| Issue |
RAIRO-Oper. Res.
Volume 58, Number 4, July-August 2024
|
|
|---|---|---|
| Page(s) | 3107 - 3117 | |
| DOI | https://doi.org/10.1051/ro/2024091 | |
| Published online | 01 August 2024 | |
Partner selection in business mergers: a data envelopment analysis approach
Faculty of Business, University of New Brunswick, Saint John, NB E2K 5E2, Canada
* Corresponding author: gamin@unb.ca
Received:
4
November
2022
Accepted:
25
April
2024
Business mergers and partnerships could create opportunities for the decision making units (DMUs) involved to collectively enhance their efficiency. Estimating potential merger gains for a set of given merging DMUs using data envelopment analysis (DEA) and inverse DEA have been discussed in the literature. This paper develops new inverse DEA models for partner selection in a merger. The developed models extend the literature by finding optimal sets of partners that would maximize merger gains among a group of potential merging partners. The results of this study are useful to business managers seeking to merge to improve competitiveness. Data from the top US commercial banks is used to show the applicability of the proposed DEA models in this study.
Mathematics Subject Classification: 90C08 / 90C05 / 90C10
Key words: Data envelopment analysis / partnership gains / inverse DEA / strategic alliance / mergers and acquisitions
© The authors. Published by EDP Sciences, ROADEF, SMAI 2024
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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