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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.

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?

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.

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.

Introduction: Lending is an age-old concept, and Peer-to-Peer (P2P) lending is not new. The reduction in the issuing of loans by banks has made people switch from traditional to online mode. The introduction of the online P2P lending industry is in its nascent stage of growth. As this industry is relatively new, understanding user experience, sentiments, and emotions would be helpful for the industry to innovate as per customer requirements.

Purpose: To explore the patterns in the sentiments expressed by users of ‘Cashkumar’ based on Google reviews.

Methodology: Sentiments have been analysed using user experience in risk, cost, ease of use, and loan processing time. Python application was used for sentiment analysis of Google reviews.

Findings: The sentiment analysis results showed that the average sentiment score was 0.7144, which indicates that the user sentiment towards ‘Cashkumar’ is positive. The reviews reflect that the users, especially borrowers were satisfied with the platform’s services and happy with loan processing time. The other factors – ease of use, cost, and risk – were not given much importance by users. Both lenders and borrowers faced a few issues, but the results of the lender’s sentiment analysis could not be generalised due to a smaller number of posted reviews.

Industry platforms can alter relations among exchange partners in such a way that the industry structure is changed. The focus of much industry platform research has been on how platform creation and leadership offers advantages to the most central firms, but platforms can also be advantageous for small specialist firms that compete with the most central firms. We examine book publishing as an example of an industry in which the central players – large publishing firms – are losing power to self-publishing authors because the distributor Amazon has a powerful platform for customers to communicate independently, and the non-publishing platform Twitter also serves as a medium for readers to discuss and review books. Our empirical analysis is based on downloaded sales statistics for Amazon Ebooks, matched with Amazon reviews of the same books and tweets that refer to the book or the author. We analyze how Ebook sales are a function of publisher, Amazon reviews, and tweets, and we are able to assess the importance of each factor in the sale of book titles. The main finding is that Amazon reviews are powerful drivers of book sales, and have greater effect on the sales of books that are not backed by publishers. Twitter also affects book sales, but less strongly than Amazon reviews.

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.

Trust has emerged as a crucial research topic in the sharing economy. However, scholarship on trust in sharing accommodation remains limited. By using stakeholder theory, this study aims to provide a systematic framework for integrating trust among multiple stakeholders and identify potential knowledge gaps and future research directions for trust in sharing accommodation.

The authors select papers using a combination of multiple keywords from EBSCOhost and Web of Science. The analysis includes 172 journal papers published between 2011 and 2021. The authors conduct a systematic review through thematic content analysis, and each paper is analyzed using manual coding.

The analysis shows that key stakeholders for trust building in sharing accommodation include consumers, hosts, platforms, residents and governments, with most studies focusing on the consumer perspective. The study integrates various trust antecedents and outcomes from the above multistakeholder. Second, this study summarizes the most commonly used theories, and more diversified theories could be applied to future research. Third, this study finds that most studies use quantitative methods, and researchers should introduce more integrated methodologies such as machine learning on a large scale. Furthermore, the current research disciplinary paradigm should be extended to multidisciplinary and interdisciplinary approaches to promote innovation in trust research. Finally, the COVID-19 pandemic has brought both challenges and opportunities to industry as well as researchers, and more institutional rather than commercial perspectives need to be addressed.

The study contributes to the trust and the sharing economy literature by providing a systematic framework for integrating trust from multistakeholder perspectives. The study also points out several future research directions by combining micro and macro multistakeholder perspectives, identifying more diversified theories and methodologies and specifying multidisciplinary and interdisciplinary approaches.

The study advances knowledge by providing a systematic framework for integrating trust among multiple stakeholders and proposing future research directions for trust in sharing accommodation.

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.

Websites have their own features in aspect preference (e.g. the relative importance platforms place on product aspects in product evaluation). The purpose of this paper is to capture characteristics of different book reviews on aspect preferences by opinion mining techniques.

The authors employ two indicators for identifying aspect preferences, and propose a method for quantifying overall differences of reviews on aspect preferences through three dimensions: aspect awareness, aspect satisfaction and comprehensive value.

The results show that book reviews on e-commerce websites contain information about external aspects of a book (e.g. hardcover), while those on social network websites pay more attention to content-related aspects of the book (e.g. stories). These results indicate that aspect preferences of reviews vary from platforms and make it hard to evaluate book comprehensively based on single-source data. Online book reviews from a wide range of sources can assess book impact from multiple perspectives and dimensions.

In order to illustrate the value of the authors’ method, the authors show book impact assessment based on multi-source data as an application of these difference analyses. Furthermore, the authors present an example of a book promotion to provide customized marketing services for different user clusters.

This study investigates the influence of different data sources on book evaluation from the content of book reviews. The authors also showcase potential applications of these analyses in book impact assessment.