• Information Systems Review
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• v.7 no.1
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• pp.275-295
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• 2005

Changes in business environment caused by globalization of the world economy and the beginning of the knowledge society forced hospitals to equip with tools for the enhanced competitiveness. In other words, hospitals must aim three targets such as acquisition of advanced medical skills and equipments, improvement of service level for patients, and achievement of superior managerial performance simultaneously. This study has been done to suggest a way to reduce the possibility of hospital bill claim reduction as an alternative for the achievement of superior managerial performance. If the reduction rate of hospital bill claim is high, it will put negative impact on the hospital's revenue stream and hospital's reliability. Thus, if they want to stay competitive, hospitals need to device ways to cut the reduction rate as much as possible. In this study, a prototype system has been developed and implemented to check the possibility to cut the reduction rate through deep analysis of causes of reduction. The prototype first developed utilizing data mining techniques and the relation rules algorithm. Then the prototype was tested its performance using the D hospital's live data.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.141-149
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• 2021

This study investigated the effect of accounting information on dividend policy as a measure of corporate conservatism from the perspective of the internal accounting management system. The verification is based on a sample of 543 companies listed on securities (excluding KOSDAQ and financial industry) among the Bank of Korea (2019) 「2018 Corporate Management Analysis」 and company analysis of the Korea Productivity Center (financial data disclosed as listed companies as a December settlement company) was composed. Using SPSS 22, empirical analysis was conducted using exploratory factor analysis and regression analysis. The first is the verification related to corporate conservatism and the role of dividend policy, and it is verification of whether internal accounting management influences financial decision-making. Second, if internal accounting management exists, it is a verification of how conservatism and investment policies (in-house reserve, debt borrowing, capital increase, dividends, etc.) affect the corporate value according to accounting information. As a result, from the perspective of the internal accounting management system, it was found that among the variables of accounting information, profitability can have a positive effect on corporate conservatism and dividend policy as a corporate valuation method of reinvestment. In addition, it has been proven that corporate conservatism has an effect on profitability-to-value through capital accumulation and reinvestment such as surplus and internal reserves. In the future, we will study and discuss the complementarity of corporate conservatism and dividend policy in relation to governance structure and improvement of the internal accounting management system.

• Information Systems Review
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• v.10 no.1
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• pp.135-164
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• 2008

This research is to offer a structured yet practical ex-ante evaluation methodology for IT investment. Benchmarking the best practices of four Korean organizations, we try to integrate core processes, relevant measures, and evaluation dimensions into a consistent and wholesome body of evaluating methodology. The best practices we considered encompass a wide range of business enterprises, including for-profit, non-profit, service-oriented, and manufacturing entities. The proposed methodology consists of three stages; the first stage checks the validity of investments by looking into comprehensiveness of planning, willingness to accomplish, justifiable grounds for the investments, overlapping investments, and obstructing risks; the second do so by putting an IT investment into economic, strategic, and technological perspectives; and the last third would produce a unified quantity that summarizes outcome of the previous stages. Incorporating the proven knowledge, guidelines, and quantifying tools, the methodology could make a valuable reference model for IT evaluation practitioners who have been bedeviled by having to going through such ex-ante evaluations.

• Management & Information Systems Review
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• v.39 no.2
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• pp.163-179
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• 2020

Recently, impact investment has attracted attention all over the world. This is intended to effectively solve problems by combining private capital and various financial techniques with social and environmental needs, as it is recognized that it is difficult to solve social and environmental problems. Impact investment means a mixture of financial, social, and environmental aspects. This refers to an investment focused on such a blended value, through which it simultaneously achieves financial and social values such as return on investment. The purpose of this study is to study whether impact investment, which has become a new issue, is actually applicable in Korea. This study first considers the concept and method of impact investment, and a prior study on social enterprises and impact investment that pursue social values. In particular, after analyzing in detail the social performance-related bonds (SIB) and operational cases, we intend to explore the possible applicability of impact investment to Korea. The results and implications of this study are, first, changes in the government's attitude toward impact finance. The government should entrust innovative public works to market-proven service providers to enhance the professionalism and efficiency of public service projects. Second, the legal system must innovate. Impact investment should provide an institutional foundation to pursue social problem solving simultaneously, not maximizing financial performance. Third, when investing in public works in the private sector, impact investment must clearly demand social performance and clarify the evaluation accordingly. The project execution process should create an impact environment that is more free and active.

• 재활복지
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• v.21 no.2
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• pp.63-88
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• 2017

This study evaluated the actual status of special needs of the hearing-impaired person for disaster response. The analysis revealed a significant level of unmet needs in disaster response for hearing-impaired person. The 5 special needs in disaster response include: 1) communication needs, which involve securing the means to make an emergency rescue request and communicating information during the rescue process; 2) transportation needs, which indicate the effective evacuation capacity and the level of training; 3) medical needs, which address the degree of preparedness for physical and mental emergency measures and the delivery of health information for rescue and first aid process; 4) maintaining functional independence needs, which refer to the level of self-preparedness to minimize damage in disaster situations, and; 5) supervision needs, which correspond to a personalized support system provided to disaster-vulnerable groups.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.123-131
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• 2016

Process mining in big data environment utilize a number of data were generated from the business process. It generates lots of knowledge and insights regarding implementation and improvement of the process through the event log of the company's enterprise resource planning (ERP) system. In recent years, various research activities engaged with the audit work of company organizations are trying actively by using the maximum strength of the mining process. However, domestic studies on applicable sales auditing system for the process mining are insufficient under big data environment. Therefore, we propose process-mining methods that can be optimally applied to online and traditional auditing system. In advance, we propose continuous monitoring information system that can early detect and prevent the risk under the big data environment by monitoring risk factors in the organizations of enterprise. The scope of the research of this paper is to design a pre-verification system for risk factor via practical examples in sales auditing. Furthermore, realizations of preventive audit, continuous monitoring for high risk, reduction of fraud, and timely action for violation of rules are enhanced by proposed sales auditing system. According to the simulation results, avoidance of financial risks, reduction of audit period, and improvement of audit quality are represented.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.2
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• pp.67-80
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• 2020

This study has been conducted at a time when Korean government continues to extend support for youth startups as part of its policy to create jobs and the focus moves from career and employment to youth startups with a growing interest in the field of youth startups. Against this background, this study aims to identify difficulty factors of youth startups in areas besides the Seoul Metropolitan Area, seek ways to overcome difficulty factors, and propose policy implications. To this end, this study set five criteria and 25 sub-criteria to evaluate the difficulties of youth startups by reviewing previous studies and conducting literature review, and performing brainstorming method. The empirical analysis of the evaluation criteria was performed, using the analytic hierarchy process (AHP) method, on youths aged 19 to 39 in Gunsan area. The analysis results showed that the largest difficulty factors facing local youths include business model establishment, business administration and management, and startup funding in the criteria. As for sub-criteria, the largest difficulty factors are market information acquisition, technology commercialization, project feasibility, technology development, and new market pioneering in descending order. Local youths have much difficulty about the process of turning a business item into a product and commercializing it. According to a comparative analysis by gender, men were a relatively high difficulty in commercializing business models than women. men were a relatively high difficulty in commercializing business models than women. On the other hand, women were higher than men in all factors (management management, entrepreneurship, improvement of entrepreneurship system, and improvement of entrepreneurship awareness) except for factors affecting business model. In addition, the factors of entrepreneurship were found to be relatively different among young people (college students, prospective entrepreneurs, entrepreneurs). In conclusion, it was suggested that in order to revitalize youth entrepreneurship in the region, it is necessary to actively resolve the difficulties of business model commercialization rather than entrepreneurship funds. In addition, it is necessary to strategically support customized entrepreneurship support and situational administrative services because gender and hierarchical difficulties are different than general solutions. This study presented practical priorities and derivation methods for the entrepreneurship difficulties faced by local youth, and suggested measures and improvements for vitalizing local youth entrepreneurship in the future.

• Journal of Venture Innovation
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• v.6 no.1
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• pp.181-198
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• 2023

This study aims to analyze the influence of organizational support perception of workers in medical institutions on intrinsic job motivation, and to check whether there is significance in the mediating effect of work-family conflict and work-family balance factors in this process. The results of empirical analysis through the questionnaire are as follows. First, it was confirmed that organizational support recognition had a significant positive effect on work-family balance as well as intrinsic job motivation, and work-family balance had a significant positive effect on intrinsic job motivation. Second, it was confirmed that organizational support recognition had a significant negative effect on work-family conflict, but work-family conflict had no significant influence on intrinsic job motivation. Third, in order to reduce job stress for medical institution workers, it is necessary to reduce job intensity, assign appropriate workload for ability. And in order to improve manpower operation and job efficiency, Job training and staffing in the right place are needed. Fourth, in order to improve positive organizational support perception and intrinsic job motivation, It is necessary to induce long-term service by providing support and institutional devices to increase attachment to the current job and recognize organizational problems as their own problems with various incentive systems. The limitations of this study and future research directions are as follows. First, it is believed that an expanded analysis of medical institution workers nationwide by region, gender, medical institution, academic, and income will not only provide more valuable results, but also evaluate the quality of medical services. Second, it is necessary to reflect the impact of the work-life balance support system on each employee depending on the environmental uncertainty or degree of competition in the hospital to which medical institution workers belong. Third, organizational support perception will be recognized differently depending on organizational culture and organizational type, and organizational size and work characteristics, working years, and work types, so it is necessary to reflect this. Fourth, it is necessary to analyze various new personnel management techniques such as hospital's organizational structure, job design, organizational support method, motivational approach, and personnel evaluation method in line with the recent change in the government's medical institution policy and the global business environment. It is also considered important to analyze by reflecting recent and near future medical trends.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.