Learning from input-output mixes in DEA: a proportional measure for slack-based efficient projections

Cherchye, Laurens; Puyenbroeck, Tom Van

doi:10.1002/(sici)1099-1468(199905)20:3<151::aid-mde924>3.0.co;2-b

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…This article applied output-oriented the (BCC) DEA variant and FDH not only because both assume VRS (Cook & Seiford, 2009; Zhu, 2009) but also because they are more suitable for managerial use than CCR (Cherchye & Van Puyenbroeck, 1999). The similarity of the assumptions ensures the comparability of results and appropriateness of identified peers and performance targets for weak DMUs.…”

Section: Methodsmentioning

confidence: 99%

Inter-Organizational Bench-Learning to Respond to Climate Change and Reduce Trade-Offs in Urban Drinking Water Supply: The Case of Grade 2B Municipalities in Oromia National Regional State, Ethiopia

Debela

2022

Sage Open

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Most studies on drinking water supply seem to focus on efficiency analysis and have failed to analyze effectiveness, cost-effectiveness, and trade-offs in performance management. Using Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH) novel reference technologies, we analyzed the efficiency, effectiveness, and cost-effectiveness, and the social, economic and ecological performance (TBL elements) of 29 municipalities in drinking water supply, in Oromia National Regional State. The results from 42 (efficiency), 173 (effectiveness), and 37 (cost-effectiveness) DEA and FDH models on production model criterion and TBL elements reveal that performance of many municipalities was weak, and the municipalities encountered trade-offs on the production model criterion and TBL elements. We argue that inter-organizational bench-learning can improve performance, reduce trade-offs, and respond to the effect of climate change on the drinking water supply. To this end, we argue for the need to map peers for inter-organizational bench-learning. We identified five inter-organizational bench-learning strategies and drew implications. The article contributes to performance management literature and the debate on sustainable development.

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Section: Methodsmentioning

confidence: 99%

Inter-Organizational Bench-Learning to Respond to Climate Change and Reduce Trade-Offs in Urban Drinking Water Supply: The Case of Grade 2B Municipalities in Oromia National Regional State, Ethiopia

Debela

2022

Sage Open

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Profit efficiency analysis under limited information with an application to German farm types

Cherchye

Puyenbroeck²

2007

Omega

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Negative data in DEA: a directional distance approach applied to bank branches

Portela

Thanassoulis²,

Simpson³

2004

Journal of the Operational Research Society

344

200

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This paper is drawn from the use of data envelopment analysis (DEA) in helping a Portuguese bank to manage the performance of its branches. The bank wanted to set targets for the branches on such variables as growth in number of clients, growth in funds deposited and so on. Such variables can take positive and negative values but apart from some exceptions, traditional DEA models have hitherto been restricted to non-negative data. We report on the development of a model to handle unrestricted data in a DEA framework and illustrate the use of this model on data from the bank concerned.

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Learning from input-output mixes in DEA: a proportional measure for slack-based efficient projections

Cited by 4 publications

References 17 publications

Inter-Organizational Bench-Learning to Respond to Climate Change and Reduce Trade-Offs in Urban Drinking Water Supply: The Case of Grade 2B Municipalities in Oromia National Regional State, Ethiopia

Inter-Organizational Bench-Learning to Respond to Climate Change and Reduce Trade-Offs in Urban Drinking Water Supply: The Case of Grade 2B Municipalities in Oromia National Regional State, Ethiopia

Profit efficiency analysis under limited information with an application to German farm types

Negative data in DEA: a directional distance approach applied to bank branches

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