| 中国众创空间科技创新效率的区域差异及空间分布 |
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| 引用本文：李 鑫1,2,陈银娥3.中国众创空间科技创新效率的区域差异及空间分布[J].财经理论与实践,2023,(2):88-95 |
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| 中文摘要:基于技术创新和区域经济理论，依据众创空间2017—2019年数据，运用三阶段超效率SBM-DEA动态模型，结合ML指数、Dagum系数和Moran’s I指数，考量我国众创空间科技创新效率的时空差异和空间分布。结果发现：众创空间科技创新整体效率较低，区域之间差异较大；科技创新效率的差异来源于组内和组间差异，呈现“东多西少”的分布格局，具有较强的局部集聚性。鉴于此，应进一步依托众创空间的科技创新动能和区位优势，构建区域优势产业，优化区域产业结构，聚集科技创新人才，提升科技创新产业水平。 |
| 中文关键词:众创空间 科技创新 时空分异 SBM-DEA动态模型 |
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| Regional Differences and Spatial Distribution of Science and Technology Innovation Efficiency in China’s Crowdsourcing Spaces |
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| Abstract:Based on theories of technological innovation and regional economy, the three-stage super-efficiency SBM-DEA dynamic model, combined with ML index, Dagum coefficient and Moran’s I index, was applied to consider the spatial and temporal differences and the spatial distribution of technological innovation efficiency of crowdsourcing innovation spaces in China based on the data of crowdsourcing innovation spaces from 2017 to 2019. The results show that the overall efficiency of science and technology innovation in crowdsourcing innovation spaces is low, and the differences between regions are large; the differences in science and technology innovation efficiency are within and between groups, and the distribution pattern is “more in the east and less in the west”, with strong local clustering. In view of this, we should further build up regional advantageous industries, optimize regional industrial structure, gather scientific and technological innovation talents, and improve the level of scientific and technological innovation industries by relying on the scientific and technological innovation kinetic energy and location advantages of crowdsourcing spaces. |
| keywords:crowdsourcing space science and technology innovation spatio-temporal differentiation SBM-DEA dynamic model |
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