The role of customer behavior in forming perceived value at restaurants: A multidimensional approach

With the increasing involvement of customers at restaurants, their behaviors have become an indispensable part in formation of perceptions of value. To examine such a subtle and complicated process, this study applies a multi-layer/multi-dimension approach to examine how customer behavior predicts perceived value, which in turn leads to satisfaction. Particularly, customer behavior, assessed with a hierarchical framework, includes two dimensions: participation behavior (with four sub-dimensions: information seeking, information sharing, responsible behavior, personal interaction) and citizenship behavior (with four sub-dimensions: feedback, advocacy, helping, and tolerance). Customer perceived value consists of three dimensions: economic, individual, and relational values. Data collected from 514 respondents reflect those with dining experience at restaurants. The results show that a customer’s perception of value gains greater impact from citizenship behavior than participation behavior. The findings of the study contribute to the evolving knowledge of customer behavior and offer industry practitioners’ effective marketing strategies to maximize customer value.     

Finding the reviews on yelp that actually matter to me: Innovative approach of improving recommender systems

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.     

Identifying unreliable online hospitality reviews with biased user-given ratings: A deep learning forecasting approach

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.     

Customer value in the hotel industry: What managers believe they deliver and what customer experience

Delivering value for customers has become a central theme in business. The paper examines customer value from two perspectives, i.e. that of service provider and that of the customer. A total 231 managers and 385 customers responded to the survey. The results indicate that managers’ perception of customer value is different to what customers’ experience as customer value. The differences persist across different classes of hotels. These results suggest there is a need to align management and customer perspectives to optimize customer value as delivered and experienced. Specifically, hotels should invest in customer understanding and customer linking activities.     

The effect of relational benefits on perceived value in relation to customer loyalty: An empirical study in the Australian coffee outlets industry

Retaining and cultivating customer loyalty has become increasingly important for coffee outlet marketers and operators due to the highly competitive environment. This study aims to develop and test a model investigating how relational benefits enhance perceived value to win customer loyalty. Using a self-administered questionnaire survey, 949 respondents from coffee outlets were used for this study. The findings show that relational benefits have direct effect on perceived value and customer loyalty. In addition, relational benefits also have indirect effect on loyalty via perceived value. Finally, perceived value positively influenced customer loyalty. The findings suggest that coffee outlet operators with a better understanding of their customers’ perceptions, and help them in developing competitive strategies that differentiate themselves from competitors and win customer loyalty in an aggressive market.     

Listening to the voice of the guest: A framework to improve decision-making processes with text data

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.     

Motivation and satisfaction of Chinese and U.S. tourists in restaurants: A cross-cultural text mining of online reviews

Tourists with dissimilar cultural backgrounds think and behave differently. Precisely capturing and correctly understanding the cultural difference will help tourism managers generate greater customer satisfaction and increased business revenue. To this end, this paper uncovers and compares the motivation and satisfaction of restaurant tourist customers coming from China and U.S. by investigating their online ratings and reviews. From two major online review communities, customer ratings and reviews have been retrieved, quantified, text-mined, compared, and interpreted using statistics, latent Dirichlet allocation, and frequency analysis. Results suggest that Chinese tourists are less inclined to assign lower ratings to restaurants, and are more strongly fascinated by the food offered, whereas U.S. tourists are more apt to be fun-seeking, and are less uncomfortable with crowdedness.     

The financial impact of online customer reviews in the restaurant industry: A moderating effect of brand equity

The salient impact of electronic Word-of-Mouth (eWOM) on firm performance has been widely noticed by scholars and practitioners. While eWOM serves as an important source of information that helps reduce perceived uncertainty risks in service purchases, it is highly likely that other sources of information are simultaneously used with eWOM in the purchase decisions. Thus, this study empirically examined the financial effects of restaurant eWOM (review volume and review rating) and their changes in the presence of brand equity. Three-stage least squares analysis was employed in the empirical investigation. Our findings revealed that review volume and review rating contributed to restaurant profitability. Brand equity was found to moderate the effects of the review attributes on restaurants profitability, which implies that eWOM has a greater influence on the financial performance of weak-branded restaurants than strong-branded ones. Implications for researchers and practitioners are discussed.     

The signaling effect of management response in engaging customers: A study of the hotel industry

Hotels today actively respond to online reviews given their tremendous influence on travelers' decisions. Yet, the questions of how to respond to online reviews continue to baffle hotel managers. By focusing on prospective travelers, we propose the effective management response signals hotels' care for customers and quality of service. Particularly, we postulate the frequency, speed and length of response influence the effectiveness of signaling in reducing information asymmetry. Based on the large-scale field data from TripAdvisor, this study demonstrates that the frequency and speed of response significantly enhance travelers’ engagement as indicated by more reviews, higher average valence, more votes for helpfulness, and higher popularity ranking. Furthermore, the frequent and speedy response is more effective for budget (vs. premium) hotels. Thus, management response to online reviews serves as a critical channel of communication to engage customers.     

Drivers and resources of customer co-creation: A scenario-based case in the restaurant industry

The aim of this study was to build and test a model that explores customer co-creation by integrating drivers and resources that promote customer co-creation. This study used a scenario-based survey that combines respondents’ personal experiences of dining at a full-service restaurant and their responses to the hypothetical scenario of the co-creation experience. The population of the study was restaurant customers in the United States who visited any full-service restaurant within the previous three months before the survey date. Using convenience sampling, respondents were recruited from the Amazon Mechanical Turk. A total of 501 responses were used for data analysis using a two-step approach of structural equation modeling. This study found that customers with a higher level of knowledge, self-efficacy, and motivation are more likely to participate in the co-creation experience. In addition, the results supported the role of the customer as a resource integrator during the co-creation experience.     

A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism

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.     

Control,use and ownership of big data: A reciprocal view of customer big data value in the hospitality and tourism industry

While the large-scale harvesting of consumer data is a common practice in the hospitality and tourism industries, the seemingly unassailable right of companies to collect and share consumer data is not without critics. The purpose of this paper is to explore the nature of data-based value co-creation under varying conditions of consumer control and benefits. Academic and press articles were used to explore the nature of big data value co-creation in a wide range of hospitality, tourism, and other industries. A forward-looking approach was adopted by considering the implications of policy and technology as key mechanisms for sharing the power dynamic regarding the ownership, control and use of personal data. The results suggest that reciprocal big data value creation can be seen as a function of the level of benefit and control afforded to consumers regarding the use of their data. Four types of reciprocal big data value creation are proposed.     

A systematic literature review of the personal value orientation construct in hospitality and tourism literature

Although personal value orientation has received the attention of scholars to explain and predict various consumer behaviors, prior research within the hospitality and tourism context has proposed the mismatched fundamental concept and operationalization of personal values and/or has adapted partial aspects of the personal value construct, such as pro-environmental behaviors. Based on the fundamental principles of personal value orientation, this study systematically reviewed studies that focused on personal value orientation by investigating their research context, definitions, theoretical background and measurements, roles and main results. The systematic literature review was conducted with articles published in top-tier hospitality and tourism journals between 2000 and 2018 by identifying research gaps and providing a direction for future research on personal value orientation in those fields.     

滨海旅游目的地游客感知价值的影响因素研究——以广西北海银滩国家级旅游度假区为例

把握影响游客感知价值的关键因素,有助于促进旅游目的地竞争力的提升。以广西北海银滩国家级旅游度假区为研究区域,采用携程旅行网北海银滩游客网络评论资料作为研究样本,运用扎根理论方法构建包括5个主范畴和18个对应范畴的滨海游客感知价值影响因素模型,并运用复杂系统的决策实验和评估实验法（Decision making Trial and Evaluation Laboratory,DEMATEL）模型构建滨海游客感知价值影响因素的关系矩阵,识别影响滨海游客感知价值的关键因素。研究发现：资源和环境、管理和保护、设施和服务、情感和成本价值构成了滨海旅游目的地游客感知价值的重要维度,天气条件、海滩规模、海滩管理和保护、游客愉悦感、游客娱乐体验项目、旅游基础设施及服务是影响滨海旅游目的地游客感知价值的关键因素。据此提出促进我国滨海旅游目的地游客感知价值提升的相关建议。