基于三阶段共享互馈网络DEA的知识创新效率评价
Knowledge Innovation Efficiency Evaluation Based on Three-Stage Network DEA with Shared Resources and Mutual Feedback
潘浩 1娄渊雨 1陈晓雷 1杨国梁 1关忠诚1
作者信息
- 1. 中国科学院科技战略咨询研究院,北京 100190;中国科学院大学公共政策与管理学院,北京 100049
- 折叠
摘要
在复杂的系统中,资源分配和系统内部交互是研究系统创新效率时需要重视的因素,但在以往的相关研究中并没有考虑到系统内部之间的相互反馈作用.基于此文章拓展了三阶段共享网络DEA模型,将系统内部子系统间存在的互馈作用纳入模型研究范围,证明了系统有效的充分必要条件,定义了系统有效和阶段有效.对54所中国高等院校的知识创新效率进行测度,将知识创新系统分为知识生产阶段和知识应用阶段,对比了不同阶段侧重方法(GP1:侧重第一阶段;GP3:侧重第三阶段)的差异,研究结果表明:不存在系统有效的DMU;相较于GP3,GP1会低估知识应用效率,且会降低各DMU知识生产效率的区分度.文章还针对不同特性对各样本高校展开分析,并给出相关政策、措施建议.
Abstract
In complex system,resource allocation and internal interaction of the system are key factors that need to be paid attention to when studying the innovation efficiency of the system,but in previous related researche,scholars did not take the mutual feedback between divisions inside the system into account.Based on what we mentioned,this paper has expanded the previous model and constructed a three-stage network DEA model with shared resources,incorporating the interaction effect between the internal divisions of the system.We prove the necessary and sufficient conditions of effective system and define effectiveness of the system and each stage.We measure the knowledge innovation efficiency of 54 Chinese universities and divide the knowledge innovation system into the knowledge production stage and the knowledge application stage.Compared with the distinction of different methods(GP1:Focusing on the first stage;GP3:Focusing on the third stage),the results show that there is no DMU with systemic efficiency;Compared with GP3,GP1 will underestimate the efficiency of knowledge application and reduce the level of distinction between DMUs at the efficiency of knowledge production.This paper also analyzes various sample universities in accordance with different characteristics and puts forward relevant policies and measures.
关键词
网络DEA/资源共享/互馈/知识创新Key words
Network DEA/resources sharing/mutual feedback/knowledge innova-tion引用本文复制引用
基金项目
国家自然科学基金面上项目(72071196)
出版年
2024
NETL
NSTL
万方数据