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1.
This study aims to examine whether customer ratings and online reviews affect hotel revenues, and if so, to quantify the effects. To achieve this objective, we articulate the mechanisms grounded on reputation theories whereby customer ratings exercise the influence on hotel performance through reputational and signaling effects. Using customer rating data from TripAdvisor and hotel revenue data from Texas, we estimate fixed effects regressions and adopt a regression discontinuity design to separate the signaling effect of customer ratings from reputational effect. We found that the signaling effect of a 1-star increase is an increase of 2.2–3.0% in hotel monthly revenues whereas the reputational effect of a 1-star increase is an increase of around 1.5–2.3% in hotel monthly revenues. Our findings are robust across alternative model specifications and provide insightful implications for hotels to manage their customer ratings. 相似文献
2.
In tourism, customer engagement has been found to boost loyalty, trust and brand evaluations. Customer engagement is facilitated by social media, but neither of these phenomena is well-researched in tourism. This research contributes in two ways. First, we validate the Customer Engagement with Tourism Brands (CETB) 25-item scale proposed by So, King & Sparks (2014) in a social media context, and offer an alternative three-factor 11-item version of the scale. Second, we replicate their proposed structural model, and test our alternative model, to predict the behavioural intention of loyalty from engagement, and to test customer involvement as an antecedent to engagement. Ultimately, we propose a customer engagement scale and a nomological framework for customer engagement, both of which can be applied in both tourism and non-tourism contexts. Managers of tourism brands on social to better assess the nature of customer engagement with the parsimonious 11-item scale. 相似文献
3.
Tourism sensory experiences represent a spatially constrained and constructed process influenced by various environmental stimuli. Although growing academic attention has been devoted to sensory tourism, few studies have incorporated spatiality into investigations of sensory experiences. This study establishes a macro–meso–micro analytical framework to explore the relationships among sensory experiences and spatial environmental characteristics based on social media big data. This research also moves beyond the conventional five-sense framework to include a sixth sense—interoception. Results (a) uncover the spatial distribution and relationships among sensory experiences in a destination; (b) demonstrate associations between attraction types and sensory experiences; and (c) illustrate interactions between environmental attributes and sensory encounters. This study theoretically clarifies relevant antecedents, extends a sense-based framework, and multidimensionally enriches tourism sensory experiences; empirically offer guidance for sensory environment planning, marketing, and management. Results also produce methodological insights for adopting social media big data to capture sensory experiences. 相似文献
4.
The purpose of this research is to examine the effects of restaurant attributes and the underlying factors impacting overall customer experience within a range of different restaurant types. To understand their experiences, this study analyses online reviews of restaurants which have become important sources of customer experience data. This current research utilises a combination of quantitative analyses to examine 935,386 Google Maps reviews of 5010 restaurants in London, Birmingham, and Manchester. The authors used the VADER sentiment analysis algorithm to measure the sentiment of four key restaurant attributes: food, service, atmosphere, and value. Logistic regression was conducted to test the relationships between these attributes and a 5-star rating. Furthermore, logistic regression was used to compare the changes of odds at different star rating levels. To understand the factors that drive positive and negative reviews, the top 30 food items of 8 types of restaurants were analysed. 相似文献
5.
This study leveraged the advantages of user-generated reviews with the aim of offering new insights into the determinants of hotel customer satisfaction by discriminating among customers by language group. From a collection of 412,784 user-generated reviews on TripAdvisor for 10,149 hotels from five Chinese cities, we found that foreign tourists, who speak diverse languages (English, German, French, Italian, Portuguese, Spanish, Japanese, and Russian), differ substantially in terms of their emphasis on the roles of various hotel attributes (“Rooms,” “Location,” “Cleanliness,” “Service,” and “Value”) in forming their overall satisfaction rating for hotels. Chinese tourists domestically exhibit distinct preferences for room-related hotel attributes when compared to foreign tourists. Major interaction effects are revealed between the attributes “Rooms” and “Service” and between “Value” and “Service”. 相似文献
6.
The main purpose of this study is based on qualitative and quantitative research procedures, and integrates the key service factors for the online food delivery (OFD) industry extracted by Internet Big Data Analytics (IBDA) to construct a OFD service quality scale (OFD-SERV). This study takes OFD customers in Taipei City as the objects. The results show that 20 key service factors for the OFD industry are extracted through IBDA. The OFD-SERV scale contains six dimensions including reliability, maintenance of meal quality and hygiene, assurance, security, system operation and traceability, a total of 28 items. The results from the structural equation modeling showed that the reliability, assurance and system operation have a positive impact on customer satisfaction. Finally, the findings provide knowledge and inspiration for the current OFD, and enable OFD operators and future researchers to more accurately identify the deficiency of service quality. 相似文献
7.
The considerable volume of online reviews for today's hotels are is difficult for review readers to manually process. Automatic review summarizations are a promising direction for improving information processing of travelers. Studies have focused on extracting relevant text features or performing sentiment analysis to compile review summaries. However, numerous reviews contain nonspecific or nonsentimental content, hindering the ability of sentiment-based techniques' to accurately summarize useful information from hotel reviews. This paper proposes a systematic approach that first constructs classifiers to identify helpful reviews and then classifies the sentences in the helpful reviews into six hotel features. Finally, the sentiment polarities of sentences are analyzed to generate the review summaries. Experiment results indicated that the performance of the proposed approach was superior to other methods. 相似文献
8.
Online reviews provide additional product information to reduce uncertainty. Hence, consumers often rely on online reviews to form purchase decisions. However, an explosion of online reviews brings the problem of information overload to individuals. Identifying reviews containing valuable information from large numbers of reviews becomes increasingly important to both consumers and companies, especially for experience products, such as attractions. Several online review platforms provide a function for readers to rate a review as “helpful” when it contains valuable information. Different from consumers, companies want to detect potential valuable reviews before they are rated to avoid or promote their negative or positive influence, respectively. Using online attraction review data retrieved from TripAdvisor, we conduct a two-level empirical analysis to explore factors that affect the value of reviews. We introduc a negative binomial regression model at a review level to explore the effects of the actual reviews. Subsequently, we apply a Tobit regression model at the reviewer level to investigate the effects of reviewer characteristics inferred from properties of historical rating distribution. The empirical analysis results indicate that both text readability and reviewer characteristics affect the perceived value of reviews. These findings have direct implications for attraction managers in their improved identification of potential valuable reviews. 相似文献
9.
Despite the acknowledged importance of social media for customer engagement, our understanding of this phenomenon is limited and new theories can help shed further light on the unique features of social media in the tourism context. Our work contributes to the literature by adopting an affordance perspective that leads us to identify three distinctive social media affordances for customer engagement in tourism: persistent engagement, customized engagement, and triggered engagement. Our work also extends prior research on customer engagement by examining the process of recognition (proprioception, exteroception and coperception) through which organizations engage customers in social media. 相似文献
10.
This study investigated the effects of consumer experience and disconfirmation on the timing of online reviews. Based on a unique dataset of restaurant reservations and online reviews, the empirical results indicate that (1) there is a reverse U-shaped relationship between consumer experience and online review posting timing, i.e., consumers who have strongly dissatisfying or satisfying experiences tend to post online reviews earlier than consumers who have moderate experience; (2) the disconfirmation between a customer’s experience and the average rating of prior reviews has a negative effect on his or her online review posting speed; and (3) the effect of disconfirmation on review posting speed is substantial for consumers who have strongly dissatisfying or satisfying experiences, while it is weaker for consumers who have moderate experience. 相似文献
11.
This study puts to empirical test a major typology in the tourism literature, mass versus special interest tourism (SIT), as the once-distinctive boundary between the two has become blurry in modern tourism scholarship. We utilize 41,747 geo-located Instagram photos pertaining to the 2017 Great American Solar Eclipse and Big Data analytics to distinguish tourists based on their choice of observational destinations and spatial movement patterns. Two types of tourists are identified: opportunists and hardcore. The motivational profile of those tourists is validated with the external data through hypothesis testing and compared with and contrasted against existing motivation-based tourist typologies. The main conclusion is that large share of tourists involved in what is traditionally understood as SIT activities exhibit behavior and profile characteristic of mass tourists seeking novelty but conscious about risks and comforts. Practical implications regarding the potential of rural and urban destinations for developing SIT tourism are also discussed. 相似文献
12.
Internet techniques significantly influence the tourism industry and Internet data have been used widely used in tourism and hospitality research. However, reviews on the recent development of Internet data in tourism forecasting remain limited. This work reviews articles on tourism forecasting research with Internet data published in academic journals from 2012 to 2019. Then, the findings ae synthesized based on the following Internet data classifications: search engine, web traffic, social media, and multiple sources. Results show that among such classifications, search engine data are most widely incorporated into tourism forecasting. Time series and econometric forecasting models remain dominant, whereas artificial intelligence methods are still developing. For unstructured social media and multi-source data, methodological advancements in text mining, sentiment analysis, and social network analysis are required to transform data into time series for forecasting. Combined Internet data and forecasting models will help in improving forecasting accuracy further in future research. 相似文献
13.
The purposes of the paper are (1) to examine the dynamic properties of online reviews, focusing on whether previous review trends contribute to herding or reactant behavior in subsequent review rating generation (dynamic flow), and (2) to explore the business value of management responses in the dynamic flow of online reviews based on Social Impact Theory and Rational Action Theory as the foundation. To this end, we analyze a series of regression and logistic models with quasi-experimental cases from a large online review dataset, collected from a leading online travel website in China. We find that both types of previous trends of reviews, positive and negative, contribute to reactant behavior in subsequent review rating generation. When the review trends are considered with management responses, we find that management responses have a positive impact on subsequent review ratings in the negative review trend, but not in the positive review trend. 相似文献
14.
Our research examines the perceptions and evaluations of prospective customers toward an online negative review and any accompanying hotel response. The study explores two main issues: whether the presence (versus absence) of an organizational response to negative customer reviews affects the inferences potential consumers draw about the target business, and which aspects of responses affect their impressions. We test the effects of four variables associated with a response: source of response, voice of responder, speed of response, and action frame on two outcomes variables (i.e., customer concern and trust inferences). The provision of an online response (versus no response) enhanced inferences that potential consumers draw regarding the business's trustworthiness and the extent to which it cares about its customers. Using a human voice and a timely response yielded favorable customer inferences. Inferences did not vary with response source or action frame. Implications are drawn for effective management of negative online reviews. 相似文献
15.
This work explores how Italian regional Destination Management Organisations (DMOs) strategically employ Facebook to promote and market their destinations, and improves on the current metrics for capturing user engagement. Based on big data analysis from the regional DMOs' Facebook pages, supplemented with semi-structured interviews conducted with DMO managers, the study sheds light on the factors contributing to superior level of social activity. The findings indicate that the way Facebook is tactically and strategically employed varies significantly across Italian regional DMOs. Visual content (namely photos) and moderately long posts have a statistically-significant positive impact on DMOs' Facebook engagement, whereas high post frequency, and early daily timing (in the morning) of posts have a negative impact on engagement. Last but not least, the study shows that most of the regional DMOs (except for Trentino, Tuscany, and Sicily) deploy Facebook with a top-down approach, allowing for little spontaneous user generated content (UGC). 相似文献
16.
Despite the popular use of social media analytics to scrutinize customer emotions, less scholarly efforts have been invested in visualizing theme park visitors' emotions. Employing the convergence of social media analytics and geospatial analytics, this paper visualized cohesive places where Disneyland visitors express distinct types of emotion in social media messages. Among 226,946 collected tweets, this study used 19,809 tweets containing one or more emotion words listed in Russell's Circumplex Model of Affect. Text mining analysis and GIS-based exploratory spatial data analysis showed that tweets reflecting each quadrant of emotions have considerable spatial variations and different topics related to visitor emotions. The research approach enabled displaying particular spots in theme park zones and areas of riding attractions where emotions of each quadrant are significantly clustered. This study highlights methodological implications of visualizing spatial patterns of visitors' emotions and provides practitioners with a useful guide to develop routes evoking pleasant emotions. 相似文献
17.
ABSTRACTOne hundred and five articles on social media in hospitality and tourism during 2004–2014 were identified from three databases and seven journals. Seven dimensions were used for analysis. Results indicated that social media research in hospitality and tourism is in its early stages with two turning points. The number of articles rose dramatically in 2010 and unexpectedly dropped in 2014. Research gaps are apparent in several industry sectors in topics beyond online reviews and in research methods, where literature reviews suffer from small numbers and few prominent researchers. Implications and future research directions are also discussed. 相似文献
18.
Online consumer reviews (OCRs) are valuable to consumers and sellers. Online price promotion is commonly used by local merchants to increase sales. However, knowledge of the differences in OCRs between consumers who received a discount and regular consumers is limited. This study investigates the effects of price discounts on restaurant OCRs by comparing the review rating and open-ended contents of OCRs from consumers who received a discount and regular consumers. The results show that the review rating is higher from consumers who received a discount, whereas the word count, image count, and diversity of review contents are higher from regular consumers. Regular consumers are more likely to mention product quality, environmental quality, service quality, geographic location, purchasing process, recommendation expression, and loyalty expression in OCRs, and there is no significant difference in the dimensions of price, cognitive attitude, and emotional attitude between the two groups. 相似文献
19.
This paper investigates how managing online reviews affects hotel performance. An international hotel chain provided the hotel performance data and the online review data. A leading social media firm for the hospitality industry collected the online review data, which the hotel company purchased. The results indicate that overall ratings are the most salient predictor of hotel performance, followed by response to negative comments. The better the overall ratings and the higher the response rate to negative comments, the higher the hotel performance. Therefore, online reviews in social media, specifically overall rating and response to negative comments, should be managed as a critical part of hotel marketing. 相似文献
20.
This study considers the review reliability problem by identifying biased user-given ratings through rating prediction on the basis of the textual content. Deep learning approaches were introduced to investigate the textual review and validate the effect of rating prediction using a dataset collected from Yelp. The definition of “biased rating” was clarified and influenced the matching rules. The approach obtains high performance on a total of 1,000,000 reviews for prediction, with user-given ratings as the benchmark. Using the revealed biased ratings, unreliable reviews were detected by combining the results of several deep learning kernels. Findings shed light on understanding review quality by distinguishing biased ratings and unreliable reviews that may cause inconsistency and ambiguity to readers. Hence, theoretical and managerial areas for social media analytics are enriched on the basis of online review meta-data in hospitality and tourism. 相似文献