Modelling generalized firms' restructuring using inverse DEA

The key consideration for firms’ restructuring is improving their operational efficiencies. Market conditions often offer opportunities or generate threats that can be handled by restructuring scenarios through consolidation, to create synergy, or through split, to create reverse synergy. A generalized restructuring refers to a move in a business market where a homogeneous set of firms, a set of pre-restructuring decision making units (DMUs), proceed with a restructuring to produce a new set of post-restructuring entities in the same market to realize efficiency targets. This paper aims to develop a novel inverse Data Envelopment Analysis based methodology, called GInvDEA (Generalized Inverse DEA), for modeling the generalized restructuring. Moreover, the paper suggests a linear programming model that allows determining the lowest performance levels, measured by efficiency that can be achieved through a given generalized restructuring. An application in banking operations illustrates the theory developed in the paper

An alternative approach to decompose the potential gains from mergers

Bogetoft and Wang proposed admirable production economic models to estimate and decompose the potential gains from mergers. They provided a good platform to quantify the merger efficiency and related it to relevant organisational changes ex-ante. In this paper, we develop an alternative approach to decompose the potential overall gains from mergers into to technical effect, size effect, and harmony effect. The proposed approach uses strongly efficient projections, and consistently calculates radial input-based measures for these three effects based on the pre-merger aggregated inputs. In addition, the proposed approach is of vital significance in two special cases where the aggregated projected inputs are not proportional to the pre-merger aggregated inputs and where the production sizes are very different for the original decision-making units. Finally, an application to the City Commercial Banks (CCBs) in China is provided to illustrate the usefulness and efficacy of the proposed approach. The application shows that there exist significant merger efficiency gains for these top 20 CCBs. Further, both the technical effect and harmony effect favour mergers, whereas the size effect would work against most mergers. Thus, in most cases the full-size merger with “organisational sense” is not proper

Investigating the impact of behavioral factors on supply network efficiency:insights from banking’s corporate bond networks

This paper highlights the role of behavioral factors for efficiency measurement in supply networks. To this aim, behavioral issues are investigated among interrelations between decision makers involved in corporate bond service networks. The corporate bond network was considered in three consecutive stages, where each stage represents the relations between two members of the network: issuer-underwriter, underwriter-bank, and bank-investor. Adopting a multi-method approach, we collected behavioral data by conducting semi-structured interviews and applying the critical incident technique. Financial and behavioral data, collected from each stage in 20 corporate bond networks, were analyzed using fuzzy network data envelopment analysis to obtain overall and stage-wise efficiency scores for each network. Sensitivity analyzes of the findings revealed inefficiencies in the relations between underwriters-issuers, banks-underwriters, and banks-investors stemming from certain behavioral factors. The results show that incorporating behavioral factors provides a better means of efficiency measurement in supply networks

DEA-based Nash bargaining approach to merger target selection

Mergers and Acquisitions (M&amp;As) are important business strategies in any industry, as they allow the parties involved to achieve, for example, higher market share, profits and influence in one or more industries. In any M&amp;A activity, the key challenge for an acquirer company is to select the target company that can most improve its performance through synergy. The goal of this research is thus to help acquirer companies model and optimally solve their merger target selection problems (MTSPs) in both horizontal integration and vertical integration settings. We apply both a data envelopment analysis (DEA) based performance evaluation framework and the Nash bargaining solution concept to mathematically model an acquirer company's MTSPs under the two types of integration settings. To the best of our knowledge, the proposed new models are the first DEA-based Nash bargaining models in the literature to help acquirer companies obtain their most desired target companies. Finally, this research provides numerical examples, including real-life examples, to illustrate various aspects and implementation details of the two types of models.</p