Partner selection in business mergers: a data envelopment analysis approach

Abstract

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.