• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.410-422
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• 2018

This paper studies the factors that increase the sustainability of Industry-University cooperation in the current structure of domestic I-U cooperation, which is highly dependent on government support. First, we examine the extent to which the 'I-U Relationship Strength' can be explained by the cumulative 'Experiences of I-U cooperation' and 'Width of various I-U cooperation channels', and the 'I-U cooperation barrier' can be explained by the 'Difference in mutual recognition' and 'Institutional barriers' on sustainability. In addition, the factors that can lower the 'I-U cooperation barrier', such as the 'University administration's efforts' and 'Trust between I-U', are examined. The researchers examined the factors affecting the sustainability of I-U cooperation and the factors affecting 'I-U cooperation barriers' in the 'I-U cooperation technology development projects' of the Ministry of Small & Medium Venture Business and Startups with its long history of domestic I-U cooperation programs. In order to clarify the data of the research sample, a questionnaire survey of organizational units was conducted for all companies participating in the 'I-U cooperation technology development projects' of the Ministry of SMVB and Startups between 2011 and 2015, and the responses of 356 organizations were used. It was found that the greater the 'Width of the I-U cooperation channels', the higher the sustainability and that the greater the 'Institutional barriers', the lower the sustainability. However, through the 'University administration's efforts' and 'Trust between I-U', it is possible to overcome the 'I-U cooperation barrier'. Ultimately, the systematization of Industry-University-Research institute subjects is needed to realize sustainability. In other words, it is necessary to have a linkage program that can broaden the link between industry and universities, that is to broaden the scope of I-U cooperation. Moreover, it is necessary for the government to provide institutional support to promote desirable I-U cooperation policies. Finally, it is essential to change the universities' core organizations in order to improve the level of university administration services.

• Journal of Korea Technology Innovation Society
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• v.21 no.2
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• pp.636-662
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• 2018

Recently, the Moon administration established the Ministry of Small and Medium-sized Enterprises (SMEs) and Startups, as part of its national strategy for start-up and innovation growth led by small and medium-sized venture companies. In a slowing economy, as venture companies with excellent internal competencies are seen to be favorable to growth, the government funding for technology development is becoming increasingly important. Previous studies examine the internal competence factors that can strengthen competitiveness through self-efforts and the influence structure of growth stage, which is an important factor in industrial environment, on business performance. As the government support for venture firms has been strengthened, the effect of government funding on the management performance and technological innovation performance of venture firms have been recently discussed in various ways. However, there is a lack of precedent research on the moderating effect of the utilization of government funding on the existing influence structure in which firm's internal competence and growth stages affects business performance. Therefore, this study examined whether the internal competencies of the venture firms and the stage of growth have direct effects on business performance and analyzed the moderating effect in connection with government funding utilization under these influence structures. The results of the study are as follows. First, the utilization of government funding in the venture firms whose R&D personnel ratio is relatively low, not to have own brands and showed an increase of employees has a significantly positive influence on business performance. Second, the moderating effects of the government funding utilization at the high growth stage of the venture firms are shown significantly. These results suggest that the venture policy linked to the job creation of the present government requires not only the support considering R&D personnel but also the necessity of supporting human resources policy to a greater extent and further study on the effectiveness of venture firms in the high growth stage.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.1-11
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• 2023

Big tech companies are further strengthening its status against the background of data accumulation, price competitiveness by the platform, and competitive advantage due to the network effect. The competition subcommittee of the European Union(EU) imposed a huge fine on Google for antitrust violations, which was interpreted as an attempt to collect Google's unpaid taxes. In fact, taxation efforts in the form of 'Google tax' are underway, targeting expedient tax avoidance by global platforms. It has power and has a considerable influence on the startup ecosystem. The domestic sales and tax scale of global platforms, which have a great impact on domestic content startups and small and medium-sized venture companies, are not accurately measured. In the case of Google, according to research literature, sales in Korea were estimated at about 2 trillion to 3 trillion won in 2017, but Google Korea reported sales of 290 billion won in 2021 and paid 13 billion won in taxes. This study aims to verify the economic effect of the global platform that has a great influence on Korea, and specifically to quantitatively estimate the annual domestic sales and taxes of Google, a representative global platform. As a result of estimating Google's annual domestic sales and taxes based on the figures presented in the document related to Google's economic effect published by Google, the result was 4 to 9 trillion won in annual sales and 390.6 to 913.1 billion won in taxes. This study is meaningful in that it provides basic data on the direction of national and tax policies in the future digital economy era by estimating the problem of tax authority by country of global platform companies with a specific example of Google.

• Journal of Agricultural Extension & Community Development
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• v.21 no.4
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• pp.1093-1124
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• 2014

Studies on the role of agricultural science colleges are mostly divided into agricultural production, which is the primary function of agriculture, and other functions, which have recently begun to be emphasized as a result of social needs. With the green revolution and the aging of the farming population, there is a strong view that the role of agricultural science colleges should remain as it is. However, agriculture is expanding in terms of concept and content by converging with other industries not traditionally associated with agricultural production. Thus, the fields that now need to form part of agricultural science knowledge are becoming more detailed and expansive. The government's perception remains at the level of merely fostering farmers. This was evident in a survey on the employment rate, a factor used to evaluate colleges, in which the role of agricultural science colleges was limited to fostering farmers. Agro- industry fields, other than agriculturalists, include general industries in which the academic fields of agricultural science are combined with other academic fields. Thus, even when someone is employed in an industry that requires background knowledge of agricultural science, there is often a perception that he or she is employed in a field that is irrelevant to the major. This study examines the role of agricultural science colleges in agriculture and farm villages by focusing on the employment of graduates of these colleges within agro-industry. We categorize academic research on agricultural science into 16 fields, based on the medium level of the National Standard Science and Technology Classification Codes. Then, we categorize the employment fields into 168 fields, based on the small classification level of the inter-industry relations classification. Thus, we investigate 220 departments of 37 colleges, nationwide. Our findings show that the average employment rate of graduates of agricultural science colleges is 69.0%. Furthermore, 33.0% of all employees work in agro-industry fields that require background knowledge in agricultural science, which is one out of three job seekers. Then, 3.6% of employees work in business startups in agro-industry. The aforementioned government survey showed that only 0.1% of all college graduates in Korea were employed as agriculturalists in 2013. However, our results showed that 13.3% of graduates were working as agriculturalists, which is significantly different to the results of the government survey. These results confirm that agricultural science colleges contribute greatly to the employment of graduates, including farmers, agro-industry, and business startups in agro-industry fields.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.1
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• pp.105-117
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• 2018

In this study, we identify the obstacles that occur through the relationship between I-U cooperation and look for factors that can overcome them in the 'university administration's efforts' and 'Trust between I-U'. In the study of existing I-U cooperation, the relationship between industry and university has accumulated experiences and various channels of bilateral cooperation by sustaining interactions and absorbing capacity of knowledge by path dependence. However, as cooperation increases, 'I-U cooperation barrier' are inevitable, which is explained by two perspectives: 'Difference in mutual recognition' and 'Institutional barriers'. In order to induce the achievement of effective I-U cooperation, it is necessary to overcome these obstacles stemming from mutual relations, and it will be possible to maintain the relationship of continuous I-U cooperation. The researchers conducted research on companies participating in the I-U cooperation technology development project of the 'Ministry of Small and Medium Venture Business', which is a representative I-U cooperation program in Korea. This project will be promoted in the 'Small & Medium Business I-U cooperation Center', an administration-dedicated organization of the university. The researchers measure 'University administration's efforts' and 'Trust between I-U'to overcome'I-U cooperation barrier' In order to clarify the data of the research sample, a questionnaire survey of organizational units was conducted for all companies participating in the 'I-U cooperation technology development projects' of the SMEs and Startups between 2011 and 2015, and the responses of 356 organizations were drawn. The results showed that the higher the level of 'University administration's efforts' and Trust between I-U', the lower 'Difference in mutual recognition' and 'Institutional barriers'. Particularly, it showed higher explanatory power to overcome 'Institutional barriers' among obstacles. Therefore, it should be accompanied by the interest, implementation and institutional support of I-U-R subjects to raise the level of these two factors that can overcome 'I-U cooperation barrier'.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.211-226
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• 2023

As we transition into the post-COVID era, economic activities that were stagnant are regaining momentum. In particular, there is a growing trend of technology entrepreneurship driven by the opportunities of digital transformation in the Fourth Industrial Revolution. However, entrepreneurship education content is struggling to keep up with the rapid pace of technological change. This study aims to emphasize the importance of entrepreneurship mentoring as a crucial component of entrepreneurship education content that requires adaptation and advancement due to the increasing demand for technology entrepreneurship. This study redefines startup mentoring, which is differentiated from general mentoring, at the present time when the demand for startups, which increases with the declining employment rate, increases, and the development of quality startup education contents and securing professional startup mentors are required. According to the start-up stage, it is divided into preliminary entrepreneurs and early entrepreneurs, and the effect of entrepreneurship knowledge and self-efficacy among start-up mentoring functions on entrepreneurial will and mentoring satisfaction is improved by empirically researching the effects of start-up mentoring functions in the case of initial entrepreneurs as a moderating effect. To confirm the importance of entrepreneurship mentoring effect for. To this end, among the mentoring functions, entrepreneurship knowledge and self-efficacy were set as independent variables, and entrepreneurial will and mentoring satisfaction were set as dependent variables. The research model was designed and hypotheses were established. In addition, empirical analysis was conducted by conducting a questionnaire survey on trainees who received entrepreneurship mentoring education at ICCE Startup School and Opus Startup School. To summarize the results of the empirical analysis, first, among the entrepreneurship mentoring functions, entrepreneurship knowledge and self-efficacy were analyzed to have a significant positive (+) effect on entrepreneurial will. Second, among the entrepreneurship mentoring functions, entrepreneurship knowledge and self-efficacy were analyzed to have a significant positive (+) effect on mentoring satisfaction. Third, it was analyzed that entrepreneurship had no significant moderating effect on entrepreneurial knowledge and entrepreneurial will. Fourth, it was analyzed that entrepreneurship had no significant moderating effect on mentoring satisfaction. Fifth, it was found that entrepreneurship had a significant moderating effect between self-efficacy and will to start a business. As a result of the research analysis, the first implication is that the mentoring function in start-up education is analyzed to produce meaningful results for both the initial entrepreneurs and the prospective entrepreneurs in the will to start a business and satisfaction. . Second, it was analyzed that there was no significant relationship between whether a business was started and the mentoring function and effect. However, it was analyzed that the will to start a business through improvement of self-efficacy through mentoring was significantly related to whether or not to start a business. turned out to be helpful. Many start-up education programs currently conducted in Korea educate both early-stage entrepreneurs and prospective entrepreneurs at the same time for reasons such as convenience. However, through the results of this study, even in small-scale entrepreneurship mentoring, it is suggested that customized mentoring through detailed classification such as whether the mentee has started a business can be a method for successful entrepreneurship and high satisfaction of the mentee.

• 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.