• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.1
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• pp.55-73
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• 2024

Unicorn companies are attracting attention around the world as they are recognized for their high corporate value in a short period of time as an innovative business models. Their growth process presents good lessons for the startup ecosystem and have a positive impact on national economic development and job creation. However, previous studies related to unicorn companies are focused on 'event studies' and 'case studies' such as characteristics of founders, environmental factors, business models and success/failure cases of companies already recognized as unicorns rather than a multifaceted approach. The occurrence of unicorn companies and Macroscopic analysis of related factors is lacking. Against this background, this study are considering the characteristics of unicorns examined through previous research and the current status unicorns with a high proportion of technology companies, the purpose was to analyze the impact of the country's technological competitiveness, such as 'technology human resource index', 'R&D index', and 'technology infrastructure index', on the increase in unicorn companies. For statistical analysis, data published by various international organizations, the Bank of Korea, and Statistics Korea from 2017 to 2020 and unicorn company data compiled by CB Insights were used as panel data for 44 countries to be tested by multiple regression analysis. As a result of the study, it was confirmed that the number of science majors had a positive (+) effect on the increase of unicorn companies in the case of technology human resource index, and in the case of R&D index, the total amount of R&D investment had a positive (+) effect on the increase of unicorn companies, while the number of Triad Patents Families and the number of scientific and technological papers published had a negative (-) effect on the increase of unicorn companies. Finally, in the case of technology infrastructure index, it was confirmed that the number of the world's 500th-ranked universities had a positive (+) effect on the increase of unicorn companies. This study is the first to reveal the causal relationship between national technological competitiveness and unicorn company growth based on country-specific and time-series empirical data, which were insufficiently covered in previous studies. and compared to the UN's ranking of the global industrial competitiveness index and the OECD's total R&D investment by country, Korea is considered to have technological and growth potential, while the number of unicorn companies driving growth as leaders of the innovative economy is relatively small, so the research results can be used when establishing policies to discover and foster unicorn companies in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.41 no.2
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• pp.35-45
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• 2023

First, The spatial distribution characteristics of old big trees were analyzed using ArcGIS figures by combining basic information such as species and ages of old big trees in Handan City, which were compiled by the local bureau of landscaping. The types of species, distribution by ages of trees, ownership status, growth status, and diversity status were comprehensively analyzed. Statistically, Styphnolobium, Acacia, Gleditsia, and Albizia of Fabaceae accounted for the majority, of which Sophora japonica accounted for the highest proportion. Sophora japonica is widely and intensively distributed to each prefecture and district in Handan city. According to the age and distribution, the old big trees over 1000 years old were mainly Sophora japonica, Zelkova serrata, Juniperus chinensis, Morus australis Koidz., Dalbergia hupeana Hance, Ceratonia siliqua L., and Pistacia chinensis, and Platycladus orientalis. Second, as found in each type of old big tree status, various types of old big tree status were investigated, the protection management system, protection management process, and protection management benefits were studied, and the protection of old big tree was closely related to the growth environment. Currently, the main driving force behind the protection of old big trees is the worship of old big trees. By depositing its sacredness to the old big tree and sublimating the natural character that nature gave to the old big tree into a guiding consciousness of social activities, nature's "beauty" and personality's "goodness" are well combined. The protection state of the old big tree is closely related to the degree of interaction with the surrounding environment and the participation of various cultures and subjects. In the process of continuously interacting with the surrounding environment during the long-term growth of old big trees, it seems that a natural sanctuary was formed around old big trees in the process of voluntarily establishing a "natural-cultural-scape" system involving bottom-up and top-down cross-regions, multicultural and multi-subjects. Third, China focused on protecting and recovering old big trees, but the protection management system is poor due to a lack of comprehensive consideration of historical and cultural values, plant diversity significance, and social values of old big trees in the management process. Three indicators of space's regional characteristics, property and protection characteristics, and value characteristics can be found in the evaluation of the natural characteristics of old giant trees, which are highly valuable in terms of traditional consciousness management, resource protection practice, faith system construction, and realization of life community values. A systematic management system should be supported as to whether they can be protected and developed for a long time. Fourth, as the perception of protected areas is not yet mature in China, "natural sanctuary" should be treated as an important research content in the process of establishing a nature reserve system. The form of natural sanctuary management, which focuses on bottom-up community participation, is a strong supplement to the current type of top-down nature reserve management in China. Based on this, the protection of old giant trees should be included in the form of a nature reserve called a natural monument in the nature reserve system. In addition, residents of the area around the nature reserve should be one of the main agents of biodiversity conservation.