• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Distribution Research
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• v.17 no.1
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• pp.65-86
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• 2012

The $20^{th}$ century was the era of manufacturer brands which built higher brand equity for consumers. Consumers moved from generic products of inconsistent quality produced by local factories in the $19^{th}$ century to branded products from global manufacturers and manufacturer brands reached consumers through distributors and retailers. Retailers were relatively small compared to their largest suppliers. However, sometime in the 1970s, things began to slowly change as retailers started to develop their own national chains and began international expansion, and consolidation of the retail industry from mom-and-pop stores to global players was well under way (Kumar and Steenkamp 2007, p.2) In South Korea, since the middle of the 1990s, the bulking up of retailers that started then has changed the balance of power between manufacturers and retailers. Retailer private labels, generally referred to as own labels, store brands, distributors own private-label, home brand or own label brand have also been performing strongly in every single local market (Bushman 1993; De Wulf et al. 2005). Private labels now account for one out of every five items sold every day in U.S. supermarkets, drug chains, and mass merchandisers (Kumar and Steenkamp 2007), and the market share in Western Europe is even larger (Euromonitor 2007). In the UK, grocery market share of private labels grew from 39% of sales in 2008 to 41% in 2010 (Marian 2010). Planet Retail (2007, p.1) recently concluded that "[PLs] are set for accelerated growth, with the majority of the world's leading grocers increasing their own label penetration." Private labels have gained wide attention both in the academic literature and popular business press and there is a glowing academic research to the perspective of manufacturers and retailers. Empirical research on private labels has mainly studies the factors explaining private labels market shares across product categories and/or retail chains (Dahr and Hoch 1997; Hoch and Banerji, 1993), factors influencing the private labels proneness of consumers (Baltas and Doyle 1998; Burton et al. 1998; Richardson et al. 1996) and factors how to react brand manufacturers towards PLs (Dunne and Narasimhan 1999; Hoch 1996; Quelch and Harding 1996; Verhoef et al. 2000). Nevertheless, empirical research on factors influencing the production in terms of a manufacturer-retailer is rather anecdotal than theory-based. The objective of this paper is to bridge the gap in these two types of research and explore the factors which influence on manufacturer's private label production based on two competing theories: S-C-P (Structure - Conduct - Performance) paradigm and resource-based theory. In order to do so, the authors used in-depth interview with marketing managers, reviewed retail press and research and presents the conceptual framework that integrates the major determinants of private labels production. From a manufacturer's perspective, supplying private labels often starts on a strategic basis. When a manufacturer engages in private labels, the manufacturer does not have to spend on advertising, retailer promotions or maintain a dedicated sales force. Moreover, if a manufacturer has weak marketing capabilities, the manufacturer can make use of retailer's marketing capability to produce private labels and lessen its marketing cost and increases its profit margin. Figure 1. is the theoretical framework based on a strategic market management perspective, integrated concept of both S-C-P paradigm and resource-based theory. The model includes one mediate variable, marketing capabilities, and the other moderate variable, competitive intensity. Manufacturer's national brand reputation, firm's marketing investment, and product portfolio, which are hypothesized to positively affected manufacturer's marketing capabilities. Then, marketing capabilities has negatively effected on private label production. Moderating effects of competitive intensity are hypothesized on the relationship between marketing capabilities and private label production. To verify the proposed research model and hypotheses, data were collected from 192 manufacturers (212 responses) who are producing private labels in South Korea. Cronbach's alpha test, explanatory / comfirmatory factor analysis, and correlation analysis were employed to validate hypotheses. The following results were drawing using structural equation modeling and all hypotheses are supported. Findings indicate that manufacturer's private label production is strongly related to its marketing capabilities. Consumer marketing capabilities, in turn, is directly connected with the 3 strategic factors (e.g., marketing investment, manufacturer's national brand reputation, and product portfolio). It is moderated by competitive intensity between marketing capabilities and private label production. In conclusion, this research may be the first study to investigate the reasons manufacturers engage in private labels based on two competing theoretic views, S-C-P paradigm and resource-based theory. The private label phenomenon has received growing attention by marketing scholars. In many industries, private labels represent formidable competition to manufacturer brands and manufacturers have a dilemma with selling to as well as competing with their retailers. The current study suggests key factors when manufacturers consider engaging in private label production.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.123-139
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• 2019

The job classification system of major job sites differs from site to site and is different from the job classification system of the 'SQF(Sectoral Qualifications Framework)' proposed by the SW field. Therefore, a new job classification system is needed for SW companies, SW job seekers, and job sites to understand. The purpose of this study is to establish a standard job classification system that reflects market demand by analyzing SQF based on job offer information of major job sites and the NCS(National Competency Standards). For this purpose, the association analysis between occupations of major job sites is conducted and the association rule between SQF and occupation is conducted to derive the association rule between occupations. Using this association rule, we proposed an intelligent job classification system based on data mapping the job classification system of major job sites and SQF and job classification system. First, major job sites are selected to obtain information on the job classification system of the SW market. Then We identify ways to collect job information from each site and collect data through open API. Focusing on the relationship between the data, filtering only the job information posted on each job site at the same time, other job information is deleted. Next, we will map the job classification system between job sites using the association rules derived from the association analysis. We will complete the mapping between these market segments, discuss with the experts, further map the SQF, and finally propose a new job classification system. As a result, more than 30,000 job listings were collected in XML format using open API in 'WORKNET,' 'JOBKOREA,' and 'saramin', which are the main job sites in Korea. After filtering out about 900 job postings simultaneously posted on multiple job sites, 800 association rules were derived by applying the Apriori algorithm, which is a frequent pattern mining. Based on 800 related rules, the job classification system of WORKNET, JOBKOREA, and saramin and the SQF job classification system were mapped and classified into 1st and 4th stages. In the new job taxonomy, the first primary class, IT consulting, computer system, network, and security related job system, consisted of three secondary classifications, five tertiary classifications, and five fourth classifications. The second primary classification, the database and the job system related to system operation, consisted of three secondary classifications, three tertiary classifications, and four fourth classifications. The third primary category, Web Planning, Web Programming, Web Design, and Game, was composed of four secondary classifications, nine tertiary classifications, and two fourth classifications. The last primary classification, job systems related to ICT management, computer and communication engineering technology, consisted of three secondary classifications and six tertiary classifications. In particular, the new job classification system has a relatively flexible stage of classification, unlike other existing classification systems. WORKNET divides jobs into third categories, JOBKOREA divides jobs into second categories, and the subdivided jobs into keywords. saramin divided the job into the second classification, and the subdivided the job into keyword form. The newly proposed standard job classification system accepts some keyword-based jobs, and treats some product names as jobs. In the classification system, not only are jobs suspended in the second classification, but there are also jobs that are subdivided into the fourth classification. This reflected the idea that not all jobs could be broken down into the same steps. We also proposed a combination of rules and experts' opinions from market data collected and conducted associative analysis. Therefore, the newly proposed job classification system can be regarded as a data-based intelligent job classification system that reflects the market demand, unlike the existing job classification system. This study is meaningful in that it suggests a new job classification system that reflects market demand by attempting mapping between occupations based on data through the association analysis between occupations rather than intuition of some experts. However, this study has a limitation in that it cannot fully reflect the market demand that changes over time because the data collection point is temporary. As market demands change over time, including seasonal factors and major corporate public recruitment timings, continuous data monitoring and repeated experiments are needed to achieve more accurate matching. The results of this study can be used to suggest the direction of improvement of SQF in the SW industry in the future, and it is expected to be transferred to other industries with the experience of success in the SW industry.

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

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

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.89-117
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• 2018

The Korean companies are intensifying competition with not only domestic companies but also foreign companies in globalization. In this environment, it is essential activities not only for large companies but also Small and Medium Enterprises (SMEs) to get and develop the core competency. Particularly, SMEs that are inferior to resources of various aspects, such as financial resources etc., can make innovation through effective R&D investment. And then, SMEs can occupy a competency and can be survive at the environment. Conventionally, the method of "self-development" by using only the internal resources of the company has been dominant. Recently, however, R&D method through cooperation, also called "Open Innovation", is emerging. Especially SMEs are relatively short of available internal resources. Therefore, it is necessary to utilize technology and resources through cooperation with external companies(such as joint development or contract development etc.) rather than self-development R&D. In this context, we confirmed the effect of SMEs' factors on sales in Korea. Specifically, the factors that SMEs hold are classified as 'Technical characteristic', 'Company competency', and 'R&D activity' and analyzed how they influence the sales achieved as a result of R&D. The analysis was based on a two-year statistical survey conducted by the Korean government. In addition, we confirmed the influence of the factors on the sales according to the R&D method(Self-Development vs. Open Innovation), and also observed the influence change in 29 industrial categories. The results of the study are summarized as follows: First, regression analysis shows that twelve factors of SMEs have a significant effect on sales. Specifically, 15 factors included in the analysis, 12 factors excluding 3 factors were found to have significant influence. In the technical characteristic, 'imitation period' and 'product life cycle' of the technology were confirmed. In the company competency, 'R&D led person', 'researcher number', 'intellectual property registration status', 'number of R&D attempts', and 'ratio of success to trial' were confirmed. The R&D activity was found to have a significant impact on all included factors. Second, the influence of factors on the R&D method was confirmed, and the change was confirmed in four factors. In addition, these factors were found that have different effects on sales according to the R&D method. Specifically, 'researcher number', 'number of R&D attempts', 'performance compensation system', and 'R&D investment' were found to have significant moderate effects. In other words, the moderating effect of open innovation was confirmed for four factors. Third, on the industrial classification, it is confirmed that different factors have a significant influence on each industrial classification. At this point, it was confirmed that at least one factor, up to nine factors had a significant effect on the sales according to the industrial classification. Furthermore, different moderate effects have been confirmed in the industrial classification and R&D method. In the moderate effect, up to eight significant moderate effects were confirmed according to the industrial classification. In particular, 'R&D investment' and 'performance compensation system' were confirmed to be the most common moderating effect by each 12 times and 11 times in all industrial classification. This study provides the following suggestions: First, it is necessary for SMEs to determine the R&D method in consideration of the characteristics of the technology to be R&D as well as the enterprise competency and the R&D activity. In addition, there is a need to identify and concentrate on the factors that increase sales in R&D decisions, which are mainly affected by the industry classification to which the company belongs. Second, governments that support SMEs' R&D need to provide guidelines that are fit to their situation. It is necessary to differentiate the support for the company considering various factors such as technology and R&D purpose for their effective budget execution. Finally, based on the results of this study, we urge the need to reconsider the effectiveness of existing SME support policies.

• Journal of Mushroom
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• v.15 no.1
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• pp.45-53
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• 2017

Profitability of farmers has decreased mainly owing to low price while the gross amount of mushroom production has increased continuously in South Korea. In this regard, analyzing patterns of mushroom consumption is believed to be meaningful. We used a panel data set consisting of 667 families, from 2010 to 2015. Based on the panel data, mushroom consumption patterns of people living in city areas were examined. Multiple descriptive analysis methods and frequency analysis approaches were adopted in this study in terms of time and space dimensions, demographic properties, and purchase behaviors. The findings of this studyshow that mushroom purchase is highly dependent on seasonal events, which implies that the product consumption timing is predictable. In addition, yearly purchase amount patterns reflect that superstores have become the major mushroomtrading venues. This directly supports the need to establish supply chain capabilities for mushroom farmers so that they gain more bargaining power against enterprise-type groceries. Finally, functional features of mushroom can be linked with marketing promotion because purchase patterns demonstrate potential needs for healthcare food in mushroom categories. Based on the analyzed patterns, this paper provides insightful implications for policy makers who want to promote mushroom consumption.

• Analytical Science and Technology
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• v.8 no.1
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• pp.91-99
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• 1995

One of alternative conventional technologies used for treatment of livestock wastes is composting process, and recently some mechanical composting processes are being practiced. It is, however, recognized the composting process also has its own limitations such as longer time requirement, and difficulties to estimate the degree of decomposition, etc. The incomplete compost contains potentially harmful materials to crops and public health due to instabilized organic contents and pathogenic organisms. The purpose of this investigation is to develop an innovative system whereby anxious livestock wastes are thoroughly stabilized and disinfected. Thus the overall management scheme should meet the following requirements. 1. A system should be in a cost-effective and environmentally sound manner. 2. Sludges must be chemically stabilized and bacteriologically safe. 3. Odor-free by product should be applied to crop land. 4. Sludges are sources of fertilizer nutrients and/or soil amendments to enhance crop production. 5. And they can be used as potential pH adjusting agent of the acidified soils. Overall effectiveness of the developed system is experimentally tested to satisfy the preset criteria and requirements. Major experiments are divided into four categories: they are 1. chemical stability test, 2. optimal condition test of stabilization process, 3. bacteriological examination and disinfection tests, and 4. deodorization tests The stabilization process is consisted of the stabilizing reaction process and the drying process. Stabilized wastes is dried by both sun dryer and rotary dryer. It is shown that an additive dosage of about g/kg solid in wastes with a minimum of 5-minutes reaction would be necessary for effective stabilization reaction. The stabilization process is consisted of the stabilizing reaction process and drying process. Stabilized wastes are dried by both sun dryer and rotary dryer. It is shown that an additive dosage of about 300g/kg solid in wastes with a minimum of 5-minutes reaction would be necessary for effective stabilization reaction. In the stabilization reaction process, the pH of wastes is lowered from initial values of 12.3 to 8.6. High pH prevents odor production and kills pathogenic organisms. Organic matter contents in the stabilized wastes are about 50% and the sum of contents of fertilizer elements such as total nitrogen, $P_2O_5$ and $K_2O$ are about 5.3%. The livestock wastes that are stabilized chemically and hygienically can be used as a good soil conditioner and/or organic fertilizer.

• Korean Journal of Environmental Biology
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• v.32 no.3
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• pp.168-175
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• 2014

In order to understand the phytoplankton community structure based on their cell size duringlow water temperature periods, we studied 10 stations in the East Sea, Korea on March, 2012. The minimum standing crops of total phytoplankton were $3.4{\times}10^6cells\;L^{-1}$ at the station 5. The maximum values were $7.6{\times}10^6cells\;L^{-1}$ at the station 8, which is two times the amount of the minimum. The carbon mass at the station 4 ($6.3{\times}10^8pg\;L^{-1}$) was more than forty times higher compared with station 5 ($0.08{\times}10^8pg\;L^{-1}$). From these results, we found a significant difference between standing crops and carbon mass which might have caused due to their differences in community structure and cell size. Therefore, we considered the types of plankton biomass to estimate the primary product in the specific location and/or time. The phytoplankton communities were classified in 3 types: microplankton (> $20{\mu}m$), nanoplankton (< $20{\mu}m$) and picoplankton (< $2{\mu}m$). In the case of picoplankton, various morphological types were observed during the study period. These various picoplankton species were further classified as S (spherical), SF (spherical&flagella), O (oval), OF (oval&flagella) or R (rod) type, and we analyzed their community structure based on these categories. The picoplankton was found to be the most dominant type at 8 stations and S type as the most popular. The picoplankton seems to be the significant organism in the marine ecology during low water temperature periods in the coastal waters of East Sea. Therefore, picoplankton \;-with scientific surveys can be considered as the database for their identification. In conclusion, we suggest that cell size of the phytoplankton would be the best criteria to accurately analyze their community structure and to reveal groups having more ecological influence.

• Journal of the East Asian Society of Dietary Life
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• v.18 no.5
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• pp.692-701
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• 2008

This study was conducted with 150 adults of 20 years or older, all of whom lived in Seoul. The awareness of traditional Hangwa (Korean cookies) was shown to be relatively low, with 2.9 points on average, and the intake of traditional Hangwa for a month was 2.1 times. For the reason that traditional Hangwa couldn't become popular, and the opinion that the reason was because the price is expensive was the highest, at 3.8. The next highest reasons given were because it is difficult to buy, and because it is inconvenient to eat, at 3.0. The traditional Hangwa was shown to enjoy positive awareness as opposed to negative awareness, while the manufacturing sanitation was also recognized to be relatively clean, with 2.6 points assigned to the opinion that it doesn't taste good, and 2.7 points assigned to the opinion that the manufacturing sanitation is unclean. With regard to the excellence of traditional Hangwa, the response that 'our tradition can be handed down' was the most often encountered, with a score of 3.9. The traditional Hangwa was appraised as excellent, with 3.6 points assigned to the opinion 'it suits our body because it is made with our agricultural products' and 3.4% for each opinion 'safe ingredients are used' and 'all ingredients are good for health'. With regard to the level of agreement for the development of functional traditional Hangwa, the positive group was higher than 25.3% of the negative group, with 27.3% for 'agree very much' and 22.0% for 'agree'. When converted into a 5-point scale for the development of functional traditional Hangwa, the group that was positive toward its development was high, with 3.4 points on average. All categories of excellence awareness were correlated with the level of agreement for the development of a functional food product (p<0.001). Consumer awareness toward the addition of traditional Hangwa functionality was generally positive, with 3.3 points or higher on average, and awareness of the aging suppression and diabetes control effects of Hangwa was also high, with 3.5 points. Next were Hangwa for diet, Hangwa for blood pressure control, Hangwa for mineral supple-mentation and vitamin additive-type Hangwa, with 3.4 for each, and Hangwa for health preservation, with 3.3 in order. With regard to the degree of interest toward functional traditional Hangwa for the treatment of diseases, obese patients cited aging suppression, at 3.2, and vitamin additive Hangwa, at 3.0, while Hangwa for dieting was assigned 2.8 points. Patients with high blood pressure, blood circulation, and diabetes were all shown as having a high degree of interest in all items, while evidencing particular interest toward Hangwa for diabetes control and Hangwa for blood pressure control. With regard to intention to purchase while developing functional traditional Hangwa, the group asserting intention to purchase was higher than 60% for all items except for Hangwa for diabetes control (58.7%). The Hangwa for aging suppression was highest, at 68.6%, and shown as having intention to purchase during development in the order of vitamin additive Hangwa at 68.0%, Hangwa for mineral supplementation at 64.6%, each of Hangwa for health preservation and Hangwa for blood pressure control at 62.7%, Hangwa for diet at 62.6% and Hangwa for diabetes control at 58.7%. The considerations during the development of functional traditional Hangwa were in the following order: storage at 4.1 points, taste and level of function at 3.9 points, size at 3.5 points, and packing at 3.4 points.