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Abstract

This study analyzes a language network of Science and Technology Basic Plan, which is the basis for science and technology policy in Korea, for the next Science and Technology Basic Plan. Language network analysis was adopted for a quantitative approach measuring the trend of policies. Several techniques such as keyword analysis, language network map analysis, quantitative characteristics analysis and keyword-related major-word analysis have been performed. Results show that there are common policies emphasized by all Science and Technology Basic Plans in the past, and there are also specific policies emphasized in each period of the Science and Technology Basic Plan. These specific policies come from a ‘change of times’ when the Science and Technology Basic Plans were established, as well as the philosophy of the national government.

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This aim of the study is to show the necessity of implementing an industrial innovation strategy with consideration given to characteristics of the industrial technology. The relationship between industrial technological characteristics and innovation performance is analyzed by using an innovation survey as well as a human capital corporate panel (HCCP). The time-lag effect is also examined. Findings of the analysis show that high-tech industries have entered the post catch-up technology innovation stage in the mid-2000s, but low-tech industries still seem to stay in the catch-up stage. In terms of technology policy, the additional technology innovation support policy should focus on enhancing the innovation capability of the middle and low technology industries, since high technology industries are already developing their own innovation capability. It is necessary to strengthen capacity building through technical cooperation with technology consulting, rather than providing technical support through suppliers.

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In this paper, we analyze the basic research system in South Korea. We propose a national basic research system consisting of value, openness, input, transformation, and output. Based on this framework, we set up interview questionnaires, and 15 key informants have been interviewed. According to our results, first, in terms of value, basic research is recognized as an activity for creating knowledge in the understanding of nature. Second, as for openness, scientists and policy experts agree that active interaction with the global community is an important value for the national research system. Third, in terms of sustainable research resources, scientists are strongly required to effectively allocate research funding, maximizing the creativity of researchers and the efficient sharing of research equipment. Fourth, in transformation, basic researchers maintain that the Korean research system has is extremly dependent on the government’s external control, and its self-regulative system has been weak for over half century onw. Fifth, for global competitiveness, the interviewees agreed that the quality of basic research in Korea is approaching that of its global competitors. Finally, we put forward some policy implications on the basis of these findings.

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Inclusive innovation refers to different types and forms of innovation activities or performance by which we can get more for lesser cost and which could cater and meet the needs and demands of more people. The essence of inclusive innovation is to help poor, marginalized and underprivileged sections of society to improve their livelihoods and enable them to climb up the socio-economic ladder. In the current phase of economic slowdown, increasing unemployment and inequalities, World Bank, OECD and various governments are turning towards inclusive innovation as a new source of optimism or even as a new innovation strategy. Whilst it is being reframed or packaged as a novel or a new strategy, one can trace its historical roots to the AT 0000000ㅡmovement and the Gandhian ideas of economy and society in the 1940s and 1950s. These ideas have inspired and influenced a range of individuals, institutions and civil society groups in inclusive innovation.

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The aim of this study is to investigate the relative impact of the image of information service on customer’s perceived value and satisfaction of R&D information. It also seeks to assess the moderating effect of service users' skills on the value of the service image on the customer. For this purpose, a field study was conducted on users of a public R&D information service called NTIS (National Technology Information Service) in Korea. The findings show that the information service image has a significant impact on customers’ perceived value and satisfaction. In addition, customers' perceived value is found to be an important indicator in strengthening customer satisfaction. Findings also reveal that individual personal computer skills moderate the relationship between service image and information value. Further research is needed to strengthen the independent variable in view of the increasing pressure to improve public service quality and customer management.

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This article proposes a logic model for assessing the performance of the outcome of public research as a technology valuation method. It consists of two parts and eight steps. The first part is a scoring system and the second part is a validation process of the performance index derived from scoring by valuation method. The scoring in the first part generally requires a focus group method to find out the value drivers and make an evaluation table. The reason why we call it the technology valuation method is that the first part is derived from the simple evaluation of technology value using checklists for value drive. The second part is the regular technology valuation process. The model is designed for the measurement of unquantifiable outcome. Is knowledge or scientific outcome comparable to the measured outcome? If possible, how big is the unquantifiable outcome? This model is based on financial valuation techniques with clear or acceptable market data. Therefore, it cannot work solely for unquantifiable outcomes without comparable measurable outcomes, unlike economic valuation.

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Abstract

When evaluating the economic value of technology or business project, we need to consider the period and cost for commercialization. Since the discounted cash flow (DCF) method has limitations in that it can not consider consecutive investment or does not reflect the probabilistic property of commercialization cost, we often take it desirable to apply the concept of real options with key metrics of underlying asset value, commercialization cost, and volatility, while regarding the value of technology and investment as the opportunity value. We at this moment provide more elaborated real options model with the effective region of volatility, which reflects the uncertainty in the option pricing model (OPM).