Abstract:Based on the micro-panel data of Chinese universities in 2008—2016,this paper firstly measures the transformation efficiency of scientific and technological achievements through the network DEA model,and explores its spatial convergence.Based on the perspective of the innovation ecosystem,this paper constructs a spatial error model to empirically test Chinese universities.The influencing factors of the transformation efficiency of scientific and technological achievements,the research results show that:①the transformation efficiency of scientific and technological achievements in Chinese universities is at a low level,and its spatial correlation is significant;the transformation efficiency of scientific and technological achievements in universities in the eastern regions is significantly higher than the transformation efficiency of scientific and technological achievements in universities in the central and western regions.The spatial agglomeration effect has a weakening trend,which has led to the trend of the transformation of scientific and technological achievements in the central and western universities without catching up with the eastern region.②Government funds have a significant role in promoting the transformation efficiency of scientific and technological achievements in universities,and it is more significant before 2012;the impact of corporate funds on the transformation efficiency of scientific and technological achievements in universities is not significant in 2008—2012,and there are in 2013—2016.Significant positive impact;other funds have no significant negative impact on the efficiency of scientific and technological achievements in universities.③The senior professional title personnel significantly promoted the transformation efficiency of scientific and technological achievements in colleges and universities.It is difficult to achieve the transformation efficiency of scientific and technological achievements in colleges and universities.Since 2012,China's reward evaluation mechanism has been improved.
林青宁, 毛世平. 高校科技成果转化效率研究[J]. 中国科技论坛, 2019(5): 144-151.
Lin Qingning, Mao Shiping. Research on Transformation Efficiency of Chinese Universities. , 2019(5): 144-151.
[1] 谷德斌,尹航,杨贵彬.高校科技成果转化驱动模式研究[J].科技进步与对策,2012,29(13):24-28. [2]康晓梅.高校科技成果转化的制约因素与对策[J].中国高校科技,2014(8):82-83. [3]胡罡,章向宏,刘薇薇,等.地方研究院:高校科技成果转化模式新探索[J].研究与发展管理,2014,26(3):122-128. [4]马晓君,潘昌伟.高校科技成果转化的困境与推进策略[J].现代教育管理,2015(1):78-82. [5]赵哲.中国高校科技成果转化的现实困境与突破路径[J].高校教育管理,2016,10(5):52-56. [6]申轶男,张超,朱国峰,等.高校科技成果转化存在的问题、成因及解决办法[J].中国高校科技,2016(3):8-11. [7]邱峰.论自主创新战略下的高校科技成果转化模式[J].中国高校科技,2016(5):79-81. [8]邵青青.高校科技成果转化“热”下的“冷”思考[J].中国高校科技,2017(6):17-19. [9]何彬,范硕.中国大学科技成果转化效率演变与影响因素[J].科学学与科学技术管理,2013,34(10):85-94. [10]罗茜,高蓉蓉,曹丽娜.高校科技成果转化效率测度分析与影响因素扎根研究—以江苏省为例[J].科技进步与对策2018:1-9. [11]ALBERT N Link,DONALD S Siegel.Generating science-based growth:an econometric analysis of the impact of organizational incentives on university-industry technology transfer[J].European journal of finance,2005,11(3):169-181. [12]CHAPPLE W,LOCKETT A,SIEGEL D,et al.Assessing the relative performance of U.K.university technology transfer offices:parametric and non-parametric evidence[J].Research policy,2005,34(3):369-384. [13]ANDERSON T R,DAIM T U,LAVOIE F F.Measuring the efficiency of university technology transfer[J].Technovation,2007,27(5):306-318. [14]DONALD S,MIKE W,WENDY C,et al.Assessing the relative performance of university technology transfer in the US and UK:a stochastic distance function approach[J].Economics of innovation and new technology,2008,17(7-8):717-729. [15]CARDOZO R,ARDICHVILI A,STRAUSS A.Effectiveness of university technology transfer:an organizational population ecology view of a maturing supplier industry[J].Journal of technology transfer,2011,36(2):173-202. [16]MACHO S,INES D,PEREZ C.Incentives in university technology transfers original research article[J].International journal of industrial organization,2010,28(4):362-367. [17]CURI C,DARAIO C,LLERENA P.University technology transfer:How (in)efficient are French universities[J].Dis technical reports,2012,36(3):629-655. [18]KIM Y.The ivory tower approach to entrepreneurial linkage:productivity changes in university technology transfer[J].The journal of technology transfer,2013,38(2):180-197. [19]叶锐,杨建飞,常云昆.中国省际高技术产业效率测度与分解——基于共享投入关联DEA模型[J].数量经济技术经济研究,2012,29(7):3-17+91. KWON H B,LEE J.Two-stage production modeling of large U.S.banks:a DEA-neural network approach.Expert systems with applications,2015,42(19):6758-6766. [21]ADNER R.Match your innovation strategy to your innova-tion ecosystem[J].Harvard business review,2006,84(4):98. [22]ADNER R,KAPOOR R.Value creation in innovation ecosystems:How the structure of technological interdependence affects firm performance in new technology generations[J].Strategic management journal,2010,31(3):306-333. [23]潘颖雯,万迪昉.研发的不确定性与研发人员激励契约的设计研究[J].科学学与科学技术管理,2007(8):175-178. [24]WEI M S,ROB K,MIHAELA U,et al.A collaborative agent-based infrastructure for Internet-enabled collaborative enterprises[J].International journal of production research,2003,41(8):1621-1638. [25]HABER S H,WERFEL S H.Patent trolls as financial intermediaries? Experimental evidence[J].Economics letters,2016(149):64-66. [26]岳书敬,刘朝明.人力资本与区域全要素生产率分析[J].经济研究,2006(4):90-96+127. [27]孟宪飞,郑永平,吴荫芳.从国家科技进步一等奖获奖情况看中国科技创新之路[J].科技进步与对策,2010,27(2):1-4. [28]彭纪生,仲为国,孙文祥.政策测量、政策协同演变与经济绩效:基于创新政策的实证研究[J].管理世界,2008(9):25-36. [29]CHE X G,YANG Y.Patent protection with a cooperative R&D option[J].Economics letters,2012,116(3):469-471.