This study suggests a novel application of Inverse Data Envelopment Analysis (InvDEA) in strategic decision making about mergers and acquisitions in banking. The conventional DEA assesses the efficiency of banks based on the information gathered about the quantities of inputs used to realize the observed level of outputs produced. The decision maker of a banking unit willing to merge/acquire another banking unit needs to decide about the inputs and/or outputs level if an efficiency target for the new banking unit is set. In this paper, a new InvDEA-based approach is developed to suggest the required level of the inputs and outputs for the merged bank to reach a predetermined efficiency target. This study illustrates the novelty of the proposed approach through the case of a bank considering merging with or acquiring one of its competitors to synergize and realize higher level of efficiency. A real data set of 42 banking units in Gulf Corporation Council countries is used to show the practicality of the proposed approach.
Mass customization (MC) as a business strategy is designed to simultaneously compete on two rival competitive priorities-the price and customization level of a product. MC academics and experts have gone a step further. They suggest that MC is a unique strategy whose implementation promises across-theboard improvement in all four of the competitive priorities (price, quality, flexibility, and speed) simultaneously. Its growing adoption by businesses in recent years, the steep rise in success stories associated with MC, and the voluminous body of publications in a short period of its existence have created a need to study the directions, trends, application potential, and research strategies embedded in these publications. Accordingly, this paper studies and analyzes the trends and directions of the research published in 1,124 MC publications that have appeared in journals and magazines since the inception of the term mass customization in 1987 by Stan Davis in his classic book Future Perfect. Statistical trend analyses are conducted to study the vitality and health of the field of MC using number of publications and number of publication outlets and their respective trends. The publication outlet data conform to an S curve, establishing maturity of the MC field. The publication data show that the MC field has passed through four stages of growth: incubation or slow factors that indicate that the dip in 2006 may be an outlier. This paper also suggests developing a clear understanding of the value and type of research embodied in MC publications through three types of taxonomic analyses. The frameworks for all three taxonomies are set forth, two of which have been previously employed in other areas of OR/MS (Reisman and Kirschnik, Oper Res 42(4):577-588, 1994; Oper Res 43(5):731-740, 1995): The first taxonomic framework first classifies the paper as a theory paper or an application paper. At the second stage, the application content of the publication is determined based on a five-point scale ranging from simple modeling of the real world to bona fide real-world application. The second taxonomic framework suggests usage of a taxonomy comprised of seven distinct types of research strategies. The former analysis provides important information about the application worthiness of the MC publications and hence their usefulness to the real world. The second analysis provides information about the type of research strategies used by MC researchers, which, in turn, allows drawing conclusions about the quality and rigor of such research. The third taxonomic framework suggested recommends classification of all publications among multilevel containers based on the disciplines that intersect with MC and their branches.
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.
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