• Land and Housing Review
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• v.11 no.1
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• pp.79-86
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• 2020

The number of business closure is one of key indicators diagnosing the status of local economy. The increases in closure are attributed to various endogenous/exogenous factors such as decreases in sales of stores, decline of local market, deterioration of global financial condition, but it is not trivial task to figure out the cause and effect mechanism among variables. The effects of those factors are expected to show geographical variations, which the empirical analysis results in this study presented. As such, the objective of this study is to estimate the effects of variables on increase in the number of business closure and examine the distributional properties of the geographic variations of the effects among spatial units of analysis. To this end, GWR (Geographically Weighted Regression) model was utilized to draw empirical analysis outcomes. It is expected that the outcomes of the sort in this research may be useful in aiding decision-making process of drafting locality-specific policies and/or deciding where to prioritize the limited public resources available.

• 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.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Health Policy and Management
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• v.20 no.3
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• pp.123-137
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• 2010

The main purpose of this article is for deriving functions related to the prediction of the closure of the hospitals, and finding out how the discriminant functions affect the closure of the hospitals. Empirical data were collected from 3 years financial statements of 41 private hospitals closed down from 2000 till 2006 and 62 private hospitals in business till now. As a result, the functions related to the prediction of the closure of the private hospital are 4 indices: Return on Assets, Operating Margin, Normal Profit Total Assets, Interest expenses to Total borrowings and bonds payable. From these discriminant functions predicting the closure, I found that the profitability indices - Return on Assets, Operating Margin, Normal Profit Total Assets - are the significant affecting factors. The discriminant functions predicting the closure of the group of the hospitals, 3 years before the closure were Normal Profit to Gross Revenues, Total borrowings and bonds payable to total assets, Total Assets Turnover, Total borrowings and bonds payable to Revenues, Interest expenses to Total borrowings and bonds payable and among them Normal Profit to Gross Revenues, Total borrowings and bonds payable to total assets, Total Assets Turnover, Total borrowings and bonds payable to Revenues are the significant affecting factors. However 2 years before the closure, the discriminant functions predicting the closure of the hospital were Interest expenses to Total borrowings and bonds payable and it was the significant affecting factor. And, one year before the closure, the discriminant functions predicting the closure were Total Assets Turnover, Fixed Assets Turnover, Growth Rate of Total Assets, Growth Rate of Revenues, Interest expenses to Revenues, Interest expenses to Total borrowings and bonds payable. Among them, Total Assets Turnover, Growth Rate of Revenues, Interest expenses to Revenues were the significant affecting factors.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.499-507
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• 2020

This paper is one of the types of 'sharing economy', a social economic model that borrows and shares things, spaces, and services from the development of the 4th Industrial Revolution. Through sharing of empty space and time in the city, I would like to suggest a way to reduce the closure of small business owners in order to create jobs, which is one of urban problems in the community. We also build a platform that utilizes the free time and space of buildings through space sharing, one of the types of sharing economy, and provides education programs for start-up education, promotion, marketing, and consulting by matching small business owners with building owners. Therefore, in this paper, by sharing the space and time, the landlord and the small business can share the profits of the small business by reducing the business owner's closure and the job creation plan. Coaching urban regeneration was proposed.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.121-126
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• 2022

This study analyzed the survival rate of commercial districts before and after the outbreak of COVID-19 by analyzing Gangnam-gu, Seoul and Suseong-gu, Daegu as independent regions, although the background and distribution of commercial districts are similar. In the basic analysis, the size of the commercial districts was much larger in Gangnam-gu, Seoul, but it was confirmed that the distribution by industry was similar. In both regions, there were more openings than closings before the outbreak of COVID-19 in common, but as the COVID-19 outbreak occurred, the closure ratio of businesses centered on face-to-face services increased significantly. As a result of the survival analysis, it was analyzed that most industries were indifferent to the risk of closure of private institutes before Corona 19, but after the outbreak of Corona 19, it was confirmed that the risk of closure of private institutes increased, especially in Daegu, which was the initial spread of Corona 19. As a result of comparing the survival rate between regions, it was analyzed that the risk of business closure in Gangnam-gu increased relatively after the outbreak of Corona 19, confirming that the contraction in the commercial area of Seoul with a large floating population was greater.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.539-541
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• 2022

Due to the prolonged covid-19 pandemic, the rate of closure of the self-employed is rapidly increasing, and the economic damage is increasing. In this study, we provide various information such as the degree of overcrowding by region and industry through the analysis of business districts in Chungcheongbuk-do due to the outbreak of covid-19, and analyze the start-up environment by using public data to help start-up success and business stability. It provided support for self-employed people to use as basic data when starting a business.

• Journal of Digital Convergence
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• v.16 no.4
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• pp.95-105
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• 2018

This study empirically test the roles of network closure and structural hole on radical innovation in technological knowledge exchange network in Gumi cluster. In doing so, we build 2,550 firm network, transforming association*firm(2-mode) to firm*firm(1-mode) network data. In addition, in order to investigate firms' attributes, we conduct survey for 101 firms in Gumi cluster using random sampling, and finally collect 86 firm samples. For analysis, we use ridge regression since network density and efficiency, indices of network closure and structural hole respectively, has a high level of multicollinearity. The findings show that structural hole has a significant and positive impact on radical innovation, but network closure has a significant and negative impact on radical innovation. This study contributes to present an empirical evidence of debate on network closure and structural hole based on past conceptual discussions and literature review and further goes a long way towards strategy formulation to establish social capital in accomplishing radical innovation. Further research is required that pays closer attention to features of technological knowledge, innovation types and interaction between network closure and structural hole, directing efforts to structural characteristics of various networks.

• Korean Journal of Construction Engineering and Management
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• v.21 no.2
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• pp.70-78
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• 2020

Construction program management requires more systematic management than traditional management methods due to the complexity of the project and various stakeholders. In particular, the systematization of closure phase of the process is in urgent need as there is a difference between the owner's expectations and the contractor's deliverables, resulting in delayed handovers, conflicts, frictions, and increased legal disputes. This study identified the process and activities of closure phase through domestic and international literature and case studies for the successful closure of the construction program. Further, the proposed processes and activities were verified through expert verification for appropriateness and the possibility of application to the site. By utilizing the processes and activities proposed in this study, the project aims to overcome the limits of closure phase and improve the satisfaction of the owner, as well as to enhance the global competitiveness of domestic construction companies.

• Korean Journal of Artificial Intelligence
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• v.6 no.2
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• pp.23-27
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• 2018

Korea has a high proportion of self-employment. Many of them start the food business since it does not require high-techs and it is possible to start the business relatively easily compared to many others in business categories. However, the closure rate of the business is also high due to excessive competition and market saturation. Cafés and restaurants are examples of food business where the business analysis is highly important. However, for most of the people who want to start their own business, it is difficult to conduct systematic business analysis such as trade area analysis or to find information for business analysis. Therefore, in this paper, we predicted business status with simple information using Microsoft Azure Machine Learning Studio program. Experimental results showed higher performance than the number of attributes, and it is expected that this artificial intelligence model will be helpful to those who are self-employed because it can easily predict the business status. The results showed that the overall accuracy was over 60 % and the performance was high compared to the number of attributes. If this model is used, those who prepare for self-employment who are not experts in the business analysis will be able to predict the business status of stores in Seoul with simple attributes.