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The purpose of this paper is to evaluate design performance of 51 gear shaping machines by using data envelopment analysis (DEA).

Existing studies extend traditional DEA by handling bounded and discrete data based on envelopment models. However, value judgment is usually neglected and fail to be incorporated in these envelopment models. In many cases, there is a need for prior preferences. Using existing DEA approaches as a backdrop, the current paper presents a methodology for incorporating assurance region (AR) restrictions into DEA with bounded and discrete data, i.e. the assurance region bounded discrete (AR-BD) DEA model. Then, the AR-BD DEA model is combined with a context-dependent DEA to obtain an efficiency stratification.

The authors examine different AR restrictions and calculate efficiency scores of five scenarios of AR restrictions by using the proposed AR-BD DEA model. It shows that AR restrictions have a great impact on the efficiency scores. The authors also identify nine efficient frontiers in total. For each decision-making unit, it could set benchmarks and improve its performance based on each higher efficient frontier.

This paper first evaluates efficiency of gear shaping machines by considering different (bounded and discrete) variable types of data and including AR restrictions. The AR-BD DEA model and context-dependent AR-BD DEA model proposed in this paper further enrich the DEA theory. The findings in this paper certainly provide useful information for both producers and consumers to make smart decisions.

While market theorists have devoted a great deal of effort to the conceptualization of service quality, the practical guidance available to service providers continues to be very limited. Utilizing the emerging role of a new marketing entity, the retail electric service provider, as an illustration, the article discusses how data envelopment analysis might be used to analyze service quality at the retail service level. Specific dimensions thought to influence consumers’ perceptions of the quality of retail electric energy services are identified, and the potential use of data envelopment analysis as a diagnostic tool for effective management of service quality by retail electric service providers is demonstrated. Generalization to different types of service providers is suggested. Empirical studies to develop practical guidance along this line of analysis are encouraged.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This paper aims to evaluate commercial bank branches' performance in dynamic and competitive conditions where decision-making units (DMUs) seek a greater proportion of shared resources as it happens in the real world. By introducing the concepts of cross-shared and serial-shared resources, the authors have emphasized the role of evaluation results of past periods on branches' total efficiency.

In this study, a new mixed-integer data envelopment analysis (MI-DEA) model has been proposed to evaluate the performance of a dynamic network in the presence of cross-shared and serial-shared resources.

The proposed model helps bank managers to find the source of inefficiencies and establish a connection between the results of the periodic performance of the DMUs and the distribution of serial and cross-shared resources. The results show that the weighting coefficients of the periods do not significantly affect the overall efficiency of commercial bank branches, unlike desirable and undesirable intermediates.

This paper presents the following factors: (1) A new mixed-integer network data envelopment analysis model is developed under dynamic competitive conditions. (2) For the first time in DEA models, the concept of cross-shared resources is proposed to consider shared resources between DMUs. (3) All controllable, uncontrollable, desirable and undesirable outputs in the model are considered with the possibility to transfer to the next periods. (4) A case study is given for the performance evaluation of 38 branches of an Iranian commercial bank from 2016 to 2020.

This paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.

Different statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their statistical counterpart, logistic regression.

The results showed that random forests and bagging outperform other methods in terms of predictive power.

This is the first study to assess the impact of environmental factors on banking performance in Middle East and North Africa countries.

Uncertainty in data, whether in real-valued or integer-valued data, may result in infeasible optimal solutions or unreliable efficiency scores and ranking of decision-making units. To handle the uncertainty in integer-valued factors in data envelopment analysis (DEA) models, this study aims to propose a robust DEA model which is applicable in the presence of such factors.

This research focuses on the application of fuzzy interpretation of efficiency to a mixed-integer DEA (MIDEA) model. The robust optimization approach is used to address the uncertain integer-valued parameters in the proposed MIDEA model.

In this study, the authors proposed an MIDEA model without any equality constraint to avoid the arise problem by such constraints in the construction of the robust counterpart of the conventional MIDEA models. We have studied the characteristics and conditions for constructing the uncertainty set with uncertain integer-valued parameters and a robust MIDEA model is proposed under a combined box-polyhedral uncertainty set. The applicability of the developed models is shown in a case study of Malaysian public universities.

This study develops an MIDEA model equivalent to the conventional MIDEA model excluding any equality constraint which is crucial in robust approach to avoid restricted feasible region or infeasible solutions. This study proposes a robust DEA approach which is applicable in cases with uncertain integer-valued parameters, unlike previous studies in robust DEA field where uncertain parameters are generally assumed to be only real-valued.

Proper performance measurement is an important issue in library operational management. A data envelopment analysis (DEA) model is applied to evaluate the relative operational efficiency of 25 U.S. private research-university library members of the Association of Research Libraries (ARL). Operations of each library decision-making unit are considered as a production process using four resource input and four service output variables. The model results are analyzed and compared with the efficient group and a peer group by using a t-test. The model provides decision-makers with more accurate information to implement better library services with appropriate resource allocation.

The long tradition in the public sector of introducing decision-making tools that then fail to live up to expectations has fueled a debate over the proper role of government. This paper suggests that the debate over government productivity may be misplaced. Public productivity may be hindered as a result of inappropriate use of decisionmaking tools for the allocation of resources. Thus, this paper argues for an enlarged repertoire of decision-making techniques. In particular, data envelopment analysis (DEA) is presented as an alternative often more appropriate than such commonly used techniques as cost-benefit ratio and regression analyses.

The purpose of this study is to evaluate the performance of mutual funds during the COVID-19 pandemic with environmental, social and governance (ESG) criteria. The main research question is whether mutual fund performance differs with respect to the level of the mutual fund’s ESG score.

The data set contains global fund data, and mutual fund performance is analyzed using two types of data envelopment analysis (DEA) models: the DEA portfolio index (DPEI) and the range direction measure (RDM) DEA. Propensity score matching and logistic regression are also applied.

The results reveal that: nonequity mutual funds present significantly higher performance compared to the performance of equity mutual funds; mutual funds with high ESG scores are associated with significantly higher performance compared to those with low to medium ESG scores; funds with high ESG scores experience higher performance irrespective of their type; and efficiency scores derived from the RDM DEA are significantly higher than those derived from the DPEI model.

Investors, fund managers and market participants can benefit from the findings of this study and improve their investment decision-making process, including more sustainable funds in their portfolios. Regulators and policymakers should further promote or even require the inclusion of more sustainable investments in the financial products offered by institutional investors. The main limitation of the study is related to data availability regarding the ESG score of mutual funds.

To the best of the authors’ knowledge, this is the first study that provides robust evidence in support of a positive association between ESG scores and mutual fund performance during the pandemic-induced crisis applying a DEA methodology.

Data envelopment analysis (DEA) has wide applications in the agricultural sector to evaluate the efficiency with crisp input and output data. However, in agricultural production, impreciseness and uncertainty in data are common. As a result, the data obtained from farmers vary. This impreciseness in crisp data can be represented in fuzzy sets. This paper aims to employ a combination of fuzzy data envelopment analysis (FDEA) approach to yield crisp DEA efficiency values by converting the fuzzy DEA model into a linear programming problem and machine learning algorithms for better evaluation and prediction of the variables affecting the farm efficiency.

DEA applications are focused on the use of a common two-step approach to find crucial factors that affect efficiency. It is important to identify impactful variables for minimizing production adversities. In this study, first, FDEA was applied for efficiency estimation and ranking of the paddy growers. Second, the support vector machine (SVM) and random forest (RF) were used for identifying the key leading factors in efficiency prediction.

The proposed research was conducted with 450 paddy growers. In comparison to the general DEA approach, the FDEA model evaluates fuzzy DEA efficiency giving the user the flexibility to measure the performance at different possibility levels.

The use of machine learning applications introduces advanced strategies and important factors influencing agricultural production, which may help future research in farms' performance.