• Asian Journal of Innovation and Policy
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• v.7 no.1
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• pp.9-29
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• 2018

Japan's manufacturing is mostly dependent on the automotive industry in Toyota-Kariya cities. However, the nearby city of Hamamatsu is the home of a start-up ecosystem known as Japan's Silicon Valley. How is it possible to evaluate the innovative potential of each regional industry? What kind of guidelines exist for continuing R&D investment when companies' net incomes are negative in the face of the 'Valley-of-Death' or financial crisis? Is it possible to measure the regional resilience ability in the context of the financial crisis? Entrepreneurial innovation is defined as a real-option portfolio consisting of investment decision to commercialize R&D findings. The subcontractor system implies a vertical and tight industrial group. However, a start-up ecosystem means a platform for horizontal and flexible partnership. In this research, the data include the financial indices of each of 18 public companies in both regions between FY2009 and FY2017. The objective of this paper is to clarify the call option or resilience function of equity for R&D investment in the context of the financial crisis in both regions by using Bayesian MCMC analysis.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.3
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• pp.299-308
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• 2009

In this paper, we have developed an evaluation model of technology green index(TGI) which includes 3 evaluation factors and 13 indicators. Furthermore, as presenting the degree of relative importance among evaluation factors and indicators for all R&D evaluation stages and all green technology areas, and applying the proposed model to 5 defense projects, we have found applicability of the model to evaluation of defense R&D projects. The results of evaluation using this model can be used to monitor the performance of project life cycle and develop R&D investment strategy of green technology using portfolio analysis.

• Journal of Korea Spatial Information System Society
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• v.9 no.3
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• pp.81-89
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• 2007

We analyzed the technology portfolio matrix for the car navigation technology using QFD method and accordingly suggested the navigation technology development direction. QFD is a useful tool to analyze the customer demands and the technologies. Depending on the survey results from the latent customers and the technology capabilities from the study of the national institutions, we suggested technology portfolio matrix. The visual HMI technology, safe driving support technology, and the navigator information management technology are the most prospective area for R&D investment according to the portfolio matrix.

• New & Renewable Energy
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• v.16 no.2
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• pp.28-34
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• 2020

The South Korean government invested about 560 billion won in research and development in the wind power sector over 30 years from 1989 to 2019. In addition, the government has been setting up conditions for the country's wind industry to grow through various policies, including the RPS (Renewable Energy Portfolio Standard). As a result, the size of Korea's wind industry has grown to 1.1 trillion won in annual sales and there are 2,000 people working in the wind energy sector. While the domestic market is weak due to the small size of Korea and there are problems with the NIMBY, the government has continued to support technology developments and support market policies. Despite insufficient performance in overseas markets, the effect of the government is still significant on the growth of the wind industry. In particular, the government's R&D investment program, which focuses on enhancing companies' competitiveness, has spurred job creation in the wind industry and a stable research environment for researchers. In this study, we compared the differences between methods of investigating employment statistics in Korea and those of other countries. We also proposed effective investment measures for the government by analyzing the investment effects according to value chain and types of organization.

• Proceedings of the Technology Innovation Conference
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• 2002.06a
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• pp.238-256
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• 2002

Competitiveness of a nation can be explained by the concept of national innovation systems(NIS). As components of NIS, industry clusters become the issue in analysing innovative activity of an economy. Innovative clusters can be identified by the innovation survey or other economic activity data. Input-output Table was used widely as a tool for quantitative analysis, This paper classifies seven clusters in Korean industry based on inter-industries trade of intermediary goods and services, Maximizing procedure method is used in analysing input-output table. Identified clusters are Textiles/chemicals, Construction/Material, Instrument/Equipment, Automobile, Services, Energy, and Agriculture/Food cluster, Among these clusters, some different characteristics in R&D portfolios are detected. R&D investment characteristics of each cluster give us significant implications in understanding innovative dynamics of Korean industry.

• Journal of Korean Library and Information Science Society
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• v.38 no.4
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• pp.441-462
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• 2007

As country try expand R&D investment and enhance its efficiency to improve the national competitiveness, research is needed to conduct qualitative enhancement and derive progressive future strategy in relation to the academic information distribution project in scientific technology field. In this study, BSC-based performance indicators were applied to an institute that is the representative of domestic academic information distribution institutes in the field of scientific technology to evaluate project performance, and then to analyze portfolio of using such evaluation results. As for the items of evaluation for the performance indicators of academic information distribution project in the scientific technology field, 12 items that includes information resource quality, information service quality, user satisfaction and economically useful value of academic information from four(4) viewpoints such as information resource, information service, user and economic viewpoints. In the portfolio analysis, it was conducted by performance indicators and by elements of the individual performance indicators as well. Based on the results of performance evaluation and portfolio analysis, the improvement method by viewpoints on academic information distribution project of an institute was suggested.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.7
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• pp.1750-1759
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• 2009

Recently from the enterprise when about reduction the product of numerous type to develop does not produce simultaneously with Life Cycle shortening and JIT productions of the product, is confronting in the environment which will not become. In order to confront in like this environment, developing the good product importance but, the product researches what kind of direction and developing with core subject of technical innovation is recognized. For the development method and an investment direction of the product proposed PPM (Product Portfolio Management) algorithms from the research which sees from like this viewpoint. With PPM algorithms which are proposed, divided the product in 6 kind groups of standardization type, discrimination type, material cost investigation type, processing cost investigation type and sale promotion type and abolition type and presented tyresearch and development direction..Research approach portfolio investigation of existing research and consulting experience of research and development field with background, presented the analytical model of algorithm, is applied of the instance which composed.

• Journal of Korea Technology Innovation Society
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• v.21 no.4
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• pp.1268-1295
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• 2018

This study examines the effects of policy mix on R&D efficiency in by using data from 2014 and 2016 Korean Innovation Survey. The DEA-Tobit analysis is used to estimate the impact of policy mix on relative R&D efficiency. As a result of the DEA analysis, the R&D efficiency of the Korean manufacturing industry firms is low, because the R&D investment has not been used effectively. According to the Tobit model, policy mix have a positive effect on R&D efficiency. In particular, the combination of market-oriented, market supply-oriented, and supply demand-oriented policy mix showed a positive relationship with R&D efficiency. R&D portfolio is necessary to improve R&D efficiencies and government has to facilitate a policy mix in view of the nature of firms and Consistency of policy tools.

• New & Renewable Energy
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• v.18 no.3
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• pp.72-84
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• 2022

As the threat of climate change escalates, 'net-zero' has become a priority for the international community, and the use of new and renewable energy sources is expected to play a significant role in reaching international carbon neutrality. Here, we evaluate technological competitiveness in terms of implementation and technology by analyzing scientific literature and patents in the new and renewable energy fields of five major countries. For the past 10 years (2009-2019), the most active areas of new and renewable energy research and development have been solar power, wind power, waste, and fuel cells. China is the forerunner in implementation, whereas the United States has the most advanced technology. Portfolio analysis revealed that Korea's fuel cell, the United States' bioenergy, China's waste, Japan's solar and fuel cell, and the European Union's wind power have shown to be in Star Field respectively. Technological competitiveness analysis found that Korea is lagging behind other countries in the new and renewable energy sector, and needs to set a new direction for future carbon-neutral research and development, investment, and policy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.22-31
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• 2023

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.