• Journal of Distribution Science
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• v.10 no.5
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• pp.37-44
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• 2012

In this paper, we investigate several factors that affect the locational decision of discount stores by using previous studies on the marketing area and the location of commercial facilities. We selected 21 primary variables that are expected to influence the decision of store location and, by factor analysis, grouped them into five underlying factors. Among these, the demographic factor, which shows the potential purchasing power level, had the greatest impact on the locational decision for the store. However, we found individual stores positioned according to unique locational characteristics in addition to the demographic factor. It means that we have to additionally consider if the vicinity of the market is based on any physical properties. Many previous studies proposed four decision factors for store location: the economic factor, the demographic factor, the land utilization factor, and traffic factor. However, the fivefold factors-our distinctive contribution-are more concrete and persuasive according to Korean reality. We show that location preference is based on the following criteria: (1) the area is densely populated, (2) houses stand close together, (3) residents have a high income level, (4) road traffic is developed and easy to access, and (5) public transportation is well developed. The demographic factor has the greatest impact on the location of a discount store. The number of households has a greater relevance to the demographic factor than does the individual consumer. Second, discount stores relatively prefer places where houses are located close together because such places offer easy access to the market. Third, a place whose residents have a high income level will be preferred, with its large cars and excellent traffic conditions. Fourth, a location would be highly rated if the roads around commercial facilities are well developed and their accessibility is good. Finally, discount stores must be located close to bus stops because female consumers, including housewives-the most important customers-evaluate stores based on distance. In this research, the variable of consumer attitude and preference was excluded, and the location factors of discount stores were analyzed according to a microscopic view through physical spatial data. In the future, the opening of new discount stores based on the five factors indicated above will require a comparatively shorter time from the first project feasibility analysis. In addition, the result of our study can be applied to the field of public policy for constructing and attracting large-scale distribution facilities.

• Journal of the East Asian Society of Dietary Life
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• v.12 no.2
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• pp.91-99
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• 2002

This study was carried out to develop a diabetic-oriented convenience flood using 7 medicinal plants (Schisandra chinensis, Coix lachryma-jobi, Dioscorea batatas, Ophipogon japonicus, Lyicium chinense, Houttuynia cordata, Polygonatum sibiricum) and chicken. Portion size was 310g, total calorie was 551.6 kcal and carbohydrate, lipid and protein were consisted of 53.0%, 20.9% and 26.1%, respectively. Calcium, zinc and iron content were 268.9mg, 5.4mg and 6.1mg, respectively. Crude fiber content was 22.9g. In sensory evaluation, the scores of taste, color, texture and overall acceptability were higher than normal diabetic meal. Hypoglycemic effect of the device meal for diabetic persons was excellent compared to that of normal diabetic meal. The above results indicate that the 7 medicinal plants can be used as functional ingredients fur diabetic-oriented convenience flood industry. Also, device meal can be used as ready-prepared food for weight control.

• The Journal of Natural Sciences
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• v.10 no.1
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• pp.209-216
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• 1998

The results of the study of diving the 21st century's Korea fashion market into consumer market, fashion market, and a new marketing strategy are as follows. The 21st consumer market is First, a fashion democracy phenomenon. As many people try to leave unconditional fashion following, consumer show a phenomenon to choose and create their own fashion by subjective judgements. Second, a phenomenon of total fashion pursuit. Consumer in the future are likely to put their goals not in differentiating small item products, but considering various fashion elements based on their individuality and sense of value. Third, world quality-oriented. With the improvement of life level, it accomplishes to emphasize consumers' fashion mind on the world wide popular use of materials, quality, design and brand image. Fourth, with the entrance of neo-rationalism, consumers show increasing trends to emphasize wisdom, solidity in goods strategy pursuing high quality fashion and to demand resonable prices. Fifth, concept-oriented. Consumers are changing into pursuing concept appropriate to individual life scene. Prospecting the composition of the 21st century's fashion market, First, sportive casual zone will draw attention more than any other zone. This is because interest in sports will grow according to the increase of leisure time and the expasion of time and space in the 21st century, and also ecology will become the important issue of sports sense because of human beings's natural habit toward nature. Second, the down aging phenomenon will accelerate its speed as a big trend. Third, a retro phenomenon, a concept contrary to digital and high-tech, will become another big trend for its remake, antique, and classic concept in fashion market with ecology trend. New marketing strategy to cope with changing fashion market is as follows. First, with the trend of borderless concept, borders between apparels are becoming vague, for example, they offer custom-made products to consumers. Second, as more enterprises take the way of gorilla and guerrilla where guerrillas who aim at niche market show up will develop. Basically, they think highly of individual creative study, and pursue the scene adherence with high sensitiveness. However this polarization becomes mutually-supplementing relationship showing gorilla's guerilla movement, and guerilla's gorilla high-tech. Third with the development of value retailing, enterprises pursuing mass merchandising of groups called category killers are expanded and amplified to new product fields, and expand business' share. Fourth, using outsourcing, the trend to use exterior function leaving each enterprise's strength by inspecting its own work is gradually strong. Fifth, with the expansion of none store sale, the entrance of the internet and the CD-ROM sales added to communication sales such as catalogues are specified. An eminent American think tank expect that 5-5% of the total sale of clothes and home goods in 2010 will be done by none store sale. Accordingly, to overcome the problems, First international, global level marketing, Second, the improvement of technology, Third, knowledge-creating marketing are needed.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Korean Journal of Materials Research
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• v.9 no.3
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• pp.274-281
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• 1999

Usually sputtering and electroless plating methods were used for manufacturing metal-alloy thin film magnetic memory devices. Since electroless plating method has many merits in mass production and product variety com­pared to sputtering method, many researches about electroless plating have been performed in the United State of America and Japan. However, electroless plating method has not been studied frequently in Korea. In these respects the purpose of this research is manufacturing Co-Mn-P alloy thin film on the corning glass 2948 by electroless plating method using sodium hypophosphite as a reductant, and analyzing deposition rate, alloy composition, microstructure, and magnetic characteristics at various pH's and temperatures. For Co-P alloy thin film, the reductive deposition reaction 0$\alpha$urred only in basic condition, not in acidic condition. The deposition rate increased as the pH and temperature increased, and the optimum condition was found at the pH of 10 and the temperature of $80^{\circ}C$. Also magnetic charac­teristics was found to be most excellent at the pH of 9 and the temperature of $70^{\circ}C$, resulting in the coercive force of 8700e and the squareness of 0.78. At this condition, the contents of P was 2.54% and the thickness of the film was $0.216\mu\textrm{m}$. For crystal orientation, we could not observe fcc for $\beta$-Co. On the other hand,(1010), (0002), (1011) orientation of hcp for a-Co was observed. We could confirm the formation of longitudinal magnetization from dominant (1010) and (1011) orientation of Co-P alloy. For Co-Mn-P alloy deposition, coercive force was about 1000e more than that of Co P alloy, but squareness had no difference. For crystal orientation, (l01O) and (lOll) orientation of $\alpha$-Co was dominant as same as that of Co- P alloy. Likewise we could confirm the formation of longitudinal magnetization.

• Journal of Distribution Science
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• v.9 no.3
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• pp.73-82
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• 2011

This study presents an effective and distinctive marketing strategy through the implementation of the aesthetic art marketing communication technique of storytelling. The reason applying art to marketing is effective is that it gives "class" and aesthetic beauty to the brand's image, which will lead to an increase in revenue and loyalty of consumers. The story stands in for the brand's subject of "desire." Luxury brand customers not only consume high-quality products, require the utmost in service, and value of the brand, they also appreciate the story the brand is telling. The story, combined with art, is called art marketing communication; it makes the brand more unique through its enhanced visual elements. The study discusses art collaboration, art exhibition, a transforming architecture project, art advertisement, a flagship store, and a human resource training center. Based on the "desire," I adopted the element and principle of storytelling. By visualizing the brand with a symbol, the company is able to relate to consumers' sentimentality. Through storytelling art marketing communication, and the strategy using relevance of brand and artist's popularity, the research shows efficient art marketing influences to the brand's image. The results of the research indicate that by using adequate art marketing communication that best reflects the identity and story of the luxury brand, it produces great results; the research also demonstrated, in various ways, that art marketing will succeed. The case showed the following outcomes. First, consumers have a tendency to choose a brand that is associated with an empathizing story. World renowned brands see through the market's "desires" for unique stories, and they also provide the ability to amuse consumers. The story in a product will become an important competitive element in future markets. Second, the art marketing communication applying a story rendered a brand with distinction. The most effective art marketing communications are art collaboration, art exhibition, locomotive architecture project, and others that are adopted as various means. Third, the brand's products were considered as an art piece, which led to not only strengthening the loyalty of consumers but also an increase in sales. In addition, the company could sustain a premium price for the goods sold. By adapting art to a brand's tradition, an innovative and creative new product provides consumer satisfaction, and producing goods in limited editions creates enthusiastic collectors. Fourth, this study suggests an abridged report, implication, limitation of the study, and directions for further research. Referring to the case for the adaptation of luxury brands, efficient art marketing strategies considering Korean company brand and efficiently study preceding Korean company brand art marketing strategy could be proposed.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.7
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• pp.876-885
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• 2017

This study aimed to identify the characteristics of Home Meal Replacement (HMR) product purchases and the need for HMR product development for Hong Kong consumers in order to suggest market segmentation strategies according to consumers' dietary lifestyle. For this, an online survey was conducted on a panel of 521 Hong Kong consumers with HMR purchase experience registered at a specialized organization. Data analysis was performed using SPSS (ver. 23.0). HMR purchase characteristics of Hong Kong consumers according to dietary lifestyle showed significant differences in all items, including 'number of purchases', 'purchase location', 'cost of single purchase', and 'reason for purchase'. According to dietary lifestyle, participants were divided into three clusters: 'High interest', 'normal interest', and 'low interest'. In the case of 'high interest in dietary life group', 'low-sodium food' was the most common, followed by 'heating food', 'low sugar food', and 'low calorie food'. In the case of 'moderate interest in dietary life group', 'low-sodium food' was the most common, followed by 'low sugar food', 'low calorie food', and 'nutritious meal'. In the case of 'low interest in dietary life group', 'low sugar food' was the most common, followed by 'low-sodium food', 'various new menu', and 'easy-to-carry dehydrated food'. For the 'high interest' group, the highest proportion of consumers were male in between the ages of 20 to 29, married, and worked in an office job. The 'high interest' consumers also showed a tendency to pay '15,000 to 20,000 KRW' per single purchase. The 'normal interest' group consisted of an even proportion of male and female consumers, with the most common age range being from 30 to 39 years, and most were married. These consumers preferred to spend 'less than 10,000 KRW' or '10,000 KRW to 15,000 KRW' per single purchase, which is in the lower price range for HMR purchases. The 'low interest in dietary life group' had more females gender-wise, were unmarried, and worked in an office job, For a single purchase, the 'low interest' group chose to pay less than 10,000 KRW, which is relatively lower than the other two clusters. The results of this study can be used as baseline data for building marketing strategies for HMR product development. It can also provide basic data and directions for new HMR export products that reflect consumer needs in order to create a market segmentation strategy for industrial applications.

• Journal of the East Asian Society of Dietary Life
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• v.22 no.6
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• pp.744-757
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• 2012

This study was designed to obtain basic data for the globalization of Korean food and the expansion of food exports through contract foodservices. A survey of dietary habits and attitudes toward school foodservices was given to students in three American international schools served by a domestic contract foodservice management company located in Seoul and Gyeonggi area. The results showed an average of three meals taken daily 3.39 times for male students and 2.95 times for female students and the time required for a meal was about 24~26 minutes. The average breakfast frequency was 5.10 times(4.59 times for male students and 5.35 times for female students) and many students reported skipping breakfast due to a lack of time. The average weekly frequency of dining out was 1.78 times(2.15 times for male students and 1.60 times for female students). In all schools, irrespective of gender and grade, students responded that a desire for snacking was 'why they want to have cookies', and snacking hours were frequently listed as 'between noon and evening'. Many also responded that an unbalanced diet is the reason some snacks are 'not to their taste'. Overall, students were highly satisfied with the foodservice menu, although there was a significant difference in what was considered proper food temperature, proper food seasoning, suitable amounts of food, and freshness of food. Male and female students were specifically highly satisfied with the 'freshness of food materials' and 'variety of menu' respectively. Overall, all students were highly satisfied with the foodservice, including the 'cleanliness of tables and trays'.