基于数据包络分析的中国城市创新能力评价

摘要/Abstract

摘要： 针对城市创新能力的应用背景，构建了重点城市创新能力的评价指标体系，其中包括人才培养和科技创新两个创新阶段，提出了综合考虑共享投入和分阶段产出的两阶段数据包络分析模型，对国内52座重点城市的总体和单阶段的创新能力进行了有效评价和区分。研究结果发现，长三角和珠三角区域的创新能力在全国范围内具有优势，而创新效率相对较低的城市主要分布于中部、西南和西北的部分地区。实证分析为这些城市进一步提高自身的科技创造和研发创新水平提供重要的参考价值和指导意义。

关键词: 数据包络分析(DEA), 效率, 两阶段, 城市创新能力

Abstract: According to the background of application to city innovation performance, a set of indices are established for evaluating innovation performance of key cities in China. The innovation process is divided into two individual stages——talent training and sci-tech creation. Two-stage data envelopment analysis (DEA) models are proposed to assess and differentiate the overall and individual-stage innovation performance for 52 Chinese key cities. It is demonstrated that the Yangtze River Delta region and Pearl River Delta region have done an excellent job in city innovation, while quite a number of cities in central, southeast and northeast China still have a lot to improve in that aspect. The results provide important implications and suggestions for those cities to effectively enhance their innovative development.

Key words: data envelopment analysis (DEA), efficiency, two-stage, city innovation performance

中图分类号:

C934
C94

杜娟, 霍佳震. 基于数据包络分析的中国城市创新能力评价[J]. 中国管理科学, 2014, 22(6): 85-93.

DU Juan, HUO Jia-zhen. DEA-based Evaluation on City Innovation in China[J]. Chinese Journal of Management Science, 2014, 22(6): 85-93.

参考文献

[1] 张微,夏恩君,王月红. 城市技术创新环境建设研究 [J]. 商业时代,2007,28: 4-5.

[2] 邵云飞,唐小我. 中国区域技术创新能力的主成分实证研究 [J]. 管理工程学报,2005,19(3): 71-76.

[3] 张立柱,郭中华,李玉珍. 山东省城市创新能力评价及"四大创新圈模式"构建 [J]. 科学学与科学技术管理,2006,6: 75-79.

[4] 刘友金,李洪铭,叶俊杰. 基于聚类分析的区域创新能力差异研究 [J]. 哈尔滨学院学报,2001,22(2): 24-29.

[5] 李晓钟,张小蒂. 江浙区域技术创新效率比较分析 [J]. 中国工业经济,2005,7(7): 57-64.

[6] 曹杨,王雪青. 基于耗散结构理论的资源型城市创新能力评价 [J]. 东北大学学报(社会科学版),2011,13(2): 124-127.

[7] 邵云飞,周敏,唐小我. 基于中心城市创新辐射的西部自主创新效率研究 [J]. 管理学报,2010,7(12): 1825-1830.

[8] 池仁勇,虞晓芬,李正卫. 我国东西部地区技术创新效率差异及其动因分析 [J]. 中国软科学,2004, 8: 45-52.

[9] 代明,张晓鹏. 基于DEA的中国创新型城市创新绩效分析 [J]. 科技管理研究,2011,6: 6-8.

[10] Charnes A, Cooper W W, Rhodes E. Measuring the efficiency of decision making units [J]. European Journal of Operational Research, 1978, 2(6): 429-444.

[11] Sarrico C S, Dyson R G. Using DEA for planning in UK university—An institutional perspective [J]. Journal of Operational Research Society, 2000, 51(7): 789-800.

[12] Sherman H D. Hospital efficiency measurement and evaluation: Empirical test of a new technique [J]. Medical Care, 1984, 22(10): 922-938.

[13] O'Neill L. Multifactor efficiency in data envelopment analysis with an application to urban hospitals [J]. Health Care Management Science, 1998, 1(1): 19-27.

[14] Harris J M, Ozgen H, Ozcan Y A. Do mergers enhance the performance of hospital efficiency [J]? Journal of the Operational Research Society, 2000, 51(7): 801-811.

[15] Lozano S, Villa G, Guerrero F, et al. Measuring the performance of nations at the Summer Olympics using data envelopment analysis [J]. Journal of the Operational Research Society, 2002, 53(5): 501-511.

[16] Churilov L, Flitaman A. Towards fair ranking of Olympics achievements: The case of Sydney 2000 [J]. Computers and Operations Research, 2006, 33(7): 2057-2982.

[17] Seiford L M, Zhu J. Profitability and marketability of the top 55 US commercial banks [J]. Management Science, 1999, 45(9): 1270-1288.

[18] Kao C, Hwang S 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.

[19] Wang C H, Gopal R, Zionts S. Use of data envelopment analysis in assessing information technology impact on firm performance [J]. Annals of Operations Research, 1997, 73: 191-213.

[20] Chen Yao, Zhu J. Measuring information technology's indirect impact on firm performance [J]. Information Technology & Management Journal, 2004, 5(1-2): 9-22.

[21] Botti L, Briec W, Cliquet G. Plural forms versus franchise and company-owned systems: A DEA approach of hotel chain performance [J]. Omega, 2009, 37(3): 566-578.

[22] Zhu J. Multi-factor performance measure model with an application to Fortune 500 companies [J]. European Journal of Operational Research, 2000, 123(1): 105-124.

[23] Cook W D, Hababou M. Sales performance measurement in bank branches [J]. Omega, 2001, 29(4): 299-307.

[24] Cook W D, Hababou M, Tuenter H. Multicomponent efficiency measurement and shared inputs in data envelopment analysis: An application to sales and service performance in bank branches [J]. Journal of Productivity Analysis, 2000, 14(3): 209-224.

[25] Sexton T R, Lewis H F. Two-stage DEA: An application to major league baseball [J]. Journal of Productivity Analysis, 2003, 19(2-3): 227-249.

[26] Chilingerian J, Sherman H D. Health care applications: From hospitals to physician, from productive efficiency to quality frontiers[M]//Cooper W W, Seiford L M, Zhu J. Handbook on data envelopment analysis. Boston: Springer, 2004.

[27] Liang L, Cook W D, Zhu J. DEA models for two-stage processes: Game approach and efficiency decomposition [J]. Naval Research Logistics, 2008, 55: 643-653.

[28] Chen Yao, Du Juan, Sherman H D, et al. DEA model with shared resources and efficiency decomposition [J]. European Journal of Operational Research, 2010, 207(1): 339-349.

[29] Banker R D, Charnes A, Cooper W W. Some models for estimating technical and scale inefficiencies in data envelopment analysis [J]. Management Science, 1984, 30(9): 1078-1092.

[30] Chen Yao, Cook W D, Li Ning, et al. Additive efficiency decomposition in two-stage DEA [J]. European Journal of Operational Research, 2009, 196(3): 1170-1176.

[31] Charnes A, Cooper W W. Programming with linear fractional functionals [J]. Naval Research Logistics Quarterly, 1962, 9(3-4):181-186.