2011
DOI: 10.1016/j.eswa.2010.12.148
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A study on the selection of benchmarking paths in DEA

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Cited by 57 publications
(30 citation statements)
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“…The context-dependent DEA (CD-DEA), originally introduced by Seiford and Zhu [26], is a well-known extension on standard DEA. The CD-DEA, consisting of stratification procedure, attractiveness measure, and progress measure, has proven a helpful method to construct the benchmark-learning pathway for inefficient DMUs [27,28]. Through the benchmark-learning pathway, inefficient DMUs can stepwise improve their efficiencies and reach the terminal frontier [29,30].…”
Section: Methodsmentioning
confidence: 99%
“…The context-dependent DEA (CD-DEA), originally introduced by Seiford and Zhu [26], is a well-known extension on standard DEA. The CD-DEA, consisting of stratification procedure, attractiveness measure, and progress measure, has proven a helpful method to construct the benchmark-learning pathway for inefficient DMUs [27,28]. Through the benchmark-learning pathway, inefficient DMUs can stepwise improve their efficiencies and reach the terminal frontier [29,30].…”
Section: Methodsmentioning
confidence: 99%
“…Next, we conducted a comparative experiment with the method proposed by Lim et al (2011) (hereafter called the L-method) to demonstrate the effectiveness of the proposed method. The L-method is regarded as a similar approach to our method in that it was designed to select the optimal benchmarking path considering multiple criteria.…”
Section: Inputsmentioning
confidence: 99%
“…In setting benchmarking goals or strategies, selecting existing units as the benchmark target can be more practical, because the inefficient DMU that wants to improve its efficiency can utilize actual information. On the other hand, selecting hypothetical units as the benchmark target can lead to unrealistic benchmarking in setting benchmarking strategies or implementing the best practice, because learning additional knowledge from hypothetical units is problematic (Lim et al, 2011). For this reason, in the present study, only existing units were considered as benchmark targets.…”
Section: Introductionmentioning
confidence: 99%
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“…The proposed model derives multiple efficient frontiers for identifying the performance factors that differentiate inefficient DMUs. Lim et al [12] advocated, for stepwise benchmark target selection, the use of the attractiveness and progress measures of context-dependent DEA along with the consideration of feasibility. In the proposed algorithm, it searches to select only local solutions, and thereby it does not guarantee a global optimum.…”
Section: Introductionmentioning
confidence: 99%