基于知识发酵的科技创新效率测度模型——以湖北省为例

俞享, 门玉英, 刘园园, 李芳

知识管理论坛 ›› 2023, Vol. 8 ›› Issue (6) : 540-553.

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知识管理论坛 ›› 2023, Vol. 8 ›› Issue (6) : 540-553. DOI: 10.13266/j.issn.2095-5472.2023.043
研究论文

基于知识发酵的科技创新效率测度模型——以湖北省为例

作者信息 +

A Model for Measuring the Efficiency of Technological Innovation Based on Knowledge Fermentation——Take Hubei Province as an example

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文章历史 +

摘要

[目的/意义]针对科技创新效率的测度有助于深入了解湖北省城市科技创新的实际情况,为地方经济政策提供理论支持和实践指导。[方法/过程] 以湖北省12个城市为研究对象,运用超效率SBM-Malmquist模型测算各城市2003—2019年的科技创新效率和全要素生产率,并结合OLS回归探究其影响因素。基于知识发酵理论,将对外开放、产业结构、经济发展水平、信息化水平、人力资本等因素与之相结合,展示其如何共同影响湖北省科技创新效率的过程。[结果/结论] 研究结果表明:2003—2019年,湖北省科技创新效率整体态势向好发展;科技创新效率存在明显地域差异;科技创新活动全要素生产率均处于上升状态,主要源于科技进步;对外开放、经济发展水平以及人力资本对湖北省科技创新效率的影响呈显著正相关。基于研究结论,提出改善科技创新环境、加强人才引进工作、提高开放水平等政策建议。

Abstract

[Objective/Significance] The measurement of science and technology innovation efficiency is helpful to understand the actual situation of urban science and technology innovation in Hubei Province, and provide theoretical support and practical guidance for local economic policies. [Method/Process] Taking 12 cities in Hubei Province as research objects, the super-efficiency SBM-Malmquist model was used to estimate the technological innovation efficiency and total factor productivity of each city from 2003 to 2019, and the influencing factors were explored with OLS regression. Based on the theory of knowledge fermentation, factors such as opening to the outside world, industrial structure, economic development level, informatization level and human capital were combined to show how they jointly affect the efficiency of science and technology innovation in Hubei Province. [Result/Conclusion] The results showed that: from 2003 to 2019, the overall trend of science and technology innovation efficiency in Hubei Province was developing well; There are obvious regional differences in the efficiency of scientific and technological innovation. The total factor productivity of scientific and technological innovation activities is on the rise, which mainly originates from scientific and progress. The influence of opening to the outside world, the level of economic development and human capital on the efficiency of science and technology innovation in Hubei Province is significantly positively correlated. Based on the research conclusions, the paper puts forward some policy suggestions such as improving the environment for scientific and technological innovation, strengthening the introduction of talents, and improving the level of opening up.

关键词

科技创新 / 知识发酵 / 超效率SBM模型 / OLS模型 / 影响因素

Key words

scientific and technological innovation / theory of knowledge fermentation / ultra-efficient SBM model / OLS model / influencing factors

引用本文

导出引用
俞享 , 门玉英 , 刘园园 , . 基于知识发酵的科技创新效率测度模型——以湖北省为例[J]. 知识管理论坛. 2023, 8(6): 540-553 https://doi.org/10.13266/j.issn.2095-5472.2023.043
Xiang Yu , Yuying Men , Yuanyuan Liu , et al. A Model for Measuring the Efficiency of Technological Innovation Based on Knowledge Fermentation——Take Hubei Province as an example[J]. Knowledge Management Forum. 2023, 8(6): 540-553 https://doi.org/10.13266/j.issn.2095-5472.2023.043
中图分类号: F204   

参考文献

[1]
刘钒,邓明亮.基于改进超效率DEA模型的长江经济带科技创新效率研究[J].科技进步与对策,2017,34(23):48-53.(LIU F, DENG M L. Research on scientific and technological innovation efficiency of Yangtze River Economic Belt based on improved superefficiency DEA model [J]. Science and technology progress and countermeasures,2017,34(23):48-53.)
[2]
朱鹏颐,刘东华,黄新焕.动态视角下城市科技创新效率评价研究——以福建九地级市为例[J].科研管理,2017,38(6):43-50.(ZHU P Y, LIU D H, HUANG X H. Evaluation on efficiency of urban science and technology innovation from a dynamic perspective: a case study of nine prefecture-level cities in Fujian Province [J]. Science research management,2017,38(6):43-50.)
[3]
CHARNES A,COOPER W W,RHODES E. Measuring the efficiency of decision-making umits[J].European journal of operational research,1978,2(6):429-444.
[4]
李晓梅,白雪飞.基于超效率CCR-DEA的国有物流企业绩效实证分析——基于16家上市物流企业的样本数据[J].中国流通经济,2016,30(4):26-32.(LI X M, BAI X F. Empirical analysis of performance of state-owned logistics enterprises based on super-efficiency CCR-DEA: based on sample data of 16 listed logistics enterprises [J]. China circulation economy,2016,30(4):26-32.)
[5]
王珍珍.我国制造业与物流业联动发展效率评价——基于超效率CCR-DEA模型[J].中国流通经济,2017,31(2):20-30.(WANG Z Z. Evaluation of joint development efficiency of manufacturing industry and logistics industry in China: based on super-efficiency CCR-DEA model [J]. China circulation economy,2017,31(2):20-30.)
[6]
BANKER R D,CHARNES A,COOPER W W. Some models for estimating technical and scale inefficiencies in data envelopment analysis[J].Management science,1984(9):1078-1092.
[7]
TONE K.A slacks‐based measure of efficiency in data envelopment analysis[J].European journal of operational research,2001,130(3):498‐509.
[8]
TONE K.A Slacks-based measure of super-efficiency in data envelopment analysis[J].European journal of operational research,2002,143(1):32-41.
[9]
赖一飞,谢潘佳,叶丽婷,等.我国区域科技创新效率测评及影响因素研究——基于超效率SBM-Malmquist-Tobit模型[J].科技进步与对策,2021,38(13):37-45.( LAI Y F, XIE P J, YE L T, et al. Evaluation and influencing factors of regional science and technology innovation efficiency in China: based on super-efficiency SBM-Malmquist-Tobit model [J]. Science and technology progress and countermeasures, 2019,38(13):37-45.)
[10]
UNGKYU H, METTE A, MARTIN K. Regional R&D efficiency in Korea from static and dynamic perspectives[J]. Regional studies, 2014, 50(7) : 1170-1184.
[11]
李健,鲁亚洲.京津冀创新能力预测与影响因素研究[J].科技进步与对策,2019,36(12):37-45. (LI J, LU Y Z. Prediction and influencing factors of innovation capability in Beijing-Tianjin-Hebei [J]. Science and technology progress and countermeasures,2019,36(12):37-45.)
[12]
李芸,雷宏振,张小筠.基于SBM模型的科技创新效率及影响因素研究[J].技术经济,2020,39(5):1-8.( LI Y, LEI H Z, ZHANG X J. Research on S&T innovation efficiency and Influencing factors based on SBM model [J]. Journal of technical economics, 2019,39(5):1-8.)
[13]
王鹏,钟誉华,颜悦.科技创新效率与区域经济韧性交互分析——基于珠三角地区的实证[J].科技进步与对策,2022,39(8):48-58.(WANG P, ZHONG Y H, YAN Y. Interaction analysis of scientific and technological innovation efficiency and regional economic resilience: an empirical study of the Pearl River Delta region [J]. Science and technology progress and countermeasures, 2002,39(8):48-58.)
[14]
和金生.知识管理与知识发酵[J].科学学与科学技术管理,2002(3):63-66.(HE J S. Knowledge management and knowledge fermentation [J]. Science of science and science and technology management,2002(3):63-66.)
[15]
刘洪伟,和金生,马丽丽.知识发酵——知识管理的仿生学理论初探[J].科学学研究,2003(5):514-518.( LIU H W, HE J S, MA LL. Knowledge fermentation, the bionics theory of knowledge management study [J].Studies in science of science, 2003 (5) : 514-518.)
[16]
李影,张鹏,曾永泉.粤港澳大湾区工业科技创新效率及其时空演变研究[J].工业技术经济,2020,39(8):21-27.( LI Y, ZHANG P, ZENG Y Q. Study on the efficiency of industrial science and technology innovation and its spatio-temporal evolution in Guangdong-Hong Kong-Macao Greater Bay Area [J]. Industrial technical economics, 2019,39(8):21-27.)
[17]
舒天楚,聂小琴,郭含文,等.京津冀城市群科技资源配置效率分析——基于DEA-Malmquist指数模型[J].科技管理研究,2021,41(4):89-96.(SHU T C, NIE X Q, GUO H W, et al. Efficiency analysis of science and technology resource allocation in Beijing-Tianjin-Hebei City cluster: based on DEA-Malmquist index model [J]. Science and technology management research,2021,41(4):89-96)
[18]
乔为国,詹文杰.中国产学研三大部门技术创新经费投入产出效率评估研究[J].科技进步与对策,2022,39(22):113-121.( QIAO W G, ZHAN W J. Evaluation of input-output efficiency of technological innovation funds in three departments of industry, university and research in China [J]. Science and technology progress and countermeasures, 2022,39(22):113-121)
[19]
于树江,王云胜,曾建丽,等.创新价值链下京津冀高技术产业技术创新效率及驱动要素研究[J].科学决策,2021(7):77-90.( YU S J, WANG Y S, ZENG J L, et al. Research on technological innovation efficiency and driving factors of Beijing-Tianjin-Hebei high-tech Industry under Innovation value chain [J]. Science decision,2021(7):77-90.)
[20]
和雪滢. 外商投资、产业集聚对绿色创新效率的影响[D].杭州:浙江财经大学,2023.( HE X Y. Foreign investment and industrial agglomeration effect on the efficiency of the green innovation [D]. Hangzhou: Zhejiang University of Finance and Economics, 2023.)
[21]
李腾觉. 基于系统GMM模型的区域一体化对城市创新能力的影响研究[D].武汉:华中科技大学,2023. (LI T J. Research on the impact of regional integration on urban innovation capability based on system GMM model [D]. Wuhan: Huazhong University of Science and Technology, 2023. )
[22]
湛泳,李珊.智慧城市建设、创业活力与经济高质量发展——基于绿色全要素生产率视角的分析[J].财经研究,2022,48(1):4-18. (ZHAN Y, LI S. Smart city construction, entrepreneurial vitality and high-quality economic development: an analysis from the perspective of green total factor productivity [J]. Study of finance and economics, 2022,48 (1) : 4-18.)
[23]
蔺鹏,孟娜娜.绿色全要素生产率增长的时空分异与动态收敛[J].数量经济技术经济研究,2021,38(8):104-124. ( LIN P, MENG N N. Green total factor productivity growth differentiation of space and time and the dynamic convergence [J]. Journal of quantitative technical economics, 2021, 38 (8): 104-124.)
[24]
郭淑芬,张俊.中国31个省市科技创新效率及投入冗余比较[J].科研管理,2018,39(4):55-63.( GUO S F, ZHANG J. Input of science and technology innovation and efficiency in China's 31 provinces redundant comparison [J]. Journal of scientific research management, 2018, 33(4) 6:55-63.)
[25]
宋美喆,柒江艺.政府行政权力调整对城市绿色全要素生产率的影响——来自“撤县设区”和“省直管县”改革的经验证据[J].中国环境管理,2023,15(4):140-150. (SONG M Z, QI J Y. The impact of government administrative power adjustment on urban green total factor productivity: empirical evidence from the reform of "setting up districts by eliminating counties" and "province directly administering counties" [J]. China's environmental management, 2023, 15(4) : 140-150.)
[26]
李瑞雪,司孟慧,张汉飞.金融集聚对工业绿色全要素生产率的影响研究——基于长三角地区的实证[J].华东经济管理,2022,36(5):34-47. ( LI R X, SI M H, ZHANG H F. The impact of financial agglomeration on industrial green total factor productivity: an empirical study in the Yangtze River Delta [J]. East China economic management, 2022, 36 (5) : 34-47.)
[27]
曾亿武,孙文策,李丽莉,等.数字鸿沟新坐标:智慧城市建设对城乡收入差距的影响[J].中国农村观察,2022(3):165-184.(ZENG Y W, SUN W C, LI L L, et al. New coordinates of digital divide: the impact of smart city construction on Urban-Rural income gap [J]. China rural observation,2022(3):165-184)
[28]
谢霜,向慧芳.产业结构调整对污染减排影响的实证分析——基于中国省际面板数据[J].经济视角,2016(1):62-69.(XIE S, XIANG H F. An empirical analysis of the impact of industrial structure adjustment on pollution reduction: based on China's provincial panel data [J]. Economic perspective,2016(1):62-69.)
[29]
何兴强,欧燕,陈平.上下游供求潜力与FDI在我国的区位选择——基于地区投入产出表和空间效应的分析[J].学术研究,2014(6):72-78,89.( HE X Q, OU Y, CHEN P. Upstream and downstream supply and demand potential and FDI location choice in China: based on regional input-output table and spatial effect analysis [J]. Academic research,2014(6):72-78,89.)
[30]
贾栋.陕西省城市科技创新能力与创新效率评价研究[D]. 西安:西安科技大学,2018.( JIA D. Research on evaluation of urban science and technology innovation ability and innovation Efficiency in Shaanxi province [D]. Xi 'an: Xi 'an University of Science and Technology,2018.)
[31]
彭刚,张文铖,李光武.国际贸易对地区经济增长率的影响——基于我国地级市数据的实证分析[J].经济问题探索,2020(10):158-169.( PENG G, ZHANG W C, LI G W. The impact of international trade on regional economic growth: an empirical analysis based on the data of prefecture-level cities in China [J]. Exploration of economic issues,2020(10):158-169.)

作者贡献说明

俞 享:数据处理及论文撰写;

门玉英:论文修改;

刘园园:论文撰写;

李 芳:论文修改。

基金

湖北省软科学研究项目“湖北省科技服务综合体建设研究”(2022EDA021)

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