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This study aims to examine the effect of hotels’ star ratings and customer ratings on online hotel prices from both supply- and demand-side perspectives.

To compile the supply-side data, a Web crawler was designed and implemented to read online prices and characteristics of available hotels from Trivago. Demand-side data were compiled from surveys conducted using the Amazon Mechanical Turk portal. Data were analyzed with an array of advanced machine learning regression models.

The results show that while a star rating is the most important predictor of price from both supply- and demand-side perspectives, customer rating influences the price much more significantly on the demand-side. Customers showed a tendency to overestimate the room price of three- and four-star hotels and underestimate the price of five-star hotels. Customers placed a heavier weight on customer ratings when estimating prices particularly when the average rating was above 7.5 (out of 10). The study also confirms the strong effect of price adjustment for customers when they were exposed to the prices of other similar hotels. Finally, the study examines the impact of demographics on the perceived hotel value. Age, ethnicity, education and income are shown to be the most significant demographic characteristics.

The results are valuable from a research perspective because they demonstrate how to price rooms more effectively based on their perceived value from consumers’ perspectives. From a practical standpoint, the findings provide useful managerial tools for pricing in competitive environments.

The purpose of this study is to develop a review rating prediction method based on a supervised text mining approach for unrated customer reviews.

Using 2,851 hotel comment card (HCC) reviews, this paper manually labeled positive and negative comments with seven aspects (dining, cleanliness, service, entertainment, price, public, room) that emerged from the content of said reviews. After text preprocessing (tokenization, eliminating punctuation, stemming, etc.), two classifier models were created for predicting the reviews’ sentiments and aspects. Thus, an aggregate rating scale was generated using these two classifier models to determine overall rating values.

A new algorithm, Comment Rate (CRate), based on supervised learning, is proposed. The results are compared with another review-rating algorithm called location based social matrix factorization (LBSMF) to check the consistency of the proposed algorithm. It is seen that the proposed algorithm can predict the sentiments better than LBSMF. The performance evaluation is performed on a real data set, and the results indicate that the CRate algorithm truly predicts the overall rating with ratio 80.27%. In addition, the CRate algorithm can generate an overall rating prediction scale for hotel management to automatically analyze customer reviews and understand the sentiment thereof.

The review data were only collected from a resort hotel during a limited period. Therefore, this paper cannot explore the effect of independent variables on the dependent variable in context of larger period.

This paper provides a novel overall rating prediction technique allowing hotel management to improve their operations. With this feature, hotel management can evaluate guest feedback through HCCs more effectively and quickly. In this way, the hotel management will be able to identify those service areas that need to be developed faster and more effectively. In addition, this review rating prediction approach can be applied to customer reviews posted via online platforms for detecting review and rating reliability.

Manually analyzing textual information is time-consuming and can lead to measurement errors. Therefore, the primary contribution of this study is that although comment cards do not have rating values, the proposed CRate algorithm can predict the overall rating and understand the sentiment of the reviews in question.

Two valuable pieces of information – reviews and their corresponding numerical ratings – are accessible to potential customers before they make a purchasing decision. An extensive body of marketing literature has scrutinized the influence of customers’ reviews by linking such aspects as the volume and valance of reviews with product sales and customers’ purchase intention. The aim of this study, for which dual coding theory was used, was to understand the relationship between reviews and their corresponding numerical ratings.

The authors used the latent Dirichlet allocation technique to categorize customers’ reviews. The present findings contribute to the literature by showing the underlying mechanisms that customers use to interpret reviews and associate them with numerical ratings.

The gradient boosted decision tree model demonstrates that non-abstract-dominant reviews (reviews mainly consist of tangible objects, actions, events or affective words) are significant predictors of their corresponding numerical ratings. However, abstract-dominant reviews (i.e. those consisting primarily of intangible objects, events or actions) cannot predict their associated numerical ratings.

The present findings contribute to the literature by showing the underlying mechanisms that customers use to interpret reviews and associate them with numerical 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.

The aim of this paper is to examine the impact of three main technologies on converting browsers into customers: impact of review rating (location rating and service rating), recommendation and search listings.

This paper estimates conversion rate model parameters using a quasi-likelihood method with the Bernoulli log-likelihood function and parametric regression model based on the beta distribution.

The results show that a high rank in search listings, a high number of recommendations and location rating have a significant and positive impact on conversion rates. However, service rating and star rating do not have a significant effect on conversion rate. Furthermore, room price and hotel size are negatively associated with conversion rate. It was also found that a high rank in search listings, a high number of recommendations and location rating increase online hotel bookings. Furthermore, it was found that a high number of recommendations increase the conversion rate of hotels with low ranks.

The findings show that hotels’ location ratings are more important than both star and service ratings for the conversion of visitors into customers. Thus, hotels that are located in convenient locations can charge higher prices. The results may also help entrepreneurs who are planning to open new hotels to forecast the conversion rates and demand for specific locations. It was found that a high number of recommendations help to increase the conversion rate of hotels with low ranks. This result suggests that a high numbers of recommendations mitigate the adverse effect of a low rank in search listings on the conversion rate.

This paper contributes to the understanding of the drivers of conversion rates in online channels for the successful implementation of hotel marketing.

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 aim of the paper is to study the degree of independence of customers' portfolio concentration measure from the pricing policy adopted by rating agencies.

The paper tests different measures of customers value (revenues or profits and customer lifetime value) and different concentration measure (top customer or Herfindahl‐Hirschman index) on the customers' portfolio of rating agencies in the time period 1999‐2008. Simulating different pricing models, the paper tests the sensitivity of these measures to discounted fees applied to best customers and identifies measures that are more and less sensitive to the discount applied.

Concentration measures that consider all the customers' portfolios and look at both cost and revenues related to the service on a multi‐period time horizon (CLV) are less sensitive to the discount policy respect to the others.

Results point out some opportunities related to apply more complete approaches defined by marketing science on the financial service industry in order to construct better measures for the economic independence. The paper works only with publicly available data and more details about the fee applied to each customer could increase the significance of the results achieved.

The paper contributes to the current debate on the economic independence of rating agencies stressing the opportunity of rethinking the measures on economic independence that are currently considered by supervisory authorities.

The paper is the first empirical application of standard marketing concepts of customers' concentration measure to the rating industry.

The paper studies the pricing policies adopted by ratings agencies.

Social media channels provide an avenue for expressing views about different services/products. However, unlike merchandise/company websites (where users can post both reviews and ratings), it is not possible to understand user's ratings for a particular service-related comment on social media unless explicitly mentioned. Predicting ratings can be beneficial for service providers and prospective customers. Additionally, predicting ratings from a user-generated content can help in developing vast data sets for recommender systems utilizing recent data. The aim of this study is to predict ratings more accurately and enhance the performance of sentiment-based predictors by combining it with the emotional content of textual data.

This study had utilized a combination of sentiment and emotion scores to predict the ratings of Twitter posts (3,509 tweets) in three different contexts, namely, online food delivery (OFD) services, online travel agencies (OTAs) and online learning (e-learning). A total of 29,551 reviews were utilized for training and testing purposes.

Results of this study indicate accuracies of 58.34%, 57.84% and 100% in cases of e-learning, OTA and OFD services, respectively. The combination of sentiment and emotion scores showed an increase in accuracies of 19.41%, 27.83% and 40.20% in cases of e-learning, OFD and OTA services, respectively.

Understanding the ratings of social media comments can help both service providers as well as prospective customers who do not spend much time reading posts but want to understand the perspectives of others about a particular service/product. Additionally, predicting ratings of social media comments will help to build databases for recommender systems in different contexts.

The uniqueness of this study is in utilizing a combination of sentiment and emotion scores to predict the ratings of tweets related to different online services, namely, e-learning OFD and OTAs.

This study aims to investigate the determinants of herd behavior in online hotel service evaluations, focusing on the cultural and geographic distance characteristics of customers.

On the basis of 381,462 TripAdvisor reviews of hotels in the USA written by more than 100,000 customers from 92 countries, this study uses the empirical analysis to explore the collective roles of cultural distance, geographic distance and hospitality experience on herd behavior in online hotel ratings.

Cultural and geographic distances between customers and product and service locations positively affect herding and these two effects are substitutable. The hospitality experience of customers attenuates the impacts of distances on herding. These results are robust for multiple hotel service ratings.

Findings help hotels understand perceptual biases of customers on hotel services under the social influence and consequently develop effective marketing strategies to boost hotel revenues and increase profitability.

The research contributes to hospitality and online review literature by understanding how cultural and geographic distances shape online hotel service evaluations under the root of the uncertainty of decision-making and the observation of others’ behavior. The research also contributes to the distances in international business literature by deepening the understanding of the substitution and heterogeneity of distance effects. Methodologically, a time-varying and monotonously increasing variable is constructed to depict customers’ hospitality experience. The extensive data volume ensures the generalizability of our results.

This study aims to examine the information transfer effects of customers’ credit rating downgrades on supplier firms.

In this study, the authors use suppliers’ cumulative abnormal returns around customers’ credit rating downgrade events to identify how shocks to customer credit impact supplier equity prices. The authors also incorporate ordinary least squares and weighted least squares regressions regression analysis of the determinants of supplier market response to customer downgrades.

The authors find that customer credit rating downgrades present significant negative shocks to the stock prices of supplier firms. Moreover, the authors show that the information transfer effects are determined by both firm- and industry-level factors, including the market anticipation of downgrades, the strength of the customer–supplier linkage, the industry rivals’ reactions to the downgrades and investor attention. The authors also find that the likelihood that a supplier will receive a rating downgrade is significantly higher following its primary customer firm’s downgrade.

To the best of the authors’ knowledge, this paper is the first to explore the information transfer effects of credit rating downgrades on primary stakeholders within the supply chain. The authors document that customer–supplier networks have valuable implications for the spillover effect across debt and equity holders. Information about customers’ financial stress is incorporated into suppliers’ equity prices outside of the context of customer bankruptcy.