There is a lot of attention given to the determinants of guest satisfaction and consumer behavior in the tourism literature. While much extant literature uses a deductive approach for identifying guest satisfaction dimensions, we apply an inductive approach by utilizing large unstructured text data of 104,161 online reviews of Korean accommodation customers to frame which topics of interest guests find important. Using latent Dirichlet allocation, a generative, Bayesian, hierarchical statistical model, we extract and validate topics of interest in the dataset. The results corroborate extant literature in that dimensions, such as location and service quality, are important. However, we extend existing dimensions of importance by more precisely distinguishing aspects of location and service quality. Furthermore, by comparing the characteristics of the accommodations in terms of metropolitan versus rural and the type of accommodation, we reveal differences in topics of importance between different characteristics of the accommodations. Specifically, we find a higher importance for points of competition and points of uniqueness among the accommodation characteristics. This has implications for how managers can improve customer satisfaction and how researchers can more precisely measure customer satisfaction in the hospitality industry.

This study inductively analyzes the topics of interest that drive customer experience and satisfaction within the sharing economy of the accommodation sector. Using a dataset of 1,086,800 Airbnb reviews across New York City, the text is preprocessed and latent Dirichlet allocation is utilized in order to extract 43 topics of interest from the user-generated content. The topics fall into one of several categories, including the general evaluation of guests, centralized or decentralized location attributes of the accommodation, tangible and intangible characteristics of the listed units, management of the listing or unit, and service quality of the host. The deeper complex relationships between topics are explored in detail using hierarchical Ward Clustering.

The study aims to investigate the formation of customer loyalty among luxury restaurant patrons in Korea. Moreover, the study investigated how the restaurants’ performance could contribute to the trust and sustainability of the Michelin restaurant guide’s reputation. The study identified meal experience, brand credibility, and brand love to influence customers’ revisit intention and willingness to pay a premium. The study surveyed 400 luxury restaurant patrons in Korea. The Michelin restaurant guide was used to classify fine dining restaurants. Measurement items from previously validated studies were adopted. The results of the study showed the meal experience scale satisfactorily measures service performance and leads to the formation of brand credibility. Subsequently, brand prestige and brand love significantly predicted customers’ loyalty intentions. Additionally, brand credibility helps form the trust of the Michelin guide and eventually predicts the long-term reputation of the guide.

This study investigates the impact of signaling on customers’ evaluations of service qualities, customer satisfaction and revisit intentions in times of service failures. Open kitchen design is selected as a signal. An online survey is conducted to collect data on customers’ evaluations of service qualities in open kitchen restaurants in comparison with closed kitchen restaurants. Then, customer satisfaction and revisit intentions are measured under service failure scenarios. The findings demonstrate that open kitchen design is perceived as a negative signal for noise and smell but not as a positive signal for food hygiene and food quality. Under service failure scenarios, signaling is not confirmed to affect customer satisfaction and revisit intentions. Rather, signaling is found to be effective even when there were relatively higher chances of service failures.