首页 | 新闻公告 | 作者指南 | 编委会 | 关于杂志 | 订阅 | 相关链接 | 下载区 | 联系我们

三阶段组合效率测度模型与技术研发效率测度
Enhanced hybrid three-stage model for efficiency measure with application totechnological R&D efficiency
摘要点击 360  全文点击 203    
查看全文  查看/发表评论  下载PDF阅读器
中文关键词  数据包络分析( DEA) ; 三阶段组合效率测度模型; 幅度调整测度( RAM) ; 随机前沿 分析( SFA) ; 技术研发效率; 中国大中型工业企业
英文关键词  data envelopment analysis ( DEA) ; hybrid three-stage model for efficiency measure; range-adjusted measure ( RAM) ; stochastic frontier analysis ( SFA) ; technological research and development efficiency; China’s large and medium-sized industrial enterprises
基金项目  国家自然科学基金资助项目(71471170;71103173) ; 国家软科学研究计划资助项目(2013GXS4B087) ; 教育部人文社会科学研究资助项目(13YJC790062)
学科分类代码  
作者单位
陈凯华 中国科学院科技政策与管理科学研究所,北京100190 
汪寿阳 中国科学院数学与系统科学研究院,北京100190 
寇明婷 中国科学院大学管理学院,北京100190 
中文摘要
      在系统梳理现有可调整环境因素或/与统计噪音影响的三阶段组合效率测度模型的基础上,利用幅度调整测度(RAM) 与随机前沿分析(SFA) 结合,构建了无需强制性调整环境因素与统计噪音影响的三阶段组合效率测度改进模型——RAM-SFA-RAM.相对现有模型,新的组合测度模型不但在估计影响与调整投入和产出时可实现较为客观地设定环境因素与松弛变量之间存在非线性生产函数关系,以降低效率估计偏误,而且充分利用了RAM 模型全面测度无效的优点,为从投入和产出两个方面同时过滤环境因素与统计噪音的差异性影响提供了新的分析途径; 更重要的是,选用的RAM 具有平移不变性,可使组合测度模型克服现有模型只能通过最值强制性对投入和产出进行正向调整以适应选择的效率估计模型无法处理非正值的不足,避免了由此产生的效率估计偏误.实证研究部分用这一组合效率测度模型分析了中国省域层次上大中型工业企业的技术研发效率,为效率的客观比较提供了可行的模型方法.
英文摘要
      This paper proposes a flexible and enhanced hybrid three-stage model for efficiency measure,RAMSFA-RAM,without the mandatory adjustment of the effects of environment factors and statistical noise,which is based on the comprehensive discussions and review of extant hybrid three-stage models for efficiency measure with adjustments for the effects of contextual environment and statistical noise. In contrast to the existing models,our hybrid model not only obeys the non-linear productive functional relationship between the environmental factors and slacks in estimating slacks and in adjusting the inputs and outputs to reduce the bias of efficiency estimation,but also takes advantage of the RAM which can present a new approach of adjusting the different effects of environmental factors and statistic noise based on both inputs and outputs. More importantly,our model takes full advantage of the translation invariance of the RAM to deal with the non-positive values,which can only work for existing models by arbitrary positive adjustments in virtue of the maximum or minimum of estimated slack datasets,and avoids the bias of efficiency estimation. In the empirical study,this article applies this new model to measure the research and development efficiency of large-and mediumsized industrial enterprises at China’s provincial level,to show that our method presents a feasible modelling approach of objectively comparing efficiency scores.
关闭

版权所有 © 2007 《管理科学学报》
通讯地址:天津市南开区卫津路92号天津大学第25教学楼A座908室 邮编:300072
联系电话/传真:022-27403197 电子信箱: jmstju@263.net