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A generalized fuzzy Multiple-Layer NDEA: An application to performance-based budgeting

Mohamad Reza Amini, Adel Azar, Hamid Eskandari Orcid Logo, Peter F. Wanke

Applied Soft Computing, Volume: 100, Start page: 106984

Swansea University Author: Hamid Eskandari Orcid Logo

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Abstract

Network data envelopment analysis (NDEA) is capable of considering operations and interdependence of a system’s component processes to measure efficiencies. There are numerous performance evaluation applications in which some indicators have hierarchical structures with a considerable number of sub-...

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Published in: Applied Soft Computing
ISSN: 1568-4946
Published: Elsevier BV 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa56031
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spelling 2021-02-01T14:22:04.2237578 v2 56031 2021-01-14 A generalized fuzzy Multiple-Layer NDEA: An application to performance-based budgeting d2a47b056b55373889a9d19d2924f634 0000-0002-5515-9399 Hamid Eskandari Hamid Eskandari true false 2021-01-14 BBU Network data envelopment analysis (NDEA) is capable of considering operations and interdependence of a system’s component processes to measure efficiencies. There are numerous performance evaluation applications in which some indicators have hierarchical structures with a considerable number of sub-indicators. This problem of ignoring the hierarchical structure of indicators weakens the discrimination power of NDEA models and may result in inaccurate efficiency scores. In this paper we propose a generalized fuzzy Multiple-Layer NDEA (GFML-NDEA) model and GFML-NDEA-based composite indicators (GFML-NDEA-CI) to incorporate the hierarchical structures of indicators in the ambit of the particular two-stage NDEA models. To demonstrate the usefulness of the GFMLNDEA-CI model proposed, its application was tested by evaluating the efficiency of the performance-based budgeting (PBB) system in 14 governmental agencies in Iran. The comparative analysis results obtained from the GFML-NDEA-CI (multi-layer) model with those from the single-layer fuzzy NDEA-CI model indicate that the number of efficient decision-making units (DMUs) in the one-layer model is eight, whereas it is solely one DMU in the multi-layer model. The discrimination power of the multi-layer model proposed is significantly increased by observing that standard deviation of efficiency scores are increased by 41%, 61%, and 84% for possibility levels 0, 0.5, and 1, respectively. This is obtained while reducing information entropy, thus suggesting that the proposed model yields more reliable scores. Journal Article Applied Soft Computing 100 106984 Elsevier BV 1568-4946 DEA; Fuzzy logic; Performance-based budgeting; Maturity model; Network structure; Hierarchical structure 1 3 2021 2021-03-01 10.1016/j.asoc.2020.106984 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2021-02-01T14:22:04.2237578 2021-01-14T14:26:59.3877066 School of Management Business Mohamad Reza Amini 1 Adel Azar 2 Hamid Eskandari 0000-0002-5515-9399 3 Peter F. Wanke 4 56031__19212__5585160386f041ed937b5ffd9df388ab.pdf 56031.pdf 2021-02-01T14:17:46.6739030 Output 942777 application/pdf Accepted Manuscript true 2021-12-10T00:00:00.0000000 ©2020 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title A generalized fuzzy Multiple-Layer NDEA: An application to performance-based budgeting
spellingShingle A generalized fuzzy Multiple-Layer NDEA: An application to performance-based budgeting
Hamid Eskandari
title_short A generalized fuzzy Multiple-Layer NDEA: An application to performance-based budgeting
title_full A generalized fuzzy Multiple-Layer NDEA: An application to performance-based budgeting
title_fullStr A generalized fuzzy Multiple-Layer NDEA: An application to performance-based budgeting
title_full_unstemmed A generalized fuzzy Multiple-Layer NDEA: An application to performance-based budgeting
title_sort A generalized fuzzy Multiple-Layer NDEA: An application to performance-based budgeting
author_id_str_mv d2a47b056b55373889a9d19d2924f634
author_id_fullname_str_mv d2a47b056b55373889a9d19d2924f634_***_Hamid Eskandari
author Hamid Eskandari
author2 Mohamad Reza Amini
Adel Azar
Hamid Eskandari
Peter F. Wanke
format Journal article
container_title Applied Soft Computing
container_volume 100
container_start_page 106984
publishDate 2021
institution Swansea University
issn 1568-4946
doi_str_mv 10.1016/j.asoc.2020.106984
publisher Elsevier BV
college_str School of Management
hierarchytype
hierarchy_top_id schoolofmanagement
hierarchy_top_title School of Management
hierarchy_parent_id schoolofmanagement
hierarchy_parent_title School of Management
department_str Business{{{_:::_}}}School of Management{{{_:::_}}}Business
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description Network data envelopment analysis (NDEA) is capable of considering operations and interdependence of a system’s component processes to measure efficiencies. There are numerous performance evaluation applications in which some indicators have hierarchical structures with a considerable number of sub-indicators. This problem of ignoring the hierarchical structure of indicators weakens the discrimination power of NDEA models and may result in inaccurate efficiency scores. In this paper we propose a generalized fuzzy Multiple-Layer NDEA (GFML-NDEA) model and GFML-NDEA-based composite indicators (GFML-NDEA-CI) to incorporate the hierarchical structures of indicators in the ambit of the particular two-stage NDEA models. To demonstrate the usefulness of the GFMLNDEA-CI model proposed, its application was tested by evaluating the efficiency of the performance-based budgeting (PBB) system in 14 governmental agencies in Iran. The comparative analysis results obtained from the GFML-NDEA-CI (multi-layer) model with those from the single-layer fuzzy NDEA-CI model indicate that the number of efficient decision-making units (DMUs) in the one-layer model is eight, whereas it is solely one DMU in the multi-layer model. The discrimination power of the multi-layer model proposed is significantly increased by observing that standard deviation of efficiency scores are increased by 41%, 61%, and 84% for possibility levels 0, 0.5, and 1, respectively. This is obtained while reducing information entropy, thus suggesting that the proposed model yields more reliable scores.
published_date 2021-03-01T04:11:23Z
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score 10.8793745