高技术产业技术创新效率评价的改进DEA方法研究
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摘要
随着高技术产业的发展和技术创新投入产出的日益增长,技术创新成为决定高技术产业长期持续发展,影响区域经济增长和区域竞争力的关键因素。由于创新资源的投入规模往往受到区域经济发展水平、区域知识基础等客观条件的制约,因此,在既定的创新资源投入规模下,提高技术创新的投入产出效率成为提高创新产出的重要途径。而在提高技术创新效率的过程中,首先要解决的是如何评价高技术产业的技术创新效率问题。
传统生产过程一般满足边际收益递减的规律,关于该规律在知识经济中是否同样适用,很多学者有不同的意见。已有学者从定性的角度讨论了知识和收益递增的关系以及知识经济中出现的边际收益递增现象。高技术产业技术创新具有明显的知识经济特点,因此,需要重新思考边际收益递减规律在高技术产业技术创新中是否具有普遍意义。DEA模型是效率评价的重要工具,该模型中生产可能集的凸性假设隐含边际收益递减或不变的前提,一旦所考察的对象出现边际收益递增的现象,所对应的生产可能集不满足凸性的要求,模型所确定的前沿面和实际不符,必然导致评价结果缺乏合理性,甚至隐蔽性地存在着严重的不合理评价。因此,有必要从定量的角度来研究边际收益特性的识别方法,探索边际收益递增特性下效率评价的新方法。
本文针对上述问题展开研究,主要有以下内容:
在明确界定相关核心概念的基础上,从投入产出的角度把高技术产业技术创新划分为技术开发阶段和技术转化阶段,相应地,把技术创新效率分解成技术开发效率和技术转化效率,并通过分析每个阶段的投入要素和产出成果,分阶段构建投入产出指标体系。
针对DEA模型应用的局限性,提出创新可能集及相关概念,并在此基础上构造边际收益特性的识别方法和高技术产业技术创新效率的评价方法。创新可能集不受凸性假设约束,可以描述不同边际收益特性下的投入产出活动。创新前沿面反映创新活动的实际前沿,创新前沿面上投影点的产出能作为相应单元能达到的理想产出,因此,新的评价方法在边际收益递减、不变或者递增的特性下都能获得合理的评价结果。此外,通过对二阶段序列DEA模型的扩展,新的效率评价法不仅考虑每个阶段的边际收益特性,并且考虑系统内部各子系统效率及其对系统整体效率的影响程度。为了剔除区域禀赋对高技术产业技术创新的影响,通过借鉴Ruggiero的三阶段模型,本文还提出了考虑禀赋差异的效率评价方法。
最后,从实证的角度分析高技术产业技术开发活动和技术转化活动的边际收益特性,并对我国各个地区的技术开发效率、技术转化效率和总体效率进行评价。
研究表明,我国高技术产业在技术开发阶段出现边际收益递增的现象,新的评价方法突破了传统DEA模型在测算技术创新效率方面的局限性,提高了评价的合理性和科学性。因此,本文的研究为评价高技术产业技术创新效率提供了新的思路和方法,为提升我国各地区高技术产业技术创新效率、增强高技术产业竞争力提供理论参考和借鉴依据,具有一定的理论意义和现实意义。
With the development of high-tech industry and the increasing inputs and outputs in the process of technological innovation, technological innovation becomes the key factors to determine the long-term sustained development of high-tech industry and influence regional economic growth and regional competitiveness. As the scale of innovation resources is often restricted to the objective conditions such as level of regional economic development, base of regional knowledge and so on, therefore, an important approach to increase innovation outputs is to improve the input-output efficiency of technological innovation in the condition of available input scale of innovative resources. First of all, we should to solve the problem that how to evaluate the technological innovation efficiency in the high-tech industry in the process of improving technological innovation efficiency.
Generally speaking, traditional production process meets the law of decreasing marginal returns while many scholars have different views on whether or not it still works in the knowledge-based economy. Scholars have discussed the relationship between knowledge and return increasing and mentioned the phenomenon of increasing marginal returns in the knowledge-based economy from a qualitative point. The technological innovation in high-tech industry has obvious characteristics of knowledge-based economy; therefore, it’s necessary to reconsider if the law of decreasing marginal returns works universally in the technological innovation in high-tech industry innovation of. The DEA model is an important instrument to evaluate the efficiency, in which the convexity of feasible production sets implies a precondition of decreasing or unchanging marginal returns. If the phenomenon of increasing marginal returns exists for the research objects, the feasible production set doesn’t meet the convexity assumption so that the frontier decided by the DEA model will not accord the facts, which will lead to the unjust results and even result in a serious evaluation of the irrational. Therefore, it’s necessary to do some research on how to identify the marginal return characteristics from a quantitative point and explore a new method of efficiency evaluation in the condition of decreasing marginal returns.
In this paper, we start the research according to above problems as following:
After clearly defining the relative key conceptions, we decompose the high-tech innovation process into technological development process and technological transformation process from the input-output perspective. On this basis, technological innovation efficiency can be decomposed into technological development efficiency and technological transformation efficiency. We also analyze the input factors and out factors and design the input-output index systems in each stage respectively.
According to the limitation in DEA application, we present the interrelated conceptions of feasible innovation sets. Base on these conceptions, we present the methods to identify the marginal return characteristics and to evaluate the technological innovation efficiency in high-tech industry. The feasible innovation sets has no restriction of convexity so that it describe the input-output activities in conditions of various characteristics of marginal returns. The innovation frontier reflects the actual frontier of innovation activities and the outputs of the projections can respect the optimum outputs the units can achieve so that the new evaluation methods can apply not only to condition of decreasing or unchanging marginal returns but also to condition of increasing marginal returns. In addition, through the expansion of two-stage sequence DEA model, the new evaluation method can not only consider the marginal return characteristics in each stage but also consider the impact of the internal structure in technological innovation in the whole process. In order to eliminate the influence of regional gifts on the technological innovation in the high-tech industry, we develop a new new model to evaluate the efficiency based on the three-steps model given by Ruggiero.
Finally, we analyze the characteristic of marginal returns in each stage and also acquire technological development efficiency, technological transformation efficiency and overall efficiency for each region in each year in the empirical research.
The research shows that marginal returns are constant in smaller scale and marginal returns are incremental in larger scale at technological development stage and there no characteristics of increasing marginal returns at technological transformation stage. The new evaluation method conquer the shortage of traditional DEA model evaluating technological innovation efficiency so that the results is more rationale and scientific. Therefore, the research we have done is meaningful in theory and practice, which provide a new idea and method about the issues of efficiency evaluation of technological innovation in high-tech industry and provide some theoretical and practical references to improve the technological innovation efficiency and increase the competitiveness in high-tech industry.
传统生产过程一般满足边际收益递减的规律,关于该规律在知识经济中是否同样适用,很多学者有不同的意见。已有学者从定性的角度讨论了知识和收益递增的关系以及知识经济中出现的边际收益递增现象。高技术产业技术创新具有明显的知识经济特点,因此,需要重新思考边际收益递减规律在高技术产业技术创新中是否具有普遍意义。DEA模型是效率评价的重要工具,该模型中生产可能集的凸性假设隐含边际收益递减或不变的前提,一旦所考察的对象出现边际收益递增的现象,所对应的生产可能集不满足凸性的要求,模型所确定的前沿面和实际不符,必然导致评价结果缺乏合理性,甚至隐蔽性地存在着严重的不合理评价。因此,有必要从定量的角度来研究边际收益特性的识别方法,探索边际收益递增特性下效率评价的新方法。
本文针对上述问题展开研究,主要有以下内容:
在明确界定相关核心概念的基础上,从投入产出的角度把高技术产业技术创新划分为技术开发阶段和技术转化阶段,相应地,把技术创新效率分解成技术开发效率和技术转化效率,并通过分析每个阶段的投入要素和产出成果,分阶段构建投入产出指标体系。
针对DEA模型应用的局限性,提出创新可能集及相关概念,并在此基础上构造边际收益特性的识别方法和高技术产业技术创新效率的评价方法。创新可能集不受凸性假设约束,可以描述不同边际收益特性下的投入产出活动。创新前沿面反映创新活动的实际前沿,创新前沿面上投影点的产出能作为相应单元能达到的理想产出,因此,新的评价方法在边际收益递减、不变或者递增的特性下都能获得合理的评价结果。此外,通过对二阶段序列DEA模型的扩展,新的效率评价法不仅考虑每个阶段的边际收益特性,并且考虑系统内部各子系统效率及其对系统整体效率的影响程度。为了剔除区域禀赋对高技术产业技术创新的影响,通过借鉴Ruggiero的三阶段模型,本文还提出了考虑禀赋差异的效率评价方法。
最后,从实证的角度分析高技术产业技术开发活动和技术转化活动的边际收益特性,并对我国各个地区的技术开发效率、技术转化效率和总体效率进行评价。
研究表明,我国高技术产业在技术开发阶段出现边际收益递增的现象,新的评价方法突破了传统DEA模型在测算技术创新效率方面的局限性,提高了评价的合理性和科学性。因此,本文的研究为评价高技术产业技术创新效率提供了新的思路和方法,为提升我国各地区高技术产业技术创新效率、增强高技术产业竞争力提供理论参考和借鉴依据,具有一定的理论意义和现实意义。
With the development of high-tech industry and the increasing inputs and outputs in the process of technological innovation, technological innovation becomes the key factors to determine the long-term sustained development of high-tech industry and influence regional economic growth and regional competitiveness. As the scale of innovation resources is often restricted to the objective conditions such as level of regional economic development, base of regional knowledge and so on, therefore, an important approach to increase innovation outputs is to improve the input-output efficiency of technological innovation in the condition of available input scale of innovative resources. First of all, we should to solve the problem that how to evaluate the technological innovation efficiency in the high-tech industry in the process of improving technological innovation efficiency.
Generally speaking, traditional production process meets the law of decreasing marginal returns while many scholars have different views on whether or not it still works in the knowledge-based economy. Scholars have discussed the relationship between knowledge and return increasing and mentioned the phenomenon of increasing marginal returns in the knowledge-based economy from a qualitative point. The technological innovation in high-tech industry has obvious characteristics of knowledge-based economy; therefore, it’s necessary to reconsider if the law of decreasing marginal returns works universally in the technological innovation in high-tech industry innovation of. The DEA model is an important instrument to evaluate the efficiency, in which the convexity of feasible production sets implies a precondition of decreasing or unchanging marginal returns. If the phenomenon of increasing marginal returns exists for the research objects, the feasible production set doesn’t meet the convexity assumption so that the frontier decided by the DEA model will not accord the facts, which will lead to the unjust results and even result in a serious evaluation of the irrational. Therefore, it’s necessary to do some research on how to identify the marginal return characteristics from a quantitative point and explore a new method of efficiency evaluation in the condition of decreasing marginal returns.
In this paper, we start the research according to above problems as following:
After clearly defining the relative key conceptions, we decompose the high-tech innovation process into technological development process and technological transformation process from the input-output perspective. On this basis, technological innovation efficiency can be decomposed into technological development efficiency and technological transformation efficiency. We also analyze the input factors and out factors and design the input-output index systems in each stage respectively.
According to the limitation in DEA application, we present the interrelated conceptions of feasible innovation sets. Base on these conceptions, we present the methods to identify the marginal return characteristics and to evaluate the technological innovation efficiency in high-tech industry. The feasible innovation sets has no restriction of convexity so that it describe the input-output activities in conditions of various characteristics of marginal returns. The innovation frontier reflects the actual frontier of innovation activities and the outputs of the projections can respect the optimum outputs the units can achieve so that the new evaluation methods can apply not only to condition of decreasing or unchanging marginal returns but also to condition of increasing marginal returns. In addition, through the expansion of two-stage sequence DEA model, the new evaluation method can not only consider the marginal return characteristics in each stage but also consider the impact of the internal structure in technological innovation in the whole process. In order to eliminate the influence of regional gifts on the technological innovation in the high-tech industry, we develop a new new model to evaluate the efficiency based on the three-steps model given by Ruggiero.
Finally, we analyze the characteristic of marginal returns in each stage and also acquire technological development efficiency, technological transformation efficiency and overall efficiency for each region in each year in the empirical research.
The research shows that marginal returns are constant in smaller scale and marginal returns are incremental in larger scale at technological development stage and there no characteristics of increasing marginal returns at technological transformation stage. The new evaluation method conquer the shortage of traditional DEA model evaluating technological innovation efficiency so that the results is more rationale and scientific. Therefore, the research we have done is meaningful in theory and practice, which provide a new idea and method about the issues of efficiency evaluation of technological innovation in high-tech industry and provide some theoretical and practical references to improve the technological innovation efficiency and increase the competitiveness in high-tech industry.
引文
1徐冬青.江苏高新技术产业发展的若干问题.市场周刊. 2004, 9: 13~15
2范柏乃.高新技术产业发展法律环境研究.上海财经出版社.2000: 3~13
3盐泽由典.复杂系统经济学入门.生产性出版社. 1997: 319
4亚当·斯密.国富论(上).商务印书馆. 1972: 5
5庞巴维克.资本实证论.商务印刷馆中译本. 1980: 80
6 G. Becker, K. Murphy. The Deviation of Labor, Coordination Costs, and Knowledge. Quarterly Journal of Economics. 1992,CVII(4): 1137~1160
7 K. J. Arrow. The Economic Implications of Learning by Doing. Review of Economic Studies. 1962,29(3): 155~173
8 P. M. Romer. Increasing Returns and Long-run Growth. Journal of Political Economy. 1986,94(5):1002~1037
9 W. B. Arthur. Competing Technologies, Increasing Returns, and Lock-in by Historical Events. Economic Journal. 1989,99(3): 116~131
10汪丁丁.知识沿时间和空间的互补性以及相关的经济学.经济研究. 1997,6: 70~77
11纪玉山.江中蛟.知识经济与边际收益递增.经济评论. 2000, 4: 16~19
12谭清美,李宗植.新经济和传统经济比较研究.科学管理研究. 2002,
20(4): 37~38
13蒋景媛.知识的特征及将其纳入生产函数的方法.财经科学. 2005, 2: 41~47
14张孟才,李雪峰.网络经济中边际收益递增的不确定性.产业经济. 2007, 9: 35~36
15郭广珍.迟爱敏.知识经济与经济增长理论.山东矿业学院学报社会科学版. 1999,1(2):115~119
16 Z. Griliches. Patent Statistics as Economic Indicators: A Survey. Journal of Economic Literature. 1990, 28(4): 1661~1707
17 A. Prencipe. Technological Competencies and Product’s Evolutionary Dynamics: a Case Study from the Aero-Engine Industry. Research Policy.1977, 25(8): 1261~1276
18 J. Schmookler. Invention and Economic Growth. Cambridge: HarvardUniversity Press. 1966: 17
19 F. M. Scherer. Firm Size, Market Size, Opportunity and Output of Patented Inventions. American Economic Review. 1965, 55(5): 1097~1125
20 K. Pavvitt. The Objectives of Technology Policy. Science and Public Policy. 1987, 14(4): 182~188
21 P. Patel, K. Pavitt. The Nature and Importance of National Innovation Systems. STI Review. 1994, 14: 9~32
22 Z. J. Acs, D. B. Audretsch, M. P. Feldman. R&D Spillover and Recipient Firm Size. Review of Economic and Statistics. 1994, 76(2): 336~340
23 A. Arundel. The Relative Effectiveness of Patents and Secrecy for Appropriation. Research Policy. 2001, 30:611~624
24 J. L. Furman, M. E. Porter, S. Stern. The Determinants of National Innovative Capacity. Research Policy. 2002, 31(6): 899~933
25 A. Arundel, I. Kabla. What Percentage of Innovations is Patented? Empirical Estimates for European Firms. Research Policy. 1998, 27(2): 127~141
26 Z. J. Acs, L. Anselin Luc, A. Varga. Patents and Innovation Counts as Measures of Regional Production of New Knowledge. Research Policy. 2002, 31(7): 1069~1085
27 M. Crosby. Patents: Innovation and Growth. Economic Record. 2000, 76(3): 255~262
28 A. B. Jaffe. The U.S. Patent System in Transition: Policy Innovation and the Innovation Process. Research Policy. 2000, 29(4-5): 531~557
29 R. Mazzoleni, R. R. Nelson. The Benefits and Costs of Strong Patent Protection: A Contribution to the Current Debate. Research Policy. 1998, 27(3): 273~284
30 J. Cantwell, F. Fai. Firms as the Source of Innovation and Growth: the Evolution of Technological Competence. Journal of Evolutionary Economics. 1999, 9(3):331~366
31 D. Guellec, B. van Pottelsberghe de la Potterie. The Internationalisation of Technology Analyzed with Patent Data. Research Policy. 2001, 30(8): 1253~1266
32 B. P. Abraham, S. D. Moitra. Innovation Assessment through Patent Analysis. Technovation. 2001, 21(4): 245~252
33 M. Waterson. The Economics of Product Patents. The American Economic Review. 1990, 80(4): 860~869
34 I. P. Mahmood, J. Singh. Technological Dynamism in Asia. Research Policy. 2003, 32(6): 1031~1054
35连燕华,徐颖,郑奕荣.国家技术创新投入产出的宏观计量方法研究.研究与发展管理. 2005, 12 : 84~89
36赵捷,柳卸林.从专利申请看我国技术创新之短.瞭望新闻周刊. 1999, 4: 7
37官建成,刘顺忠.区域创新机构对创新绩效影响的研究.科学学研究. 2003, 2: 210~214
38唐福国,陈光.我国技术创新能力的地区差异分析:框架、指标与评价.研究与发展管理. 2001, 13(5): 32~36
39邵运飞,唐小我,陈光.中国区域技术创新能力的主成分实证研究.管理工程学报. 2005, 19(3): 71~76
40刘耀彬,王启仿,宋字峰.转型时期中国区域技术创新能力评价与分析.科技进步与对策2004, 9: 13~15
41时鹏将,许晓雯,蔡虹. R&D投入产出效率的DEA分析.科学学与科学技术管理. 2004, 1: 28~30
42古利平,张宗益,康继军.专利与R&D资源:中国创新投入产出分析.管理工程学报2006, 5: 113~118
43叶采飞,郑明贵.有色金属行业科技创新投入与产出评价指标体系研究.江西有色金属. 2004, 6: 1~6
44中国科技发展战略研究小组. 2002中国区域创新能力报告.经济管理出版社. 2003: 2~33
45陈劲,景劲松,周笑磊.我国企业R&D国际化的影响因素分析.科学学研究. 2003, 1: 51~57
46赵喜仓,陈海波.我国R&D状况的区域比较分析.统计研究. 2003, 3: 38~42
47 R. Fare, S. Grosskopf, M. Norris, Z. Zhang. Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries. American Economic Review. 1994, 84(1): 66~83
48王丽,魏煜.企业效率研究方法比较.预测. 1999, 5: 76~79
49韩松,王稳.几种技术效率测量方法的比较研究.中国软科学. 2004, 4:147~151
50谢洪军,任玉珑.技术效率研究中的前沿分析方法及其比较.科技管理研究. 2006(8): 237~239
51 A. Charnes, W.W. Cooper, B.Golany, L. Seiford. Foundation of Data Envelopment Analysis for Pareto-Koopmans Efficient Empirical Production Functions. Journal of Econometrics (Netherlands). 1985, 30(1-2): 91~107
52 C. Kao. A Linear Formulation of the Two-level DEA Model. Omega. 2008, 36(6): 958~962
53 A. Johnson, L. Mcginnis. Outlier Detection in Two-stage Semi Parametric DEA Models. European Journal of Operational Research. 2008,187(2): 629~635
54 G. R. Jahanshahloo, M. Soleimani-damaneh, A. Mostafaee. A Simplified Version of the DEA Cost Eifficiency Model. European Journal of Operational Research.2008,184(2): 814~815
55 M. R. Alirezaee and, M. Afsharian. A complete ranking of DMUs Using Restrictions in DEA models. Applied Mathematics and Computation. 2007,189(2): 1550~1559
56 A. H. Vencheh, A. A. Foroughi. A Generalized DEA Model for Inputs / Outputs Estimation. Mathematical and Computer Modelling. 2006, 43(5-6):447~457
57王晓红,王雪峰,翟爱梅等.具有边际收益递增特性的DEA模型求解方法.哈尔滨工业大学学报. 2004, 36(10): 1297~1300
58王晓红,王雪峰,翟爱梅等.具有边际收益递增特性的数据包络分析模型.上海交通大学学报. 2005, 39(3): 484~487
59 R. Fare, S. Grosskopf. Productivity and Intermediate Products: A Frontier Approach. Economics Letters. 1996, 50(1): 65~70
60 R. Fare, S. Grosskopf. Network DEA. Socio-Economic Planning Sciences, 2000, 34(1): 35~49
61 Y. Chen, L. Liang, F. Yang, J. Zhu. Evaluation of Information Technology Investment: A Data Envelopment Analysis Approach. Computers & Operations Research. 2006, 33(5): 1368~1379
62 C. H. Wang, R. Gopal, S. Zionts. Use of Data Envelopment Analysis in Assessing Information Technology Impact on Firm Performance. Annals ofOperations Research. 1997, 73(10): 1991~213
63 Y. Chen, J. Zhu. Measuring Information Technology's Indirect Impact on Firm Performance. Information Technology and Management. 2004, 5(1-2): 9~22
64 H. F. Lewis, T. R. Sexton. Network DEA: Efficiency Analysis of Organizations with Complex Internal Structure. Computers & Operations Research. 2004, 31(9):1365~1410
65 T. R. Sexton, H. F. Lewis. Two-stage DEA: An Application to Major League Baseball. Journal of Productivity Analysis. 2003, 19 (2-3): 227~49
66 G. E. Battese, T. J. Coelli. Prediction of Firmed-level Technical Efficiencies with a Generalized Frontier Production Function and Panel Data. Journal of Econometrics. 1988,38(3):387~399
67 R. E. Stevenson. Likelihood Functions for Generalized Stochastic Frontier Estimation. Journal of Econometrices. 1980,13(1):57~66
68 W. H. Greene. A Gamma-distributed Stochastic Frontier Model. Journal of Econometrics. 1990, 46(1-2):141~164
69 C. Ritter, L. Simar. Pitfall of Normal-Gamma Stochastic Frontier Models. Journal of Productivity Analysis. 1997, 82(5):167~182
70 M. M. Pitt, L. F. Lee. Measurement and Sources of Technical Inefficiency in the Indonesian Weaving Industry. Journal of Development Economics. 1981,9(1):43~64
71 P. Schmidt, R. C. Sickes. Production Frontiers and Panel Data. Journal of Business and Economic Statistics. 1984, 2(4):367~374
72 C. Cornwell, P. Schmidt, R. C. Sickes. Production Frontiers with Cross-sectional and Time-series Variation in Efficiency Levels. Journal of Econometrics. 1990, 46(1-2):185~200
73 C. A. K. Lovell, S. S. Schmidt. The Measurement of Productive Efficiency: Techniques and Application. Oxford University Press, New York, 1993:237~255
74 S. C. Kumbhakar. Production Frontiers, Panel Data and Time-Varying Technical Inefficiency. Journal of Econometrics. 1990,46(1-2):201~211
75 G. E. Battese, T. J. Coelli. Frontier Production Functions, Technical Efficiency and Panel Data: With Application of Paddy Farmers in India.Journal of Productivity Analysis. 1992, 3(1-2):153~169
76 I. Herrero. Different Approaches to Efficiency: An Application to the Spanish Trawl Fleet Operating in Moroccan Waters. European Journal of Operational Research. 2005, 167(1):257~271
77 C. Fernandez, G. Koop, M. Steel. A Bayesian Analysis of Mutiple-output Production Frontier. Journal of Econometrics. 2000, 98(1):47~79
78 T. J. Coelli. Estimators and Hypothesis Tests for Stochastic Frontier Function: A Monte Carlo Analysis. Journal of Productivity Analysis. 1995,6(3):247~268
79 T. Sueyoshi. Stochastic Frontier Production Analysis: Measuring Performance of Public Telecommunication in 24 OECD Countries. European Journal of Operational Research. 1997, 74(3):466~478
80 J. P. Florens, L. Smiar. Parametric Approximation of Nonparametric Frontier. Journal of Econometrics. 2005, 124(1):91~116
81 P. Cooke, M. G. Uranga, G. Etxebarria. Regional Systems of Innovation: an Evolutionary Perspective. Environment and Planning. 1998, 30: 1563~1584
82 P. N. Cooke, H. J. Braczyk.H.J, M. Heidenreich. Regional Innovation Systems: The Role of Governances in the Globalized World. London. UCL Press. 1996, 23~86
83 H. Wiig, M. Wood. What Comprises a Regional Innovation System? An Empirical Study. 1999. http://web.sol.no/step/195/195.htm
84 B. T. Asheim, P. Cooke. Local Learning and Interactive Innovation Networks in a Global Economy. Ashgate Publishers. 1999: 3~15
85 B. A. Lundvall. National System of Innovation: Towards a Theory of Innovation and Interactive Learning.London. Printer Publisher. 1992: 46~61
86 B. L. Lundvall, B. Johnson, E. S. Andersen, B. Dalum. National Systems of Production, Innovation and Competence Building. Research Policy. 2002, 31 (2): 213~231
87 K. Pavitt, P. Patel. Corporate Technology Strategies and National Systems of Innovation. STI Review.1995: 78~91
88 OECD. National Innovation System. Pairs . 1997: 1~48
89 M. J. Enright. Regional Clusters: What We Know and What We Should Know. Innovation Clusters and Interregional Competition. 2003, 3: 99~129
90 M. P. Feldman. The Geography of Innovation. Boston: Kluwer Academic Publishers.1998: 113~151
91 M. E. Porter. Clusters and the New Economics of Competition. Harvard Business Review. 1998,76(6): 77~90
92 M. Fritsch. Interregional Differences in R&D Activities - an Empirical Investigation. European Planning Studies. 2002,8(4): 409~427
93 Z. J. Acs, D. B. Audretsch, M. P. Feldman. The Real Effects of Academic Research. American Economic Review. 1982,1: 363~367
94 L. Anselin, A. Varga, Z. J. Acs. Local Geographic Spillovers between University Research and High technology Innovation. Journal of Urban Economic. 1997, 42(3): 422~448
95 A. B. Jaffe, M. Trajtenberg, R. Henderson. Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations. Quarterly Journal of Economics. 1993, 108(3): 576~598
96 M. E. Porter. Clusters and the New Economics of Competition. Harvard University Press. 1998: 18~62
97 R. Baptista, G. M. P. Swann. A Comparison of Clustering Dynamics in the US and UK Computer Industries. Journal of Evolutionary Economics. 1999,9(3): 373~399
98 P. Almeida, B. Kogut. The Exploration of Technological Diversity and the Geographic Localization of Innovation. Small Business Economics. 1997,9(1):21~31
99 D. B. Audretsch, M. P. Feldman. R&D Spillovers and the Geography of Innovation and Production. American Economic Review. 1996, 86(3): 630~640
100 R. E. Caves, D. R. Barton. Efficiency in US Manufacturing Industries. Cambridge. MIT Press. 1990: 47~49
101 R. Moreno, R. Paci, S. Usai. Geographical and Sectoral Clusters of Innovation in Europe. Annals of Regional Science.2005, 39(4): 715~739
102 M. Fritsch, A. Stephan. The Distribution and Heterogeneity of Technical Efficiency within Industries– An Empirical Assessment. Faculty of Economics and Business Administration. Technical University of Freiberg. 2004: 1~76
103 M. Fritsch, J. Mallok. Machinery and Productivity– A Comparison of East and West German Manufacturing Plants. Technological Change and Regional Development in Europe. 2004, 12(3): 61~73
104 D. Mayes, C. Harris. M. Lansbury. Inefficiency in Industry. Hemel Hampstead. Harvester Wheatsheaf. 2004: 50~52
105 M. Fritsch. Measuring the Quality of Regional Innovation Systems– A Knowledge Production Function Approach. International Regional Science Review. 2002, 25(1): 86~101
106 C. Lawson, E. Lorenz. Collective Learning, Tacit Knowledge and Regional Innovative Capacity. Regional Studise. 1999, 33(4): 305~317
107 B. T. Asheim, A. Isaksen. Location, Agglomeration and Innovation: Towards Regional Innovation Systems in Norway? Europe Planning Studies. 1997, 5(3): 299~330
108 M.A. Diez, M.S. Esteban. The Evaluation of Regional Innovation and Cluster Policies: Looking for New Approaches. Conference paper on Decentralization and Evaluation. Lausanne. 2000: 1032~1038
109钱燕云.企业技术创新效率和有效性的综合评价研究.科技管理研究. 2004, 1: 51~53
110刘顺忠,官建成.区域创新系统创新绩效的评价.中国管理科学. 2002,10(5): 75~78
111官建成,何颖.基于DEA方法的区域创新系统的评价.科学学研究. 2005, 23 (2): 265~272
112池仁勇,唐根年.基于投入与绩效评价的区域技术创新效率研究.科研管理,2004,4: 23~27
113易伟明,刘满凤.区域创新系统创新绩效分析与评价.科技进步与对策. 2005, 3: 119~121
114唐厚兴,梁威.区域创新系统创新绩效分析和评价.科技与产业. 2005, 5(6): 10~15
115虞晓芬,李正卫,池仁勇,施鸣炜.我国区域技术创新效率:现状与原因.科学学研究. 2005, 23(2): 258~264
116王伟光.中国工业行业技术创新效率的实证研究(1990-1999).沈阳师范大学学报社会科学版. 2003, 1: 57~63
117刘和东,梁东黎,耿修林.技术创新绩效的“南北差异”分析——以我国西南和西北地区为对象的实证研究.经济体制改革. 2003, 4: 109~113
118余佩琨,林水山.基于分形理论的中国区域创新绩效研究.技术经济与管理研究. 2005, 4: 50~51
119李晓钟,张小蒂.江浙区域技术创新效率比较分析.中国工业经济. 2005, 7: 57~64
120赵付民,邹珊刚.区域创新环境及对区域创新绩效的影响分析.统计与决策. 2005, 4: 17~18
121顾朝林.中国高技术产业与园区.中信出版社. 1998: 34
122魏心镇.新的产业空间.北京大学出版社. 1993: l0
123李悦.产业经济学.中国人民大学出版社. 1998: 257
124郭励弘.高技术产业:发展规律与风险投资.中国发展出版社. 2000: 14
125 K. Iwai. A Contribution to the Evolutionary Theory of Innovation, Imitation and Growth. Journal of Economic Behavior & Organization. 2000,43(2): 167~198
126邵云飞,唐小我,陈光.我国技术创新研究综述.电子科技大学学报社科版. 2002, 4(1): 48~52
127 E. Mansfield, M. Schwartz, S. Wagner. Imitation Costs and Patents: An Empirical Study. Economic Journal. 1981, 91(364): 907~918
128 C. Freeman. The National System of Innovation in Historical Perspective. Cambridge Journal of Economics. 1995, 19(1): 5~24
129 K. Rennings. Analysis Redefining Innovation-Eco-innovation Research and the Contribution from Ecological Economics. Ecological Economics. 2000, 32(2): 319~332
130 E. Kaukomen. The Evaluation of Scientific Research: Selected Experiences. OECD.1997: 124 ~136
131柳卸林.企业技术创新管理.北京:科学技术文献出版社. 1997: 21~53
132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原因分析.中国软科学. 2004, 8: 128~132
133盛世豪.知识经济与工业经济的知识化过程.中国软科学. 1998,12: 73~74
134梁莱歆,张永榜.我国高新技术企业R&D投入与绩效现状调查分析.研究与发展管理. 2006, 1: 47~51
135刘作义,陈晓田.科学研究评价的性质、作用、方法及程序.科研管理.2002,23(2):33~40
136 J. Ruggiero. Non-discretionary Inputs in Data Envelopment Analysis. European Journal of Operational Research. 1998, 111(3):461~469
2范柏乃.高新技术产业发展法律环境研究.上海财经出版社.2000: 3~13
3盐泽由典.复杂系统经济学入门.生产性出版社. 1997: 319
4亚当·斯密.国富论(上).商务印书馆. 1972: 5
5庞巴维克.资本实证论.商务印刷馆中译本. 1980: 80
6 G. Becker, K. Murphy. The Deviation of Labor, Coordination Costs, and Knowledge. Quarterly Journal of Economics. 1992,CVII(4): 1137~1160
7 K. J. Arrow. The Economic Implications of Learning by Doing. Review of Economic Studies. 1962,29(3): 155~173
8 P. M. Romer. Increasing Returns and Long-run Growth. Journal of Political Economy. 1986,94(5):1002~1037
9 W. B. Arthur. Competing Technologies, Increasing Returns, and Lock-in by Historical Events. Economic Journal. 1989,99(3): 116~131
10汪丁丁.知识沿时间和空间的互补性以及相关的经济学.经济研究. 1997,6: 70~77
11纪玉山.江中蛟.知识经济与边际收益递增.经济评论. 2000, 4: 16~19
12谭清美,李宗植.新经济和传统经济比较研究.科学管理研究. 2002,
20(4): 37~38
13蒋景媛.知识的特征及将其纳入生产函数的方法.财经科学. 2005, 2: 41~47
14张孟才,李雪峰.网络经济中边际收益递增的不确定性.产业经济. 2007, 9: 35~36
15郭广珍.迟爱敏.知识经济与经济增长理论.山东矿业学院学报社会科学版. 1999,1(2):115~119
16 Z. Griliches. Patent Statistics as Economic Indicators: A Survey. Journal of Economic Literature. 1990, 28(4): 1661~1707
17 A. Prencipe. Technological Competencies and Product’s Evolutionary Dynamics: a Case Study from the Aero-Engine Industry. Research Policy.1977, 25(8): 1261~1276
18 J. Schmookler. Invention and Economic Growth. Cambridge: HarvardUniversity Press. 1966: 17
19 F. M. Scherer. Firm Size, Market Size, Opportunity and Output of Patented Inventions. American Economic Review. 1965, 55(5): 1097~1125
20 K. Pavvitt. The Objectives of Technology Policy. Science and Public Policy. 1987, 14(4): 182~188
21 P. Patel, K. Pavitt. The Nature and Importance of National Innovation Systems. STI Review. 1994, 14: 9~32
22 Z. J. Acs, D. B. Audretsch, M. P. Feldman. R&D Spillover and Recipient Firm Size. Review of Economic and Statistics. 1994, 76(2): 336~340
23 A. Arundel. The Relative Effectiveness of Patents and Secrecy for Appropriation. Research Policy. 2001, 30:611~624
24 J. L. Furman, M. E. Porter, S. Stern. The Determinants of National Innovative Capacity. Research Policy. 2002, 31(6): 899~933
25 A. Arundel, I. Kabla. What Percentage of Innovations is Patented? Empirical Estimates for European Firms. Research Policy. 1998, 27(2): 127~141
26 Z. J. Acs, L. Anselin Luc, A. Varga. Patents and Innovation Counts as Measures of Regional Production of New Knowledge. Research Policy. 2002, 31(7): 1069~1085
27 M. Crosby. Patents: Innovation and Growth. Economic Record. 2000, 76(3): 255~262
28 A. B. Jaffe. The U.S. Patent System in Transition: Policy Innovation and the Innovation Process. Research Policy. 2000, 29(4-5): 531~557
29 R. Mazzoleni, R. R. Nelson. The Benefits and Costs of Strong Patent Protection: A Contribution to the Current Debate. Research Policy. 1998, 27(3): 273~284
30 J. Cantwell, F. Fai. Firms as the Source of Innovation and Growth: the Evolution of Technological Competence. Journal of Evolutionary Economics. 1999, 9(3):331~366
31 D. Guellec, B. van Pottelsberghe de la Potterie. The Internationalisation of Technology Analyzed with Patent Data. Research Policy. 2001, 30(8): 1253~1266
32 B. P. Abraham, S. D. Moitra. Innovation Assessment through Patent Analysis. Technovation. 2001, 21(4): 245~252
33 M. Waterson. The Economics of Product Patents. The American Economic Review. 1990, 80(4): 860~869
34 I. P. Mahmood, J. Singh. Technological Dynamism in Asia. Research Policy. 2003, 32(6): 1031~1054
35连燕华,徐颖,郑奕荣.国家技术创新投入产出的宏观计量方法研究.研究与发展管理. 2005, 12 : 84~89
36赵捷,柳卸林.从专利申请看我国技术创新之短.瞭望新闻周刊. 1999, 4: 7
37官建成,刘顺忠.区域创新机构对创新绩效影响的研究.科学学研究. 2003, 2: 210~214
38唐福国,陈光.我国技术创新能力的地区差异分析:框架、指标与评价.研究与发展管理. 2001, 13(5): 32~36
39邵运飞,唐小我,陈光.中国区域技术创新能力的主成分实证研究.管理工程学报. 2005, 19(3): 71~76
40刘耀彬,王启仿,宋字峰.转型时期中国区域技术创新能力评价与分析.科技进步与对策2004, 9: 13~15
41时鹏将,许晓雯,蔡虹. R&D投入产出效率的DEA分析.科学学与科学技术管理. 2004, 1: 28~30
42古利平,张宗益,康继军.专利与R&D资源:中国创新投入产出分析.管理工程学报2006, 5: 113~118
43叶采飞,郑明贵.有色金属行业科技创新投入与产出评价指标体系研究.江西有色金属. 2004, 6: 1~6
44中国科技发展战略研究小组. 2002中国区域创新能力报告.经济管理出版社. 2003: 2~33
45陈劲,景劲松,周笑磊.我国企业R&D国际化的影响因素分析.科学学研究. 2003, 1: 51~57
46赵喜仓,陈海波.我国R&D状况的区域比较分析.统计研究. 2003, 3: 38~42
47 R. Fare, S. Grosskopf, M. Norris, Z. Zhang. Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries. American Economic Review. 1994, 84(1): 66~83
48王丽,魏煜.企业效率研究方法比较.预测. 1999, 5: 76~79
49韩松,王稳.几种技术效率测量方法的比较研究.中国软科学. 2004, 4:147~151
50谢洪军,任玉珑.技术效率研究中的前沿分析方法及其比较.科技管理研究. 2006(8): 237~239
51 A. Charnes, W.W. Cooper, B.Golany, L. Seiford. Foundation of Data Envelopment Analysis for Pareto-Koopmans Efficient Empirical Production Functions. Journal of Econometrics (Netherlands). 1985, 30(1-2): 91~107
52 C. Kao. A Linear Formulation of the Two-level DEA Model. Omega. 2008, 36(6): 958~962
53 A. Johnson, L. Mcginnis. Outlier Detection in Two-stage Semi Parametric DEA Models. European Journal of Operational Research. 2008,187(2): 629~635
54 G. R. Jahanshahloo, M. Soleimani-damaneh, A. Mostafaee. A Simplified Version of the DEA Cost Eifficiency Model. European Journal of Operational Research.2008,184(2): 814~815
55 M. R. Alirezaee and, M. Afsharian. A complete ranking of DMUs Using Restrictions in DEA models. Applied Mathematics and Computation. 2007,189(2): 1550~1559
56 A. H. Vencheh, A. A. Foroughi. A Generalized DEA Model for Inputs / Outputs Estimation. Mathematical and Computer Modelling. 2006, 43(5-6):447~457
57王晓红,王雪峰,翟爱梅等.具有边际收益递增特性的DEA模型求解方法.哈尔滨工业大学学报. 2004, 36(10): 1297~1300
58王晓红,王雪峰,翟爱梅等.具有边际收益递增特性的数据包络分析模型.上海交通大学学报. 2005, 39(3): 484~487
59 R. Fare, S. Grosskopf. Productivity and Intermediate Products: A Frontier Approach. Economics Letters. 1996, 50(1): 65~70
60 R. Fare, S. Grosskopf. Network DEA. Socio-Economic Planning Sciences, 2000, 34(1): 35~49
61 Y. Chen, L. Liang, F. Yang, J. Zhu. Evaluation of Information Technology Investment: A Data Envelopment Analysis Approach. Computers & Operations Research. 2006, 33(5): 1368~1379
62 C. H. Wang, R. Gopal, S. Zionts. Use of Data Envelopment Analysis in Assessing Information Technology Impact on Firm Performance. Annals ofOperations Research. 1997, 73(10): 1991~213
63 Y. Chen, J. Zhu. Measuring Information Technology's Indirect Impact on Firm Performance. Information Technology and Management. 2004, 5(1-2): 9~22
64 H. F. Lewis, T. R. Sexton. Network DEA: Efficiency Analysis of Organizations with Complex Internal Structure. Computers & Operations Research. 2004, 31(9):1365~1410
65 T. R. Sexton, H. F. Lewis. Two-stage DEA: An Application to Major League Baseball. Journal of Productivity Analysis. 2003, 19 (2-3): 227~49
66 G. E. Battese, T. J. Coelli. Prediction of Firmed-level Technical Efficiencies with a Generalized Frontier Production Function and Panel Data. Journal of Econometrics. 1988,38(3):387~399
67 R. E. Stevenson. Likelihood Functions for Generalized Stochastic Frontier Estimation. Journal of Econometrices. 1980,13(1):57~66
68 W. H. Greene. A Gamma-distributed Stochastic Frontier Model. Journal of Econometrics. 1990, 46(1-2):141~164
69 C. Ritter, L. Simar. Pitfall of Normal-Gamma Stochastic Frontier Models. Journal of Productivity Analysis. 1997, 82(5):167~182
70 M. M. Pitt, L. F. Lee. Measurement and Sources of Technical Inefficiency in the Indonesian Weaving Industry. Journal of Development Economics. 1981,9(1):43~64
71 P. Schmidt, R. C. Sickes. Production Frontiers and Panel Data. Journal of Business and Economic Statistics. 1984, 2(4):367~374
72 C. Cornwell, P. Schmidt, R. C. Sickes. Production Frontiers with Cross-sectional and Time-series Variation in Efficiency Levels. Journal of Econometrics. 1990, 46(1-2):185~200
73 C. A. K. Lovell, S. S. Schmidt. The Measurement of Productive Efficiency: Techniques and Application. Oxford University Press, New York, 1993:237~255
74 S. C. Kumbhakar. Production Frontiers, Panel Data and Time-Varying Technical Inefficiency. Journal of Econometrics. 1990,46(1-2):201~211
75 G. E. Battese, T. J. Coelli. Frontier Production Functions, Technical Efficiency and Panel Data: With Application of Paddy Farmers in India.Journal of Productivity Analysis. 1992, 3(1-2):153~169
76 I. Herrero. Different Approaches to Efficiency: An Application to the Spanish Trawl Fleet Operating in Moroccan Waters. European Journal of Operational Research. 2005, 167(1):257~271
77 C. Fernandez, G. Koop, M. Steel. A Bayesian Analysis of Mutiple-output Production Frontier. Journal of Econometrics. 2000, 98(1):47~79
78 T. J. Coelli. Estimators and Hypothesis Tests for Stochastic Frontier Function: A Monte Carlo Analysis. Journal of Productivity Analysis. 1995,6(3):247~268
79 T. Sueyoshi. Stochastic Frontier Production Analysis: Measuring Performance of Public Telecommunication in 24 OECD Countries. European Journal of Operational Research. 1997, 74(3):466~478
80 J. P. Florens, L. Smiar. Parametric Approximation of Nonparametric Frontier. Journal of Econometrics. 2005, 124(1):91~116
81 P. Cooke, M. G. Uranga, G. Etxebarria. Regional Systems of Innovation: an Evolutionary Perspective. Environment and Planning. 1998, 30: 1563~1584
82 P. N. Cooke, H. J. Braczyk.H.J, M. Heidenreich. Regional Innovation Systems: The Role of Governances in the Globalized World. London. UCL Press. 1996, 23~86
83 H. Wiig, M. Wood. What Comprises a Regional Innovation System? An Empirical Study. 1999. http://web.sol.no/step/195/195.htm
84 B. T. Asheim, P. Cooke. Local Learning and Interactive Innovation Networks in a Global Economy. Ashgate Publishers. 1999: 3~15
85 B. A. Lundvall. National System of Innovation: Towards a Theory of Innovation and Interactive Learning.London. Printer Publisher. 1992: 46~61
86 B. L. Lundvall, B. Johnson, E. S. Andersen, B. Dalum. National Systems of Production, Innovation and Competence Building. Research Policy. 2002, 31 (2): 213~231
87 K. Pavitt, P. Patel. Corporate Technology Strategies and National Systems of Innovation. STI Review.1995: 78~91
88 OECD. National Innovation System. Pairs . 1997: 1~48
89 M. J. Enright. Regional Clusters: What We Know and What We Should Know. Innovation Clusters and Interregional Competition. 2003, 3: 99~129
90 M. P. Feldman. The Geography of Innovation. Boston: Kluwer Academic Publishers.1998: 113~151
91 M. E. Porter. Clusters and the New Economics of Competition. Harvard Business Review. 1998,76(6): 77~90
92 M. Fritsch. Interregional Differences in R&D Activities - an Empirical Investigation. European Planning Studies. 2002,8(4): 409~427
93 Z. J. Acs, D. B. Audretsch, M. P. Feldman. The Real Effects of Academic Research. American Economic Review. 1982,1: 363~367
94 L. Anselin, A. Varga, Z. J. Acs. Local Geographic Spillovers between University Research and High technology Innovation. Journal of Urban Economic. 1997, 42(3): 422~448
95 A. B. Jaffe, M. Trajtenberg, R. Henderson. Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations. Quarterly Journal of Economics. 1993, 108(3): 576~598
96 M. E. Porter. Clusters and the New Economics of Competition. Harvard University Press. 1998: 18~62
97 R. Baptista, G. M. P. Swann. A Comparison of Clustering Dynamics in the US and UK Computer Industries. Journal of Evolutionary Economics. 1999,9(3): 373~399
98 P. Almeida, B. Kogut. The Exploration of Technological Diversity and the Geographic Localization of Innovation. Small Business Economics. 1997,9(1):21~31
99 D. B. Audretsch, M. P. Feldman. R&D Spillovers and the Geography of Innovation and Production. American Economic Review. 1996, 86(3): 630~640
100 R. E. Caves, D. R. Barton. Efficiency in US Manufacturing Industries. Cambridge. MIT Press. 1990: 47~49
101 R. Moreno, R. Paci, S. Usai. Geographical and Sectoral Clusters of Innovation in Europe. Annals of Regional Science.2005, 39(4): 715~739
102 M. Fritsch, A. Stephan. The Distribution and Heterogeneity of Technical Efficiency within Industries– An Empirical Assessment. Faculty of Economics and Business Administration. Technical University of Freiberg. 2004: 1~76
103 M. Fritsch, J. Mallok. Machinery and Productivity– A Comparison of East and West German Manufacturing Plants. Technological Change and Regional Development in Europe. 2004, 12(3): 61~73
104 D. Mayes, C. Harris. M. Lansbury. Inefficiency in Industry. Hemel Hampstead. Harvester Wheatsheaf. 2004: 50~52
105 M. Fritsch. Measuring the Quality of Regional Innovation Systems– A Knowledge Production Function Approach. International Regional Science Review. 2002, 25(1): 86~101
106 C. Lawson, E. Lorenz. Collective Learning, Tacit Knowledge and Regional Innovative Capacity. Regional Studise. 1999, 33(4): 305~317
107 B. T. Asheim, A. Isaksen. Location, Agglomeration and Innovation: Towards Regional Innovation Systems in Norway? Europe Planning Studies. 1997, 5(3): 299~330
108 M.A. Diez, M.S. Esteban. The Evaluation of Regional Innovation and Cluster Policies: Looking for New Approaches. Conference paper on Decentralization and Evaluation. Lausanne. 2000: 1032~1038
109钱燕云.企业技术创新效率和有效性的综合评价研究.科技管理研究. 2004, 1: 51~53
110刘顺忠,官建成.区域创新系统创新绩效的评价.中国管理科学. 2002,10(5): 75~78
111官建成,何颖.基于DEA方法的区域创新系统的评价.科学学研究. 2005, 23 (2): 265~272
112池仁勇,唐根年.基于投入与绩效评价的区域技术创新效率研究.科研管理,2004,4: 23~27
113易伟明,刘满凤.区域创新系统创新绩效分析与评价.科技进步与对策. 2005, 3: 119~121
114唐厚兴,梁威.区域创新系统创新绩效分析和评价.科技与产业. 2005, 5(6): 10~15
115虞晓芬,李正卫,池仁勇,施鸣炜.我国区域技术创新效率:现状与原因.科学学研究. 2005, 23(2): 258~264
116王伟光.中国工业行业技术创新效率的实证研究(1990-1999).沈阳师范大学学报社会科学版. 2003, 1: 57~63
117刘和东,梁东黎,耿修林.技术创新绩效的“南北差异”分析——以我国西南和西北地区为对象的实证研究.经济体制改革. 2003, 4: 109~113
118余佩琨,林水山.基于分形理论的中国区域创新绩效研究.技术经济与管理研究. 2005, 4: 50~51
119李晓钟,张小蒂.江浙区域技术创新效率比较分析.中国工业经济. 2005, 7: 57~64
120赵付民,邹珊刚.区域创新环境及对区域创新绩效的影响分析.统计与决策. 2005, 4: 17~18
121顾朝林.中国高技术产业与园区.中信出版社. 1998: 34
122魏心镇.新的产业空间.北京大学出版社. 1993: l0
123李悦.产业经济学.中国人民大学出版社. 1998: 257
124郭励弘.高技术产业:发展规律与风险投资.中国发展出版社. 2000: 14
125 K. Iwai. A Contribution to the Evolutionary Theory of Innovation, Imitation and Growth. Journal of Economic Behavior & Organization. 2000,43(2): 167~198
126邵云飞,唐小我,陈光.我国技术创新研究综述.电子科技大学学报社科版. 2002, 4(1): 48~52
127 E. Mansfield, M. Schwartz, S. Wagner. Imitation Costs and Patents: An Empirical Study. Economic Journal. 1981, 91(364): 907~918
128 C. Freeman. The National System of Innovation in Historical Perspective. Cambridge Journal of Economics. 1995, 19(1): 5~24
129 K. Rennings. Analysis Redefining Innovation-Eco-innovation Research and the Contribution from Ecological Economics. Ecological Economics. 2000, 32(2): 319~332
130 E. Kaukomen. The Evaluation of Scientific Research: Selected Experiences. OECD.1997: 124 ~136
131柳卸林.企业技术创新管理.北京:科学技术文献出版社. 1997: 21~53
132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原因分析.中国软科学. 2004, 8: 128~132
133盛世豪.知识经济与工业经济的知识化过程.中国软科学. 1998,12: 73~74
134梁莱歆,张永榜.我国高新技术企业R&D投入与绩效现状调查分析.研究与发展管理. 2006, 1: 47~51
135刘作义,陈晓田.科学研究评价的性质、作用、方法及程序.科研管理.2002,23(2):33~40
136 J. Ruggiero. Non-discretionary Inputs in Data Envelopment Analysis. European Journal of Operational Research. 1998, 111(3):461~469