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This study aims to investigate the relationships between service quality, perceived value and hotel guest satisfaction, drawing upon data from TripAdvisor – an online travel agent (OTA) platform. The study also investigates the mediating role of perceived value on the relationship between service quality and satisfaction, as well as the moderating role of hotel star ratings on all direct and indirect relationships.

Data for this study were collected via Web scraping from August–October 2018. Data were collected from 192 three- to five star-rated hotels in Kuala Lumpur, Malaysia. Partial least squares – structural equation modeling was used for data analysis. Furthermore, importance-performance map analysis (IPMA) was performed to identify the most important items of service quality and perceived value in improving customer satisfaction.

The findings of this study provide support for all direct and indirect relationships for three-star and four- and five-star hotels. Moreover, the results indicate that perceived value mediates the relationship between service quality and customer satisfaction. These results support the moderating role of hotel star ratings for the relationship between service quality and perceived value. The results also show that after perceived value, three-star hotels looking to improve customer satisfaction should prioritize improving the quality of their services, sleep quality, cleanliness and rooms. Four- and five-star hotels, on the other hand, should prioritize service, cleanliness, room and sleep quality.

OTA platforms collect a wealth of data pertaining to large number of hotels; nevertheless, few studies to date have drawn on this data to examine a pre-determined conceptual framework developed based on the literature. As such, this study makes a valuable methodological contribution to the tourism and hospitality literature. In terms of theoretical contributions, this study examines the mediating role of perceived value between service quality and satisfaction using OTA data. In addition, this study assesses the moderating role of hotel star ratings for the direct and indirect effects of service quality on satisfaction. Using IPMA, this study compares the importance and performance of service quality indicators to generate satisfaction between three-star and four- and five-star hotels.

本论文检测了服务质量、价值感知、和酒店顾客满意度之间的关系, 使用TripAdvisor的数据—OTA。本论文还检测了价值感知对服务质量和满意度之间的中介作用, 以及酒店星级评价对其中直接和间接关系的调节作用。.

本论文采样通过网络爬虫技术, 截取了2018年八月至十月之间的数据。研究样本为192家马来西亚Kuala Lumpur地区的三星-五星酒店。样本分析方法为PLS-SEM。此外, 本论文采样IPMA分析法来找出提高顾客满意度中的服务质量和价值感知中最重要的因子。.

研究结果指出了三星、四星、五星酒店的直接和间接关系。此外, 研究还显示了服务质量和顾客满意度关系的价值感知中介作用。研究结果还指出了酒店星级评价对服务质量和价值感知关系的调节作用。此外, 研究还指出, 除了价值感知, 如果三星酒店想提高顾客满意度, 那么他们应该优先提高其服务质量、睡眠质量、清洁度、和房间。另一方面, 四星和五星酒店应该优先提高其服务质量、清洁度、房间、和睡眠质量。.

OTA平台搜集大量酒店数据, 但是很少作品研究这些数据, 以检测根据文献提出的理论模型。因此, 本论文在方法论上对旅游酒店文献做出宝贵贡献。理论贡献而言, 本论文使用OTA数据检测了价值感知对服务质量和满意度关系之间的中介作用。此外, 本论文检测了酒店星级评价对服务质量和满意度之间直接和间接关系的调节作用。本论文使用IPMA方法, 比较各种服务质量指标的重要性对在三星、四星、五星酒店的提高满意度的不同作用。.

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.

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.

The importance of online reviews on online hotel booking has been widely acknowledged. However, not all online reviews affect consumers equally. Compared with common online reviews, key online reviews (KORs) have a greater influence on consumers' decisions and online hotel booking. This study takes the first step to investigate the factors affecting the identification of KORs and the role of KORs in online hotel booking.

To test the research hypotheses, this study develops a crawler to obtain 551,600 online reviews of 650 hotels in ten representative large cities in China. This study first uses a binary logistic regression to identify KORs by combining review content quality and reviewer characteristics and then uses a log-regression model to investigate the role of KORs in online hotel booking.

This study mined the factors affecting the identification of KORs by analyzing review contents and reviewer characteristics. Our results revealed that KORs play a mediating role in the effects of review content and reviewer characteristics on online hotel booking.

This study focuses on KORs, which have received limited attention in research but are important to practitioners. Specifically, this study investigates the antecedents and consequences of KORs. Our results enable hotel managers to manage online reviews effectively, particularly KORs.

The authors investigated the effects of the characteristics of reviews, reviewers and corporate factors on review helpfulness and assessed the role of culture in moderating these relationships.

A research model was established based on the elaboration likelihood and information adoption models. To empirically analyze this research model, 10,611 TripAdvisor reviews from 9 countries were collected. In addition, a zero-inflated negative binomial model and multilevel analysis were employed in consideration of the data characteristics.

The results revealed that review depth had a positive effect on review helpfulness, and review ratings and reviewer expertise had a negative effect. As a corporate characteristic, hotel size had a negative effect on review helpfulness. In addition, the effects of review rating, reviewer expertise and hotel rating exhibited significant differences based on the moderating effects of uncertainty avoidance and power distance level.

The results of this study expand the review helpfulness literature by explaining the inconsistent findings of previous studies via cultural theory. In addition, past research in this field has mainly focused on analyzing only review and reviewer characteristics, while this study demonstrated that company size negatively affects review helpfulness based on the signaling theory. Finally, this study contributes to cultural comparison literature by discovering that the processing of review information by consumers differs according to their cultural background.

This study investigates the service quality dimensions of hotel resorts in renowned beach destinations of Thailand. It also explores the relationship between review text sentiment expressed in online platforms and the satisfaction ratings provided for those reviews.

The study employs a two-step analysis approach: first, supervised and unsupervised machine learning via support vector machine (SVM) and latent Dirichlet allocation (LDA) are used to identify service quality dimensions, and second, SmartPLS with PROCESS macro is applied to analyze the moderating roles of quality signals and reviewer’s experience on the relationship between sentiment and satisfaction rating. The dataset comprises 102,179 online reviews from TripAdvisor, focusing on 187 selected hotels rated from 3 to 5 stars.

Eight service quality dimensions were identified, including leisure activities, tangibles and surroundings, reliability, responsiveness, service process, food, empathy and ambience. The study underscores that the service process stands as the sole dimension exhibiting negative sentiment. Furthermore, the analysis revealed a robust positive association between sentiment of review texts and satisfaction, and reviewers’ experience and brand affiliation influenced the relationship between customer sentiment and satisfaction.

Hotel managers should focus efforts on maintaining tangible aspects while enhancing existing service quality level of other dimensions, particularly those related to intangible elements. Independent hotels might implement quality audit to ensure that service quality gaps are monitored.

This study contributes an examination of the moderating roles of quality signals and reviewer’s experience on the relationship between review sentiment and satisfaction rating in online reviews.

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.

The purpose of this study is to explore the effect of reviewer qualification and credibility (RQC) and hotel classification involving online hotel reviews (OHRs). The study examines the effects of the reviewer level as a proxy of RQC on review helpfulness and reviewing behavior (review rating, review length). The study also included hotel classification as a moderating variable.

Data from 1,968 reviews were collected from TripAdvisor.com using a web data-harvesting technique. Hypothesized relations in the model were tested with t-test and MANOVA analysis.

The empirical results show that the effect of reviewer level on review helpfulness is not significant. In addition, a high-level reviewer tends to leave a lower rating and a lengthier review than a low-level reviewer. Regarding the moderating effects, for the high-level reviewer, three-star independent hotels have a greater effect on review helpfulness.

The study has several useful implications for researchers, hotel industry when managing OHR and disseminating information to their potential consumers.

The findings help online review website organizers manage the operation of RQC and hotel classification in a proper manner. Marketing managers, especially those of three-star independent hotels, can effectively utilize review management to the desired effect.

Unlike previous studies, this study explores the effect of RQC on review helpfulness and reviewing behaviors across the hotel classification. In addition, this study contributes to the hotel industry developing more effective online reviews from the reviewer level and diverse hotel types (three-star independent, four-star chain, five-star luxury hotels).

Research analyzing online travelers’ reviews has boomed over the past years, but it lacks efficient methodologies that can provide useful end-user value within time and budget. This study aims to contribute to the field by developing and testing a new methodology for sentiment analysis that surpasses the standard dictionary-based method by creating two hotel-specific word lexicons.

Big data of hotel customer reviews posted on the TripAdvisor platform were collected and appropriately prepared for conducting a binary sentiment analysis by developing a novel bag-of-words weighted approach. The latter provides a transparent and replicable procedure to prepare, create and assess lexicons for sentiment analysis. This approach resulted in two lexicons (a weighted lexicon, L1 and a manually selected lexicon, L2), which were tested and validated by applying classification accuracy metrics to the TripAdvisor big data. Two popular methodologies (a public dictionary-based method and a complex machine-learning algorithm) were used for comparing the accuracy metrics of the study’s approach for creating the two lexicons.

The results of the accuracy metrics confirmed that the study’s methodology significantly outperforms the dictionary-based method in comparison to the machine-learning algorithm method. The findings also provide evidence that the study’s methodology is generalizable for predicting users’ sentiment.

The study developed and validated a methodology for generating reliable lexicons that can be used for big data analysis aiming to understand and predict customers’ sentiment. The L2 hotel dictionary generated by the study provides a reliable method and a useful tool for analyzing guests’ feedback and enabling managers to understand, anticipate and re-actively respond to customers’ attitudes and changes. The study also proposed a simplified methodology for understanding the sentiment of each user, which, in turn, can be used for conducting comparisons aiming to detect and understand guests’ sentiment changes across time, as well as across users based on their profiles and experiences.

This study contributes to the field by proposing and testing a new methodology for conducting sentiment analysis that addresses previous methodological limitations, as well as the contextual specificities of the tourism industry. Based on the paper’s literature review, this is the first research study using a bag-of-words approach for conducting a sentiment analysis and creating a field-specific lexicon.

论可推广性的情感分析法以创建酒店字典：以TripAdvisor酒店评论为样本的大数据分析

对于在线游客评论的研究在过去的几年中与日俱增, 但是仍缺乏有效方法能在有限的时间喝预算内提供终端用户价值。本论文开发并测试了一套情感分析的新方法, 创建两套酒店相关的词库, 此方法超越了标准词典式分析法。

研究样本为TripAdvisor酒店客户评论的大数据, 通过开发崭新的有配重的词库法, 来开展两极式情感分析。这个崭新的具有配重的词库法能够呈现透明化和可复制的程序, 准备、创建、并检验情感分析的词条。这个方法用到了两种词典（有配重的词典L1和手动选择的词典L2）, 本论文通过对TripAdvisor大数据进行使用词类划分精准度, 来检测和验证这两种词典。本论文采用两种热门方法（公共词典法和复杂机器学习算法）来对比词典的准确度。

精确度对比结果证实了本论文的方法, 相较于机器学习算法, 显著地超越了以字典为基础的方法。研究结果还表明, 本论文的方法可以就预测用户情感趋势进行推广。

本论文开发并验证了一项方法, 这种方法通过创建可信的词典进行大数据分析, 以判定用户情感。本论文创建的L2酒店词库对分析客人反馈是可靠有用的工具, 这个词库还能帮助酒店经理了解、预测、以及积极相应客人的态度和改变。本论文还提出了一项可以了解每个用户情感的简易方法, 这项方法可以通过对比的方式来检测和了解客人不同时间的情感变化, 以及根据其不同背景和经历的不同用户之间的变化。

本论文提出并检测了一项新方法, 这项情感分析方法可以解决之前方法的局限并立脚于旅游行业。基于文献综述, 本论文是首篇研究, 使用词库法来进行情感分析和创建特别领域词典的方式。

This study aims to adopt a cognitive heuristic approach to investigate the interaction effect of a message source characteristic (reviewer expertise [RE]) and two message structure characteristics (review rating consistency [RC] and review valence [RV]) on the perceived credibility of hotel online reviews.

Data were collected from 242 university students and were analyzed by three-way analysis of variance through a 2 × 2 factorial experiments using a simulated hotel review page on TripAdvisor.

Results show a three-way interaction effect of RE, RC and RV on the perceived credibility of hotel online reviews. The main effects of the three factors are also determined. Higher perceived credibility scores are found for negative reviews, reviews written by experts and reviews with a consistent rating.

This study adopts an experimental approach and is the first to investigate the three-way interactions of message source and message structure characteristics of online hotel reviews. Data were collected from students in a university in Hong Kong. Results may not be generalizable to other markets.

Results suggest that reviews written by experts have higher perceived credibility. Hotels should pay attention to the content of online reviews and the expertise level of reviewers. Efforts should be exerted to create positive experiences for hotel guests that motivate expert reviewers to write positive reviews. Note that negative reviews have higher perceived credibility than positive ones. Hotels should promptly address negative reviews and provide professional responses to reviewers. Platform operators of user-generated content (UGC) should create well-defined reviewer profiles that can serve as cues that communicate the different expertise of reviewers.

This study is the first to test the three-way interaction effect of RE, RC and RV on the perceived credibility of hotel online reviews. Results provide recommendations to hotels and UGC operators and enable them to benefit from emerging UGC usage.