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This paper examines differences in bank efficiency between banks affiliated with single-bank holding companies and those affiliated with multi-bank holding companies by applying a fuzzy multi-objective two-stage data envelopment analysis technique. Using a sample of U.S. commercial banks covering 1994–2018, the results show that banks affiliated with multi-bank holding companies are more efficient than those affiliated with single-bank holding companies, suggesting that the former takes advantage of their parents' resources to enhance their efficiency, consistent with the internal capital market theory. They also show that banks with a powerful CEO exhibit lower efficiency than others. Moreover, there is an inverted U shape relationship between multi-bank holding company structure and bank efficiency, suggesting the presence of an optimal number of multi-bank holding subsidiaries that maximizes efficiency. [ABSTRACT FROM AUTHOR]

This paper provides a new method to define a Euclidean common set of weights (ECSW) in data development analysis (DEA) that (1) allows ranking both efficient and inefficient firms, (2) is more realistic in terms of determination of weights, and (3) generates rankings for banks consistent with their credit ratings. We first use DEA to determine the efficient frontier and then estimate a common set of weights that can minimize the Euclidean distance between the firms and that frontier. This process is illustrated by a simple numerical example and is extended to a real-life situation using the Eurozone banking sector. Our ECSW approach outperforms other common set of weights approaches in both numerical and real-life examples, and in terms of providing rankings consistent with banks' credit ratings. [ABSTRACT FROM AUTHOR]

This paper proposes that data envelopment analysis (DEA) should be viewed as a method (or tool) for data-oriented analytics in performance evaluation and benchmarking. While computational algorithms have been developed to deal with large volumes of data (decision making units, inputs, and outputs) under the conventional DEA, valuable information hidden in big data that are represented by network structures should be extracted by DEA. These network structures, e.g., transportation and logistics systems, encompass a broader range of inter-linked metrics that cannot be modelled by conventional DEA. It is proposed that network DEA is related to the value dimension of big data. It is shown that network DEA is different from standard DEA, although it bears the name of DEA and some similarity with conventional DEA. Network DEA is big data enabled analytics (big DEA) when multiple (performance) metrics or attributes are linked through network structures. These network structures are too large or complex to be dealt with by conventional DEA. Unlike conventional DEA that are solved via linear programming, general network DEA corresponds to nonconvex optimization problems. This represents opportunities for developing techniques for solving non-linear network DEA models. Areas such as transportation and logistics system as well as supply chains have a great potential to use network DEA in big data modeling. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) strives to evaluate the production units under their best conditions. DEA flexibility in selecting the appropriate input/output weights always results in unreal and zero weights. Treating decision-making units (DMUs) as black-box regardless of their internal structures misleads the DEA performance evaluation. While considering units as a network process, it is more likely to identify more inefficiency sources. This paper suggests using a new common set of weights (CSWs) approach to evaluate the units in both black-box and two-stage structures based on a unified criterion. Indeed, our contribution to this line of research is as follows: Firstly, we improve the model proposed by Kao and Hung (J Oper res Soc 56(10): 1196–1203, 2005) to calculate the CSWs in a linear-based optimization model. Secondly, a new CSWs method is suggested in the two-stage network DEA (NDEA) as multiple objectives fractional programming (MOFP) problem. Thirdly, the MOFP problem is converted into a single objective linear programming problem in the two-stage network case. Finally, an enlightening application is presented. [ABSTRACT FROM AUTHOR]

Due to the urban expansion and population increasing, bus network design is an important problem in the public transportation. Functional aspect of bus networks such as the fuel consumption and depreciation of buses and also spatial aspects of bus networks such as station and terminal locations or access rate to the buses are not proper conditions in most cities. Therefore, having an efficient method to evaluate the performance of bus lines by considering both functional and spatial aspects is essential. In this paper, we propose a new model for the bus terminal location problem using data envelopment analysis with multi-objective programming approach. In this model, we want to find efficient allocation patterns for assigning stations terminals, and also we investigate the optimal locations for deploying terminals. Hence, we use a genetic algorithm for solving our model. By using the simultaneous combination of data envelopment analysis and bus terminal location problem, two types of efficiencies are optimized: Spatial efficiency as measured by finding allocation patterns with the most serving amount and the terminals' efficiency in serving demands as measured by the data envelopment analysis efficiency score for selected allocation patterns. This approach is useful when terminals' efficiency is one of the important criteria in choosing the optimal terminals location for decision-makers. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is an analytical method for measuring, benchmarking, and improving efficiency performance. While DEA has traditionally been a deterministic ex-post approach, when operational data are not available, it can be positioned to represent an ex-ante perspective, if it is feasible to simulate production behaviors. However, there are two limitations of ex-ante DEA. First, is the representation of uncertainties associated with the contextual conditions and their impact on production. Second, is the inability to incorporate risk preferences that relate to the organizations' attitude towards production uncertainty. This paper addresses these two gaps through a method that integrates Monte-Carlo simulations, the Subjective Expected Utility Theory, and ex-ante DEA. Our method allows to explicitly model and incorporate the uncertainties associated with the contextual variables and the production behavior, while facilitating the representation of risk preferences. We demonstrate our method through a case study that is framed around a hypothetical community evaluating Micro-Grid (MG) design alternatives. We utilize the National Renewable Energy Lab data to model the stakeholder energy demand and environmental uncertainties; and use the engineering specifications for the MG technology to populate the production possibility set. We then investigate the distribution of efficiency scores by using various stakeholder risk attitudes and find that DEA rankings are insensitive to risk preferences. The proposed method and the documented risk insensitivity are significant contributions as they allow to reposition ex-ante DEA as a nonparametric alternative to multi-criteria decision-making, which can be generalized across different application areas, including uncommon DEA applications such as engineering. [ABSTRACT FROM AUTHOR]

This paper combines the Cobb-Douglas (CD)with the method of Data Envelopment Analysis and Analytic Hierarchy Process(DEA-AHP) and applies it to the input efficiency evaluation of fiscal and taxation policies. Based on the input of the number of fiscal policy items and the output model design of transformation and upgrading, the overall efficiency of fiscal policy is calculated by the method of Data Envelopment Analysis and Analytic Hierarchy Process. It turns out that the overall efficiency of fiscal and taxation policies is 0.397, and the efficiency of pure policy factors is 0.616. There is a difference in input efficiency between preferential tax policies and fiscal subsidy policies. Tax preferential policy has insufficient investment, and financial subsidy has investment redundancy. Furthermore, the absolute value of tax incentives and financial subsidies are introduced into the CD Function to calculate the contribution rate, and the government and enterprise factors that are not effective in the efficiency of fiscal and taxation policies are attempted to be separated. As a result, it is found that the contribution rate of financial subsidies is weakly negatively correlated. It shows that the conversion rate of enterprises to the financial subsidy policy is low, and the tax preference policy has a better conversion rate. The results of the study reveal that the scope and intensity of tax optimization needs to be increased at the government level, and the utilization of fiscal subsidy policies needs to be improved at the enterprise level. The government should reduce financial subsidies and establish a universal tax preferential policy system. [ABSTRACT FROM AUTHOR]

The present paper examines the Mergers and Acquisitions' (M&As') effects on the European banking efficiency levels based on a sample of 43 listed commercial banks in eight countries (Portugal, Italy, Ireland, Greece, Spain, Germany, France and Finland). We applied a Data Envelopment Analysis (DEA) in two groups of countries and conducted a second-stage analysis under two different models adjusted for credit-risk factors. This is the first time that DEA, an effective nonparametric method for evaluating efficiency, has been applied in the exploration of the European banking sector M&As that occurred during a crisis period characterized by uncertainty and inconvenient circumstances that influenced performance. The results imply significant effects of the capital adequacy ratio and non-performing loans on DEA scores, and demonstrate the past effects on their performance. Moreover, the results reveal that M&As negatively affect the efficiency levels in both "strong" and "weak" banking systems, whereas technical efficiency levels are positively affected by the growth perspective of the origin countries, regardless of whether banks are involved in one or more M&As. The M&As' inability to improve the financial stability of the banking system during the examined crisis period of 2007–2015 led to an examination of the merging banks' concentration levels, before and after the M&A strategy. The results revealed a positive merger effect, only when a M&A takes place under specific levels of market concentrations (i.e., when competition increases). [ABSTRACT FROM AUTHOR]

Liquidity risk was at the heart of 2007–2008 global financial crisis, which has led to a series of financial institutions failure. We test whether and how liquidity risk impacts European banks' stability (i.e., a bank risk-return profile) under different levels of engagement in non-traditional banking activities after the global financial crisis and during the implementation of the Basel III liquidity rules. To calculate financial stability, we adopt an efficiency perspective based on the combination of the CAMELS rating system with the data envelopment analysis technique. We implement a nonlinear panel smooth transition regression approach, where transitional factors of income diversification are endogenously captured from the data. We find that, liquidity risk stemming from liquidity creation has a positive impact on bank stability, implying that income diversification can serve as a "buffer" through which banks can ensure their liquidity creation and offset for the compression of intermediation margin in lending and deposit activities. This suggests that diversification does not impede the ability of banks to operate with lower liquidity holdings but allows them to make greater use of their balance sheets to fulfill their primary roles of credit provision and liquidity creation. The results offer interesting implications for regulators and bank managers in managing liquidity risk. [ABSTRACT FROM AUTHOR]

The Indian Railways is India's biggest employer and undeniably influences the country's transportation network, economy, and social and cultural systems. The network is split into zones for operational reasons. It is vital to evaluate these railway networks to identify their strengths and shortcomings and to improve their performance. Past works often use Data Envelopment Analysis to evaluate railway services. Our contribution lies in the inclusion of several aspects not previously considered, such as (i) both tangible and intangible criteria, (ii) the hierarchical nature of the problem, and (iii) additional useful criteria and data to analyze the performance of the zones, including physical assets, operating ratio, accidents, comfort, travel experience, flexibility, transparency, etc. We use the novel Hierarchical Fuzzy Axiomatic Design method to evaluate the performance of sixteen zones in the Indian Railways since it suits our problem well. We find that the Southern Railway zone performs best, while the Northeast Frontier zone is ranked last. We also identify the strengths and weaknesses of all railway zones. [ABSTRACT FROM AUTHOR]

The emergence of sports tourism has compelled sports managers to rethink the management and improvement of sports facilities. Through service quality analysis, sports managers can identify the strengths and weaknesses of their activities for possible advancement. Hence, this study aims to develop a decision support model based on integrating online reviews and data envelopment analysis to measure the degree of tourist satisfaction and provide benchmarking goals for service improvement. The proposed model employs text mining techniques to discover service quality attributes from text reviews. According to the discovered service quality attributes, we conduct sentiment analysis to reveal the sentiment polarities of the text reviews. Then, we refine the polarities and ratings of online reviews into linguistic distribution assessments. Furthermore, we develop a linguistic distribution output-oriented non-discretionary bestpoint slack-based measure (BP-SBM) to compute the degree of tourist satisfaction and benchmarking goals. The linguistic distribution output-oriented non-discretionary BP-SBM can handle both positive and negative data values, thus overcoming the flaws of the traditional model. Meanwhile, the proposed decision support model investigates how the service-quality attributes interact to provide improvement pathways for an underperforming stadium based on association rule mining. We test the applicability of the proposed decision support model on some Elite stadia in Europe. [ABSTRACT FROM AUTHOR]

Economic-environmental performance or eco-efficiency is a topic of great interest due to the "green movement." Data Envelopment Analysis (DEA) is a non-parametric method for measuring the eco-efficiencies in comparable Decision-Making Units (DMUs) under various technology assumptions, e.g., constant or variable returns to scale. In the case of variable returns to scale, the returns to scale (RTS) values show whether the DMUs under consideration have the correct scale size or can be improved by upsizing or downsizing. However, sometimes the RTS values for some DMUs are unusually high or low and hence useless in practice. The RTS-mavericks test is devised to propose RTS bounds to fix this flaw. However, these bounds can be ineffective in practice. Even if this flaw is rectified, it needs to be clarified how the concept of RTS-mavericks influences eco-efficiency analysis. For the case of a single technology and a combination of two technologies (a so-called pollution-generating technology), we derive RTS equations and develop new median-based optimization problems to correct this flaw and show that the new concept can lead to non-convex technologies. We also demonstrate the applicability and exhibit the efficacy of the proposed model in the context of eco-efficiency analysis in the European Union. [ABSTRACT FROM AUTHOR]

A centrally managed system (CMS) typically comprises several decision making units (DMUs) that operate under a central DMU. The central DMU allocates the total available resources under its control among different DMUs to optimize the performance of the whole system. This distinguishing feature is at the heart of centralized resource allocation (CRA) methods and should be taken into account when assessing individual efficiency of each DMU in CMS. We introduce a slacks-based model for measuring individual efficiency of each DMU in CMS. As we will discuss, there are different possible CRA plans leading different projection points of DMUs on the frontier of the production possibility set (PPS). We will however show that all DMUs are projected on the same supporting hyperplane of the PPS under all CRA plans. We therefore have a common reference base, a subset of the ordinary efficient frontier, using which individual efficiency of each DMU can be measured in CMS. Having measured the individual efficiency of each DMU, we can categorize the DMUs into CRA-efficient and CRA-inefficient. To distinguish between CRA-efficient DMUs, we further introduce an influence index that measures the maximum effect of a specific CRA-efficient DMU on the construction of the projection points of the DMUs in CMS. We then propose a linear model to measure the influence of each CRA-efficient DMU. We can therefore provide a complete ranking of the DMUs in CMS. The proposed approach is demonstrated using a real data set. [ABSTRACT FROM AUTHOR]

According to the extant literature, improving the leanness of a production system boosts a company's productivity and competitiveness. However, such an endeavor usually involves managing multiple, potentially conflicting objectives. This study proposes a framework that analyzes lean production methods using simulation and data envelopment analysis (DEA) to accommodate the underlying multi-objective decision-making problem. The proposed framework can help identify the most efficient solution alternative by (i) considering the most common lean production methods for assembly line balancing, such as single minute exchange of dies (SMED) and multi-machine set-up reduction (MMSUR), (ii) creating and simulating various alternative assembly line configuration options via discrete-event simulation modeling, and (iii) formulating and applying DEA to identify the best alternative assembly system configuration for the multi-objective decision making. In this study, we demonstrate the viability and superiority of the proposed framework with an application case on an automotive spare parts production system. The results show that the suggested framework substantially improves the existing system by increasing efficiency while concurrently decreasing work-in-process (WIP). [ABSTRACT FROM AUTHOR]

This study constructs an index of humanitarian labor efficiency to measure labor utilization based on a factor-specific data envelopment analysis model. Compared with the traditional labor productivity index, humanitarian labor efficiency is a more reliable and comprehensive measure; it not only excludes the contribution of non-labor input but also considers the undesirable output, such as accidental deaths. The results show that China's humanitarian labor efficiency is low, ranging from 0.3 to 0.7, and it should be improved further. Unlike labor productivity, humanitarian labor efficiency did not increase markedly between 2007 and 2017. Further analysis revealed that pure labor efficiency declined while pure humanitarian efficiency increased for this period, which offset each other. Tobit regression shows that industrial structure has a significant influence on humanitarian labor efficiency. Baumol's disease, caused by the growing tertiary industry, may result in the decline of labor utilization efficiency in China. This study presents suggestions on how to deal with Baumol's disease. [ABSTRACT FROM AUTHOR]

While humanitarian efforts are critical for assisting those affected by natural disasters, it is also essential for those affected by poverty, such as China's rural poor. In this regard, China introduced the rural minimum living security system to provide humanitarian relief to its rural poor. The aim of this study is to explore the influencing factors and efficiency of humanitarian supply chains funds using rural minimum living security funds (RMLSF) as an example. Based on data from 31 provinces (autonomous regions and municipalities) in China from 2007 to 2016, this study employs the logarithmic mean Divisia index approach to decompose the RMLSF and investigates the contributions of seven factors on the change therein. This study also uses the three-stage data envelopment analysis method to assess the poverty reduction efficiency of RMLSF. The results show that the economic development level, the extent to which minimum living security funds are tilted toward rural areas, and the fiscal expenditure scale are the three main factors for the increase in RMLSF. Moreover, the technical efficiency in most provinces and the average technical efficiency in the eastern and central regions are underestimated due to external ambient factors, whereas the average technical efficiency in the western region is overestimated. These results provide a basis for increasing the scale and efficiency of RMLSF. [ABSTRACT FROM AUTHOR]

In the past 10 years, a large number of consensus-reaching approaches for group decision making (GDM) have been proposed. While these methods either focus on the cost of the consensus reaching or the convergency of the consensus process, the consensus efficiency has long been ignored. Meanwhile, the measurements of consensus threshold are often determined by some subjective and intuitive judgements, such as management experience and estimations for the degree of satisfaction, which lack a theoretical foundation. In management applications, how to measure consensus and how to evaluate a consensus reaching method are also ambiguous. To tackle these questions, we introduce efficiency measures into the consensus reaching process of GDM and achieve a comprehensive evaluation of current consensus methods through an efficiency analysis of consensus costs and consensus improvement. From the perspective of efficiency, we propose a benchmark in consensus reaching by data envelopment analysis without explicit input benchmark models, and then present an objective method for consensus threshold determination in GDM. Finally, we use numerical examples to illustrate the usability of our method. [ABSTRACT FROM AUTHOR]

The purpose of this research is to introduce an innovative decision architecture to assess corporate risks by utilizing accounting narratives and to further examine the association between those risks and operating performance. We run text mining algorithms to identify the types of risks from narratives and incorporate them with a readability measure (i.e., linguistic cue(s)) to conjecture a firm manager's attitude toward corporate risk. We consider a two-stage network data envelopment analysis (TNDEA) with the benefit of opening up the black-box of a production process via considering internal activities. The outcome derived from TNDEA is determined by users who decide "a priori" what the specification of the model should be, without considering any alternatives. The study further incorporates the fuzzy rough set theory (FRST) with TNDEA by considering inclusion/exclusion or different combinations of inputs, intermediates, and outputs so as to realize a corporate's underlying business situation. Decision attributes taken from the outcome of FRST-TNDEA and condition attributes gathered from annual reports are jointly inserted into an artificial intelligence (AI) technique to establish the forecasting model. The integrated circuit (IC) industry has long been viewed as an essential backbone of Taiwan's economy, and therefore it is taken as our research target. The results show that the introduced linguistic cues have positive and considerable impacts on firm performance. Overall, the findings herein provide direct support for recent regulators that require corporates to add a new section on risk factors in accounting narratives so as to prevent users from making biased judgments. [ABSTRACT FROM AUTHOR]

Following the emergence of the Internet, the Internet of things (IOT) brought about another wave of technological and economic revolutions. Through the lens of the production process, this study utilises the dynamic network slack-based measure model in data envelopment analysis to evaluate 32 IOT companies in Taiwan in terms of their innovation efficiency, operational efficiency and overall efficiency for the period of 2007–2017. Empirical results reveal that the average operational and overall efficiencies of IOT companies in Taiwan have been decreasing considerably since 2008. However, their average innovation efficiency remains stable over the sample period owing to government reductions in enterprise research and development (R&D) tax credit incentives. Through the impulse response function method, this study further confirms that the Statute for Industrial Innovation, which was implemented in 2010 and revised and reimplemented in 2016, specifically, policies concerning enterprise R&D tax credits, affect the efficiencies of IOT companies in Taiwan. Overall, this study reveals the performance evaluation process of IOT companies by showing that their innovation capability affects their operational efficiency. Thus, the government is advised to incorporate innovation measures into relevant industrial policies to achieve policy effectiveness. [ABSTRACT FROM AUTHOR]

This study evaluates the management efficiency and quality effectiveness of Taiwanese local auditing institutions (LAIs), whereby two outputs leave the first stage without being inputted to the second stage under the assumption of variable returns to scale. To address these considerations, this study integrates multiplicative efficiency aggregation (MEA) in a two-stage network data envelopment analysis (DEA) model into the form of second order cone programming (SOCP). Under a cone structure, our DEA findings indicate that management efficiency increased, and quality effectiveness decreased over the sample period 2013–2015. Besides, a truncated regression analysis indicates that differences in operating environments, particularly urbanization level and LAIs' attributes, have significant impacts on the management efficiency of LAIs. The potential applications and strengths of SOCP and MEA-based two-stage network DEA in assessing the LAIs are highlighted. [ABSTRACT FROM AUTHOR]

The purpose of this contribution is to compute the popular Malmquist productivity index while adding a component representing plant capacity utilisation. In particular, this is—to the best of our knowledge—the first empirical application estimating both input- and output-oriented Malmquist productivity indices in conjunction with the corresponding input- and output-oriented plant capacity utilisation measures. Our empirical application focuses on a provincial data set of tourism activities in China over the period 2008–2016. The results contain the output- and input-oriented Malmquist productivity indices, some Spearman rank correlations between both, a t-test whether these indices differ from unity, and some bootstrapping analysis. [ABSTRACT FROM AUTHOR]

This study assesses whether foreign currency (FC) hedging improves firm productive efficiency. Using a unique sample of French non-financial listed firms belonging to the CAC All-Tradable index (former SBF250 index) index over the period 2004–2012, we employ a non-parametric method—data envelopment analysis—to estimate a firm's efficiency frontier and analyze the role of financial hedging in addressing agency conflicts in France. The empirical results show that FC hedging has a significant positive effect on efficiency. This finding supports the theoretical view that hedging is a disciplinary device that can mitigate the owner–manager agency conflicts, leading to a better firm efficiency. The results are robust to a battery of sensitivity and endogeneity tests. [ABSTRACT FROM AUTHOR]

We use a novel three-stage network data envelopment analysis (DEA) model (based on production, intermediation, and revenue generation operations) with bootstrapping to evaluate the performance of 14 of the largest Canadian credit unions for the period 2007–2017 and the impact of various events on this performance. For each analysis, we contrast the results of the network DEA with those of a black box DEA. We show that the former provides more insightful information regarding the sources of the inefficiencies. We first found that while overall, the credit unions showed high-efficiency ratios, there is room for improvement, especially for the production sub-process. Moreover, the efficiency of individual credit unions is not consistent across the three different stages. Through the years 2007–2017, the credit union system exhibits a relatively sharp decline in its efficiency, mainly due to managerial issues at the revenue generation stage. Our analyses show that the various stages of Canadian credit union operations have been affected by the 2007–2009 financial crisis, the low policy interest rates that occurred in the following years, and the fact that in Canada, the federal government has eliminated the discount on the federal tax rate. The credit unions can improve their performance at the different stages by exploring Fintech Solutions to reduce their operating costs, seeking a better mix of loans and securities investments, and improving their interest and saving rate settings. [ABSTRACT FROM AUTHOR]

Chen et al. Omega 41:621–625, 2013 propose a DEA model which measures the super-efficiency with directional distance function. Their model avoids the infeasibility when the inputs have zero and it also prevents the negative projection. However, their model might be infeasible when there is zero in the outputs. Besides, their model relies on complex predetermined parameters. Recently, Lin and Chen (Lin and Chen Journal of the Operational Research Society 66:1506–1510, 2015) propose a new model which prevents infeasibility and negative projection. However, Lin and Chen's method introduces a constant which might dominate the input of the target DMU. To overcome the drawbacks of the models proposed in Chen et al. Omega 41:621–625, 2013 and Lin and Chen Journal of the Operational Research Society 66:1506–1510, 2015, we will develop a new DEA model to compute the super-efficiency, which prevents negative projection and the infeasibility when zero data occurs and works in the same way as the N-L model (Ray, Ray Journal of the Operational Research Society 59:788–797, 2008) when no abnormal input occurs. [ABSTRACT FROM AUTHOR]

We develop an output-oriented data envelopment analysis framework to examine the efficiency of Chinese banks over the period 2007–2017 and further test the relationship between efficiency and corporate social responsibility (CSR). We are the first piece of research considering the number of employees as one bank input and potential increase in the number of employees as one CSR indicator. Additionally, we innovatively propose another three specific CSR indicators: namely donation, balance of green credits and loans to small and medium sized enterprises. The results show that the gain from improving allocative output-efficiency by reallocating variable inputs is less than the gain attained by improving technical output-efficiency. Evidence from the second-stage regression analysis shows that the overall indirect technical efficiency is significantly and negatively affected by the volumes of green credits, while an increase in the volumes of donations will improve the indirect allocative efficiency. [ABSTRACT FROM AUTHOR]

In the present era of complex environments, banks operate in a more dynamic environment, which in turn, affects their relative efficiency. Traditional Data envelopment analysis (DEA) models are widely used to measure efficiency. However, environmental/exogenous variables can significantly influence the DEA efficiency scores. Therefore, identifying the most important environmental variables is crucial in the evaluation of bank performances. This study introduces a three-stage DEA framework that employs a random forest as a powerful ensemble method for variable selection to search for the most influential environmental variables. The direction of influence of the selected environmental variables and their predictive power for predicting bank performances are investigated in the third stage, through a regression analysis. The proposed framework is tested with a sample of 110 banks in Middle East and North Africa countries, observed over a period of 3 years (2014 till 2016). Accordingly, a relevant set of environmental variables is identified and its effects on bank efficiency are studied. The findings indicate that the country where the bank operates has a significant effect on the bank's efficiency. Results also show that the overall average efficiency score is stable (around 87%) for all banks. The study concludes with the limitations and suggested directions for further research. [ABSTRACT FROM AUTHOR]

In response to the limitation of classical Data Envelopment Analysis (DEA) models, the super efficiency DEA models, including Andersen and Petersen (Manag Sci 39(10): 1261–1264, 1993)'s model (hereafter called AP model) and Li et al. (Eur J Oper Res 255(3): 884–892, 2016)'s cooperative-game-based model (hereafter called L–L model), have been proposed to rank efficient decision-making units (DMUs). Although both models have been widely applied in practice, there is a paucity of research examining the performance of the two models in ranking efficient DMUs. Consequently, it is unclear how close the rankings obtained by the two models are to the "true" ones. Among the very few studies, Banker et al. (Ann Oper Res 250(1): 21–35, 2017) pointed out that the ranking performance of the AP model is unsatisfactory; Li et al. (Eur J Oper Res 255(3): 884–892, 2016) and Hinojosa et al. (Exp Syst Appl 80(9): 273–283, 2017) demonstrated the L–L model's capability of ranking efficient DMUs without addressing the ranking performance. In this study, we, thus, examine the ranking performance of the two super-efficiency models. In evaluating their performance, we carry out Monte Carlo simulations based on the well-known Cobb–Douglas production function and adopt Kendall rank correlation coefficient. Unlike Banker et al. (Ann Oper Res 250(1): 21–35, 2017), we use the rankings obtained based on the two models and the "true" ones as the basis of performance evaluation in our simulations. Moreover, we consider several types of returns to scale (RS) and study the impact of changes of some parameters on the ranking performance. In view of the importance, we also carry out additional simulations to examine the influence of technical inefficiency on the two models' ranking performance. Based on the simulation results, we conclude: (1) Under different RS, the ranking performance of the two models remains the same when changing parameters, e.g., the distribution of input variables; (2) Under different RS, when technical inefficiency (in comparison with random noise) is more important, the two models have satisfactory performance by providing rankings that are close to, or the same as, the "true" ones; (3) The L–L model has better performance than the AP model and is more robust. This is especially true when technical inefficiency is less important; (4) Under different RS, when technical inefficiency is less important, both models have unsatisfactory ranking performance; and (5) The relative importance of technical inefficiency plays an prominent role in ranking efficient DMUs. [ABSTRACT FROM AUTHOR]

In this study, we develop an approach that integrates corporate image of corporate social responsibility (CSR) activities, stock price crash risk and profitability into a dynamic slack-based measure model to evaluate corporate sustainability performance. We illustrate our methodology by applying it empirically to examine the sustainability performance of firms that won a Top 50 Corporate Citizenship Award from the Commonwealth Magazine of Taiwan. Our proposed model yields an accurate estimation of time-specific dynamic corporate sustainability performance over multiple periods. Specifically, we find that it is essential to integrate the corporate image of CSR activities into the evaluation of dynamic corporate sustainability performance. We also find that performance results have a better explanation and practical significance to the actual situation. Thus, our performance ranking results and cluster analysis can help stakeholders identify investment decisions as they seek out the most worthwhile investment firms and groups of firms. Additionally, our findings can help policymakers identify inter-temporal corporate sustainability performance trends. [ABSTRACT FROM AUTHOR]

This contribution focuses on testing the empirical impact of the convexity assumption in estimating costs using nonparametric specifications of technology and cost functions. Apart from reviewing the scant available evidence, the empirical results based on two publicly available data sets reveal the effect of the convexity axiom on cost function estimates: cost estimates based on convex technologies turn out to be on average between 21% and 38% lower than those computed on nonconvex technologies. These differences are statistically significant when comparing kernel densities and can be illustrated using sections of the cost function estimates along some output dimension. Finally, also the characterization of returns to scale and economies of scale using production and cost functions for individual units yields conflicting results for between 19% and 31% of individual observations. The theoretical known potential impact as well as these empirical results should make us reconsider convexity in empirical production analysis: clearly, convexity is not harmless. [ABSTRACT FROM AUTHOR]