Volume 55, Number 5, September-October 2021
|Page(s)||2861 - 2881|
|Published online||29 September 2021|
Directional scale elasticity considering the management preference of decision-makers
School of Business Administration, Hunan University, Changsha 410082, P.R. China
2 Institute of Data Science and Decision Optimization, Hunan University, Changsha 410082, P.R. China
3 Beijing Normal University, Zhuhai, 519087, P.R. China
4 DBM, BNU-HKBU United International College, Zhuhai 519087, P.R. China
Accepted: 14 August 2021
Most data envelopment analysis (DEA) studies on scale elasticity (SE) and returns to scale (RTS) of efficient units arise from the traditional definitions of them in economics, which is based on measuring radial changes in outputs caused by the simultaneous change in all inputs. In actual multiple inputs/outputs activities, the goals of expanding inputs are not only to obtain increases in outputs, but also to expect the proportions of such increases consistent with the management preference of decision-makers. However, the management preference is usually not radial changes in outputs. With the latter goal into consideration, this paper proposes the directional SE and RTS in a general formula for multi-output activities, and offers a DEA-based model for the formula of directional SE at any point on the DEA frontier, which is straightforward and requires no simplifying assumptions. Finally, the empirical part employs the data of 16 basic research institutions in Chinese Academy of Sciences (CAS) to illustrate the superiority of the proposed theories and methods.
Mathematics Subject Classification: 90B30 / 90B50 / 90C08 / 90C90
Key words: Data envelopment analysis / returns to scale / management preference / directional scale elasticity
© The authors. Published by EDP Sciences, ROADEF, SMAI 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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