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The main purpose of this study is to explore the effect of three dimensions of “home feeling” – home-as-practical, home-as-social and home-as-attachment – on the online rating of homestays and additionally considers the accommodation’s attribute performance and level of sharing.

To achieve the research aims, more than 9,738,335 items of user-generated content concerning 743,953 Airbnb listings covering 35 cities were collected as the study data. These data are analyzed through hierarchical regression.

The results show that all three dimensions of home feeling positively affect the online rating; all three dimensions negatively moderate the relationship between attribute performance and online rating; the size of the moderating effect of each dimension on the relationship between attribute performance and online rating gradually increases in the order home-as-practical, home-as-social and home-as-attachment; and as the level of sharing increases, the moderating effect of home feeling on the relationship between attribute performance and online rating diminishes.

This study contributes to the literatures on the role of home feeling in homestays, the online rating of homestays and the motivations of guests who choose different room types. The findings of this study can help hosts better understand the formation of online rating of homestays, make targeted improvements in rooms and services and create a home feeling for specific degrees of sharing. This in turn will help them to improve the online rating of their homestays, establish an excellent online reputation and, ultimately, increase sales.

This study advances knowledge by confirming three dimensions of home feeling not only have direct positive impacts on online rating but also mitigate the impact of attribute performance on online rating. This effect differs significantly in magnitude with the degree of sharing.

Using data from online guest ratings, the purpose of this paper is to explore how the performance of hotels in terms of various criteria influences loyalty behavior of customers.

Ratings of 333 hotels in the UK were collected from the web site www.laterooms.com and statistical analysis performed.

The criterion related to marketing management (Value for money) was found to be the most important criterion influencing loyalty behavior of customers in UK hotels. Further, it was found that good performance of hotels in terms of physical‐product management can significantly influence the intentions of business guests to stay again, whereas leisure guests expect good performance both in terms of physical‐product management and people and process management. While guests of independent hotels value performance in terms of people and process management, guests of chain hotels value both physical‐product management and people and process management. Finally, it was found that the significance of criteria related to physical‐product management and people and process management generally varies across star ratings.

Expectations of guests have been found to be quite different across various categories – star classifications, chain and independent hotels, and leisure and business guests. This implies that a general solution may not satisfy guests belonging to all these categories. For example, business and leisure guests perceive facilities differently and hence hotel managers need to provide different kinds of services to satisfy them. Similarly, the finding that business guests attach higher importance to criteria related to physical‐property management, in deciding their intention to stay again in the UK hotels, compared to people and process management, has interesting practical implications. This indicates that good performance of hotels in maintaining room quality and cleanliness can significantly influence the intentions of business guests to stay again.

This is one of the first studies to statistically analyze online guest ratings. It extends the applicability of the frameworks developed in the earlier literature by employing them with the new data source (online ratings).

In this paper, the authors aim to examine the impact of resource capabilities on customer loyalty of UK hotels. Understanding this impact will help organisations to improve customer satisfaction in order to obtain improved customer loyalty.

The authors use a relatively innovative data source, namely online ratings. They measure resource capabilities of a firm using customer ratings in terms of various operational criteria. Similarly, customer loyalty is measured using guests ' ratings on their intention to use the same service (stay again in the same hotel) and their intention to recommend the service to friends. The authors employ structural equation modelling to test research hypotheses.

The authors ' results indicate that there is a significant positive influence of resource capabilities on customer loyalty. They further find that the significant influence of resource capabilities on customer loyalty does not differ across hotels with various star ratings.

The authors looked at the online guest ratings available on a particular website, but it is only one of the many websites offering online hotel reservations, and not all customers that made hotel reservations using this e-booking facility would be inclined to leave feedback after their stay in the hotel. This limitation can be partially overcome by pooling similar data from a number of online hotel booking sites.

The most important managerial implication is that good resource capabilities of firms translate well into customer loyalty. Thus, managers should ensure good performance in terms of various hotel attributes – cleanliness, quality of room, facilities, and customer service – and also ensure that customers perceive good value for their money while staying in the hotel.

The authors applied structural modelling framework to verify the resource capability – performance link in the context of hotels. They used a relatively novel data source – online guest ratings of hotels – to understand the relationships between resource capabilities and customer loyalty.

This paper seeks to explore the performance of UK hotels, in terms of various service attributes, and whether it influences customers' intention to stay again.

Data are used from online customer ratings of 664 hotels in the UK for the purpose. The approach is based on an interesting use of statistical regression reported in the literature that attempted to classify different cues in hotels as critical, satisfier, dissatisfier, etc. In this study, six prominent attributes are considered, namely: customer service, cleanliness, room quality, value for money, quality of food, and family friendliness, rated by guests, based on their experiences of staying in hotels.

The findings reveal that “Value for money” is a critical attribute, while “Customer service”, “Room quality” and “Quality of food” are dissatisfiers. Business guests, and guests of independent hotels, exhibit similar behavior, but for leisure guests, and guests of chain hotels, “Value for money” is a dissatisfier.

“Value for money” is a critical attribute, in that good performance, in terms of this attribute, is critical for positively influencing guests' intention to stay again; however, failures in terms of this attribute cannot be compensated by improving service in terms of other attributes. There are three dissatisfier attributes (“Customer service”, “Room quality”, and “Quality of food”), implying that an inadequate performance in terms of these attributes could significantly adversely impact guests' intention to stay again.

This study would appear to be the first to use the extensive data available on the internet on guest ratings of hotels.

Hotel classification systems are used to convey information about facilities and services. Yet, they have been prone to criticism for overemphasizing facilities at the expense of other matters of importance to service quality. In contrast, online travel agents (OTAs) use innovative methods to evaluate satisfaction with hotels. Conventional systems will lose relevance if they do not step up to consider service aspects associated with customer satisfaction. This chapter probes five hotel classification systems along with one OTA and leverages the literature to propose an improved framework classification. This is based on nine critical areas that include service quality, infrastructure, facilities and services, human resources, sustainability, safety and security, accessibility, quality systems, and online hotel ratings.

Online hotel ratings, a form of electronic word of mouth (eWOM), are becoming increasingly important to tourism and hospitality management. Using sentiment analysis based on the big data technique, this paper aims to investigate the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM, and to further identify the moderating effects of review characteristics.

The authors first retrieve 273,457 customer-generated reviews from a well-known online travel agency in China using automated data crawlers. Next, they exploit two different sentiment analysis methods to obtain sentiment scores. Finally, empirical studies based on threshold regressions are conducted to establish the asymmetric relationship between customer sentiment and online hotel ratings.

The results suggest that the relationship between customer sentiment and online hotel ratings is asymmetric, and a negative sentiment score will exert a larger decline in online hotel ratings, compared to a positive sentiment score. Meanwhile, the reviewer level and reviews with pictures have moderating effects on the relationship between customer sentiment and online hotel ratings. Moreover, two different types of sentiment scores output by different sentiment analysis methods verify the results of this study.

The moderating effects of reviewer level and reviews with pictures offer new insights for hotel managers to make different customer service policies and for customers to select a hotel based on reviews from the online travel agency.

This paper contributes to the literature by applying big data analysis to the issues in hotel management. Based on the eWOM communication theories, this study extends previous study by providing an analysis framework for the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM.

Tourism and hospitality are industries that have been dramatically transformed by information technology (IT). This study aims to use a keyword analysis to quantitatively review how IT reshaped these industries.

In total, 3,282 keywords were collected from 24 high-impact tourism and hospitality journals and a social network analysis was used for the analysis.

This study contributes to research and practice by providing a visual digital knowledge map for tourism and hospitality, and seven research hotspots were identified from the results of the keyword analysis.

A parsimonious eMarketing model for tourism and hospitality is proposed to direct future studies concerning these themes and guide practitioners in allocating the appropriate resources for IT investment.

This map not only identifies seven themes that explain, which and how IT-related factors influence tourism and hospitality but also demonstrates the patterns and intellectual structure of the related body of knowledge. The trend analysis indicates how IT transforms the tourism and hospitality industries in terms of mode and scope.

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.

The purpose of this paper is to investigate how hotels respond to online reviews on a third-party Web site (such as TripAdvisor) based on the hotel’s star rating.

Content analysis was used to compare responses to online hotel reviews at five different levels of hotel based on a star-rating system ranging from one star to five stars.

Most hotel managers’ response rates were low, and they paid the most attention to positive comments. Managers at four- and five-star hotels more often responded to negative online reviews. Guest service manager was the most common job title of managers who responded to guests’ reviews.

This paper is limited to an analysis of ten hotels, two for each of the five-star ratings. More hotel cases with long-term data collection involving the use of the star-rating system may provide more insights on this discussion.

The exploratory study sought to identify strategies for managing online reviews in the lodging industry. Hotel managers should respond to negative online reviews with appreciation, apology and an explanation of what went wrong. Moreover, hotels may need a designated person to observe and respond to guest comments on their Web sites and third-party Web sites. A designated person is also needed to monitor online comments and communicate with guests to better manage the hotel’s online reputation.

As an exploratory research project, this paper expands the understanding of hotel managers’ responses to their guests’ online reviews in an attempt to identify best practices for the industry.