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dc.contributor.authorTsolas, I.E.
dc.contributor.authorVincent, Charles
dc.contributor.authorGherman, T.
dc.date.accessioned2020-07-05T23:25:44Z
dc.date.accessioned2020-08-13T14:50:52Z
dc.date.available2020-07-05T23:25:44Z
dc.date.available2020-08-13T14:50:52Z
dc.date.issued2020-12
dc.identifier.citationTsolas IE, Vincent C and Gherman T (2020) Supporting better practice benchmarking: A DEA-ANN approach to bank branch performance assessment. Expert Systems with Applications. 160: 113599.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17952
dc.descriptionNo
dc.description.abstractThe quest for best practices may lead to an increased risk of poor decision-making, especially when aiming to attain best practice levels reveals that efforts are beyond the organization’s present capabilities. This situation is commonly known as the “best practice trap”. Motivated by such observation, the purpose of the present paper is to develop a practical methodology to support better practice benchmarking, with an application to the banking sector. In this sense, we develop a two-stage hybrid model that employs Artificial Neural Network (ANN) via integration with Data Envelopment Analysis (DEA), which is used as a preprocessor, to investigate the ability of the DEA-ANN approach to classify the sampled branches of a Greek bank into predefined efficiency classes. ANN is integrated with a family of radial and non-radial DEA models. This combined approach effectively captures the information contained in the characteristics of the sampled branches, and subsequently demonstrates a satisfactory classification ability especially for the efficient branches. Our prediction results are presented using four performance measures (hit rates): percent success rate of classifying a bank branch’s performance exactly or within one class of its actual performance, as well as just one class above the actual class and just one class below the actual class. The proposed modeling approach integrates the DEA context with ANN and advances benchmarking practices to enhance the decision-making process for efficiency improvement.en_US
dc.language.isoenen_US
dc.subjectArtificial neural network
dc.subjectData envelopment analysis
dc.subjectBanking
dc.subjectPerformance
dc.subjectBest practice
dc.subjectBenchmarking
dc.subjectArtificial intelligence
dc.titleSupporting better practice benchmarking: A DEA-ANN approach to bank branch performance assessmenten_US
dc.status.refereedYes
dc.date.Accepted25/05/2020
dc.date.application30/05/2020
dc.typeArticle
dc.type.versionNo full-text in the repository
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2020.113599
dc.date.updated2020-07-05T22:25:55Z
dc.openaccess.statusclosedAccess


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