• The Korean Journal of Food And Nutrition
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• v.29 no.6
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• pp.835-848
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• 2016

The purpose of this study was to find efficient and customized tools for delivering the benefit of health functional foods (HFFs). Delivery tools which could influence the impact of advertising were images, explanations of ingredients, diagrams of health benefit, patents, and comments from authority. Six advertisements were developed using these tools: "A": relevant image + explanation of ingredients + scientific diagram of efficacy; "B": relevant image + explanation of ingredients; "C": relevant image; "D": irrelevant image; "E": irrelevant image + explanation of ingredient + patent; "F": irrelevant image + explanation of ingredient + comments from authority. To analyze the consumer perceptions on HFFs and advertisement effects, 300 respondents were requested to answer a questionnaire comprising of the following questions: 5 questions of attitudes (necessity of HFFs, trust in HFFs, gathering information, watching advertisements and trust in advertisement claims) and 6 questions on the 6 developed advertisements (attention, understanding, sufficiency of information, sympathy, trust, and purchase). Scoring was done as per the 5 Likert scale. There was a higher proportion of females and the elderly, as compared to males and youngsters. The overall consumer attitudes were positive. Explanation of ingredients, scientific diagram of health benefit, patents and expert comments were helpful factors in increasing the advertisement evaluation by consumer, but the images were not. Advertisement evaluation of consumer did not differ with gender and age. However, differences were observed between some of the consumer attitudes (necessity of HFFs, trust in HFFs, gathering information and trust in advertisements claim) and advertisement evaluations (attention, understanding, sympathy and purchase). Our results indicate that for consumers utilizing the HFFs, advertisements with concrete tools such as diagrams, patent, and expert comments are more helpful. However, for consumers who do not have interest in HFFs, the scientific information was irrelevant. We believe that to maximize the effect of health information in advertisements, consumers should be segmented, and customized tools for each segment needs to be developed.

• 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.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.37-46
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• 2016

Patent application as achievements from R&D institutions in public sector have quantitatively increased by expanding R&D investment for enhancing competitiveness but there have been few tangible outputs from the investment. From this reason, TLO(Technology Transfer&Licensing Organization) and its operation becomes more important to implement technology transfer and commercialization and to bring success in the related business. To get work done more efficiently and to improve utilizing products of the R&D in the TLO, this research is to draw domains and activities of TLO and establish its task systems by prioritizing activities. From literature reviews and expert interviews, we generated 6 work domains and 21 task items. Applying AHP analysis, we discriminated the relative importance from task items and analyzed its priority. The finding of this research can provide implications for TLO to increase work efficiency and improve its performance.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.159-173
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• 2017

In order to provide technical financial support to small and medium-sized venture companies based on technology, the government implemented the TCB evaluation, which is a kind of technology rating evaluation, from the Kibo and a qualified private TCB. In this paper, we briefly review the current state of TCB evaluation and available indicators related to technology evaluation accumulated in the Korea Credit Information Services (TDB), and then use indicators that have a significant effect on the technology rating score. Multiple regression techniques will be explored. And the relative importance and classification accuracy of the indicators were calculated by applying the key indicators as independent features applied to the generalized boosting model, which is a representative machine learning classifier, as the class influence and the fitness of each model. As a result of the analysis, it was analyzed that the relative importance between the two models was not significantly different. However, GBM model had more weight on the InnoBiz certification, R&D department, patent registration and venture confirmation indicators than regression model.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.211-218
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• 2020

This study compared and analyzed the efficiency of the research results of the convergence research project operated by the National Science and Technology Research Council and the general research project carried out by the Government-funded research institute and proposed measures to enhance the efficiency of the operation and management of convergence research. Research data were collected from 21 government-funded research institutes through an information disclosure claim and DEA analysis was conducted for efficiency assessment. The SCI papers of convergence research showed high efficiency, and the patent registration and technology transfer sector showed high efficiency of general research projects. This indicates that convergence research projects for securing lead and original technologies are highly efficient, but their performance is low due to lack of participation by businesses in terms of the utilization of derived technologies, and more companies' participation and opportunities are needed for practical use of convergence research results. Through the analysis of efficiency of convergence research project performance, this study provided policy and guidance for R&D planning for rational investment of limited manpower and research costs. Further, future research on identifying efficiency is proposed depending on the type of technology subject to convergence research as a method for managing convergence research.

• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4331-4338
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• 2014

$N^1$-(2, 5-dimethoxyphenyl)-$N^8$-hydroxyoctanediamide (N25) is a novel SAHA cap derivative of HDACi, with a patent (No. CN 103159646). This invention is a hydroxamic acid compound with a structural formula of $RNHCO(CH_2)6CONHOH$ (wherein R=2, 5dimethoxyaniline), a pharmaceutically acceptable salt which is soluble. In the present study, we investigated the effects of N25 with regard to drug distribution and molecular docking, and anti-proliferation, apoptosis, cell cycling, and $LD_{50}$. First, we designed a molecular approach for modeling selected SAHA derivatives based on available structural information regarding human HDAC8 in complex with SAHA (PDB code 1T69). N25 was found to be stabilized by direct interaction with the HDAC8. Anti-proliferative activity was observed in human glioma U251, U87, T98G cells and human lung cancer H460, A549, H1299 cells at moderate concentrations ($0.5-30{\mu}M$). Compared with SAHA, N25 displayed an increased antitumor activity in U251 and H460 cells. We further analyzed cell death mechanisms activated by N25 in U251 and H460 cells. N25 significantly increased acetylation of Histone 3 and inhibited HDAC4. On RT-PCR analysis, N25 increased the mRNA levels of p21, however, decreased the levels of p53. These resulted in promotion of apoptosis, inducing G0/G1 arrest in U251 cells and G2/M arrest in H460 cells in a time-dependent and dose-dependent manner. In addition, N25 was able to distribute to brain tissue through the blood-brain barrier of mice ($LD_{50}$: 240.840mg/kg). In conclusion, our findings demonstrate that N25 will provide an invaluable tool to investigate the molecular mechanism with potential chemotherapeutic value in several malignancies, especially human glioma.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.79-92
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• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.