西部典型城市创新效率测算及影响因素路径分析
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摘要
本文结合网络DEA模型和超效率DEA模型构建的关联两阶段超效率DEA模型(TSS-DEA),对西部14个典型城市的创新效率进行测算,得到技术研发和经济转化两阶段创新效率值和城市创新综合效率值。在定性分析的基础上梳理创新效率影响因素,构建偏最小二乘法结构方程模型和路径假设,并利用效率测算结果对影响因素和路径进行实证分析,得出创新环境、创新主体特征和创新交流因素发展不同是西部典型城市创新效率差距的主要原因,提高城市创新效率可以从三个维度中包含的因素为导向制定政策和措施。最后,根据研究结论提出西部城市创新效率提升路径建议。
Firstly,the paper measures the innovation efficiency of 14 western typical cities with a two-stage dynamic super-efficiency DEA model which is established combining the network DEA model and the super efficiency DEA model. Innovation efficiency of technical research and development stage and economic transformation stage, and comprehensive efficiency are calculated by the model. Then,the paper analyzes the influence factors of innovation efficiency qualitatively and constructs the PLS-SEM model and path hypothesizes. Empirical analysis is carried out by using the efficiency measurement results. This paper discovers that,differences of the factors—innovation environment,innovator features and innovation exchanges are the main reasons for the gap of innovation efficiency in typical western cities. Finally suggestions on how to improve innovation efficiency in western cities are proposed.
Firstly,the paper measures the innovation efficiency of 14 western typical cities with a two-stage dynamic super-efficiency DEA model which is established combining the network DEA model and the super efficiency DEA model. Innovation efficiency of technical research and development stage and economic transformation stage, and comprehensive efficiency are calculated by the model. Then,the paper analyzes the influence factors of innovation efficiency qualitatively and constructs the PLS-SEM model and path hypothesizes. Empirical analysis is carried out by using the efficiency measurement results. This paper discovers that,differences of the factors—innovation environment,innovator features and innovation exchanges are the main reasons for the gap of innovation efficiency in typical western cities. Finally suggestions on how to improve innovation efficiency in western cities are proposed.
引文
[1]郭淡泊,雷家,张俊芳,彭勃.国家创新体系效率及影响因素研究———基于DEA-Tobit两步法的分析[J].清华大学学报(哲学社会科学),2012,27(2):142-150+160.
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[4]孙红兵,向刚.基于DEA的城市创新系统创新效率评价分析[J].科技进步与对策,2011,28(12):130-135.
[5]梁瑞敏,彭佑元.基于网络DEA的山西省区域科技创新绩效评价研究[J].科技管理研究,2014,34(18):112-116.
[6]LIANG L,YANG F,COOK W D,et al. DEA models for supply chain efficiency evaluation[J].Annals of operations research,2006,145(1):35-49.
[7]ROFEFARE,SHAWNA G. Network DEA[J].Socio-economic planning sciences,2005,34(1):35-49.
[8]KAO C,HWANG N. Efficiency decomposition in two-stage data envelopment analysis:an application to non-life insurance companies in Taiwan[J].European journal of operational research,2008,185(1):418-429.
[9]CHEN Y,COOK W D,LI N,et al. Additive efficiency decomposition in two-stage DEA[J]. European journal of operational research,2009,196(3):1170-1176.
[10]查勇,梁睴,许传永.基于BCC模型的几何平均最优意义下的两阶段合作效率[J].系统工程理论与实践,2008,28(10):53-58.
[11]张鸿,郝添磊,汪玉磊.基于创新链视角的科技创新效率实证分析[J].西安财经学院学报,2017(6):56-61.
[12]李婧,谭清美,白俊红.中国区域创新效率及其影响因素[J].中国人口·资源与环境,2009(6)142-147.
[13]王松.我国区域创新主体协同研究[D].武汉:武汉理工大学,2013.
[14]王帅.开放式创新视角下区域创新系统演化机制及其绩效影响因素研究[D].合肥:中国科学技术大学,2016.
[15]贺小桐.产学研合作对创新型城市发展的影响力研究[D].合肥:中国科学技术大学,2014.
[16]FRITSCH M. Measuring the quality of regional innovation systems:a knowledge production function approach[J].International regional science review,2002,25(1):86-101.
[17]连燕华,马晓光.我国产学研合作发展态势评价[J].中国软科学,2001(1):54-59.
[18]叶鹰,鲁特·莱兹多夫,武夷山.三螺旋模型及其量化分析方法研讨[J].中国软科学,2014(11):131-139.
[19]邹华,何得雨.开放式创新视角下政用产学研联动机制研究[J].沈阳工业大学学报(社会科学版),2007(1):1-5.
[20]JRESKOG K G,WOLD H. The ML and PLS techniques for modeling with latent variables:historical and competitive aspects[M].Leiden:Herod and Augustus Brill,1982:361-382.
[21]FALK Frank,MILLER Nancy B. A primer for soft modeling[J].University of Akron press,1992.
[2]官建成,何颖.基于DEA方法的区域创新系统的评价[J].科学学研究,2005(2)265-272.
[3]孙凯.基于DEA的区域创新系统创新效率评价研究[J].科技管理研究,2008(3):139-141.
[4]孙红兵,向刚.基于DEA的城市创新系统创新效率评价分析[J].科技进步与对策,2011,28(12):130-135.
[5]梁瑞敏,彭佑元.基于网络DEA的山西省区域科技创新绩效评价研究[J].科技管理研究,2014,34(18):112-116.
[6]LIANG L,YANG F,COOK W D,et al. DEA models for supply chain efficiency evaluation[J].Annals of operations research,2006,145(1):35-49.
[7]ROFEFARE,SHAWNA G. Network DEA[J].Socio-economic planning sciences,2005,34(1):35-49.
[8]KAO C,HWANG N. Efficiency decomposition in two-stage data envelopment analysis:an application to non-life insurance companies in Taiwan[J].European journal of operational research,2008,185(1):418-429.
[9]CHEN Y,COOK W D,LI N,et al. Additive efficiency decomposition in two-stage DEA[J]. European journal of operational research,2009,196(3):1170-1176.
[10]查勇,梁睴,许传永.基于BCC模型的几何平均最优意义下的两阶段合作效率[J].系统工程理论与实践,2008,28(10):53-58.
[11]张鸿,郝添磊,汪玉磊.基于创新链视角的科技创新效率实证分析[J].西安财经学院学报,2017(6):56-61.
[12]李婧,谭清美,白俊红.中国区域创新效率及其影响因素[J].中国人口·资源与环境,2009(6)142-147.
[13]王松.我国区域创新主体协同研究[D].武汉:武汉理工大学,2013.
[14]王帅.开放式创新视角下区域创新系统演化机制及其绩效影响因素研究[D].合肥:中国科学技术大学,2016.
[15]贺小桐.产学研合作对创新型城市发展的影响力研究[D].合肥:中国科学技术大学,2014.
[16]FRITSCH M. Measuring the quality of regional innovation systems:a knowledge production function approach[J].International regional science review,2002,25(1):86-101.
[17]连燕华,马晓光.我国产学研合作发展态势评价[J].中国软科学,2001(1):54-59.
[18]叶鹰,鲁特·莱兹多夫,武夷山.三螺旋模型及其量化分析方法研讨[J].中国软科学,2014(11):131-139.
[19]邹华,何得雨.开放式创新视角下政用产学研联动机制研究[J].沈阳工业大学学报(社会科学版),2007(1):1-5.
[20]JRESKOG K G,WOLD H. The ML and PLS techniques for modeling with latent variables:historical and competitive aspects[M].Leiden:Herod and Augustus Brill,1982:361-382.
[21]FALK Frank,MILLER Nancy B. A primer for soft modeling[J].University of Akron press,1992.