How to Improve Performance and Diversity of Government-Funded Research Institute Ecosystem? Focus on Result Sharing and Feedback Policy
1
Technology Management, Economics and Policy Program, College of Engineering, Seoul National University, Seoul 08826, Korea
2
Department of Biomedical Convergence, College of Medicine, Chungbuk National University, Cheongju 28644, Korea
*
Author to whom correspondence should be addressed.
J. Open Innov. Technol. Mark. Complex. 2019, 5(3), 66; https://doi.org/10.3390/joitmc5030066
Received: 17 July 2019 / Revised: 18 August 2019 / Accepted: 20 August 2019 / Published: 5 September 2019
(This article belongs to the Special Issue Second IT Revolution and Dynamic Open Innovation: From Smart City, Autonomous Car, Intelligent Robot, and Block Chain to Sharing Economy)
Despite its importance to the performance outcome of an organization, there are very few studies on how feedback mechanism impacts ecosystems of government-funded research institutes (GIs). This study focuses on the effect of the feedback mechanism on the average performance and diversity of a GI ecosystem. Feedback mechanisms consisted of feedback strategy and degree of result sharing. An agent-based model that embeds a genetic algorithm to replicate a real GI ecosystem was used. It was found that relational patterns between average performance and degree of result sharing varied by type of feedback policy. In contrast, convergence time, which refers to the average period of settling the stable state in the perspective of ecosystem diversity, depends on the ratio of result openness rather than the type of feedback policy. This study suggests two plans to improve the GI assessment system by changing the degree of result sharing and feedback type.
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Keywords:
organizational assessment; government-funded research and development institute; governmental institute; incentive; feedback; agent based model
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MDPI and ACS Style
Cha, N.; Hwang, J.; Kim, E. How to Improve Performance and Diversity of Government-Funded Research Institute Ecosystem? Focus on Result Sharing and Feedback Policy. J. Open Innov. Technol. Mark. Complex. 2019, 5, 66.
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