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This study aimed to identify the characteristics of excellent peer reviewers by using Publons.com (an open and free online peer review website).

Reviewers of the clinical medicine field on Publons were selected as the sample (n = 1,864). A logistic regression model was employed to examine the data.

The results revealed that reviewers' verified reviews, verified editor records, and whether they were the Publons mentors had significant and positive associations with excellent peer reviewers, while their research performance (including the number of articles indexed by Web of Science (WOS), citations, H-index and high-cited researcher), genders, words per review, number of current/past editorial boards, whether they had experiences of post-publication review on Publons and whether they were Publons academy graduates had no significant associations with excellent peer reviewers.

This study could help journals find excellent peer reviewers from free and open online platforms.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2021-0604.

The purpose of this study is to present a methodology for enhancing the quality and usefulness of online reviews for prospective customers to investigate how this contemporary form of instrumental support can be facilitated to strengthen customer-to-customer support.

This study develops an analytics framework with applications of machine learning models using customer review data from Amazon.com. Linear regression is commonly used for review helpfulness and sales prediction. In this study, Random Forest model is applied because of its strong performance and reliability. To advance the methodology, a custom script in Python is created to generate Partial Dependence Plots for intensive exploration of the dependency interpretations of review helpfulness and sales. The authors also apply K-Means to cluster reviewers and use the results to generate reviewer qualification scores and collective reviewer scores, which are incorporated into the review facilitation process.

The authors find the average helpfulness ratio of the reviewer as the most important determinant of reviewer qualification. The collective reviewer qualification for a product created based on reviewers’ characteristics is found important to customers’ purchase intentions and can be used as a metric for product comparison.

The findings of this study suggest that service improvement efforts can be performed by developing software applications to monitor reviewer qualifications dynamically, bestowing a badge to top quality reviewers, redesigning review sorting interfaces and displaying the consumer rating distribution on the product page, resulting in improved information reliability and consumer trust.

This study adds to the research on customer-to-customer support in the service literature. As customer reviews perform as a contemporary form of instrumental support, the authors validate the determinants of review helpfulness and perform an intensive exploration of its dependency interpretation. Reviewer qualification and the collective reviewer qualification scores are generated as new predictors and incorporated into the helpfulness-based review facilitation services.

The study aims to provide an analytical overview of current innovations in peer review and their potential impacts on scholarly communication.

The authors created a survey that was disseminated among publishers, academic journal editors and other organizations in the scholarly communication ecosystem, resulting in a data set of 95 self-defined innovations. The authors ordered the material using a taxonomy that compares innovation projects according to five dimensions. For example, what is the object of review? How are reviewers recruited, and does the innovation entail specific review foci?

Peer review innovations partly pull in mutually opposed directions. Several initiatives aim to make peer review more efficient and less costly, while other initiatives aim to promote its rigor, which is likely to increase costs; innovations based on a singular notion of “good scientific practice” are at odds with more pluralistic understandings of scientific quality; and the idea of transparency in peer review is the antithesis to the notion that objectivity requires anonymization. These fault lines suggest a need for better coordination.

This paper presents original data that were analyzed using a novel, inductively developed, taxonomy. Contrary to earlier research, the authors do not attempt to gauge the extent to which peer review innovations increase the “reliability” or “quality” of reviews (as defined according to often implicit normative criteria), nor are they trying to measure the uptake of innovations in the routines of academic journals. Instead, they focus on peer review innovation activities as a distinct object of analysis.

Online reviews in online health communities (OHCs) have been a vital information source for patients. The extant literature on the bias effects of helpful reviews mainly concentrates on traditional e-commerce, whereas research on OHCs is still rare. Thus, based on the heuristic-systematic model (HSM), this research explores how two unique reviewer characteristics in OHCs, which may induce attribution bias and confirmation bias, affect review helpfulness and how review length moderates these relationships.

This research analyzed 130,279 reviews collected from haodf.com (one of the representative OHCs in China) by adopting the negative binomial regression to test our research model.

The results indicate that reviewer cured status positively influences review helpfulness, whereas reviewer recommendation source negatively affects review helpfulness. Moreover, the effects of the two reviewer cues on review helpfulness will be weaker for longer reviews.

First, as one of the initial attempts, the current study investigates the effects of confirmation bias and attribution bias of online reviews in OHCs by exploring the effects of two unique reviewer characteristics on review helpfulness. Second, the weakening moderating effects of review length on the two bias effects provide empirical support for the theoretical arguments of the HSM in OHCs.

This study aims to integrate reviewers’ and readers’ discrepant perspectives on extremely negative reviews. Specifically, this study examines the relationship between negative emotion intensity levels and reviews helpfulness on two platforms: integrated websites and social networking sites (SNS) to emphasize the role of platform types on customers’ purchase decisions.

This research adopts a mixed-method approach of business intelligence approach and quasi-experimental design. Study 1 performed text mining and Welch’s t-test to compare reviewers’ negative emotion intensity levels on two platforms. Study 2 adopted a 2*2 factorial quasi-experimental design to examine how intense negative emotions impact the perceived reviews helpfulness on two platforms. A 3*2 factorial design in Study 3 also tested social tie strength’s moderating effect between the intensity of negative emotions and review helpfulness.

The current study reveals that integrated website reviewers tend to express more extreme negative emotions than SNS reviewers. SNS and integrated website readers deem reviews that embed severe negative emotions as less helpful. The moderating role of social tie strength between extremely negative emotions on review helpfulness was insignificant in the study.

This study enriches the online review literature by comparing writers’ and readers’ perspectives on online reviews with extremely negative emotions across two online platform types: integrated websites and SNS. From the writers’ perspective, this study highlights anonymity and the presence of an audience as essential factors that reviewers consider in selecting an online review platform to express themselves. This research also sheds light on how readers’ perspectives on extremely negative reviews conflict with the presumptions of writers of extremely negative reviews on integrated websites by demonstrating that content embedding extremely negative emotions is less helpful regardless of platform type.

This research provides online negative review management strategies to platform and hotel managers. The findings suggest hotel and review platform managers should consider adopting review alignment or monitoring systems based on negative emotions intensity levels since readers on both platforms perceive reviews embedding extremely negative emotions as less helpful. Additionally, hotel managers can progress promotions to guests who share online reviews on SNS since SNS reviewers are more likely to attenuate their extremely negative emotions when writing reviews.

This research innovatively provides a comprehensive overview of negative reviews’ production and consumption process from reviewers’ and readers’ perspectives. This research also provides practitioners insight into the nature of two different platform types and the management of negative reviews on these platforms.

Online reviewers' identity information is an essential cue by which consumers judge reviews on ecommerce platforms. However, few studies have explored how prior anonymous reviews and focal reviews affect reviewers' preference for anonymity. The purpose of this paper is to investigate why reviewers seek anonymity in terms of prior anonymous reviews and focal reviews.

Based on restaurant reviews collected from meituan.com, one of the largest group-buying ecommerce platforms in China, this study employed logistic regression to examine how prior anonymous reviews and focal reviews are associated with reviewers' preference for anonymity.

Results show that the volume and sequence of prior anonymous review are positively associated with the likelihood of reviewers' preference for anonymity, whereas focal review valence is negatively correlated with this preference. Focal review length is positively correlated with reviewers' preference for anonymity but negatively moderates the roles of review valence and prior anonymous reviews on this preference.

This study expands the information disclosure literature by exploring determinants of user identity disclosure from a reviewer perspective. This research also offers a methodological contribution by employing a more accurate measure to calculate reviewers' preference for anonymity, enhancing the empirical results. Lastly, this work supplements the online review literature on how prior anonymous reviews and focal reviews are associated with reviewers' identity disclosure.

Nowadays, online platforms which provide products or services try to implement their homegrown communities to facilitate users' social interactions. Reviewers' activities in these communities can reflect their interests. Based on the theory of homophily, the authors aim to explore the impacts of the reviewer preference similarity and opinion similarity on the rate of product diffusion.

First, the authors construct reviewer similarity network based on their common interests and propose typical network metrics to measure reviewer preference similarity. Second, the authors measure reviewer opinion similarity with natural language processing. Finally, based on a panel data from an online video platform in China, both the fixed-effect and random-effect panel data models are constructed.

The authors find that reviewer preference similarity has a positive effect on the product diffusion, whereas reviewer opinion similarity has a negative effect on the diffusion. Furthermore, temporal distance moderates the relationship between reviewer similarity and the product diffusion. As a double-edged sword, review preference similarity hinders product diffusion in the initial phase, whereas benefits it in the later phase. Reviewer opinion similarity is always detrimental to product diffusion, especially in the initial phase.

This paper extends the understanding of homophily from the micro peer level to the group level by constructing reviewers' similarity network and highlights the important role of reviewer preference similarity and opinion similarity in product diffusion. The results also provide important insights for managers to design and implement diversity strategies for better product adoption in the community context.

The purpose of this paper is to explore how much one's academic development and environment influence the way one performs, evaluates, writes and tries to publish basic, scholarly marketing research.

A sample of results from initial journal submissions is used to empirically examine the potential reasons for rejection and the size and strength of main effects and interactions comprising the author's country of origin and the reviewer's country of origin.

The findings, taken together, suggest a small but statistically significant advantage to US authors in avoiding rejection. This advantage is attributable to a relative superiority in expressing ideas in the English language and a better ability to logically develop theory. There is little evidence that suggests the advantage is due to a substantially more quantitative approach than that of others.

The paper examines results of the review process from an actual sample of submitted papers and specifically addresses issues related to the “American” paradigm of marketing research.

The experiential nature of travel and tourism services has popularized the importance of electronic word-of-mouth (EWOM) among potential customers. EWOM has a significant influence on hotel booking intention of customers as they tend to trust EWOM more than the messages spread by marketers. Amid abundant reviews available online, it becomes difficult for travelers to identify the most significant ones. This questions the credibility of reviewers as various online businesses allow reviewers to post their feedback using nickname or email address rather than using real name, photo or other personal information. Therefore, this study aims to determine the factors leading to reviewer credibility.

The paper proposes an econometric model to determine the variables that affect the reviewer’s credibility in the hospitality and tourism sector. The proposed model uses quantifiable variables of reviewers and reviews to estimate reviewer credibility, defined in terms of proportion of number of helpful votes received by a reviewer to the number of total reviews written by him. This covers both aspects of source credibility i.e. trustworthiness and expertness. The authors have used the data set of TripAdvisor.com to validate the models.

Regression analysis significantly validated the econometric models proposed here. To check the predictive efficiency of the models, predictive modeling using five commonly used classifiers such as random forest (RF), linear discriminant analysis, k-nearest neighbor, decision tree and support vector machine is performed. RF gave the best accuracy for the overall model.

The findings of this research paper suggest various implications for hoteliers and managers to help retain credible reviewers in the online travel community. This will help them to achieve long term relationships with the clients and increase their trust in the brand.

To the best of authors’ knowledge, this study performs an econometric modeling approach to find determinants of reviewer credibility, not conducted in previous studies. Moreover, the study contracts from earlier works by considering it to be an endogenous variable, rather than an exogenous one.

Online reviews are increasingly available for a wide range of products and services. Several studies have demonstrated the benefits of the presence of customer reviews to an online retailer, but the issue of what makes online reviews helpful to a consumer in the process of making a purchase decision remains uninvestigated. The paper aims to discuss this issue.

Given the strategic potential of online reviews, this study drew on past research to develop a conceptual understanding of the components of helpfulness and to further empirically test the model using actual online review data from iPeen.com in Taiwan. A content analysis of 989 reviews across four products identified the interplay effects of review sidedness, reviewer’s expertise, and product type on the helpfulness of an online review.

For search goods, consumers consider two-sided reviews to be more helpful than one-sided reviews when the reviewers are experts in writing such articles, whereas they consider two-sided reviews to be equally helpful as one-sided reviews when the reviewers are novices. Conversely, for experience goods, consumers consider one-sided reviews to be more helpful than two-sided reviews when the reviewers are experts in writing review articles, but they consider one-sided reviews to be equally helpful as two-sided reviews when the reviewers are novices.

With an understanding of how review sidedness affects online review helpfulness, online retailers could establish the policy for promoting the helpfulness of reviews more effectively.

This research yields at least three important contributions: first, it contributes to the message sidedness literature by showing which arguments (one- or two-sided) are deemed to be helpful; second, it contributes to the online peer review literature by demonstrating the importance of considering product type and heuristic cues (i.e. the reviewer’s expertise) when explaining helpfulness; and third, the results in this research demonstrate that people are drawn to dual-processing; that is, the judgment of online review helpfulness is determined by heuristic cues (e.g. the status of the reviewer) and systematic processing (e.g. review content).