基于TOPSIS和DEA模型比较的国家重点实验室宏观效率评价
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摘要
国家重点实验室投入产出效率评价备受学术界和实践界关注。采用基于信息熵的理想解排序法(TOPSIS)和数据包络分析(DEA)-CCR综合评价模型评估国家重点实验室1990—2015年宏观效率变化情况。研究结果表明:两种模型分析的结论存在明显差异,前者参照理想解的临近距离得到年份排序总体呈上升趋势,反映出随着投入的增加,实验室的产出值也快速增加;后者参照投入冗余效率得到年份排序总体呈下降趋势,说明实验室的科研产出相对于增加的投入而言,仍具有很大的提升空间。比较分析后提出国家重点实验室宏观效率变化的4个阶段,以期为合理评价国立科研机构效率提供新的视角。
The efficiency evaluation on the input and output of state key laboratories has attracted much attention from academics and administration. This paper evaluates the efficiency changes of state key laboratories from 1991 to 2016 based on the information-entropy TOPSIS and DEA-CCR model comparatively. The results show that: by TOPSIS,there is an increase trend in general from the length of distance from the ideal value, meaning that the outputs has risen up quite fast compared with the added inputs; on the opposite, by DEA, the trend is on the downside, which means that there should have been much better outputs because of the rising amount of inputs. On the basis of the above models, four phases of efficiency changes of state key laboratories are proposed, in order to provide a new perspective to evaluate the efficiency of government-funded institutes.
The efficiency evaluation on the input and output of state key laboratories has attracted much attention from academics and administration. This paper evaluates the efficiency changes of state key laboratories from 1991 to 2016 based on the information-entropy TOPSIS and DEA-CCR model comparatively. The results show that: by TOPSIS,there is an increase trend in general from the length of distance from the ideal value, meaning that the outputs has risen up quite fast compared with the added inputs; on the opposite, by DEA, the trend is on the downside, which means that there should have been much better outputs because of the rising amount of inputs. On the basis of the above models, four phases of efficiency changes of state key laboratories are proposed, in order to provide a new perspective to evaluate the efficiency of government-funded institutes.
引文
[1]王婉娟,危怀安.内部创新网络对协同创新能力的影响机理:基于国家重点实验室的实证研究[J].科研管理, 2018,39(1):143-152.
[2]辛督强.基于DEA的国家重点实验室投入产出效率评价[J].实验技术与管理, 2014(4):220-223.
[3]张静一,仇笳熙.国家重点实验室运行过程的问题与管理经验[J].科研管理,2017(增刊1):691-694.
[4]田雅娟,杨志萍,周涛,等.科研成果评价研究:国家重点实验室评价案例[J].科研管理, 2016(增刊1):264-269.
[5]李旭彦,杨晓秋,宋英华.论文数据分析在国家重点实验室学术评价中的应用探讨[J].科技进步与对策,2016,33(11):122-128.
[6]徐志玮.美国国家实验室的科研评估和启示:以美国劳伦斯伯克利国家实验室为例[J]. 实验技术与管理,2014(1):201-206.
[7]葛少卫,杨晓江.基于人因工程的实验室评估指标体系构建[J].科技管理研究, 2016,36(9):57-61.
[8]王磊,周亚楠,张宇.基于熵权-TOPSIS法的低碳城市发展水平评价及障碍度分析[J].科技管理研究,2017,37(17):239-245.
[9]鞠航,徐帅.基于组合赋权-TOPSIS模型的军工企业装备维修能力评价研究[J].科技管理研究,2017,37(22):60-64.
[10]ZHANG H, GU CH L, GU L W,et al.The evaluation of tourism destination competitiveness by TOPSIS & information entropy:a case in the Yangtze River Delta of China[J]. Tourism Management, 2011,32(2):443-451.
[11]俞立平,张晓东.基于熵权TOPSIS的地区高校科技竞争力评价研究[J].情报杂志, 2013(11):181-186.
[12]杨默函,姚卫浩,李君,等.科研实验室评估方法及指标体系对比[J].中国高校科技, 2013(12):52-55.
[13]魏楚,沈满洪.能源效率及其影响因素:基于DEA的实证分析[J].管理世界, 2007(8):66-76.
[14]MARCHETTI D, WANKE P.Brazil's rail freight transport: efficiency analysis using two-stage DEA and cluster-driven public policies [J].Socio-Economic Planning Sciences, 2017,59:26-42.
[15]COOK W D, ZHU J. DEA Cobb-Douglas frontier and crossefficiency[J].Journal of the Operational Research Society, 2014,65(2):265-268.
[16]赵琦.基于DEA的义务教育资源配置效率实证研究:以东部某市小学为例[J].教育研究, 2015(3):84-90.
[2]辛督强.基于DEA的国家重点实验室投入产出效率评价[J].实验技术与管理, 2014(4):220-223.
[3]张静一,仇笳熙.国家重点实验室运行过程的问题与管理经验[J].科研管理,2017(增刊1):691-694.
[4]田雅娟,杨志萍,周涛,等.科研成果评价研究:国家重点实验室评价案例[J].科研管理, 2016(增刊1):264-269.
[5]李旭彦,杨晓秋,宋英华.论文数据分析在国家重点实验室学术评价中的应用探讨[J].科技进步与对策,2016,33(11):122-128.
[6]徐志玮.美国国家实验室的科研评估和启示:以美国劳伦斯伯克利国家实验室为例[J]. 实验技术与管理,2014(1):201-206.
[7]葛少卫,杨晓江.基于人因工程的实验室评估指标体系构建[J].科技管理研究, 2016,36(9):57-61.
[8]王磊,周亚楠,张宇.基于熵权-TOPSIS法的低碳城市发展水平评价及障碍度分析[J].科技管理研究,2017,37(17):239-245.
[9]鞠航,徐帅.基于组合赋权-TOPSIS模型的军工企业装备维修能力评价研究[J].科技管理研究,2017,37(22):60-64.
[10]ZHANG H, GU CH L, GU L W,et al.The evaluation of tourism destination competitiveness by TOPSIS & information entropy:a case in the Yangtze River Delta of China[J]. Tourism Management, 2011,32(2):443-451.
[11]俞立平,张晓东.基于熵权TOPSIS的地区高校科技竞争力评价研究[J].情报杂志, 2013(11):181-186.
[12]杨默函,姚卫浩,李君,等.科研实验室评估方法及指标体系对比[J].中国高校科技, 2013(12):52-55.
[13]魏楚,沈满洪.能源效率及其影响因素:基于DEA的实证分析[J].管理世界, 2007(8):66-76.
[14]MARCHETTI D, WANKE P.Brazil's rail freight transport: efficiency analysis using two-stage DEA and cluster-driven public policies [J].Socio-Economic Planning Sciences, 2017,59:26-42.
[15]COOK W D, ZHU J. DEA Cobb-Douglas frontier and crossefficiency[J].Journal of the Operational Research Society, 2014,65(2):265-268.
[16]赵琦.基于DEA的义务教育资源配置效率实证研究:以东部某市小学为例[J].教育研究, 2015(3):84-90.