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Development and Evolution of Slacks‐Based Measure Models in Data Envelopment Analysis: A Comprehensive Review of the Literature.

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Titel: Development and Evolution of Slacks‐Based Measure Models in Data Envelopment Analysis: A Comprehensive Review of the Literature.
Autoren: Emrouznejad, Ali1 (AUTHOR) a.emrouznejad@surrey.ac.uk, Brzezicki, Łukasz2 (AUTHOR), Lu, Chxiao1 (AUTHOR)
Quelle: Journal of Economic Surveys. Dec2025, Vol. 39 Issue 5, p2106-2133. 28p.
Schlagwörter: *NONPARAMETRIC statistics, *DATA assimilation, DATA envelopment analysis, EVALUATION methodology, INFERENTIAL statistics
Abstract: This paper provides a comprehensive review of slacks‐based measure (SBM) models within the nonparametric data envelopment analysis (DEA) framework. The review reveals that the development and modifications of SBM models have progressed in multiple directions. Key areas of modification include (1) the nature of inputs and outputs, (2) data specificity, (3) super‐efficiency for ranking decision‐making units, (4) the inclusion of networks in organizational structures or production processes, (5) the dynamic nature of the analysis, and (6) various other methodological aspects. Increasingly, complex SBM models addressing multiple aspects, such as input/output characteristics and data specificity, are appearing in the literature. Another notable trend is the integration of various methodological proposals from different authors into unified SBM models. Some publications even attempt to forecast future efficiency levels and incorporate large datasets (big data). Despite numerous modifications and advancements, SBM models still lack robust statistical inference capabilities, unlike radial models, which possess more developed statistical foundations. [ABSTRACT FROM AUTHOR]
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Datenbank: Business Source Index
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Abstract:This paper provides a comprehensive review of slacks‐based measure (SBM) models within the nonparametric data envelopment analysis (DEA) framework. The review reveals that the development and modifications of SBM models have progressed in multiple directions. Key areas of modification include (1) the nature of inputs and outputs, (2) data specificity, (3) super‐efficiency for ranking decision‐making units, (4) the inclusion of networks in organizational structures or production processes, (5) the dynamic nature of the analysis, and (6) various other methodological aspects. Increasingly, complex SBM models addressing multiple aspects, such as input/output characteristics and data specificity, are appearing in the literature. Another notable trend is the integration of various methodological proposals from different authors into unified SBM models. Some publications even attempt to forecast future efficiency levels and incorporate large datasets (big data). Despite numerous modifications and advancements, SBM models still lack robust statistical inference capabilities, unlike radial models, which possess more developed statistical foundations. [ABSTRACT FROM AUTHOR]
ISSN:09500804
DOI:10.1111/joes.12682