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| Research on Multiple Modes for Improving Technological Innovation Efficiency of High-tech Industry——Based on the Perspective of Innovation Environment |
| Fan Decheng,Gu Xiaomei |
| (School of Economics and Management,Harbin Engineering University,Harbin 150001,China) |
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Abstract Since the innovation environment can provide supporting conditions for technological innovation in high-tech industry,it is of great significance to clarify the modes for improving technological innovation efficiency from the perspective of innovation environment. This study adopts the CCR-DEA model to measure the technological innovation efficiency of high-tech industry of 29 province-level regions in China from 2012 to 2016. Then this study utilizes the GMDH algorithm to identify the key environmental factors affecting technological innovation efficiency. Finally this study employs the fsQCA method to explore the different configurations of key environmental factors and the multiple modes for improving technological innovation. The results show that the average value of technological innovation efficiency of China's high-tech industry is relatively low during the five years,and more than half of the provinces do not reach the national average; economic foundation,market demand,openness,industrial R&D foundation,the tendency of independent innovation,government innovation intervention and intellectual property protection are key environmental factors affecting technological innovation efficiency; though none of the key environmental factors can improve technological innovation efficiency separately,four kinds of combinations of them are sufficient conditions for that,which provide four modes to improve technological innovation efficiency; the demand driven mode,exchange collision mode and subject driven mode are suitable for the regions with weak economic foundation,while the mode of increasing quality and reducing bundle can be applied in the regions with good economic foundation.
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Received: 13 July 2020
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[1] 孙早，徐远华. 信息基础设施建设能提高中国高技术产业的创新效率吗——基于2002-2013年高技术17个细分行业面板数据的经验分析[J].南开经济研究，2018，35(2): 72-92.
[2] 支燕，白雪洁.我国高技术产业创新绩效提升路径研究——自主创新还是技术外取[J].南开经济研究，2012，29(5): 51-64.
[3] 洪银兴. 科技创新体系的完善与协同发展探讨[J]. 经济学动态，2016，57(2): 4-9.
[4] 肖仁桥，陈忠卫，钱丽. 异质性技术视角下中国高技术制造业创新效率研究[J].管理科学，2018，31(1): 48-68.
[5] 刘思明，赵彦云，侯鹏. 区域创新体系与创新效率:中国省级层面的经验分析[J].山西财经大学学报，2011，33(12): 9-17.
[6] 戴魁早. 制度环境、区域差异与知识生产效率——来自中国省际高技术产业的经验证据[J]. 科学学研究，2015，33(3): 369-377.
[7] HONG J,FENG B,WU Y R,et al. Do government grants promote innovation efficiency in China's high-tech industries[J]. Technovation,2016: S0166497216301018.
[8] 王斌，谭清美. 市场因素与高技术产业创新成果转化：促进还是抑制[J]. 科学学研究，2016，34(6): 850-859.
[9] 叶丹，黄庆华. 区域创新环境对高技术产业创新效率的影响研究——基于DEA-Malmquist方法[J].宏观经济研究，2017，39(8):132-140.
[10] LIU Y Z,CHEN X F,CHU J F,et al. Industrial development environment and innovation efficiency of high-tech industry: analysis based on the framework of innovation systems[J]. Technology Analysis&Strategic Management,2017（8）: 1-13.
[11] 李彦龙. 税收优惠政策与高技术产业创新效率[J]. 数量经济技术经济研究，2018，35(1): 60-76.
[12] 张涵，杨晓昕. 创新环境约束下高技术产业区域创新效率及收敛性研究[J]. 科技进步与对策，2018，35(3): 43-51.
[13] CAO S L,FENG F,CHEN W Y,et al. Does market competition promote innovation efficiency in China′s high-tech industries[J]. Technology Analysis&Strategic Management,2019(2): 1-14.
[14] 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.
[15] MALADA H R,IVAKHNENKO A G. Inductive learning algorithms for complex system modeling [M]. Florida: CRC Press,1994: 69-86.
[16] 刘光中，颜科琦，康银劳. 基于自组织理论的GMDH神经网络算法及应用[J].数学的实践与认识，2001，31(4): 464-469.
[17] 贺昌政．自组织数据挖掘与经济预测[M].北京：科学出版社，2005.
[18] RAGIN C C．Redesigning social inquiry: Fuzzy sets and beyond[M]．Chicago: University of Chicago Press,2008．
[19] SCHNEIDER C Q,WAGEMANN C. Set-theoretic methods for the social sciences:a guide to qualitative comparative Analysis[M].Cambridge: Cambridge University Press,2012.
[20] 赵文，王娜. 二元网络背景下中国海归企业绩效提升路径研究——基于模糊集的定性比较分析[J]. 科学学与科学技术管理，2017，38(5): 128-139.
[21] 张明， 陈伟宏， 蓝海林. 中国企业“凭什么”完全并购境外高新技术企业——基于 94 个案例的模糊集定性比较分析（fsQCA）[J].中国工业经济，2019，36(4): 117-135.
[22] 杜宝贵，王欣. 众创空间创新发展多重并发因果关系与多元路径——基于模糊集定性比较分析[J].科技进步与对策，2020,37（7）：120-129.
[23] 刘伟. 考虑环境因素的高新技术产业技术创新效率分析——基于2000-2007年和2008-2014年两个时段的比较[J].科研管理，2016，37(11):18-25.
[24] 邓凯. 空间差异视角下西部地区高新技术企业创新环境的外生优势分析[J]. 统计与信息论坛，2018，33(9): 88-96.
[25] 侯鹏，刘思明，建兰宁. 创新环境对中国区域创新能力的影响及地区差异研究[J]. 经济问题探索，2014,35(11): 73-80.
[26] 中国科技发展战略研究小组，中国科学院大学中国创新创业管理研究中心. 中国区域创新能力评价报告2016[M]. 北京：科学技术文献出版社，2016:205-212.
[27] GRILICHES Z,LICHTENBEGR F. Inter-industry technology flows and productivity growth:a reexamination [J].Review of Economics Statistics,1984,66(2): 324-329.
[28] 李向东，李南，白俊红，等. 高技术产业研发创新效率分析[J].中国软科学，2011(2): 52-61.
[29] 张军，吴桂英，张吉鹏. 中国省际物质资本存量估算：1952-2000[J]. 经济研究，2004,50(10): 35-44.
[30] 陈珊. 科技金融对我国高技术产业区域创新效率影响分析[J]. 经济问题探索，2019,40(3): 166-172.
[31] 汪玲. 长江经济带创新环境与创新绩效耦合协调研究[J].安徽农业大学学报，2017,61(5): 50-55.
[32] CODURAS A,ANTONIO CLEMENTE J,RUIZ J.A novel application of fuzzy-set qualitative comparative analysis to GEM data[J]. Journal of Business Research,2016,69(4): 1265-1270.
[33] 王蓉娟，吴建祖. 环保约谈制度何以有效——基于29 个案例的模糊集定性比较分析[J].中国人口·资源与环境，2019，29(12): 103-111.
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