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Policy Research on Science and Technology
Motohashi Laboratory

Data Analytics to Understand Innovation Dynamics and Applications to Science and Technology Policy Making

Scientification of Economy : Co-evolution of Science and Innovation and Ecosystem Formation

Scientific foundation becomes more and more important for industrial innovation process. The genome science has changed its R&D process substantially and concurrent progress of academic research and its industrialization (innovation) occurs in AI and robotics field (scientification of economy). We are conducting empirical research on science and innovation coevolution, by using large bibliometric datasets (patents, research articles) and economic statistics. The results of our analysis are inputted to actual policy formation in relevant ministries. The concrete research theme includes

・Co-evolution of science and innovation: New role of university and policy implications to effective industry collaborations
・Economic analysis of AI/Big Data/IoT and platform business
・Global competition in science innovation (vs. US and China) and regional innovation ecosysytem (Silcon Valley, Shenzhen)

Emprical research on science, technology and innovation policy

・International R&D collaboration
・Interactions of IPR and competition policy
・Public research funding and open science

Big Data Analytics for Empirical Innovation Research

We are also conducting the research on database construction and new methodologies of technology forecasting, based on bibliometric information (research articles and patents). Advanced computer science techniques (such as deep neural network) are used for natural language processing in multi lingual environment (Chinese, English, Thai as well as Japanese).

Framework of science, technology and industry indicator
Framework of science, technology and industry indicator
Participating a panel discussion at CSIS in Washington DC
Regional innovation ecosystem in Shenzhen
Technology mapping for characterizing university inventions (natural language process of patent documents)
Technology mapping for characterizing university inventions (natural language process of patent documents)

Member

  • Kazuyuki MOTOHASHI
  • Specialized field:Technology Management Strategy, Global Business Strategy, Science and Technology Policy, Bibliometrics
  • Seok Beom KWON
  • Specialized field:Patent Policy, Data Economy, Research on Interdisciplinary Research, Bibliometrics
<As of May 2020>

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