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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 review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases in the unique digital environment of “Chinanet,” this paper aims to shed new light on the multiple sources of biases embedded in online reviews and potential interactions among users, technical platforms and the broader social–cultural norms.

In the first study, online restaurant reviews were collected from Dianping.com, one of China's largest review platforms. Their distribution and underlying biases were examined via comparisons with offline reviews collected from on-site surveys. In the second study, user and platform ratings were collected from three additional major online review platforms – Koubei, Meituan and Ele.me – and compared for possible indications of biases in platform's review aggregation.

The results revealed a distinct exponential-curved distribution of Chinese users’ online reviews, suggesting a deviation from previous findings based on Western user data. The lack of online “moaning” on Chinese review platforms points to the social–cultural complexity of Chinese consumer behavior and online environment that goes beyond self-selection at the individual user level. The results also documented a prevalent usage of customized aggregation methods by review service providers in China, implicating an additional layer of biases introduced by technical platforms.

Using an online–offline design and multi-platform data sets, this paper elucidates online review biases among Chinese users, the world's largest and understudied (in terms of review biases) online user group. The results provide insights into the unique social–cultural cyber norm in China's digital environment and bring to light the multilayered nature of online review biases at the intersection of users, platforms and culture.

The purpose of this study is to explore the antecedents of consumers’ online review intention in e-commerce platforms from a unique perspective of consumer commitment and platform design. Meanwhile, for the dual-platform strategy, i.e. providing both the web and mobile platforms simultaneously, which is widely adopted in the industry but lacks theoretical concerns, this study aims to examine the differences that platform design influences consumer commitment, consequently contributing to online review intention, between the web and mobile contexts.

A cross-sectional online survey is employed, and a structural equation model-based approach is utilized to analyze the data collected from both the website-preferred consumers (N = 167) and the mobile app-preferred consumers (N = 247).

The results indicate that instrumental support design factors and socio-emotional support factors positively influence consumer commitment, which further affect online review intention positively. Furthermore, design factors in different use contexts generate different impacts, and consumer commitment generates a greater effect on online review intention in the mobile than in the web context. Empathy is found to be an important motivator of consumer commitment in both contexts.

To the best of the authors’ knowledge, as one of the first attempts to capture the differences in the relationship between platform design on consumer commitment and online review intention in different use contexts within the dual-platform e-commerce, this study provides insights for e-commerce platform managers and designers to promote consumer commitment and online review engagement by prioritizing the platform design.

This study aims to elicit the preferences of potential travelers for different property listings' attributes (online review number, positive valence rate of reviews and discount strategy) when selecting hotels and peer-to-peer (P2P) accommodation sharing on online booking platforms.

A discrete choice experiment (DCE) was conducted with 291 respondents with accommodation needs. They were asked to choose between pairs of listings.

The authors found that when booking accommodation online, complex discount strategies were not determinant both in selecting hotels and P2P accommodations. Positive valence rate of reviews has a higher impact on the selection of traditional hotels than P2P accommodations, while the number of online reviews has a higher impact on the selection of P2P accommodations than traditional hotels. The authors further discuss the effect of each attribute on online accommodation selection in terms of price ranges of the property listings.

The findings provide suggestions for platform operators and product/service providers to improve their marketing strategies and optimize their management efforts.

To the best of the authors’ knowledge, this is one of the first studies that investigate the role of property listings' attributes on the selections between hotels and P2P accommodations. The findings from this research study could be generalized to other online platforms and electronic commerce-related transactions.

This article aims to advance a new theoretical perspective on the basis of prosumption theory, namely, that online review platforms can be conceptualized as prosumer communities (and online reviews as prosumer-generated content). This perspective meshes with message tuning research to suggest specific mechanisms through which peer-to-peer prosumption takes place in online review communities. Overall, this article enriches and deepens theoretical understanding of prosumption behavior in the product review context and offers practical advice for inducing high-value, prosumer-generated content in online communities.

Exploratory observations of current practices across a wide spectrum of review platforms were conducted. The observed platforms include independent review sites (e.g. Yelp) and review sites affiliated with e-tailers (e.g. Amazon), general review sites (e.g. Viewpoints) and product-specific review sites (e.g. Healthgrades), large-scale review sites (e.g. TripAdvisor) and review sites of a smaller scale (e.g. Judy’s Book) and review sites based in different geographic regions, including Australia (e.g. Productreview.com.au), China (e.g. Taobao), Europe (e.g. Reevoo), India (e.g. Zomato) and North America (e.g. Foursquare).

Theoretical analysis suggests that high-quality review content is the result of collaborative prosumption characterized by three distinct value-adding processes: history-based message tuning, audience-based message tuning and norm-based message tuning. In-depth observations reveal that today’s review platforms are leveraging these value-adding processes to varying degrees. The overwhelming diversity of the observed platform features points to the need for more research on platform design and management.

This research identifies three distinct dimensions of review quality – novelty, relevancy and congruency – that can be systematically managed through platform design. The exploratory nature of this research necessitates follow-up work to further investigate how high-quality review content emerges in the historical, interpersonal and cultural contexts of online prosumer communities.

The prosumption-inducing mechanisms identified in this research have major consumer welfare and strategy implications. First, they may lead to novel, relevant and congruent consumer reviews. Second, they may enhance the value of brand communities (which rely heavily on collaborative prosumption).

This research addresses two intriguing questions pertinent to marketing theory and practice in the digital era. First, how do high-quality reviews emerge on product review platforms (which consist of ordinary consumers)? Second, what constitutes high-quality review content and how can platform managers facilitate the collaborative creation of high-quality review content by ordinary consumers?

This paper investigates online evaluation processes on peer-to-peer platforms to highlight how online peer evaluation enacts neoliberal subjects and collectives.

The paper uses netnography (Kozinets, 2002) to study the online community of Airbnb. It is also based on 18 interviews, mostly with Airbnb users, and quantitative data about reviews.

Results indicate that peer-to-peer platforms constitute biopolitical infrastructures. They enact and consolidate narcissistic entrepreneurs of the self through evaluation processes and consolidating a for-show community. Specifically, three features make evaluation a powerful neoliberal agent. The object of evaluation shifts from the service to the user's own worth (1). The public nature of the evaluation (2) and symetrical accountability between the evaluator and the evaluatee (3) contribute to excessively positive reviews and this keeps the market fluid.

This paper calls for problematization of the idea of sharing in the so-called “sharing economy”. What is shared on peer-to-peer platforms is the comfort of engaging with people like ourselves.

This paper contributes to the literature on online accounting by extending consideration of evaluation beyond the review process. It also stresses that trust in the evaluative infrastructure is fostered by narcissistic relationships between users, who come to use the platform as a mirror. The peer-to-peer context refreshes the our knowledge on evaluation in a corporate context by highlighting phenomena of standardized spontaneity and euphemized evaluation language. This allows evaluation processes to incorporate a market logic without having to fuel competition.

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.

Based on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.

This study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.

Based on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.

In terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.

This study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.

“Prosumers” (combining “producer” and “consumers”) describes consumers’ ability to openly share their product/service experiences and thereby drive sales and digital marketing. Understanding what motivates active prosumers to engage in electronic word-of-mouth (eWOM) and share or review their hotel experiences online can help organizations empathize with consumers and use their messages to co-create value. Identifying prosumers’ motivators can enable companies to properly target them as resources for review or consumer feedback studies. This paper aims to investigate the influence of motivators (intrinsic and extrinsic), service quality and age on consumers’ eWOM communication behaviors.

A panel of 204 travelers was surveyed regarding their hotel travel experiences, propensity to write online reviews, preferred review-writing platform, motivations for writing reviews and impressions of service quality. To test the hypotheses, a multivariate regression analysis was performed with eWOM as the dependent variable. Differences in eWOM as a function of preferred review platform were also tested using ANOVA, with a multiple comparison analysis that underlines the differences between prosumers who prefer different types of review platforms and their eWOM behaviors.

Both intrinsic and extrinsic motivators, as well as service reliability, had a significant influence on eWOM behavior, while service tangibility had a negative relationship. Additionally, prosumers’ engagement in eWOM about their hospitality experience differed according to their preferred review platform.

The findings of the analysis underline the importance of consumer motivations and of satisfaction with service quality in the context of digital review behavior. For marketing and hospitality research, this shows the benefits of including not only individual characteristics and demographics when analyzing review behavior but also elements such as perceptions of service quality. Given the differences in how the dimensions of service quality affect consumers’ engagement in online review behavior, this represents a very important topic for research and can be included in future studies that analyze the consumer review behavior model.

Regarding the implications for practitioners, this study highlights the important role played by consumer satisfaction with service in the hospitality industry and its effect on their involvement in online reviews. Managers should be focused constantly on offering great service to their guests, while, at the same time, offering them motivations to engage in posting positive reviews about their vacation. Moreover, as the results of this study imply that various dimensions of service quality have a different impact, managers should focus especially on the aspects that consumers consider important and constantly include in their reviews, such as the tangibles dimension. The results of this study also have the potential to provide to businesses more information to improve the social aspects of vacationing, which can not only improve perceptions about service quality but can also have a positive influence on consumers’ motivations.

This paper develops a better understanding of what motivates people to engage in the eWOM communication behavior of writing online hotel reviews, by showing the effect of consumer motivations and service quality variables on prosumers’ engagement in online review behavior.

This study aims to compare the rating dynamics of the same hotels in two online review platforms (Booking.com and Trip Advisor), which mainly differ in requiring or not requiring proof of prior reservation before posting a review (respectively, a verified vs a non-verified platform).

A verified system, by definition, cannot host fake reviews. Should also the non-verified system be free from “ambiguous” reviews, the structure of ratings (valence, variability, dynamics) for the same items should also be similar. Any detected structural difference, on the contrary, might be linked to a possible review bias.

Travelers’ scores in the non-verified platform are higher and much more volatile than ratings in the verified platform. Additionally, the verified review system presents a faster convergence of ratings towards the long-term scores of individual hotels, whereas the non-verified system shows much more discordance in the early phases of the review window.

The paper offers insights into how to detect suspicious reviews. Non-verified platforms should add indices of scores’ dispersion to existing information available in websites and mobile apps. Moreover, they can use time windows to delete older (and more likely biased) reviews. Findings also ring a warning bell to tourists about the reliability of ratings, particularly when only a few reviews are posted online.

The across-platform comparison of single items (in terms of ratings’ dynamics and speed of convergence) is a novel contribution that calls for extending the analysis to different destinations and types of platform.