Modeling Sustainable Development of Cryptocurrencies by a Fractional Pure-Jump Process in DEA Framework

Sustainable development and sustainability assessment have been of great interest to both academe and practitioners in the past decades. In this study, we review the literature on data envelopment analysis (DEA) applications in sustainability using citation-based approaches. A directional network is constructed based on citation relationships among DEA papers published in journals indexed by the Web of Science database from 1996 to 2019. We first draw the citation chronological graph to present a complete picture of literature development trajectory since 1996. Then we identify the local main DEA development paths in sustainability research by assigning an importance index, namely search path count (SPC), to each link in the citation network. The local main path suggests that the current key route of DEA applications in sustainability focus on the environmental sustainability. Through the Kamada–Kawai layout algorithm, we find four research clusters in the literature including corporate sustainability assessment, regional sustainability assessment, sustainability composite indicator construction, and sustainability performance analysis. For each of the clusters, we further identify the key articles based on citation network and local citation scores, demonstrate the developmental trajectory of the literature, and suggest future research directions.KeywordsData envelopment analysis (DEA)SustainabilityLiterature surveyCitation analysis

One of the major research streams in data envelopment analysis (DEA) is ranking decision-making units (DMUs). Utilizing a multicriteria decision-making technique, we develop a novel approach to fully rank all units. Motivated by the convex cone-based total order for multiple criteria alternatives proposed by Dehnokhalaji et al. (Nav Res Logist 61(2):155–163, 2014), we consider DMUs in DEA as multiple criteria alternatives and obtain their total ordering. Initially, some pairwise preference information is provided by the decision maker for units and the concepts of convex cones and polyhedral sets are defined in a DEA framework, correspondingly. We apply a modification of Dehnokhalaji et al. method to extract additional preference information for each pair of units and consequently obtain a full ranking (strict total ordering) of DMUs. The benefit of our approach to their method is that we apply non-radial models to overcome the instability drawback of radial models and their infeasibility occurring in DEA applications. The proposed approach is implemented for two numerical examples, and the accuracy of it is investigated through a computational test.

The financial situation of transitional period in Russia makes companies in the country economize their resources, and as a consequence increase their efficiency. Data Envelopment Analysis (DEA) proposed by A. Charnes and W. Cooper is a powerful approach to determine the efficiency of the complex production systems[1,2,3]. At present, we witness a real scientific boom of DEA approach development, both theory and applications. Many scientific publications are devoted to DEA applications in the financial services sector. In our economic situation, the straight application of conventional DEA approach frequently leads to “strange” results. The analysis of these situations has driven us to the conclusion that we must introduce some additional constructions to the classical approach. In our work, we consider some new constructions in the DEA framework and apply them to efficiency analysis of the leading Russian banks and vertically integrated oil companies. The DEA approach generalizes many notions of macro- and microeconomics for the case of the multidimensional space of production parameters (inputs and outputs). However, it is very difficult for the manager to operate in the multidimensional space of parameters. A family of parametric optimization methods developed by our group enables us to make a cross-section of the efficient frontier by any pair of given directions. Thus, this will reduce the analysis of the complex system to the investigation of the well-known functions in economics: production function, isoquant, isocost, etc. This paper develops results that were presented at the 4th International Congress on Industrial and Applied Mathematics, Edinburgh, 1999 [4] and at the International DEA Symposium 2000, Brisbane [5].KeywordsData Envelopment AnalysisProduction UnitData Envelopment Analysis ModelEfficient FrontierData Envelopment Analysis ApproachThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Purpose– The purpose of this paper applies to Indian steel manufacturing industries to evaluate the technical and scale efficiency (SE).Design/methodology/approach– Data envelopment analysis (DEA) has been employed to calculate the relative efficiency of the steel manufacturing units. The selection criteria for the inclusion of a steel manufacturing unit in the analysis has been annual income of more than 50 crores and units manufacturing pig iron, steel and sponge iron. Within the DEA framework, the output-oriented model with constant returns to scale and variable returns to scale were studied. Four input variables, namely, gross fixed assets, total energy cost, total number of employees and currents assets were considered. Among the output variables, the four variables considered are income, sales, PBIT and PAT.Findings– The result of the efficiency scores have been categorized into three parts. The pure technical efficiency represents local efficiency and the reason of inefficiency is due to inefficient operations. Technical efficiency indicates that the respective decision-making units are globally efficient in case the efficiency is 100 per cent. The SE explains that the inefficiency is caused by disadvantageous conditions. As the result shows, that public sector undertaking (PSUs) are operating under disadvantageous conditions as compared to private manufacturing units. One of the possible reasons of location disadvantage condition is manufacturing units for PSUs are scattered throughout India. Some of the units are located in such places where, the raw material, supply chain could be difficult. It has been found that 45 per cent of the private manufacturing units are technically as well as scale inefficient units.Practical implications– The result of the study would benefit the steel industry to develop a performance benchmarking as steel companies must be profitable in the long term to ensure sustainable achievements.Originality/value– This is an original study to apply DEA to get insights on productivity efficiency of the steel manufacturing units in India. Though the manufacturing units were selected on the basis of annual income, the analysis of productivity does not reflect any impact of income on the efficiency of the manufacturing firms.

Data envelopment analysis application in sustainability: The origins, development and future directions

The supplier selection process is a strategic decision-making process that influences the company’s sustainability. Lately, the sustainability concept has been highlighted as an organization’s source of success and profitability. Therefore, the selection of a sustainable supplier has become an imperative for organizations and is the focus of this manuscript. Suppliers are key stakeholders in the supply chain, and their proper selection is a key factor in a successful and sustainable supply chain. For this reason, it is crucial to determine how and which methods are mostly used by companies when choosing sustainable suppliers with the aim of examining whether the Data Envelopment Analysis (DEA) contributes to the same. This article is the first to present a comprehensive bibliometric analysis of 87 articles dealing with the application of DEA in the sustainable supplier selection in the period 2010–2022, with the application of the keywords “Data Envelopment Analysis”, “Supplier”, and “Sustainable” in Scopus and Web of Science databases. The main goal of this manuscript is to explore the applications of DEA in a sustainable supplier selection and to provide an analysis and visualization of bibliometric data to reveal the annual trends of published articles in this area, the top contributing journals, the most cited papers, the most contributing authors, citations, affiliations, and countries’ analysis, and an in-depth keyword visualization analysis. The findings of this study provide valuable insights and emphasize the ever-growing trend toward the selection of sustainable partners and suppliers in business using DEA methodology. Notably, this work shows the applicability and efficacy of DEA in specialized areas of supply chain management and should contribute to the construction of an overview of the existing literature on DEA studies regarding the process of selection of sustainable suppliers in supply chain management as well as stimulate the interest in the topic. This article gives an overview of a research field that is actually insufficiently explored through the scientific literature and presents a wide area and guidelines for future work.

Combining ranking scales and selecting variables in the DEA context: The case of industrial branches

This article provides a systematic analysis of renewable energy performance using data envelopment analysis (DEA) to understand the diverging paths of renewable energy development for countries. In this review, 72 quantitative studies were identified using a multi-stage selection process. The review found that the DEA method can be used as an appropriate tool for performance evaluation of renewable energy studies' research. The DEA method can be applied critically for decision making, especially for policymakers in the renewable energy sector. The review also demonstrated that the DEA method, either traditional or advanced, can be comprehensively used to evaluate the performance of renewable energy studies depending on the objective of the research, as well as the complexity and accuracy of data issues. This review revealed that the selection of input and output factors used in DEA is sufficient enough to evaluate renewable energy performance. This review contributed to the current energy literature and filled in the gap with the addition of new knowledge on assessing renewable energy research studies intensively using a formal systematic literature review process. The review revealed that the development of DEA methodologies and applications in renewable energy should be expanded in the future. The results obtained from this review are both beneficial and inspirational for further research regarding the DEA application in renewable energy and provide valuable input for policymakers in decision-making processes.

PurposeResearchers have noticed that in efficiency assessment, some attributes exhibit specializations including non-discretionary, non-controllable or undesirable. This paper aims to focus on other special factors which have target levels to achieve, i.e. the inputs (outputs) are no longer the-less-the-better (the-more-the-better).Design/methodology/approachIn this paper, the authors further study the target variables when some attributes have multiple levels of targets. Such a situation can be found in many operational efficiency evaluations with various targets or bounded scale in inputs or outputs. They suppose that decision-making units (DMUs), reaching any target level, are identical efficient. To some extent, it is mitigation between common targets and individual targets. Using the closest target rule, the authors propose a target-level-oriented method to evaluate DMUs locally.FindingsFirst, the authors found that some factors have multiple levels of targets to improve its efficiency in real world practice. Second, the proposed technique is able to deal with the multiple-levels-targets problems in data envelopment analysis (DEA) framework. Third, the decision-maker can select the improvement directions more freely than that in the traditional setting.Originality/valueFirst, this is the first paper to discuss the multiple-levels-targets problems in DEA framework. Second, the proposed technique can help the decision-maker to select the best improvement strategies. Third, the technique developed in this paper can be used in many areas. For example, it can support the environmental efficiency evaluation with different standards of pollution emission.

Purpose Despite the growing reliance on Data Envelopment Analysis (DEA) to evaluate the performance of tourism destinations and hospitality organizations, significant gaps in its application need to be addressed. Thus, the purpose of this study is to provide a critical overview of the application of DEA in tourism and hospitality research and to offer a list of actionable recommendations for researchers using this methodology. Design/methodology/approach This research systematically reviews DEA studies in tourism and hospitality based on a set of preidentified issues that are critical to DEA estimation. The final pool of papers included 162 studies published over the past 22 years (2003–2024). Findings The findings reveal that the majority of studies exhibit significant shortcomings regarding critical aspects of DEA application, ranging from the nature and number of inputs/outputs to model specification and orientation. Considering such findings and the characteristics of the tourism and hospitality industry, this paper proposes the DEA Suitability and Reporting Framework (DSRF) – a comprehensive checklist for ensuring transparency and rigor in DEA applications. Practical implications The study offers important implications for tourism and hospitality researchers. The paper presents researchers with suggestions for the correct application of DEA to derive meaningful conclusions. These suggestions are provided with a clear explanation of the importance of each procedure, along with the tourism and hospitality industry characteristics. Originality/value DEA, as a tool to estimate performance, continues to attract interest in the tourism and hospitality field. Thus, to ensure that future DEA studies are more robust, this critical reflection paper emphasizes and calls for greater transparency and consistency in the application of DEA.

A survey of data envelopment analysis applications in the insurance industry 1993–2018

This study discusses a guideline on a proper use of Data Envelopment Analysis (DEA) that has been widely used for performance analysis in public and private sectors. The use of DEA is equipped with Strong Complementary Slackness Conditions (SCSCs) in this study, but an application of DEA/SCSCs depends upon its careful use, as summarized in the guideline. The guideline consists of the five suggestions. First, a data set used in the DEA applications should not have a ratio variable (e.g., financial ratios) in an input(s) and/or an output(s). Second, radial DEA models under variable and constant Returns to Scale (RTS) need a special treatment on zero in a data set. Third, the DEA evaluation needs to drop an outlier. Fourth, an imprecise number (e.g., 1/3) may suffer from a round-off error because DEA needs to specify it in a precise expression to operate a computer code. Finally, when a large input or output variable may dominate other variables in DEA computation, it is necessary to normalize the data set or simply to divide each observation by its average. Such a simple treatment produces more reliable DEA results than the one without any data adjustment. This study also discusses how to handle an occurrence of zero in DEA multipliers by applying SCSCs. The DEA/SCSCs can serve for a multiplier restriction approach without any prior information. Thus, the propesed DEA/SCSCs can provide more reliable results than a straight use of DEA.

Six sigma provides a methodology for problem solving by using project management tools systematically. Theprimary aim of six sigma applications is minimizing defects. Minimizing defects will ensure the outputs andcustomers’ satisfaction increase and the costs decrease. However, the number of the firms that state that theycouldn’t get the results from six sigma programs as they expected is not few. The main reason for failure of sixsigma programs is not to balance the costs with gains of six sigma projects. So, evaluating the performance of sixsigma projects is an important issue for the firms that apply six sigma programs.In this paper, data envelopment analysis was used to evaluate the performance of six sigma projects which isstated as critical success factor for six sigma programs. Data envelopment analysis which is based on linearprogramming helps to determine the efficient and inefficient decision making units by comparing the relativeefficiencies of decision making units. Recently, there are some papers in the literature which apply dataenvelopment analysis for evaluating the performance of R&D and software projects. There are also some paperswhich discuss the application of data envelopment analysis in the selection of six sigma projects. Howeverevaluating the performance of six sigma projects with data envelopment analysis was not discussedcomprehensively in the literature. This paper is original by showing that data envelopment analysis which can beused in evaluating the performance of organizations can also be used for evaluating the performance of six sigmaprojects. The application of data envelopment analysis for evaluating the performance of six sigma projects wasshown by a case study.

In Data Envelopment Analysis (DEA) applications involving multiple inputs and outputs, inputs are aggregated into the total amounts of each type of input. For example, if input types ‘labour’ and ‘capital’ are used to produce multiple outputs, the total amount of labour used to produce all outputs is treated as one aggregated input and the total amount of capital as another. Resources are not disaggregated into input variables measuring the amount of labour used to produce the first output, the amount of labour used to produce the second output, the amount of labour used to produce the third output and so on, for both labour and capital. It is shown that such intra-input aggregation causes downward bias in reported technical efficiency scores, with variations in bias unrelated to true technical efficiency. Therefore, with few exceptions, any technical efficiency comparisons among DMUs are invalid. The presence of intra-input aggregation bias is demonstrated mathematically, simulation is used to exhibit its severity, and the exceptions that permit intra-input aggregation without causing bias are identified. It is concluded that, for multiple-input, multiple-output DEA applications, inputs must be disaggregated into the amounts used to produce each output in order to validly report technical efficiency, unless one of the exceptions is present.

A literature study for DEA applied to energy and environment