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Zakat is an important Islamic economic instrument that plays significant role in Sustainable Development Goals. Accordingly, Zakat Institutions must manage zakat in a proper and efficient manner. This study aims to examine the efficiency of Zakat Institutions based on their clusters which are government, business and social organizations.

This study uses three quantitative methods: data envelopment analysis (DEA), free disposal hull and super-efficiency DEA. The analytical method is based on production approach, variable return to scale assumption and output orientation. The sample consists of 14 Zakat Institutions from three clusters: Zakat Institutions managed by government, Zakat Institutions managed by corporation and Zakat Institution managed by social organizations.

The results revealed that all of three techniques culminate the same ranking order of efficiency. Zakat Institution managed by the government is the most efficient Zakat Institution, with the average value of 0.87 by using three approaches combined. Meanwhile, Zakat Institutions owned by company and social institutions cluster are in second and third position, with the average value of 0.65 and 0.4, respectively, based on the results of the three approaches. This study contends that the level of efficiency of Zakat Institutions may be supported by clusters (affiliations) in their management.

This study’s limitation is the inadequacy of the required data. Nonetheless, this study provides insights to improve the efficiency of Zakat Institutions based on their clusters. Zakat Institutions in each cluster can improve their efficiency by optimizing inputs to produce multiple outputs.

This study enhances research on the efficiency of Zakat Institutions using three methods to assess the consistency and strength of Zakat Institutions’ efficiency values. In addition, this study examines the efficiency level of Zakat Institutions based on their clusters.

The purpose of this study is to present a hybrid analytic network process (ANP) and data envelopment analysis (DEA) method for ranking organizational units as well as prioritizing organization’s human capital management criteria.

In the proposed DEA model, human capital management criteria are considered as the inputs and organizational commitment considered as the output of DEA model. Afterward, the organizational unit’s efficiency and the weight of human capital management criteria are evaluated through DEA and the data are considered as the basis of ANP model.

The results of prioritizing the drivers of human capital management were showed that leadership practices, human capital management and learning capacity are the most important ones. Also, the findings proved that the proposed integrated DEA/ANP can be helpful in managerial issues.

Filling the super-matrix and designing the questionnaire of ANP have always been the challenge of scholars because of the large number of data it requires. One of the main advantages of the proposed hybrid method in this research is that it resolves the above-mentioned problem of ANP.

The results of the proposed method may provide managers this opportunity to better analyze the condition of organizational units from human capital management perspective and focus on the most important activities in this context. Moreover, the proposed hybrid method can help scholars to better use both DEA and ANP techniques and obtain more reliable findings.

In this study, DEA efficiency measures of units, and the weights of inputs and outputs of DEA model are used to fill the super-matrix of ANP. In addition to having a logical approach, this method provides more reliable results and enjoys the advantages of both ANP and DEA techniques.

The paper aims to enrich previous findings for an emerging banking industry such as Vietnam, reporting the difference between the parametric and nonparametric methods when measuring cost efficiency. The purpose of the study is to assess the consistency in issuing policies to improve the cost efficiency of Vietnamese commercial banks.

The cost efficiency of banks is assessed through the data envelopment analysis (DEA) and the stochastic frontier analysis (SFA). Next, five tests are conducted in succession to analyze the differences in cost efficiency measured by these two methods, including the distribution, the rankings, the identification of the best and worst banks, the time consistency and the determinants of efficiency frontier. The data are collected from the annual financial statements of Vietnamese banks during 2005–2017.

The results show that the cost efficiency obtained under the SFA models is more consistent than under the DEA models. However, the DEA-based efficiency scores are more similar in ranking order and stability over time. The inconsistency in efficiency characteristics under two different methods reminds policy makers and bank administrators to compare and select the appropriate efficiency frontier measure for each stage and specific economic conditions.

This paper shows the need to control for heterogeneity over banking groups and time as well as for random noise and outliers when measuring the cost efficiency.

The purpose of this paper is to employ an efficiency analysis and compare the efficiency scores of publicly or privately operated airports in Turkey.

This study uses analytic hierarchy process, which is one of the widely known multi-criteria decision-making methods to calculate the relative weights of input and outputs. This study also uses data envelopment analysis and assurance region (AR) method to calculate the efficiency scores of airports at the empirical analysis stage.

The empirical results reveal that DEA-BCC and DEA-CCR methods produced almost the same efficiency scores, and 14 airports were found as efficient. Also, AR method was employed and under this method, it was only two airports operated by the private sector that were found as efficient. None of the publicly operated airports was found as efficient.

The main practical implication of this study is that publicly operated airports must improve their efficiency levels in Turkey. This situation indicates that the government policy for the aviation sector must be changed. It is not a coincidence that all publicly operated airports are inefficient. To cope with this situation, it may be a useful policy that establishes a regional airport system or applies the privatization process to all airports.

The most significant contribution of this study to literature is that the AR method, which was never used before in a single country’s airport performance evaluation study, was applied for the first time. Also, this technique was applied first time to Turkish airports for measuring their efficiency levels.

The purpose of this paper is to provide scientific guidance for the integration of industrialization and information (TIOII). In recent years, TIOII has promoted the development of intelligent manufacturing in China. However, many enterprises blindly invest in TIOII, which affects their normal production and operation.

This study establishes an efficiency evaluation model for TIOII. In this paper, entropy analytic hierarchy process (AHP) constraint cone and cross-efficiency are added based on traditional data envelopment analysis (DEA) model, and entropy AHP–cross-efficiency DEA model is proposed. Then, statistical analysis is carried out on the integration efficiency of enterprises in Guangzhou using cross-sectional data, and the traditional DEA model and entropy AHP–cross-efficiency DEA model are used to analyze the integration efficiency of enterprises.

The data show that the efficiency of enterprise integration is at a medium level in Guangzhou. The efficiency of enterprise integration has no significant relationship with enterprise size and production type but has a low negative correlation with the development level of enterprise integration. In addition, the improved DEA model can better reflect the real integration efficiency of enterprises and obtain complete ranking results.

By adding the entropy AHP constraint cone and cross-efficiency, the traditional DEA model is improved. The improved DEA model can better reflect the real efficiency of TIOII and obtain complete ranking results.

Competition in dynamic markets requires operational excellence and effective operations strategy. Hence, organizations have to evaluate their operational performance in comparison to their rivals. The purpose of this paper is to present an operational performance evaluation framework for analyzing and ranking the organizations based on the effectiveness of their operations strategies.

In this study three uncertain data envelopment analysis (DEA) models consist of fuzzy DEA (FDEA), imprecise DEA (IDEA) and Grey DEA are applied to assess the performance of organizations in terms of the operations. The applicability of the proposed framework is demonstrated through a case study at Fars province cement manufacturers of Iran.

The ranking results assist the mangers of service and manufacturing enterprises in finding out the current positions of their companies and the effectiveness of their operations strategies in comparison to their rivals. Accordingly they can make proper strategic and operational decisions to improve the performance of the operations.

Based on the previous literature, this is the first study which uses FDEA and IDEA methods in an operational performance evaluation problem. Furthermore this study proposes a novel framework for evaluating and ranking the operational performance of firms from the perspective of operations strategy.

Resource allocation has always been a critical problem with significant economic relevance. Many industries allocate the resources based on classical methods such as overall equipment effectiveness (OEE) and data envelopment analysis (DEA). The lack of OEE factors’ weight, how it is defined, analyzed, interpreted and compared in OEE and selection of unrealistic weights, self-appraisal and disability of complete ranking in DEA are challenges that are possible to occur. These defects may result in unfair allocation of the resources. This study aims to overcome the mentioned weaknesses.

In this paper, an approach using a set of various DEA models and Nash bargaining solution (NBS) is designed to solve the resource allocation problem based on OEE, among a set of comparable and uniform DMUs (decision-making units) in a fair way.

The results show that a unique Pareto optimal allocation solution is obtained by the proposed DEA–NBS model among the DMUs. This allocation is more acceptable for players, because the allocation results are commonly determined by all DMUs rather than a specific one. Furthermore, the rankings achieved by the utilized methods and TOPSIS (technique for order preference by similarity to ideal solution) are compared by Spearman’s rank correlation coefficient to validate the resource allocation plan. The findings indicate that the DEA–NBS method has the best correlation with the TOPSIS approach.

To the best of authors’ knowledge, no research has considered the use of DEA and NBS with OEE.

The purpose of this paper is to combine the uncertain methods of type-2 fuzzy sets and data envelopment analysis (DEA) evaluation model together. A new type-2 fuzzy DEA efficiency assessment method is established. Then the proposed procedure is applied to the poverty alleviation problem.

The research method is the DEA model, which is an effective method for efficiency assessment of social–economic systems. Considering the existence of the same efficiency values that cannot be ranked in the proposed DEA model, the balance index is introduced to solve the ranking problem of decision-making units effectively.

The results show that the proposed method can not only measure the efficiency of the existence of uncertain information but also deal with the ranking of multiple efficient decision-making units.

This paper selects type-2 fuzzy DEA model to express a lot of uncertain information in efficiency evaluation problems. We use the parameter decomposition method of type-2 fuzzy programming or the type-2 expectation values indirectly. The balance index is proposed to further distinguish the multiple effective decision-making units. Furthermore, this paper selects rural poverty alleviation in Hainan Province as a case study to verify the feasibility of the method. The relative efficiency values in different years are calculated and analyzed.

A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

The purpose of this paper is to present a tie-breaking procedure for computing performance efficiencies to improve benchmarking and performance evaluation process in a business situation.

The authors propose a unified approach based on data envelopment analysis (DEA) and technique for order of preference by similarity to ideal solution (TOPSIS), to overcome the difficulty of unique ranking in the prevalent benchmarking and performance evaluation processes such as DEA, Super efficiency DEA model, etc., under constant return to scale (CRS) assumption. This model is called as efficiency ranking method using DEA and TOPSIS (ERM-DT). In order to check the consistency of the approach, various input-output combinations (to calculate the efficiencies) have been illustrated. Further, the authors present a case of an Indian Bank to illustrate an application of the proposed approach.

The proposed approach, ERM-DT enables assign a unique rank to decision making units and provides a tie breaking procedure. Results obtained using the proposed approach are statistically compared with those obtained from the CRS DEA approach and super efficiency DEA approach using Friedman’s test.

The proposed model provides an efficiency ranking method based on a score obtained by considering the minimum distance from the best value and maximum distance from the worst value. The proposed methodology is capable of handling negative data and undesirable output variables. This approach is unit invariant and makes the calculations simple. The authors present an application to compute the efficiency of various branches of an Indian bank. The authors hope the proposed method can enhance the decision-making ability of the management in complex situations.

The authors propose an integrated DEA and TOPSIS framework for better benchmarking and performance evaluation. This approach provides a tie-breaking procedure for the efficiencies computed using CRS DEA approach. Ranks are assigned based on score obtained by considering the distance from the worst and the best solution. The proposed approach can be used with non-positive data points and undesirable output variables.