We lead academic research in science and innovation policy, especially focusing on developing a methodology to analyze a vast amount of academic and industry information with infometric approach including text mining and link mining in order to offer a structured information to relevant stakeholders. The following research projects are ongoing.

Research Projects

Modeling Innovation Processes `Open the Black Box

As the sources of competitiveness shifts to intangible assets like knowledge and technology, it becomes the critical issue to continuously produce core technology driving future innovation for any organizations and nations. In addition, it also becomes important for engineers and managers that leveraging the value of core technology with appropriate product and service design, business strategy and market orientation is essential to meet social needs and demands. In this project, we focus on the innovation processes from basic research to applied research and product development to market cultivation. We also analyze the process that core technologies open new market and new industry. Specifically, we study the interactions among scientist, engineer, technology, organization, management, and institution during innovation processes.

Visualizing Science `Computational intelligence to comprehend an academic landscape

The advancement of science has been achieved by specialization and segmentation of a discipline and has contributed the development of our society. On the other side, the specialization and segmentation hamper us to grasp an overall structure and research front even in a discipline, which makes the effective funding, research, cooperation difficult especially in cross-disciplinary field. Therefore, it is necessary for policy makers and R&D managers to organize a pile of academic information to know an overall structure, research front, trend, relationships with the other disciplines for their effective decision making. In this project, we develop tools visualizing academic landscape by natural language procession and citation network analysis. Our tools are also expected to enhance smooth communication among individual researchers, engineers, managers, and policy makers.

Empowering Innovation System `Enhancing Partnerships among Diverse Organizations

There is a resurgence of interest in open innovation, which reflects the current situation we face. Firstly, social issues which we must solve become so complex that one organization has enough knowledge and skills, and therefore we must integrate diverse organizations. Secondly, to keep competitiveness in a condition where globalization proceeds and speed of innovation increases, we do not have enough time to develop knowledge and skills within our organization. And a wide recognition on the importance of scientific knowledge makes academia-industry partnerships indispensable. However, the activity is not enough and it is not a rudimentary task to find appropriate partners and make relevant alliance. In this project, we develop a recommendation system for both academia and industry to find plausible partners by using information technology including web engineering, text mining, link mining, and machine learning to support new cooperation and alignment developments.

Service Innovation `Bridging Basic and Applied Science with Service Science and Innovation

Service innovation is a promising key issue to develop our economy and enrich our life. In this study, we analyze what and how basic and applied science can contribute to service innovation in each sector such as business service, health and medical services or individual services. We also develop infometric methodology like data, text, and link mining, which is a common and fundamental component of service science.

Green Innovation `Analysis of Academic and Industrial Structure for Strategic Management of R&D~

This study aims to analyze the structure of green innovation which is currently under competitive situation. We target renewable energies, and analyze the research front of advanced technologies by utilizing the methodology developed by IPRC. We also analyze their production cost with life cycle cost accounting. Based on the above results, we identify the potential cost reduction and performance improvement by the current and advanced technologies. The results can give a useful guideline for science and innovation policy setting to know the investment target and expected outcome in advance.