• 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 Physiology & Pathology in Korean Medicine
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• v.35 no.5
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• pp.151-161
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• 2021

Traditional Korean medicine (TKM) takes a holistic view that emphasizes the balance between the elements constituting the human body or between the human body and the external environment. To investigate the holistic properties of TKM, here we propose to apply the methodology of complexity science to the TKM research. Complexity science is a discipline for studying complex systems with interactions between components that raise the behaviour as a whole which can be more than the sum of their parts. We first provide an introduction to the complexity science and its research methods, particularly focusing on network science and data science approaches. Next, we briefly present the current status of TKM research employing these methods. Finally, we provide suggestions for future research elucidating the underlying mechanism of TKM, both in terms of biomedicine and humanities.

• The Journal of Korean Medicine
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• v.45 no.1
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• pp.203-214
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• 2024

Objectives: In the context of competency-based education emphasized in Korean Medicine, this study aimed to develop a pilot version of a CPX (Clinical Performance Examination) Practicing Chatbot utilizing large language models with prompt engineering. Methods: A standardized patient scenario was acquired from the National Institute of Korean Medicine and transformed into text format. Prompt engineering was then conducted using role prompting and few-shot prompting techniques. The GPT-4 API was employed, and a web application was created using the gradio package. An internal evaluation criterion was established for the quantitative assessment of the chatbot's performance. Results: The chatbot was implemented and evaluated based on the internal evaluation criterion. It demonstrated relatively high correctness and compliance. However, there is a need for improvement in confidentiality and naturalness. Conclusions: This study successfully piloted the CPX Practicing Chatbot, revealing the potential for developing educational models using AI technology in the field of Korean Medicine. Additionally, it identified limitations and provided insights for future developmental directions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.213-227
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• 2021

The purpose of this study is to identify factors that are causal and intention to revisit healing agricultural facilities and to identify the relationship between factors. the effect of experience type was analyzed on revisit intention by using facility experience satisfaction as a meditator for healing agriculture participants. The sub-variables of experience types were divided into educational experience, devious experience, entertainment experience, and aesthetic experience. 259 questionnaires collected from the people who experienced healing agriculture programs in the whole country were used for empirical analysis. The PROCESS macro v3.4 and SPSS v22.0 were used to analyze based on the singular simple mediation model which contains a single mediator. First, all types of experience had a positive (+) effect on satisfaction. Second, satisfaction had a positive (+) effect on revisit intention independently of experience type. Third, all types of experience had a positive (+) effect on revisit intention. Fourth, satisfaction mediated between experience type and revisit intention except for deviating experience among experience types. As a follow-up study, it is necessary to study for the adjustment of mediators other than the mediator introduced in the study or the controlled mediated analysis through the conditional process model in which the moderator variable is introduced.

• Journal of Service Research and Studies
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• v.11 no.3
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• pp.1-16
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• 2021

Recently, crowdfunding has been receiving increased attention due to the variety of novel ways it provides for connecting funders and fundraisers. Concurrently, however, it has been criticized on the grounds of information asymmetry. We are interested in whether information collected from an asymmetric information source significantly influences crowdfunding success. Through this study, we would like to establish a social field of convergence pursued by Serve Science and to conduct practical research and practice together. First, we investigated the importance of interaction between funders and fundraisers within the crowdfunding platform. In the "comments" section, communication between funders and fundraisers reduces the degree of asymmetric information. In the aspect of presumable funders, they should make the best of the "comments" section to collect more solid information without losing return on their investment. Second, we analyze sentiment-based optimistic and pessimistic information from the "story" (asymmetric source) and "comments" sections. In conclusion, it shows that the optimistic and pessimistic information of the "comments" section has a great impact on crowdfunding success and that the influence of the "story" section is limited to pessimistic information under certain conditions. The crowdfunding platform is an innovative and productive way for startups and entrepreneurs to start a business, and since information an important role in the success of crowdfunding, It can be seen that it is essential to focus on establishing the best communication methods.

• Journal of Korea Technology Innovation Society
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• v.21 no.4
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• pp.1366-1385
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• 2018

Recently, government policy focuses on R&D subsidies for venture firms in the early and medium stage. However, due to the 'asymmetric information' on those firms, a concern about the possibility of adverse selection of government policy, that is, whether the R&D subsidies are offered to the less-growth potential venture firms is on the rise. Therefore, based on the "2015 venture firm's survey" data in Korea, we compared the managerial efficiency of venture firms in manufacturing sectors by dividing them into beneficiary and non-beneficiary groups at government R&D subsidies. We found that the beneficiary groups showed lower managerial efficiency than non-beneficiary groups, even if they are superior to non-beneficiary groups in technological performance. We also observed that the phenomenon involve 'low managerial efficiency in the beneficiary groups' is more relevant in mid-high tech. manufacturing sectors. Our findings provide an exploratory empirical evidence of the concern about adverse selection in the selection of R&D subsidies beneficiary groups. Therefore, the government should consider managerial performance as the key criteria for selecting R&D subsidies beneficiary groups, rather than depending on technological performance solely. Furthermore, the government should develop other complementary policies to support financial performance of the groups. Lastly, the government should make those policies attract venture firms with potential to achieve financial performance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.1-13
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• 2019

As a way to find new and innovative technologies, many companies have invested in and acquired skilled startups. Because startups are usually small in size and have a small history of past business experience, there are many risks involved in acquiring them as they have limited technical skills and business feasibility verification methods. Thus, venture capital plays an important role in discovering and investing competitive startups. While Independent Venture Capital generally values financial returns, Corporate Venture Capital, which plays investment roles in the firm, values business synergies with the parent company from a strategic perspective. In an industry sector where development of technology is rapid and whether new technology is held determines a company's competitiveness, existing companies incorporate startups with innovative technologies into their investment portfolios, collaborate together, and take over for comprehensive cooperation. In addition, new investments and acquisitions are carried out through the management of portfolio companies to obtain and utilize industry information. In this paper, major U.S. companies listed in the U.S. verified their investment activities through corporate venture capital and their impact on parent companies and startups through regression, while the parent company's acquisition performance was analyzed through an event study based on a stock price analysis. The criteria for startup were defined as companies with less than 12 years of experience, and the analysis showed that the parent companies with corporate venture capital with a larger number of investments actively take over startups. In addition, increasing corporate venture capital's financial investment activities shows a negative impact on the parent companies' acquisition activities, and the acquisition performance increased when the parent companies took over startups in its portfolio.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.237-258
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• 2022

As the global market environment changes, the domestic ICT industry has a growing influence on the world economy. This industry is regarded as an important driving force in the national economy from a technological and social point of view. In particular, small and medium-sized enterprises (SMEs) in the ICT industry are regarded as essential actors of domestic economic development in terms of company diversity, technology development and job creation. However, since it is small compared to large-sized enterprises, it is difficult for SMEs to survive with a differentiated strategy in an incomplete and rapidly changing environment. Therefore, SMEs must make a lot of efforts to improve their own capabilities, and the government needs to provide the desirable help suitable for corporate internal resources so that they can continue to be competitive. This study classifies the types of ICT SMEs participating in government support programs, and analyzes the relationship between resources and performance creation of each type. The data from the "ICT Small and Medium Enterprises Survey" conducted annually by the Ministry of Science and ICT was used. In the first stage, ICT SMEs were clustered based on common factors according to their experiences with government support programs. Three clusters were meaningfully classified, and each cluster was named "active participation type," "initial support type," and "soloist type." As a second step, this study compared the characteristics of each cluster through profiling analysis for each cluster. The third step carried out in this study was to find out the mechanism of R&D performance creation for each cluster through regression analysis. Different factors affected performance creation for each cluster, and the magnitude of the influence was also different. Specifically, for "active participation type", "current manpower", "technology competitiveness", and "R&D investment in the previous year" were found to be important factors in creating R&D performance. "Initial support type" was identified as "whether or not a dedicated R&D organization exists", "R&D investment amount in the previous year", "Ratio of sales to large companies", and "Ratio of vendors supplied to large companies" contributed to the performance. Lastly, in the case of "soloist type", "current workforce" and "future recruitment plan", "technological competitiveness", "R&D investment", "large company sales ratio", and "overseas sales ratio" showed a significant relationship with the performance. This study has practical implications of showing what strategy should be established when supporting SMEs in the future according to the government's participation in the startup program and providing a guide on what kind of support should be provided.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.468-488
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• 2018

The maker movement is a phenomenon of society and culture where people who make necessary things come together and share knowledge and experience through creativity. However, as the maker movement has grown rapidly over the past decade, there is still a lack of consensus for how far they will be viewed as a maker movement. We need to look at how the maker movement has changed so far in order to find the direction of development of the maker movement. This study analyzes the media articles using text-based big data analysis methodology to understand how the issue of the maker movement has changed in general media. In particular, we apply Keyword Network Analysis and DTM(Dynamic Topic Model) to analyze changes of interest according to time. The Keyword Network Analysis derives major keywords at the word level in order to analyze the evolution of the maker movement, and DTM helps to identify changes in interest in different areas of the maker movement at three levels: word, topic, and document. As a result, we identified major topics such as start-ups, makerspaces, and maker education, and the major keywords have changed from 3D printer and enterprise to education.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.2
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• pp.73-78
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• 2022

Since Traditional Korean medicine (TKM) doctors use various knowledge systems during treatment, diagnosis results may differ for each TKM doctor. However, it is difficult to explain all the reasons for the diagnosis because TKM doctors use both explicit and implicit knowledge. In this study, an upgraded random forest (RF)-based evaluation tool was proposed to extract clinical knowledge of TKM doctors. Also, it was confirmed to what extent the professor's clinical knowledge was delivered to the trainees by using the evaluation tool. The data used to construct the evaluation tool were targeted at 106 people who visited the Sasang Constitutional Department at Kyung Hee University Korean Medicine Hospital at Gangdong. For explicit knowledge extraction, four TKM doctors were asked to express the importance of symptoms as scores. In addition, for implicit knowledge extraction, importance score was confirmed in the RF model that learned the patient's symptoms and the TKM doctor's constitutional determination results. In order to confirm the delivery of clinical knowledge, the similarity of symptoms that professors and trainees consider important when discriminating constitution was calculated using the Jaccard coefficient. As a result of the study, our proposed tool was able to successfully evaluate the clinical knowledge of TKM doctors. Also, it was confirmed that the professor's clinical knowledge was delivered to the trainee. Our tool can be used in various fields such as providing feedback on treatment, education of training TKM doctors, and development of AI in TKM.