On indication, strict monotonicity, and efficiency of projections in a general class of path-based data envelopment analysis models

Margaréta Halická, Mária Trnovská and Aleš Černý

European Journal of Operational Research, 2025, vol. 320, issue 1, 175-187

Abstract: Data envelopment analysis (DEA) theory formulates a number of desirable properties that DEA models should satisfy. Among these, indication, strict monotonicity, and strong efficiency of projections tend to be grouped together in the sense that, in individual models, typically, either all three are satisfied or all three fail at the same time. Specifically, in slacks-based graph models, the three properties are always met; in path-based models, such as radial models, directional distance function models, and the hyperbolic function model, the three properties, with some minor exceptions, typically all fail.

Keywords: Data envelopment analysis; Directional distance function; Hyperbolic distance function; Indication; Strict monotonicity (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:320:y:2025:i:1:p:175-187

DOI: 10.1016/j.ejor.2024.08.009

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