• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.455-456
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• 2018

산업구조를 이해하고 기업의 신제품 개발에 있어 밸류체인 분석은 시장 성공의 핵심 열쇠라고 할 수 있다. 기존 정부에서 추진해 왔던 다양한 기술로드맵 분석사업에서 밸류체인 분석은 산업분석의 핵심으로 활용되고 있다. 그러나 일반적으로 실질적인 통계데이터를 기반으로 하기보다는 일부 전문가의 연구결과에 의지하고 있는 실정이다. 이에 본 연구를 통해 좀 더 객관적이고 실질적인 산업구조내에서 실제 기업 거래정보 기반으로 밸류체인을 분석할 수 있는 새로운 분석 모델에 관한 연구를 수행하고자 한다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.3 no.1
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• pp.1-28
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• 2008

In this research, an empirical analysis was performed to determine the correlation between management performance and Empirical Research on the R&D investment for domestic venture businesses in each industry. Specifically, an empirical analysis for each industry was attempted not only to clarify the general hypothesis on the relationship between management performance and R&D investment for venture businesses but also to demonstrate that differences exist for each industry. Empirical analysis was conducted for eight industries with respect to the $2002{\sim}2006$ panel data extracted as investigative results from the "Investigation Report on Science and Technology R&D Activities" published by the Ministry of Science and Technology. Industrial classification was limited to the middle-level classification (2-digit) in the Korea Standard Industry Code (KSIC) owing to the limited number of panels. Although this research only verified the overall positive effect of R&D activities and funds for existing research on corporate value or productivity and management performance, it was able to document the difference for each individual industry and each business size unlike existing research. Furthermore, the reliability of the research results was enhanced by targeting companies that have been continuously conducting R&D and management activities using consistent 5-year panel data in the analysis. Again, this was something that existing research did not have. Finally, through the use of recent data from 2002 after the IMF economic crisis up to 2006 in the empirical analysis, this research proposed the problems due to the prevailing circumstances at the time of entering the advanced nation stage based on an empirical analysis; the prevailing problems during the pursuit of advanced nation status before the IMF crisis broke out were not tackled. The key empirical analysis yielded several results. First, capital and size of the labor force have a positive correlation with the management performance for the entire company or the venture business. This applies to all eight industries as the subjects of the analysis. Second, although the number of years since a company has been established can have positive or negative correlation with management performance for the entire company or venture business in specific industries, a definite overall trend cannot be identified. Third, R&D investment can be said to have an overall positive effect on corporate management performance. Fourth, the size of the research staff cannot be said to be a factor unilaterally affecting the management performance of the entire company or the venture business. Fifth, the number of years a research institute has been in operation, which was assumed to have a positive effect on the management performance of a company because of the accumulated R&D know-how -- definitely acts as a positive factor contributing to the management performance of a company.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.1-20
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• 2021

As the Great Reset is discussed at the World Economic Forum due to the COVID-19 pandemic, artificial intelligence, the driving force of the 4th industrial revolution, is also in the spotlight. However, corporate research in the field of artificial intelligence is still scarce. Since 2000, related research has focused on how to create value by applying artificial intelligence to existing companies, and research on how startups seize opportunities and enter among existing businesses to create new value can hardly be found. Therefore, this study analyzed the cases of startups using the comprehensive framework of the multi-level perspective with the research question of how artificial intelligence based startups, a sub-industry of software, have different innovation patterns from the existing software industry. The target firms are gazelle firms that have been certified as venture firms in South Korea, as start-ups within 7 years of age, specializing in machine learning modeling purposively sampled in the medical, finance, marketing/advertising, e-commerce, and manufacturing fields. As a result of the analysis, existing software companies have achieved process innovation from an enterprise-wide integration perspective, in contrast machine learning technology based startups identified unit processes that were difficult to automate or create value by dismantling existing processes, and automate and optimize those processes based on data. The contribution of this study is to analyse the birth of artificial intelligence-based startups and their innovation patterns while validating the framework of an integrated multi-level perspective. In addition, since innovation is driven based on data, the ability to respond to data-related regulations is emphasized even for start-ups, and the government needs to eliminate the uncertainty in related systems to create a predictable and flexible business environment.

• Journal of Technology Innovation
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• v.18 no.1
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• pp.21-51
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• 2010

This paper examines the knowledge transfer effect in Korean venture systems. Existing literature has provided rich evidence of the effect of knowledge transfer, but we do not have micro mechanisms inherent in the process of knowledge transfer. This paper argues that knowledge transfer effects vary depending on the knowledge types, sources, and legacy. This paper also tests role of the two important pillars in knowledge transfer of Korean venture startups; venture capital and government. This paper also examines the role of absorptive capacity in the knowledge transfer process. With 1,862 sample of Korean venture firms, this study employed three methods depending on 3 different types of dependent variables: hierarchical regression, logistic regression, and survival analysis. Main findings include that 1) knowledge characteristic itself and its alignment with industry influence the knowledge transfer effects, 2) government support has a negative effect on financial performance of venture firms, but does not have significant interaction effect on knowledge transfer, and 3) the absorptive capacity of each firm moderates the knowledge transfer effects. The theoretical and practical implications are discussed.

• Korea Information Processing Society Review
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• v.19 no.2
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• pp.70-78
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• 2012

• Proceedings of the Korea Technology Innovation Society Conference
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• 2015.11a
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• pp.368-374
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• 2015

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.138-144
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• 2021

In the electronic component assembly business, when product defects occur, it is important to track incoming raw material defects or work defects, and it is important to improve suppliers or work sites according to the results. The core task of the smart factory is to build an integrated data hub to process storage, management, and analysis in real time, and to manage cluster processes, energy, environment, and safety. In order to improve reliability through accurate analysis and collection of production data by real-time monitoring of production site management for electronic parts-related small and medium-sized enterprises (SMEs), the establishment of a smart factory is essential. This paper was developed to be utilized in the construction by defining the system configuration method, smart factory-related technology and application cases, considering the characteristics of SMEs related to electronic components that want to introduce a smart factory.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.165-177
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• 2023

In this paper analyzed the impact of innovation of CEOs of small and medium-sized companies, which are rapidly shifting to a digital economy, on corporate performance and how data literacy performs mediating functions. It was confirmed that innovation has a positive effect on corporate performance and that data literacy partially mediates the relationship between innovation and corporate performance. Transformational leadership shows a moderating effect in the relationship between innovation and corporate performance, and transactional leadership showed no moderating effect. Laissez-faire leadership has a moderating effect in the relationship between innovation and data literacy. These results show that innovation is an effective means of improving the organization's management performance, and are expected to awaken the importance of laissez-faire leadership and contribute to the establishment of management strategies.

• Journal of Korea Technology Innovation Society
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• v.18 no.2
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• pp.264-291
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• 2015

This research is to empirically analyze the effects of governmental policy including R&D subsidiary and tax reduction, which are both direct and indirect financial supports, during the examination period (2007~2009). The analysis was based on 2,751 firms that received governmental support via both R&D subsidiary and tax reduction with 7,038 panel events during the economic recession (2008~2009) and found that governmental support drives R&D investment of firms during the recession. The contribution of this research is that investigation of policy effectiveness categorized by firm sizes, particularly during the economic crisis. The result of the study is that during the recession, large firms had more elasticity increase towards tax reduction whereas smaller firms and ventures had it towards direct financial subsidiary. The elasticity increase of both large and small firms was in positive association with firms' R&D investment. The result indicates that government support obviously has positive influence on R&D investment of firms during the crisis, even enforcing the investment.

• 한국벤처창업학회:학술대회논문집
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• 2022.04a
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• pp.153-155
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• 2022

고객을 세분화하여 맞춤화된 서비스를 제공하는 것은 고객 관계 관리에 있어 중요하다. 빅데이터 분석 기법과 기계 학습 등을 활용한 분석 기법의 발전은 더욱 세밀한 고객 세분화를 가능케 했다. 하지만 새로운 분석 기법을 기업에서 효과적으로 적용하는 것은 여러 어려움이 존재한다. 본 연구는 특히 국내 보험 산업에서 데이터 분석 기법을 활용해 더욱 향상된 고객 세분화를 수행할 수 있는 방법에 대해 논의한다. 이를 위하여 실제 보험 설계사와의 심층 인터뷰를 통해 국내 보험 회사의 현상을 파악하고, 이를 기반으로 보험 산업에서 활용할 수 있는 가이드라인을 제시하고자 한다.