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科技成果转化效果研究——基于熵权法和SSB模型 被引量:11

The Research on the Effectiveness of Transforming Scientific and Technological Achievements into Products:Based on Entropy Method and SSB Model
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摘要 以科技成果转化效果为研究对象建立评价体系,先采用熵权法得到每一个子系统及指标的权重和得分。再利用非径向且考虑松弛变量的Super-SBM模型对科技成果转化两阶段过程中的投入产出效率进行分析,并提出进一步归一化提取出的松弛变量和剔除前沿面效率值的有效部分,进而分析各指标的指数波动程度。再针对该评价体系构建BP神经网络模型进一步验证模型预测的精确性和相关评价体系的科学性和可实践性。研究结果表明:陕西省科技成果转化效果在构建的7个子系统维度发展各异,总体转化效果呈上升趋势,但科技成果经济转化能力不足,且科技成果研发与经济转化两阶段效率不均衡。重点把控科研经费、人力资源和科研环境子系统,进一步提升科技成果转化效率。最后结合Super-SBM-BP(SSB)模型评价结果为科技成果转化工作提出可行性建议。 Taking the transformation effect of scientific and technological achevements as the research object to establish the evaluation system,the entropy weight method is used to get the weight and score of each subsystem and index.Super‐SBM model based on non‐radial method of slacks‐based measure is used to analyze the input‐output efficiency of the two‐stage process of transforming Sci‐tech achievements into products.Furthermore,the extracted relaxation variable is normalized and the valid part after removing the efficiency value of the frontier is used to get the index fluctuation degree of each index.Then a BP neural network model is constructed to verify the accuracy of the model’s prediction and to ensure the evaluation index system is reasonable and practical.Making analysis of the data of Shaanxi Province,the results show that the transformation effectiveness of Sci‐tech achievements in Shaanxi Province is different in the seven subsystems,and the overall effectiveness is rising.However,the ability of economic transformation of Sci‐tech achievements is insufficient,and the efficiency of R&D and the efficiency of economic transformation of Sci‐tech achievements are unbalanced.The key point is to manage the three subsystems,such as scientific research funding,the human resources input,and the scientific research environment,and to further improve the efficiency of the transformation of Sci‐tech achievements.Finally,combined with the above evaluation results of Super‐SBM‐BP(SSB)model,feasible suggestions for the transformation of scientific and technological achievements are provided.
作者 艾时钟 陈正道 王慧雪纯 Ai Shizhong;Chen Zhengdao;Wang Huixuechun(School of Economics and Management,Xidian University,Xi’an 710071,China)
出处 《技术经济》 CSSCI 北大核心 2021年第3期1-10,共10页 Journal of Technology Economics
基金 西安市软科学项目“基于知识转移的硬科技成果转化模式研究”(201805071RK2SF5(10))。
关键词 科技成果转化 熵权法 Super-SBM 神经网络 transformation of scientific and technological achievements entropy method Super‐SBM neural network
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