• Journal of Distribution Science
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• v.4 no.2
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• pp.5-20
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• 2006

Retail is called location business because it is one of the most important factors to estimate management of stores for retailers who are going to sell products directly to customers. Retailers' management achievements are shown in sale in general. Therefore, retailers tend to focus on ways to increase the numbers of customers in order to raise sales. First of all, in this research, I am going to examine the most fundamental models such as Reilly's retail gravitation, converse model, huff probability model and multiful losit model in selecting stores. Secondly, I am going to provide the process and analyzing ways to predict estimated sales amount with the previous theory model. Also I am going to predict estimated sales amount of the department store L which is located in D metorpolitan city. Lastly, I am going to argue about the problem of this research and the next research subject. Our main goal is to provide ways to complement and inspect sales estimation models, which can be used in fields after taking characters of high class structure of Korea into consideration on the base of previous researches. According to the result of the research, my conclusion is that if the process of analysis and changing factors are complemented, revise model, which can reflect reality of Korea, will be provided. Therefore, in the future study, we have to build up theory models to suit for our retail market through critic reviews about the existing high class structure of Korea.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.6
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• pp.10-27
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• 2017

Recently, the importance of recognizing the natural environment and the need for its conservation are increasing due to rapid urbanization. Suncheon Bay, designated as Scenic Site No. 41 and one of the World's Five Greatest Coastal Wetlands, is the only tideland among the tidal flats in Korea, which has salt marsh reserves. It has high conservation value from the ecological aspect. In addition to the Suncheon Bay National Garden, it provides various benefits not only to visitors but to local residents as well in terms of economics, environmental issues, and history and cultural aspects. Two million tourists visit the site annually, which has constantly highlighted the limits of ecological capacity. The valuation of the Suncheon Bay wetland is more important for the sustainability of the Suncheon Bay wetland than for its value as a tourism resource for the activation of the local economy. This study used the Logit model, which is commonly used among probabilistic choice models, to evaluate the economic value of Suncheon Bay wetland with the contingent valuation method(CVM). Applying the conservation value of the Suncheon Bay wetland to the benefit of KRW 8,200 for 1 person and 1 day, the benefit from exploration is KRW 2,050, the management and conservation value is KRW 3,034, and the heritage value is KRW 3,116. The results of this study are that benefit from the annual exploration of Suncheon Bay wetland was KRW 44.3 in billion, the management and conservation value was KRW 6.55 in billion, and the heritage value was KRW 6.73 in billion. When converted to the number of paying visitors per year, the conservation value is about KRW 177.1 billion. This study was conducted to evaluate the use and conservation aspects of the economic value of Suncheon Bay wetland. Based on the latent value of the Suncheon Bay wetland, it provides basic data about the efficient management and policy establishment of Suncheon Bay wetland. The study is significant in that the ecological sustainability of the Suncheon bay wetland and the value of non-marketable were evaluated based on the recognition of 'benefit through exploration', 'management and conservation value' and 'value of heritage'. It can be used as policy decision data on the integrated collection of the admission fee of the Suncheon Bay wetland and Suncheon Bay National Garden.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.125-133
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• 2014

This paper develops a bankruptcy prediction model on an Artificial Neural Network for hotel management. A bankruptcy prediction model has a specific feature to predict a bankruptcy of the whole hotel business after evaluate bankruptcy possibility on the basis of business performance data of each branch. here are many traditional statistical models for bankruptcy prediction such as Multivariate Discriminant Analysis or Logit Analysis. However, we chose Artificial Neural Network because the method has accuracy rates of prediction better than those of other methods. We first selected 100 good enterprises and 100 bankrupt enterprises as experimental data and set up a bankruptcy prediction model by use of a tool for Artificial Neural Network, NeuroShell. The model and its experiments, which demonstrated high efficiency, can certainly provide great help in decision making in the field of hotel management and in deciding on the bankruptcy or financial solidity of each branch of serviced residence hotel.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.11
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• pp.2238-2249
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• 2015

The purpose of this study is to analyze the relations of innovation and productivity following the introduction of ICT and the effects in the process of innovative investments activity-innovation-productivity, not only by finding causes and effects. For this purpose we conducted surveys of SMEs classified into 7 categories by type of business. To put it concretely, this study was performed to find out the foactors which allow companies to secure competitiveness by enhancing of innovative measures through ICT, and to further analyze the political implications for the development of small and medium-size business by conducting an empirical analysis of the process, from the determination of innovative investments all the way through to production. Analysis model used CDM model using econometric methods such as multiple regression analysis and multinominal logit analysis to produce results. Also we established and analyzed models of innovation investment determinants, innovation determinants and productivity determinants to analyze specifically the relations between ICT and productivity.

• Korean Journal of Health Education and Promotion
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• v.17 no.1
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• pp.19-39
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• 2000

The purpose of this study was to examine the prevalence and correlated factors of sexual behavior among high school students in Seoul A sample of 233 male and 248 female high school students were analyzed using cross-tabulation and logit regression models. Correlated factors examined include type of school, level of mothers education, perceived living status of family, whether family has two parents or not, and whether students have ever lived away from the family, whether students received reproductive health education at school and whether they have friends with sexual experience, whether students have ever smoking and alcohol drinking. The prevalence of alcohol drinking was 73% among boys and 55% among girls and the prevalence of smoking was 64% of boys and 40% of girls, whereas the prevalence of sexual activity was 27% among boys and 15% among girls. Risk taking was more prevalent among boys than among girls. Multiple risk taking behavior was common for both boys and girls. Students who did not have two parents were more likely to engage in risk taking behavior than those who had two parents. For both boys and girls, the factor that affects their own sexual activity most was having a friend who was sexually active and having an experience of living away from their family also increases the odds. For girls, the factor that affects having experience of alcohol drinking and smoking. Receiving reproductive health education at school had no effect on students sexual behavior. Much higher risk taking behavior with sexual behavior among students in Seoul implies that the overall prevalence of risk taking behavior among high school students was likely to rise as South Korea continues its modernization. In-school and community health education programs need to be modified to be effective in protecting students from risk taking sexual behavior.

• Journal of Animal Science and Technology
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• v.58 no.1
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• pp.1.1-1.6
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• 2016

Background: Genotype imputation is an important process of predicting unknown genotypes, which uses reference population with dense genotypes to predict missing genotypes for both human and animal genetic variations at a low cost. Machine learning methods specially boosting methods have been used in genetic studies to explore the underlying genetic profile of disease and build models capable of predicting missing values of a marker. Methods: In this study strategies and factors affecting the imputation accuracy of parent-offspring trios compared from lower-density SNP panels (5 K) to high density (10 K) SNP panel using three different Boosting methods namely TotalBoost (TB), LogitBoost (LB) and AdaBoost (AB). The methods employed using simulated data to impute the un-typed SNPs in parent-offspring trios. Four different datasets of G1 (100 trios with 5 k SNPs), G2 (100 trios with 10 k SNPs), G3 (500 trios with 5 k SNPs), and G4 (500 trio with 10 k SNPs) were simulated. In four datasets all parents were genotyped completely, and offspring genotyped with a lower density panel. Results: Comparison of the three methods for imputation showed that the LB outperformed AB and TB for imputation accuracy. The time of computation were different between methods. The AB was the fastest algorithm. The higher SNP densities resulted the increase of the accuracy of imputation. Larger trios (i.e. 500) was better for performance of LB and TB. Conclusions: The conclusion is that the three methods do well in terms of imputation accuracy also the dense chip is recommended for imputation of parent-offspring trios.

• The Journal of Fisheries Business Administration
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• v.48 no.2
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• pp.67-82
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• 2017

The study intends to examine the effects of the fishing license system on fisheries resources in order to reduce the adverse effects of recreational fishing, such as fishery resource reduction and environmental pollution. In doing so, the research question of the study is to determine what factors influence anglers' willingness to support fishing licenses. Based on the extended theory of planned behavior, we further included explanatory variables such as recreation specialization and motivations besides anglers' attitudes, norms and self-efficacy towards the environment and proposed six research hypotheses. The data were collected through on-site and online surveys in Gwangju and Cheonnam province and a total of 337 effective questionnaires were collected for data analysis. Three different binary logit models were employed with the dependent variable of anglers'willingness to support fishing licenses to assess the effects of explanatory variables. Study results show that social norms, the level of recreation specialization, motivation factors related to environmental experiences positively affected anglers'willingness to support fishing licenses. However, anglers'consumptive orientation attitudes such as catching big fish, motivation factors related to activity general experience preferences and previous fishing experience had negative effects on the dependent variables. Study results indicate that public outreach and education programs are essential to successfully introduce the fishing license system. Managerial and policy-related implications are further discussed to make recreational fishing a more environment-friendly recreational activity. This study investigated the effects of diverse variables derived from anglers' social-psychological characteristics on their support for fishing licenses and suggest diverse policy-related and managerial implications.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.1
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• pp.1-16
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• 2013

This study investigates the spatial distribution characteristics of Korean fashion industries during the last decade, in which the economic geography of fashion industries has changed dynamically with economic globalization and "thus resulted in increased" demand "of" diversification. In particular, this study examines the spatial distribution patterns of fashion industries in the Seoul metropolitan area where fashion industries are highly agglomerated. For the purpose, this study applies Moran's I Index of spatial autocorrelation analysis for seven functional sectors of fashion industries related to fashion production. The global and local agglomeration patterns are examined for each functional sector. The results clarify the distinction in the spatial agglomeration patterns among the seven functional sectors of fashion industries in the Seoul Metropolitan area. Logit models are developed to examine the interrelationships among functional sectors in their spatial agglomeration distribution patterns. By conducting binary logistic regression analysis, we find out how the spatial agglomeration of each functional sector is related to the others.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1559-1569
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• 2013

this study is to deduct the difference between regional and urban commercial trips by analyzing the characteristics of the regional and urban truck movements. To achieve this, we investigated the relation between the number of truck trips and various truck generation attributes such as truck attributes, origin and destination attributes, and commodity type using ordered logit models, which are separately estimated for regional and urban truck movements using truck diary data of Korea Transport Database (KTDB). According to the estimation results, regional and urban truck movements have different characteristics in truck attributes, origin and destination attributes and commodity type. Especially, the number of regional trucks trips increased as origin and destination are manufactural area and as the total value of products of industrial area in origin and destination increase.

• The Korean Journal of Community Living Science
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• v.20 no.2
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• pp.145-156
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• 2009

The purpose of this study was to explore socio-economic factors as determinants of food behavior and self-evaluation on meeting dietary guidelines. The data were derived from the KNHANES collected in 2007. A multidimensional framework of the determinants of food behavior was used, including age, gender, region, occupation, education, income and nutritional knowledge. The determinants of food behavior and self-evaluation were estimated by ordered logistic regression models. Food behavior was measured by dietary diversity scores including six food groups, which were cereals, vegetables, meats, fruits, milk, and oils. Self-evaluation on meeting dietary guidelines was based on responses from questionnaires for implementing Korean dietary guidelines. In general, the respondents who fulfilled all criteria were few. There were some differences between dietary diversity scores and self-evaluation on meeting dietary guidelines. Age, gender, and educational level showed effect on food behavior and self-evaluation. For dietary diversity scores, the individuals who were younger male, graduated from college were more likely to consume more various foods. The individuals who were older female, graduated from high school were more likely to meet dietary guidelines. Occupation was associated only with self-evaluation. Age and gender were associated with food behavior as well as self-evaluation. Income and marital status were associated only with dietary diversity scores. Reading food label and occupation were associated only with self-evaluation. The food behavior of married individuals was less in line with the dietary diversity scores than singles. In conclusion the differences between objective measure and subjective measure on individuals' diet showed more efforts like segmented nutritional education would be needed to increase the quality of dietary life.