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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 reviews are often likely to be socially influenced by prior reviews. This study aims to examine key review and reviewer characteristics which may influence the social influence process.

Restaurant review data from Yelp.com are analyzed using an ordered logit model and text mining approach.

This study reveals that prior average review rating exerts a positive influence on subsequent review ratings for the same restaurant, but the effect is attenuated by the variance in existing review ratings. Moreover, social influence is stronger for consumers who had a moderate dining experience or invested less cognitive effort in writing online reviews. Compared to reviewers classified by Yelp as “elite,” non-elite reviewers appear more susceptible to the social influence of prior average review rating.

This study provides guidelines for mitigating the social influence of prior reviews and improving the accuracy of online product/service ratings, which will eventually enhance business and the reputation of online review platforms.

The findings from this study contribute to the electronic word-of-mouth (eWOM) literature and social influence literature in terms of the bidirectional nature of social influence on eWOM.

Product ratings and reviews are popular tools to support buying decisions of consumers. Many e-commerce platforms now offer product ratings and reviews as ratings and reviews are valuable for online retailers. However, luxury goods industry is somewhat slow to adapt to the digital terrain. The purpose of this paper is to answer “how luxury consumers see user-generated product ratings and reviews for their online shopping experience and what important factors or values are perceived by the luxury consumers when they shop online?”

To understand how luxury consumers use product ratings and reviews before buying online, a survey with a situational set up of variations of rating, review and price options in association with a number of hypothetical luxury goods was conducted among 421 global luxury consumers out of over 6,000 people. The study was carried out from September to October 2018 for six weeks in the form of online and mobile survey. User population is high net-worth individuals or luxury consumers derived from the author’s various professional and social networks and communities. Their geographical coverage would be global, but concentrated around the major cities.

The survey shows that ratings and reviews can be important source of information for luxury consumers. Online ratings and reviews are rated as helpful by 76.01% of the participants. People who chose the highly rated one (4.8/5) over the poorly rated (3.7/5) was 86.94%, while all else such as product category, star rating and price range are about the same. Feedback from the open question survey indicates that the perceived helpfulness of rating and review systems could vary. Comparing user reviews is time-consuming because of unstructured nature of contextual reviews and the relative nature of human perception on the rating scale.

There are two aspects of ratings and reviews playing an important role for luxury consumers’ buying decision. First, it is about helpfulness of collective rating score. Luxury consumers see a user-generated rating score and use the score when they make a choice even if the rating is not an absolute, but relative figure, not exactly like the star rating system in the hotel industry. Second, there is discrepancy between the status of the brand in association with its price position and perceived value as the industry does not cope with classifying their brands in any official star rating system.

Consumers need compact and concise information about the products they need. When there are only a few potential products left in their short wish-list, full user reviews can be helpful to get more details and general opinions about the products on the short list before making a final decision. In that regard, a primary indicator that will guide through the decision-making process of the luxury consumers would be the trustworthiness of user rating of each product in an aggregated score along with a potential use of sub-ratings, which has to be visible from the product landing page.

Even if there is a wide use and ubiquitous nature of product ratings and reviews in other consumer products, the author is curious about how luxury consumers use ratings and reviews for their buying decision because there are not that many researches done previously in spite of the importance of this issue. Luxury goods industry has hit €320bn in 2017 according to Bain and Co., and 25% of the trading volume will be replaced by the digital commerce by 2025.

This study aims to investigate how prior reviews posted by other consumers affect subsequent consumers’ evaluations and to what extent the review temporal distance can increase or reduce the social influence of prior reviews. In this study’s restaurant context, review temporal distance refers to the duration between dining time and review time of a dining experience.

The data of paired online restaurant reservations and reviews are analyzed using Ordered Logit Model. Two robustness checks are conducted to test the stability of the main estimation results.

The empirical results demonstrate that consumers’ restaurant evaluation is socially influenced by both the prior average review rating and number of prior reviews; review temporal distance has a direct negative effect on consumers’ restaurant evaluation; and review temporal distance increases the social influence of prior reviews.

This study suggests that online review matters. Both restaurants and the online review platforms should encourage consumers to share their experiences and post online reviews immediately after their consumption.

The study contributes to the literature on electronic word-of-mouth, social influence and psychological distance. First, the bi-directional nature of social influence on electronic word-of-mouth for experience-oriented product is documented. Second, for the first time, this study examines how review temporal distance could affect the social influence on consumers’ restaurant evaluation.

Online ratings (review valence) have been found to exert a strong influence on hotel room prices. This study aims to systematically synthesize research estimating the impact of online ratings on room rates using a meta-analytical method.

From major academic databases, a total of 163 estimates of the effects of online ratings on room rates were coded from 22 studies across different countries through a systematic review of relevant literature. All estimates were converted into elasticity-type effect sizes, and a hierarchical linear meta-regression was used to investigate factors explaining variations in the effect sizes.

The median elasticity of online ratings on hotel room rates was estimated to be 0.851. Meta-regression results highlighted four categories of factors moderating the size of this elasticity: data characteristics, research settings, variable measures and publication outlet. Among sub-ratings, results revealed value rating and room rating to exert the largest impact on room rates, whereas staff and cleanliness ratings demonstrated non-significant impacts.

This study provides practical implications on the relative importance of different types of online ratings for online reputation and revenue management.

This study represents the first research effort to understand factors moderating the effects of online ratings on hotel room rates based on a quantitative review of the literature. Moreover, this study provides beneficial insights into the specification of empirical hedonic pricing models and data-collection strategies, such as the selection of price variables and choices of model functional forms.

Building on a small body of work, the authors' study aims to investigate some important antecedents of online review characteristics in the Chinese restaurant industry.

Using a data set of restaurant reviews collected from a most popular review platform in China, the authors conduct a series of analyses to examine the influence of travel experience and travel distance on travelers' review characteristics in terms of review rating and media richness. The moderating effect of restaurant price on the influence is also investigated.

Travelers with a longer travel distance and more travel experience tend to provide higher and lower online ratings, respectively, which can be explained by the construal level theory (CLT) and the expectation-confirmation theory (ECT), respectively. Furthermore, these strong feelings can then induce travelers to post enriched reviews with more pictures, more words and more affective words to release consumption tension. Besides, restaurant price can moderate these relationships.

Distinguished from most studies which mainly focus on the consequences of online review characteristics or antecedents of review helpfulness, the authors pay attention to the effects of travelers' individual differences in terms of travel distance and travel experience on travelers' online reviewing behavior. In addition to review rating, the authors also focus on media richness in terms of visual and textual information. The authors' research findings can benefit restaurant consumers and managers for their online word-of-mouth utilization and management.

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 star rating summarises the review content and conveys the message faster than other review components. Star ratings influence helpfulness of the reviews, and extreme reviews are considered as less helpful in the decision process. However, literature has rarely addressed variations in star ratings across product categories and variations between two online retailers. In this paper, the authors have compared the distribution of star ratings across 11 products and among the retailers.

Online reviews for 11 product categories have collected, and the authors compared the distribution of star ratings across 11 products and retailers. Correspondence analysis has been applied to show the association between star ratings and product categories for the e-retail firms.

The Amazon site contains proportionately more number of 1-star rated reviews than Flipkart. In Amazon reviews, few product categories are closely associated with 1-star and 2-star reviews, whereas no product categories are closely associated with 1-star and 2-star reviews in Flipkart reviews. The results indicate two distinct communication strategies followed by the firms in managing online consumer reviews.

The authors did not analyse data across demographic details because of access restriction policies of the websites.

Understanding the distribution of review characteristics will improve the consumer’s decision-making ability and using online review content judiciously.

This study’s results show significant insights on online retailing by providing cues in using shopping sites and online review characteristics of two prominent retailers.

This paper has brought out a distinct distribution pattern of online review between Amazon and Flipkart. Amazon allows a higher degree of negative contents, whereas Flipkart allows more number of positive reviews.

Online consumer reviews have become increasingly important for consumer decision-making. One of the most prominent examples is the hotel industry where consumer reviews on websites, such as Bookings.com, TripAdvisor and Venere.com, play a critical role in consumers’ choice of a hotel. There have been a number of recent studies analyzing various aspects of online reviews. The purpose of this paper is to investigate their effects in terms of hotel occupancy rates.

This paper measures through regression analysis the impact of three dimensions of consumer reviews (i.e. review score, review variance and review volume) on the occupancy rates of 346 hotels located in Rome, isolating a number of other factors that might also affect demand.

Review score is the dimension with the highest impact. The results suggest that after controlling for other variables, a one-point increase in the review score is associated to an increase in the occupancy rate by 7.5 percentage points. Regardless the review score, the number of reviews has a positive effect, but with decreasing returns, implying that the higher the number of reviews, the lower the beneficial effect in terms of occupancy rates is.

The findings quantify the strong association of online reviews to occupancy rates suggesting the use of appropriate reputational management systems to increase hotel occupancy and therefore performance.

A major contribution of this paper is its comprehensiveness in analyzing the relation between online consumer reviews and occupancy across a heterogeneous sample of hotels.

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.