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We investigate the joint impacts of three trust cues – content, sentiment and helpfulness votes – of online product reviews on the trust of reviews and attitude toward the product/service reviewed.

We performed three studies to test our research model, presenting participants with scenarios involving product reviews and prior users' helpful and unhelpful votes across experimental settings.

A high helpfulness ratio boosts users’ trust and influences behaviors in both positive and negative reviews. This effect is more pronounced in attribute-based reviews than emotion-based ones. Unlike the ratio effect, helpfulness magnitude significantly impacts only negative attribute-based reviews.

Future research should investigate voting systems in various online contexts, such as Facebook post likes, Twitter microblog thumb-ups and up-votes for article comments on platforms like The New York Times.

Our findings have significant implications for voting system-providers implementing information techniques on third-party review platforms, participatory sites emphasizing user-generated content and online retailers prioritizing product awareness and reputation.

This study addresses an identified need; that is, the helpfulness votes as an additional trust cue and the joint effects of three trust cues – content, sentiment and helpfulness votes – of online product reviews on the trust of customers in reviews and their consequential attitude toward the product/service reviewed.

The helpfulness vote is a type of aggregate user representation that, by measuring the quality of an online review based on certain criteria, can allow readers to find helpful reviews more quickly. Although widely applied in practice, the effectiveness of the voting mechanism is unsatisfactory. This paper uses the heuristic–systematic model and the theory of dynamics of reviews to shed light on the effect of various information cues (product ratings, word count and product attributes in the textual content of reviews) on online reviews’ aggregative voting process. It proposes a conceptual model of seven empirically tested hypotheses.

A dataset of user-generated online hotel reviews (n = 6,099) was automatically extracted from Ctrip.com. In order to measure the variable of product attributes as a systematic cue, the paper uses Chinese word segmentation, a part-of-speech tag and word frequency statistics to analyze online textual content. To verify the seven hypotheses, SPSS 17.0 was used to perform multiple linear regression.

The results show that the aggregative process of helpfulness voting can be divided into two stages, initial and cumulative voting, depending on whether voting is affected by the previous votes. Heuristic (product ratings, word count) and systematic cues (product attributes in the textual content) respectively exert a greater impact on the two stages. Furthermore, the interaction of heuristic and systematic cues plays an important role in both stages, with a stronger impact on the cumulative voting stage and a weaker one on the initial stage.

This paper’s findings can be used to explore improvements to helpfulness voting by aligning it with an individual’s information process strategy, such as by providing more explicating heuristic cues, developing different methods of presenting relevant cues to promote the voting decision at different stages, and specifying the cognitive mechanisms when designing the functions and features of helpfulness voting.

This study explores the aggregative process of helpfulness votes, drawing on the study of the dynamics of online reviews for the first time. It also contributes to the understanding of the influence of various information cues on the process from an information process perspective.

This study aims to study to what extent the helpfulness votes others attach to a review affect a consumer’s perceived helpfulness of that review. In addition, the purpose of this study is to investigate whether this social influence moderates the relationships among several content presentation factors and perceived helpfulness.

A choice-based conjoint experiment was carried out in which 201 respondents evaluated different reviews and chose the review they perceive as most helpful.

Consumers perceive reviews as more (less) helpful in the presence of clearly valenced positive (negative) helpfulness votes. In addition, helpfulness votes of others diminish the positive impact of structure and the negative impact of spelling errors.

The experimental setup may limit the external validity of the study.

Providing a helpfulness button gives firms an instrument to offer content that consumers perceive as more useful and to exert some influence on the effects of content presentation factors on the review’s helpfulness.

Consumers tend to follow other consumers’ opinions without forming their own opinion. Firms could misuse this tendency by hiring people to vote on reviews that are not necessarily helpful for consumers, but are helpful for the firm.

This study is the first to assess the extent to which social influence affects consumers’ evaluation of reviews. Given that consumers use helpfulness votes to distinguish reviews, it is important to understand to what extent these votes reflect the actual helpfulness of the information in the review and to what extent they reflect previous helpfulness votes.

The reviews submitted by users are the foundation of user-generated content (UGC) platforms. However, the rapid growth of users brings the problems of information overload and spotty content, which makes it necessary for UGC platforms to screen out reviews that are really helpful to users. The authors put forward in this paper the factors influencing review helpfulness voting from the perspective of review characteristics and reviewer characteristics.

This study uses 8,953 reviews from 20 movies listed on Douban.com with variables focusing on review characteristics and reviewer characteristics that affect review helpfulness. To verify the six hypotheses proposed in the study, Stata 14 was used to perform tobit regression.

Findings show that review helpfulness is significantly influenced by the length, valence, timeliness and deviation rating of the reviews. The results also underlie that a review submitted by a reviewer who has more followers and experience is more affected by review characteristics.

Previous literature has discussed the factors that affect the helpfulness of reviews; however, the authors have established a new model that explores more comprehensive review characteristics and the moderating effect reviewer characteristics have on helpfulness. In this empirical research, the authors selected a UGC community in China as the research object. The UGC community may encourage users to write more helpful reviews by highlighting the characteristics of users. Users in return can use this to establish his/her image in the community. Future research can explore more variables related to users.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2020-0186.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

The purpose of this paper is to examine the mechanism of how peer review helpfulness evaluation in online review communities is established, drawing upon the internalization and identification routes of persuasion effect.

Based on book reviews selected from Douban.com (a prestigious review community in China), this study used econometric models to investigate the effects of both reviews and reviewers’ characteristics on peer review helpfulness evaluation in review communities.

Review internalization is more persuasive than reviewers’ identification in peer evaluations, in terms of both short and long reviews. Reviews with extreme negative ratings tend to obtain higher level of helpfulness evaluation than those with positive or moderate ratings. The influence of reviewers’ characteristics is a significant cue in helping consumers to establish the trust perception in the context of short reviews, while its function diminishes in the context of long reviews, thus suggesting the importance of reviewers’ identification for short reviews in review communities.

The findings will enhance current understanding of peer review review helpfulness evaluation in online review communities and help practitioners administrate community reviews intelligently, help members write better reviews and customers in their product browsing experience.

First, this study enriches review evaluation research in review communities by demonstrating the importance of internalization and identification lens of persuasion effect when explaining review helpfulness; second, this study helps to confirm the existing findings that reviews with extreme negative ratings are more helpful than those with moderate or positive ratings in review communities; third, this study proposes a new perspective pertaining to the relationship between reviewers’ identification and helpfulness evaluation.

The purpose of this paper is to suggest important determinants for helpfulness from the reviews’ product data, review characteristics, and textual characteristics, and identify the more crucial factors among these determinants by using statistical methods. Furthermore, this study intends to propose a classification-based review recommender using a decision tree (CRDT) that uses a decision tree to identify and recommend reviews that have a high level of helpfulness.

This study used publicly available data from Amazon.com to construct measures of determinants and helpfulness. To examine this, the authors collected data about economic transactions on Amazon.com and analyzed the associated review system. The final sample included 10,000 reviews composed of 4,799 helpful and 5,201 not helpful reviews.

The study selected more crucial determinants from a comprehensive group of product, reviewer, and textual characteristics through using a t-test and logistics regression. The five important variables found to be significant in both t-test and logistic regression analysis were the total number of reviews for the product, the reviewer’s history macro, the reviewer’s rank, the disclosure of the reviewer’s name, and the length of the review in words. The decision tree method produced decision rules for determining helpfulness from the value of the product data, review characteristics, and textual characteristics. The prediction accuracy of CRDT was better than that of the k-nearest neighbor (kNN) method and linear multivariate discriminant analysis in terms of prediction error. CRDT can suggest better determinants that have a greater effect on the degree of helpfulness.

The important factors suggested as affecting review helpfulness should be considered in the design of websites, as online retail sites with more helpful reviews can provide a greater potential value to customers. The results of the study suggest managers and marketers better understand customers’ review and increase the value to customers by proving enhanced diagnosticity to consumers.

This study is different from previous studies in that it investigated the holistic aspect of determinants, that is, product, review, and textual characteristics for classifying helpful reviews, and selected more crucial determinants from a comprehensive group of product, reviewer, and textual characteristics by using a t-test and logistics regression. This study utilized a decision tree, which has rarely been used in predicting review helpfulness, to provide rules for identifying helpful online reviews.

This paper seeks to propose and empirically validate a conceptual model on the antecedents of review helpfulness comprising three constructs, namely, valence dissimilarity, lexical dissimilarity and review order.

A panel dataset of customer reviews was collected from Amazon. Using deep learning and text processing techniques, 650,995 reviews on 13,612 products from 570,870 reviewers were analyzed. Using negative binomial regression, four hypotheses were tested.

The results indicate that new reviews with high valence dissimilarity and lexical dissimilarity compared to existing reviews are less helpful. However, over the sequence of reviews, the negative effect of review dissimilarity on review helpfulness can be moderated. This moderation differs for valence and lexical dissimilarity.

This study explains review dissimilarity in the context of online review helpfulness. It draws on the elaboration likelihood model and explains how the impacts of peripheral and central cues are moderated over the sequence of reviews.

The findings of this study provide benefits to online retailers planning to implement online reviews to improve user experience.

This paper highlights the importance of review dissimilarity in identifying user perception of online review helpfulness and understanding the dynamics of this perception over the sequence of reviews, which can lead to improved marketing strategies.

Based on more than 2.7 million online reviews (ORs) collected with big data analytical techniques from Booking.com and TripAdvisor.com, this paper aims to explore if and to what extent environmental discourse embedded in ORs has an impact on electronic word-of-mouth (e-WOM) helpfulness across eight major destination cities in North America and Europe.

This study gathered, by means of Big Data techniques, 2.7 million ORs hosted on Booking.com and TripAdvisor, and covering hospitality services in eight different destinations cities in North America (New York City, Miami, Orlando and Las Vegas) and Europe (Barcelona, London, Paris and Rome) over the period 2017–2018. The ORs were analysed by means of ad hoc content analytic dictionaries to identify the presence and depth of the environmental discourse included in each OR. A negative binomial regression analysis was used to measure the impact of the presence/depth of online environmental discourse in ORs on e-WOM helpfulness.

The findings indicate that the environmental discourse presence and depth influence positively e-WOM helpfulness. More specifically those travelers who write explicitly about environmental topics in their ORs are more likely to produce ORs that are voted as helpful by other consumers.

Implications highlight that both hotel managers and platform developers/managers should become increasingly aware of the importance that customer attach to environmental practices and initiatives and therefore engage more assiduously in environmental initiatives, if their objective is to improve online review helpfulness for other customers reading the focal reviews. Future studies might include more destinations and other operationalizations of environmental discourse.

This study constitutes the first attempt to capture how the presence and depth of hospitality services consumers’ environmental discourse influence e-WOM helpfulness on multiple digital platforms, by means of a big data analysis on a large sample of online reviews across multiple countries and destinations. As such it makes a relevant contribution to the area at the intersection between big data analytics, e-WOM and sustainable tourism research.