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To enhance customer experiences, firms are increasingly adding human-like features to their self-service technology (SST) machines. To that end, the purpose of the present study is to examine customer interactions with an anthropomorphic machine in a service failure context. Specifically, the authors investigate the joint effects of machine voice, an individual’s sense of power and the presence of other customers in influencing customers’ switching intentions following an SST failure.

In this study, the authors used a quasi-experimental design in which they manipulated voice type (anthropomorphic vs robotic) and the presence of other customers (present vs absent) in video-based scenarios while measuring customers’ sense of power. The scenarios reflected a service failure experience with a self-service kiosk at an airport. The authors tested the hypotheses using PROCESS analyses with the Johnson–Neyman technique.

Consumer reactions to SST failures vary depending on the degree of anthropomorphism associated with an SST machine, an individual’s sense of power and the presence of other customers.

Field inquiry and an investigation in other SST contexts or of other anthropomorphic features are needed to generalize the findings.

Service providers targeting powerful consumers should consider the social presence of others when incorporating anthropomorphic features into their SST facilities.

This study is the first to examine consumer responses to service failures in an anthropomorphic SST context.

Consumers increasingly depend on the internet as the information source to make their hospitality decisions, which highlights the need for more research in online recommendation. Due to the globalization, culture and its effects on marketing become an increasingly important subject to investigate. Therefore, this paper aims to offer a cross-cultural investigation of consumers’ different trustworthiness and credibility perceptions when facing online recommendations from different information resources.

This research uses the source-credibility theory to examine consumers’ responses to online recommendations from two sources. Participants were recruited from two equivalent marketing panels in each culture. A 2 (online recommendation source: in-group vs out-group) by 2 (culture: American vs Chinese) between-subjects quasi-experiment was conducted to test the hypotheses.

The results demonstrate that culture moderates consumer responses to the two types of online sources. Chinese consumers, due to their more collectivist nature, exhibit higher levels of purchase intent when the recommendation originates from an in-group rather than from an out-group. Such differences are not observed among the more individualist American consumers. Furthermore, trustworthiness plays an important role in influencing Chinese consumers’ perception of recommendation credibility and the consequent purchase intent.

This research provides guidelines to hospitality practitioners when developing their social networking sites and online marketing strategies across different cultures.

The current study conducts an in-depth investigation of cultural differences in consumers’ perceptions of and reactions to online recommendations from other customers with various social distances.

This paper aims to investigate how the contextual marketing strategy facilitated by mobile technology enhances consumers’ purchase intention and experiential quality in a hedonic service setting – museum. Specifically, this study explores the impacts of a context-triggered push notification promoting context-related souvenirs embedded in the museum mobile app on visitors’ souvenir purchase intention and visit experience.

This study adopted a scenario-based experimental design with video stimuli and a follow-up survey questionnaire. A total of 151 participants were randomly assigned to one of the two art museum visiting scenarios (i.e. with vs without the in-app push notification). A series of one-way ANOVA comparisons and serial mediation tests were conducted for hypotheses testing.

The results show that the mobile app promoting context-related souvenirs positively affects museum visitors’ souvenir purchase intention without undermining their experiential quality. The serial mediation effects through contextual perceived value and impulse buying tendency further explain how the in-app push notification influences visitors’ souvenir purchase intention.

This study extends the contextual marketing research to a unique service domain (i.e. museum) and contributes to the service technology literature. The findings present empirical evidence for the effectiveness of mobile contextual marketing and its potential to enhance consumers’ experiential quality in a hedonic service setting. The current research provides practical guidelines to both the museum management and mobile app developers.

The purpose of this paper is to identify how consumer engagement practices shape the dynamics of a soccer club virtual brand community.

A netnographic approach was employed. Using a Python script, more than 7,000 tweets about São Paulo FC soccer club on Twitter were collected and analyzed using the Virtual Brand Community Engagement Practices typology.

The dynamics of engagement relies on two types of practices: those that comprise the actions of tweeting, retweeting, replying to, mentioning and liking messages from and about the São Paulo FC soccer club profile and those derived from the proposition of Hollebeek et al. (2017). Given the dynamics of Twitter, some practices have performed differently than the original proposal, resulting in a slight adaptation of the framework.

Given the length and the netnographic nature of this study, its findings should be considered exploratory. Future studies can build upon the ideas presented here by researching different contexts and focal objects of engagement.

This paper provides the refinement of social media strategies and content development to make them more efficient and to establish a relevant communication channel with audiences. This knowledge can contribute to a better understanding of goals and metrics.

This paper is based on the behavioral nature of engagement in virtual brand communities, which is an increasing topic of interest. To date, few studies have examined online engagement using a practice approach specifically in the sports marketing context.

This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research.

The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization.

Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research.

This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed.

This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data.

This paper aims to introduce a hierarchical fuzzy system for an online review analysis named FLORA. FLORA enables tourists to decide their destination without reading numerous reviews from experienced tourists. It summarizes reviews and visualizes them through a hierarchical structure. The visualization does not only present overall quality of an accommodation, but it also presents the condition of the bed, hospitality of the front desk receptionist and much more in a snap.

FLORA is a complete system which acquires online reviews, analyzes sentiments, computes feature scores and summarizes results in a hierarchical view. FLORA is designed to use an overall score, rated by real tourists as a baseline for accuracy comparison. The accuracy of FLORA has achieved by a novel sentiment analysis process (as part of a knowledge acquisition engine) based on semantic analysis and a novel rating technique, called hierarchical fuzzy calculation, in the knowledge inference engine.

The performance comparison of FLORA against related work has been assessed in two aspects. The first aspect focuses on review analysis with binary format representation. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, is achieved with the highest values in precision, recall and F-measure. The second aspect looks at review analysis with a five-point rating scale rating by comparing with one of the most advanced research methods, called fuzzy domain ontology. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, returns the closest results to the tourist-defined rating.

This research advances knowledge of online review analysis by contributing a novel sentiment analysis process and a novel rating technique. The FLORA system has two limitations. First, the reviews are based on individual expression, which is an arbitrary distinction and not always grammatically correct. Consequently, some opinions may not be extracted because the context free grammar rules are insufficient. Second, natural languages evolve and diversify all the time. Many emerging words or phrases, including idioms, proverbs and slang, are often used in online reviews. Thus, those words or phrases need to be manually updated in the knowledge base.

This research contributes to the tourism business and assists travelers by introducing comprehensive and easy to understand information about each accommodation to travelers. Although the FLORA system was originally designed and tested with accommodation reviews, it can also be used with reviews of any products or services by updating data in the knowledge base. Thus, businesses, which have online reviews for their products or services, can benefit from the FLORA system.

This research proposes a FLORA system which analyzes sentiments from online reviews, computes feature scores and summarizes results in a hierarchical view. Moreover, this work is able to use the overall score, rated by real tourists, as a baseline for accuracy comparison. The main theoretical implication is a novel sentiment analysis process based on semantic analysis and a novel rating technique called hierarchical fuzzy calculation.

The purpose of this paper is to provide a review of hospitality and tourism studies that have used social media analytics to collect, examine, summarize and interpret “big data” derived from social media. It proposes improved approaches by documenting past and current analytic practice addressed by the selected studies in social media analytics.

Studies from the past 18 years were identified and collected from five international electronic bibliographic databases. Social media analytics-related terms and keywords in the titles, keywords or abstracts were used to identify relevant articles. Book chapters, conference papers and articles not written in English were excluded from analysis. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) guided the search, and Stieglitz and Dang-Xuan’s (2013) social media analytics framework was adapted to categorize methods reported in each article.

The research purpose of each study was identified and categorized to better understand the questions social media analytics were being used to address, as well as the frequency of each method’s use. Since 2014, rapid growth of social media analytics was observed, along with an expanded use of multiple analytic methods, including accuracy testing. These factors suggest an increased commitment to and competency in conducting comprehensive and robust social media data analyses. Improved use of methods such as social network analysis, comparative analysis and trend analysis is recommended. Consumer-review networks and social networking sites were the main social media platforms from which data were gathered; simultaneous analysis of multi-platform/sources of data is recommended to improve validity and comprehensive understanding.

This is the first systematic literature review of the application of social media analytics in hospitality and tourism research. The study highlights advancements in social media analytics and recommends an expansion of approaches; common analytical methods such as text analysis and sentiment analysis should be supplemented by infrequently used approaches such as comparative analysis and spatial analysis.

本文对酒店旅游学科中采用社交媒体数据分析的文献进行梳理。本文通过审阅其相关分析方法的文献来提出分析方法的改进策略。

样本数据包括过去18年中五个国际在线文献索引库中的文献。搜索通过标题、关键词、或者摘要中出现社交媒体数据分析等相关字样的文章。书章节、会议文章、以及非英文文章未被收录在索引中。系统回顾和文献综述的方法（PRISMA）指导本文文献索引, Stieglitz和Dang-Xuan（2013）社交媒体数据分析框架作为本文文献分类的方法。

本文汇报了每篇文献的研究目的以及系统归类以更好理解社交媒体数据分析的研究问题以及每种方法的使用频率。自2014年起, 社交媒体数据分析快速增长, 以及其他相关分析方法, 包括精度测试（accuracy testing）。这些结果表明更多全面、稳定的分析方法需求增强以及竞争激烈。本文推荐使用改良方法, 比如社交网络分析法、比较分析、趋势分析等。消费者评价网络和社交网站成为主要社交媒体网络数据的提供平台。本文推荐多源数据应该同步分析以提高有效性和全面性的理解。

本文是首篇酒店旅游领域中对社交媒体数据分析的系统文献回顾型文章。本文强调了社交媒体数据分析的先进性以及扩展其方法的全面性；常见分析方法比如文本分析和情感分析应该结合非常见的分析方法比如比较分析法和空间分析法进行系统分析。

关键词 –关键词 对比分析, 情感分析, 用户原创内容,社交媒体分析, 主题模型, 空间分析, 文本分析文章类型 文献综述

This study aims to quantify the underlying feelings of online reviews and discover the role of seasonality in customer dining experiences.

This study applied sentiment analysis to determine the polarity of a given comment. Furthermore, content analysis was conducted based on the core attributes of the customer dining experiences.

Positive feelings towards the food and the service do not show a linear relationship, while the overall dining experiences increase in line with the positive feelings on food quality. Moreover, feelings towards the atmosphere of the restaurants are the most positive in peak season.

This study provides guidelines for restaurateurs regarding the aspects that need more attention in different seasons.

The paper contributes to the knowledge of customer feelings in local restaurants/gastronomy and the role seasonality plays in fostering such feelings. In addition, the novel methodological procedures provide insights for tourism research in discovering new dimensions in theories based on big data.

本论文旨在量化在线评论中的情感导向以及发掘季节性对消费者用餐体验的作用。

本论文采用情感分析法对既定评论做出情感判断。此外, 本文还依据消费者用餐体验中的核心价值采用了内容分析法。

研究发现消费者对食物和服务的正向情感并不是线性关系。然而, 整体用餐体验与对食物质量的正向情感是线性正向的关系。此外, 消费者对饭店氛围的情感在旺季的时节是最为突出的。

本论文对饭店从业者在不同季节的关注点上起到了指导作用。

本论文对地方饭店/美食的消费者情感认知做出了贡献, 此外, 本论文还对季节性如何促进消费情感的作用做出了研究。本论文还采用了新型的研究方法, 这对于旅游研究来说, 做出了基于大数据的新理论研究方向。

Few studies to date have explored factors contributing to the dining experience from a visitor’s perspective. The purpose of this study is to investigate whether different restaurant attributes are critical in evaluating the restaurant experience in online reviews for visitors (non-local) and local guests.

In all, 100,831 online restaurant reviews retrieved from TripAdvisor are analyzed by using domain-specific aspect-based sentiment detection. The influence of different restaurant features on the overall evaluation of visitors and locals is determined and the most critical factors are identified by the frequency of their online discussion.

There are significant differences between locals and visitors regarding the impact of busyness, payment options, atmosphere and location on the overall star rating. Furthermore, the valence of the factors drinks, facilities, food, busyness and menu found in the reviews also differs significantly between the two types of guests.

The findings of this study help restaurant managers to better understand the different customer needs. Based on the results, they can better decide which restaurant aspects should receive the most attention to ensure that customers are satisfied.

Research on online reviews has largely neglected the role of different visitation motives. This study assumes that the reviews of local and non-local restaurant visitors are based on different factors and separates them to gain a more fine-grained and realistic picture of the relevant factors for each particular group.

The purpose of this study is to quantify the impact of online customer reputation on financial profitability.

Online reputation is captured by extracting the most recurring textual themes associated with customer satisfaction and dissatisfaction, expressed within positive vs negative online guest reviews on Booking.com. Latent semantic analysis is used for textual analysis. Proxies of overall financial performance are manually constructed for the sample hotels, using financial data from the Financial Analysis Made Easy (FAME) database. Ordinary least squares is used to gauge the effect of online customer reputation on financial profitability.

Empirical findings indicate that recurring textual themes from positive online reviews (in contrast to negative reviews) exhibit a higher degree of homogeneity and consensus. The themes repeated in positive, but not in negative reviews, are found to significantly associate with hotel financial performance. Results contribute to the discussion about the measurable effect of online reputation on financial performance.

Contemporary quantitative methods are used to extract online reputation for a sample of UK hotels and associate this reputation with bottom-line financial profitability. The relationship between online reputation, as manifested within hotel guest reviews, and the financial performance of hotels is examined. Financial profitability is the result of revenues, reduced by the costs incurred in order to be able to offer a given level of service. Previous studies have mainly focused on basic measures of performance, i.e. revenue generation, rather than bottom-line profitability. By combining online guest reviews from travel websites (Booking.com) with financial measures of enterprise performance (FAME), this study makes a meaningful contribution to the strategic management of hotel businesses.