Abstract
Super-efficiency model in the presence of negative data is a relatively neglected issue in the DEA field. The existing super-efficiency models have some shortcoming in practice. In this paper, the radial super-efficiency model based on Directional Distance Function (DDF) is modified to provide a complete ranking order of the DMUs (including efficient and inefficient DMUs). This model shows more reliability on differentiating efficient DMUs from inefficient ones via a new super-efficiency measure. The properties of proposed model include feasibility, monotonicity and unit invariance. Moreover, the model can produce positive outputs when data are non-negative. An empirical study in bank sector demonstrates the superiority of the proposed model.
Highlights
Data Envelopment Analysis (DEA) is a powerful tool in the context of production management for performance measurement
The purpose of DEA is to measure the relative efficiency of a set of decision making units (DMUs) where multiple inputs convert into multiple outputs (Charneset al. (1978))
Lin and Chen (2017) proposed a novel DDFbased variable returns to scale (VRS) radial super-efficiency DEA model which is feasible and is able to handle negative data. They claimed that their proposed model can provide a measure of efficiency for all DMUs in the presence of negative data
Summary
Data Envelopment Analysis (DEA) is a powerful tool in the context of production management for performance measurement. Lin and Chen (2015) considered the model in Chen et al (2013) when zero data exist in outputs All these modified super-efficiency DEA models are proposed for the nonnegative data and the infeasibility issue when there are negative inputs or outputs still exists. Lin and Chen (2017) proposed a novel DDFbased VRS radial super-efficiency DEA model which is feasible and is able to handle negative data. They claimed that their proposed model can provide a measure of efficiency for all DMUs in the presence of negative data.
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