• The Korean Journal of Franchise Management
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• v.2 no.1
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• pp.173-199
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• 2011

A franchisee has to depend on the overall system, such as knowhow and management support, from a franchisor in the franchise system and the two parties do not start with the same position in economic or information power because the franchisor controls or supports through selling or management styles. For this, unfair trades the franchisor's over controlling and limiting the franchisee might occur and other side effects by the people who give the franchisee scam trades has negatively influenced on the development of franchise industry and national economy. So, the purpose of this study is preventing unfair trade for the franchisee from understanding the causes and problems of dispute between the franchisor and the franchisee focused on the dispute cases submitted the Korea Fair Trade Mediation Agency and seeking ways to secure the transparency of recruitment process and justice of franchise management process. The results of the case analysis are followed; first, affiliation contracts should run on the franchisor's exact public information statement and the surely understanding of the franchisee. Secondly, the franchisor needs to use their past experiences and investigated data for recruiting franchisees. Thirdly, in the case of making a contract with the franchisee, the franchisor has to make sure the business area by checking it with franchisee in person. Fourthly, the contracts are important in affiliation contracts, so enacting the possibility of disputes makes the disputes decreased. Fifthly, lots of investigation and interests are needed for protecting rights and interests between the franchisor and franchisee and preventing the disputes by catching the cause and more practical solutions of the disputes from the government.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.6
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• pp.840-849
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• 2005

In order to investigate the relationship between intake conditions of health food and health-related factors by residence and sex in Tong-Young area, a survey was carried out from 1,303 adults. Health foods were classified 3 groups including vitamin and mineral supplements, toner foods and manufactured health food supplements. Health-related factors were stress, fatigue, smoking and drinking. The $29.5\%$ of the subjects had taken some health food for health. Especially the male took more toner foods habitually than the female did. In take of vitamin and mineral supplements by residence, there was a significant difference $(p\leq0.01)$ as follows. The subjects in island $(20.0\%)$ who took vitamin/mineral supplements were about two times as compared with the subjects in Dong $(10.8\%)$, or Eub-Myeon $(10.0\%)$. The subjects taking supplementary food replied over fair $(82.8\%)$, the subjects taking toner food replied over fair (90.3$\%$) scored higher than who replied bad or very bad in self-perceived health status. Therefore, the better the subjects felt self-perceived health status, the more they took health foods for health themselves. In self-perceived stress status, the subjects who replied a little $(50.0\%,\;45.3\%)$ or little $(19.9\%,\;26.4\%)$, took vitamin and mineral supplements or manufactured health foods a lot. In toner food there was a significant correlation $(p\leq0.05)$ as follows. The less the subjects felt stress, the more they took dietry supplement. No smoker $(12.9\%)$intake rate of vitamin and mineral supplements was higher than smoker $(8.8\%)$. Smokers $(6.5\%)$ intake rate of toner food was higher than no smoker $(4.0\%)$. It was not significant the relationship between intake condition of health food and drinking. The main motivation for taking health food were by self-decision and invitation of friends or neighbors.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• 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.

• Korean journal of food and cookery science
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• v.17 no.6
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• pp.583-588
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• 2001

In this study, the differences in the contents of vitamin C and minerals in perilla leaves were compared according to the age of leaf and storage conditions. The content of vitamin C in perilla leaves the younger the perilla leaves, the higher the content of vitamin C: Vitamin C content of mature leaf at the lowest part of the stem was 63.7mg/100g, and that of young leaf at the top of the stem was 129.0mg/100g. However, the contents of Ca and Fe by the leaf age were in an opposite tendency with vitamin C: Ca content of the leaves at the lowest and the top part of the stem were 449.4 mg and 311.8mg/100g, respectively. But, the contents of Na and Mg were poor in mature leaf, and the content of K showed no particular difference according to leaf age. When stored at 5$^{\circ}C$, residual rate of vitamin C content was 92.56, 81.52 and 77.05%, after stored 1, 5 and 10 days, respectively. In the case of 20$^{\circ}C$, residual rate of vitamin C was 85.80, 79.90 and 72.53%, respectively. When stored at 5$^{\circ}C$, Ca content in perilla leaves was in the range of 348.93∼408.81mg/100g, and at 20$^{\circ}C$, the content of Ca was 360.26∼392.25mg/100g. Storage time and temperature did not make a significant difference in the mineral content of perilla leaves.

• Journal of Ginseng Culture
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• v.2
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• pp.71-83
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• 2020

College students are the potential income classes preparing for income activities and are the main consumers of the future that are very important. In order to understand the current state of ginseng-related consumption culture of young consumers, this study wanted to examine university students' attitudes toward ginseng-related products and services in the future, and to find out their purchasing status, satisfaction, and intent to purchase them. In doing so, we looked at the relative influence of the relevant variables that affect the intent of the purchase. As a result, the variables that affect college student consumers' intention to purchase ginseng-related products were shown in the order of their reliability, economy, purchasing experience, subjective interest, monthly income, monthly allowance, pride in ginseng, and age. Of these, most of the variables had positive effects, but the average monthly income, monthly allowance, and age had negative effects. In other words, the higher the reliability, economy, and self-esteem of ginseng, the higher the willingness to buy ginseng, the higher the subjective interest in ginseng, the higher the age, the lower the monthly allowance, and the lower the income, the lower the willingness to buy ginseng. To promote college students' consumption of ginseng products, it is necessary to cut prices for the younger generation, enhance the quality of the products for the younger generation, improve the taste to overcome the negative aspects of rejecting the bitter and bitter taste, and, above all, induce consumers' attention. It is also necessary to expand accessibility through the development of convenient and easy-to-eat products for young people and the expansion of sales outlets. Recently, young consumers are interested in new products they have never experienced before, products that are good to certify because of their unique design or packaging, and retro products that stimulate nostalgia in the past, so they need to promote and provide information related to consumption of ginseng products in this regard. Considering the practicality and convenience of consumers, we propose consideration of personal consumers' taste curation services, which reflect their preference for products that are convenient to carry with them in line with various living environments, and can have synergy with other products.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.12
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• pp.1991-2001
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• 2009

This study provides basic information that is needed to build marketing strategies related to consumer brand-switching, through the investigation of consumer motivations for brand-switching, as determined by the types of fashion products. The study was implemented by a descriptive survey method using a questionnaire. The survey was conducted during the period of May $11^{th}$ through July $5^{th}$ 2008. A total of 184 completed responses were analyzed. All respondents were from the Seoul area and between the ages of 20 and 31. Factor analysis and Cronbach's alpha coefficients, one-way ANOVA and Duncan test were employed for the analysis of data. Significant differences were found in brand-switching motives according to the types of fashion products. For clothing, shift behavior occurred more often in conjunction with the attributes of the products itself, such as design, color, price, size, and fiber content. In general, clothes more than shoes, were likely subject to brand-switching most often on the basis of situational factors. On the other hand, for shoes, more brand-switching activities occurred because of non-product attributes, such as discounts, coupons, desire for a change, and wearing of friends. In light of the results, there is a need to differentiate brand related marketing strategies with respect to clothing and shoes. For clothing, efforts focusing on the improvement of the product attributes will be more effective in minimizing brand-switching. There is also a need to improve instructions for increasing the product understanding of salespersons as well as the ability to give advice in accordance with personal consumer characteristics. With regard to shoes, greater efforts should be given to promotional activities, and the desire of consumers for a change in order to prevent brand-switching of customers.