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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 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.

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.

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.

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.

The purpose of this study is to investigate the effect of online reviews on consumers’ intention to visit restaurants, with the moderating role of involvement.

The research framework was built on signaling theory, message appeals and involvement theory. To test the proposed framework, three experiments were conducted online with real customer samples. T-tests, ANOVA and SPSS PROCESS macro were used for data analysis.

The results revealed that online reviews with higher online ratings and emotional appeal led to higher restaurant visit intention. Review appeal significantly moderated the effect of online ratings on restaurant visit intention. Customers with low restaurant involvement were more impacted by emotional comments than by functional comments.

The present study extends our knowledge on the effects of online reviews moderated by levels of customer involvement. By combining signaling theory with involvement theory, it adds value to the literature on customer online behavior, especially in the foodservice context. The present study has limitations that might provide opportunities for future research. It used evaluations (TripAdvisor scores) and only positive reviews (texts), so customers’ intentions considering negative reviews could not be examined. The level of hedonism concerning consumption in restaurants and prior knowledge regarding restaurant reviews was not controlled for. It is possible that the level of hedonism perceived and prior review knowledge may moderate the customers’ intention to visit the restaurant.

The present study shows the importance of online comments for the promotion of restaurants that have low evaluation scores. It is essential that restaurant owners and managers encourage potential customers by using comments to elaborate on their marketing strategies and promotion. At the same time, they should invite customers to share their emotional experiences, and not just their views on service efficiency (a functional aspect). During the COVID-19 pandemic, the use of the internet and mobile devices has become more prominent. Managers could therefore use emotional messages on the restaurant’s website or apps to attract customers with low restaurant involvement. Also, a system to identify the involvement of customers with restaurants could be implemented online or on mobile devices to present specific messages. The present study also recommends the use of online tools as virtual tours, photographs taken from different angles, smiling faces, floor plans and sittings and pre-determined emotional expressions. Also, the restaurant could promote lives on cooking different dishes to motive customer’s interaction and comments. These would help to increase customers’ visit intentions.

This study extends knowledge about the effect of restaurant online reviews (both ratings and appeals) moderated by the level of customer involvement. The present study also adds value to the customer online behavior literature showing that customers with low involvement are more sensitive to emotional content as they use the affective route to process information rather than the central route.

在线评论对餐厅到访意愿的影响：运用信号理论和参与理论

本研究旨在探索以顾客参与度作为调节变量, 关于在线评论对餐厅到访意愿的影响。

本文以信号理论, 信息诉求, 参与理论来建立研究框架。为测试提出的理论框架, 本研究进行了三个在线消费者实验。T-检验, 方差分析, 和SPSS PROCESS 来作为统计方法。

研究发现评论分值越高, 运用感情诉求往往导致更高度的到访意愿。评论的诉求形式显著调节了评分对到访意愿的影响。对于参与度较低的顾客, 情感诉求比功能性诉求更加能影响顾客意愿。

本研究对餐厅在线评论（评分和诉求种类）对顾客到访意愿影响, 以及如何被消费者参与度所调节贡献了新知识。本研究对消费者在线行为做出了贡献, 发现参与度较低的顾客对和情感有关的内容更敏感, 相对于中央路径, 由于此类顾客更倾向于情感路径来处理信息。

Online ratings and reviews have recently emerged as mechanisms to facilitate accountability and transparency in the provision of goods and services. The purpose of this paper is to examine the nature and outcome of the accountability that online ratings and reviews create in the sharing economy.

The study draws on 30 face-to-face and Skype interviews with Airbnb guests and hosts as well as on secondary materials, including content from Airbnb data analytic reports.

The authors demonstrate that face-saving practices widely condition user ratings and comments. Face saving occurs when individuals attempt to preserve their own identity and the identity of others during a social interaction. At Airbnb, the authors find that reviewers adopt three distinct face-saving strategies: the use of private reviewing channels, the creation of tactful reviews and refraining from reviewing entirely. The authors also find that users are sceptical of rating metrics and public comments and draw upon a wide range of alternative sources, such as private messaging and other publicly available resources, in their decision making.

This paper highlights the overwhelmingly positive character of Airbnb ratings and reviews. It proposes the concept of crowdbased accountability as a limited, partial form of assurance for sharing economy users. Guests and hosts alike prioritise face-saving practices over reviewer responsibilities to provide authentic, reliable accounts to the public. Consequently, reviewers effectively remove the risk of sanctions for those in the network who underperform.

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.