Conventional Data Envelopment Analysis Research Articles

Technical efficiency of sugarcane farming in East Java, Indonesia: A bootstrap data envelopment analysis

Abstract Sugarcane is an essential commodity in Indonesia. However, climate change negatively affects the sugarcane production efficiency. This study aims to measure the technical efficiency of sugarcane farming using a bootstrap data envelopment analysis (DEA) approach, compare the efficiency of irrigated and rain-fed sugarcane farming, and determine the factors affecting the technical efficiency of sugarcane farming in East Java, Indonesia. Primary data were collected from 451 sugarcane farmers during the 2020–2021 planting season. The results indicate that the “bias-corrected” technical efficiency scores of the single- and double-bootstrap approach (0.624 and 0.561) were lower than that of conventional DEA (0.714). The efficiency score of irrigated sugarcane farms (0.593) was higher than that of rain-fed farms (0.529). Moreover, the farmers’ age, household size, dependency, farming experience, training, subsidies, crop diversification, and access to irrigation impacted sugarcane farming’s technical efficiency. Improvement of 1% in training, irrigation access, and subsidies increased the technical efficiency by 0.034, 0.032, and 0.030, respectively. This strategy is expected to enhance the productivity and technical efficiency and reduce the poverty in rural households in East Java.

A compromise solution approach for efficiency measurement with shared input: The case of tourist hotels in Taiwan

Shared input occurs often when a production system performs two or more functions and some of the functions share the same input. To determine the proportion of the shared input devoted to each function, the conventional data envelopment analysis (DEA) models that allow each decision making unit (DMU) to select the most favorable value to attain the highest efficiency score is usually used. The result is that many DMUs assign proportions of different bound values to the same function, which is not reasonable. This paper proposes a compromise solution approach that aggregates the viewpoints of all DMUs to obtain a consensus regarding the proportion of the shared input used by each function. The approach is actually a parametric DEA approach for constructing the production function. To increase the explanation power, transcendental functions are considered. The tourist hotel, where the accommodation and catering functions share the input of management employees is used for illustration. The results show that a transcendental function has stronger explanation power, and reasonable compromised values for the proportions of the effort of the management employees devoted to the two functions are obtained.

Regret cross-efficiency evaluation using attitudinal entropy approach

Data envelopment analysis (DEA) is a popular nonparametric tool for measuring the performance of decision making unit (DMU). Cross-efficiency evaluation is an efficient means to rank DMUs, which compensates for efficiency overestimation and incomplete ranking of conventional DEA models. With respect to incomplete rational cross-efficiency evaluation, the common methods construct models with two extreme efficiencies as reference, such as efficiency values of the aggressive and benevolent models. In addition, they only explore the irrational preferences of decision maker (DM) at a certain stage in the evaluation process. They fail to investigate the cross-efficiency evaluation synthetically under incomplete rationality and characterize the complexity of the individuals’ decision making. To fill this gap, a new method must be proposed. This paper proposes a new cross-efficiency termed regret cross-efficiency model using attitudinal entropy approach (RACE), which constructs a secondary goal model from a comprehensive perspective under the framework of regret theory, and introduces attitudinal entropy to aggregate the cross-efficiencies. Besides, this paper presents some empirical examples to illustrate the validity and robustness of the RACE method.

Precision in decision-making: a novel Z-number DEA approach for European country rankings

PurposeConventional approaches such as Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) cannot effectively account for uncertainty, which can lead to imprecise decision-making. Furthermore, these methods frequently rely on precise numbers, ignoring the inherent uncertainty of real-world data. To address this gap, the research question arises: How can we develop a methodology that combines Z-number theory and FDEA to provide a comprehensive assessment of residency preferences in European countries while accounting for uncertainty in information reliability? The proposed methodology aims to fill this gap by incorporating Z-number theory and FDEA.Design/methodology/approachThe proposed study assesses residency preferences across 39 European countries, focusing on key factors like environment, sustainability, technology, education, and development, which significantly influence individuals' residency choices. Unlike conventional DEA and FDEA approaches, the proposed method introduces a novel consideration: dependability. This inclusion aims to refine decision-making precision by accounting for uncertainties related to data reliability. The proposed methodology utilizes an interval approach, specifically employing the a-cut approach with interval values in the second step. Unlike using crisp values, this interval programming resolves formulations to determine the efficiencies of decision-making units (DMUs).FindingsThe comprehensive findings provide valuable insights into the distinctive factors of European nations, aiding informed decision-making for residency choices. Malta (75.6%-76.1%-75.8%), Austria (78.2%-78%-76.1%), and the United Kingdom (79.3%-78.4%-77%) stand out with distinct characteristics at levels of a = 0-a = 0.5-a = 1, assuming the independence of variables of the overall evaluation. Individual consideration of each factor reveals various countries as prominent contenders, except for the environmental factor, which remains consistent across countries.Originality/valueTraditional DEA models encounter challenges when dealing with uncertainties and inaccuracies, particularly in the evaluation of large systems. To overcome these limitations, we propose integrating Z-numbers—a powerful mathematical tool for modeling uncertainty—into the conventional DEA process. Our methodology not only assesses the effectiveness of countries across various socio-economic and environmental metrics but also explicitly addresses the inherent uncertainties associated with the data. By doing so, it aims to enhance the precision of decision-making and provide valuable insights for policymakers and stakeholders.

Planning improvements through data envelopment analysis (DEA) benchmarking based on a selection of peers

Incorporating preferences on suitable peers into benchmarking analyses may ensure the setting of appropriate targets, which enable designing plans for improving performance that are aligned with management. This paper deals with target setting in situations where decision makers (DMs) have previously made a selection of peer candidates for the benchmarking of a given organization. A first approach is developed within the framework of conventional Data Envelopment Analysis (DEA), which is the technology mostly used in non-parametric frontier analysis. It provides targets from reference sets consisting of peer candidates that span a face of the strong efficient frontier of the production possibility set (PPS). These targets result from solving a DEA-like model, thus preventing from the need to identify all of the maximal efficient faces (MEFs) of the DEA frontier. We also propose a second approach where the convexity in DEA is somehow relaxed to allow additionally for reference sets consisting of candidates that are Pareto-efficient, provided that their convex hull is not dominated by other units. In that sense, the targets found can be seen as representing best practices. This approach broadens the range of alternatives when planning improvements, and may eventually provide closer targets.

The relation between efficiency of credit operation and non-performing loans—An application of network DEA model with undesirable outputs

To evaluate the efficiency of decision-making units, researchers continually develop models simulating the production process of organizations. This study formulates a network model integrating undesirable outputs to measure the efficiency of Vietnam’s banking industry. Employing methodologies from the data envelopment analysis (DEA) approach, the efficiency scores for these banks are subsequently computed and comparatively analyzed. The empirical results indicate that the incorporation of undesirable output variables in the efficiency evaluation model leads to significantly lower efficiency scores compared to the conventional DEA model. In practical terms, the study unveils a deterioration in the efficiency of banking operations in Vietnam during the post-Covid era, primarily attributed to deficiencies in credit risk management. These findings contribute to heightening awareness among bank managers regarding the pivotal importance of credit management activities.

Semi-Additive Integer-Valued Production Technology for Analyzing Public Hospitals in Mashhad

Conventional Data Envelopment Analysis (DEA) models assume real-valued input-output data and ignore the collaboration among decision-making units (DMUs) in the analysis of efficiency. This paper proposes a novel DEA production technology that is capable of dealing with the collaboration concept and resource sharing for both integer and real-valued data in the efficiency measurement and target setting. This is accomplished by way of a mixed integer linear programming (MILP) efficiency analysis model. We also deal with the computational aspect of the proposed model and invent a MILP with the computational complexity of [Formula: see text] rather than [Formula: see text]. We explain the proposed models by numerical examples and graphical illustrations. We apply our models for efficiency analysis of 15 governmental (public) hospitals in Mashhad City in Iran and demonstrate the practical relevance and advanced future of the proposed methods.

Weight-restricted approach on constant returns to scale DEA models: Efficiency of internet banking in Turkey

The performance measurement of decision-making units (DMUs) can be optimised by strategically choosing the optimal input and output weights. This approach circumvents the potential complexities associated with establishing a common basis for ranking homogeneous units and managing the results from multiple efficient frontiers. Efficient frontier ranking is simplified by limiting the weights based on the intrinsic or technical connections between relevant inputs and outputs. It also creates a coherent framework that coordinates various considerations across all under-evaluated DMUs and variables. This method effectively examines the complexities related to the inputs and outputs of homogeneous units using conventional data envelopment analysis (DEA) models to evaluate the efficiency of these units.In the banking sector, the relationship between inputs and outputs is complex. This complexity requires careful attention, particularly in cases where the existence of one input or output is conditional on the existence of another. This dynamic is accentuated by the integration of advanced fintech solutions, which inherently provide significant benefits to banking operations. The use of appropriate analytical models for banking data is important. The synergy between advanced financial technologies and analytical methods has reached its peak in reducing losses and strengthening profits. This, in turn, facilitates the optimal allocation of financial resources to growth-oriented projects.The empirical application of weight-restricted DEA models to Turkey's mobile-internet-banking sector underscores their efficacy. These modified models contrast with the standard DEA versions, which often encounter challenges stemming from the allocation of weights to pivotal variables. This study demonstrates the tangible benefits of our approach and its potential to revolutionise the efficiency assessment landscape in the banking sector.The proposed unconventional model which is applied to Turkish Internet banking for the period extending from 2006 to 2018 and which takes a comprehensive weight restriction approach to DEA and encompasses all relevant input and output variables, resulted in strictly positive weights and a reduced count of inflated efficient units along with their scores. This is practically beneficent to banking sector in so far as it facilitates decision-making and related economic and financial planning, and as it proves to be a reliable benchmark by local or international banks intending to provide or develop Internet banking services.

Financial efficiency of healthcare companies in an emerging market

AbstractUnderstanding the financial dynamics of healthcare companies is essential for reducing costs while improving access to care and patient outcomes. This study aims to explore the financial efficiency of healthcare companies operating in an emerging stock market. We use conventional and fuzzy data envelopment analysis. We consider liquidity and leverage ratios as input criteria and profitability ratios as output criteria, each taken from Borsa Istanbul for the year 2022. The results indicate that financially inefficient healthcare companies can increase their efficiency by effectively managing debt and working capital, restructuring their asset base, and focusing on equity management.

Panel Data Analysis of Subjective Well-Being in European Countries in the Years 2013–2022

Panel Data Analysis of Subjective Well-Being in European Countries in the Years 2013–2022

Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984-2022.

This study reviews scholarly publications on data envelopment analysis (DEA) studies on acute care hospital (ACH) efficiency published between 1984 and 2022 in scholarly peer-reviewed journals.We employ systematic literature review (SLR) method to identify and analyze pertinent past research using predetermined steps. The SLR offers a comprehensive resource that meticulously analyzes DEA methodology for practitioners and researchers focusing on ACH efficiency measurement. The articles reviewed in the SLR are analyzed and synthesized based on the nature of the DEA modelling process and the key findings from the DEA models. The key findings from the DEA models are presented under the following sections: effects of different ownership structures; impacts of specific healthcare reforms or other policy interventions; international and multi-state comparisons; effects of changes in competitive environment; impacts of new technology implementations; effects of hospital location; impacts of quality management interventions; impact of COVID-19 on hospital performance; impact of teaching status, and impact of merger. Furthermore, the nature of DEA modelling process focuses on use of sensitivity analysis; choice of inputs and outputs; comparison with Stochastic Frontier Analysis; use of congestion analysis; use of bootstrapping; imposition of weight restrictions; use of DEA window analysis; and exogenous factors. The findings demonstrate that, despite several innovative DEA extensions and hospital applications, over half of the research used the conventional DEA models. The findings also show that the most often used inputs in the DEA models were labor-oriented inputs and hospital beds, whereas the most frequently used outputs were outpatient visits, followed by surgeries, admissions, and inpatient days. Further research on the impact of healthcare reforms and health information technology (HIT) on hospital performance is required, given the number of reforms being implemented in many countries and the role HIT plays in enhancing care quality and lowering costs. We conclude by offering several new research directions for future studies.

Inverse Data Envelopment Analysis Model to Improve Efficiency by Increasing Outputs

Inverse Data Envelopment Analysis (IDEA) has gained significant attention among researchers as an analytical tool for assessing efficiency. Estimating input values while ensuring cost efficiency through changes in output quantities is complex and sensitive. In contrast to conventional data envelopment analysis methods, IDEA enables the quantification of input/output variations resulting from output/input reductions or expansions while preserving the measurement efficiency level. This paper aims to introduce a novel approach for estimating input values by incrementally increasing the value of each output of decision-making units (DMUs) during the evaluation process, thereby maintaining or improving cost efficiency. By utilizing IDEA and manipulating the output values of the DMU under evaluation, the input values are estimated while ensuring constant or enhanced cost efficiency. A simple numerical example and a case study from a Turkish automotive company are presented to validate the proposed method. The obtained results demonstrate significant improvements and hold promising prospects, indicating the potential applicability of this approach to other similar problems and research areas.

Efficiency assessment of public sector management and culture-led urban regeneration using the enhanced Russell-based directional distance function with stochastic data

Generic data envelopment analysis (DEA) models are based on deterministic input and output. However, input and output vectors are often interrupted by random factors, such as measurement errors and data noise, in real economic situations. This study proposes a new chance-constrained network DEA model based on the modified directional distance function (DDF) and enhanced Russell measure (ERM) model for assessing government management and culture-led urban regeneration. In addition to exploring the randomness of data, this study integrates the advantages of both ERM and DDF and considers the inefficiency level from a non-oriented viewpoint, the direction vector, and each input and output simultaneously. Each input and output of the two production stages can use non-radials to measure efficiency. Results show that the urban-rural gap has gradually widened since 2015. To validate the legitimacy of the model, this study utilizes the bootstrapping method to verify the results of the stochastic network DEA model and the conventional two-stage network DEA approach. This study also considers different alpha values as basis for comparison to confirm whether the results obtained differ by uncertainty level.

Improved efficiency assessment in network DEA through interval data analysis: An empirical study in agriculture

Conventional Network Data Envelopment Analysis (NDEA) models often make an assumption of data precision, which frequently does not align with the realities of many real-world scenarios. When dealing with ambiguous data, whether it involves input, output, or intermediate products represented as bounded or ordinal data, the accurate assessment of efficiency scores poses a significant challenge. This study addresses the crucial issue of handling interval data within NDEA structures by introducing an innovative methodology that integrates both optimistic and pessimistic strategies. Our proposed methodology goes beyond the mere determination of upper and lower bounds for efficiency scores; it also incorporates target-setting and improvement approaches. Through the calculation of interval efficiency for each decision-making unit (DMU), our approach offers a comprehensive framework for efficiency classification. To underscore the effectiveness of this methodology, the study presents empirical evidence through a case study in the agriculture industry. The results not only showcase the advantages of our proposed methodology but also emphasize its potential for practical application in diverse and complex real-world contexts.

Spatial agglomeration analysis on a circular economy's energy efficiency: A study of European Union countries

Due to the shortcoming of data envelopment analysis (DEA) not presenting any spatial meaning in regard to decision-making units (DMUs) and their peers, this research uses spatial agglomeration analysis to fill in this gap. It also establishes a closed-loop circular economy DEA model that replaces a conventional linear DEA approach to investigate whether low energy efficiency DMUs are adjacent to high energy efficiency peers by examining a spatial scatter diagram, because geographical analysis and spatial learning recommendations can help upgrade the energy efficiency of a low energy efficiency country. The observation data cover 27 European Union (EU) countries and the United Kingdom (UK) and span the period 2001 to 2014. The empirical results show that: (i) no matter whether the EU country is bordered by the sea, or is an island country, or is a landlocked country, this does not prevent the country from achieving energy efficiency; (ii) there are four energy efficiency centers in the EU, but they do not distinctly agglomerate; and (iii) there is a phenomenon of radical degradation in energy efficiency from high energy efficiency centers to low energy efficiency countries.

Development of intuitionistic fuzzy cost efficiency model in data envelopment analysis

Data envelopment analysis (DEA) is a non-parametric linear programming (LP) based technique to measure the relative efficiencies of decision-making units (DMUs). The conventional DEA models assume that input-output data is crisp, which may not always be feasible in practical situations due to the presence of ambiguity and imprecision. Therefore, to handle uncertain and imprecise data, the concept of intuitionistic fuzzy sets (IFS) has been introduced. In this study, the relative efficiencies of DMUs with uncertain data will be determined. For this reason, the conventional cost efficiency (CE) model of DEA is extended to IF environment. Also, the lower and upper-cost efficiency models are developed using α-cut and β-cut approach. The data for inputs, outputs and input prices are considered intuitionistic fuzzy numbers (IFNs), in particular triangular intuitionistic fuzzy numbers (TIFNs). To demonstrate the practical application of the proposed intuitionistic fuzzy cost efficiency models (IFCEMs), a numerical example is presented.

Examining the efficiency of regional university technology transfer in China: A mixed-integer generalized data envelopment analysis framework

In university technology transfer performance assessment, some variables, such as labor and contracts, must be treated as integer-valued variables. Usually, the reference points obtained by the conventional data envelopment analysis (DEA) method are unrealistic when one of the input/output measures is an integer value. As a result, several integer DEA models have been proposed over the last decade. However, these models are based only on self-evaluation. In some cases, when a large gap exists between the best- and worst-performing units, the improvement path based on self-evaluation can be inappropriate. To overcome this issue, we propose a mixed-integer generalized DEA model based on peer evaluation to evaluate the efficiency level of regional university technology transfer in China. A sample of 27 provinces in China from 2008 to 2015 is used in the study. The results reveal that the eastern region, on average, obtained the highest level of efficiency among the three regions in China, whereas the western region presented a poor efficiency level. This is consistent with the current stage of development. Several recommendations are suggested for promoting technology transfer in the western region.

Efficiency analysis of engineering colleges in India: Decomposition into parallel sub-processes systems

Analyzing engineering colleges in India with Data Envelopment Analysis (DEA) is the focus of this study. Engineering colleges pioneer in teaching and research, creating two sub-processes within engineering education. Thus, our study considers each engineering college to comprise two parallel non-homogenous sub-processes, such as teaching and researching. Unlike the conventional DEA models, where the system is treated as a black box, this work attempts to independently assess the impact of each parallel sub-process so that it is possible to identify specific changes in the respective processes to improve the overall efficiency. In particular, our study analyzed the efficiency of engineering colleges based on the National Institute Ranking Framework (NIRF) data. Further, the comparison of the efficiency scores from teaching and research indicates that, on average, the institutes are more efficient in teaching than researching. The current study helps policymakers set priorities and course corrections for performance improvement programs of engineering colleges in India.

Maintenance groups evaluation under uncertainties: a novel stochastic free disposal hull in the presence of lognormally distributed data

Maintenance groups play an essential role in the successful operation of large companies and factories. Additionally, data envelopment analysis (DEA) is known as a valuable tool for monitoring the performance of maintenance groups. Especially, in contrast to the conventional DEA models that impose the convexity assumption into the technology, the free disposal hull (FDH) model provides a method for assessing the efficiency without the assumption of convexity and can be considered a valuable tool for determining one of the observed groups as the benchmark for each maintenance group. Meanwhile, because of the stochastic structure of data with lognormal distribution in the maintenance groups, this paper extends the FDH model in stochastic data with the lognormal distribution. Moreover, the method’s capabilities are confirmed based on some theorems, and a simulation study that illustrated the properties of the developed procedure is also performed. The developed methodology is applied to assess the performance of 21 maintenance groups of AZCO under uncertainty conditions.

Setting closer targets based on non-dominated convex combinations of Pareto-efficient units: A bi-level linear programming approach in Data Envelopment Analysis

Data Envelopment Analysis (DEA) very often sets unrealistic targets, which require from the decision-making units (DMUs) a huge amount effort, perhaps non-assumable, for their achievement. For the identification of best practices in the benchmarking, this paper proposes considering as peers not only DEA efficient DMUs but also those that are Pareto efficient, and allowing for reference sets spanning convex combinations that are not dominated by observed DMUs. It is therefore an approach that somehow relaxes the convexity in DEA, and sets targets representing best practices in the sense that they define a course of action leading to results that are not worse than those of the real plans. A bi-level linear programming (BLP) DEA model is developed which finds the closest targets from a convex combination of the DMUs in a reference set satisfying such non-dominance requirement. This approach has proven to be successful in setting targets that require an effort significantly smaller than that needed to achieve the closest targets on the strong efficient frontier of both the DEA and the free disposal hull (FDH) technologies. In the empirical illustration, we have observed reductions in the order of 40 percentage points on average in the total effort required for their achievement, thus setting more realistically implementable directions for improving performance towards best practices than those provided by conventional DEA and FDH.