The fast food restaurant business is one of the fastest-growing industries in the world. International and local restaurant chains are trying to satisfy the demands of customers for a variety of products and services. Along with changing market trends, customers are now becoming more sophisticated and demanding. Customer satisfaction is an essential business issue, as entrepreneurs have realized that favorable customer feedback is key for a long-term sustainable operation. Customers who have an excellent experience at a restaurant may recommend the restaurant to others, spread positive information, or become a loyal customer. The fast food industry has only recently developed in Mongolia and an increasing number of global fast food chains are now entering the market every year. The purpose of this paper is to examine and evaluate the factors affecting customer satisfaction, revisit intention, and likelihood of recommendation for Mongolian fast food restaurants, as well as a global fast food restaurant in Mongolia using the DINESERV scale. This study focuses on comparing directly competing food chains; only two brands were studied because of the limited fast food presence in Ulaanbaatar. Then, it aims to analyze how satisfaction levels influence a customer’s revisit intention and likelihood of recommending a restaurant. Furthermore, an in-depth analysis of the difference between local and global fast food brands is a key element that this paper analyzes. Moreover, this paper investigates how results can be different according to whether the respondent resides in Mongolia or Korea and discusses business implications. The results of this paper show that four factors (food quality, service quality, price, and atmosphere of a restaurant) positively influence customer satisfaction, revisit intention, and likelihood of recommendation for Mongolian and global fast food restaurants, and customer satisfaction has a positive influence on customer revisit intention and likelihood of recommendation for both types of restaurants. However, depending on whether it is a Mongolian fast food restaurant or a global fast food restaurant, the factors affecting customer satisfaction, revisit intention, and recommendation are different.

Consumer reviews on the web have rapidly become an important information source through which consumers can share their experiences and opinions about products and services. It is a form of text-based communication that provides new possibilities and opens vast perspectives in terms of marketing. Reading consumer reviews gives marketers an opportunity to eavesdrop on their own consumers. This paper examines consumer reviews of three different competitive automobile brands and analyzes the advantages and disadvantages of each vehicle using text mining and association rule methods. The data were collected from an online resource for automotive information, Edmunds.com, with a scraping tool “ParseHub” and then processed in R software for statistical computing and graphics. The paper provides detailed insights into the superior and problematic sides of each brand and into consumers’ perceptions of automobiles and highlights differences between satisfied and unsatisfied groups regarding the best and worst features of the brands.

Previous studies have introduced different potential pricing strategies for cloud services. However, not much research has been done comparing subscription pricing and pay-per-use pricing, which are commonly used pricing schemes. Also, there are very few studies which analyze a two-part tariff pricing scheme for cloud services, even though this option may increasingly attract service providers as the cloud market becomes more competitive and the profit margin grows narrower. Previous research has focused on firms' profitability rather than social welfare due to the limitations of free services. This study uses theoretical and numerical analysis to compare the social welfare and profitability of three pricing schemes commonly used by firms: subscription pricing, pay-per-use pricing, and two-part tariff pricing. It shows that the pay-per-use pricing is the best solution from the perspective of social welfare, which contrasts with the conclusion of a previous study stating that social welfare is maximized under a two-part tariff. This paper also shows that the two-part tariff is the most profitable pricing scheme for firms.Sustainability 2020, 12, 49 2 of 15 but a comparative analysis of subscription pricing and pay-per-use pricing has not been done, although these pricing methods are commonly used in cloud service systems. Also, an analytical study is required for the two-part tariff pricing scheme, since the concept of a two-part tariff is nowadays adopted as a cloud services' pricing strategy and could gain in popularity among service providers as the cloud market becomes more competitive and the profit margin grows narrower. Previous research has focused on firms' profitability with respect to the limitations of free services. Chun and Choi [7] analyzed the two pricing schemes of subscription and pay-per-use pricing models from a provider's standpoint and discussed their implications for consumer surplus and social welfare. Chun et al. [8] compared the three pricing schemes of subscription pricing, pay-per-use pricing, and two-part tariff from the perspectives of a provider's profit, consumer surplus, and social welfare.This study differentiates itself from previous ones in several aspects. Firstly, I applied two-part tariff pricing to cloud services by considering subscription pricing and pay-per-use pricing and extended the previous two-part tariff to a more generalized customer heterogeneous model, in order to utilize it for a cloud service pricing strategy. Secondly, I clarified the previous two-part tariff model and compared the results from subscription, pay-per-use, and two-part tariff pricing with those obtained from analytical and numerical approaches and recent real-world examples. In particular, I show how two-part tariff pricing can include the other two pricing schemes of subscription and pay-per-use pricing, through theoretical and numerical analysis. Thirdly, I discuss some possible conflicting issues between customers and providers, in terms of consumer and producer surpluses.This paper shows tha...

This study investigates liability issues in electronic transactions when security or privacy breaches occur. As data is transferred using various devices, such as PCs, mobile phones, tablets, sensors, smart meters, and cars, and various architecture, such as the cloud, IoT, as well as in well-defined network structures in electronic commerce, privacy and security breaches happen. These have become a major hindrance to the development and use of commercial activities on the Internet. There have been many security breach cases, such as those of Target Corporation's security and payment system (2013), eBay's cyberattack (2014), Uber's hacking incident (2016), Facebook's personal data use and privacy breach (2018), and many others. Therefore, when a dispute regarding electronic transactions arises between a customer and a firm, the allocation of liability is very important for the sustainability of e-businesses. Many cases show that firms are held liable for those incidents. However, the liability allocation rule tends to vary slightly from country to country depending on the application areas. EU countries seem to favor customers. In the United States, there are actually no uniform federal laws relating to business cybersecurity. Also, in the case of cryptocurrency, liability tends to lie with customers. Why is the ruling different? In this regard, this paper analyses the legal framework for security and privacy breaches for sustainable e-businesses. In particular, this paper focuses on the optimal liability in terms of enhancing social welfare when considering both sides-the customer and the firm (or service provider). This paper shows that liability can be generally imposed on the firm's side when the possibility of security or privacy breaches is high, and the customer's loss is relatively large. However, the liability depends on the customer's attitude towards risk, customer's losses, and the efficiency of security investment.

Case based reasoning is a knowledge discovery technique that uses similar past problems to solve current new problems. It has been applied to many tasks, including the prediction of temporal variables as well as learning techniques such as neural networks, genetic algorithms, decision trees, etc. This paper presents a geometric criterion for selecting similar cases that serve as an exemplar for the target. The proposed technique, called geometric Case Based Reasoning, uses a shape distance method that uses the number of sign changes of features for the target case, especially when extracting nearest neighbors. Thus, this method overcomes the limitation of conventional case-based reasoning in that it uses Euclidean distance and does not consider how nearest neighbors are similar to the target case in terms of changes between previous and current features in a time series. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index. The results show that the proposed technique is significantly better than the random walk model at p < 0.01. However, it was not significantly better than the conventional CBR model in the hit rate measure and did not surpass the conventional CBR in the mean absolute percentage error.