共查询到17条相似文献，搜索用时 0 毫秒
1.
Systems beyond firm control are information systems neither designed nor commissioned by an organization, that the firm must use to compete. We extend Effective Use Theory (EUT) to theorize how hotels can leverage systems beyond firm control within the constraints they impose. Specifically, we study the competitive effect of managerial responses to online reviews in the lodging industry. Writing managerial responses is the only action hotels can take in response to customer comments within online review systems. We use an advanced text mining technique, topic modeling, to develop a non-perceptual measure of informed action: review-response congruence. We then empirically test the association between informed action and performance. Our work shows that specific responses are more effective than generic responses and that the degree of review-response congruence positively affects hotel's competitive performance. 相似文献
2.
As many readers struggle with massive textual information on review websites, developing optimized recommender systems that assist readers in identifying relevant reviews is critical. The present study aims to explore and predict the relationship between a reviewer’s evaluation of distinct attributes (i.e., importance and sentiment of a restaurant aspect)2 and overall satisfaction (i.e., generic numerical rating of a restaurant). Latent Aspect Rating Analysis is modified to achieve the goal. The study identifies five restaurant attributes: food & drinks, customer service, dining atmosphere, restaurant value, and location. Restaurant value contributes most from the importance perspective and food & drinks contributes most from the sentiment perspective. Restaurant value ranks the first as the overall satisfaction of attributes (i.e., combination of importance and sentiment). Accordingly, the present study suggests a supplement of the “dynamic” recommender systems. This study offers scholars and practitioners a refined approach to analyze wealthy review content. 相似文献
3.
The amount of texts available on the web is growing continuously and making sense of this unstructured data efficiently and effectively, therefore, poses a demanding challenge for organizations. Although computer science community has developed many techniques, there is ample room for improvement on organizational utilization of such text data, especially when referring to decision-making support. In this article, we propose and validate a framework towards an effective use of text data inside hotel industry, bringing tourism sector to this discussion. We combined three text mining techniques for text classification, sentiment analysis and topic modeling in a novelty way to allows managers to analyze guests’ comments and compares competitors in hospitality industry based on SERVQUAL. Our objective is to present an automatized process involving text data collection and analysis, improving decision-making process. 相似文献
4.
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
5.
As an online reputation management tool, hotel managers increasingly rely on management responses to online reviews to improve the electronic word of mouth (eWOM). Due to the substantial heterogeneity of textual reviews with different topics, it is particularly challenging to personalize the response for customer relationship management. Based on a panel data of 500 hotels in the state of Texas collected from TripAdvisor, this study examines the influence of personalized management responses on rating increase from a topic matching perspective. The empirical results show that (1) a high level of topic matching of management response leads to an increase of hotel online rating; (2) a high valence and a large variation of existing rating weaken the positive influence of such personalized management response; (3) the influence is stronger for economy hotels compared to luxury ones. Lastly, practical implications are provided. 相似文献
6.
This study aims to use computational linguistics, visual analytics, and deep learning techniques to analyze hotel reviews and responses collected on TripAdvisor and to identify response strategies. To this end, we collected and analyzed 113,685 hotel reviews and responses and their semantic and syntactic relations. We are among the first to use visual analytics and deep learning-based natural language processing to empirically identify managerial responses. The empirical results indicate that our proposed multi-feature fusion, convolutional neural network model can make different types of data complement each other, thereby outperforming the comparisons. The visualization results can also be used to improve the performance of the proposed model and provide insights into response strategies, which further shows the theoretical and technical contributions of this study. 相似文献
7.
The crucial role of sensory dimensions in customer experiences has been supported in literature. However, traditional self-reported sensory measurements have limited capacity in capturing the multi-dimensional experiences sensed by individuals and articulating the distinct effect of different sensory dimensions on actual behavior. This study is the first attempt to test the effects of positive and negative experiences involving all five senses (sight, smell, sound, taste, and touch) on customer ratings. The sensory experiences reported in social media reviews were captured and explored using text mining and sentiment analysis. The findings show that although the majority of customers’ experiences were positive, the negative sensory experiences had higher effect on customer rating. Furthermore, the five senses had different weights in forming overall experience, which provides theoretical contributions to the literature on sensescapes, prospect theory, and discourses on satisfiers and dissatisfiers. 相似文献
8.
Online tourism has received increasing attention from scholars and practitioners due to its growing contribution to the economy. While related issues have been studied, research on forecasting customer purchases and the influence of forecasting variables, online tourism is still in its infancy. Therefore, this paper aims to develop a data-driven method to achieve two objectives: (1) provide an accurate purchase forecasting model for online tourism and (2) analyze the influence of behavior variables as predictors of online tourism purchases. Based on the real-world multiplex behavior data, the proposed method can predict online tourism purchases accurately by machine learning algorithms. As for the practical implications, the influence of behavior variables is ranked according to the predictive marginal value, and how these important variables affect the final purchase is discussed with the help of partial dependence plots. This research contributes to the purchase forecasting literature and has significant practical implications. 相似文献
9.
Restaurant management requires customer responsiveness to deal with increasingly higher expectations and market competitiveness. This study proposes an approach to simplify the decision-making process of restaurant managers by combining both live social media customer feedback and historical sales data in a sales forecast model (based on TripAdvisor data and the Bass model).Our approach was validated with internal and external (i.e., online reviews) data gathered from six restaurants. The collected data was processed using data analytics for developing a dashboard that provides value for restauranteurs by taking advantage of online reviews and sales forecast. Such dashboard was evaluated by restaurant management experts, which provided positive feedback, highlighting in particular the time saved in the decision-making process. 相似文献
10.
The purpose of this research was to explore and assess factors influencing perceptions of consumers with food allergies toward restaurants when accommodating allergen-free requests. Mixed approaches, including big data analytics (i.e., topic modeling), content analysis, and multiple regression analyses were performed to analyze user-generated reviews for restaurants listed on AllergyEats.com, an information-sharing platform for consumers with food allergies. Among the 40 topics identified, “knowledgeable staff” was the most prevalent topic. Results of topic correlation analyses revealed five groups of topics: customized orders, efforts of staff, menu options, fried foods and oil, and communication with shared latent features. Four topics in the group of “efforts of staff” had the highest positive impact on restaurant ratings, while two topics in the group of “communications” had the strongest negative impacts. Foodservice managers and educators may use the results of this study to better accommodate consumers with food allergies and develop appropriate training programs. 相似文献
11.
The 7 Ps model is a very useful tool in helping service firms solve managerial issues in marketing. Guided by the 7 Ps marketing mix framework, a big-data, supervised machine learning analysis was performed with 1,148,062 English reviews of 37,092 Airbnb listings in San Francisco and New York City. The results disclose similar patterns in both markets, where travelers shared their experience about Service Product and Physical Evidence most often; Price and Promotion were the least mentioned elements. Furthermore, through a series of comparisons of Airbnb’s 7 Ps marketing mix among the listings managed by different types of hosts, multi-unit and single-unit hosts seem to offer similar services with a small observable difference; whereas superhosts and the ordinary hosts deliver different services. This study makes valuable methodological contributions and provides practical marketing insights for hoteliers and the hosts and webmasters on home-sharing websites. Policymakers should pay special attention to multi-unit hosts. 相似文献
12.
This study reports an attempt to validate a customer well-being (CWB) index related to natural wildlife tourism. It was hypothesized that the CWB index related to wildlife tourism has a positive influence on travel outcomes (length of stay, number of visits, and total expenses), mediated by perceived value and customer loyalty. These hypotheses were tested using four waves of surveys of customers (overnight visitors) intercepted at the park in a two-year period. The survey data provided support for the hypotheses, which, in turn, lend validation support to the CWB index. Managerial implications of the customer well-being index are also discussed. 相似文献
13.
This study investigates how free add-on services affect customers’ perceived value in horizontal and vertical competition. We collected 349,879 reviews about over 3000 hotels in 25 U.S. cities from TripAdvisor. Using three balanced data sets generated by coarsened exact matching, the ordered logistic regressions show that free hotel add-on services (including free breakfast, parking, and WiFi) positively affect consumers’ perceived value. However, increased horizontal and vertical competition differentially weakens the positive effects of free add-on services. We not only observe a negative moderating effect of horizontal competition, but also identify three patterns of the marginal effects of these three add-ons in horizontal competition. The moderating effect of vertical competition exists from the higher-grade hotel segment to a lower-grade hotel, but such an effect is insignificant from the lower-grade hotel segment to a higher-grade hotel. Therefore, hotel managers should consider diverse external competitive environments and design appropriate differentiated service strategies. 相似文献
14.
Online consumer reviews have been studied for various research problems in hospitality and tourism. However, existing studies using review data tend to rely on a single data source and data quality is largely anecdotal. This greatly limits the generalizability and contribution of social media analytics research. Through text analytics this study comparatively examines three major online review platforms, namely TripAdvisor, Expedia, and Yelp, in terms of information quality related to online reviews about the entire hotel population in Manhattan, New York City. The findings show that there are huge discrepancies in the representation of the hotel industry on these platforms. Particularly, online reviews vary considerably in terms of their linguistic characteristics, semantic features, sentiment, rating, usefulness as well as the relationships between these features. This study offers a basis for understanding the methodological challenges and identifies several research directions for social media analytics in hospitality and tourism. 相似文献
15.
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
16.
This conceptual paper provides a brief review of hospitality consumers’ information search behavior and the factors that are likely to influence their information search behavior and their utilization and processing of online reviews. This study focuses on the factors such as information overload, confusion, information processing, information presentation format, trust and evaluation mode that has not received much attention from hospitality scholars in addition to discussing the impacts of perceived risk and familiarity on information search and information processing approaches utilized by hospitality consumers. This study also discusses opportunities for hospitality researchers to empirically examine the extent to which each of the factors discussed might influence hospitality consumers information search, information processing and especially their utilization of online reviews in their decision making process. 相似文献
17.
Covid-19 created tremendous uncertainty in the tourism industry; in this study, we use social media data to explore differences in the preferences and attitudes of tourism consumers, both before and during the pandemic. We use natural language processing (NLP) techniques to analyze over one million Reddit posts on travel-related subreddits. We investigate the preference for city and nature-oriented tourism in selected destinations; the analysis demonstrates that nature tourism gained interest during Covid-19 in destinations with rich nature resources, whereas city tourism lost interest in destinations known for city tourism. We also classify Reddit authors into two categories: conservation and openness, according to a psychological theory of personal values, and show that this is predictive, with openness associated with positive travel sentiment and low risk awareness. This points to the potential for value-based segmentation of travel consumers based on theoretically-grounded NLP analysis of social media data. 相似文献