doi.org/10.1016/j.tourman.2009.12.005

Park, S., & Nicolau, J. L. (2015). Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, 67–83.
https://doi.org/10.1016/j.annals.2014.10.007

Price, L. L., Feick, L. F., & Higie, R. A. (1989). Preference heterogeneity and coorientation as determinants of perceived informational influence. Journal of Business Research, 19(3), 227–242.
https://doi.org/10.1016/0148-2963(89)90021-0

Purnawirawan, N., De Pelsmacker, P., & Dens, N. (2012). Balance and sequence in online reviews: How perceived usefulness affects attitudes and intentions. Journal of Interactive Marketing, 26(4), 244–255.
https://doi.org/10.1016/j.intmar.2012.04.002

Qi, L., Lei, X., & Qiang, Y. (2012, September 20–22). Investigating the impact of online word-of mouth on hotel sales with panel data [Paper presentation]. 19th Annual International Conference on Management Science and Engineering, Dallas, TX, USA.
https://doi.org/10.1109/ICMSE.2012.6414153

Qiu, J., Liu, C., Li, Y., & Lin, Z. (2018). Leveraging sentiment analysis at the aspects level to predict ratings of reviews. Information Sciences, 451, 295–309.
https://doi.org/10.1016/j.ins.2018.04.009

Racherla, P., & Friske, W. (2012). Perceived ‘usefulness’ of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications, 11(6), 548–559.
https://doi.org/10.1016/j.elerap.2012.06.003

Roy, D., & Banerjee, S. (2014). Identification and measurement of brand identity and image gap: A quantitative approach. Journal of Product & Brand Management, 23(3), 207–219.
https://doi.org/10.1108/JPBM-01-2014-0478

Salehi-Esfahani, S., & Kang, J. (2019). Why do you use Yelp? Analysis of factors influencing customers’ website adoption and dining behavior. International Journal of Hospitality Management, 78, 179–188.
https://doi.org/10.1016/j.ijhm.2018.12.002

Schaffner, F. (2016, July 18). Information transmission in high dimensional choice problems: The value of online ratings in the restaurant market [Paper presentation]. 2016 German Economic Association VfS Annual Conference, Hamburg, Germany. https://bit.ly/3iAI9PI

Sparks, B. A., & Browning, V. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 32(6), 1310–1323.
https://doi.org/10.1016/j.tourman.2010.12.011

Sun, M. (2012). How does the variance of product ratings matter? Management Science, 58(4), 696–707.
https://doi.org/10.1287/mnsc.1110.1458

Vana, P., & Lambrecht, A. (2021). The effect of individual online reviews on purchase likelihood. Marketing Science, 40(4), 708–730.
https://doi.org/10.1287/mksc.2020.1278

Wang, Y., Wang, Z., Zhang, D., & Zhang, R. (2019). Discovering cultural differences in online consumer product reviews. Journal of Electronic Commerce Research, 20(3), 169–183. https://bit.ly/3lOVMNa

Wei, W., Miao, L., & Huang, Z. J. (2013). Customer engagement behaviors and hotel responses. International Journal of Hospitality Management, 33, 316–330.
https://doi.org/10.1016/j.ijhm.2012.10.002

Yang, J., & Mai, E. S. (2010). Experiential goods with network externalities effects: An empirical study of online rating system. Journal of Business Research, 63(9–10), 1050–1057.
https://doi.org/10.1016/j.jbusres.2009.04.029

Yang, S.-B., Hlee, S., Lee, J., & Koo, C. (2017). An empirical examination of online restaurant reviews on Yelp.com: A dual coding theory perspective. International Journal of Contemporary Hospitality Management, 29(2), 817–839.
https://doi.org/10.1108/IJCHM-11-2015-0643

Ye, Q., Law, R., & Gu, B. (2009). The impact of online user reviews on hotel room sales. International Journal of Hospitality Management, 28(1), 180–182.
https://doi.org/10.1016/j.ijhm.2008.06.011

Ye, Q., Law, R., Gu, B., & Chen, W. (2011). The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings. Computers in Human Behavior, 27(2), 634–639.
https://doi.org/10.1016/j.chb.2010.04.014

Yelp, Inc. (2019). 10 things you should know about Yelp. Yelp.com. https://bit.ly/3s39HAi

Zhang, H., & Choi, Y. K. (2018). Preannouncement messages: Impetus for electronic word-of-mouth. International Journal of Advertising, 37(1), 54–70.
https://doi.org/10.1080/02650487.2017.1391679

Zhang, H., Liu, X., & Ying, K. (2017, March). Reviews usefulness prediction for Yelp dataset [Working paper]. University of California, San Diego, CA, USA.

Zhang, L., Ma, B., & Cartwright, D. K. (2013). The impact of online user reviews on cameras sales. European Journal of Marketing, 47(7), 1115–1128.
https://doi.org/10.1108/03090561311324237

Zhang, Z., & Li, X. (2010). Controversy is marketing: Mining sentiments in social media. In R. H. Sprague, Jr. (Ed.), Proceedings of the 43rd Hawaii International Conference on System Sciences (pp. 1–10). IEEE.
https://doi.org/10.1109/HICSS.2010.121

Table/Figure

Figure 1. The Proposed Effect of Review Ratings on Perceived Usefulness


Table 1. Operationalization of Variables

Table/Figure

Table/Figure

Table 2. Frequencies of Raw Star Ratings

Table/Figure

Table 3. Perceived Usefulness of Yelp Reviews as a Function of Reference-Based Star Rating (Equation I)

Table/Figure

Note. N = 951,178.
* p < .001.


Table 4. Perceived Usefulness of Yelp Reviews as a Function of Reference-Based Star Rating and Variance (Equation II)

Table/Figure

Note. N = 951,178.
* p < .001.


Table 5. Perceived Usefulness of Yelp Reviews as a Function of Reference-Based Star Rating and Average (Equation III)

Table/Figure

Note. N = 951,178.
* p < .001.


This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2020S1A5B8103855).

Sung-Byung Yang, School of Management, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea. Email: [email protected]

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