• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.2
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• pp.121-134
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• 2015

This study empirically analyzed the effects of international entrepreneurial proclivity of exporting small and medium enterprises on corporate capability and export performance according to product type of industrial and consumer goods. International entrepreneurial proclivity of exporting small and medium enterprises consists of risk-taking, proactiveness, and innovativeness, and corporate capability consists of technological capability and product differentiation capability. Risk-taking, innovativeness, and proactiveness had a significant impact on technological capability in case of industrial goods, and in case of consumer goods, only risk-taking and innovativeness had significant impact. Product differentiation capability of consumer goods was significantly influenced by the order of innovativeness, proactiveness, and risk-taking while only innovativeness had a negative impact on industrial goods. When the impact of corporate capability on export performance was examined, only technological capability had a significant impact on both financial and strategic performance in case of industrial goods while both technological capability and product differentiation capability had significant impact in case of consumer goods. After examining the direct impact of international entrepreneurial proclivity on financial performance, it was found that financial performance in the case of industrial goods was significantly influenced by the order of proactiveness and risk-taking, and in the case of consumer goods by the order of innovativeness and proactiveness. However, the impact of international entrepreneurial proclivity on strategic performance showed different results. In case of industrial goods, only risk-taking had a significant impact on strategic performance while in the case of consumer goods it was significantly influenced by the order of innovativeness, proactivenesspro, and risk-taking. The direct impact of international entrepreneurial proclivity on export performance was different in case of financial and strategic performance, and there was difference regarding product type as well. It suggests that different approach is needed according to product type in order to increase export performance since the impact of international entrepreneurial proclivity on corporate capability, the impact of corporate capability on export performance, and the impact of international entrepreneurial proclivity on export performance were all different according to product type.

• Journal of Venture Innovation
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• v.2 no.2
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• pp.31-46
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• 2019

Materials and Parts acts as the bridge in the manufacturing industry. In 2018, the materials and parts industry became the leading industry in Korea as its export reached $316.2 billion, accounting for 52.3 percent of the country's total exports. As such, it is the main industry of Korea leading the trade surplus, but when it comes to Japan, it is not. The trade deficit with Japan shrinks to $24 billion last year but the materials and parts industry still accounts for 60 percent of total deficit, which is about $15.1 billion. Today Japan has the top competitiveness in the high-tech materials and parts industry and the factors can be found in cooperation and symbiosis among companies, monotsukuri spirit, and long-term government policy. In order for Korean economy to pursue the Japan's high-tech materials and parts industry, the following change of perception is necessary. First, the material and parts industry requires win-win cooperation. In general, materials and parts are intermediate products. Therefore, it is important to understand the characterist that the transactions are all made up between companies not the with consumers. Second, expansion of joint technology development is absolutely necessary. South Korea is a leading country in the field of general-purpose materials and parts. However, the research shows that South Korea has structure which small and medium-sized companies could have difficulties in developing high-tech products as finding demand and developing market are hard due to low participation of large corporations at R&D stage. It is necessary for large corporations to participate in joint R&D and share opinions of customers from the beginning stage of R&D. Third, a long-term approach is needed. Structural vulnerabilities in the Korea's materials and parts industry, including the lack of advanced technologies is the main reason of solidification of Korea's trade deficit with Japan but there are also cultural differences about technology in the background. Even if it takes time, a long-term approach is absolutely necessary to build up technology and know-how in order to secure competitiveness in the high-tech materials and parts industry. This approach applies to act of corporation and government policy.

• 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.16 no.6
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• pp.69-84
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• 2021

As industrial structural changes in the 4th Industrial Revolution have recently led to the need for fostering high-tech industries and high-tech manufacturing industries have been showing high value-added creation, the importance of high-tech manufacturing ventures has increased a lot as well. As a result of this, the government is actively supporting and fostering them. However, it appears that high-tech manufacturing ventures seem to have a lot of difficulty in securing competitive advantages due to the lack of internal core competencies and experience in the rapidly changing international economic conditions. In order for high-tech manufacturing ventures to strengthen internal core competencies, external collaborations with other companies or institutions which have diverse experience, technology skills and abundant resources are actively promoted. Accordingly, based on resource-based theory and transaction cost theory, the authors analyzed the effects of the high-tech manufacturing ventures'external collaborations on internal core competencies and management performance in this study. In order to verify the hypothesis of this study, the 2020 data on"The Research on the Precision Status of Ventures'compiled by the Ministry of SMEs and Startups since 1999 were utilized. According to the results of this study, the experience of external collaborations had a positive impact on the internal core competencies and non-financial management performance, while there was no direct impact on financial management performance. Moreover, the relationship between the experience of external collaborations and management performance is mediated by the internal core competencies. Additionally, it was found that the internal core competencies positively affected both non-financial and financial management performances, and non-financial management performance again had a significant impact on the financial management performance. Finally, the experience of external collaborations had a positive impact on both development, manufacturing, and marketing factors forming the internal core competencies. However, the impacts of individual factors were different in the management performance. Development and marketing factors were shown to have a significant impact on both non-financial and financial management performance, while the manufacturing factor had a significant impact only on financial management performance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.4
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• pp.143-161
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• 2022

The government is promoting policies to increase policy efficiency by supporting corporate growth through corporate support and establishing the Ministry of SMEs and Startups as a control tower for corporate support projects. However, opinions on the efficiency of the government's corporate support project are divided, and this study aims to check how the government's corporate support project affects corporate performance and how absorption capacity and satisfaction, which are internal factors, affect corporate growth. Research was conducted on companies receiving government corporate support projects, and previous studies focused on financial support among government corporate support projects, while the effect of government corporate support was analyzed by dividing government support projects into financial and non-financial support, and absorption capabilities and corporate support satisfaction were analyzed. Through this, the effect on corporate financial performance and non-financial performance was empirically analyzed according to the mediating effect of absorption capacity and corporate support satisfaction in the government's corporate support project. As a result, both the government's financial and non-financial support had a positive effect on financial and non-financial performance, and it was confirmed that both absorption capacity and corporate support satisfaction mediate both financial and non-financial performance, and it was analyzed that it had a positive (+) effect. In order to improve the absorption capacity of a company, it is expected that it will be meaningful to improve the efficiency of the business by defining the problems faced by the company and suggesting solutions through the establishment of a supplier and consumer network.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.215-235
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• 2024

As artificial intelligence (AI) technology is recognized as a key technology that will determine future national competitiveness, competition for AI technology and industry promotion policies in major countries is intensifying. This study aims to present implications for domestic policy making by analyzing the policies of major countries on the start-up of AI companies, which are the basis of the AI industry ecosystem. The top four countries and the EU for the number of new investment attraction companies in the 2023 AI Index announced by the HAI Research Institute at Stanford University in the United States were selected, The United States enacted the National AI Initiative Act (NAIIA) in 2021. Through this law, The US Government is promoting continued leadership in the United States in AI R&D, developing reliable AI systems in the public and private sectors, building an AI system ecosystem across society, and strengthening DB management and access to AI policies conducted by all federal agencies. In the 14th Five-Year (2021-2025) Plan and 2035 Long-term Goals held in 2021, China has specified AI as the first of the seven strategic high-tech technologies, and is developing policies aimed at becoming the No. 1 AI global powerhouse by 2030. The UK is investing in innovative R&D companies through the 'Future Fund Breakthrough' in 2021, and is expanding related investments by preparing national strategies to leap forward as AI leaders, such as the implementation plan of the national AI strategy in 2022. Israel is supporting technology investment in start-up companies centered on the Innovation Agency, and the Innovation Agency is leading mid- to long-term investments of 2 to 15 years and regulatory reforms for new technologies. The EU is strengthening its digital innovation hub network and creating the InvestEU (European Strategic Investment Fund) and AI investment fund to support the use of AI by SMEs. This study aims to contribute to analyzing the policies of major foreign countries in making AI company start-up policies and providing a basis for Korea's strategy search. The limitations of the study are the limitations of the countries to be analyzed and the failure to attempt comparative analysis of the policy environments of the countries under the same conditions.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.