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Millions of ratings and reviews about products are available on the Internet for free, and they are used by academic researchers in the tourism sector. Data from websites like TripAdvisor are replacing or complementing traditional questionnaires and interviews. The authors are proposing a methodology to estimate the percentage accounted for by the sample of self-interviewed individuals over the total study population, in order to calculate the reliability of the results obtained. Average percentages obtained for hotels cannot be easily generalized due to the high dispersion in participation rates among hotels, even in the same city. Participation levels for tourist attractions are substantially lower than those for hotels and are likely biased, due to the fact that some tourists evaluate places without actually visiting them, merely after viewing them from the outside.

The purpose of this paper is to examine the significance of TripAdvisor on reputation within the hotel industry. TripAdvisor encapsulates key themes in establishing an online reputation strategy in an evolving digital landscape.

Through the use of an exploratory case study, data were gathered primarily by means of a series of expert interviews within the hotel industry in Cyprus, today a mature holiday destination in Europe. Further data collection included a document search of presentations, annual reports, past surveys and sales and marketing literature from the examined industry.

Hotel communication practitioners are fully aware of the impact of social media in managing reputation. Constant monitoring, prompt responses, training and transparency were identified as key factors. Online reputation management needs to be taken into consideration when designing a comprehensive integrated communication strategy.

Congruence amongst interviewees in certain areas could be on account of the homogeneity of practitioners, of their background and training and of similar organisational cultures across the locale of study. This leads to limits in the generalisations from this study’s findings.

Encouragement and training of employees were amongst the primary suggestions that emerged. An internal and external environmental scan, recognising possible strengths, weaknesses, opportunities and threats, which could assist in the effective engagement and monitoring of the organisation’s online presence, were also suggested.

The uniqueness of the study lies in its exploration of reputation management of a well-known traveller’s platform by addressing social media content in both a proactive and reactive manner.

Recent figures show that users are discontinuing their usage of TripAdvisor, the leading user-generated content (UGC) platform in the tourism sector. Hence, it is relevant to study the factors that influence travelers’ continued use of TripAdvisor.

The authors have integrated constructs from the technology acceptance model, information systems (IS) continuance model and electronic word of mouth literature. They used PLS-SEM (smartPLS V.3.2.8) to test the hypotheses using data from 297 users of TripAdvisor recruited through Prolific.

Findings reveal that perceived ease of use, online consumer review (OCR) credibility and OCR usefulness have a positive impact on customer satisfaction, which ultimately leads to continuance intention of UGC platforms. Customer satisfaction mediates the effect of the independent variables on continuance intention.

Managers of UGC platforms (i.e. TripAdvisor) can benefit from the findings of this study. Specifically, they should improve the ease of use of their platforms by facilitating travelers’ information searches. Moreover, they should use signals to make credible and helpful content stand out from the crowd of reviews.

This is the first study that adopts the IS continuance model in the travel and tourism literature to research the factors influencing consumers’ continued use of travel-based UGC platforms. Moreover, the authors have extended this model by including new constructs that are particularly relevant to UGC platforms, such as performance heuristics and OCR credibility.

The purpose of this study is to demonstrate how to use customer spontaneous comments to identify which aspects influence the overall customer satisfaction with restaurant services from a nonlinear perspective.

The authors collected data from 399 spontaneous comments about a chain of fast-food restaurants in Brazil. The comments are freely available on the TripAdvisor portal and were extracted and classified according to seven dimensions related to the quality of services: tangibles, reliability, responsiveness, assurance, empathy, quality of the food and price. Next, the authors combine the critical incident technique (CIT) and the penalty-reward contrast analysis (PRCA) to investigate the nonlinear relationship between service quality assessment and overall customer satisfaction.

The method of integrating CIT with PRCA explains 64.7% of the variation in the customer's assessment of the services provided (RAdj2 = 0.647). This shows that spontaneous comments from customers are related to their overall satisfaction with the service provided. Besides, the findings suggest that consumers tend to comment more about positive than negative experiences regarding aspects related to food, attendants' empathy and service assurance, and more negative comments about aspects related to responsiveness and price. However, it was found that negative comments have a stronger influence on overall satisfaction than positive comments.

Using comments available for free on the Internet and evaluating how positive and negative comments can jointly influence customer satisfaction, the proposed methodology demonstrates how restaurants can use their customers' spontaneous comments to identify critical aspects to be managed and improved. To the best of authors’ knowledge, this is the first study presenting how restaurants can use customer spontaneous comments, freely available on the internet, to identify the relevance of different aspects of the services provided from a nonlinear perspective. In addition, the present study shows that although customers spontaneously tend to share more positive than negative comments about restaurant services, events related to negative experiences have a stronger influence on overall satisfaction.

To test the applicability of the user-generated content (UGC) derived from social travel network sites for online reputation management, the purpose of this study is to analyze the spatial clustering of the reputable hotels (based on the TripAdvisor Best-Value indicator) and reputable outdoor seating restaurants (based on ranking indicator).

This study used data mining techniques to obtain the UGC from TripAdvisor. The Hierarchical Density-Based Spatial Clustering method based on algorithm (HDBSCAN) was used for robust cluster analysis.

The findings of this study revealed that best value (BV) hotels and reputable outdoor seating restaurants are most likely to be located in and around the central districts of the urban tourist destinations where population and economic activities are denser. BV hotels' spatiotemporal cluster analysis formed clusters of different sizes, densities and shape patterns.

This study showed that reputable hotels and restaurants (H&Rs) are concentrated within districts near historic city centers. This should be an impetus for applied research on urban investment environments.

The findings would be rational guidance for entrepreneurs and potential investors on the most attractive tourism investment environments.

There has been a lack of studies focusing on analyzing the spatial clustering of the H&Rs using UGC. Therefore, to the best of the authors’ knowledge, this study is the first to map and analyze the spatiotemporal clustering patterns of reputable hotels (TripAdvisor BV indicator) and restaurants (ranking indicator). As such, this study makes a significant methodological contribution to urban tourism research by showing pattern change in H&Rs clustering using data mining and the HDBSCAN algorithm.

为了测试社交旅游网站 (STNS) 的用户生成内容 (UGC) 对在线声誉管理 (ORM) 的适用性, 本研究分析了知名酒店的空间聚类（基于 TripAdvisor 最佳价值指标) 和信誉良好的户外座位 (ODS) 餐厅(基于排名指标)。

该研究使用数据挖掘技术从 TripAdvisor 获取 UGC。 基于（HDBSCAN）算法的分层基于密度的空间聚类方法用于鲁棒聚类分析。

调查结果显示, 最具价值 (BV) 酒店和信誉良好的 ODS 餐厅最有可能位于人口和经济活动较为密集的城市旅游目的地的中心区及其周边地区。 BV 酒店的时空聚类分析形成了不同大小、密度和形状模式的聚类。

目前的文献扔缺乏专注于分析利用 UGC 的酒店和餐厅 (H&R) 空间聚类的研究。 因此, 本研究首次绘制并分析了知名酒店（TripAdvisor BV 指标）和餐厅（排名指标）的时空聚类模式。 因此, 本研究通过利用数据挖掘和 HDBSCAN 算法显示 H&Rs 聚类的模式变化, 为城市旅游研究做出了重要的方法论贡献。

这项研究表明, 著名的 H&R 集中在历史悠久的市中心附近的地区。 这应该是对城市投资环境的应用研究的推动力。

研究结果将为企业家和潜在投资者提供最具吸引力的旅游投资环境的理性指导。

Social media use has increased in recent years, and businesses are looking to capitalize on the plethora of marketing opportunities afforded by this digital shift by paying attention to user-generated content (UGC) posted on review websites. Leveraging UGC can help small businesses gain a competitive advantage over late-adopters. At the same time, there is evidence to suggest that small businesses do not have the time, resources or skill level to properly use social media to create a competitive advantage. This paper aims to explore how wine tourism businesses can analyze consumer feedback on online review websites to evaluate customer perceptions and expectations and generate more effective ways to improve customer satisfaction.

The qualitative method of thematic analysis was used to map out consumer reviews online to assess service satisfaction and dissatisfaction. A total of 848 reviews were gathered and qualitatively analyzed from two online review websites (TripAdvisor.com and Yelp.com) using open and axial coding and thematic analysis.

The results show that wine consumers are interested in the hedonic aspects of their experience, are most often attracted to wineries as a special outing and focus on factors such as scenery and atmosphere, service quality and products in their reviews. Hence, service and sales personnel have a key opportunity to capitalize on generating better service experiences through social media analysis.

The present study fills a gap by providing a more in-depth, qualitative exploration of the wine consumers’ psychology and experience, including factors such as atmosphere and special occasions. Furthermore, this study uses interpretive, manual coding to pick up on nuanced themes that are often missed by using automated qualitative analysis software or by looking at frequency counts in isolation.

Based on more than 2.7 million online reviews (ORs) collected with big data analytical techniques from Booking.com and TripAdvisor.com, this paper aims to explore if and to what extent environmental discourse embedded in ORs has an impact on electronic word-of-mouth (e-WOM) helpfulness across eight major destination cities in North America and Europe.

This study gathered, by means of Big Data techniques, 2.7 million ORs hosted on Booking.com and TripAdvisor, and covering hospitality services in eight different destinations cities in North America (New York City, Miami, Orlando and Las Vegas) and Europe (Barcelona, London, Paris and Rome) over the period 2017–2018. The ORs were analysed by means of ad hoc content analytic dictionaries to identify the presence and depth of the environmental discourse included in each OR. A negative binomial regression analysis was used to measure the impact of the presence/depth of online environmental discourse in ORs on e-WOM helpfulness.

The findings indicate that the environmental discourse presence and depth influence positively e-WOM helpfulness. More specifically those travelers who write explicitly about environmental topics in their ORs are more likely to produce ORs that are voted as helpful by other consumers.

Implications highlight that both hotel managers and platform developers/managers should become increasingly aware of the importance that customer attach to environmental practices and initiatives and therefore engage more assiduously in environmental initiatives, if their objective is to improve online review helpfulness for other customers reading the focal reviews. Future studies might include more destinations and other operationalizations of environmental discourse.

This study constitutes the first attempt to capture how the presence and depth of hospitality services consumers’ environmental discourse influence e-WOM helpfulness on multiple digital platforms, by means of a big data analysis on a large sample of online reviews across multiple countries and destinations. As such it makes a relevant contribution to the area at the intersection between big data analytics, e-WOM and sustainable tourism research.

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 broad goal of the study is to determine how travelers communicate in the cyberspace in relation to their positive and negative experiences they had when staying in a particular hotel. Further goals of this study include identifying the main themes that motivate consumers to evaluate hotel experiences in online environments and categorize the most frequently mentioned areas in the online hotel reviews.

Content analysis techniques were applied by using the software tool NVivo 8 in order to analyze comments extracted using an automated web spider. The spider extracted qualitative data in the form of reviews and comments and quantitative data in the form of demographic information and ratings. The reviews were considered as a primary data for analysis, these reviews portrayed both positive and negative experiences. During this process, the spider collected data on 3,124 hotels and 17,357 traveler reviews from the TripAdvisor site.

By reviewing and understanding traveler comments of their hotel experiences, managers could gain knowledge concerning which element influence to form a positive brand image. Cleanliness of the hotel generally is a common concern in traveler's expectations. Words about deficiency of cleanliness (dirty) appeared more regularly when travelers write negative reviews about the hotel. Travelers showed to be more likely to write positive reviews of hotels with convenient location to good areas such as attractions, shopping, airports, and restaurants. The data in this research shows that travelers can be positively influenced by quality of service received a friendly and well trained staff. When travelers are pleased with the quality of human contact offerings of a well‐trained employee, they tend to feel more satisfied and to form a positive brand image that it is translated into a positive review.

Limitations could be listed using a relatively small sample size, and a relatively limited geographical capacity. Future studies are advised to include bigger sample sizes and also advised to explore a diverse pool of geographical.

The study identifies the possible areas that hoteliers need to pay close attention to improve service. Further, it is one of the first studies in hospitality that highlights strategies to create and reinforce brand image by using online reviews.

This paper aims to explore how emotional expressions embedded in online hotel reviews influence consumers’ helpfulness perceptions. In particular, this study develops and tests hypotheses analyzing empirical data with a text-mining method in the context of hotels to investigate how review valence influences the perceived helpfulness of online hotel reviews and to examine the role of negative emotional expressions embedded in online consumer reviews with respect to perceived helpfulness.

This study collected 520,668 online reviews involving 488 hotels in New York City (NYC) on Tripadvisor.com. Of these reviews, 69,202 reviews (13.29 per cent) that had received helpfulness votes were analyzed by a text mining method and negative binomial regression.

This study demonstrates that negative reviews are considered more helpful than positive reviews when potential customers read online hotel reviews for their future stay. However, when intensively negative emotions were expressed, the degree of helpfulness regarding negative reviews was diminished.

While emotional expressions prevail in online consumer reviews, surprisingly little attention has been devoted to the consequences of emotional expressions in consumers’ information processing and decision-making. Due to the nature of service, given the inseparability of production and consumption, which often hinders the execution of flawless service, consumers tend to be more dependent on reviews to minimize any potential failures they may encounter later on. Therefore, this study fills a gap by demonstrating that negative reviews and emotional expressions play a more crucial role in consumers’ information processing and decision-making.