• Journal of Korean Society of Rural Planning
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• v.27 no.1
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• pp.85-94
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• 2021

The purpose of this study is to investigate the factors affecting the types of the 6th industrialization of fishery households. In this study we tried to explain the significance of the demographic and managerial characteristics of fishery households when they choose the types of the 6th industrialization business. Multinomial logistic model was used for this analysis. This study shows that the household and fishery management characteristics, main method of fishing, and regional factors matters for fishery households to choose their business types. Our results implies that it is necessary to reflect the detailed support measures differentiated by business types when implementing the 6th industrialization policy for fishery sector. In addition, the sixth industrialization of fishery should not be limited to marine products, but agricultural products produced in fishing villages should be included.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.9-14
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• 2021

This paper predicted a model that indicates whether to buy a car based on primary health insurance customer data. Currently, automobiles are being used to land transportation and living, and the scope of use and equipment is expanding. This rapid increase in automobiles has caused automobile insurance to emerge as an essential business target for insurance companies. Therefore, if the car insurance sales are predicted and sold using the information of existing health insurance customers, it can generate continuous profits in the insurance company's operating performance. Therefore, this paper aims to analyze existing customer characteristics and implement a predictive model to activate advertisements for customers interested in such auto insurance. The goal of this study is to maximize the profits of insurance companies by devising communication strategies that can optimize business models and profits for customers. This study was conducted through the Microsoft Azure program, and an automobile insurance purchase prediction model was implemented using Health Insurance Cross-sell Prediction data. The program algorithm uses Two-Class Logistic Regression and Two-Class Boosted Decision Tree at the same time to compare two models and predict and compare the results. According to the results of this study, when the Threshold is 0.3, the AUC is 0.837, and the accuracy is 0.833, which has high accuracy. Therefore, the result was that customers with health insurance could induce a positive reaction to auto insurance purchases.

• Journal of the Korean Society of Safety
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• v.30 no.4
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• pp.113-119
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• 2015

This study is to examine the workers' awareness of the safety in logistic centers. For that purpose, the exploratory factor analysis of workers' safety awareness in logistic centers was performed at first, and the 6 variables extracted from the factor analysis were then used to investigate the difference in intergroup awareness of the safety environment in the logistic centers. We administered a survey to 147 workers attending the logistic centers and collected data from them. The results of the study showed that the intergroup awareness of the safety environment turned out to be statistically different from each other in terms of working environment, safe behavior, work risk, safety knowledge and effort, risk justification and compromising attitudes. Experiences in industrial accidents influenced awareness of working environment, work risk and risk justification. The group who experienced accidents is more likely to feel risky and unsatisfied with working place, and their awareness toward risk justification was high as well. It was also observed that there exists awareness difference between manager group and worker group. The group who manages the working place showed more positive awareness of working environment, safe behavior, work risk, safety knowledge and effort, risk justification and compromising attitudes than the worker group. On the contrary, the worker group showed high recognition in risk of working place, and felt that they are willing to compromise on safety for increasing production. The scale of the logistic center produced negative influence on awareness of safety. The group in small logistic center showed the highest awareness in safety, whereas the group in large logistic center with more than 100 workers showed the highest awareness in risk. They are more likely to deviate from correct and safe work procedures due to over-familiarity with the job, as well. The findings suggest that there is a need for the safety management and education to change the workers' understanding and attitudes towards safety.

• Health Policy and Management
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• v.10 no.2
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• pp.1-21
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• 2000

This study reviewde various parametic and nonparametic method for forexasting hospital closures in Korea. We compared multivariate discriminant analysis, multivartiate logistic regression, classfication and regression tree, and neural network method based on hit ratio of each model for forecasting hospital closure. Like other studies in the literture, neural metwork analysis showed highest average hit ratio. For policy and business purposes, we combined the four analytical method and constructed a foreasting model that can be easily used to predict the probabolity of hospital closure given financial information of a hospital.

• IE interfaces
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• v.13 no.3
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• pp.288-295
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• 2000

As the industrial environment becomes more competitive, supply chain management(SCM) has become recognized as a major strategy in the business world. Despite its current widespread popularity, its basic concepts are confused with those of other exiting logistic tools and not clearly understood. This paper introduces the current status of SCM in terms of physical logistics, interrelationship with other logistic tools as well as the general history and definitions of SCM. Researches are categorized into deterministic models, stochastic models, simulation models, and several future research areas are discussed in this paper.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.2
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• pp.79-93
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• 2015

Today, consumer needs are diversified, continually changing and differentiated. Under the market conditions of unlimited competition to satisfy those needs, businesses focus on relationship-building with customers, as well as on quality of service. As interests in the coffee market rise, in these days, coffee culture diffusion and coffee franchise chain increase have been proceeding under the lead of the young people. This study aims to find the effects of service quality on customer (Korean, Chinese and Foreigner) satisfaction using logistic regression. The results of this study show somewhat different characteristics depending on the characteristics of country people. It is expected that this study can be very useful in regard to similar business.

• Nuclear Engineering and Technology
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• v.48 no.2
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• pp.559-571
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• 2016

This paper studies the feasibility of renewable energy as a substitute for nuclear and energy by considering Korean customers' willingness to pay (WTP). For this analysis, we use the contingent valuation method to estimate the WTP of renewable energy, and then estimate its value using ordered logistic regression. To replace nuclear power and fossil energy with renewable energy in Korea, an average household is willing to pay an additional 102,388 Korean Won (KRW) per month (approx. US $85). Therefore, the yearly economic value of renewable energy in Korea is about 19.3 trillion KRW (approx. US $16.1 billion). Considering that power generation with only renewable energy would cost an additional 35 trillion KRW per year, it is economically infeasible for renewable energy to be the sole method of low-carbon energy generation in Korea.

• Journal of Industrial Convergence
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• v.15 no.1
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• pp.17-32
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• 2017

The purpose of this study is to evaluate financial models that can predict corporate bankruptcy with diverse studies on evaluation models. The study uses discriminant analysis, logistic model, decision tree, neural networks as analyses tools with 18 input variables as major financial factors. The study found meaningful variables such as current ratio, return on investment, ordinary income to total assets, total debt turn over rate, interest expenses to sales, net working capital to total assets and it also found that prediction performance of suggested method is a bit low compared to that in literature review. It is because the studies in the past uses the data set on the listed companies or companies audited from outside. And this study uses data on the companies whose credibility is not verified enough. Another finding is that models based on decision tree analysis and discriminant analysis showed the highest performance among many bankruptcy forecasting models.

• Journal of the Korea Safety Management & Science
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• v.14 no.2
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• pp.221-228
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• 2012

This paper analyzed twenty-six production-related KPI(Key Performance indicator) factors of business diagnosis, such as personnel, equipment, materials, operations and quality affecting company business competition to 186 small machinery manufacturers in 2010. Also, we explained the concept of Business Positioning and divided research subjects into four Business Positioning Groups formed break-even point ratio & fixed cost ratio to sales and then we compared between the 4 groups using Logistic Regression analysis by SAS statistical software package. The objective of this study is two-fold. The first is to find out production-related KPI factor of superior Business Positioning Group. The second is to suggest improvement ways for small manufacturers in order to get better profitable Business Positioning.