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我国省区规模工业创新绩效实证研究——基于HCA-SEM(超效率)-DEA模型 被引量:9

Innovation Performance of the Provincial Scale Industries in China——Based on the HCA-SEM( Super Efficiency)-DEA Model
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摘要 对我国各省区规模工业进行系统聚类,分为创新效率较高区域、创新效率中等区域和创新效率较低区域3个层次,运用一般化(非)导向超效率DEA模型进行创新绩效分析,得出如下结论:我国省区规模工业创新基本有效率,70%的省区处于效率前沿;"创新效率较高区域"在技术输出方面具有绝对优势;要素驱动呈现R&D、技改、资本投入与创新效率较高、中等、较低区域依次匹配的显著特征;产出目标呈现重利润、产值、专利与创新效率较高、中等、较低区域依次匹配特征.层次分析得到如下结论:在"创新效率较高区域"中,北京处于顶端,上海绩效较差;在"创新效率中等区域"中,江西、吉林、湖南等具有较好的创新效率,黑龙江、重庆和河北具有效率提升空间;在"创新效率较低区域"中,新疆、内蒙古创新效率较高,新疆、海南规模效率较高,山西两项指标都比较低.从层次、比例和松弛3个方面提出改进建议:提升层次应该成为各省区规模工业创新绩效提高的首要目标;进行投入与产出的双向比例改进,适应我国当前的供给侧结构性改革;进行精细化成本管理,推进松弛改进. On the cluster, Chinese provincial industries were divided into three levels : region with higher innovation efficiency, region with medium innovation efficiency and region with poor innovation efficiency. Non-oriented super effi- ciency DEA model was used to analyze the innovation performance. The conclusion is drawn as follows : Chinese provincial scale industrial innovation is efficient, with 70% provinces at the forefront ; "region with higher innovation efficien- cy" has absolute advantage in technical output; driving factors present the characteristic that R&D, technology innovation, capital investment match with regions of higher, medium and poor innovation efficiency in turns; output target present the characteristic that profits, output, patent match with regions of higher, medium and poor innovation efficiency in turns. The following conclusion can be achieved from the facet analysis: Beijing is on the top in "the region with higher innovation efficiency", while Shanghai is poor; in "the region with medium innovation efficiency", Jiangxi, Jilin and Hunan have better innovation efficiency, and Heilongjiang, Chongqing and Hebei possess space of efficiency promotion; in "the region with poor innovation efficiency", Xinjiang and Inner Mongolia have higher innovation efficiency, Xinjiang and Hainan have higher scale efficiency, and Shanxi lies the lowest for the two indicators. It made recommendations respectively from three aspects, including level, proportion and relaxation, promoting level should be the primary goal for improvement of provincial industrial innovation performance; bi-directional proportional improvement of input and output should be carried out to adapt to the Supply Side Structural Reforms; precise cost management should be conducted to promote the relaxation improvement.
作者 陈四辉
出处 《研究与发展管理》 CSSCI 北大核心 2017年第2期102-115,共14页 R&D Management
基金 国家自然科学基金资助项目"基于‘两型社会’的城市规模研究"(71073135) 湛江市科技计划项目"湛江融入‘海丝之路’发展战略的科技创新体系研究"(2015A01045)
关键词 规模工业 创新绩效 超效率DEA模型 系统聚类分析 scale industry innovation performance super-efficiency DEA model system clustering analysis
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