• Journal of Venture Innovation
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• v.7 no.1
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• pp.201-223
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• 2024

This study undertook an empirical analysis to examine the impact of various factors on entrepreneurial intention among young people, with a particular focus on the role of startup mentoring. Employing a survey distributed nationwide, data from 250 valid respondents were subjected to structural equation modeling to investigate these dynamics. The analysis uncovered that workplace stress, subjective norms, and perceived behavioral control positively influence the entrepreneurial intentions of youth. Meanwhile, technological constraints negatively affected these intentions. The study did not explore the potential effects of future uncertainty and the burden of failure. Significantly, it was found that startup mentoring plays a crucial role in mitigating the negative impacts that may deter young individuals from pursuing entrepreneurship. Mentoring was instrumental in reducing negative influences, thereby fostering a more conducive environment for entrepreneurial ambition. By integrating the Push-Pull-Mooring (PPM) and Theory of Planned Behavior (TPB) models, this research not only validates these frameworks within the context of youth entrepreneurship but also underscores the essential function of startup mentoring in enhancing entrepreneurial intentions. The insights from this study highlight the importance of mentoring programs in nurturing the entrepreneurial spirit among the youth, suggesting that targeted mentoring support can play a pivotal role in overcoming barriers to entrepreneurship.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.3
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• pp.60-75
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• 2024

Research on health outcomes of environmental factors has been implemented by multiple and interacting factors, including environmental, socio-demographic, economic, and traffic aspects. There are still significant challenges and limitations in constructing databases for the connections between contributing factors and an integrated approach to environmental health research even though there has been a dramatic increase in data availability and incredible technological advance in data storage and processing. This study emphasizes the necessity of establishing a geospatial data pipeline to analyze the impact of environmental factors on health. It also highlights the difficulties and solutions related to the construction and utilization of a geospatial database. Key challenges include diverse data sources and formats, different spatio-temporal data structures, and coordinate system inconsistencies over time within the same geospatial data. To address these issues, a data pipeline was constructed with pre-processing and post-processing for the data, resulting in refined datasets that could be used for calculating geographic variables. In addition, an AWS-based relational database and shared platform were established to provide an efficient environment for data storage and analysis. Guidelines for each step of the process, including data management and analysis, were developed to enable future researchers to effectively use the data pipeline.

• Informatization Policy
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• v.31 no.3
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• pp.48-71
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• 2024

In the modern era of digital sophistication, effective public administration warrants a citizen-centric approach that not only anticipates the needs of public service users but also comprehends their behaviors in undertaking proactive measures to deliver public services as needed. This study adopts a typological perspective by viewing e-government users as distinct consumer groups with individualized demands, behavioral tendencies, and perceptual attributes. Utilizing data from a 2021 survey on e-government service utilization, a two-step cluster analysis was conducted to delineate user typology through an empirical study. The analysis incorporated variables such as the purpose of using e-government, selected e-government channels, subjective perceptions of technological risk, and personal innovativeness. Accordingly, e-government users were classified into five distinct typological groups labeled "Unilateral Active Users Geared to Social Media," "Versatile Power Users," "Unilateral Pragmatic Active Users," "Occasional Passive Users," and "Minimal Users." This typological differentiation of e-government user groups is intended to help identify unique user demands and characteristics so as to facilitate the delivery of tailored e-government services and informed policy decisions catering to the diverse needs of users.

• Journal of Korea Technology Innovation Society
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• v.20 no.4
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• pp.1089-1121
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• 2017

'Technological convergence' is the recent innovation trend which facilitates to solve social crux as well as to generate new industries. Korean government research institutes (GRIs) have taken a pivotal role for economic growth which capitalized on technology-oriented strategies. Recently, the policy interests on the transition of their role and mission towards multidisciplinary research organization is increasingly shed lights. This study regards the collaborative activities as one of the key success factors in the multidisciplinary research. In this sense, this study sets research purposes as follows: First, we intend to define a concept and to confine a scope of multidisciplinary research from the view point of R&D purposes and problem-solving process. Second, we categorize the collaboration and the relevant performances which reflect the characteristics of the multidisciplinary research. Third, we analyze the characteristics of collaborative activities and the effects of strength on the research performances. To this end, this study conducted a survey of 104 research project directors, which have experienced at least one of two types of multidisciplinary research projects through National R&D project or NST (National Research Council of Science & Technology) convergence research project. Then, we conducted regression analysis by utilizing the survey results in order to verify the relation between the collaborative activities and the performances. As results of analyses, first, the diversification of collaboration partners was a salient factor in the process of knowledge creation. Second, collective works among the researchers in similar area and domain enhanced mission-oriented technology development projects such as patent creation or technology transfer. Third, we verified that the diversity of created knowledge and the degree of relation continuity between researchers increased in the condition of guaranteeing individual researcher's independence and autonomy as well as sharing various technological capabilities. These results provide the future policy directions related to the methods to measure the collaboration and performance analysis for multidisciplinary research.

• Korean Journal of Heritage: History & Science
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• v.46 no.1
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• pp.208-227
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• 2013

Twenty-seven bronze bowls excavated from the Goryo burial complex at Deobu-gol were examined for their microstructure and chemical composition to characterize the bronze technology practiced by commoners at the time. Results showed that the objects examined can be classified into four groups: 1) objects forged out of Cu-near 22%Sn alloys and then quenched; 2) objects cast from Cu-below 10% Sn alloys containing lead; 3) objects cast from Cu-10%~20% Sn alloys containing lead and then quenched; 4) objects forged out of Cu-10~20% Sn alloys containing lead and then quenched. This study revealed that the fabrication technique as determined by alloy compositions plays an important role in bronze technology. The use of lead was clearly associated with the selection of quenching temperatures, the character of inclusions and the color characteristics of bronze surfaces. It was found that the objects containing lead were quenched at temperatures of $520^{\circ}{\sim}586^{\circ}C$ while those without lead were quenched at the range of $586^{\circ}{\sim}799^{\circ}C$. The presence of selenium in impurity inclusions was detected only in alloys containing lead, suggesting that the raw materials, Cu and Sn, used in making the lead-free alloys for the first group were carefully selected from those smelted using ores without lead contamination. Furthermore, the addition of lead was found to have significant effects on the color characteristics of the surface of bronze alloys when they are subjected to corrosion during interment. In leaded alloys, corrosion turns the surface light green or dark green while in unleaded alloys, corrosion turns the surface dark brown or black. It was found that in fabrication, the wall thickness of the bronze bowls varies depending on the application of quenching; most of the quenched objects have walls 1mm thick or below while those without quenching have walls 1mm thick or above. Fabrication techniques in bronze making usually reflect social environments of a community. It is likely that in the late Goryo period, experiencing lack of skilled bronze workers, the increased demand for bronze was met in two ways; by the use of chief lead instead of expensive tin and by the use of casting suitable for mass production. The above results show that the Goryo bronze workers tried to overcome such a resource-limited environment through technological innovations as apparent in the use of varying fabrication techniques for different alloys. Recently, numerous bronze objects are excavated and available for investigation. This study shows that with the use of proper analytical techniques they can serve as a valuable source of information required for the characterization of the associated technology as well as the social environment leading to the establishment of such technology.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.114-129
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• 2023

In the era of the 4th industrial revolution, the importance of the 4th industrial revolution technology is increasing in the service industry. The purpose of this study is to identify the development and utilization status of the 4th industrial revolution technology in the service industry and to derive the characteristics and implications of the 4th industrial revolution technology innovation in the service industry. In this study, research and analysis were conducted based on the business activity survey data in order to identify the technological innovation characteristics of the 4th industrial revolution in the service industry. The 4th industrial revolution technology in the service industry was analyzed in terms of company ratio, technology development and utilization rate, development/utilization technology, technology application field, and technology development method. In addition, the trend of the 4th industrial revolution technology change in the service industry was also analyzed. The 4th industrial revolution technology utilization and development status of other industries was compared and analyzed. In particular, the service industry 4th industrial revolution technology innovation type was divided into 4 types from the perspective of the 4th industrial revolution company ratio and the 4th industrial revolution company ratio growth rate, and types for each service industry were derived. The characteristics and implications of the 4th industrial revolution technology innovation in the service industry were presented from nine perspectives. As a result of the study, it was found that companies in the service industry were developing or using 4th industrial revolution technologies more actively than companies in other industries, and it was analyzed that the gap was further widening. By service industry, information and communication, finance and insurance, and educational service showed relatively high rates of developing or utilizing 4th industrial revolution technologies. The service industries in which the share of 4th industrial revolution companies increased the most were real estate, education service, health and social welfare service. In particular, cloud, big data, and artificial intelligence were analyzed as the three core technologies of the fourth industrial revolution. The service industry can be classified into 4 types in terms of the 4th industrial revolution company ratio and growth rate, and service industry innovation measures that reflect the differentiated innovation characteristics of each type are needed.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.143-159
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• 2017

Due to the rapid development of IT in recent years, not only personal information but also the key technologies and information leakage that companies have are becoming important issues. For the enterprise, the core technology that the company possesses is a very important part for the survival of the enterprise and for the continuous competitive advantage. Recently, there have been many cases of technical infringement. Technology leaks not only cause tremendous financial losses such as falling stock prices for companies, but they also have a negative impact on corporate reputation and delays in corporate development. In the case of SMEs, where core technology is an important part of the enterprise, compared to large corporations, the preparation for technological leakage can be seen as an indispensable factor in the existence of the enterprise. As the necessity and importance of Information Security Management (ISM) is emerging, it is necessary to check and prepare for the threat of technology infringement early in the enterprise. Nevertheless, previous studies have shown that the majority of policy alternatives are represented by about 90%. As a research method, literature analysis accounted for 76% and empirical and statistical analysis accounted for a relatively low rate of 16%. For this reason, it is necessary to study the management model and prediction model to prevent leakage of technology to meet the characteristics of SMEs. In this study, before analyzing the empirical analysis, we divided the technical characteristics from the technology value perspective and the organizational factor from the technology control point based on many previous researches related to the factors affecting the technology leakage. A total of 12 related variables were selected for the two factors, and the analysis was performed with these variables. In this study, we use three - year data of "Small and Medium Enterprise Technical Statistics Survey" conducted by the Small and Medium Business Administration. Analysis data includes 30 industries based on KSIC-based 2-digit classification, and the number of companies affected by technology leakage is 415 over 3 years. Through this data, we conducted a randomized sampling in the same industry based on the KSIC in the same year, and compared with the companies (n = 415) and the unaffected firms (n = 415) 1:1 Corresponding samples were prepared and analyzed. In this research, we will conduct an empirical analysis to search for factors influencing technology leakage, and propose an early warning system through data mining. Specifically, in this study, based on the questionnaire survey of SMEs conducted by the Small and Medium Business Administration (SME), we classified the factors that affect the technology leakage of SMEs into two factors(Technology Characteristics, Organization Characteristics). And we propose a model that informs the possibility of technical infringement by using Support Vector Machine(SVM) which is one of the various techniques of data mining based on the proven factors through statistical analysis. Unlike previous studies, this study focused on the cases of various industries in many years, and it can be pointed out that the artificial intelligence model was developed through this study. In addition, since the factors are derived empirically according to the actual leakage of SME technology leakage, it will be possible to suggest to policy makers which companies should be managed from the viewpoint of technology protection. Finally, it is expected that the early warning model on the possibility of technology leakage proposed in this study will provide an opportunity to prevent technology Leakage from the viewpoint of enterprise and government in advance.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• The Korean Society of Law and Medicine
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• v.23 no.4
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• pp.29-74
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• 2022

As social disasters occur under the Disaster Management Act, which can damage the people's "life, body, and property" due to the rapid spread and spread of unexpected COVID-19 infectious diseases in 2020, information collected through inspection and reporting of infectious disease pathogens (Article 11), epidemiological investigation (Article 18), epidemiological investigation for vaccination (Article 29), artificial technology, and prevention policy Decision), (3) It was used as an important basis for decision-making in the context of an infectious disease crisis, such as promoting vaccination and understanding the current status of damage. In addition, medical policy decisions using infectious disease data contribute to quarantine policy decisions, information provision, drug development, and research technology development, and interest in the legal scope and limitations of using infectious disease data has increased worldwide. The use of infectious disease data can be classified for the purpose of spreading and blocking infectious diseases, prevention, management, and treatment of infectious diseases, and the use of information will be more widely made in the context of an infectious disease crisis. In particular, as the serious stage of the Disaster Management Act continues, the processing of personal identification information and sensitive information becomes an important issue. Information on "medical records, vaccination drugs, vaccination, underlying diseases, health rankings, long-term care recognition grades, pregnancy, etc." needs to be interpreted. In the case of "prevention, management, and treatment of infectious diseases", it is difficult to clearly define the concept of medical practicesThe types of actions are judged based on "legislative purposes, academic principles, expertise, and social norms," but the balance of legal interests should be based on the need for data use in quarantine policies and urgent judgment in public health crises. Specifically, the speed and degree of transmission of infectious diseases in a crisis, whether the purpose can be achieved without processing sensitive information, whether it unfairly violates the interests of third parties or information subjects, and the effectiveness of introducing quarantine policies through processing sensitive information can be used as major evaluation factors. On the other hand, the collection, provision, and use of infectious disease data for research purposes will be used through pseudonym processing under the Personal Information Protection Act, consent under the Bioethics Act and deliberation by the Institutional Bioethics Committee, and data provision deliberation committee. Therefore, the use of research purposes is recognized as long as procedural validity is secured as it is reviewed by the pseudonym processing and data review committee, the consent of the information subject, and the institutional bioethics review committee. However, the burden on research managers should be reduced by clarifying the pseudonymization or anonymization procedures, the introduction or consent procedures of the comprehensive consent system and the opt-out system should be clearly prepared, and the procedure for re-identifying or securing security that may arise from technological development should be clearly defined.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.