Flexible target setting in mergers using inverse data envelopment analysis

In a merger, one important issue is the estimation of the levels of inputs and outputs required from each merging decision-making unit (DMU) so that the merged entity can realise a desired efficiency target. This paper uses the potential of inverse data envelopment analysis (InvDEA) to build a flexible target setting of the inputs and outputs. This study expands the application of the InvDEA methodology in a merger by introducing a flexible target setting that allows the decision maker to favour specific input in the target setting. We use a dataset of 30 universities to illustrate the practical scope of the proposed flexible target setting method, which can obviously be employed in any other merging context.