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Online ratings and reviews have recently emerged as mechanisms to facilitate accountability and transparency in the provision of goods and services. The purpose of this paper is to examine the nature and outcome of the accountability that online ratings and reviews create in the sharing economy.

The study draws on 30 face-to-face and Skype interviews with Airbnb guests and hosts as well as on secondary materials, including content from Airbnb data analytic reports.

The authors demonstrate that face-saving practices widely condition user ratings and comments. Face saving occurs when individuals attempt to preserve their own identity and the identity of others during a social interaction. At Airbnb, the authors find that reviewers adopt three distinct face-saving strategies: the use of private reviewing channels, the creation of tactful reviews and refraining from reviewing entirely. The authors also find that users are sceptical of rating metrics and public comments and draw upon a wide range of alternative sources, such as private messaging and other publicly available resources, in their decision making.

This paper highlights the overwhelmingly positive character of Airbnb ratings and reviews. It proposes the concept of crowdbased accountability as a limited, partial form of assurance for sharing economy users. Guests and hosts alike prioritise face-saving practices over reviewer responsibilities to provide authentic, reliable accounts to the public. Consequently, reviewers effectively remove the risk of sanctions for those in the network who underperform.

This study aims to explain why online reviews in Airbnb are skewed toward positive ratings. The authors examine customer perceptions of the service quality of an Airbnb stay as a relevant antecedent of whether customers leave an online review of that Airbnb stay. To this end, the authors test the hypothesis that the relationship between service quality and leaving an online review is linear and positive.

To test the hypothesis, the authors rely on primary survey data from 177 Airbnb customers combined with secondary data coming from their personal online Airbnb accounts. The authors conducted a binary logistic regression analysis to test the hypothesis.

The results show that customers’ service quality perceptions are positively and linearly related to leaving an online review of an Airbnb stay. In other words, satisfied customers are more likely to leave a review after an Airbnb stay than those who are dissatisfied.

The study is original in two respects. First, it reconsiders the role of customer experiences in explaining online customer reviews. In doing so, it empirically shows that the conventional wisdom of a U-shaped relationship between customer experiences and online reviewing does not hold in the context of the sharing economy. Second, by relying on primary survey data, the authors reveal the risk of dissatisfied customers creating an underreporting bias in online reviews, which ultimately make online reviews of Airbnb skewed toward positive ratings.

This study aims to provide an in-depth understanding of the complex nature of Airbnb user experience by analyzing the pattern and sentiment of online reviews and assessing the relationships among review scores.

Big data analysis is conducted using Airbnb users’ online reviews of 16 US cities; correlation is run on review scores.

The key themes of Airbnb users’ online reviews are “clean,” “location,” “stay,” “home,” “place,” “host,” “neighborhood” and “recommend” and users have positive Airbnb experiences in general. The score of “cleanliness” significantly affects the “overall review” score.

This study is exploratory in nature; mixed methods should be used in the future to measure the relationship between user experience and extracted themes. As the context is in the USA in the current study, comparisons of review patterns across different countries and regions are necessary for later studies. Furthermore, future studies should consider Airbnb users’ demographics, personality and lodging preferences.

It is important for Airbnb hosts to maintain a clean and accessible property. Both Airbnb hosts and hoteliers should enhance the attributes that generate positive customer reviews. Each city should develop different strategies based on the performance of “cleanliness” and “overall review.”

This study supplements the existing literature in Airbnb user experience by analyzing online reviews in 16 US cities via Leximancer 4.0.

分析Airbnb网络评论：论美国16城市的用户体验

本论文旨在深入了解Airbnb用户体验的复杂属性, 通过分析网络评论以及网络评分之间的关系来找寻规律和判断用户情感偏差。

本论文采用大数据方式, 样本为美国16个城市的Airbnb用户网络评论; 本论文还对网络评分做出相关性分析。

研究结果表明, Airbnb用户网络评论的关键主题为“清洁性”、“地理位置”、“居住体验”、“家”、“地点”、“招待主人”、“邻里”、以及“推荐”和用户总体来说对Airbnb有着积极体验。“清洁度”分数对“整体评论”有显著影响。

本论文属于开拓性研究作品; 未来项目可以采用混合方法来衡量用户体验与主题的关系。本论文的研究背景是美国, 未来研究可以做不同国家和区域的评论模式对比分析。此外, 未来研究还应该考虑Airbnb用户的统计数据指标、个性、以及住宿设施偏好。

对于Airbnb住宿提供者来说, 保持设施的清洁和可使用性是非常重要的。对于Airbnb住宿主人和酒店经营者来说, 加强服务属性来提高网络好评是非常重要的。每个城市都应该基于“清洁度”和“整体评论”来开发不同的战略。

本论文补充了现有Airbnb用户体验的文献, 通过Leximancer 4.0 软件来分析美国16个城市的网络评论

Building upon uncertainty reduction theory, this work aims to explore how four uncertainty reduction factors (i.e. online property review, online textual description, online visual description and online instant messenger) mitigate property quality uncertainty and property fit uncertainty, which further influence Airbnb use intention.

This work tests the proposed research model using a structural equation modeling approach with 335 Airbnb users.

The findings reveal that the online property review, online textual description, online visual description and online instant messenger can efficiently mitigate property quality uncertainty and property fit uncertainty, which ultimately influence Airbnb use intention.

This study provides useful insights on mitigating property uncertainty in the peer-to-peer (P2P) accommodation platforms. Researchers are encouraged to investigate the boundary conditions that influence the effectiveness of uncertainty reduction strategies in alleviating property uncertainty.

P2P accommodation service providers are suggested to take actionable uncertainty reduction strategies to mitigate property uncertainty in online P2P accommodation platforms.

First, this study advances research on P2P accommodation by identifying two key types of property uncertainty, namely, property quality uncertainty and property fit uncertainty. Second, this study extends research on P2P accommodation by proposing contextualized passive, active and interactive uncertainty reduction strategies in mitigating property uncertainty. Third, this study extends uncertainty reduction theory to the P2P accommodation context. Fourth, this study enriches uncertainty reduction theory by verifying the mediating effects of property quality uncertainty and property fit uncertainty.

This paper aims to investigate customers’ experiences with Airbnb by text-mining customer reviews posted on the platform and comparing the extracted topics from online reviews between Airbnb and the traditional hotel industry using topic modeling.

This research uses text-mining approaches, including content analysis and topic modeling (latent Dirichlet allocation method), to examine 1,026,988 Airbnb guest reviews of 50,933 listings in seven cities in the USA.

The content analysis shows that negative reviews are more authentic and credible than positive reviews on Airbnb and that the occurrence of social words is positively related to positive emotion in reviews, but negatively related to negative emotion in reviews. A comparison of reviews on Airbnb and hotel reviews shows unique topics on Airbnb, namely, “late check-in”, “patio and deck view”, “food in kitchen”, “help from host”, “door lock/key”, “sleep/bed condition” and “host response”.

The topic modeling result suggests that Airbnb guests want to get to know and connect with the local community; thus, help from hosts on ways they can authentically experience the local community would be beneficial. In addition, the results suggest that customers emphasize their interaction with hosts; thus, to improve customer satisfaction, Airbnb hosts should interact with guests and respond to guests’ inquiries quickly.

Hotel managers should design marketing programs that fulfill customers’ desire for authentic and local experiences. The results also suggest that peer-to-peer accommodation platforms should improve online review systems to facilitate authentic reviews and help guests have a smooth check-in process.

This study is one of the first to examine consumer reviews in detail in the sharing economy and compare topics from consumer reviews between Airbnb and hotels.

The current paper aims at exploring negative aspects in reviews about Airbnb listings in Athens, Greece.

The aspect-based sentiment approach (ABSA), a subset of sentiment analysis, is used. The study analyzed 8,200 reviews, which had at least one negative aspect. Based on dependency parsing, noun phrases were extracted, and the underlying grammar relationships were used to identify aspect and sentiment terms.

The extracted aspect terms were classified into three broad categories, i.e. the location, the amenities and the host. To each of them the associated sentiment was assigned. Based on the results, Airbnb properties could focus on certain aspects related to negative sentiments in order to minimize negative reviews and increase customer satisfaction.

The study employs the ABSA, which offers more advantages in order to identify multiple conflicting sentiments in Airbnb comments, which is the limitation of the traditional sentiment analysis method.

The purpose of this paper is to investigate attributes that influence Airbnb customer experience by analysing online reviews from users staying in London. It presents a text mining approach to identify a set of broad themes from the textual reviews. It aims to highlight the customers’ changing perception of good quality of accommodations.

This paper analyses 169,666 reviews posted by Airbnb users who stayed in London from 2011 to 2015. Hierarchical clustering algorithms are used to group similar words into clusters based on their co-occurrence. Longitudinal analysis and seasonal analysis are conducted for a more coherent understanding of the Airbnb customer behaviour.

This paper provides empirical insights about how Airbnb users’ mindset of good quality of accommodations changes over a five-year timespan and in different seasons. While there are common attributes considered important throughout the years, exclusive attributes are discovered in particular years and seasons.

This paper is confined to Airbnb experiences in London. Researchers are encouraged to apply the proposed methodology to investigate Airbnb experiences in other cities and detect any change in customer perception of quality stay.

This paper offers implications for the prioritisation of customer concerns to design and improve services offerings and for alignment of services with customer expectations in the sharing economy.

This paper fulfils an identified need to examine the change in customer expectation across the timespan and seasons in the case of Airbnb. It also contributes by illustrating how big data can be used to uncover key attributes that facilitate the engagement with the sharing economy.

The purpose of this paper is to examine sharing economy online marketplaces with the aim of understanding how trust perceptions form and get communicated through sharing economy platforms.

The authors build on online user comments and reviews as aggregated by independent third-party websites, and apply a qualitative analysis.

The findings show that the quantity of information and communication are important drivers towards building trust perceptions, while an overall lack of interaction between users and the marketplace provider intensifies perceived risks.

The authors validated the importance of trust and the authors have illustrated that the critical conditions that hinder trust formation are information asymmetry as well as the lack of interaction. What is also an interesting implication is that the impact of both of these can be exacerbated when there is a perceived lack of support among users and between them and the marketplace operator.

By building on cooperation–competition theory, this study aims to investigate the multidimensional flipped effects of neighborhood hotels on Airbnb listings’ popularity, examining the degree to which such impacts are influenced by hotel types and geographical areas.

This study explores the interdependent and competitive relationship between neighborhood hotels and Airbnb from the perspective of effects on Airbnb listings’ popularity by exploring a data set covering 10,492 Airbnb listings and 2,691 hotels from Ctrip.

Results reveal that neighborhood hotels’ number of reviews, review ratings and prices each have positive spillover effects on Airbnb listings’ popularity, while quality assurance labels and negative review topic sentiments exert competitive effects on Airbnb popularity. Moreover, the number of budget chain hotels and high-star hotels have positive and negative effects on Airbnb popularity, respectively. Geographical areas also have a moderating effect on the relationship between various hotel-related influencing factors and Airbnb.

This study can offer hotel managers and Airbnb operators a clearer understanding of these businesses’ coexisting relationship. Findings can also provide Airbnb-specific guidelines for practitioners in terms of site selection, promotional features and development strategies for Airbnb listings.

This study establishes a cooperation–competition relationship model between hotels and Airbnb and considers the flipped effects of hotels on Airbnb for the first time. It expands previous studies by considering the multidimensional effects of hotels on Airbnb listings’ popularity and by examining the influences of hotel types and geographical areas on hotels’ impacts on Airbnb.

This study aims to explore the dimensions and sub-factors of Airbnb hosts’ affinity-seeking strategies. It also aims to build a conceptual framework of hosts’ affinity-seeking strategies and their impact on Airbnb guests’ feelings of affection toward their host and/or the homestay and their behavioral intentions.

Based on 150,161 Inside Airbnb online reviews in three major US cities, this study uses semantic analysis to explore processes through which hosts’ affinity-seeking strategies are constructed.

A conceptual framework is proposed to identify two dimensions (warmth and competency) and their sub-factors (“presenting friendly attitudes,” “showing personality traits,” “providing service and help” and “promoting social interaction and sharing”) of Airbnb homestay hosts’ affinity-seeking strategies. The framework shows a positive relationship between these strategies and guests’ affection and behavior in response.

The research findings provide valuable insights to hosts for improving their affinity and strengthening their competitive advantages. They also offer guidance to destination management organizations on how to build a positive destination image.

To the best of the authors’ knowledge, this study is the first to conceptualize Airbnb homestay hosts’ affinity-seeking strategies. It contributes to the literature by incorporating social cognition theory and service theory in the analysis of these strategies.