• Journal of Information Technology Services
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• v.18 no.2
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• pp.1-16
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• 2019

Social media is one of the important marketing channels for companies, changing the way interacting with customers. Marketers attract participation from customers' in social media platforms by producing branded content, which helps them gain various marketing results such as brand awareness, web traffic, and sales. The number of the empirical studies on the effects of social media on marketing performance is still low although various success stories and studies have been published. In particular, IT companies are trying to attract users onto their websites with social media content and promotions; however, they regard the number of the visitors as a vanity metric, which has little effectiveness. The study examined the Effect of the site introduced via Facebook, a typical social medium, on customers' revisit. Precedent studies proved that revisit, one of forms of major visit for satisfactory results of a website, is suitable for analyzing the operational output on Facebook pages. The results of the study demonstrated that Facebook content has a positive impact on website inflows and revisits. Also, it turns out that the higher the average website visit depth reinforces the positive relationship between the rate of the inflow and that of the site revisit.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.1
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• pp.1-13
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• 2006

In this paper we consider a design problem of a cyclic queueing network with two stages, each with a local buffer of limited capacity. Based on the theory of reversibility and product-form solution, we derive the throughput function of the network as a key performance measure to maximize. Two cases are considered. In case each stage consists of a single server, an optimal allocation policy of a given buffer capacity and work load between stages as well as the optimal number of customers is identified by exploiting the properties of the throughput function. In case each stage consists of multiple servers, the optimal policy developed for the single server case doesn't hold any more and an algorithm is developed to allocate with a small number of computations a given number of servers, buffer capacity as well as total work load and the total number of customers. The differences of the optimal policies between two cases and the implications of the results are also discussed. The results can be applied to support the design of certain manufacturing and computer/communication systems.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• The Korean Journal of Franchise Management
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• v.12 no.1
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• pp.21-34
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• 2021

Purpose: This study examines the structural relationship between perceived quality, relationship quality, and revisit intention in the context of coffee shop. In this model, perceived quality consists of product, service, and experience quality, and relationship quality consists of satisfaction, trust, and commitment, and performance consists of revisit intention. More specially, this study identifies whether perceived quality plays a mediating role in the relationship between perceived quality and relationship quality and the direct/indirect effects of perceive quality on intention to revisit. Research design, data and methodology: The survey was conducted from September 1 to 30, 2019. The data were collected from 320 respondents and analyzed using structural equation modeling (SEM) with AMOS program. Results: The findings are as follows. First, quality perception of coffee specialty stores had a statistically positive effect on relationship quality, indicating supports H1. Therefore, customers can know that they are aware of the quality of coffee specialty stores, including quality of service and experience as well as products, and that they form relationship quality with coffee specialty stores. Second, relationship quality between coffee shops and customers had a significant positive effect on performance. Thus, H2 was supported. The results show that if the coffee shop does not consider relationship quality as important, customer loyalty decreases, the number of customers decreases, and the number of customers who switch to another coffee shop increases, which can lead to a threat to the coffee shop. Third, in the case of hypothesis H3, it was found that there was a partial mediating effect of satisfaction and trust between quality perception and reuse intention of coffee specialty stores, so hypothesis H3 was partially supported. As commitment appears to have no mediating effect, it can be said that customers who use coffee shops are not only difficult to maintain as regular customers of a particular coffee shop, but also have ample room to move to other coffee shops. Conclusions: Although many scholars point out the importance of service quality, few studies were conducted in the context of the Korean food service industry (including coffee shops). From this perspective, this study tested several hypotheses that the quality (product, service, experience) perceived by customers can have a positive effect on relationship quality and performance (re-visit intention), either directly or indirectly. The findings of this study demonstrate that if the manager of a coffee shop understands the characteristics of quality perceived by customers and the role of relationship quality, the effect of quality perceptions on customers can be maximized in order to maintain the relationship with customers.

• Culinary science and hospitality research
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• v.6 no.1
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• pp.25-55
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• 2000

The Development of a new menu item in a restaurant usually begins when your chef prepares a creative "brainchild" that he serves to you, the owner. If you are satisfied with the new dish, It gets a "go-ahead" signal, under the assumption that what appeals to you will appeal to a significant number of your customers. While many excellent and successful items have been created this way, It is not reasonable to expect that your tastes can represent the wide variety of your potential guests tastes. The purpose of this study is to examine the factors that have an influence on customers′ menu evaluation. A menu is a marketing tool which delivers restaurant′s image and message to customers needs and wants to restaurant. So far, studies on menu have been done in an analytical way from a management′s viewpoint and inadequate to reflect customers′ needs and wants. In this study is to examine the factors that have an influence on customers′ menu evaluation. The factors affected the customers′ menu evaluation of hotel restaurant in the three factors. Three factors are food service factor, menu copy factor, value of food. Correlation between the evaluation of criteria and selection of menu was examined. All Three evaluation criteria have shown strong correlation with selection of menu of these criteria, menu-copy factor was found to be most strongly correlated with selection of menu. In conclusion, As a study on the Customer′s evaluation factors of the Korean restaurant menus in hotels, It raises to exert us strength in the menu management of Korean restaurant.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.297-304
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• 2021

In a duopoly situation, one firm can gain competitive advantage by attracting the price-sensitive customers from its competitor leading to higher profits through higher sale revenue for the competing company. A simulation study in which there are two electric car manufacturers with agent based modelling was conducted in order to verify this contention. The first step consisted of defining the baseline. Simulations of 1000 times and agent-based modelling were conducted with the assumption that company 1 reduced its price to the maximum of 20% thereby contributing to the switch-over of a maximum of 40% of the price sensitive customers of company 2. The results of 1000 simulations and agent-based modelling highlighted that price reduction by company 1 resulted in a significant increase in the number of customers, presumably due to switch-over from company 2 and there was a corresponding increase in revenues from both of the sales avenues. Thus, Company 1 achieved competitive advantage by marketing its cars to the customers of Company 2 using price reduction strategy to attract them. This study has ramifications for companies that aim to sway the price sensitive customers from a competitor.

• Journal of the FoodService Safety
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• v.3 no.1
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• pp.28-37
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• 2022

With the increased development of the dining sector, several restaurants have emerged and are becoming more advanced. Since the Michelin Guide Seoul publication in Korea, the fine-dining market has attracted attention from guests. Moreover, the number of customers visiting fine-dining restaurants has increased comparatively. However, it has been a mere 6 years since the publication of the Michelin Guide Seoul, and domestic materials and research related to the Michelin Guide are insufficient. Therefore, this research examines the factors responsible for restaurants to gain Michelin stars, and the effect of the Michelin guide on customers to select restaurants. Customers who have visited and experienced the star restaurants were interviewed, as well as the owner chefs and personnel managing the restaurant. The interviews were conducted from August to September 2020 using the Colaizzi method. The survey was designed to extract meaningful sentences or phrases from the participant interviews, categorize general and abstract statements into topic clusters, and describe the essential experience. This research aimed to determine the effect of the Michelin guide on customers for a selection of the restaurants and star restaurants. It is important to understand the development of Michelin restaurants and the effective factors which make customers choose the restaurants. Also, results of this study will be a future guide for the possibility of developing domestic fine-dining restaurants, normal restaurants, and the food service industry.

• Culinary science and hospitality research
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• v.16 no.2
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• pp.199-214
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• 2010

This study aims to investigate the effects of selection attributes on customers' satisfaction and behavioral intention for hotel weddings depending on their life style. The developed hypotheses were tested using a sample of customers who have ever had hotel weddings in the age of 20~30 living in Seoul and Daejeon. The total number of 300 self-administrated questionnaire copies were distributed and 248 valid samples were used for the analysis. In order to examine the proposed model, statistical tests were conducted using SPSS (14.0). The results showed that the customers' attributes selecting a hotel wedding were significantly different depending on their life style. It was also found that customers' satisfaction has a significantly positive effect on their behavioral intention for hotel weddings.

• Management Science and Financial Engineering
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• v.2 no.1
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• pp.123-145
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• 1996

We consider a priority-based multiclass queue with probabilistic feed-back. There are J service stations. Each customer belongs to one of the several priority classes, and the customers of each class arrive at each station in a Poisson process. A single server serves queued customers on a priority basis with a nonpreemptive scheduling discipline. The customers who complete their services feed back to the system instantaneously and join one of the queues of the stations or depart from the system according to a given probability. In this paper, we propose a new method to simplify the analysis of these queueing systems. By the analysis of busy periods and regenerative processes, we clarify the underlying system structure, and systematically obtain the mean for the sojourn time, i.e., the time from the arrival to the departure from the system, of a customer at every station. The mean for the number of customers queued in each station at an arbitrary time is also obtained simultaneously.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.489-497
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• 2000

The M/M/1 queue with impatient customers is studied. Impatient customers wait for service only for limited time K/0 and leave the system if their services do not start during that time. Notice that in the analysis of virtual waiting time, the impatient customer can be considered as the customer who enters the system only when his/her waiting time does not exceed K. In this paper, we apply martingale methods to the virtual waiting time and obtain the expected period from origin to the point where the virtual waiting time crosses over K or reaches 0, and the variance of this period. With this results, we obtain the expected busy period of the queue, the distribution, expectation and variance of the number of times the virtual waiting time exceeding level K during a busy period, and the probability of there being no impatient customers in a busy period.