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This paper aims to examine the effects of traditional customer satisfaction (CS) relative magnitude and social media review ratings on hotel performance and to explore which online travel intermediaries’ review ratings serve as the most reliable and valid predictor for hotel performance.

In 2014, CS and hotel performance data were collected from the internal database of full-service hotels operated and managed by a large hotel chain in the USA. Each property’s social media review ratings data were hand-collected from major online travel intermediaries and social media websites.

The results of this study indicate that social media review rating is a more significant predictor than traditional CS for explaining hotel performance metrics. Additionally, the social media review rating of TripAdvisor is the best predictor for hotel performance out of the other intermediaries.

This research contributes to the hospitality literature because it examines the incremental explanatory power of social media review rating and traditional CS on hotel performance. Among the leading online travel intermediaries, the findings show that TripAdvisor’s social media review rating has the most salient effect on hotel performance.

The result of this study provides useful practical implications for hotel marketers and revenue managers. This study assists hotel marketers and revenue managers in better allocating their budget for marketing and suggests ways for channel optimization.

The finding of this study will help revenue managers, marketing managers, and hotel owners make decisions regarding their marketing budget allocation to their social media marketing campaign and select the optimal online travel intermediaries as part of their channel management strategies.

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.

Online reputation management (ORM) plays a significant role in the tourism industry. Tourists are more and more interested to express their opinions about their experiences/satisfaction not only with their friends but also on social media. ORM is largely used not only by tourist destinations but also by other companies operating in the tourism industry. This chapter aims to draw attention to the importance of intermediaries in tourism, their reputation in general, and especially their ORM and its specifics. This contribution also characterizes different types of intermediaries and their different roles in the distribution process of tourism services. These roles are important and can be even more significant in the “new normal” post-COVID-19 time. In the scientific literature and research, there is not much attention given to intermediaries as a whole and even less to their ORM and its specific solutions. But practical contributions can be found. Due to the specific activities and roles of different types of intermediaries, also their reputation is influenced not only by tourists but also by their suppliers. Their ORM has also some specifics and needs specific solutions. Their reputation is depending not only on customers' satisfaction with their own services but also on the reputation of tourism service providers, whose services they offer and mediate either individually or in the form of their own product, e.g., package tours. Specific attention in this chapter is given to intermediaries such as OTAs (Online Travel Agencies/Agents) and tour operators. At some time, these two types of intermediaries help to increase the reputation of tourism services providers, e.g., hotels. The chapter describes the situation in the field of intermediaries with a conceptual model, their ORM, and summarizes its specifics.

The purpose of this paper is to review the impact electronic word-of-mouth (eWOM) has had on the hospitality and tourism industry and discuss the changes that will affect its future. The paper’s touchpoint is the authors’ earlier paper (Litvin et al., 2008), which proposed that eWOM was to become a major influence as a conduit of travelers’ views and opinions.

The paper summarizes the arguments of the authors’ earlier paper, describing ways in which eWOM has evolved into the influential system it has become, with special emphasis on the growth of mobile media as a platform for eWOM dissemination.

The authors conclude that eWOM has fulfilled its promise to become a major influence on the hospitality and tourism industry and will continue to play an essential role in hospitality marketing for the foreseeable future.

The authors provide examples of successful media campaigns and propose strategies for hospitality and tourism businesses.

eWOM has emerged to become a highly influential element of modern marketing strategy. This look back at an early eWOM paper, with reflection on changes that have occurred and a view to the future, is of value as validation of an often cited article that set the stage for much subsequent hospitality research.

The purpose of this study is to understand the use of social networking sites (SNSs) by hotels. Specifically, drawn upon a resource and capability-based perspective, this study addresses two research questions: (1) the relationship between a hotel’s resources and its use of Twitter and (2) the relationship between the use of Twitter by hotels and their RevPAR.

The research data include the hotel chain scales, Twitter user profiles and Twitter activities of the hotel parent companies in the USA and the hotels’ RevPAR. To more clearly understand the effect of the use of SNSs, the study uses two dimensions: electronic word-of-mouth and customer engagement. The two dimensions of the hotels’ Twitter use are calculated based on the data extracted from their Twitter user profiles and historical tweets. For a practical purpose, a social media index (SMI), which combines electronic word-of-mouth and the customer engagement score, was used to determine the overall level of Twitter use by hotels.

For RQ1, the results indicate there is a positive association between a hotel’s resources and Twitter use. For RQ2, this study shows there is also a positive association between Twitter use by hotels and their RevPAR.

Twitter use appears to be associated with hotels’ resources. In turn, Twitter use is positively associated with hotel RevPAR. Thus, hotels should look at Twitter as a potential strategic tool for business operation and attempt to increase their ability to leverage Twitter (and other SNSs) for organizational goals (e.g. sales, promotion, customer service).

To the authors’ knowledge, this is the first study empirically investigating the use of SNSs by hotels with the data drawn from actual firm-generated content (e.g. tweets, retweets) and hotels’ user profile information from Twitter.

This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems.

This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating.

Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment.

This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languages.

Social relationships on the internet through the emergence of Web 2.0 applications created new opportunities for business. This is mainly because of the growth of social networking sites, which has also developed e‐commerce. The current development in e‐commerce opened a new stream, entitled social commerce, which is using social technologies to create an environment for generating social interactions. These social interactions can drive online social support in e‐commerce, which in turn is creating trust and an increased intention to use social commerce.

This research used social support theory and related theories on intention to use to propose a theoretical framework for the adoption of social commerce.

The model predicts that forums and communities, ratings, reviews, referrals and recommendations are helping to introduce new business plans for e‐vendors. The model also shows trust is an on‐going issue in e‐commerce and can be built through social commerce constructs.

There is limited research in the area of social commerce which this study seeks to redress. This study proposes a new model which can be extended by other constructs. However, the research needs to empirically test the constructs of the proposed model and their relationship.

This paper introduces social commerce constructs, namely; recommendations and referrals, forums and communities and rating and reviews. The bases of the model proposed in this research are IT adoption and literature in the area such as PU and intention to buy or trust. These highlight the key role of ICT in the behaviour of online customers. This can be a development for e‐commerce adoption models and the results signify that IS has a reference discipline for the behaviour of online consumers. This is an issue in marketing where not enough attention is paid to the importance of IT and IS.

Much research regarding social media posts and relevancy has resulted in mixed findings. Furthermore, the mediating role of relevancy has not previously been examined. This paper aims to examine the correlating relationship between types of posts made by hotels and the resulting occupancy rates. Then, the mediating role of relevancy is examined and ways that posts can increase/decrease relevancy of the post to potential hotel users.

Within the context of the hotel industry, three studies were conducted – one including hotel occupancy data from a corporate chain – to examine the impact of social media posts on relevancy and intentions to stay at the hotel. Experimental studies were conducted to explain the results of the real-world hotel data.

The findings show that relevancy is an important mediator in linking social media posts to service performance. A locally (vs nationally) themed post can decrease both the relevancy of a post and the viewer’s intentions to stay at a hotel. This relationship, however, can be weakened if a picture is included with the post, as a visual may increase self-identification with a post.

These results have important theoretical and practical implications as social media managers attempt to find the best ways to communicate to their customers and followers. Specifically, there are lower and upper limits to how many times a hotel should be posting to social media. The data also show many hotels post about local events, such as school fundraisers or a job fair, that can be harmful to stay intentions, likely due to the irrelevant nature of local posts to customers who are likely to stay in a hotel. National posts are seen as more relevant and likely to increase stay intentions, and the inclusion of a picture can help local posts seem more relevant.

Due to product diversity, traditional quality signals in the hotel industry such as star ratings and brand affiliation do not work well in the accommodation booking process on the sharing economy platform. From a suppliers’ perspective, this study aims to apply the signaling theory to the booking of Airbnb listings and explore the influence of quality signals on the odds of an Airbnb listing being booked.

A binomial logistic model is used to describe the influences of different attributes on the market demand. Because of the large sample size, sequential Bayesian updating method is utilized in hospitality and tourism field for the first attempt.

Results show that, in addition to host-specific information such as “Superhost” and identity verification, attributes including price, extra charges, region competitiveness and house rules are all effective signals in Airbnb. The signaling impact is more effective for the listings without any review comments.

This study contributes to the literature by incorporating the signaling theory in the analysis of booking probability of Airbnb accommodation. The research findings are valuable to hosts in improving their booking rates and revenue. In addition, government and industrial management organizations can have more efficient strategy and policy planning.

The purpose of this study is to examine the role and influence of online reviewers’ cultural traits and perceived experience on online review ratings of Russian hotels by taking a direct measurement approach.

The authors adopt an explanatory sequential research design consisting of two stages. In the first stage, based on a sample of almost 75,000 Booking.com online reviews covering hotels located in Moscow (Russia), this study examines quantitatively to what extent the cultural traits of online reviewers and hotel guests’ perceived experience in online reviewing affect online ratings also using censored regressions. In the second stage, it interprets the results in light of semi-structured interviews conducted with a convenience sample of managers.

Each of the Hofstede’s cultural dimensions (namely, individualism, masculinity, uncertainty avoidance and power distance) exerts a significantly negative influence on the hotel online ratings. More specifically, the higher the levels of individualism, masculinity, uncertainty avoidance and power distance, the lower the hotel’s online ratings. Reviewers’ perceived experience in online reviewing is negatively related to online ratings.

The study’s findings bear relevant practical implications for hotel managers and online platform managers in countries that are not typically covered by online consumer behavior studies in hospitality such as Russia. From a theoretical viewpoint, this study contributes to cultural studies in hospitality management and marketing with a further development of the nascent research stream taking a direct measurement approach to the study of cultural influences on consumers’ behaviors. Furthermore, this study offers a better and in-depth understanding of the role of cultural traits on electronic word of mouth, as well as international market segmentation theory in online settings.

The conjoint exploration of the effects of cultural differences and perceived experience in online reviewing adds to the nascent research stream taking a direct measurement approach to the study of the Hofstede’s cultural dimensions on online consumers’ behaviors. The authors make multiple theoretical and methodological contributions, highlighting that online hospitality customers cannot be considered as one homogeneous mass. Instead, the application of Hofstede’s cultural dimensions allows identifying distinctively different online behaviors across international online customers: different online customer groups can be clustered into segments, as they display different online behaviors and give different online evaluations.