创新活动的动态绩效测度指数研究-基于高校科学创新活动的建模与应用

科研管理 ›› 2012, Vol. 33 ›› Issue (1): 103-111.

创新活动的动态绩效测度指数研究-基于高校科学创新活动的建模与应用

陈凯华¹, 官建成^2,3

1. 中国科学院科技政策与管理科学研究所, 北京 100190;
2. 中国科学院研究生院管理学院, 北京 100180;
3. 复旦大学管理学院, 上海 200433

收稿日期:2010-03-18 修回日期:2011-03-25 出版日期:2012-01-27 发布日期:2012-02-03
作者简介:陈凯华(1980- ),男(汉),山东郓城人,中国科学院科技政策与管理科学研究所助理研究员,博士后,研究方向:科技政策与发展战略,创新测度与管理,管理科学方法与应用。官建成(1955- ),男(汉),江西余江人,中国科学院研究生院管理学院教授,博士生导师,研究方向:科学计量学、研发管理与技术创新管理。
基金资助:
国家自然科学基金青年资助项目(71103173),中国博士后科学基金面上资助项目(20100480479),中国博士后科学基金第四批特别资助项目(201104158);中国科学院王宽诚博士后工作奖励项目。

The measurement index for the dynamic performance of innovation activities:Modeling and applications based on scientific innovations in Chinese universities of science and engineering

Chen Kaihua¹, Guan Jiancheng^2,3

1. Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100090, China;
2. School of Management, Graduate University of Chinese Academy Sciences, Beijing 100080, China

Received:2010-03-18 Revised:2011-03-25 Online:2012-01-27 Published:2012-02-03

摘要/Abstract

摘要： 本文借助"曼奎斯特指数"理论,在考虑规模效应的影响下,基于非定向且非径向的"增强型罗素测量"模型,从绩效的动态性角度构建了纯技术效率变化、生产技术变化、规模效率变化、组织管理绩效变化以及综合效率变化等五个绩效指数,以从多角度来考查中国重点理工科高校科学创新的动态绩效水平,即绩效变动的程度与趋势。以12所具有代表性的"985"工程重点建设的理工科高校为研究对象,基于它们在两个考查期(2002-2003年与2004-2005年)内的科学创新投入产出数据展开实证分析。比较前后两期的测度结果发现,各理工高校的五个绩效指数的变动趋势几乎都发生了质的变化,从"衰退(状态值<1)"变为"增长(状态值>1)";生产技术变化与综合效率变化之间存在稳定的显著相关关系;纯技术效率变化、规模效率变化和组织管理绩效变化等软性动态绩效指数与综合绩效变化之间相关关系增强。

关键词: 创新活动, 动态绩效, 理工科高校, 数据包络分析, 增强型罗素测量, 曼奎斯特指数

Abstract: Based on the theory of Malmquist index associated with Enhanced Russell Measure (ERM), five dynamic performance indexes, that is, pure technical efficiency change, technology change, scale efficiency change, organization and management performance change as well as comprehensive efficiency change are formulated to measure the dynamic performance (related to the trend and degree of performance change) of the scientific innovations in the Chinaese key universities of science and engineering. The empirical analysis is implemented based on the inputs and outputs in scientific activities of 12 representative "985" universities over the two comparable observed periods, i.e.,2002-2003 and 2004-2005. The comparisons between the statistical results over two periods show that five performance indexes are in a transition, which change from decline (<1) into growth (>1); there is a robust and significant correlation between the technology change and the comprehensive efficiency change; those soft performance indexes (pure technical efficiency change, scale effects change, organization and management performance change) are increasingly related to the comprehensive efficiency change.

Key words: innovation activity, dynamic performance, university of science and engineering, DEA, ERM, Malmquist index

中图分类号:

陈凯华, 官建成. 创新活动的动态绩效测度指数研究-基于高校科学创新活动的建模与应用[J]. 科研管理, 2012, 33(1): 103-111.

Chen Kaihua, Guan Jiancheng. The measurement index for the dynamic performance of innovation activities:Modeling and applications based on scientific innovations in Chinese universities of science and engineering[J]. Science Research Management, 2012, 33(1): 103-111.

参考文献

[1] Moed HF, Gl nzel W, Schmoch U. The Use of Publication and Patent Statistics in Studies of S&T Systems[M]. New York:Kluwer academic publishers, 2004:1. [2] 周静,王立杰,石晓军.我国不同地区高校科技创新的制度效率与规模效率研究[J].研究与发展管理,2005,17(1):109-117. [3] Ng YC, Li SK. Measuring the Research Performance of Chinese Higher Education Institutions:An Application of Data Envelopment Analysis[J]. Education Economics, 2000, 8(2):139-156. [4] 陆根书,刘蕾.教育部直属高校自然科学研究效率及发展趋势研究[J].高等工程教育研究, 2006(1):12-16. [5] Johnes J, Li Y. Measuring the research performance of Chinese higher education institutions using data envelopment analysis[J]. China Economic Review, 2008, 19(4):679-696. [6] 陈凯华,官建成. 重点学科建设效率的科学评估[J]. 学位与研究生教育,2008(5):59-64. [7] Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units[J]. European Journal of Operational Research, 1978, 2(6):429-444. [8] Banker RD. Estimating most productive scale size using data envelopment analysis [J]. European Journal of Operational Research, 1984,17(1):35-44. [9] Cooper WW, Huang ZM, Li SX, Parker BR, Pastor JT. Efficiency aggregation with enhanced Russell measures in data envelopment analysis[J]. Socio-Economic Planning Sciences, 2007, 41(1):1-21. [10] Aigner DJ, Lovell CAK, Schmidt P. Formulation and Estimation of Stochastic Frontier Models[J]. Journal of Econometrics, 1977, 6(1):21-37. [11] Pastor JT, Ruiz JL, Sirvent I. An enhanced DEA Russell graph efficiency measure[J]. European Journal of Operational Research, 1999, 115(3):596-607. [12] Chambers RG, Chung Y, F re R. Benefit and Distance Functions[J]. Journal of Economic Theory, 1996,70(2):407-419. [13] 官建成,陈凯华. 我国高技术产业技术创新效率的测度[J].数量经济技术经济研究,2009,26(10):19-33. [14] Andersen P, Petersen, NC. A Procedure for Ranking Efficient Units in Data Envelopment Analysis[J]. Management Science, 1993,39(10), 1261-1264. [15] Tone K. Malmquist production index, efficiency change over time. In:Cooper WW, Seiford LM, Zhu J. (eds.), Handbook on Data Envelopment Analysis. Dordrecht:Kluwer Academic Publishers, 2004:203-227. [16] Nishimizu M, Page JM. Total Factor Productivity Growth, Technological Progress and Technical Efficiency Change[J]. Economic Journal, 1982, 92(368):920-936. [17] Ray SC, Desli E. Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries:Comment[J]. American Economic Review, 1997, 87(5):1033-1039. [18] Caves DW, Christensen LR, Diewert WE. Multilateral comparisons of output, input and productivity using superlative index numbers[J]. Economic Journal, 1982, 92(365):73-86. [19] Byrnes P, Fare R, Grosskopf S. Measuring Productive Efficiency:An Application to Illinois Strip Mines[J]. Management Science, 1984, 30(6):671-682. [20] Fare R,Grosskopf S,Norris M,Zhang ZY. Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries[J]. American Economic Review, 1994, 84(1):66-83. [21] Ggifell-tatjé E, Lovell CAK. A DEA-based analysis of productivity change and intertemporal managerial performance [J]. Annals of Operations Research, 1997, 73(1):177-189. [22] Chen Y, Ali AI. DEA Malmquist productivity measure:New insights with an application to computer industry[J]. European Journal of Operational Research, 2004, 159(1):239-249. [23] Liu FHF, Wang PH. DEA Malmquist productivity measure:Taiwanese semiconductor companies [J]. International Journal of Production Economics, 2008, 112(1):367-379. [24] Cooper WW, Seiford LM, Zhu J. Slacks and congestion:response to a comment by R. F re and S. Grosskopf[J]. Socio-Economic Planning Sciences, 2001, 35(3):205-215. [25] Guan JC, Chen KH. Modeling macro-R&D production frontier performance:an application to Chinese province-level R&D[J]. Scientometrics, 2010, 82(1):165-173. [26] Lovell CAK. The Decomposition of Malmquist Productivity Indexes[J]. Journal of Productivity Analysis, 2003, 20(3):437-458. [27] Guan JC, Chen KH. Measuring the innovation production process:A cross-region empirical study of China’s high-tech innovations [J]. Technovation,2010,30(1):348-358. [28] Simar L, Wilson PW. Sensitivity analysis of efficiency scores:How to bootstrap in nonparametric frontier models[J]. Management Science, 1998, 44(2), 49-61.