BRADFORD SCHOLARS

    • Sign in
    View Item 
    •   Bradford Scholars
    • Management and Law
    • Management and Law Publications
    • View Item
    •   Bradford Scholars
    • Management and Law
    • Management and Law Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Bradford ScholarsCommunitiesAuthorsTitlesSubjectsPublication DateThis CollectionAuthorsTitlesSubjectsPublication Date

    My Account

    Sign in

    HELP

    Bradford Scholars FAQsCopyright Fact SheetPolicies Fact SheetDeposit Terms and ConditionsDigital Preservation Policy

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Supporting better practice benchmarking: A DEA-ANN approach to bank branch performance assessment

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Publication date
    2020-12
    Author
    Tsolas, I.E.
    Vincent, Charles
    Gherman, T.
    Keyword
    Artificial neural network
    Data envelopment analysis
    Banking
    Performance
    Best practice
    Benchmarking
    Artificial intelligence
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    The 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.
    URI
    http://hdl.handle.net/10454/17952
    Version
    No full-text in the repository
    Citation
    Tsolas 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.
    Link to publisher’s version
    https://doi.org/10.1016/j.eswa.2020.113599
    Type
    Article
    Collections
    Management and Law Publications

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.