• Korean Journal of Food Science and Technology
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• v.43 no.1
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• pp.91-97
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• 2011

This study was performed to assess the microbiological quality and potential health risk of fresh-cut produce and organic vegetables sampled from supermarkets and department stores in Korea. A total of 96 samples comprised three types of fresh-cut produce (sprouts, mixed-vegetables, fruit) and three types of organic vegetables (lettuce, perilla leaf, green pepper). The samples were analyzed for total viable cell counts, coliforms, Enterobacteriaceae, Escherichia coli, Salmonella spp., Listeria monocytogenes, Vibrio parahaemolyticus, Bacillus cereus, and Staphylococcus aureus. The microbiological counts of fruit were very low. Sprouts were highly contaminated by total viable cell counts ($8.3{\pm}0.57$ log CFU/g), Enterobacteriaceae ($7.1{\pm}0.76$ log CFU/g), and coliforms ($4.9{\pm}0.40$ log MPN/g), and showed a high incidence level of B. cereus ($2.9{\pm}0.48$ log CFU/g). Of the fresh-cut produce analyzed, six (13.6%) mixed-vegetable salads were E. coli positive. S. aureus was detected in only one sprout sample and one mixed-vegetable salad, and its contamination levels were under 2 log CFU/g, which is appropriate for Korean standards (<3 log CFU/g) of fresh-cut produce. Of the organic vegetables, lettuces were highly contaminated by total viable cell counts ($6.4{\pm}0.74$ log CFU/g), Enterobacteriaceae ($5.7{\pm}0.98$ log CFU/g), and coliforms ($3.7{\pm}1.72$ log MPN/g). Two (13.6%) organic lettuce and one (7.1%) perillar leaf sample were E. coli positive, and S. aureus was detected in one lettuce and two perilla leaf samples. Salmonella spp., Vibrio parahaemolyticus, and Listeria monocytogenes were not detected in any of the fresh-cut produce or organic vegetables analyzed.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.40 no.3
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• pp.269-278
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• 2014

This study aimed to provide the fundamental data on the field of cosmetics by comparing heavy metal concentration in terms of domestic/foreign products, types and colors. The study determined the concentrations of lead, cadmium, arsenic, chromium, antimony, nickel, copper and cobalt in cosmetics such as lipstick, lip gloss, lip balm, foundation and eye liner. From the period of January to August, 2013, 121 samples were collected from cosmetic stores distributing to the general market. The average metal concentrations were as follows; $0.663{\mu}g/g$ for lead, $0.010{\mu}g/g$ for cadmium, $0.056{\mu}g/g$ for arsenic, $1.144{\mu}g/g$ for chromium, $0.008{\mu}g/g$ for antimony, $0.405{\mu}g/g$ for nickel, $0.319{\mu}g/g$ for copper and $0.108{\mu}g/g$ for cobalt. Except for chromium, the heavy metal concentrations were significantly higher in foreign products than in domestic products (p < 0.05). Also, The mean concentrations of heavy metal were significantly different (p < 0.05) when classified by cosmetic type. The highest mean concentrations shown in lipstick were $1.430{\mu}g/g$ of chromium, $0.616{\mu}g/g$ of lead and $0.385{\mu}g/g$ of nickel, in foundation $1.155{\mu}g/g$ of lead and $1.023{\mu}g/g$ of chromium. In eye liner, the highest mean concentrations were $1.424{\mu}g/g$ of chromium and $0.830{\mu}g/g$ of nickel. Additionally, The concentrations of heavy metal were significantly different by color (p < 0.05). Brown colored cosmetics were found to have the highest mean concentrations of chromium, nickel and copper, ivory colored cosmetics the highest mean concentrations of chromium and lead, and pink colored cosmetics the highest concentrations of lead and chromium.

• Journal of the Korean association of regional geographers
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• v.2 no.1
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• pp.133-150
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• 1996

This study is to clarify the market cycle of travelling merchants and the spatial behavior of consumer's commodity purchasing. its reasons and purchasing region of each commodity in Chongsan of Chongsan Myun(village) periodic markets, that is one of the lowest central places in Okchon county. The data used are the results of interviews with 58 travelling merchants on June 22 and July 17, 1994, and questionaire survey taken to parents of students of Chongsan middle school of Chongsan Myun in Okchon county. Study area is typical agricultural regions taking the role of central places to provide rural service and is comparatively important periodic markets. Some of findings are summarized as follows: (1) Until 1980's. appearance of closed periodic markets is caused by the population decrease in rural region, income increase, and rising of living level according to the Five Years Planning of Economic Development, appearance of chain stores of agricultural co-coperative and of supermarkets,. changes in distribution mechanisim by increasing consignment volume of agricultural products through agricultural co-coperative, and the development of transportation in Okchon county. These, too, became the reasons for the decline of the Chongsan periodic markets in Okchon county. (2) Most of the travelling merchants visiting the Chongsan periodic markets are in their 50's of age, and they sell the miscellaneous commodities and agricultural products. And about one-fourths of travelling merchants reside in regions with periodic markets and in Okchon of higher order central places. (3) Travelling routes visting periodic markets can be simplified to five types. Major types of travelling routes are Chongsan periodic market$\rightarrow$Wonnam$\rightarrow$Boun, and Chongsan periodic market$\rightarrow$Yungdong$\rightarrow$Yongsan. The patterns of travelling merchants visiting periodic markets are classified into the type of everyday visiting of periodic markets over three days of five days from merchant's residence to market, and the type of merchants or consumers visiting one day's of five days. On days that travelling merchants don't visit periodic markets they purchase the commodities in Seoul, Taejon and Chongju. (4) Consumers who use periodic markets are from thirties to fifties years of age and most of them are employed in agriculture. Consumers visit periodic markets on foot or by bus, and visit two or three times in a month, and mainly purchase the commodities for one or two hours from about ten o'clock in the morning. (5) Consumers purchase the necessaries of life in periodic markets, and other commodities are purchased in Taejon city, Youngdong, and Boun Eup(town). But consumers purchase the goods(convenience goods, shopping goods, and specialied goods) largerly in Chongsan, because additional expense and disadvantage after service with poor transportation service for purchased goods in others regions. Therefore, the hierarchies of central places by the consumer's purchasing behaviour can not be seem in dewellers in Chongsan and Chongseong Myun.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.305-316
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• 2011

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• Journal of the Korean Society of Food Culture
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• v.9 no.4
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• pp.379-394
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• 1994

The purpose of this study was to identify the correlation between the factors influencing on housewives' decision-making processes of organic foods and the relating variables, and the 5 stages of decision-making processes of the EBK model is utilized in this study. The sample was selected from 411 housewives living in Seoul from 1st of September through 20th of September, 1993. Frequency, Percentage, Mean, Factor analysis, One-way ANOVA, Duncan's multiple range test, t-Test, Correlation, Multiple regression analysis and Path analysis were measured. Major results are as follows: 1. Purchasing motivation of the organic foods were in order of the health care, nutritive value and taste care. 2. The major informations source for the knowledge of organic foods were in order of TV/radio, newspaper/magazine, recommendations informations and advice through a family/friends/acquaintances. 3. Evalution criteria in shopping of organic foods, the total degree of consideration over the purchasing factors of organic foods was fairly high level: consumers thought much of the sanitation/freshness, nutritive value and the food safety. In this regard opinion leaders was dominantly mass media. Consumers have a tendency to purchase organic foods in consideration of their children and husband. 4. Major place to purchase organic foods are super markets and department stores. And When shopping organic foods, housewives by all means confirm the check points in their own mind, which were expiry date, manufactured date and packing condition, but unexpectedly manufactured company was out of concern. 5. Housewives usually satisfy with decision after purchasing organic foods, while they were fairly unsatisfied with the price, quality, incomplete description for ingredients and manufactured date. 6. The variables influencing to the sincerity when selecting the most desired organic foods is how be cares about the natural freshness of the foods and the types of residents in order. Another interesting tendency is the richer they are very considerate to decide. It is to say the people who cares more about the natural freshness is the sincerer when making decision and also the class who lives in the apartment house enjoying high income do not easily accept the product quality.

• Journal of Distribution Research
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• v.15 no.5
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• pp.103-126
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• 2010

The development of an industry including distribution sector is influenced by not only government policy but the related firms' behaviors. Recently the large-scale retailers have had more enormous channel power than any other distributors including monopolistic makers. Now is the time for government to prepare some policies against the unfair transaction behaviors by large-scale retailers. In this paper I tried to inquire into the distribution competition policy from a political correspondent point of view related with the transition of distribution system. For the purpose of this article I compared the case of Korea with Japan. According to the results so far inquired, there are some commons and differences in the cases of the two. Some suggestions are as follows. Considering the predominant position the concept of large-scale retailers is to be extended from a single store to numerous chain stores in the political level. Government needs to examine the standard propriety for large-scale retailer; the size of selling area and amount of sales a year. When a large-scale retailer store is to be established, it need to be taken a permit or a pre-inspection. The Fair Trade Commission have to secure the neutrality from Government's strategies. And government should find out the examples of unfair transaction behavior types and prepare some proper guidelines continually. For the last time statistical data by distributors are to be fitted out and the actual investigations for estimating the effects of government policies need to be enforced.

• Journal of Nutrition and Health
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• v.55 no.4
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• pp.476-491
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• 2022

Purpose: The purpose of this study was to evaluate the energy and nutrient contents of home meal replacement (HMR) rice products per single serving package based on nutrition labels. Methods: The market research was conducted from February to July 2021 on products sold on the internet, at convenience stores, etc. A total of 406 products were investigated. The products were divided into the following 6 classifications: instant rice (n = 45), cup rice (n = 64), frozen rice (n = 188), rice bowls with toppings (n = 32), gimbap (n = 38), and triangular gimbap (n = 39). Results: The mean packaging weight per serving was the highest in the rice bowl with toppings at 297.1 g, followed by cup rice (264.0 g), frozen rice (239.5 g), gimbap (230.2 g), instant rice (193.4 g), and triangular gimbap (121.6 g) (p < 0.001). The energy per serving package for the rice bowl with toppings was significantly the highest at 496.0 kcal (p < 0.001). The sodium content per serving package of gimbap was the highest at 1,021.8 mg and that of the instant rice was lowest at 37.4 mg (p < 0.001). The price per serving package of the rice bowl with toppings at 4,333.8 won was the highest. The contribution to the daily nutritional value per serving package of all types of HMR rice products surveyed showed an average range of 10-25% for energy, 11-22% for carbohydrates, and 2-51% for sodium. Conclusion: These results indicate the energy and nutrient contents of HMR rice products, vary by type. Therefore, consumers should review the nutrition labeling to select an appropriate HMR rice product based on their intended consumption.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of Distribution Research
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• v.15 no.2
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• pp.1-34
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• 2010

Introduction As consumers' purchase behavior change into a rational and practical direction, the discount store industry came to have keen competition along with rapid external growth. Therefore as a solution, distribution businesses are concentrating on developing PB(Private Brand) which can realize differentiation and profitability at the same time. And as improvement in customer loyalty beyond customer satisfaction is effective in surviving in an environment with keen competition, PB is being used as a strategic tool to improve customer loyalty. To improve loyalty among PB users, it is necessary to develop PB by examining properties of a customer group, first of all, quality level perceived by consumers should be met to obtain customer satisfaction and customer trust and consequently induce customer loyalty. To provide results of systematic analysis on relations between antecedents influenced perceived quality and variables affecting customer loyalty, this study proposed a research model based on causal relations verified in prior researches and set 16 hypotheses about relations among 9 theoretical variables. Data was collected from 400 adult customers residing in Seoul and the Metropolitan area and using large scale discount stores, among them, 375 copies were analyzed using SPSS 15.0 and Amos 7.0. The findings of the present study followed as; We ascertained that the higher company reputation, brand reputation, product experience and brand familiarity, the higher perceived quality. The study also examined the higher perceived quality, the higher customer satisfaction, customer trust and customer loyalty. The findings showed that the higher customer satisfaction and customer trust, the higher customer loyalty. As for moderating effects between PB and NB in terms of influences of perceived quality factors on perceived quality, we can ascertain that PB was higher than NB in the influences of company reputation on perceived quality while NB was higher than PB in the influences of brand reputation and brand familiarity on perceived quality. These results of empirical analysis will be useful for those concerned to do marketing activities based on a clearer understanding of antecedents and consecutive factors influenced perceived quality. At last, discussions about academical and managerial implications in these results, we suggested the limitations of this study and the future research directions. Research Model and Hypotheses Test After analyzing if antecedent variables having influence on perceived quality shows any difference between PB and NB in terms of their influences on them, the relation between variables that have influence on customer loyalty was determined as Figure 1. We established 16 hypotheses to test and hypotheses are as follows; H1-1: Perceived price has a positive effect on perceived quality. H1-2: It is expected that PB and NB would have different influence in terms of perceived price on perceived quality. H2-1: Company reputation has a positive effect on perceived quality. H2-2: It is expected that PB and NB would have different influence in terms of company reputation on perceived quality. H3-1: Brand reputation has a positive effect on perceived quality. H3-2: It is expected that PB and NB would have different influence in terms of brand reputation on perceived quality. H4-1: Product experience has a positive effect on perceived quality. H4-2: It is expected that PB and NB would have different influence in terms of product experience on perceived quality. H5-1: Brand familiarity has a positive effect on perceived quality. H5-2: It is expected that PB and NB would have different influence in terms of brand familiarity on perceived quality. H6: Perceived quality has a positive effect on customer satisfaction. H7: Perceived quality has a positive effect on customer trust. H8: Perceived quality has a positive effect on customer loyalty. H9: Customer satisfaction has a positive effect on customer trust. H10: Customer satisfaction has a positive effect on customer loyalty. H11: Customer trust has a positive effect on customer loyalty. Results from analyzing main effects of research model is shown as