Analysing reviews in the Web 2.0: Small and medium hotels in Portugal

This study presents a characterisation of online reviews for small and medium hotels (SMH) in Portugal. We collected and analysed 1500 online reviews from 50 SMH. The findings show that room, staff and location were the concepts most frequently appeared in the reviews, with cleanliness, friendliness, helpfulness, and centrality of position the terms most frequently used to qualify the concepts. This study points out the main features which SMH managers should prioritize.     

The impact of online reviews on hotel booking intentions and perception of trust

A growing reliance on the Internet as an information source when making choices about tourism products raises the need for more research into electronic word of mouth. Within a hotel context, this study explores the role of four key factors that influence perceptions of trust and consumer choice. An experimental design is used to investigate four independent variables: the target of the review (core or interpersonal); overall valence of a set of reviews (positive or negative); framing of reviews (what comes first: negative or positive information); and whether or not a consumer generated numerical rating is provided together with the written text. Consumers seem to be more influenced by early negative information, especially when the overall set of reviews is negative. However, positively framed information together with numerical rating details increases both booking intentions and consumer trust. The results suggest that consumers tend to rely on easy-to-process information, when evaluating a hotel based upon reviews. Higher levels of trust are also evident when a positively framed set of reviews focused on interpersonal service.     

Do Airbnb’s “Superhosts” deserve the badge? An empirical study from China

The “Superhost” badge is that Airbnb entitles the host provides good services. This study verifies Airbnb’s “Superhost” mechanism by applying text mining technologies, combined with Long Short-Term Memory (LSTM) and K-Means, to the entire dataset of tourists’ online reviews of Hangzhou city, China. Six kinds of hosts’ good services are identified, including “Three Meals or Night Snacks,” “Fruits, Drinks or Snacks,” “Travel Guides,” “Free Shuttle or Helping with Luggage,” “Chats,” and “Replies or Communications.” The study reveals the minority of “Superhosts” are mentioned of providing the majority of six kinds of good services, which means “Superhosts” do deserve the badge.     

Be a “Superhost”: The importance of badge systems for peer-to-peer rental accommodations

Many sharing-economy websites like Airbnb that offer vacation-rental options for travelers are very popular. However, few studies targeting the vacation-rental industry have investigated online reviews. To narrow this gap, this study focuses mainly on the gamification design developed by Airbnb that awards a “Superhost” badge to hosts who receive good reviews and observes how this can impact an accommodation's review volume and ratings. All available information regarding Airbnb accommodation offered in Hong Kong was retrieved from Airbnb's website. We then constructed a negative binomial model and a Tobit model with different independent variables and controlled a set of variables relating to accommodation characteristics. The results show that an accommodation with the “Superhost” badge is more likely to receive reviews and higher ratings. In addition, guests are willing to spend more on “Superhost” accommodations. Based on our findings, we present implications for research and host practice.     

What makes Airbnb likeable? Exploring the nexus between service attractiveness,country image,perceived authenticity and experience from a social exchange theory perspective within an emerging economy context

As a result of the growth of the notions of collaborative consumption and sharing economy in the tourism industry, this paper applies social exchange theory to investigate how the Airbnb platform influences the Airbnb experience and authenticity, which might lead consumers to like Airbnb and influence their behavioural patterns. By recruiting 466 tourists who had stayed in Airbnb accommodation in Istanbul, Turkey via travel-related Telegram, Twitter, travel blogs, and Facebook groups, this study revealed the importance of the platform and its features in enhancing service attractiveness, perceived authenticity and experience. Furthermore, the results revealed that visitors’ experiences have an influence on Airbnb likability, where Airbnb likability influences their intention to re-visit and to recommend. Significant implications for tourism planning, management and researchers are highlighted.     

What prevents consumers that are aware of Airbnb from using the platform? A mixed methods approach

The hospitality sector is now characterised by the co-existence of traditional providers and sharing economy enterprises. It is important to better understand what prevents the use of peer-to-peer accommodation rentals in the tourism marketplace. Adopting a mixed-methods approach, this study first examines travellers’ Airbnb use constraints, and then profiles 252 Airbnb non-users based on their constraints and characteristics. The findings suggest that travellers do not use Airbnb because of distrust in the providers, in the platform and in other users, as well as perceived risk and unfamiliarity. Loss of service quality, lack of local experience, legal and regulatory issues, and disinterest also prevented consumers to book Airbnb accommodations. A cluster analysis divided non-users into three segments: Traditional travellers, Sharing economy misbelievers and Airbnb prospective users. Significant differences based on age confirmed that young travellers are more confident in using peer-to-peer accommodation platforms and more interested in the philosophy.     

The 7 Ps marketing mix of home-sharing services: Mining travelers’ online reviews on Airbnb

The 7 Ps model is a very useful tool in helping service firms solve managerial issues in marketing. Guided by the 7 Ps marketing mix framework, a big-data, supervised machine learning analysis was performed with 1,148,062 English reviews of 37,092 Airbnb listings in San Francisco and New York City. The results disclose similar patterns in both markets, where travelers shared their experience about Service Product and Physical Evidence most often; Price and Promotion were the least mentioned elements. Furthermore, through a series of comparisons of Airbnb’s 7 Ps marketing mix among the listings managed by different types of hosts, multi-unit and single-unit hosts seem to offer similar services with a small observable difference; whereas superhosts and the ordinary hosts deliver different services. This study makes valuable methodological contributions and provides practical marketing insights for hoteliers and the hosts and webmasters on home-sharing websites. Policymakers should pay special attention to multi-unit hosts.