Abstract
The relationship between science and innovation has been heavily investigated using patent data, by looking at university and industry joint applications and patent citations to research paper information. In this presentation, new methodologies of understanding science and innovation co-evolution are based on text analysis and geographical information of research papers, patents, and firm activities datasets (IPDB: Innovation process database). First, the text analysis of the research paper and patent dataset elucidates the dynamic relationship between science and technology. Second, the fine-grained geographical identification dataset by patent and firm activities investigates the university's role in local high-tech entrepreneurship in Japan. The presentation concludes with policy implications based on the findings of these two papers.
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Chaired Professor, International IP College, Tongji University
Kazuyuki Motohashi is the head of the division and a professor at the Department of Advanced Interdisciplinary Studies, Graduate School of Engineering, the University of Tokyo. He is also appointed as a chaired professor of the International IP College of Tongji University, as well as a visiting scholar at RIETI (Research Institute of Economy, Trade and Industry) and NISTEP (National Institute of Science and Technology Policy). Until this year, he had taken various positions at the Ministry of Economy, Trade and Industry of the Japanese Government and as an economist at OECD. His research interests cover a broad range of issues in economic and statistical analysis of innovation, including the economic impacts of information technology, international comparisons of productivity, the national innovation system focusing on science and industry linkages, and SME innovation and entrepreneurship policy. He has published several papers and books on the above issues, including Global Business Strategy: Multinational’s Venturing Into Emerging Economies (2014), and he serves as one of the editors of Research Policy.
The seminar will showcase the relationship between science and innovation has been heavily investigated using patent data, by looking at university and industry joint applications and patent citations to research paper information. In this presentation, new methodologies of understanding science and innovation co-evolution are based on text analysis and geographical information of research papers, patents, and firm activities datasets (IPDB: Innovation process database). First, the text analysis of the research paper and patent dataset elucidates the dynamic relationship between science and technology. Second, the fine-grained geographical identification dataset by patent and firm activities investigates the university's role in local high-tech entrepreneurship in Japan. The presentation concludes with policy implications based on the findings of these two papers.