List of Review Helpfulness Studies (2010 ~ 2019): Citations and Abstracts

List of Empirical Studies on the influencing factors for online review helpfulness since Mudambi and Schuff (2010)

1. Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon. com. MIS quarterly, 34(1), 185-200.

Customer reviews are increasingly available online for a wide range of products and services. They supplement other information provided by electronic storefronts such as product descriptions, reviews from experts, and personalized advice generated by automated recommendation systems. While researchers have demonstrated the benefits of the presence of customer reviews to an online retailer, a largely uninvestigated issue is what makes customer reviews helpful to a consumer in the process of making a purchase decision. Drawing on the paradigm of search and experience goods from information economics, we develop and test a model of customer review helpfulness. An analysis of 1,587 reviews from Amazon.com across six products indicated that review extremity, review depth, and product type affect the perceived helpfulness of the review. Product type moderates the effect of review extremity on the helpfulness of the review. For experience goods, reviews with extreme ratings are less helpful than reviews with moderate ratings. For both product types, review depth has a positive effect on the helpfulness of the review, but the product type moderates the effect of review depth on the helpfulness of the review. Review depth has a greater positive effect on the helpfulness of the review for search goods than for experience goods. We discuss the implications of our findings for both theory and practice.

2. Connors, L., Mudambi, S. M., & Schuff, D. (2011, January). Is it the review or the reviewer? A multi-method approach to determine the antecedents of online review helpfulness. In 2011 44th Hawaii International Conference on System Sciences (pp. 1-10). IEEE.

As online reviews increasingly become part of the purchasing process, it is important to understand which components of these reviews consumers consider most helpful in facilitating the purchase decision process. Online retailer and rating websites with more helpful reviews offer greater potential value to their consumers. Through two studies, we seek to identify and better understand what makes a helpful consumer review. After an open-ended analysis of the qualities of a review identified by subjects' as helpful, we conducted a controlled experiment that manipulated both the review content and the description of the reviewer. One key finding is that reviews written by a self-described expert are more helpful than those that are not. This information can provide guidance to online retailers and rating websites in their efforts to provide value to their customers.

3. Ghose, A., Ipeirotis, P.G., 2011. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. Knowledge and Data Engineering, IEEE Transactions on 23, 1498-1512.

With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we reexamine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. By using Random Forest-based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: “reviewer-related” features, “review subjectivity” features, and “review readability” features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.

4. Otterbacher, J. (2011). Being heard in review communities: Communication tactics and review prominence. Journal of Computer-Mediated Communication, 16(3), 424-444.

Review communities typically display contributions in list format, using participant feedback in determining presentation order. Given the volume of contributions, which are likely to be seen? While previous work has focused on content, we examine the relationship between communication tactics and prominence. We study three communities, comparing front-page reviews versus those on latter pages. We consider 3 types of devices: structural features, textual features, and persuasive writing. Structural features, such as profiles, convey information about authors. Textual properties, such as punctuation use, can make an impression on others. Rhetorical writing strategies are used by reviewers to convince readers of their opinions. When controlling for content, the most salient tactics distinguishing prominent reviews are textual properties and persuasive language.

5. Pan, Y., & Zhang, J. Q. (2011). Born unequal: a study of the helpfulness of user-generated product reviews. Journal of Retailing, 87(4), 598-612.

Online user-generated product reviews have become an indispensible tool for consumers and thus for retailers who want to attract and retain consumers. Yet, relatively little is known about what causes consumers to find an online peer review helpful to their shopping tasks. Prior research examines mostly the effects of product reviews on consumer product attitude, product choice, and product sales. This paper, however, provides an analysis of the determinants of review helpfulness. In two studies, we examine the effects of review characteristics, product type, and reviewer characteristics on perceived review helpfulness. With data collected from a real online retailer, we provide empirical evidence to support our conceptual predictions. Specifically, both review valence and length have positive effects on review helpfulness, but the product type (i.e., experiential vs. utilitarian product) moderates these effects. Using content analysis of reviews, we develop a measure of expressed reviewer innovativeness (i.e., the predisposition toward new products as revealed in review content). A curvilinear relationship exists between expressed reviewer innovativeness and review helpfulness. These findings lead to pertinent managerial implications.

6. Wu, P. F., Van Der Heijden, H., & Korfiatis, N. (2011, August). The influences of negativity and review quality on the helpfulness of online reviews. In International conference on information systems.

Building on the interpersonal evaluation theory in social psychology, this study explores the existence of a negativity bias in evaluating the helpfulness of online reviews, i.e., whether users perceive a negative review to be more helpful than a positive review. An analysis of 7659 book reviews from Amazon.co.uk shows that a negativity bias disappears after controlling for moderating factors related to evaluation quality such as readability and length. The finding demonstrates that the negativity bias suggested by the social psychology literature is not readily applicable to consumer-generated online reviews. The study contributes to the theorization of word-of-mouth by exploring the qualitative characteristics of consumer-generated reviews in addition to their valence. The study also makes a theoretical contribution to information systems research by introducing and extending the interpersonal evaluation theory to online review research.

7. Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers' objectives and review cues. International Journal of Electronic Commerce, 17(2), 99-126.

With the growth of e-commerce, online consumer reviews have increasingly become important sources of information that help consumers in their purchase decisions. However, the influx of online consumer reviews has caused information overload, making it difficult for consumers to choose reliable reviews. For an online retail market to succeed, it is important to lead product reviewers to write more helpful reviews, and for consumers to get helpful reviews more easily by figuring out the factors determining the helpfulness of online reviews. For this research, 75,226 online consumer reviews were collected from Amazon.com using a Web data crawler. Additional information on review content was also gathered by carrying out a sentiment analysis for mining review text. Our results show that both peripheral cues, including review rating and reviewer's credibility, and central cues, such as the content of reviews, influence the helpfulness of reviews. Based on dual process theories, we find that consumers focus on different information sources of reviews, depending on their purposes for reading reviews: online reviews can be used for information search or for evaluating alternatives. Our findings provide new perspectives to online market owners on how to manage online reviews on their Web sites.

8. Schindler, R. M., & Bickart, B. (2012). Perceived helpfulness of online consumer reviews: The role of message content and style. Journal of Consumer Behaviour, 11(3), 234-243.

The rise of online reviews written by consumers makes possible an examination of how the content and style of these word‐of‐mouth messages contribute to their helpfulness. In this study, consumers are asked to judge the value of real online consumer reviews to their simulated shopping activities. The results suggest the benefits of moderate review length and of positive, but not negative, product evaluative statements. Non‐evaluative product information and information about the reviewer were also found to be associated with review helpfulness. Stylistic elements that may impair clarity (such as spelling and grammatical errors) were associated with less valuable reviews, and elements that may make a review more entertaining (such as expressive slang and humor) were associated with more valuable reviews. These findings point to factors beyond product information that may affect the perceived helpfulness of an online consumer review. Copyright © 2012 John Wiley & Sons, Ltd.

9. Li, M., Huang, L., Tan, C. H., & Wei, K. K. (2013). Helpfulness of online product reviews as seen by consumers: Source and content features. International Journal of Electronic Commerce, 17(4), 101-136.

Online product reviews are important determinants of consumers' purchase decision. Although prior research has articulated various benefits of online product reviews, there are few investigations into whether or not they are perceived as helpful by consumers. Product review helpfulness is conceptualized as a second-order formative construct, which is manifested by perceived source credibility, perceived content diagnosticity, and perceived vicarious expression of the product review. In this study, we conduct a laboratory experiment to investigate product review helpfulness as well as its corresponding antecedents from the product review feature perspective (i.e., source- and content-based review features). Findings from the study are threefold. First, the results of the data analysis support the theoretical conceptualization of product review helpfulness as a formative construct. Second, the results support the notion that the source- and content-based review features have direct impact on product review helpfulness. Consumers perceive customer-written product reviews as more helpful than those written by experts; they also perceive a concrete review as more helpful than an abstract review. Third, we find an interaction effect of the source- and content-based features of product reviews on review helpfulness. A customer-written product review with a low level of content abstractness yields the highest perceived review helpfulness.

10. Wu, P. F. (2013). In search of negativity bias: An empirical study of perceived helpfulness of online reviews. Psychology & Marketing, 30(11), 971-984.

A basic tenet of psychology is that the psychological effects of negative information outweigh those of positive information. Three empirical studies show that the negativity bias can be attenuated or even reversed in the context of electronic word of mouth (eWoM). The first study analyzes a large sample of customer reviews collected from Amazon.com and concludes that negative reviews are no more helpful than positive ones when controlling for review quality The second study follows up with a virtual experiment that confirms the lack of negativity bias in evaluating the helpfulness of online reviews. The third study demonstrates that the negativity effect can be reversed by manipulating the baseline valences. This work challenges the conventional wisdom of “bad is stronger than good” and contributes to the understanding of the eWoM phenomenon.

11. Yin, D., Bond, S., & Zhang, H. (2013). Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews.

This paper explores effects of the emotions embedded in a seller review on its perceived helpfulness to readers. Drawing on frameworks in the emotion and cognitive processing literatures, we propose that over and above a well-known negativity bias, the impact of discrete emotions in a review will vary, and that one source of this variance is reader perceptions of reviewers’ cognitive effort. We focus on the roles of two distinct, negative emotions common to seller reviews: anxiety and anger. In Studies 1 and 2, experimental methods were utilized to identify and explain the differential impact of anxiety and anger in terms of perceived reviewer effort. In Study 3, seller reviews from Yahoo! Shopping websites were collected to examine the relationship between emotional review content and helpfulness ratings. Our findings demonstrate the importance of examining discrete emotions in online word-of-mouth, and they carry important practical implications for consumers and online retailers.

12. Lee, S., & Choeh, J. Y. (2014). Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Systems with Applications, 41(6), 3041-3046.

With the great development of e-commerce, users can create and publish a wealth of product information through electronic communities. It is difficult, however, for manufacturers to discover the best reviews and to determine the true underlying quality of a product due to the sheer volume of reviews available for a single product. The goal of this paper is to develop models for predicting the helpfulness of reviews, providing a tool that finds the most helpful reviews of a given product. This study intends to propose HPNN (a helpfulness prediction model using a neural network), which uses a back-propagation multilayer perceptron neural network (BPN) model to predict the level of review helpfulness using the determinants of product data, the review characteristics, and the textual characteristics of reviews. The prediction accuracy of HPNN was better than that of a linear regression analysis in terms of the mean-squared error. HPNN can suggest better determinants which have a greater effect on the degree of helpfulness. The results of this study will identify helpful online reviews and will effectively assist in the design of review sites.

13. Ngo-Ye, T. L., & Sinha, A. P. (2014). The influence of reviewer engagement characteristics on online review helpfulness: A text regression model. Decision Support Systems, 61, 47-58.

The era of Web 2.0 is witnessing the proliferation of online social media platforms, which develop new business models by leveraging user-generated content. One rapidly growing source of user-generated data is online reviews, which play a very important role in disseminating information, facilitating trust, and promoting commerce in the e-marketplace. In this paper, we develop and compare several text regression models for predicting the helpfulness of online reviews. In addition to using review words as predictors, we examine the influence of reviewer engagement characteristics such as reputation, commitment, and current activity. We employ a reviewer's RFM (Recency, Frequency, Monetary Value) dimensions to characterize his/her overall engagement and investigate if the inclusion of those dimensions helps improve the prediction of online review helpfulness. Empirical findings from text mining experiments conducted using reviews from Yelp and Amazon offer strong support to our thesis. We find that both review text and reviewer engagement characteristics help predict review helpfulness. The hybrid approach of combining the textual features of bag-of-words model and RFM dimensions produces the best prediction results. Furthermore, our approach facilitates the estimation of the helpfulness of new reviews instantly, making it possible for social media platforms to dynamically adjust the presentation of those reviews on their websites.

14. Quaschning, S., Pandelaere, M., & Vermeir, I. (2014). When consistency matters: The effect of valence consistency on review helpfulness. Journal of Computer-Mediated Communication, 20(2), 136-152.

When evaluating the helpfulness of online reviews, review valence is a particularly relevant factor. This research argues that the influence of review valence is highly dependent on its consistency with the valence of other available reviews. Using both field and experimental data, this paper show that consistent reviews are perceived as more helpful than inconsistent reviews, independent of them being positive or negative. Experiments show that this valence consistency effect is driven by causal attributions, such that consistent reviews are more likely to be attributed to the actual product experience, while inconsistent reviews are more likely to be attributed to some reviewer idiosyncrasy. Supporting the attribution theory framework, reviewer expertise moderates the effect of consumers' causal attributions on review helpfulness.

15. Zhu, L., Yin, G., & He, W. (2014). Is this opinion leader's review useful? Peripheral cues for online review helpfulness. Journal of Electronic Commerce Research, 15(4), 267.

With the growing popularity of online user-generated reviews, research has emerged to understand the mechanism of how a review is voted helpful, focusing on the central-route influences of review content and quality, yet little research has studied the roles of peripheral cues such as reviewer credibility and contextual factors. Drawing on the theories of elaboration likelihood model and source credibility model, this study developed an integrative model of online review helpfulness, focusing on the direct influence of reviewer credibility and the moderating effects of service price and rating extremity. An econometrics regression analysis of 16,265 hotel reviews on Yelp showed that reviewer expertise in terms of the number of “Elite” badges, and reviewer online attractiveness in terms of the number of friends both helped a review receive helpfulness votes. The findings further revealed that a review written by an opinion leader (i.e., a reviewer with more Elite badges and more online friends) did not necessarily receive more helpfulness votes. Hotel price weakened the enhancing effect of reviewer expertise. Rating extremity also diluted the influence of reviewer credibility. These findings contribute to the knowledge of online review helpfulness and offer practical implications on how to position valuable reviews.

16. Ahmad, S. N., & Laroche, M. (2015). How do expressed emotions affect the helpfulness of a product review? Evidence from reviews using latent semantic analysis. International Journal of Electronic Commerce, 20(1), 76-111.

Customers often search online for product reviews to make an informed buying decision. People write about their experience with the product in a review that often expresses a variety of emotions. How these emotions affect the helpfulness of the review is an intriguing but insufficiently studied question. Do discrete emotions have differential informational value in this case? Here, we build on cognitive appraisal theory to examine how discrete emotions (e.g., hope, happiness, anxiety, and disgust) embedded in the reviews affect the helpfulness votes of potential customers. We hypothesize that reviews where emotions associated with certainty are expressed will have a positive effect on review helpfulness and vice versa, regardless of their valence. Moreover, certainty mediates this effect. We adopted a quantitative content analysis approach (latent semantic analysis or LSA) to measure emotional content in these reviews. Findings demonstrate that discrete emotions have differential effects on the helpfulness of the reviews. The paper contributes to the better understanding of framing effects of discrete emotions.

17. Bjering, E., Havro, L. J., & Moen, Ø. (2015). An empirical investigation of self-selection bias and factors influencing review helpfulness. International Journal of Business and Management, 10(7), 16.

This paper build on 1 489194 product reviews from 30 product categories retrieved from Amazon.com. Product categories are classified by use of natural language analysis tools with computing of subjectivity scores reflecting a search/experience product dimension. Results show a distinct effect of self-selection where the average review score gradually decreases. For most products, no undershooting period was observed, even though a limited number of products groups had this development pattern. Review length, verified purchase and use of real names contributed to increasing helpfulness ratings. The results further suggest search products to be more influenced by review length than experience products.

18. Chua, A. Y., & Banerjee, S. (2015). Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth. Journal of the Association for Information Science and Technology, 66(2), 354-362.

This article examines review helpfulness as a function of reviewer reputation, review rating, and review depth. In drawing data from the popular review platform Amazon, results indicate that review helpfulness is positively related to reviewer profile and review depth but is negatively related to review rating. Users seem to have a proclivity for reviews contributed by reviewers with a positive track record. They also appreciate reviews with lambasting comments and those with adequate depth. By highlighting its implications for theory and practice, the article concludes with limitations and areas for further research.

19. Huang, A. H., Chen, K., Yen, D. C., & Tran, T. P. (2015). A study of factors that contribute to online review helpfulness. Computers in Human Behavior, 48, 17-27.

Helpfulness of online reviews is a multi-faceted concept that can be driven by several types of factors. This study was designed to extend existing research on online review helpfulness by looking at not just the quantitative factors (such as word count), but also qualitative aspects of reviewers (including reviewer experience, reviewer impact, reviewer cumulative helpfulness). This integrated view uncovers some insights that were not available before. Our findings suggest that word count has a threshold in its effects on review helpfulness. Beyond this threshold, its effect diminishes significantly or becomes near non-existent. Reviewer experience and their impact were not statistically significant predictors of helpfulness, but past helpfulness records tended to predict future helpfulness ratings. Review framing was also a strong predictor of helpfulness. As a result, characteristics of reviewers and review messages have a varying degree of impact on review helpfulness. Theoretical and practical implications are discussed.

20. Krishnamoorthy, S. (2015). Linguistic features for review helpfulness prediction. Expert Systems with Applications, 42(7), 3751-3759.

Online reviews play a critical role in customer’s purchase decision making process on the web. The reviews are often ranked based on user helpfulness votes to minimize the review information overload problem. This paper examines the factors that contribute towards helpfulness of online reviews and builds a predictive model. The proposed predictive model extracts novel linguistic category features by analysing the textual content of reviews. In addition, the model makes use of review metadata, subjectivity and readability related features for helpfulness prediction. Our experimental analysis on two real-life review datasets reveals that a hybrid set of features deliver the best predictive accuracy. We also show that the proposed linguistic category features are better predictors of review helpfulness for experience goods such as books, music, and video games. The findings of this study can provide new insights to e-commerce retailers for better organization and ranking of online reviews and help customers in making better product choices.

21. Ullah, R., Zeb, A., & Kim, W. (2015). The impact of emotions on the helpfulness of movie reviews. Journal of applied research and technology, 13(3), 359-363.

Online customer reviews have become a significant source of product-related information for consumers. As a result of the growing number of customer reviews, determining which customer reviews are the most helpful is important in reducing information overload. The ways in which reviews can be helpful need to be identified. In this study, we examine the impact of emotional content in online customer reviews on the number of votes those customer reviews receive that indicate they were helpful. We find that content that is more emotional yields more votes. Furthermore, our findings suggest that reviews with positive emotional content have a positive effect on review helpfulness whereas reviews with negative emotional content have no effect on review helpfulness. This study contributes to an understanding of emotional content in word of mouth and has important implications for online retailers and consumers.

22. Agnihotri, A., & Bhattacharya, S. (2016). Online review helpfulness: Role of qualitative factors. Psychology & Marketing, 33(11), 1006-1017.

Consumers are increasingly reading online reviews before making any purchasing decisions. The significance of online reviews has only grown over the years. Though in the past, scholars have emphasized the impact of quantitative factors (e.g., review ratings) on online reviews, only recently have they begun to explore the role of qualitative aspects of online reviews. Content readability and associated sentiments in text provide two important qualitative cues that influence the helpfulness of online reviews. However, the extant literature has overemphasized the linear association between these aspects and the helpfulness of reviews. Using the elaboration likelihood model and the classic ideal point concept, the current work asserts that after an ideal point is attained, lucid and sentimental reviews diminish in utility (i.e., helpfulness of an online review for consumers decreases). This may happen because consumers are wary of fraudulent reviews. This study proposes that if experienced reviewers give such extreme reviews, then consumers might still draw utility from these reviews. In other words, this study explains the moderating role of reviewer experience, which heuristically influences consumers’ trust of online reviews, thus making even too simplistic or extremely sentimental reviews helpful.

23. Chen, X., Sheng, J., Wang, X., & Deng, J. (2016). Exploring determinants of attraction and helpfulness of online product review: A consumer behaviour perspective. Discrete Dynamics in Nature and Society, 2016.

To assist filtering and sorting massive review messages, this paper attempts to examine the determinants of review attraction and helpfulness. Our analysis divides consumers’ reading process into “notice stage” and “comprehend stage” and considers the impact of “explicit information” and “implicit information” of review attraction and review helpfulness. 633 online product reviews were collected from Amazon China. A mixed-method approach is employed to test the conceptual model proposed for examining the influencing factors of review attraction and helpfulness. The empirical results show that reviews with negative extremity, more words, and higher reviewer rank easily gain more attraction and reviews with negative extremity, higher reviewer rank, mixed subjective property, and mixed sentiment seem to be more helpful. The research findings provide some important insights, which will help online businesses to encourage consumers to write good quality reviews and take more active actions to maximise the value of online reviews.

24. Hu, Y. H., & Chen, K. (2016). Predicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratings. International Journal of Information Management, 36(6), 929-944.

The tourism industry has been strongly influenced by electronic word-of-mouth (eWOM) in recent years. Currently, there are only limited studies available that look into hotel review helpfulness. This present study addresses three hidden assumptions prevalent in online review studies: (1) all reviews are visible equally to online users, (2) review rating (RR) and hotel star class (HSC) affect review helpfulness individually with no interaction, and (3) characteristics of reviews and reviewer status stay constant. Four categories of input variables were considered in the present study: review content, sentiment, author, and visibility. Our findings confirmed the interaction effect between HSC and RR. The data set was sub-divided into eight subsets as a result. Three review visibility indicators (including days since a review was posted, days since a review has remained on the home page, and number of reviews with the same rating at the time a review was written) had a varying and strong effect on review helpfulness. The model performance was greatly improved after taking account of review visibility features, the interaction effect of HSC and RR, and a more accurate measurement of variables. Model tree (M5P) outperformed linear regression and support vector regression as it better modeled the interaction effect.

25. Kwok, L., & Xie, K. L. (2016). Factors contributing to the helpfulness of online hotel reviews: Does manager response play a role?. International Journal of Contemporary Hospitality Management, 28(10), 2156-2177.

Purpose: This paper aims to examine the factors contributing to the helpfulness of online hotel reviews and to measure the impact of manager response on the helpfulness of online hotel reviews.

Design/methodology/approach: This investigation used a linear regression model that drew upon 56,284 consumer reviews and 10,797 manager responses from 1,405 hotels on TripAdvisor.com for analysis.

Findings: The helpfulness of online hotel reviews is negatively affected by rating and number of sentences in a review, but positively affected by manager response and reviewer experience in terms of reviewer status, years of membership, and number of cities visited. Manager response moderates the influence of reviewer experience on the helpfulness of online hotel reviews.

Research limitations/implications: Using the data from hotels in five major cities in Texas, the results may not be necessarily generalized to other markets, but the important role that manager response plays in online reviews is assessed with big data analysis.

Practical implications: The results suggest hospitality managers should strategically identify opinion leaders among reviewers and proactively influence the helpfulness of the reviews by providing manager response. Additionally, this study makes recommendations to webmasters of social media platforms in terms of advancing the algorithm of featuring the most helpful online reviews.

Originality/value: This study is at the frontier of research to explain how hotel managers can proactively identify opinion leaders among consumers and use manager response to influence the helpfulness of consumer reviews. Additionally, the results also provide new insights to the influence of reviewer demographic background on the helpfulness of online reviews. Finally, this study analyzed a large data set on a scale that was not available in traditional guest survey studies, responding to the call for big data applications in the hospitality industry.

26. Lee, S., & Choeh, J. Y. (2016). The determinants of helpfulness of online reviews. Behaviour & Information Technology, 35(10), 853-863.

More and more people are gravitating to reading online product reviews prior to making purchasing decisions. Because a number of reviews that vary in usefulness are posted every day, much attention is being paid to measuring their helpfulness. The goal of this paper is to investigate the various determinants of the helpfulness of reviews, and it also intends to examine the moderating effect of product type, that is, the experience or search goods in relation to the helpfulness of online reviews. The study results show that reviewer reputation, the disclosure of reviewer identity, and review depth positively affect the helpfulness of an online review. The moderating effects of product type exist for these determinants on helpfulness. That is, the number of reviews for a product and the disclosure of reviewer identity have a greater influence on the helpfulness for experience goods, while reviewer reputation, review extremity, and review depth are more important for helpfulness in relation to search goods. The interaction effects exist for average review rating and average review depth for a product with review helpfulness on product sales. The results of the study will identify helpful online reviews and assist in designing review sites effectively.

27. Hong, H., Xu, D., Wang, G. A., & Fan, W. (2017). Understanding the determinants of online review helpfulness: a meta-analytic investigation. Decision Support Systems, 102, 1-11.

Online consumer reviews can help customers reduce uncertainty and risks faced in online shopping. However, the studies examining the determinants of perceived review helpfulness produce mixed findings. We review extant research about the determinant factors of perceived online review helpfulness. All review related determinants (i.e., review depth, review readability, linear review rating, quadratic review rating, review age) and two reviewer related determinants (i.e., reviewer information disclosure and reviewer expertise) are found to have inconsistent conclusions on how they affect perceived review helpfulness. We conduct a meta-analysis to examine those determinant factors in order to reconcile the contradictory findings about their influence on perceived review helpfulness. The meta-analysis results affirm that review depth, review age, reviewer information disclosure, and reviewer expertise have positive influences on review helpfulness. Review readability and review rating are found to have no significant influence on review helpfulness. Moreover, we find that helpfulness measurement, online review platform, and product type are the three factors that cause mixed findings in extant research.

28. Karimi, S., & Wang, F. (2017). Online review helpfulness: Impact of reviewer profile image. Decision Support Systems, 96, 39-48.

Today’s consumers rely heavily on the opinion of other consumers when making purchase decisions. Understanding the degree to which a review contributes to a purchase decision, its “helpfulness”, is important to online businesses. Despite the growing number of studies on online reviews, the impact of visual cues on consumer’s evaluation of review helpfulness has remained underexplored. This chapter examines the effect of reviewer image, along with previously examined review attributes, such as review depth, valence, and equivocality, on review helpfulness. With a sample of 1400 reviews from mobile gaming applications, we report that reviewer image can significantly enhance consumers’ evaluation of review helpfulness. We did not find significant differential effects of image types (i.e. self, family, or random images) on review helpfulness. The results call for further research on the impact of visual cues as well as reviewer attributes on review helpfulness.

29. Yang, S. B., Shin, S. H., Joun, Y., & Koo, C. (2017). Exploring the comparative importance of online hotel reviews’ heuristic attributes in review helpfulness: a conjoint analysis approach. Journal of Travel & Tourism Marketing, 34(7), 963-985.

Online travel reviews have become increasingly important as a result of the intangible and heterogeneous characteristics of the tourism industry as well as the recent diffusion of social media. This exploratory case study intends to examine the comparative importance of the six heuristic attributes (reviewer location, reviewer level, reviewer helpful vote, review rating, review length, and review photo) with respect to review helpfulness in the online hotel review context. Moreover, the salience of the individual levels within each review attribute and the importance of the attributes in positive and negative review-rating groups are compared. In total, 1,158 reviews of a hotel on TripAdvisor were collected and analyzed through conjoint analyses. This study found that the review rating and reviewer helpful vote attributes are the two most important factors. Finally, three major propositions are suggested based on findings of the study, and several theoretical and managerial implications are discussed.

30. Cao, C., Yan, J., & Li, M. (2018). Understanding the Determinants of Online Consumer Review Helpfulness in Social Networking Service Context.

Consumers are unclear about how to process and identify helpful information despite the fact that the popularization of the social network service (SNS) enhances the information acquisition of consumers and help them reduce uncertainties and risks in online shopping. With the Theory of Planned Behavior (TPB) and the online trust model as the theoretical underpinnings, we identify relevant factors and framework that influence the helpfulness of online consumer reviews (OCRs) in the SNS environment, that is, the accuracy, completeness, currency and format of information and the trustworthiness of reviewers. The analysis results of 221 samples through applying the partial least squares structural equation modeling (PLS-SEM) indicate that the accuracy and completeness of information are the most influential factors. The currency has no significant influence on the helpfulness. This research extends the application of the OCR helpfulness in the SNS environment and therefore can be used as reference for enterprise’s online marketing

31. Siering, M., Muntermann, J., & Rajagopalan, B. (2018). Explaining and predicting online review helpfulness: The role of content and reviewer-related signals. Decision Support Systems, 108, 1-12.

Online reviews provide information about products and services valuable for consumers in the context of purchase decision making. Online reviews also provide additional value to online retailers, as they attract consumers. Therefore, identifying the most-helpful reviews is an important task for online retailers. This research addresses the problem of predicting the helpfulness of online product reviews by developing a comprehensive research model guided by the theoretical foundations of signaling theory. Thereby, our research model posits that the reviewer of a product sends signals to potential buyers. Using a sample of Amazon.com product reviews, we test our model and observe that review content-related signals (i.e., specific review content and writing styles) and reviewer-related signals (i.e., reviewer expertise and non-anonymity) both influence review helpfulness. Furthermore, we find that the signaling environment affects the signal impact and that incentives provided to reviewers influence the signals sent. To demonstrate the practical relevance of our results, we illustrate by means of a problem-specific evaluation scenario that our model provides superior predictions of review helpfulness compared to earlier approaches. Furthermore, we provide evidence that the proposed evaluation scenario provides deeper insights than classical performance metrics. Our findings are highly relevant for online retailers seeking to reduce information overload and consumers' search costs as well as for reviewers contributing online product reviews.

32. Lee, S., & Choeh, J. Y. (2018). The interactive impact of online word-of-mouth and review helpfulness on box office revenue. Management Decision, 56(4), 849-866.

Purpose: While a number of studies examined the eWOM (online word-of-mouth) factors affecting box office, the studies on the impact of review helpfulness on box office are lacking. The purpose of this paper is to fill the void in previous studies and further extend prior work regarding eWOM and box office. In order to explain the interaction effect of helpfulness with other variables on product sales, this study posits that review characteristics such as number of reviews, review rating, review length interact with review helpfulness to have an influence on box office. Further, as the studies that have examined whether eWOM factors are significant in box office performances for the international markets other than US are lacking, this study is targeting Korean markets to validate the effect of eWOM on box office.

Design/methodology/approach: This study used publicly available data from www.naver.com to build a sample of online review data concerning box office. The final sample of the study included 2090 movies.

Findings: The results indicated that in cases when the review is helpful, the number of reviews and review length are more greatly influencing box office. Review rating, review extremity, and helpfulness for reviewer are important determinants for review helpfulness.

Practical implications: Managers can concentrate on the review rating and review extremity of online customer reviews in the design of online sites for movies. The design of user review systems can follow the direction that promotes more helpfulness for online user reviews based on an enhanced understanding of what drives helpfulness voting.

Originality/value: Given that previous studies on the effect of review helpfulness on box office are lacking, it contributes to eWOM literature by investigating the impact of review helpfulness on box office revenue.

33. Malik, M. S. I., & Hussain, A. (2018). An analysis of review content and reviewer variables that contribute to review helpfulness. Information Processing & Management, 54(1), 88-104.

Review helpfulness is attracting increasing attention of practitioners and academics. It helps in reducing risks and uncertainty faced by users in online shopping. This study examines uninvestigated variables by looking at not only the review characteristics but also important indicators of reviewers. Several significant review content and two reviewer variables are proposed and an effective review helpfulness prediction model is built using stochastic gradient boosting learning method. This study derived a mechanism to extract novel review content variables from review text. Six popular machine learning models and three real-life Amazon review data sets are used for analysis. Our results are robust to several product categories and along three Amazon review data sets. The results show that review content variables deliver the best performance as compared to the reviewer and state-of-the-art baseline as a standalone model. This study finds that reviewer helpfulness per day and syllables in review text strongly relates to review helpfulness. Moreover, the number of space, aux verb, drives words in review text and productivity score of a reviewer are also effective predictors of review helpfulness. The findings will help customers to write better reviews, help retailers to manage their websites intelligently and aid customers in their product purchasing decisions.

34. Malik, M., & Iqbal, K. (2018). Review helpfulness as a function of Linguistic Indicators. Int J Comput Sci Netw Secur, 18, 234-240.

Online reviews are playing an important role in customer’s decision making procedure for buying any product online. As buying products online is becoming customer’s first choice while shopping. It is very helpful to make purchase decision for any product by reading online reviews related to that particular product. However, such a large volume of online reviews that is being generated can be considered as a big data challenge for both entities i.e. e-commerce websites and customers. These online reviews are usually ranked on the basis of helpful votes. This article examined the important factors that contribute to the helpfulness of online reviews and built a helpfulness predictive model for online reviews. Five novel linguistic characteristics are proposed and popular machine learning algorithms are applied to construct an effective predictive model for review helpfulness. LCM and visibility features are also used as baseline. We have performed experimental analysis on two popular Amazon review datasets and results reveals that hybrid set of features deliver the best predictive performance. We also found that the proposed Linguistic features are better predictors for review helpfulness as a standalone model. The findings of our study can provide new wisdom to e-commerce vendors for effective ranking of online reviews on the basis of their helpfulness. This research would also help customers in making better decisions before purchasing any product.

35. Filieri, R., Raguseo, E., & Vitari, C. (2019). What moderates the influence of extremely negative ratings? The role of review and reviewer characteristics. International Journal of Hospitality Management, 77, 333-341.

Online customer reviews are increasingly used by travelers to inform their purchase decisions. However, the vast amount of reviews available nowadays may increase travellers’ effort in information processing. In order to facilitate traveller’s decisions, social commerce organizations must help travellers rapidly identify the most helpful reviews to reduce their cognitive effort. Academic literature has often documented that negative reviews are judged as helpful by consumers. However, extremely negative reviews are not always perceived as such. This study is the first that unveils what factors moderate the influence of extremely negative reviews on review helpfulness. The study has adopted a sample of 7455 online customer reviews of hotels to test hypotheses. Findings show that reviews with extremely negative ratings are more likely to be helpful when the review is long and easy to read and when the reviewer is an expert or discloses his identity (i.e. geographical origin).

36. 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.

Online reviews from consumers are critically important to the restaurant business. This study identified a heuristic processing of content richness and source credibility and applied both for utilitarian and hedonic evaluations. Furthermore, we analyzed the moderating effect of restaurant type (casual, luxury fine dining restaurant). A total of 2629 online reviews were used, with 1323 reviews for three casual restaurants and 1306 reviews for three luxury restaurants. To collect the data, web harvesting and web content mining were conducted to extract useful information by employing an R program. which automatically extract online data. The results reveal that the effect of content richness and source credibility on utilitarian evaluations are greater for a casual restaurant than for a luxury restaurant, whereas only the number of content-rich images had a higher effect on hedonic evaluations of a casual restaurant. The implications of the findings can contribute to the development of marketing strategies.

37. Ismagilova, E., Dwivedi, Y. K., & Slade, E. (2019). Perceived helpfulness of eWOM: Emotions, fairness and rationality. Journal of Retailing and Consumer Services.

Consumers use online reviews to help make informed purchase decisions. This paper extends existing research by examining how content of online reviews influences perceptions of helpfulness by demonstrating how different emotions can influence helpfulness of both product and service online reviews beyond a valence-based approach using cognitive appraisal theory and attribution theory. This research contributes to existing knowledge regarding the theory of information processing, attribution theory, and cognitive appraisal theory of emotions. Using findings from this study, practitioners can make review websites more user-friendly which will help readers avoid information overload and make more informed purchase decisions.

38. Li, S. T., Pham, T. T., & Chuang, H. C. (2019). Do reviewers’ words affect predicting their helpfulness ratings? Locating helpful reviewers by linguistics styles. Information & Management, 56(1), 28-38.

Locating helpful reviewers in opinion-sharing communities is an important issue. Numerous studies that examine this using social relations have some shortcomings. This study investigates language use, differing from person to person, and develops a novel prediction model to alleviate the problems. We identify four stylistic aspects and explore their impacts on predicting reviewers’ helpfulness ratings. The analyses show that the proposed model can more accurately locate helpful reviewers than the baseline model. In addition, reviewers’ words impact more than social relations do, although a combination of these will boost prediction performance to a greater extent than one alone.

39. Liang, S., Schuckert, M., & Law, R. (2019). How to improve the stated helpfulness of hotel reviews? A multilevel approach. International Journal of Contemporary Hospitality Management, 31(2), 953-977.

Purpose: The prevalence of online review websites and the ever-growing difficulty of judging review quality result in the increasing need for consumers to reduce cognitive costs. Thus, the purpose of this study is to find out the determinants of review helpfulness based on a comprehensive theoretical framework and empirical model.

Design/methodology/approach: This study applied a comprehensive framework, including both review content quality and reviewer background, to investigate the determinants of review helpfulness. It also presents empirical models to further control factors around product features.

Findings: Consumers are more likely to give helpful votes to those informative and readable reviews accompanied by extreme ratings. Reviewers who disclose information, have a high reputation and report a poor experience are always identified as helpful. Consumers also tend to signal suggestions from users with a local cultural background as subjective and useless.

Research limitations: This study focuses on upscale hotels in China. Information registered on TripAdvisor was used presenting a residential address not nationality. Only few controlling factors available because of the limited information are shown on online review websites.

Practical implications: Managers of both hotels and online review websites need to focus on reviews and/or reviewers as KOLs who attract consumers’ attention and affect their subsequent decisions. A dialogue with those KOLs can be by focusing on responding to reviews with certain characteristics. A reward system for reviews and KOLs may benefit review quality on online review websites and reduce cognition costs.

Originality/value: This positivistic research design, with multilevel approach, presenting a comprehensive conceptual framework and empirical model not only considering review- and reviewer-related factors but also controlled factors in product or service level (hotel-related characteristics).

40. Wang, X., Tang, L. R., & Kim, E. (2019). More than words: Do emotional content and linguistic style matching matter on restaurant review helpfulness?. International Journal of Hospitality Management, 77, 438-447.

Many consumers feel overwhelmed by the unwieldy glut of information on peer-review websites (e.g., Yelp). Review helpfulness as a peer-rating mechanism on these websites enable consumers to quickly identify the most informative reviews and thus decreasing the information overload. The purpose of the study was to examine the influential factors on review helpfulness for restaurants on Yelp from affective content and communication style perspectives. The affective content was evaluated with eight emotional dimensions (joy, sadness, anger, fear, trust, disgust, anticipation, and surprise) in Plutchik’s emotion wheel. The communication style perspective was assessed with linguistic style matching (LSM). 262,205 pieces of consumer reviews were analyzed with negative binomial model. Both LSM and six out of the eight emotional dimensions (except anticipation and surprise) were found to have significant impact on review helpfulness. The study contributes to the knowledge body of review effectiveness from an innovative angle and provide pertinent managerial implications.