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• Theoretical mechanism of the non-uniqueness optimal solution for cross-efficiency is revealed. • Weight priority is constructed to reflect the comparative advantage of inputs and outputs. • Weight priority strategy can serve as both an independent strategy and a supplementary strategy. • The uniqueness of optimal result obtained by weight priority strategy is theoretically proved. Due to a large number of studies on cross-evaluation strategies, the theoretical problem of DEA cross-efficiency approach has been transformed from the non-uniqueness of the optimal solution to the relative uniqueness, because the existing cross-evaluation strategies can only reduce the possibility of non-unique solutions, but cannot prove the existence of a unique optimal solution. This paper illustrates the relative uniqueness of optimal solution of the cross-efficiency approach with traditional cross-evaluation strategies, and the graphic method and theoretical analysis are used to explain the reasons of relative uniqueness. Based on the data characteristic of inputs and outputs, the weight priority rule is made for constructing the new cross-evaluation strategy, and its optimal solution is proved to be unique. In addition, the weight priority strategy can also be introduced into traditional strategies as a supplementary strategy, and then the unique optimal solution can be obtained on the premise of preserving the characteristics of traditional strategies. In general, this paper solves the non-uniqueness and relative uniqueness problem of optimal solution in the DEA cross-efficiency approach, and then the reliability and robustness of the approach are thus improved. [ABSTRACT FROM AUTHOR]

While data availability provides adequate support for decision-making, asymmetrically distributed uncertainty remains an inevitable factor in the decision-making process. Robust data envelopment analysis (RDEA) is one of the most popular methods to handle uncertainty in input and output data. However, previous RDEA studies assume that the uncertainty of the data is symmetrically distributed, which is often violated in practical applications, such as in sports and finance. This paper aims to relax that assumption and extend RDEA models to more general scenarios. To this end, this paper proposes a new RDEA method that can capture asymmetrically distributed uncertainties from historical data and can be easily solved. In addition, a comprehensive case study of player efficiency evaluation is conducted to illustrate the merits of the proposed method. The contributions can be concluded as follows. This study introduces a novel approach to considering asymmetrically distributed uncertainty sets in the RDEA domain, which has not been previously explored. The proposed model outperforms existing RDEA models, providing more reliable and complete ranking results. Additionally, we demonstrate a comprehensive data-driven application that showcases how to build uncertainty sets from historical data and utilize our RDEA model for efficiency evaluation. Furthermore, our model's flexibility allows decision-makers to adjust its conservativeness according to their preferences, offering more dependable results. By utilizing our proposed model, decision-makers can make better-informed decisions while also receiving guidance on how to enhance the reliability of their results. [ABSTRACT FROM AUTHOR]

• The concept of value efficiency introduced in data envelopment analysis. • The novel algorithm proposed for calculating the value efficiency. • A new approach comes to incorporating preference information in the process of evaluating efficiency. • The value efficiency is calculated based on input and output components. • The method has been applied for real world data. Value efficiency analysis is an approach to incorporate the Decision-Maker's (DM) priori knowledge (preference information) into efficiency analysis. This paper presents a multi-objective programming model to measure the value efficiency. The model's feasible region is the production possibility set in data envelopment analysis (DEA) and the objective functions are the value efficiency scores corresponding to input and output components. Considering the structure of the proposed model and the concept of value efficiency, the step method (STEM) method to solve the model and gain the value efficiency is used in this paper. By selecting MPU (most preferred unit) units based on the manager's choice, the suitable benchmarks and value efficiency scores could be obtained corresponding to the unit under evaluation, then one could see that the obtained benchmarks and value efficiency scores are strongly dependent on the selected MPU units. Finally, a numerical example is proposed to illustrate the credit of the proposed approach. The method has been applied for real world data. [ABSTRACT FROM AUTHOR]

This paper develops an efficiency-based interactive dynamic technique to design the post-disaster rescue route by combining the data envelopment analysis (DEA), traveling salesman problem (TSP) and interval-valued hesitant fuzzy constraint cone (IVHFCC). The traditional DEA potentially presumes that the weights of the input (output) criteria are unrestricted, resulting in its inapplicability to some complex decision problems. To restrict the weights of input (output) criteria legitimately, this paper constructs an IVHFCC based on expert's subjective preferences in the form of interval-valued hesitant fuzzy sets. This paper defines the additive consistent normal interval-valued hesitant fuzzy preference relation (NIVHFPR) and analyses its desirable properties. Based on these properties, three linear programming models are proposed to repair the incomplete NIVHFPR, judge the additive consistency of the repaired NIVHFPR and generate the additive consistent NIVHFPR. By incorporating the TSP, DEA and the constructed IVHFCC, an efficiency-based interactive dynamic model is originally proposed to generate the optimal rescue route for the rescue team. Some desirable properties of this efficiency-based interactive dynamic model are analyzed. The validity of this model is demonstrated with a real post-disaster rescue route planning problem of Wenchuan earthquake. Some comparative analyses are provided to show the superiorities of the proposed technique. [ABSTRACT FROM AUTHOR]

The Malmquist index constructed from distance functions is a useful tool for measuring productivity growth. In this paper, we assume the presence of random noise in empirical data and enhance the literature by introducing a panel-data model in stochastic nonparametric envelopment of data (StoNED) for the consistent estimation of the Malmquist-type index. The method we propose initially employs directional distance functions to evaluate both within and intertemporal efficiencies of Malmquist-type indices based on StoNED. A major feature of our approach is that it measures productivity changes over time while capturing both inefficiency and noise in a nonparametric multiple-input multiple-output setting. As an illustrative application, we investigate the productive performance of Japanese regional banks using a balanced panel dataset spanning 2008 to 2017. • We propose a stochastic nonparametric estimation of the Malmquist-type indices. • We establish a consistent estimator for measuring intertemporal efficiencies. • The proposed approach measures productivity changes under noise. • The results reveal the drivers of productivity growth of Japanese regional banks. [ABSTRACT FROM AUTHOR]

Highlights • This paper studies the estimation of the right and left returns to scale. • The new method detects both the type and the value of returns to scale. • The advantage of the new method is that it uses no parameter in model formulation. • A case study in banking system validates the new method. Abstract One of the concepts that has gained important attention from economic and management-science research is that of the concept of returns to scale (RTS). There are several research papers on the behavior of the right and left RTS, as two specific directions of RTS, based on Data Envelopment Analysis (DEA) models. However, the main weakness of most of these methods is that they are based on the defined parameters, which itself leads to a high sensitivity of the models to variations in the magnitudes of the parameters and, thus, unreliable results. In this paper, we specially address this issue and propose a simple procedure for detecting the right and left RTS classification with an important feature that is independent of any predetermined parameters. In addition to the type of RTS, we also suggest a method to determine the value of the right and left RTS corresponding to each of the efficient DMUs. Finally, the superiority of the proposed parameter-free procedure over a parameter-based methods is elaborated by a real case in the banking system. [ABSTRACT FROM AUTHOR]

Highlights • A novel inverse problem in environmental efficiency is suggested. • A new inverse DEA model for minimizing GHG emissions is proposed. • For a set of firms and certain production, the model obtains minimum GHG emissions. • An application in oil and gas sector is proposed. Abstract The industry dynamic creates a difficult problem that must be solved if we are to address climate change. How can we lower greenhouse gas (GHG) emissions while simultaneously increasing production? This paper develops a new inverse data envelopment analysis (DEA) model for optimizing GHG emissions. The inverse DEA model minimizes the overall GHG emissions generated by a set of decision making units (DMUs) for producing a certain level of outputs, given that the DMUs maintain at least their existing performance status. The usefulness of the proposed method in this paper is demonstrated through an application in the oil and gas industry. We find that roughly 57% of our sample DMUs are inefficient. Ample room exists within the current efficiency frontier to lower GHG emissions in the oil and gas sector. We recommend that environmental regulators look into targeting regulations at these inefficiencies that exist within the industry as low bearing fruit for potential GHG emissions reductions. [ABSTRACT FROM AUTHOR]

Highlights • Both fuzzy DEA and fuzzy TOPSIS are applied to a common dataset of logistics service providers. • TOPSIS outperforms DEA in both calculation complexity and sensitivity to number of suppliers. • Output rankings from the two models are found to be less than perfectly correlated. • Utilizing both methods produces a useful pooled shortlist of potential sustainable suppliers. • The results provide a basis for negotiating price and service quality commitments. Abstract This paper presents a comparative analysis of the outcomes achieved when two widely applied methods for supplier selection—the 'technique for order of preference by similarity to ideal solution' (TOPSIS) and 'data envelopment analysis'—are applied to the problem of identifying the most preferred sustainable suppliers. Both fuzzy DEA and fuzzy TOPSIS are applied to a common dataset of logistics service providers in Sweden. The results reveal that TOPSIS outperforms DEA in terms of both calculation complexity and sensitivity to changes in the number of suppliers. However, output rankings from the two models are found to be less than perfectly correlated. The paper concludes that utilizing both methods, as applied to just a small number of evaluation criteria and a relatively low level of detail in the data, produces a useful pooled shortlist of potential sustainable suppliers. This can then form the basis for a second stage application where either of the methods may be applied to a greater number of criteria that are specified to a higher level of detail. Even more critically, the results also have the potential to point to specific aspects for discussion when negotiating price and service quality commitments with potential sustainable suppliers. [ABSTRACT FROM AUTHOR]

Highlights • An uncertain resource allocation-targets setting approach is proposed. • Uncertainties of resources and targets are modeled using type-II & type I fuzzy sets. • Fuzzy De-Novo programming for resource allocation and target setting is developed. • A real case of the natural gas supply chain is analyzed. Abstract Developing effective approaches to design optimal resources of system based on the concepts of benchmark in DEA and optimal design in De-Novo programming is one of the important managerial decision making problems. In this paper, a decision support system is developed for allocation of resources and setting the targets across a set of entities in an equitable manner in presence of uncertainty. The proposed approach has two main modules. First, the most suitable system is designed using De-Novo programming. De-Novo programming. De-Novo programming is used to optimally determine the inputs (i.e., resources) and outputs (i.e., targets) of DMUs in network DEA rather than optimizing existing DMUs. Then, the optimal values of resources are allocated and optimal values of the targets are set in a complex network structure. Furthermore, in real-world problems budget of resources and targets are usually mixed with uncertainties, so in this paper, two concept of fuzzy and interval type-II fuzzy resources and target are developed for resource allocation and target setting. Finally numerical example based on real case of natural gas supply chain is also used to evaluate the applicability and efficacy of the proposed models. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

Strategic alliances and partnerships based on mergers and acquisitions can be used to improve the performance of decision-making units (DMUs). In this paper, we present different types of strategic alliance cooperation and partnership between DMUs and their corresponding aggregation DMUs. We propose two different strategies, we were able to carry out the process of strategic alliances and partnerships in semi-additive production technology. In the first strategy, two partner DMUs can compensate for the lack of resources in some of their input components by using excess input resources in some of each other's input components. Thus these DMUs can improve their efficiency score among all DMUs. The modeling of strategic alliances and partnerships in the first strategy was based on data envelopment analysis (DEA) models. In the second strategy, the two DMUs selected as partners adjust their input and output levels with the help of each other in such a way that these two DMUs achieve a target efficiency level predetermined by the DM. The modeling of strategic alliances and partnerships in the second strategy was based on inverse DEA models. We also apply the proposed approach to show importance strategic alliances and partnerships in banking. In the following, we compared the results of the models in carrying out the partnership process in two different technologies. The results showed that in semi-additive production technology, we can achieve potential gains for banks. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a frontier analysis procedure for evaluating the relative performance of decision making units (DMUs) with multiple inputs and multiple outputs. To improve its discrimination power, an important extension is proposed as cross-efficiency, which uses peer DMUs’ optimal relative weights to evaluate the relative performance. However, the existing cross-efficiency methods show an inconsistent and unbalanced evaluation standard, since each DMU might determine a different total (or mean) efficiency value across all DMUs. The different values imply that the DMUs that have assigned larger cross-efficiency scores will have a larger effect in aggregating the ultimate cross-efficiency scores and different DMUs’ effects are unbalanced in cross-efficiency methods. In this paper, we will deal with this unbalanced cross-efficiency evaluation problem. To this end, we first suggest a practical adjustment measure to rectify the traditional cross-efficiency, which will provide a common evaluation standard for all DMUs and make each DMU dispatch an identical total efficiency score across all DMUs. Further, we propose a game-like iterative procedure to obtain the optimal balanced cross-efficiency. Finally, we present both a numerical example and an empirical study derived from the literature and a real-world problem to demonstrate the usefulness and efficacy of the new balanced cross-efficiency evaluation approach. The work presented in this paper can extend the traditional cross-efficiency approaches to situations involving unbalanced evaluation standards, and make the evaluation results more practical significance. [ABSTRACT FROM AUTHOR]

Performance measurement is one of the most essential real life decision making problems faces several criteria and different types of uncertainty and vagueness. Human expertise is mandatory for such situation although it is not enough to overcome such complicated cases. Expert systems which can sense, analyze and incorporate all details of real life decision making problems are required. In this paper an expert system is proposed in form of an uncertain Data Envelopment Analysis (DEA) approach. More formally a mixed fuzzy-robust uncertainty is addressed in the proposed DEA. Two scenario-based robust DEA models under Constant Return to Scale (CRS) and Variable Return to Scale (VRS) conditions have been proposed. Since the observed values for the inputs and outputs of Decision Making Units (DMUs) in each scenario may be ambiguous or vague, fuzzy DEA models corresponding to each of the robust scenarios are developed. So, this paper proposes four fuzzy-robust DEA models, which simultaneously maintain the advantage of each of the fuzzy and robust approaches, and at same time calculate the upper and lower bounds of the efficiency scores of DMUs under CRS and VRS conditions. Finally, to evaluate the validity and applicability of the proposed models, two numerical examples and a real case study in Small and Medium-sized Enterprises (SMEs) are presented and discussed. [ABSTRACT FROM AUTHOR]

The institutions of world repute, like the United Nations, the World Bank, etc., assess and rank countries or firms based on various criteria pertaining to key areas to evaluate their performance and publish them as Indexes. These ranking tools aid decision-makers in formulating policies and procedures 4 4 PAP-Policies and procedures. (PAP) to achieve performance efficiency. In the real world, identifying the closest, most efficient country or firm whose PAP can be emulated with minimal effort is a management dilemma. These Indexes contain only output data, which compounds the problem. This paper uses Data envelopment analysis 5 5 DEA- Data envelopment analysis. (DEA) to resolve these issues. Each country or firm under evaluation is referred to as Decision-making unit 6 6 DMU-Decision-making unit. (DMU). The novelty of this study emanates from 1) segregating countries or firms into several frontiers using Pure Output Model-based Context-Dependent Data envelopment analysis 7 7 POMCDD-Pure output model-based context-dependent data envelopment analysis. (POMCDD), 2) proposing a technique to identify the most attractive nearest DMU that is performing better and is easy to emulate, 3) identifying the "Region of introspection" by using the translation technique of Pillar-Index scores concerning DMU under evaluation to demarcate reference DMU for the concerned Pillar, and 4) suggesting techniques such as "Rapid gain," "Best yield", and "Least effort" which are proposed to identify reference DMUs for the concerned Pillar. A case study analysis of the Logistics Performance Index 8 8 LPI-Logistics Performance Index. (LPI) is carried out to substantiate the contributions. The countries ranked in the Logistics Performance Index are segregated into several efficient frontiers using the POMCDD technique. The attractiveness of each country is evaluated relative to the frontiers of poorly performing countries. The translation of Pillar-Index scores concerning the country under evaluation is used to identify reference countries for that Pillar. The developed framework is helpful to the policy-makers in analyzing published Indexes. [ABSTRACT FROM AUTHOR]

Over recent decades, China's economy has continued to develop steadily and rapidly, contributing to a large environmental cost. Therefore, many researchers propose various methods to evaluate environmental efficiency to deal with these serious issues accurately. This paper is the first to evaluate environmental efficiency using a model featuring not only a parallel structure with three component processes (primary, secondary, and tertiary industries) in the first stage but also a sewage treatment process in the second stage. Simultaneously, we propose radial but also nonradial versions of the Environmental Multiple Variable Return to Scale (EMVRS) model. These two models both consider shared inputs as well as shared outputs, and the proportion of the shared inputs and shared outputs utilized in each component process is assumed to be unknown. Additionally, we further analyze the factors influencing environmental efficiency by utilizing the Tobit model. The results when EMVRS is applied to Chinese provinces can be divided into 3 parts: (1) Most provinces do not purify wastewater appropriately contributing to the bad environmental efficiency. (2) The efficiency values of the three industry types are similar or identical to each other in most provinces. (3) The population of people employed negatively impacts environmental efficiency. [ABSTRACT FROM AUTHOR]

In traditional multi-attribute group decision-making (MAGDM) methods, the aggregation of expert evaluation information, the normalization of attribute scales, and the determination of attribute prior weights can lead to information loss and inconsistent results. In addition, these methods cannot identify and improve the shortcomings of non-optimal alternatives. Considering these, this paper proposes a MAGDM method based on data envelopment analysis (DEA) by setting up a parallel expert evaluation system, thus integrating the initial opinions of all experts to rank the alternatives from a global perspective. First, we construct the multi-expert evaluation process as a parallel system from the global perspective so that the decision-making process is based on the initial expert evaluation information. Then, a new directional distance function model based on endogenous projection directions is proposed to measure the alternatives under each expert subsystem, eliminating data processing and artificial weight assignment. The non-optimal alternatives can be improved to an efficient state by adjusting the attribute levels to target values. Next, we propose a DDF super-efficiency model to perform a full ranking of the alternatives. Finally, a case study is provided to demonstrate the rationality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The variable returns to scale data envelopment analysis (VRS-DEA) benchmarking models are developed by assuming convexity on technology. However, this assumption may be not consistent with the classical microeconomic theory where marginal product initially increases but diminishing returns eventually set in. Accordingly, the selected benchmarks may aggregate best practices, which are less attainable in performance improvement. This paper thus proposes a VRS-DEA benchmarking model with quasiconcave production function, consistent with classical microeconomic theory. A novel approach for determining the returns to scale of Decision-Making Units (DMUs) is developed. The hyperplane of the quasiconcave production function is characterized. Benchmarks are selected by minimizing the weighted distance between the evaluated DMU and the hyperplane. An empirical application of China's tourism sector is used to validate the approach. The empirical results show that Mergers and Acquisitions are beneficial for smaller hotels as the production of most regions in China's tourism sector is subject to increasing returns to scale. [ABSTRACT FROM AUTHOR]

As an effective approach in ranking decision making units (DMUs), cross-efficiency evaluation is only extended to two basic network systems: series and parallel, which is not applicable to the common complex network system in reality. By using the directional distance function (DDF), this paper extends the cross-efficiency evaluation to a complex system called the parallel two-stage system, which consists of several independent parallel processes and each of them is a sub-system with a general two-stage series structure. The proposed method is able to decompose the cross-efficiency measure of the system into the weighted average of those of processes, stages and overall stages, respectively, and as a result, successfully achieves the layer-by-layer decomposition of cross-efficiency measures under complex nested networks. An empirical study on 40 open-ended equity funds in China from 2018 to 2020 verifies that the newly proposed method not only has a stronger performance discrimination power than self-evaluation, but also can identify the relationships among efficiencies of the system, processes and stages, and meanwhile it can reflect the effects of processes and stages on the system. More importantly, our method has a better practical value for actual fund investment selection. [ABSTRACT FROM AUTHOR]

Large-scale group decision-making (LSGDM) is a complex process involving numerous decision-makers (DMs). However, considering such a large number of DMs increases the complexity of the process. it seems necessary to pay much more attention to aspects such as a proper dimensionality reduction for scalability, consensus processes with automatic feedback, and effective management of non-cooperative DMs. To address such aspects, this paper presents a novel framework for LSGDM, based on Extended Comparative Linguistic Expressions With Symbolic Translation (ELICIT). We first extend the K-means clustering algorithm by incorporating individual assessments and trust relationships to classify DMs into subgroups, enhancing decision-making efficiency. We then develop a feedback mechanism based on two optimization consensus models for ELICIT information, that automatically provides optimal recommendations. An essential aspect of our proposal is the management of non-cooperative behaviors by utilizing the normal distribution to detect and penalize misbehaviors. Furthermore, we introduce a Data Envelopment Analysis (DEA) cross-efficiency method based on ELICIT values to rank all alternatives once an acceptable group consensus degree is reached. The framework's effectiveness is demonstrated through a practical application case study, accompanied by a parametric analysis. Comparisons with existing LSGDM methods highlight the superiority of our proposal in terms of efficiency. • A K-Means clustering algorithm based on ELICIT information and trust relationships is generalized. • A novel feedback mechanism based on two optimization models is built. • A novel approach for identifying and managing non-cooperative behavior is constructed. • An extended DEA cross-efficiency model for ranking alternatives is employed. • A novel large-scale group decision-making framework is developed. [ABSTRACT FROM AUTHOR]

In this paper we propose robust efficiency scores for the scenario in which the specification of the inputs/outputs to be included in the DEA model is modelled with a probability distribution. This probabilistic approach allows us to obtain three different robust efficiency scores: the Conditional Expected Score, the Unconditional Expected Score and the Expected score under the assumption of Maximum Entropy principle. The calculation of the three efficiency scores involves the resolution of an exponential number of linear problems. The algorithm presented in this paper allows to solve over 200 millions of linear problems in an affordable time when considering up 20 inputs/outputs and 200 DMUs. The approach proposed is illustrated with an application to the assessment of professional tennis players. [ABSTRACT FROM AUTHOR]

Several researchers have adapted the data envelopment analysis (DEA) models to deal with two inter-related problems: weak discriminating power and unrealistic weight distribution. The former problem arises as an application of DEA in the situations where decision-makers seek to reach a complete ranking of units, and the latter problem refers to the situations in which basic DEA model simply rates units 100% efficient on account of irrational input and/or output weights and insufficient number of degrees of freedom. Improving discrimination power and yielding more reasonable dispersion of input and output weights simultaneously remain a challenge for DEA and multiple criteria DEA (MCDEA) models. This paper puts emphasis on weight restrictions to boost discriminating power as well as to generate true weight dispersion of MCDEA when a priori information about the weights is not available. To this end, we modify a very recent MCDEA models in the literature by determining an optimum lower bound for input and output weights. The contribution of this paper is sevenfold: first, we show that a larger amount for the lower bound on weights often leads to improving discriminating power and reaching realistic weights in MCDEA models due to imposing more weight restrictions; second, the procedure for sensitivity analysis is designed to define stability for the weights of each evaluation criterion; third, we extend a weighted MCDEA model to three evaluation criteria based on the maximum lower bound for input and output weights; fourth, we develop a super-efficiency model for efficient units under the proposed MCDEA model in this paper; fifth, we extend an epsilon-based minsum BCC-DEA model to proceed our research objectives under variable returns to scale (VRS); sixth, we present a simulation study to statistically analyze weight dispersion and rankings between five different methods in terms of non-parametric tests; and seventh, we demonstrate the applicability of the proposed models with an application to European Union member countries. [ABSTRACT FROM AUTHOR]

• A scientometric review on stock market forecasting was conducted over two decades. • Notable metrics of science mapping were analysed using VOSviewer software. • Critical areas in stock market forecasting were identified and discussed. • Modified Data Envelopment Analysis (DEA) methodology was proposed for future works. Stock Market Forecasting (SMF) has become a spotlighted area and is receiving increasing attention due to the potential that investment returns can generate profound wealth. In the past, researchers have made significant efforts to forecast the stock market trends and predict the best time to buy, sell, or hold. The essence of past investigators' various techniques and methods was to maximise the abundant opportunities that abound in the stock market trading and amass huge wealth from it. Over the years, no scientometric review has been conducted to scientifically map out the trends, progress, and limitations in the subject area. In this regard, this paper presents a pioneering scientometric review in SMF. It investigates a total of 220 reputable articles (2001–2021) to identify trends and patterns in stock market forecasting studies. VOSviewer software was used to conduct science mapping analysis. Actionable insights from the analysis explain significant metrics such as the top research outlets, most-cited articles, most co-occurred keywords, most influential countries, and much more. More so, a key finding in this paper is the introduction of a less computational approach that has the possibility of making a better forecast. Yet, past researchers have not thoroughly explored this option. This paper is beneficial to Early Stage Researchers (ESR), governments, funding bodies, managers, analysts, financial enthusiasts, practitioners, and investors, so as to understand the current progress and focus areas in stock market prediction. [ABSTRACT FROM AUTHOR]

Evaluating the efficiency of innovation systems can serve as a substantial enabling tool for policymaking serving to identify best practices and develop potential improvements of actions and strategies. It also serves to provide valuable insight in understanding the nature and dynamics of innovation process at its different stages and levels. The main aim of the paper is to present an integrated assessment and classification framework for national and regional innovation efficiency. The proposed model is based on Data Envelopment Analysis and is formulated as a multiobjective mathematical program in order to consider the objectives and constraints of the different stages and levels of the innovation process. Additionally, the model copes with DEA inconsistencies when ratio measures are employed. In the second part of the study, a multicriteria decision aid approach, based on an ordinal regression model, is applied in order to study how environmental factors on innovation and entrepreneurship affect the estimated efficiency scores. The proposed approach is applied to a set of 23 European countries and their 185 corresponding regions. The results show that there are large differences regarding the efficiency scores of the different stages and levels, implying the existence of significant divergences from the expected norm concerning innovation efficiency. The contribution of the paper lies (i) in the proposed multiobjective model, which is able to model the multiple stages and levels of the innovation process and handle ratio measures without requiring the same set of inputs and outputs at different levels and (ii) in the presented application of the model in the efficiency evaluation of innovation systems, including a meta-analysis of the results based on the framework of the Quadruple Innovation Helix. Such an approach may provide a valuable tool for country/region comparison and policy formulation. [ABSTRACT FROM AUTHOR]

In this paper we propose an alternative cross-efficiency aggregation scheme that is based on the theory underlying the aggregation of self-appraisal efficiency scores. For this purpose, we apply the denominator rule to aggregate the cross-efficiency scores obtained from the multiplier form of the DEA model. This results in a share-weighted average cross-efficiency that takes explicitly into account the relative importance of each element of the cross efficiency matrix by means of either the opportunity cost of resources used or the value of outputs produced, depending upon the orientation chosen to measure efficiency. It turns out that the proposed share-weighting average cross-efficiency uses the simple arithmetic average of the estimated input and output weights from the self-appraisal form of the DEA model to compute the ultimate cross-efficiency. As this is a set of common (across decision-making units) input and output weights, it allows for complete ranking and comparison of all (efficient and inefficient) decision-making units. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a crucial method for assessing the efficiency of decision-making units (DMUs). In practice, it is common to encounter situations where the performance of a DMU at a certain stage is evaluated, especially in policy effect evaluation. The key to stage performance evaluation is to eliminate the influence of pre-stage inputs (outputs) on stage performance. This paper proposes stage ratio DEA models that consider two types of inputs and outputs: accurate ratio data and interval ratio data. The first form considers the evaluation of stage efficiency when accurate ratios of inputs and outputs are used at the beginning and end of the stage. The second form assesses stage efficiency using interval ratio data for interval inputs and outputs. To verify the validity of the proposed models, the numerical example validates the first form of stage ratio DEA models. And the second form is applied to evaluate the sustainable efficiency of 14 energy saving and environmental protection clean enterprises (ESEPCEs). The empirical results demonstrate that the proposed models provide a more accurate assessment of stage efficiency compared to the traditional DEA-CCR model. [ABSTRACT FROM AUTHOR]

Heterogeneous multi-attribute group decision making (HMAGDM) is a kind of complex decision making problem, which exists widely in reality. The purpose of this paper is to extend data envelopment analysis (DEA) for solving the HMAGDM problems whose attribute values include real numbers, intervals, intuitionistic fuzzy numbers, triangular fuzzy numbers, trapezoidal fuzzy numbers and linguistic variables. For this purpose, a method to HMAGDM based on a fuzzy DEA cross-efficiency model is proposed. In the proposed method, heterogeneous information is uniformly transformed into intervals, which are expressed as variables with unknown parameters. On this basis, a fuzzy DEA cross-efficiency model is established to calculate the individual cross-efficiency intervals of decision making units (DMUs). The model takes into consideration all possible attribute values of each DMU in the intervals, and obtains stable evaluation results through peer evaluation mode and iteration. Then, a maximizing group consensus model is established to calculate the weights of decision makers (DMs). Given the weights of DMs, the individual cross-efficiency intervals can be aggregated into the collective cross-efficiency interval, and DMU can be ranked based on this. An example of supplier selection is presented to illustrate the practicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Russell measure (RM) and enhanced Russell measure (ERM) are popular non-radial measures for efficiency assessment of decision-making units (DMUs) in data envelopment analysis (DEA). Input and output data of both original RM and ERM are assumed to be deterministic. However, this assumption may not be valid in some situations because of data uncertainty arising from measurement errors, data staleness, and multiple repeated measurements. Interval DEA (IDEA) has been proposed to measure the interval efficiencies from the optimistic and pessimistic viewpoints while the robustness of the assessment is questionable. This paper draws on a class of robust optimisation models to surmount uncertainty with a high degree of robustness in the RM and ERM models. The contribution of this paper is fivefold; (1) we develop new robust non-radial DEA models to measure the robust efficiency of DMUs under data uncertainty, which are adjustable based upon conservatism levels, (2) we use Monte-Carlo simulation in an attempt to identify an appropriate range for the budget of uncertainty in terms of the highest conformity of ranking results, (3) we introduce the concept of the price of robustness to scrutinise the effectiveness and robustness of the proposed models, (4) we compare the developed robust models in this paper with other existing approaches, both radial and non-radial models, and (5) we explore an application to assess the efficiency of the Master of Business Administration (MBA) programmes where data uncertainties influence the quality and reliability of results. [ABSTRACT FROM AUTHOR]

• Game method is used in interval cross evaluation. • Build a variety of evaluation strategies to linearize the model. • Using entropy theory to aggregate the results of different evaluation strategies. Cross-efficiency evaluation is an effective method for the ranking of decision-making units (DMUs) in data envelopment analysis, which is the performed method for peer-evaluation and self-evaluation. In most cross-efficiency evaluation methods, the peer evaluation result is completely constrained by the evaluator's optimal self-evaluation weights. DMUs are connected by a competitive relationship; therefore, they can be regarded as players in the game and the cross efficiency value can be regarded as payment because of this competition. DMUs need to consider the impact of their own weights on other DMUs to identify a set of weights for evaluating themselves. Based on the idea of non-cooperative games in the cross evaluation of DMUs, this paper considers the interval cross game efficiency of DMUs with the interval data. The interval data theory is combined, the best production state and the worst production state are taken as reference states, and four evaluation strategy models are proposed according to the opposition and coordination relationship. Then, the bijective and injective interval game cross models, as well as the algorithm corresponding to the four evaluation strategies are constructed. Moreover, the result of the self-evaluation is used as the initial parameter to prove the existence of the solution and the convergence of the algorithm. Based on the priority relationship of the evaluation results under different strategies, the interval entropy method is used to aggregate the results and obtain the final cross efficiency of the interval game. Furthermore, both the applicability and superiority of this model are exemplified by an example. Finally, based on the method of this paper, the efficiency of forest carbon sequestration in China are studied, seven natural regions in China are ranked by forest carbon sequestration efficiency values reasonably, and the current regions development of forest carbon sequestration in China are evaluated. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore an innovative performance model for a two-stage sequential production process by combining data envelopment analysis (DEA) and back propagation neural network (BPNN). Recent literature shows a growing interest on performance modeling of two-stage production process using DEA. But, most previous studies on the scope of two-stage modeling are still limited to the efficiency measurement and also have neglected the progressive direction of predictive value and capacity. As an optimization technique, two-stage DEA model lacks predictive capacity. Despite an adaptive prediction model being a practical necessity, this area has rarely been addressed in the previous studies. This paper demonstrates an integrative approach to constructive performance modeling of a two-stage sequential production process by exploring predictive capacity of BPNN in conjunction with DEA. The effectiveness of our jointly integrated performance model through this study is empirically supported by its practical application to the financial banking operations across large U.S. banks. [ABSTRACT FROM AUTHOR]

• Propose a framework for measuring a maturity level of performance-based budgeting. • Develop a parallel network data envelopment analysis model. • Consider the hierarchical configuration of performance indicators. • Use fuzzy sets theory to deal with vagueness and ambiguity. • Present a case study to demonstrate the applicability of the developed framework. Performance-based budgeting (PBB) aims to formulate and manage public budgetary resources to improve managerial decisions based on actual performance measures of agencies. Although the PBB system has been overwhelmingly applied by various agencies, the progress and maturity of its implementation process are not satisfactory at large. Therefore, it warrants to find, evaluate and improve the performance of organisations in relation to implementing a PBB system. To do so, the composite indicators (CIs) have been proposed to aggregate multiple indicators associated with the PBB system, but their employment is contentious as they often lean on ad-hoc and troublesome assumptions. Data envelopment analysis (DEA) methods as a powerful and established tool help to contend with key limitations of CIs. Although the original DEA method ignores an internal production process, the knowledge of the internal structure of the PBB systems and indicators is of importance to provide further insights when assessing the performance of PBB systems. In this paper, we present a budget assessment framework by breaking a PBB system into two parallel stages including operations performance (OP) and financial performance enhancement (FPE) to open up the black-box structure of the system and consider the indicator hierarchy configuration of each stage. In situations of the hierarchical configuration of indicators, we develop a multilayer parallel network DEA-based CIs model to measure the PBB maturity levels of the system and its stages. It is shown that the discrimination power of the proposed multilayer model is better than the existing models with one layer and in situations of relatively small number of DMUs the model developed in this paper can be a good solution to the dimension reduction of indicators. Moreover, this research leverages fuzzy logic to surmount the subjective information that is often available in collecting indicators of the PBB systems. The major contribution of this research is to examine a case study of a PBB maturity award in Iran, as a developing country with a myriad of financial challenges, to adopt a PBB maturity model as well as point towards the efficacy and applicability of the proposed framework in practice. [ABSTRACT FROM AUTHOR]

This paper is motivated by recent concerns, prompted by the recent financial crisis, that regulatory capital guidelines on loan loss reserves can generate dysfunctional outcomes and, moreover, by the fact that the Greek bonds held by the banks have an important impact on the risk level of the bank portfolio. The purpose of this paper is to incorporate risk into bank efficiency and to provide a snapshot of the efficiency profile of the Greek banking industry. Efficiency is measured by means of a satisficing data envelopment analysis (DEA) model in which the financial risk is proxied by credit risk provisions and by the participation of banks in the Private Sector Involvement (PSI), a controllable and an uncontrollable factor by the bank management, respectively. The results of the proposed probabilistic DEA model derived through the Monte-Carlo simulation are compared with the results of the respective deterministic model. As the constructed stochastic frontier screens further some of the ‘best-in-class’ banks, the merit of the proposed metric is evident. [ABSTRACT FROM AUTHOR]

• Presents a novel fuzzy DEA based on a general fuzzy measure. • Develops an adjustable and flexible fuzzy DEA model to consider DMUs' preferences. • Applying the adjustable FDEA model for measuring efficiency of hospitals in USA. Possibilistic Data Envelopment Analysis (PDEA) is one of the most applicable and popular approaches in the literature to deal with imprecise and ambiguous data in DEA models. In this approach, with respect to tendency of decision maker (DM) in taking optimistic, pessimistic and compromise attitude, three measures including possibility, necessity and credibility measures are used to form the Fuzzy DEA (FDEA) models, respectively. However, decision makers may have different preference and so it is necessary to customize fuzzy DEA models according to properties of DMUs. This paper proposes a novel fuzzy DEA model based on general fuzzy measure in which the attitude of DMUs could be determined by the optimistic-pessimistic parameters. As a result, the proposed FDEA model is general, applicable, flexible, and adjustable based on each DMUs. A numerical example is used to explain the proposed approach while usefulness and applicability of this approach have been illustrated using a real data set to measure efficiency of 38 hospital in United States. [ABSTRACT FROM AUTHOR]

• Develop a fractal framework for virtual network optimisation and assessment; • Introduce the windows multiplicative DEA model in the presence of ratio data; • Show that devices on virtual networks have distinct fractal behaviour over time; • Prediction of a virtual setting with higher and stable TCP performance by long time; • The DEA results' dataset is available at URL: http://dx.doi.org/10.17632/776sjbz7z5.5. Recently, the prediction of the most efficient configuration of a vast set of devices used for mounting an optimised cloud computing services and virtual networks environments have attracted growing attention. This paper proposes a paradigm shift in modelling transmission control protocol (TCP) behaviour over time in virtual networks by using data envelopment analysis (DEA) models. Firstly, it proves that self-similarity with long-range dependency is presented differently in every network device. This study implements a novel fractal dimension concept on virtual networks for prediction, where this key index informs if the transport layer forwards services with smooth or jagged behaviour over time. Another substantial contribution is proving that virtual network devices have a distinct fractal memory, TCP bandwidth performance, and fractal dimension over time, presenting themselves as important factor for forecasting of spatiotemporal data. Thus, a continuous stepwise fractal performance evaluation framework methodology is developed as an expert system for virtual network assessment and performs a fractal analysis as a knowledge representation. In addition, due to the limitations of classical DEA models, the windows multiplicative data envelopment analysis (WMDEA) model is used to dynamically assess the fractal time series from virtual network hypervisors. For knowledge acquisition, 50 different virtual network hypervisors were appraised as decision-making units (DMU). Finally, this expert system also acts as a math hypervisor capable of determining the correct fractal pattern to follow when delivering TCP services in an optimised virtual network. [ABSTRACT FROM AUTHOR]

The existing parallel DEA models assume that all decision making units (DMUs) should contain identical subsystems. This paper proposes a cross-efficiency evaluation method to handle the non-homogeneous DMUs (NHDMUs) in the parallel network system, where the non-homogeneity of DMUs is shown in that the input and output sets of subsystems and the internal structure of DMUs are not the same. The method considers the scenario wherein quantitative and qualitative data exist in the inputs and outputs simultaneously. To do this, first, the NHDMUs are divided into different mutually exclusive unit groups according to the subsystems in which DMUs participate. Then, several interval cross-efficiency models are constructed for the cross-efficiency evaluation of DMUs in the same group and the cross-efficiency evaluation of DMUs with overlapping internal outlines in different groups to obtain the interval cross-efficiencies of NHDMUs. Further, the evidence theory and grey relation analysis method are used to determine a set of weights that aggregate the cross-efficiency matrix. Finally, the proposed method is discussed using an application case of talent introduction performance. The results show that the proposed method can improve the rationality and distinguishability of efficiency evaluation and find the reasons for the performance decline of a DMU. [ABSTRACT FROM AUTHOR]

Operating rooms (ORs) are one of the most stressful and complex work environments, and the surgical staff are highly exposed to ergonomic problems. In addition, the satisfaction of the surgical staff is directly affected by these factors. In this paper, an integrated intelligent algorithm is introduced to evaluate the performance of ORs based on ergonomics indicators, quantitatively and qualitatively and to examine the impact of these indicators on job satisfaction. Initially, the required data was collected by distributing a standard questionnaire covering items associated with ergonomics and job satisfaction among the surgical staff of a mega-hospital. The study benefited from a combination of artificial neural network (ANN) and statistical methods to calculate the efficiency scores for each decision-making unit (DMU). The data envelopment analysis (DEA) method was used to validate the proposed algorithm. Afterward, sensitivity analysis and statistical tests were performed to evaluate the appropriateness of OR performance based on each of the ergonomics indicators. The results reveal that OR performance was appropriate based on knowledge, situation assessment, and situation analysis (KSS), specifications of work and equipment (SW), and mental power (MP). However, it was inappropriate in terms of information and communication system (IC), teamwork and effective communication (TE), physical condition (PC), and OR condition (OC). [ABSTRACT FROM AUTHOR]

Operating rooms (ORs) are one of the most stressful and complex work environments, and the surgical staff are highly exposed to ergonomic problems. In addition, the satisfaction of the surgical staff is directly affected by these factors. In this paper, an integrated intelligent algorithm is introduced to evaluate the performance of ORs based on ergonomics indicators, quantitatively and qualitatively and to examine the impact of these indicators on job satisfaction. Initially, the required data was collected by distributing a standard questionnaire covering items associated with ergonomics and job satisfaction among the surgical staff of a mega-hospital. The study benefited from a combination of artificial neural network (ANN) and statistical methods to calculate the efficiency scores for each decision-making unit (DMU). The data envelopment analysis (DEA) method was used to validate the proposed algorithm. Afterward, sensitivity analysis and statistical tests were performed to evaluate the appropriateness of OR performance based on each of the ergonomics indicators. The results reveal that OR performance was appropriate based on knowledge, situation assessment, and situation analysis (KSS), specifications of work and equipment (SW), and mental power (MP). However, it was inappropriate in terms of information and communication system (IC), teamwork and effective communication (TE), physical condition (PC), and OR condition (OC). [ABSTRACT FROM AUTHOR]

This paper contributes by developing new models for assessing dynamic inefficiency that incorporate machine learning techniques. In particular, the new approaches apply decision trees models for the estimation of dynamic production technologies that account for investment adjustment costs. Methodologically, the new models build on the recently developed techniques of Efficiency Analysis Trees (EAT) and Convexified Efficiency Analysis Trees (CEAT) and extend them even further to a dynamic framework comprising dynamic EAT and CEAT models. The study compares dynamic inefficiency scores estimated assuming the new models against the traditional dynamic free disposal hull (FDH) and dynamic data envelopment analysis (DEA). Our empirical application focuses on dairy manufacturing firms in the main dairy processing countries in the European Union for the years 2014 and 2018. The results show that inefficiency related to the dynamic CEAT or EAT is higher than their corresponding values calculated through the dynamic DEA or FDH. The discriminating power of dynamic DEA (dynamic FDH) drastically improves when switching to dynamic CEAT (dynamic EAT). Finally, the differences between countries are observed regarding the development of dynamic inefficiency in the period associated with milk quota abolition. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is used for not only assessing the performance of a set of homogeneous decision-making units (DMUs), but finding the target operating points to improve inefficient DMUs. When inefficient DMUs are far from their target, it will be impossible to reach those in a single move, therefore, a strategy of gradual improvements with intermediate targets has been proposed. In this study, we propose a method to determine benchmarking paths, which consider the improvement steps as the learning of the production functions, not the learning of inputs and outputs. Under the constant returns-to-scale (CRS) assumption, the paper, for each inefficient DMU, sets the ultimate target as the closest target and determines the shortest improvement path with existing DMUs by Dijkstra's algorithm, and this is illustrated with a numerical experiment. As a real-world example, the method is introduced to the irons and steel enterprises in China, so that the specific steps to make efficient ones has been pointed out. The proposed method is the first study to discuss stepwise benchmarking path from the perspective of the production function. • The benchmarking paths are composed of existing DMUs. • The ultimate target is set as the closest efficient DMU. • The inefficient DMU learns the production functions of DMUs. • The distance between two production functions is calculated. • Dijkstra's algorithm is used to find the optimal benchmarking path. [ABSTRACT FROM AUTHOR]

This paper presents a new method for group decision-making (GDM) under multi-granular hesitant information based on the data envelopment analysis (DEA) cross-efficiency approach with regret theory (MGDM-RCE), which addresses some limitations of classic DEA for hesitant linguistic information, such as ignoring decision-makers (DMs)'s non-rational behavior and the use of single-granularity scales. The proposed MGDM-RCE method, on the foundation of cross-efficiency and regret theory with multi-granular hesitant fuzzy linguistic term sets (HFLTSs), allows for constructing two cross-efficiency models based on total regret-rejoice utility values to determine cross-efficiency intervals of decision-making units (DMUs). Additionally, an extended stochastic cross-efficiency technique is developed for obtaining the final ranking of alternatives in the correlative GDM problem. The performance of the MGDM-RCE method is shown using a numerical example consisting of selecting new energy sources and its validity and superiority are analyzed through sensitivity and comparative analysis. The sensitivity analysis revealed that variations in the parameters of the MGDM-RCE method do not significantly affect the ranking results. Moreover, compared to the classic methods VIKOR and TOPSIS, the MGDM-RCE method exhibits higher stability characteristics in addressing the GDM problem. [ABSTRACT FROM AUTHOR]

Transportation activities, especially road transportation, have a great impact on economic growth. On the other hand, sustainability is a major concern for transportation planning. In this work, a data-oriented network is developed to evaluate the sustainability of vehicle types. Then, this network is integrated with a multi-objective optimization model in order to provide the planning of a three-stage transportation problem, according to traffic congestion. Some criteria including total profit, efficiency of different vehicle types, relationship among the customers supplied by a specified retailer, risk of underestimating unmet demand, and selling price are used to determine the objective functions. The Chance-Constrained Programming (CCP) and Chebyshev Goal Programming (CGP) approaches are applied to solve the proposed integrated model. To the best of the authors' knowledge, it is the first time that traffic congestion under the conditions of simultaneous fuzzy and stochastic uncertainty has been integrated into sustainable transportation planning. In addition, the applicability and validity of the developed model are assessed on a case study. The results are then analyzed and appraised by Data Envelopment Analysis (DEA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. The findings prove that the components of the proposed model have a very beneficial effect on the solution, and also perform much better than the competing approaches in the literature. Two important points from the results of this paper are that (a) traffic congestion is more effective in the initial levels of the supply chain, and (b) transportation planning using efficient vehicles may reduce the desirability of the objective function values. [ABSTRACT FROM AUTHOR]

In Data Envelopment Analysis (DEA), a variety of approaches have been used in the context of single-stage and basic serial two-stage systems to attain fairness in the evaluation of decision-making units (DMUs). Little work, however, has been done to address this challenge in a generalised two-stage structure featuring additional inputs in the second stage and a proportion of first-stage outputs as final outputs. In this paper, we argue that in this context, fairness is enhanced by increasing measures related to the discriminatory power and the weighting scheme of the method. We describe a mechanism that gives prominence to a more contemporary concept of fairness, incorporating diversity and inclusion of minority opinions. These aspects have, to our knowledge, not yet received explicit attention in the methodological development of DEA. We propose a novel combination of an additive self-efficiency aggregation model, a minimax secondary goal model, and the CRiteria Importance Through Inter-criteria Correlation (CRITIC) method, in order to promote these aspects of fairness, and thus achieve a better degree of cooperation between the stages of a DMU and among DMUs. The additive aggregation model is chosen over the alternative multiplicative approach for a variety of reasons relating to the emphasis on the intermediate products exchanged and the simplification. The minimax model offers peer evaluation in which each DMU aims to evaluate the worst of the others in the best possible light. Application of the CRITIC method to DEA addresses the aggregation problem within the cross-efficiency concept. Practical applications of this approach could include supporting the determination of training needs in job rotation manufacturing, or evaluation of sustainable supply chains. The paper includes a description of a numerical experiment, illustrating the approach. • Developed modelling approaches to attain fairness in a network DEA structure. • Minimax secondary model highlights the best behaviour of the worst performing DMUs. • CRITIC as a mechanism to highlight minority opinions within a network DEA context. • Envisaged applications include job rotation systems and sustainable supply chains. [ABSTRACT FROM AUTHOR]

Abstract: The efficiency of distribution systems is largely affected by the performances of distribution centres. The main objective of this paper is to develop and propose a DEA model for distribution centres efficiency measuring that can help managers in decision making and improving the efficiency. Due to numerous indicators that describe DCs operating, the main problem is indicators selection. In order to improve discriminatory power of classical DEA models PCA–DEA approach is used. This paper analysis the efficiency of distribution centres of one trading company in Serbia. Proposed models integrate operational, quality, energy, utilisation and equipment warehouse and transport indicators. Several hypotheses are tested in this paper. The results showed that small distribution centres are more efficient than large. [Copyright &y& Elsevier]

Abstract: The main purpose of the paper is to apply a non-parametric methodology for measuring public police performance in Slovenia. The paper considers how previous studies have modeled the role of policing in their specifications of inputs and output. In particular, the multiple-stage Data Envelopment Analysis (DEA) technique is presented and then applied to measure the relative efficiency of police-work-related data for eleven Police Directorates (PDs) in 2010 and also compared with the efficiency scores in 2005. The data obtained from the police databases is analyzed through the Frontier Analyst 4.0 and SPSS 19.0 statistical package software. The results show that the technical efficiency varies significantly across the police directorates and across the selected years, even when we allow for environmental factors and control for these non-discretionary inputs. Most police directorates in Slovenia could attain a higher output if they were fully efficient. Indeed, all of the inefficient police directorates can learn how to ensure a “good” performance from the efficient ones (i.e. the Maribor PD and the Novo mesto PD) by observing how they are processing their inputs and outputs. Finally, the empirical results of the paper are important indicators of the relative efficiency (or inefficiency) of police directorates that can serve as a guide to the General Police Directorate when further investigating how to enhance the performance efficiency of the various units. [Copyright &y& Elsevier]

Abstract: One of the main problems in efficiency analysis is to determinate the environmental variables that have an impact on the production process. This paper shows that applying bootstrap to data envelopment analysis (DEA) before performing classification and regression trees (CART) increase the quality of the results. In particular, employing data on the Italian Health System, the paper highlights that bias corrected DEA allows to individuate variables affecting health efficiency which would remain undiscovered when the traditional DEA model is applied. [Copyright &y& Elsevier]

Abstract: In this paper we evaluate the efficiency and productivity of Intellectual Capital (IC) through the assessment of Bests Practices, that have successfully implemented strategies of Intellectual Capital management. The techniques selected for appraising the productivity of intangibles are the Data Envelopment Analysis (DEA) and the Malmquist Productivity Index (MPI). This approach allows a direct comparison between firms of the same industry in the perspective of improvement through benchmarking. It overcomes one of the main limitations of the current intangibles metrics comparing enterprises on the basis their Intellectual Capital management. The paper gives both academic and practical insights that could be used for the operational and strategic Intellectual Capital management. Actually, the outcome of the application gives to inefficient companies some directions for progress, that should constitute the basis for the formulation of future Intellectual Capital management strategies. Finally, we apply the analysis to the Italian yacht manufacturing sector in order to offer yachting companies guidelines for Intellectual Capital management. [Copyright &y& Elsevier]

Abstract: The objective of this paper is to deal with the performance evaluation problem in the presence of large dimensionality of data set, in which there exist multiple correlations among the original variables. Firstly, this paper analyzes the weakness of principal component analysis (PCA), and introduces a maximum variance method to reduce data dimension via Gram–Schmidt process, through which the corresponding transformed variables (Schmidt variables) can be obtained. And at the same time, a measure of investigating the cumulative variation information for selected Schmidt variables is described. Then based upon the chosen Schmidt variables, a new modified DEA approach with a natural assurance region (AR) is presented. In the developed DEA approach, the selected Schmidt variables are treated as outputs. The proposed approach will be then contrasted with the traditional CCR DEA model and the DEA model using principal components obtained from PCA with simulated data, and it is also applied to real world data set that characterizes economic performance of 31 provinces or cities in China. [Copyright &y& Elsevier]

Abstract: In a recent paper in this journal by Liu and Chuang [Liu, S. -T., & Chuang, M. (2009). Fuzzy efficiency measures in fuzzy DEA/AR with application to university libraries. Expert Systems with Applications, 36(2), 1105–1113]. Proposition 1 is proved for finding lower and upper bounds of the α-cut of . However, we find that the above proof omit the verification of constraints of assurance regions and the models (11) and (12) omit the constraints of DMUr, which makes it incredible. In this paper, we correct the models and proof of Proposition 1 in . We also propose a generalized fuzzy data envelopment model with assurance regions, whose lower and upper bounds at given levels could be obtained similarly. [Copyright &y& Elsevier]

Highlights • DMU ranking approach that synergistically combines different EIS techniques. • TOPSIS used to integrate second-order features extracted from raw input-output data. • In-strength, PageRank, inefficiency radius come from dominance network analysis. • Crowding distance, hypervolume, spacing indicators from multiobjective optimization. • The proposed approach has been applied to ranking major US airlines. Abstract This paper presents a new approach for ranking organizational units within a benchmarking context. Instead of the conventional optimization-based techniques, the proposed approach uses Social network analysis and Multiobjective optimization concepts to extract second-order features from the input-output data, integrating the resulting multidimensional information using TOPSIS. It shows how several expert and intelligent systems techniques can be harmoniously integrated and applied to performance assessment. The proposed approach has been used for ranking the performance of 27 major US airlines, comparing the results with some existing Data Envelopment Analysis methods. It is shown that the use of a richer information set instead of the raw input-output data leads to an innovative and more effective way of discriminating between efficient units. [ABSTRACT FROM AUTHOR]

Highlights • Decomposition of SBM measure into PTE, SE, and ME measures is proposed. • Contribution of PTE, SE and ME in developing DPMs are analysed. • Contribution of market and management efficiency measures in DPMs are analysed. • A comprehensive comparison between static and dynamic two-stage DPMs is provided. Abstract On feature selection, as one of the critical steps to develop a distress prediction model (DPM), a variety of expert systems and machine learning approaches have analytically supported developers. Data envelopment analysis (DEA) has provided this support by estimating the novel feature of managerial efficiency, which has frequently been used in recent two-stage DPMs. As key contributions, this study extends the application of expert system in credit scoring and distress prediction through applying diverse DEA models to compute corporate market efficiency in addition to the prevailing managerial efficiency, and to estimate the decomposed measure of mix efficiency and investigate its contribution compared to Pure Technical Efficiency and Scale Efficiency in the performance of DPMs. Further, this paper provides a comprehensive comparison between two-stage DPMs through estimating a variety of DEA efficiency measures in the first stage and employing static and dynamic classifiers in the second stage. Based on experimental results, guidelines are provided to help practitioners develop two-stage DPMs; to be more specific, guidelines are provided to assist with the choice of the proper DEA models to use in the first stage, and the choice of the best corporate efficiency measures and classifiers to use in the second stage. [ABSTRACT FROM AUTHOR]

Highlights • Process improvement via benchmarking requires addressing context-related factors. • Non-obvious common causal structures can describe a context of benchmarking. • Association Rules Mining can be used to discover context-related factors. Abstract Modern organizations typically regard information and communication technologies (ICTs) as one of the significant direct or indirect inputs for achieving operational excellence and competitive advantage. Since the concept of competitive advantage involves a relative comparison of the performance of organizational entities, then the concepts of organizational capabilities, context, and benchmarking are relevant. In this paper we present a new multi-method methodology for benchmarking that explicitly takes into consideration context-specific factors impacting the performance of organizational entities. This novel methodology allows for obtaining actionable information, in the form of non-obvious common causal structures, for improving the performance of the less efficient entities vis-à-vis their more efficient counterparts. The new methodology is state-of-the-art and is novel because it explicitly takes into consideration the context within which the organizational entities perform. Such "context awareness" allows for expanding the universe of discourse within which the process improvement initiatives are usually considered, thus allowing to consider the impact of external to the process factors on internal to the process mechanisms. This methodology involves the creative integration of several Information Systems (IS)' artifacts (i.e. multiple data mining methods) with Data Envelopment Analysis (DEA). We present an illustrative application of this methodology to an IS/ICT & Productivity research problem in the 'developing' countries context. [ABSTRACT FROM AUTHOR]

Highlights • A new method for target setting in mergers is proposed. • The method combines goal programming and inverse data envelopment analysis. • The method allows decision makers to save desired resources. • An application in banking sector is proposed. Abstract This paper suggests a novel method to deal with target setting in mergers using goal programming (GP) and inverse data envelopment analysis (InvDEA). A conventional DEA model obtains the relative efficiency of decision making units (DMUs) given multiple inputs and multiple outputs for each DMU. However, the InvDEA aims to identify the quantities of inputs and outputs when efficiency score is given as a target. This study provides an effective method that allows decision makers to incorporate their preference in target setting of a merger for saving specific input(s) or producing certain output(s) as much as possible. The proposed method is validated through an illustrative application in banking industry. [ABSTRACT FROM AUTHOR]