• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.47-59
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• 2017

Korean Economy has been developed by the Korean Government's Support for the Large-sized Firms. This Government Policy causes the Polarization between Large-sized and Micro-sized Firms Aggravated. Micro-sized Firms are distributed over the Whole Industry Area, and can also cause the Economic Crisis If They are crashed down. Therefore Government Policy for the Micro-sized Manufacturing Industry is very Important Issue. This Paper Focused on the Analysis of Current Status for the Shoes Micro Manufacturing Industry. For the Effective Analysis, This Paper uses the Statistical Data Open to the Public and also conducts the Survey for the Micro-sized Firms in Busan. Statistical Program is used for Analyzing the Collected Data and the Major Findings are as Follows. First, Shoes Industry is led by the Micro and Small & Medium sized Firms rather than the Large-sized. And the Micro-sized Firms are getting the High Rate among the Whole Shoes Industry. Busan is heavily populated Area as the Origin of Shoes Industry. Second, even though Most of the Owner of the Micro-sized Firms have the High Technology Skill Level, Worker's Aging Phenomenon gets Worse and causes the Technology Handing down to the Next Generation Difficult. Third, Because the Factory Facility of the Micro-sized Firms is Dirt and Unstable, the Modernized Manufacturing Infrastructure such as the Apartment Factory Facility is Necessary. Forth, as the Micro-sized Firms which have the Intangible Asset such as Patent is Few, the Government Policy for Encouraging the Patent Application is strongly Needed. Fifth, Entrepreneurship and Collaboration Mind between Micro-sized Firms are Lacked, so Establishment of the Cooperative Union is required. Finally, the Effort for the Systemic Planning for the Management is lacked, and the Introduction of the Management Innovation is strongly needed. The Limitation and Future Research Direction is also discussed.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.37-44
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• 2020

AI technology has developed in the form of decision support technology in law, patent, finance and national defense and is applied to disease diagnosis and legal judgment. To search real-time information with Deep Learning, Big data Analysis and Deep Learning Algorithm are required. In this paper, we try to predict the entrance rate to high-ranking universities using a Deep Learning model, RNN(Recurrent Neural Network). First, we analyzed the current status of private academies in administrative districts and the number of students by age in administrative districts, and established a socially accepted hypothesis that students residing in areas with a high educational fever have a high rate of enrollment in high-ranking universities. This is to verify based on the data analyzed using the predicted hypothesis and the government's public data. The predictive model uses data from 2015 to 2017 to learn to predict the top enrollment rate, and the trained model predicts the top enrollment rate in 2018. A prediction experiment was performed using RNN, a Deep Learning model, for the high-ranking enrollment rate in the special education zone. In this paper, we define the correlation between the high-ranking enrollment rate by analyzing the household income and the participation rate of private education about the current status of private institutes in regions with high education fever and the effect on the number of students by age.

• The Journal of Society for e-Business Studies
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• v.22 no.3
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• pp.87-103
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• 2017

In recent years, many small and medium-sized manufacturing companies are making process innovation and product innovation through the public knowledge services. K Research institute provides different types of knowledge services in combination and due to this complexity, it is difficult to analyze the performance of knowledge service programs precisely. In this study, we derived performance items from bottom-up viewpoints, rather than top-down approaches selecting those items as in previous performance analysis. As a result, 74 items were finded from 82 companies in the K Research Institute case book, and the final result was refined to 17 items. After that a case-performance matrix was constructed, and binary data was entered to analyze. As a result, three clusters were identified through K-means clustering as 'enhancement of core competitiveness (product and patent),' 'expansion of domestic and overseas market,' and 'improvement of operational efficiency.'

• The Journal of Society for e-Business Studies
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• v.25 no.3
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• pp.109-130
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• 2020

Recently, the importance of research and development for technological innovation is increasing. The rapid development of research and development has a number of positive effects, but at the same time there are also negative effects that accelerate crimes of information and technology leakage. In this study, a research security level measurement model was developed that can safely protect the R&D environment conducted at the organizational level in order to prepare for the increasingly serious R&D result leakage accident. First, by analyzing and synthesizing security policies related to domestic and overseas R&D, 10 research security level evaluation items (Research Security Promotion System, Research Facility and Equipment Security, Electronic Information Security, Major Research Information Security Management, Research Note Security Management, Patent/Intellectual Property Security Management, Technology Commercialization Security Management, Internal Researcher Security Management, Authorized Third Party Researcher Security Management, External Researcher Security Management) were derived through expert interviews. Next, the research security level evaluation model was designed so that the derived research security level evaluation items can be applied to the organization's research and development environment from a multidimensional perspective. Finally, the validity of the model was verified, and the level of research security was evaluated by applying a pilot target to the organizations that actually conduct R&D. The research security level evaluation model developed in this study is expected to be useful for appropriately measuring the security level of organizations and projects that are actually conducting R&D. It is believed that it will be helpful in establishing a research security system and preparing security management measures. In addition, it is expected that stable and effective results of R&D investments can be achieved by safely carrying out R&D at the project level as well as improving the security of the organization performing R&D.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.68-74
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• 2008

Application areas of Knowledge Discovery in Database(KDD) have been expanded to many R&D management processes including technology trends analysis, forecasting and evaluation etc. Established research field such as informetrics (or scientometrics) has utilized techniques or methods of KDD. Various systems have been developed to support works of analyzing large-scale R&D related databases such as patent DB or bibliographic DB by a few researchers or institutions. But extant systems have some problems for korean users to use. Their prices is not moderate, korean language processing is impossible, and user's demands not reflected. To solve these problems, Korea Institute of Science and Technology Information(KISTI) developed stand-alone type information analysis system named as KnowledgeMatrix. KnowledgeMatrix system offer various functions to analyze retrieved data set from databases. KnowledgeMatrix's main operation unit is composed of user-defined lists and matrix generation, cluster analysis, visualization, data pre-processing. Matrix generation unit help extract information items which will be analyzed, and calculate occurrence, co-occurrence, proximity of the items. Cluster analysis unit enable matrix data to be clustered by hierarchical or non-hierarchical clustering methods and present tree-type structure of clustered data. Visualization unit offer various methods such as chart, FDP, strategic diagram and PFNet. Data pre-processing unit consists of data import editor, string editor, thesaurus editor, grouping method, field-refining methods and sub-dataset generation methods. KnowledgeMatrix show better performances and offer more various functions than extant systems.

• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.123-141
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• 2021

As the dependence of technology on the economic structure increases, the importance of National Core Technology is increasing. However, due to the nature of the technology itself, it is difficult to determine the scope of the technology to be protected because the scope of the relation is abstract and information disclosure is limited due to the nature of the National Core Technology. To solve this problem, we propose the most appropriate literature type and method of analysis to distinguish important technologies related to National Core Technology. We conducted a pilot test to apply TF-IDF, and LDA topic modeling, two techniques of text mining analysis for big data analysis, to four types of literature (news, papers, reports, patents) collected with National Core Technology keywords in the field of Display industry. As a result, applying LDA theme modeling to patent data are highly relevant to National Core Technology. Important technologies related to the front and rear industries of displays, including OLEDs and microLEDs, were identified, and the results were visualized as networks to clarify the scope of important technologies associated with National Core Technology. Throughout this study, we have clarified the ambiguity of the scope of association of technologies and overcome the limited information disclosure characteristics of national core technologies.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Journal of Korea Technology Innovation Society
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• v.19 no.1
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• pp.80-104
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• 2016

Existing methodologies of technology planning about promising new technology focused on target technology itself, so it is true that socio-environmental context which the relevant technology has influence on is not well understood. In this respect, this study is aimed to questingly examine that news content analysis methodologies widely available in the field of science communication can be applied as a complementary methodology for contextual understanding of socio-environment in terms of technology planning about promising new technology. In the co-evolutionary environment of technology-society, promising new technology shows hype phenomenon regarding the relation with the society. Based on this, this study performed news content analysis and examined if the consequences of analysis would match hype cycle. It tried to explore substantive content understanding by socio-environment factors according to specific news frame content. To do this, new content analysis was performed targeting cloud computing as a representative promising new technology. The result of news content analysis targeting general newspapers, business news, IT special newspapers revealed that the tendency of news reporting matched the trend of hype cycle. Particularly, it was verified that reporting attitude and news frame analysis provided useful information to understand contextual content depending on social, economic, and cultural environment factors about promising new technology. The results of this study implied that news content analysis could overcome the limitation of technology information analysis focusing on academic journal patent usually applied for technology planning and could be used as a complementary methodology for understanding the context depending on macro-environment factors. In conclusion, application of news content analysis on the phase of macro-environment analysis of technology planning could contribute to the securement of mutually balanced view in the co-evolutionary perspective of technology-society.

• Journal of Distribution Science
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• v.17 no.2
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• pp.53-69
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• 2019

Purpose - National scientific technology R&D investment is exceeding 60 trillion won per year, and the results of patent applications and technology transfers are visually improving. However, despite the improving research results of national R&D, the practical results of technology startups are mediocre. It is now time to expand the construction of the technology commercialization ecosystem, where the expansion of national R&D leads to the results of technology startups. Therefore, this study discussed the measures to increase the competitiveness of technology startups through the factual survey of the companies that benefitted from R&D support programs. Research design, data, and methodology - This study targeted 996 companies that benefitted from the R&D projects of the Technology Transfer Center for National R&D Programs, and deducted itemized issues through the survey replies. Survey questions were prepared to estimate the national R&D results, and the technology recognition path, the purpose of detailed introduction of the technology, investment of the commercialization fund, economic results, and the factors of success and failure were analyzed. Results - As for the recognition rate of technology during the process of corporate technology commercialization through the technology transfer, recognition through project participation showed a high response rate, and diverse implications of technology commercialization were deducted through the analysis of economic results. As for the resolution alternatives, the proliferation of technology commercialization platform that can create excellent technology for the companies in early stages and the measure of expanding the distribution of technology infrastructure were suggested. In this study, public technology commercialization strategy is established, and the innovative marketing strategy is presented. Conclusions - This study reveal that the result of creating scientific technology jobs should be deducted, in order to produce the revolutionary results of job creation by suggesting the success models of technology commercialization based on domestic scientific technology. In particular, even though the support systems for public research results are being diversely suggested, accurate studies on their actual conditions are currently lacking. Therefore, this study suggest realistic political alternatives to assure results in the process of public technology commercialization, by examining the current state of public research results of R&D support institutions and diagnosing the issues.