摘要
客观准确地测度创新能力是制订科技政策、加快推进创新驱动发展战略的基础性工作。创新测度方法的形成完善是创新认知和创新理论不断深化发展的结果。20世纪50年代以来,三者共同演进,先后出现“创新线性模型”“创新体系”“创新生态系统”等创新理论;在创新测度实践中则有“投入产出法”“综合指标法”“DEA效率评价法”“数据挖掘法”等与之对应。依托“创新生态系统”理论,在数据集成基础上引入数据挖掘法将是创新测度的重要趋势和方向。数据挖掘法是对其他测度方法的补充而非替代;有效整合不同方法下的测度结果将有助于提高测度的客观性和准确性。
The emerging and consummating of innovation measurement methods can be treated as the result of the improvement of inno vation recognition and theory.Ever since 1950s,with the co evolution of recognition,theory and measurement,there came out the“linear model of innovation”,“innovation system approach”,and“theory of innovation ecosystem”,and the measuring methods of“in novation input and output”,“composite indicator”,“DEA”and“data mining”,etc.The former 3 can measure the innovation capacity of the target unit from different angles,and have been widely used in practice.However,the complex,systematic,networking,inter acting features of innovation activity can hardly be reflected with these method.In the guidance of the innovation ecosystem theory,da ta mining based on data integration may be a good solution and a prospective trend.And combing different methods will improve the ac curacy of measuring results.
作者
罗庆朗
蔡跃洲
沈梓鑫
Luo Qinglang;Cai Yuezhou;Shen Zixin(Institute of Quantitative&Technological Economics,Chinese Academy of Social Sciences,Beijing 100732,China)
出处
《技术经济》
CSSCI
北大核心
2020年第2期185-191,共7页
Journal of Technology Economics
基金
国家社会科学基金重点项目“数字经济对中国经济发展的影响研究”(18AZD006)
2020年度中国社会科学院哲学社会科学创新工程“新一代ICT、数据要素与数字经济”。
关键词
创新理论
创新测度
数据集成
数据挖掘
innovation theory
measurement of innovation capacity
data integration
data mining
