Abstract
This article proposes a new data envelopment analysis (DEA)-based approach to deal with mergers and acquisitions (M&As) matching. To derive reliable matching degrees between bidder and target firms, we consider both technical efficiency and scale efficiency. Specifically, an inverse DEA model is developed for measuring the technical efficiency, while a conventional DEA model is employed to identify the return of scale of the merged decision-making units (DMUs). Then, an optimization model is formulated to generate matching results to improve DMUs’ performance. An empirical study of M&As matching Turkish energy firms is examined to illustrate the proposed approach. This study shows that both technical efficiency and scale efficiency have impacts on M&As matching practices.
Highlights
Mergers and acquisitions (M&As) refer to amalgamation or consolidation of firms through various types of business and financial transactions (Braguinsky et al, 2015; Vizcaıno-Gonzalez & Navıo-Marco, 2018)
This study aims to address the strategic matching in M&A considering both technical efficiency and scale efficiency, where the technical efficiency is measured using an optimal efficiency of each decision-making units (DMUs) and scale efficiency captures the impact of scale size on productivity (Banker et al 1984)
This article presents an M&A matching framework for the strategic decision based on data envelopment analysis (DEA) techniques
Summary
Mergers and acquisitions (M&As) refer to amalgamation or consolidation of firms through various types of business and financial transactions (Braguinsky et al, 2015; Vizcaıno-Gonzalez & Navıo-Marco, 2018) Often such consolidation is represented as a bidder (acquirer) firm takes over another target firm, and establishes itself as a new entity. In the framework of TSM, the matching degrees among the candidates (acquirer or target) on two sides need to be derived before applying a specific strategy. For achieving this a variety of evaluating methods have been utilized. Data envelopment analysis, which based on linear programming, is one of the most important techniques among them
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