Four types of dependence relationship in two consecutive stage data envelopment analysis model

A. S. A. Aminuddin, N. Abu, M. M. Kasim, M. Nawawi
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Abstract

Data Envelopment Analysis (DEA) model usually does not consider the interaction between the decision making units (DMU). The interaction can be represented in the form of two consecutive stages in which the outputs from the precedent stage will be the inputs for the latter stage. The two consecutive stage DEA model can be represented as Non-separable DEA (NS-DEA) which integrates both desirable and undesirable output. The undesirable output unlike desirable output, indicates a higher efficiency if the output is lower or not productive. The different orientation between desirable and undesirable output may affect the efficiency score especially if it was formed in two consecutive stages. Thus, this research attempts to address four different types of dependence relationship which can occur in the formation of two consecutive stage DEA models and to investigate the impact towards the overall efficiency of the DMUs. The finding shows that the determination of positive or negative correlation between the two stages which combines both desirable and undesirable output, are more likely to be influenced by the orientation of the first precedent stage.Data Envelopment Analysis (DEA) model usually does not consider the interaction between the decision making units (DMU). The interaction can be represented in the form of two consecutive stages in which the outputs from the precedent stage will be the inputs for the latter stage. The two consecutive stage DEA model can be represented as Non-separable DEA (NS-DEA) which integrates both desirable and undesirable output. The undesirable output unlike desirable output, indicates a higher efficiency if the output is lower or not productive. The different orientation between desirable and undesirable output may affect the efficiency score especially if it was formed in two consecutive stages. Thus, this research attempts to address four different types of dependence relationship which can occur in the formation of two consecutive stage DEA models and to investigate the impact towards the overall efficiency of the DMUs. The finding shows that the determination of positive or negative correlation between the two ...
连续两阶段数据包络分析模型中的四种依赖关系
数据包络分析(DEA)模型通常不考虑决策单元(DMU)之间的相互作用。这种相互作用可以用两个连续阶段的形式来表示,其中前一阶段的输出将成为后一阶段的输入。连续两阶段的DEA模型可以表示为不可分DEA (non -分离式DEA, NS-DEA),它集成了期望输出和不期望输出。不期望产出与期望产出不同,如果产出较低或没有生产力,则表示效率较高。理想产出和不理想产出的不同取向会影响效率得分,特别是在连续两个阶段形成的情况下。因此,本研究试图解决在两个连续阶段DEA模型形成过程中可能出现的四种不同类型的依赖关系,并探讨其对决策单位整体效率的影响。研究结果表明,两个阶段之间的正相关或负相关的确定,结合了理想和不希望的输出,更有可能受到第一个先例阶段的取向的影响。数据包络分析(DEA)模型通常不考虑决策单元(DMU)之间的相互作用。这种相互作用可以用两个连续阶段的形式来表示,其中前一阶段的输出将成为后一阶段的输入。连续两阶段的DEA模型可以表示为不可分DEA (non -分离式DEA, NS-DEA),它集成了期望输出和不期望输出。不期望产出与期望产出不同,如果产出较低或没有生产力,则表示效率较高。理想产出和不理想产出的不同取向会影响效率得分,特别是在连续两个阶段形成的情况下。因此,本研究试图解决在两个连续阶段DEA模型形成过程中可能出现的四种不同类型的依赖关系,并探讨其对决策单位整体效率的影响。这一发现表明,两者之间正相关或负相关的测定…
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