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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.

This paper aims to explore how emotional expressions embedded in online hotel reviews influence consumers’ helpfulness perceptions. In particular, this study develops and tests hypotheses analyzing empirical data with a text-mining method in the context of hotels to investigate how review valence influences the perceived helpfulness of online hotel reviews and to examine the role of negative emotional expressions embedded in online consumer reviews with respect to perceived helpfulness.

This study collected 520,668 online reviews involving 488 hotels in New York City (NYC) on Tripadvisor.com. Of these reviews, 69,202 reviews (13.29 per cent) that had received helpfulness votes were analyzed by a text mining method and negative binomial regression.

This study demonstrates that negative reviews are considered more helpful than positive reviews when potential customers read online hotel reviews for their future stay. However, when intensively negative emotions were expressed, the degree of helpfulness regarding negative reviews was diminished.

While emotional expressions prevail in online consumer reviews, surprisingly little attention has been devoted to the consequences of emotional expressions in consumers’ information processing and decision-making. Due to the nature of service, given the inseparability of production and consumption, which often hinders the execution of flawless service, consumers tend to be more dependent on reviews to minimize any potential failures they may encounter later on. Therefore, this study fills a gap by demonstrating that negative reviews and emotional expressions play a more crucial role in consumers’ information processing and decision-making.

Cross-cultural research constitutes a pivotal topic for marketing; however, the literature indicates that there are a few studies analyzing social media reviews from a cross-cultural perspective using cultural proximity (supra-national level) as a proxy of culture. Therefore, the purpose of this study is to identify cross-cultural differences in service evaluations and specifically, in hotel appraisals among tourists from Central, Eastern (including Post-Soviet States), Northern and Southern Europe.

A quantitative approach has been taken by studying online user-generated ratings of hotels on Trip Advisor. In total, 1,055 reviews of five hotels in Greece were used for the study.

Multivariate analysis of variance and analysis of variances results confirm cultural differences in overall service evaluations and attributes (value, location, sleeping quality, rooms, cleanliness and service) of tourists from various European regions. Specifically, Eastern Europeans uploaded more reviews than any other European group, whereas Northern Europeans were more generous in their appraisals than Eastern, Southern and Central Europeans.

The results of the study could be used for segmentation purposes of the European tourism market and for recognizing, which aspects of their services need to be improved based on the segments they serve. Moreover, managers should encourage Northern and Eastern Europeans to upload their reviews as both groups are more generous in their evaluations. Moreover, the findings are useful to marketers of other services.

To the author’s knowledge, this is the first study that examines cross-cultural differences in hotel appraisals from a supra-national perspective including developed (Northern and Western Europe), developing (Southern Europe) and emerging tourism markets (Eastern Europe).

The length of stay (LoS) is of major importance from the perspective of the management of tourist destinations. As tourists heavily rely on the online reviews of other travelers as a primary information source, this study aims to empirically examine how the LoS can influence the online reviews for hotels, with special emphasis on the textual review content.

This study analyzes online review data collected from Booking.com by using the Linguistic Inquiry and Word Count program to operationalize review depth, analytical thinking and the authenticity reflected in customer reviews. Based on the analyzed data, this study used a series of regression analyses to understand the impacts of the LoS on online reviews.

The author’s analysis found that a longer stay at a hotel causes consumers to be more likely to post online reviews that not only include a numerical rating as well as written content but also lengthier and more detailed descriptions of their hotel experiences. Further analysis found that the LoS at hotels causes systematic differences in the linguistic attributes of the review content. Specifically, consumers who stay longer tend to write reviews with more analytical information, resulting in consumers perceiving the online reviews as more authentic.

Although the LoS has been considered a significant issue in tourism, studies examining the impact of different lengths of stay on consumers’ post-purchase behaviors are limited. In this light, the author’s findings demonstrate how the LoS can change the linguistic attributes of online reviews. It expands the body of knowledge of the LoS in tourism.

This study represents the first attempt to empirically examine and reveal how the different length of stay at a hotel systemically influences consumer review-posting behaviors.

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.

The study aims to examine the effects of ulterior motives in peer and expert supplementary online hotel reviews on consumers' perceived deception, dissatisfaction, and its downstream effects on altruistic response and repurchase intentions. The research also examines the moderating role of hotel attribute performance on perceived deception and its consequents.

This study used convenient non-probability sampling and collected data from 448 inbound tourists in China. It used partial least square structural equation modeling technique and SmartPLS 3.0 for analyzing the main and moderating effects of the variables.

The ulterior motives in peer and expert supplementary reviews significantly affect perceived deception, further leading to consumers' dissatisfaction and engagement in altruistic response. Noticeably, consumers' dissatisfaction is positively associated with repeat purchase intentions. Hotel attribute performance significantly moderates the relationship between the ulterior motives in supplementary reviews and consumers' perceived deception.

The study examines the key issue in online hotel reviews using the expectancy disconfirmation theory and identifies consumers' altruistic behavior because of their dissatisfaction, contributing to ethics and consumer behavior literature. Moreover, the research offers prolific implications for hotel and travel websites and hoteliers in the study context.

The purpose of this study is to quantify the impact of online customer reputation on financial profitability.

Online reputation is captured by extracting the most recurring textual themes associated with customer satisfaction and dissatisfaction, expressed within positive vs negative online guest reviews on Booking.com. Latent semantic analysis is used for textual analysis. Proxies of overall financial performance are manually constructed for the sample hotels, using financial data from the Financial Analysis Made Easy (FAME) database. Ordinary least squares is used to gauge the effect of online customer reputation on financial profitability.

Empirical findings indicate that recurring textual themes from positive online reviews (in contrast to negative reviews) exhibit a higher degree of homogeneity and consensus. The themes repeated in positive, but not in negative reviews, are found to significantly associate with hotel financial performance. Results contribute to the discussion about the measurable effect of online reputation on financial performance.

Contemporary quantitative methods are used to extract online reputation for a sample of UK hotels and associate this reputation with bottom-line financial profitability. The relationship between online reputation, as manifested within hotel guest reviews, and the financial performance of hotels is examined. Financial profitability is the result of revenues, reduced by the costs incurred in order to be able to offer a given level of service. Previous studies have mainly focused on basic measures of performance, i.e. revenue generation, rather than bottom-line profitability. By combining online guest reviews from travel websites (Booking.com) with financial measures of enterprise performance (FAME), this study makes a meaningful contribution to the strategic management of hotel businesses.

Leisure and tourism activities have proliferated and become important parts of modern life, and the hotel industry plays a necessary role in the supply for and demand from consumers. The purpose of this paper is to develop guidelines for hotel service development by applying a service development approach integrating Kansei engineering and text mining.

The online reviews represent the voice of customers regarding the products and services. Consumers’ online comments might become a key factor for consumers choosing hotels when planning their tourism itinerary. With the framework of Kansei engineering, this paper adopts text mining to extract the sets of Kansei words and hotel service characteristics from the online contents as well as the relationships among Kansei words, service characteristics and these two sets. The relationships are generated by using link analysis, and then the guidelines for hotel service development are proposed based on the obtained relationships.

The results of the present research can provide the hotel industry a comprehensive understanding of hotels’ customers opinions, and can offer specific advice on how to differentiate one’s products and services from competitors’ in order to improve customer satisfaction and increase hotels’ performance in the end. Finally, this study finds out the service development guidelines to meet customers’ requirements which can provide suggestions for hotel managers. The implications both for academic and industry are also drawn based on the obtained results.

Now, in the internet era, consumers can comment on their hotel living experience directly through the internet. The large amount of user-generated content (UGC) provided by consumers also provides chances for the hospitality industry to understand consumers’ opinions through online review mining. The UGC with consumers’ opinions to hotel services can be continuously collected and analyzed by hoteliers. Therefore, this paper demonstrates how to apply the hybrid approach integrating Kansei engineering and online review mining to hotel service development.

This paper aims to examine the factors contributing to the helpfulness of online hotel reviews and to measure the impact of manager response on the helpfulness of online hotel reviews.

This investigation used a linear regression model that drew upon 56,284 consumer reviews and 10,797 manager responses from 1,405 hotels on TripAdvisor.com for analysis.

The helpfulness of online hotel reviews is negatively affected by rating and number of sentences in a review, but positively affected by manager response and reviewer experience in terms of reviewer status, years of membership, and number of cities visited. Manager response moderates the influence of reviewer experience on the helpfulness of online hotel reviews.

Using the data from hotels in five major cities in Texas, the results may not be necessarily generalized to other markets, but the important role that manager response plays in online reviews is assessed with big data analysis.

The results suggest hospitality managers should strategically identify opinion leaders among reviewers and proactively influence the helpfulness of the reviews by providing manager response. Additionally, this study makes recommendations to webmasters of social media platforms in terms of advancing the algorithm of featuring the most helpful online reviews.

This study is at the frontier of research to explain how hotel managers can proactively identify opinion leaders among consumers and use manager response to influence the helpfulness of consumer reviews. Additionally, the results also provide new insights to the influence of reviewer demographic background on the helpfulness of online reviews. Finally, this study analyzed a large data set on a scale that was not available in traditional guest survey studies, responding to the call for big data applications in the hospitality industry.

This paper aims to examine the influences of the number of hotel management responses and especially the textual similarity in hotel management responses to online reviews on hotel online booking.

This study used the data from 437 hotels in New York City on Expedia. The data specifically include online reviews, management responses and real-time number of online hotel bookings, which were merged to create one dataset for this study. To calculate the management response similarity, three widely recognized text mining functions of calculating textual similarity were adopted in this model. Fixed-effect panel data model was then used to examine the influence of management response to consumer online reviews on online hotel booking volume.

The empirical results demonstrate that the number of management responses to consumer online reviews does not significantly affect hotel booking; compared to none or only one management response, or management responses with low similarity, management responses with high similarity can significantly reduce the hotel booking on Expedia.

This study suggests that the similarity of management responses influences customers’ hotel booking, and hotel managers should avoid providing too similar management responses.

First, this study, for the first time, proposes the concept of management response similarity and its measurement methods. Second, this study takes an initial attempt to empirically test the influence of response similarity on hotel booking by using secondary data online.