引用本文:朱钰,杨锋,江利景,等.基于共享回馈DEA模型的中国省际 高技术产业创新效率研究[J].控制与决策,2020,35(8):1997-2005
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基于共享回馈DEA模型的中国省际 高技术产业创新效率研究
朱钰1,2, 杨锋2, 江利景2, 刘培2
(1. 安徽工程大学管理工程学院,安徽芜湖241000;2. 中国科学技术大学管理学院,合肥230026)
摘要:
数据包络分析(data envelopment analysis,DEA)已被证明是测度高技术产业创新绩效的好方法,但现有文献忽略了现实中企业会将创新带来的经济效益回馈至两个子阶段进行再研发和生产,从而保证持续创新.基于此,结合高技术产业创新过程的特征,将其分为技术研发和商业转化两个子阶段,提出考虑共享回馈DEA模型的两阶段效率测度模型,不仅拓展了数据包络分析方法,也促进了创新绩效管理研究.实证结果表明:中国高技术产业整体效率良好,仍有提升空间,但省际发展不均衡,各省内部不同阶段效率也存在明显的差异,实行有针对性的管理是提升绩效的有效措施.
关键词:  高技术产业  数据包络分析  共享回馈  两阶段  创新效率
DOI:10.13195/j.kzyjc.2018.1737
分类号:F204;F276.44
基金项目:安徽省哲学社科青年项目(AHSKQ2015D51).
Innovation efficiency of Chinese provincial high-tech industries based on shared feedback DEA model
ZHU Yu1,2,YANG Feng2,JIANG Li-jing2,LIU Pei2
(1. School of Management Engineering,Anhui Polytechnic University,Wuhu241000,China;2. School of Management,University of Science and Technology of China,Hefei230026,China)
Abstract:
Data envelopment analysis(DEA) has been proved to be an excellent approach for measuring of high-tech industry innovation performance, but the existing literatures ignore that enterprises will return the economic benefits of innovation to two-stages for further development and production, so as to ensure continuous innovation. Therefore, this paper combines the characteristics of high-tech industry innovation process into two sub-stages of technology research and development and commercial transformation. A two-stage efficiency measure model considering shared feedback is proposed, which not only extends the DEA methods, but also promotes the research of innovation performance management. The empirical results show that the overall efficiency of Chinese high-tech industries is good, and there is still room for improvement. However, the inter-provincial development is unbalanced, and there are obvious differences in the efficiency of different stages within the provinces. The implementation of targeted management is an effective measure to improve the innovation performance.
Key words:  high-tech industries  data envelopment analysis(DEA)  shared feedback  two-stage  innovation efficiency

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