期刊名称：Journal of Open Innovation: Technology, Market, and Complexity
电子版ISSN：2199-8531
出版年度：2019
卷号：5
期号：3
页码：1-19
DOI：10.3390/joitmc5030066
语种：English
出版社：Springer
摘要：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.
关键词：organizational assessment; government-funded research and development institute; governmental institute; incentive; feedback; agent based model
