• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.61-72
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• 2019

Since the financial crisis in 1997, large scale unemployment and poverty have become serious, but there has been a surge in public and social job creation projects. However, with the limitations of low-wage and short-term jobs, the need for long-term, high quality jobs gradually began to garner attention. In recent years, social enterprises have grown both quantitatively and qualitatively and interest in social enterprises has increased; more specifically, scholars are interested in the determinants of success and failure of social enterprises in the academic field. In this study, we examined the effects of social enterprise characteristics on financial and social performance. In particular, we empirically analyzed social enterprises registered in the Korea Social Enterprise Agency. The financial performance of the social enterprise was measured using the net income ratio, operating income ratio, and the return on asset. The social performance of the social enterprise was measured by the total number of workers and the employment rate of vulnerable social groups. The characteristics of the social enterprise included CEO characteristics (gender, age, experience in operating the social enterprise), firm size, and the elapsed time of authentication. The results of the empirical analysis are as follows. First, as a result of analysis for the effect on financial performance, we found that the financial performance has a statistically significant, positive relationship with firm size, organizational form, government subsidies, and capital adequacy ratio. And we found that the social performance has a statistically significant, negative relationship with CEO age and credit debt dependence. Second, as a result of analysis for the effect on social performance, we found that the total number of workers had a significant, positive relationship with CEO gender and CEO age, as well as firm size, government subsidies; whereas the total number of workers had a significant, negative relationship with certification type and industry dummy. Comparatively, the employment rate of the vulnerable social groups had a significant, positive relationship with CEO gender and certification type, but there was no statistically significant relationship with the government subsidies or firm size.

• KDI Journal of Economic Policy
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• v.30 no.1
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• pp.71-130
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• 2008

Using data from the Korean Labor & Income Panel Study (KLIPS), this study investigates private income transfers in Korea, where adult children have undertaken the most responsibility of supporting their elderly parents without well-established social safety net for the elderly. According to the KLIPS data, three out of five households provided some type of support for their aged parents and two out of five households of the elderly received financial support from their adult children on a regular base. However, the private income transfers in Korea are not enough to alleviate the impact of the fall in the earned income of those who retired and are approaching an age of needing financial assistance from external source. The monthly income of those at least the age of 75, even with the earning of their spouses, is below the staggering amount of 450,000 won, which indicates that the elderly in Korea are at high risk of poverty. In order to analyze microeconomic factors affecting the private income transfers to the elderly parents, the following three samples extracted from the KLIPS data are used: a sample of respondents of age 50 or older with detailed information on their financial status; a five-year household panel sample in which their unobserved family-specific and time-invariant characteristics can be controlled by the fixed-effects model; and a sample of the younger split-off household in which characteristics of both the elderly household and their adult children household can be controlled simultaneously. The results of estimating private income transfer models using these samples can be summarized as follows. First, the dominant motive lies on the children-to-parent altruistic relationship. Additionally, another is based on exchange motive, which is paid to the elderly parents who take care of their grandchildren. Second, the amount of private income transfers has negative correlation with the income of the elderly parents, while being positively correlated with the income of the adult children. However, its income elasticity is not that high. Third, the amount of private income transfers shows a pattern of reaching the highest level when the elderly parents are in the age of 75 years old, following a decreasing pattern thereafter. Fourth, public assistance, such as the National Basic Livelihood Security benefit, appears to crowd out private transfers. Private transfers have fared better than public transfers in alleviating elderly poverty, but the role of public transfers has been increasing rapidly since the welfare expansion after the financial crisis in the late 1990s, so that one of four elderly people depends on public transfers as their main income source in 2003. As of the same year, however, there existed and occupied 12% of the elderly households those who seemed eligible for the National Basic Livelihood benefit but did not receive any public assistance. To remove elderly poverty, government may need to improve welfare delivery system as well as to increase welfare budget for the poor. In the face of persistent elderly poverty and increasing demand for public support for the elderly, which will lead to increasing government debt, welfare policy needs targeting toward the neediest rather than expanding universal benefits that have less effect of income redistribution and heavier cost. Identifying every disadvantaged elderly in dire need for economic support and providing them with the basic livelihood security would be the most important and imminent responsibility that we all should assume to prepare for the growing aged population, and this also should accompany measures to utilize the elderly workforce with enough capability and strong will to work.

• Journal of Korean Academy of Nursing
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• v.22 no.4
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• pp.636-652
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• 1992

This study identified the impact of childhood cancer on the Korean family. The purpose was to contribute knowledge for family nursing and pediatric hospice care practice with sick children and their families. This descriptive study was conducted during a 6 month period with children who were being treated for cancer at six university hospitals in Seoul. The data were gathered from members of 68 families ; 24(Group A), with a child newly diagnosed with cancer : 27(Group B), with a child under treatment and without complications, and 17 (Group C), with a child in relapse. Medical records, structured questionnaires and interviews were used for data collection. The questionnaires and interview schedules had been used previously in Martinson's research in the USA and China. The findings, conclusions, and suggestions are as follows. 1. The impact of childhood cancer on the family. Members of the family experienced fear, helplessness, guilty feelings, and anger at the time of the initial diagnosis and at relapse. Mothers complained of headache, anorexia and poor appetite, weight loss, sleep disturbance, and bad dreams. Many of the fathers either lost or changed jobs, and all working mothers stopped working. Half the parents reported changes in their marital relationships such as frequent quarrels but also stronger unity. Family members perceived cancer as the most frightening disease. Change in their world view was expressed as living on faith understanding suffering, determining to live a better life, wanting to live an upright life and valuing health as the most important. Religious activities are found most helpful through this difficult experience. Financial debt due to the treatment and care of the sick child, burdened 22 families. The above mentioned impact was most evidant in Group B(those presently undergoing treatment) and Group C(those in relapse). Findings indicate that nursing care should embrace the family of a child who is being treated for cancer. 2. Characteristics of the child with cancer The majority of the children in this sample had a diagnosis of leukemia. Their mean age was 6.8 and the ratio of boys to girls was 1.12 ; 1. The mean hospitalization frequency was 13.5 times and the mean duration of illness was 16.8 months. Most of 1.he children perceived cancer as the most frightening disease ; 32.7% of the children described their sickness as serious. Children in Group C were hospitalized more frequently, stayed in hospital for longer periods, and expressed their sickness as quite serious more often than the other two groups. These findings indicate how much comprehensive pediatric hospice nursing care services are needed along with relevant research and nursing education. 3. Characteristics of the families. The mean age of the father was 39.5 and the mother, 36,6 ; they are in their most productive life period. Mothers especially expressed feelings of financial uneasiness and powerlessness about giving up their jobs, and guilty feelings for not providing enough care and concern to other children due to taking care of the sick one. The burden of caring for the sick child can bring negative changes in family dynamics which they think provoke potential health problems in members of the family These findings suggest a need for nursing support and counselling resources. Findings also suggest the need for ethical inquiry about such questions as who should give information to the child in regard to diagnosis and prognosis, when, and how. Other suggestions included : 1) Quality health care for childhood cancer such as home care and pediatric hospice programs should be established. 2) Special and practical consideration for long-term patients should be made in the present insurance coverage. The reimbursement period for long-term patients should be lengthened. 3) Further in-depth qualitative studies are needed. 4) Education programs including guided practice experience for pediatric hospice care practitioners are needed.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.15-26
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• 2018

This study examines the effects of failure experience, re-startup's motivation, government support business and education for re-startup on the performance of re-startup firms after failure. In addition, we analyzed how the above factors affect the performance of re-startup firms by the condition of debt and credit problems. As a result of the analysis, the failure experience had no significant effect on the re-startup performance regardless of the credit problem, while re-startup's motivation, government support business and education for re-startup had a significant effect on re-startup firms' performance. In the re-startup group with the credit problem, the re-startup's motivation and the failure experience had a significant influence on the re-startup firms' performance. On the other hand, in the group that did not solve the credit problem, the re-startup's motivation and the failure experience had no significant effect on the re-startup performance, but the government support business and education for re-startup had a significant effect on re-startup performance. The results of this study are as follows: First, it shows that the characteristics of re-startups and the determinants of re-startups are different according to credit problems. Second, this study shows that it takes 56 months on average from the close of business to the re-start, and it may take more than 7 years due to the credit problems and bankruptcy. This suggests the necessity to consider re-startup in the concept of obsolete in consideration of time, when studying the direct/indirect influence of failure experience and re-startup, and establishing policy.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.