• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2358-2370
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• 2010

This research aims to empirically analyze the factors that affect the success of technology marketing by Korean universities. The total of 207 universities which successfully made technology transfers from 2006 to 2008 was examined to test the nine hypotheses. For the purpose of testing the hypotheses, technology infrastructure (research costs and the number of SCIE papers), the compensation system for the patents (application and registration), the number of patents (application and registration), TLO staff (the number of people in charge of technology transfer and the job experience in industries), the compensation system for technology transfers (researchers and contributors), and attitudes of university management and industries were analyzed with structural equation methods to figure out their effects on the revenues of technology transfer. The results of this research are summarized as follows. First, technology infrastructures of universities were found to have positive effects on securing patents. As the university research costs in the field of science and technology are increases, the research capabilities are enhanced and this a larger number of researchers are conducted. Second, this research shows that compensation systems for patent application and registration in universities have motivated researchers to take out patents for the outputs of their research. Third, the number of patents universities possess was found to have a positive effect on technology transfer. An increase in the number of patents universities possess implies an increase in the diversity and excellence of the target technologies for transfer. Fourth, the number of patents universities possess turned out to have a positive effect on TLO staff. The number of experts in charge of technology transfer including technology dealers, valuation analysis and patent attorneys should be increased as target technologies for transfer increase according to the increase of patents possessed. Because the technologies are transferee from universities to businesses, businesses (job) experience of TLO staff in industries are also important. This research is meaningful because it has identified the factors affecting the results of technology transfer by employing structural equation methods. In particular, an official governmental survey data for the academic-industrial cooperation were analyzed systematically in terms of technology infrastructure, compensation systems related to patents, the number of patents, TLO staff, compensation systems for technology transfer, and attitudes of university management and industries. All these facts might could differentiate this study from the previous studies.

• Journal of the Korean earth science society
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• v.31 no.4
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• pp.392-402
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• 2010

The purpose of this study is to analyze and evaluate the self-directed learning contents of Earth science area in the basic course of the 7th grade. For this purpose, we applied the 'Cyber Home Study Content Quality Control Tool' presented in 'Elementary Secondary Education e-Learning Quality Management Guidelines (Ver.2.0)' of Korea Education & Research Information Service (2008). The results of contents analysis are as follow: First, it was presented that the study guide introduced the contents which should be studied for one class, properly. And it was not analyzed that the diagnosis assesment was not completed in the initiative study; Second, it was possible to study choosing the contents fitting the learner's level of learning in the main study, it was comprised of about 15 minutes. Third, it was performed without feedback for incorrect answers in the learning assessment, just the number of wrong questions. And the learning arrangement present the important contents learned in that class, summarizing and arranging again. The results of content evaluation are as follows: First, a big difference was not showed against the needs analysis, instructional design, interaction in each class. And the evaluation of the ethics was not included a word or sentence not suitable. The evaluation of copyright, it was analyzed that Work within the content display in compliance with international copyright Second, the evaluation of instructional design presented mainly the description of a simple picture based, the visible resources like flash card were poor. And in the evaluation of Supporting System, it was presented that the contents were installed so that it was freely available for learners. But it was analyzed that there was no memo-function learners were able to jot down something during the studying contents. And in the evaluation for evaluation, the clear valuation basis about the described content was not presented. So there were slightly differences for each class. Third, in the evaluation and analysis for learning content, it was presented that there were some big differences for each class because it was not composed of the latest information, not corrected and complementary.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.360-373
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• 2017

The BTO-a projects is the types, which has a demand risk among the type of PPP projects in Korea. When demand risk is realized, private investor encounters financial difficulties due to lower revenue than its expectation and the government may also have a problem in stable infrastructure operation. In this regards, the government has applied various risk sharing policies in response to demand risk. However, the amount of government's risk sharing is the government's contingent liabilities as a result of demand uncertainty, and it fails to be quantified by the conventional NPV method of expressing in the text of the concession agreement. The purpose of this study is to estimate the value of investment risk sharing by the government considering the demand risk in the profit sharing system (BTO-a) introduced in 2015 as one of the demand risk sharing policy. The investment risk sharing will take the form of options in finance. Private investors have the right to claim subsidies from the government when their revenue declines, while the government has the obligation to pay subsidies under certain conditions. In this study, we have established a methodology for estimating the value of investment risk sharing by using the Black - Scholes option pricing model and examined the appropriateness of the results through case studies. As a result of the analysis, the value of investment risk sharing is estimated to be 12 billion won, which is about 4% of the investment cost of the private investment. In other words, it can be seen that the government will invest 12 billion won in financial support by sharing the investment risk. The option value when assuming the traffic volume risk as a random variable from the case studies is derived as an average of 12.2 billion won and a standard deviation of 3.67 billion won. As a result of the cumulative distribution, the option value of the 90% probability interval will be determined within the range of 6.9 to 18.8 billion won. The method proposed in this study is expected to help government and private investors understand the better risk analysis and economic value of better for investment risk sharing under the uncertainty of future demand.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.1
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• pp.117-137
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• 2019

In young entrepreneurs, the individual situation of opportunity discovery is very important. It is very important that the opportunities that are created for a particular individual entrepreneur are also recognized and assessed through the process. The need for the development of entrepreneurial opportunity competencies, which have a low proportion of opportunistic entrepreneurship, is low in the entrepreneurship education. In particular, young entrepreneurs are in desperate need of opportunistic entrepreneurship. The purpose of this study is to examine the effect of entrepreneurship motivation on entrepreneurial opportunity competence, using entrepreneurial orientation and entrepreneurship orientation as mediation variables for preliminary young entrepreneurs (19-39 old). In the case of young entrepreneurs, there is a tendency to study entrepreneurship policies and education through the system of youth entrepreneurship schools, mainly on college students and youths, and on the effects of institutional support on entrepreneurship. There is little research on the effect of a entrepreneurial motivation on the entrepreneurial opportunity competence needed to promote an entrepreneurial venture in a model with multiple mediators. The purpose of this study is to investigate the effect of start - up motivation on the entrepreneurial opportunity competence. To do this we analyzed 374 questionnaires collected from preliminary young entrepreneurs in Seoul and Gyeonggi provinces. The results of the analysis using SPSS v22.0 and Process macro v3.0 showed that the motivation of start - up had a significant effect on both opportunity recognition and opportunity evaluation of entrepreneurial opportunity competence. Second, motivation of entrepreneurs has a significant effect on entrepreneurial efficacy. Third, entrepreneurial efficacy has a significant effect on entrepreneurial orientation. Fourth, entrepreneurial orientation has a significant effect on entrepreneurial opportunity competence. Fifth, there is a significant indirect effect between entrepreneurial motivation and entrepreneurial opportunity recognition when passing through entrepreneurial orientation, entrepreneurial efficacy and entrepreneurial orientation at the same time, But indirect effects was insignificant when only entrepreneurship efficacy is passed. There is a significant indirect effect on all mediators between entrepreneurial motivation and entrepreneurial opportunity valuation. It is suggested that strengthening education on entrepreneurship is necessary to cultivate awareness of entrepreneurship opportunities and strengthening education on both entrepreneurial efficacy and entrepreneurship is necessary to cultivate evaluation of entrepreneurship opportunities by type of entrepreneurial motivation.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.