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A DEA cross-efficiency approach based on bargaining theory

dc.contributor.authorContreras Rubio, Ignacio
dc.contributor.authorLozano, Sebastián
dc.contributor.authorHinojosa, Miguel
dc.date.accessioned2024-02-06T10:11:00Z
dc.date.available2024-02-06T10:11:00Z
dc.date.issued2021
dc.descriptionThe research of the authors has been supported by the Spanish Ministry of Science, Innovation and Universities under project PGC2018-095786-B-I00.
dc.description.abstractCross-efficiency evaluation is a Data Envelopment Analysis (DEA) approach that enables the efficiency and ranking of Decision-Making Units (DMUs) to assess the possibility of idiosyncratic weighting schemes. However, the existence of multiple optimal weights in multiplier DEA models implies the non-uniqueness of the cross-efficiency evaluation. This is normally dealt with using secondary goals that enable the optimal weighting vectors to be chosen. In this paper, a new method to discriminate between optimal weighting profiles is proposed based on bargaining problems and the Kalai-Smorodinsky solution. In this approach, the input and output multipliers are agreed upon by the peer DMUs.
dc.description.sponsorshipDepartamento de Economía, Métodos Cuantitativos e Historia Económica
dc.format.mimetypeapplication/pdf
dc.identifier.citationI. Contreras, S. Lozano & M. A. Hinojosa (2021) A DEA cross-efficiency approach based on bargaining theory, Journal of the Operational Research Society, 72:5, 1156-1167, DOI: 10.1080/01605682.2020.1755898
dc.identifier.doi10.1080/01605682.2020.1755898
dc.identifier.urihttps://hdl.handle.net/10433/19752
dc.language.isoen
dc.publisherTaylor & Francis
dc.rights.accessRightsrestricted access
dc.subjectData envelopment analysis
dc.subjectCross-efficiency
dc.subjectBargaining theory
dc.subjectKalai- Smorodinsky solution
dc.titleA DEA cross-efficiency approach based on bargaining theory
dc.typejournal article
dc.type.hasVersionVoR
dspace.entity.typePublication
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relation.isAuthorOfPublication9f5ed1ef-40b2-41ff-a39b-03d42ab8c89c
relation.isAuthorOfPublication.latestForDiscovery6f870f6c-0c10-4ba6-895e-be1956636c92

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