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dc.contributor.advisorBryan R. Moser.en_US
dc.contributor.authorNagura, Masaruen_US
dc.contributor.otherSystem Design and Management Program.en_US
dc.coverage.spatiala-ja---en_US
dc.date.accessioned2018-02-08T16:27:34Z
dc.date.available2018-02-08T16:27:34Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/113522
dc.descriptionThesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2017.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 113-120).en_US
dc.description.abstractMany public policies related to innovation have been implemented in Japan, especially focusing on promoting startups from universities, aiming at economic growth from technology. However, innovation ecosystem is complicated and dynamic that make difficult for policymakers to understand the system and evaluate policy effect. In this study, we analyze innovation ecosystem around a university and build a system dynamics model to have policy implications. We study the University of Tokyo and MIT, major universities for a large number of spin-off startups in each country, as cases of ecosystems. The study begins with policy and literature review of innovation and entrepreneurship, to understand present studies and policies. Next, stakeholders, system boundary, and causal relationships are analyzed to frame the system. Then, we build a system dynamics model of innovation ecosystem around a university. We included several causal loop structures in the model. For example, an Entrepreneur boom loop is a reinforcing loop which accelerates foundation of university spin-off startups and conversion of students to become entrepreneurial. A Risk capital depletion loop is a balancing loop which decelerates growth of startups when too many startups look for investment. Multiple loops and stakeholders interact closely in the systems, and the interrelated structures cause delay and side effects in simulation runs of our model. Results of the simulation infer policymakers need to consider combinations of policies rather than implement a single policy. Another interpretation from simulation runs is that patient policy implementation can lead to better outcomes because time delays in the loops make it difficult for policymakers to observe the effect of policies in the short term. Although additional data points are required for further calibration of the model, insights from this study and the model contribute to better understanding of innovation ecosystems around a university.en_US
dc.description.statementofresponsibilityby Masaru Nagura.en_US
dc.format.extent120 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectEngineering and Management Program.en_US
dc.subjectIntegrated Design and Management Program.en_US
dc.subjectSystem Design and Management Program.en_US
dc.titleDynamics of innovation policies and ecosystems in Japanen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Engineering and Managementen_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering and Management Programen_US
dc.contributor.departmentSystem Design and Management Program.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Integrated Design and Management Programen_US
dc.identifier.oclc1020074104en_US


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