| Project/Area Number |
18J11566
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 国内 |
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| Research Field |
Economic statistics
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| Research Institution | Doshisha University |
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Principal Investigator |
DUONG BINH AN THI (2019) 同志社大学, 文化情報学研究科, 特別研究員(DC2)
DUONG Binh An Thi (2018) 同志社大学, 文化情報学研究科, 特別研究員(DC2)
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| Project Period (FY) |
2018-04-25 – 2020-03-31
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| Project Status |
Granted (Fiscal Year 2019)
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| Budget Amount *help |
¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 2019: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2018: ¥1,000,000 (Direct Cost: ¥1,000,000)
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| Keywords | MMOLR / Resonant influence |
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| Outline of Annual Research Achievements |
Our research objective is applying new methods and models of Structural Equation Modeling (SEM) to describe complex system and concepts of Supply Chain Management (SCM). We divide our research into two stages: proposing new Linear Regression model, and proposing new SEM model and applying to SCM. The first stage has been finished with Multivariate Multiple Orthogonal Linear Regression, a new model was proposed. Moreover, together with two group from Uminho University - Portugal and Bach Khoa University - Vietnam, we applied SEM to study about resonant influence in SCM. Both works were published.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Last year, we actively attended international conferences not only to present our results but also for listening the trend of the world and building up research networks. From these relationships and at conferences, we could receive many suggestions or advice from professors. Therefore, we can adjust research aim and direction on time when facing problems. More important that is we received supports from other research groups for these results.
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| Strategy for Future Research Activity |
For future, we intend to apply Generalized Maximum Entropy estimation for Structural Equation Modeling (GME-SEM) to an empirical research in Management. However, we realized that current algorithm violates the normalization constraint. Therefore, before going to the second stage, we have to conduct a sub-stage in the year of 2019 for doing overview of Generalized Maximum Entropy (GME) estimation method, and revising algorithm of GME-SEM.
In the next year, the following activities are expected to conduct:
i. Participate to summer school of “Next generation enterprise modelling in the digital transformation age” at Vienna University to update the trend of enterprises and continue building up relationships.
ii. Publish the result of sub-stage.
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