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The purpose of this paper is to provide an insight in the future of hotel rating. It reviews the impact of social media, technology that provides integration of data for the consumer and the hotels, and the way that rating bodies may respond to the changing environment on how hotels are selected and reviewed.

By reviewing current trends, practices and technological possibilities, the impact of online reviews on conventional hotel rating systems is projected into the future.

The paper predicts a full integration of conventional rating systems with online guest reviews from the different guest review platforms leading to greater transparency for the consumer and better positioning opportunities for innovative hotels. It is further predicted that those conventional rating systems that do not seek integration and alignment will see a continued drop in hotel participation and will cease to exist.

Little research has been done on the relation between online guest reviews and conventional hotel rating systems. The paper presents new insights into how current and future trends influence the way in which consumers select hotels and how this influences the way that hotels are rated.

This paper aims to understand how guests’ trip purpose and hotel star rating influence the effects of the value for money perceived at hotels and service quality on guest satisfaction and word of mouth (WOM) recommendation.

Using TripAdvisor, 25 Singaporean hotels were randomly selected for the study, which yielded hotel reviews from 2,040 respondents. Hierarchical and logistic regression analysis was conducted to investigate the relationships proposed in the study.

Results indicate significant differences between leisure and business guests’ perception of value for money and service quality at hotels with various star ratings. While perceived value for money and service quality were found as significant predictors for both leisure and business guests’ satisfaction and WOM, the effects were moderated by the hotel star rating. Despite the significant effect of hotel star rating on guest satisfaction, the study found no significant relationships between hotel star rating and WOM for leisure and business guests.

The findings suggest that managers in the hotel industry should understand the purpose of guests’ trip and offer services based on their expectations. As the star rating of a hotel creates certain expectations for both leisure and business guests, providing an appropriate level of services and assuring value for money in accordance with the hotel rating contributes to guest satisfaction and WOM recommendation.

This study contributes to the hospitality literature by investigating how hotel star rating moderates the relationship of value for money and service quality on leisure and business guests’ satisfaction and WOM recommendation.

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.

The purpose of this study is, first, to determine which developments have shaped official hotel classification systems over recent years (including the impact of guest-review platforms) and second to establish the future of those systems through the eyes of the people who are actually in charge of operating them.

Semi-structured interviews were chosen as the most suitable method for approaching hotel classification system administrators. This method is in line with previous research on approaching key informants in their respective fields. Sixteen people representing 12 different official national hotel classification systems from across the world as well as one commercial hotel star rating system participated in the online interviews.

The first main conclusion is that hotel classification systems – especially voluntary ones – would not have survived the enormous impact of guest-review platforms without quickly adjusting to the ever-changing hotel industry landscape. The frequent review of classification criteria and procedures has become the main survival strategy of classification systems. The second conclusion is that system operators are strongly optimistic about the future outlook of hotel classification based on their proven flexibility to swiftly adapt to new market conditions.

Research about hotel classification systems is usually based on the views of the systems' users, i.e. hotels or hotel guests, whereas the present paper reflects the perspective of the systems' operators, an angle rarely analyzed in the literature.

This paper aims to examine hotel star ratings on selected third‐party distribution websites, taking Hong Kong hotels as the sample.

Star rating information from 11 online distribution websites was retrieved and analyzed for all hotels in Hong Kong.

About 60 percent of the hotels were found on at least six of the selected distribution channels, and only 24 percent of the hotels have consistent star rating across different distribution channels. Results of data analysis indicated that consistent star rating becomes more difficult to maintain as the number of distribution channels used increases.

Findings of the study are limited to the selected hotels and electronic distribution channels. Still, the online distribution channels represent some of the most widely used electronic distribution channels.

Findings of this research will be of use to hotel managers and guests for better understanding the standard, in terms of star ratings, of hotels.

Despite the importance of hotel star ratings on consumers and the hotel industry, prior studies in the existing hospitality literature rarely examined the difference of hotel stars. This novel study should, thus, make a meaningful contribution to knowledge development.

The purpose of this paper is to determine whether different scales and ways to collect reviews and ratings found on online travel agencies (OTAs) can affect hotels, and whether hotels obtain the same or different evaluations.

Hotel ratings from five OTAs in four European markets were collected and compared in pairs. An initial comparison was made with the hotel scores of each OTA to show what a typical user would see. Then, a rescaled score (0-10) was used to compare all the OTA scales appropriately and to distinguish between what customers observe and what the reality is.

The results reveal that Booking.com that uses a scale (2.5-10) and Agoda with a scale (2-10) seem to give higher rating scores than Atrapalo (1-10), Travel Republic (0-10) and hotel reservation service (1-10). However, when the scores are rescaled (0-10), the worst ratings are found on Booking.com followed by Agoda.

OTAs should include, next to the scores, the scale used to rate hotels so as to provide users with better and clearer information. Moreover, rating questionnaires should match the verbal denominations with their numerical values to avoid biased ratings.

OTAs and hotel managers are losing information provided by customers because customers are not aware of the scale when rating hotels. Moreover, hotel ratings are used by potential customers to obtain a clearer image of an establishment. However, if some hotels are being overrated by some scales, customers might have higher expectations, which may not be met.

The unique rating scales of Booking.com and Agoda provide additional insights into their hotel evaluations, which seem to be apparently higher when in fact they are not.

在线旅行评论评分量表及其对酒店得分和竞争力的影响

摘要

这项研究旨在研究在线旅行社（OTA）上评论和评级的不同量表和方式是否会影响酒店获得的评估。

本研究收集并比较了来自四个欧洲市场中五个OTA的酒店等级数据。研究首先对每个OTA的酒店得分进行了比较, 以显示一般用户会看到的内容。然后研究使用重新缩放的得分（0-10）来恰当地比较所有OTA的酒店等级, 并区分顾客观察到的内容和现实。

结果显示, Booking.com使用的量表（2.5-10）和Agoda的量表（2-10）, 似乎高于Atrapalo（1-10）, Travel Republic（0-10）和 hotel reservation service （1-10）的评分。但是, 当分数重新调整为（0-10）时, 最差的评分是在Booking.com上, 其次是Agoda。

OTA应在评分旁边注明用于对酒店进行评分的量表, 以便为用户提供更好, 更清晰的信息。此外, 评级问卷应使评价描述与其数值相匹配, 以避免评级出现偏差。

OTA和酒店经理正在丢失客户所提供的信息, 因为客户在对酒店进行评级时并不了解其使用的量表。此外, 潜在客户使用酒店评级来获得更清晰的企业形象。但是, 如果某些酒店被某些网站的评级量表高估, 那么客户可能会有偏高的期望, 而这些期望可能无法被满足。

Booking.com和Agoda的独特评分等级标准为酒店提供了更多见解, 而实际上酒店的情况可能并非如此。

Las escalas de calificación de las opiniones de los viajes online y sus efectos en la valoración y competitividad de los hoteles.

El objetivo de esta investigación es determinar si las diferentes escalas y formas de recopilar opiniones y valoraciones de las Agencias de Viajes Online (OTAs), pueden afectar a si los hoteles tienen las mismas o distintas calificaciones.

Las calificaciones de hoteles de cinco OTAs en cuatro mercados europeos, se recopilaron y compararon por pares. Se realizó una comparación inicial con las puntuaciones de los hoteles de cada OTA, para mostrar lo que vería un usuario típico. Luego, se utilizó una puntuación de reescalado (0-10), para comparar todas las escalas de las OTAs de manera apropiada y así poder diferenciar entre lo que los clientes observan y lo que es en realidad.

Los resultados revelan que Booking.com, que utiliza una escala (2.5-10) y Agoda con una escala (2-10), parecen puntuar con calificaciones más altas que Atrapalo (1-10), Travel Republic (0-10) y hotel reservation service (1-10). Sin embargo, cuando se vuelven a escalar las puntuaciones (0-10), las peores calificaciones se encuentran en Booking.com, seguida de Agoda.

Las OTAs deben incluir, junto a las puntuaciones, la escala utilizada para calificar los hoteles a fin de proporcionar a los usuarios una información mayor y más clara. Además, los cuestionarios de calificación deben hacer coincidir las denominaciones verbales con sus valores numéricos para evitar calificaciones sesgadas.

Por un lado las OTAs y los gerentes de hoteles, están perdiendo información proporcionada por los clientes, porque los clientes no son conscientes del tipo de escala utilizada cuando califican los hoteles. Por otro lado, los clientes potenciales utilizan las calificaciones de los hoteles para obtener una imagen más clara de un establecimiento. Por lo que en muchos casos, los clientes pueden tener expectativas más altas, que pueden no cumplirse, si los hoteles están siendo sobrevalorados por algunas escalas.

Las escalas de calificación únicas de Booking.com y Agoda, brindan información adicional sobre las evaluaciones de sus hoteles que parecen ser aparentemente más altas cuando en realidad no lo son.

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.

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.

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.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.