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机构地区:[1]长安大学,陕西西安710064
出 处:《西安财经学院学报》2015年第4期32-37,共6页Journal of Xi’an University of Finance & Economics
基 金:国家自然科学基金项目"寡头垄断企业R&D博弈模式及其动力学问题研究"(71072160);国家社科基金项目"财政补贴退出后的多主体创新网络运行机制研究"(12CGL009);中央高校基本科研业务费专项"创新网络中企业地位变迁机理与控制实证研究"(CHD2009JC012)
摘 要:文章基于高技术产业知识产出和科技成果转化为生产力两阶段视角,考虑“中间产品产出再投入”和“初始投入在两个子过程间的分配结构”,构建共享投入关联DEA模型,使用1999—2013年中国高技术产业11个细分行业数据,测算并分解效率。高技术产业11个行业系统效率水平介于0.128-0.592之间,知识产出效率水平介于0.124~0.590之间,科技成果转化效率水平介于0.119~0.398之间。研究表明:共享投入关联DEA模型测算效率水平的同时,还可得到初始投入在两个生产阶段的配置比例和中间产品的转化信息,利于做出正确的管理决策;我国高技术产业科技成果转化效率较低,在注重研发投入的同时,须增强科技成果商品化和市场化的应用能力。A network DEA for a relational two-stage production system with shared inputs is developed in this paper on the view of two stage of knowledge out and achievement transfer and on the assumption of "re-input of the intermediate products" and "allocation information of the initial input in the two subprocesses", and then with the model, system efficiency and pure technical efficiency of each stage is calculated with the data of 29 provinces from 1999 to 2013, the level of inefficiency and the proportion of initial input is tested simultaneously. It also reveals the efficiency level of 11 industries is in 0. 128-0. 592, the knowledge transformation efficiency is in between 0. 124-0. 590 science and technology achievement transformation efficiency is generally low, ranging from 0. 119-0. 398. The results shows that network DEA not only can calculate efficiency of system and its subrocesses, but also production information embedded in the intermediate products as well as the allocation information of various inputs among the individual subprocesses. While maintaining a high level of R&D investment, it is necessary to increase the ability to transform scientific and technological achievements.
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