Arabi, B., S. Munisamy and A. Emrouznejad (2015), “A new Slacks-Based Measure of Malmquist-Luenberger Index in the Presence of Undesirable Outputs,” OMEGA, 51:29-37.

In the majority of production processes, noticeable amounts of bad byproducts or bad outputs are produced. The negative effects of the bad outputs on efficiency cannot be handled by the standard Malmquist index to measure productivity change over time. Toward this end, the Malmquist-Luenberger index (MLI) has been introduced, when undesirable outputs are present. In this paper, we introduce a Data Envelopment Analysis (DEA) model as well as an algorithm, which can successfully eliminate a common infeasibility problem encountered in MLI mixed period problems. This model incorporates the best endogenous direction amongst all other possible directions to increase desirable output and decrease the undesirable outputs at the same time. A simple example used to illustrate the new algorithm and a real application of steam power plants is used to show the applicability of the proposed model.

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Osman, I. H., A. L. Anouze and A. Emrouznejad (2015). Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis. IGI Global, USA, ISBN: 978-1-4666-4474-8.

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Emrouznejad A. and E. Thanassoulis (2015).  Introduction to Performance Improvement Management Software (PIM-DEA), in Osman et al. (Eds.) Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis: 256-275. IGI Global, USA.

 This chapter provides information on the use of Performance Improvement Management Software (PIM-DEA[1]). This advanced DEA software enables you to make the best possible analysis of your data, using the latest theoretical developments in Data Envelopment Analysis (DEA). PIM-DEA software gives you the capacity to assess efficiency and productivity, set targets, identify benchmarks and much more allowing you to truly manage the performance of organizational units. PIM-DEA is easy to use and powerful and it has an extensive range of the most up-to-date DEA models and which can handle large sets of data.

[1] For latest information please see: www.DEAsoftware.co.uk

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Emrouznejad A. and E. Cabanda (2015).  Introduction to Data Envelopment Analysis and its applications, in  Osman et al. (Eds.) Handbook of Research on Strategic Performance Management and Measurement Using Data Envelopment Analysis: 235-255. IGI Global, USA.

 This chapter provides the theoretical foundation and background on data envelopment analysis (DEA) method and some variants of basic DEA models and applications to various sectors. Some illustrative examples, helpful resources on DEA including DEA software package are also presented in this chapter. DEA is useful for measuring relative efficiency for variety of institutions and has its own merits and limitations. This chapter concludes that DEA results should be interpreted with much caution to avoid giving wrong signals and providing inappropriate recommendations.

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Emrouznejad, A. R. Banker, A.L.M Lopes, M. R. de Almeida (2014) “Data Envelopment Analysis in the Public Sector”. Socio-Economic Planning Sciences, 48 (1): 2-3.

Since its introduction in 1978, Data Envelopment Analysis (DEA) has become one of the preeminent non-parametric methods for measuring efficiency and productivity of Decision Making Units (DMUs). DEA models are now employed routinely in areas that range from assessment of public sectors such as hospitals and health care systems, schools and universities to private sectors such as banks and financial institutions. The advantage of DEA is to accommodate multiple inputs and multiple outputs for measuring the relative efficiencies of a set of homogeneous DMUs.  DEA does not require that output and input prices be available for this analysis, which is a significant advantage for public sector applications where such prices are not available.

The scope of this issue was extended beyond that of just the papers presented at the conference via an open invitation to the broader academic community working in the area of theory and applications of efficiency and productivity analysis. Papers were included in the special issue after a rigorous refereeing process, and represent only a small fraction of the total number of submitted manuscripts; there were 41 submissions to this issue.

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Matin, R. K., G.R. Amin and A. Emrouznejad (2014), A Modified Semi-Oriented Radial Measure for target setting with negative data, Measurement 54: 152–158.

Over the last few years Data Envelopment Analysis (DEA) has been gaining increasing popularity as a tool for measuring efficiency and productivity of Decision Making Units (DMUs). Conventional DEA models assume non-negative inputs and outputs. However, in many real applications, some inputs and/or outputs can take negative values. Recently, Emrouznejad et al. (2010a) introduced a Semi-Oriented Radial Measure (SORM) for modelling DEA with negative data. This paper points out some issues in target setting with SORM models and introduces a modified SORM approach. An empirical study in bank sector demonstrates the applicability of the proposed model.

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Gattoufi S., G. R. Amin, A. Emrouznejad (2014). “A new inverse DEA method for merging banks.” IMA Journal of Management Mathematics, 25: 73–87.

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.

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Emrouznejad, A. and M. Marra (2014) Ordered Weighted Averaging Operators 1988–2014: A Citation-Based Literature Survey, International Journal of Intelligent Systems, 29 (11): 994–1014. DOI 10.1002/int.21673.

This study surveys the Ordered Weighted Averaging (OWA) operator literature using a citation network analysis. The main goals are the historical reconstruction of scientific development of the OWA field, the identification of the dominant direction of knowledge accumulation that emerged since the publication of the first OWA paper and to discover the most active lines of research. The results suggest, as expected, that Yager (1988) [Yager, Ronald R. On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 183–190.] is the most influential paper and the starting point of all other research using OWA. Starting from his contribution other lines of research developed and we describe them.

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Hanen, H., A. Emrouznejad, M. N. Ouertani (2014), Technical efficiency determinants within a dual banking system: a DEA-bootstrap approach, International Journal of Applied Decision Sciences, 7 (4): 382 – 404.

The purpose of this study is to provide a comparative analysis of the efficiency of the Islamic banking sector in Gulf Cooperation Council (GCC) countries. To this end, we employ a semi-parametric two-stage methodology, where we derive technical efficiency scores via a Data Envelopment Analysis in the first stage. Then scores obtained are regressed on a series of determinants of bank efficiency using a double bootstrapping procedure. Our findings indicate that during the eight years of study, conventional banks largely outperform Islamic banks with an average technical efficiency score of 81% compared to 95.57%. However, it’s clear that since 2008 conventional banks efficiency was in a downward trend while the efficiency of their Islamic counterparts were in an upward trend since 2009. This indicates that Islamic banks have succeeded to maintain a level of effectiveness during the dark period of the subprime crisis after certainly, coming under their secondary effects during 2008-2009.  An investigation of the determinants of bank’s efficiency show that bank size have a significant positive impact on, only Islamic bank’s efficiency, while z-score is related negatively to efficiency of both departments showing that a higher (lower) distance from insolvency reduces (increases) banks’ efficiency. In other words, a stable and reliable system is crucial to foster the efficiency of GCC banks. Finally, for the whole sample, the analysis demonstrates the strong link of macroeconomic indicators with efficiency for GCC banks. But, surprisingly, there is no significant relationship in the case of Islamic banks.

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Hanafizadeh P., H. R. Khedmatgozar, A. Emrouznejad, M. Derakhshan (2014), Neural Network DEA for Measuring the Efficiency of Mutual Funds, International Journal of Applied Decision Sciences, 7 (3): 255-269.

Efficiency in the Mutual Fund (MF), is one of the issues that is attracted many investors in countries with advanced financial market for many years. Due to the need for frequent study of MF‘s efficiency in short–term periods, investors need a method that not only having high accuracy, but also high speed. Data Envelopment Analysis (DEA) is proven to be one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper uses neural network back-propagation DEA in measurement of mutual funds efficiency and shows the requirements, in the proposed method, for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large set of MFs.

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