• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• Korean Journal of Heritage: History & Science
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• v.54 no.1
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• pp.206-219
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• 2021

The warming of the climate is increasing the damage caused by termites to wooden buildings, cultural properties and houses. A group removal system can be installed around the building to detect and remove termite damage; however, if the site is not visited regularly, every one to two months, you cannot observe whether termites have spread within, and it is difficult to take prompt effective action. In addition, since the system is installed and operated in an exposed state for a long period of time, it may be ineffective or damaged, resulting in a loss of function. Furthermore if the system is installed near a cultural site, it may affect the aesthetic environment of the site. In this study, we created a detection system that uses wood, cellulose, magnets, and magnetic sensors to determine whether termites have entered the area. The data was then transferred to a low power LoRa Network which displayed the results without the necessity of visiting the site. The wood was made in the shape of a pile, and holes were made from the top to the bottom to make it easier for termites to enter and produce a cellulose sample. The cellulose sample was made in a cylindrical shape with a magnet wrapped in cellulose and inserted into the top of a hole in the wood. Then, the upper part of the wood pile was covered with a stopper to prevent foreign matter from entering. It also served to block external factors such as light and rainfall, and to create an environment where termites could add cellulose samples. When the cellulose was added by the termites, a space was created around the magnet, causing the magnet to either fall or tilt. The magnetic sensor inside the stopper was fixed on the top of the cellulose sample and measured the change in the distance between the magnet and the sensor according to the movement of the magnet. In outdoor experiments, 11 cellulose samples were inserted into the wood detection system and the termite inflow was confirmed through the movement of the magnet without visiting the site within 5 to 17 days. When making further improvements to the function and operation of the system it in the future, it is possible to confirm that termites have invaded without visiting the site. Then it is also possible to reduce damage and fruiting due to product exposure, and which would improve the condition and appearance of cultural properties.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.19-42
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• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.

• The Korean Society of Law and Medicine
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• v.21 no.1
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• pp.223-259
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• 2020

With COVID-19 spreading rapidly around the world, research and development issues on treatments and vaccines for the virus are of high interest. Among them, Remdesivir was the first to show noticeable therapeutic effects and began clinical trials, with each country authorizing the use of the drug through emergency approval. However, Gilead Co., Ltd., the developer of Remdesivir, received a lot of criticism from civic groups for submitting the application for the marketing authorization as an orphan drug. This is because when a new drug got a marketing authorization as an orphan drug could be granted an exclusive status for seven year. The long-term exclusive status of an orphan drug comes from the policy purpose of motivating pharmaceutical companies to develop treatment opportunities for patients suffering from rare diseases, which was not appropriate to apply to infectious disease treatments. This paper provides a review of the problems and improvement directions of the domestic system through comparative legal consideration against the United States, Europe and Japan for the statutes which give exclusive status to medicines. The domestic system has a fundamental problem that it does not have explicit provisions in the statute in the manner of granting exclusive status, and that it uses the review system to give it exclusive status indirectly. In addition, in the case of orphan drugs, the "Rare Diseases Management Act" and the "Regulations on Examination of Items Permission and Reporting of Drugs" provide overlapping review periods, and despite the relatively long monopoly period, there seems to be no check clause to recover exclusive status in the event of a change in circumstances. Given that biopharmaceuticals are difficult to obtain patents, the lack of such provisions is a pity of domestic legislation, although granting exclusive rights may be a great motivation to induce drug development. In the United States, given that the first biosimilar also has a one-year monopoly period, it can be interpreted that domestic legislation is quite strictly limited to granting exclusive status to biopharmaceuticals. The need for improvement of the domestic system will be recognized in that it could undermine local pharmaceutical companies' willingness to develop biopharmaceuticals in the future, and in that it is also necessary to harmonize international regulations. Taking advantage of the emergence of COVID-19 as an opportunity, we look again at the problems of the domestic system that grants exclusive rights to medicines and hope that an overall revision of the relevant legislation will be made to establish a unified legal basis.

• Journal of Distribution Research
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• v.11 no.1
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• pp.41-68
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• 2006

Today, Manufacturing companies execute the logistics outsourcing that commits the non-core logistic function to the specialized logistics corporation, which makes the manufacturing company focus on its core competence, product development and marketing, reduces logistics cost and improves customer service level. Recently, Logistics outsourcing is developed into cooperative sourcing based on the partnership. Case study on the logistics outsourcing will provide the good guideline for planning of the outsourcing strategy. The objective of this research is making a sense about 4PL through the case of UPS-Samsung Electro-Mechanics and catching major issue to provide the guideline for the cooperation outsourcing strategy. We investigated historical backgrounds of the logistics outsourcing between UPS and Samsung Electro-Mechanics. We also investigated problems occurred in outsourcing process at the five dimensions-organizational problem, CEO's short-term views, cultural gap between two companies, integration of IT system, and different understanding about outcomes. We expect to give many implications to manufacturing companies which want to cooperate with specialized logistics corporation.

• Information Systems Review
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• v.12 no.3
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• pp.75-88
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• 2010

Business models in open market systems targeting smart phone users are determined by several important factors. First, by providing developers efficient technical platforms, it contains a setting for developers to learn, apply and improve the skills relating to the product category easily while they stay beyond a corporate boundary. Second, by the first condition, a huge population of talented developers becomes to join a specific open market where will invite more customers to use their applications. Hence it will attract more and more developer participants who will finally give a rise to a persistent market growth. Third, the evaluation system between platform providers and application producers, and one between application producers and application users may underlie the trust relationships between them. The research conducted a multiple embedded case study to test the success factors of open market based business models. It focused on smart phone game communities that have installed user evaluation, and feedback systems. The user innovation empowerment model within the social game networks has highlighted the theories on the roles and characteristics of lead users, and lead user network behaviors for future NPD participations.

• Culinary science and hospitality research
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• v.10 no.3
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• pp.97-118
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• 2004

As the economy grows rapidly and the national income level increases, the service industry has become more important and its size gets larger. Especially, the food industry undertakes a continuing growth of consumer expenditures through no nationalization of food, consumers varying patterns of eating out, and individualization. However, it includes many problems with improving service quality towards customers owing to the absence of systems and philosophy to realize customer satisfaction management Therefore, this study conducted a study with Korean traditional restaurant customers to measure their perceived service quality, to verify what factors most influence consumer satisfaction, and to suggest ways to meet the costumer needs by integrating the study results and developing high service quality. To sum up the results of this study, Korean traditional restaurant visitors evaluated three dimensions of service quality overall favorably. Interestingly, background music received a lower level of satisfaction. This implies that the Korean traditional restaurant managers do not do a fine performance in managing music as an ambient element to evoke the atmosphere of the restaurant. Customers showed a lower satisfaction with menu price among the factors measuring product quality. As a result of this study, several suggestions have been made as follows: First, it is necessary for the Korean traditional restaurants to provide systematic employee training to instigate a service culture of customer focus. Secondly, it is important to develop traditional foods using ingredients produced in the region based on a literature review of food in Gyeongju, Silla. Thirdly, it is an urgent task to develop regional menus to revitalize the restaurant business. Fourthly, it is important to provide safety food through systematic sanitary administration. Lastly, it is necessary to limit the number of menu items and attempt specialization of them. Such an attempt with specialized menu items will help their food taste and quality improve and thereby reduce inventory burdens. However, this study has a few limitations. Since this study conducted a survey of the restaurants which provides only Korean traditional food in Gyeongju, not all the restaurants in it, there is a limitation in measuring customer satisfaction with service quality. Therefore, the study results cannot be generalized to all the restaurants in Gyeongju and the nation. Finally, this study suggests that studies on the relationships between customer satisfaction and menu price and customer value system or further customer satisfaction studies with Korean traditional restaurants should be continued.

• The Journal of Korean Academic Society of Nursing Education
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• v.6 no.2
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• pp.233-246
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• 2000

The purpose of this study was to investigate the real condition of hospital marketing and concrete strategy on medical consumers need for enhance the effectiveness of hospital management. The data were collected from January 27 to February 3, 2000 at a general hospital located in M city to 205 in-patients and out-patients by questionnaires. The research tool was based on literatures. The data was analysed by the use of percentage, mean, t-test and ANOVA by SAS(Statistical Analysis System) Package Program. The results of this study are summerized as follows: 1. Mean score of the marketing mix (4P 's) was 3.1 in total and each mean score was 3.6 in product, 2.5 in prices, 3.3 in place and 3.1 in promotion. The following are the items which received the highest scores in each: 'The hospital is clean' in service category; 'the prices of meals at the restaurant the patients' families use are reasonable ' in price sphere. In distribution, 'it is easy to locate the hospital' and in promotion,'we'll use this hospital again' and 'we'll recommend this hospital to others'. 2. Marketing mix factors(4P's) was significantly different on the general characteristics in terms of marriage status, occupations and the period of hospitalization. 3. The most important reason for choosing this hospital was 'This is a general hospital with good facilities' and the next were 'The hospital staffs are kind and the hospital is clean' and 'The traffic is convenient to come'. The most important factors contributing to a good image formation of the hospital were 'this is a clean hospital', 'This is a hospital with kind people' and 'this hospital is equipped with good medical facilities'. 4. The factors for making a good image concerning the nursing service were professional knowledge, good nursing skills and kindness. After grasping the marketing conditions of the hospital aided by the above-mentioned research results, the researchers concludes that it is necessary to develop an institutionalized service strategy to increase the satisfaction the patients feel about the hospital facilities and kindness of the staff and as a result, to differentiate it from other medical institutions.