Malmquist Productivity index

The Malmquist Index (MI) is a tool for analyzing the productivity. Considering its importance, different suggestions and studies have been offered on the Cost Malmquist Productivity Index (CMPI) according to existing conditions of decision making units (DMUs) and the available data. The present research aimed to provide a CMPI in a non-competitive environment in which the price data changes from one evaluation unit to another. Given the deficiency of Farrel’s cost efficiency (1957) and also the cost efficiency model presented by Tone (2002), and by taking advantage of the idea of changing the productivity of DMUs at different time periods, we presented cost Malmquist Productivity Index in the presence of non-identical prices for various DMUs, Then, we evaluate the data of the Iranian Stock Exchange by aforementioned Cost Malmquist Productivity index

The purpose of this paper is to develop an output oriented methodology for calculating productivity growth by using Malmquist productivity index (MPI) and two different data envelopment analysis (DEA) views (optimistic and pessimistic) simultaneously, and apply it to five Iranian Commercial Banks over the four time period (2009-2013). Consequently, we have proposed a new approach called the double frontiers two-stage DEA or DFTDEA for simultaneous measurement of the MPI from both different DEA views. Furthermore, this paper has used two-stage DEA with reference to the variable return to scale technology (VRS) and applied a new viewpoint to measure the overall efficiency of the process. JEL Classification: E01, E17, E37

In this paper, we present a comprehensive approach for evaluating efficiency in complex networks by integrating network data envelopment analysis (NDEA) with the Malmquist productivity index. The proposed method addresses the inherent challenge of accommodating negative data within the network efficiency evaluation framework, which is a common occurrence in real-world network operations. Through the introduction of a two-stage structure, the model not only effectively manages the presence of negative values, but also provides a robust and insightful assessment of network efficiency. A case study from banking sector is employed to demonstrate the efficacy of the proposed approach, showcasing its capacity to offer valuable and actionable insights for decision-making in complex network environments. The results highlight the practical applicability and importance of the extended approach in addressing the complexities associated with evaluating efficiency in diverse network settings.