• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1171-1186
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• 2011

Identifying Emerging Technology Trends is crucial for decision makers of nations and organizations in order to use limited resources, such as time, money, etc., efficiently. Many researchers have proposed emerging trend detection systems based on a popularity analysis of the document, but this still needs to be improved. In this paper, an emerging trend detection classifier is proposed which uses both academic and industrial data, SCOPUS and PATSTAT. Unlike most pre-vious research, our emerging technology trend classifi-er utilizes supervised, semi-automatic, machine learning techniques to improve the precision of the results. In addition, the citation information from among the SCOPUS data is analyzed to identify the early signals of emerging technology trends.

• Journal of Korea Technology Innovation Society
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• v.14 no.1
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• pp.1-21
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• 2011

Technical change and technological innovation have become major drivers of economic progress in the knowledge oriented economies where growth, productivity, and competitiveness are increasingly based on improved technologies, novel products, upgraded processes or customized services. The creation of new knowledge, modifying or improving existent knowledge, or imitation of others, has become central to economic development. New discoveries, state-of-the-art information gathering procedures, or successful problem solving routines are often at he core of these innovations. Despite the generally acknowledged importance of science in many high-tech areas of major economic relevance, there is few science-related statistics to be found in high-profile international benchmarking reports. This paper aims to provide an answer by advancing our understanding of the possibilities of indicators quantifying linkages between science and technology. Central are the concepts of innovation capability and science/technology interface, which are used to assemble a wide range of empirical studies and quantitative indicators to summarize their possibilities and limitations for producing comparative statistics. For the purpose of the study, we extracted the US patents by Korean assignees or inventors, scientific papers cited in the patents in order to analyze the characteristics of linkage of scientific knowledge flows. The review focuses on indicators dealing with flows of written or codified information, and indicators of inventiveness that capture the non-codifiable tacit knowledge dimension. General conclusions will be drawn with a view towards further developments in the foreseeable future, suggesting new avenues for the design and implementation of patent-based and inventor-based relationships between scientific research and technical development within the context of regional or national systems of innovation.

• Journal of Technology Innovation
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• v.28 no.3
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• pp.29-53
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• 2020

Existing main path analysis is useful to clarify the backbone of technology developments over the past, but has difficulty in identifying future technology candidates, and also in anticipating changes in the mainstream technology. Our method develops a growth velocity indicator, and combines it with key-route analysis and traversal counts measure in the main path analysis. It enables us to identify rapidly growing paths of future technology candidates, and further to evaluate the relative growth potential of such paths by which can replace the mainstream technology in the main path. Our method can contribute to identifying future technology candidates in a quantitative way by using patents, and broaden the scope of main path analysis research toward foresight. It can be useful for technology strategy in practice. Biofuel technology is exemplified.