• 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.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.87-99
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• 2013

Massive increases in data available to an organization are creating a new opportunity for competitive advantage. In this era of big data, developing analytics capabilities, therefore, becomes critical to take advantage of internal and external data and gain insights for data-driven decision making. However, the use of data in education is in its infancy, in comparison with business and government, and the potential for data analytics to impact education services is growing. In this paper, I survey how universities are currently using education data to improve students' performance and administrative efficiency, and propose new ways of extending the current use. In addition, with the so-called data scientist shortage, universities should be able to train professionals with data analytics skills. This paper discusses which skills are valuable to data scientists and introduces various training and certification programs offered by universities and industry. I finally conclude the paper by exploring new curriculums where students, by themselves, can learn how to find and use relevant data even in any courses.

• Journal of the Korea Convergence Society
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• v.8 no.4
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• pp.1-8
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• 2017

In the modern society, the size of the fashion market is continuously increasing both overseas and domestic. When purchasing a product through e-commerce, the evaluation data for the product created by other consumers has an effect on the consumer's decision to purchase the product. By analysing the consumer's evaluation data on the product the company can reflect consumer's opinion which can leads to positive affect of performance to company. In this paper, we propose a method to construct a model to analyze user's sentiment using word embedding space formed by learning review data of amazon fashion products. Experiments were conducted by learning three SVM classifiers according to the number of positive and negative review data using the formed word embedding space which is formed by learning 5.7 million Amazon review data.. Experimental results showed the highest accuracy of 88.0% when learning SVM classifier using 50,000 positive review data and 50,000 negative review data.