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Mukbang is widely recognized as a new type of food video on user-generated content (UGC) platforms. The purpose of this study was to identify motivators to watch mukbangs and to examine the relationship between these motivators and the intention to watch mukbangs via attitudes toward mukbangs and para-social relationships. In addition, this study examined how the intention to watch mukbangs affected purchase intention. Interviews were conducted to determine the motivation factors for watching mukbangs by collecting data from mukbang viewers. The results of the interviews suggested that vicarious satisfaction, enjoyment, information, exposure, and attractiveness were motivators for watching mukbangs. Using a survey, this study collected data from 399 participants who watched mukbangs to test relationships. Using SmartPLS, structural equation modeling (SEM) was conducted. The outcomes of the SEM indicated that vicarious satisfaction, enjoyment, and information influenced the intention to watch mukbangs via attitudes toward mukbangs. The results also indicated that exposure and attractiveness had an impact on the intention to watch mukbangs via para-social relationships. Furthermore, the intention to watch mukbangs influenced the intention to purchase food items portrayed in the mukbang content. This study contributes to the literature by empirically confirming the effect of watching mukbang on purchase intention.

Due to the advancement of Information Technology (IT), the hospitality industry is seeing a great value in gathering various kinds of and a large amount of customers' data. However, many hotels are facing a challenge in analyzing customer data and using it as an effective tool to understand the hospitality customers better and, ultimately, to increase the revenue. The authors' research attempts to resolve the current challenges of analyzing customer data in hospitality by utilizing the big data analysis tools, especially Hadoop and R. Hadoop is a framework for processing large-scale data. With the integration of new approach, their study demonstrates the ways of aggregating and analyzing the hospitality customer data to find meaningful customer information. Multiple decision trees are constructed from the customer data sets with the intention of classifying customers' needs and customers' clusters. By analyzing the customer data, the study suggests three strategies to increase the total expenditure of the customers within a limited amount of time during their stay.

With the prevalence of obesity in adolescents, and its long-term influence on their overall health, there is a large body of research exploring better ways to reduce the rate of obesity. A traditional way of maintaining an adequate body mass index (BMI), calculated by measuring the weight and height of an individual, is no longer enough, and we are in need of a better health care tool. Therefore, the current research proposes an easier method that offers instant and real-time feedback to the users from the data collected from the motion sensors of a smartphone. The study utilized the mHealth application to identify participants presenting the walking movements of the high BMI group. Using the feedforward deep learning models and convolutional neural network models, the study was able to distinguish the walking movements between nonobese and obese groups, at a rate of 90.5%. The research highlights the potential use of smartphones and suggests the mHealth application as a way to monitor individual health.