Data Envelopment Analysis Window Research Articles

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.

Efficiency and Stability of Zakat Institutions in Malaysia and Indonesia: DEA Window Analysis

The purpose of this study is to measure the efficiency and stability of zakat institutions in Indonesia and Malaysia. There are 14 zakat institutions that will be analyzed using data envelopment analysis (DEA) window analysis during the period of 2016 to 2020. The result shows that the average efficiency of zakat institution is 78% during the observation period. The inefficiency of zakat institutions from both countries is caused by the less-than-optimal distribution of zakat funds. The best performing zakat institutions are Selangor, BSMU, BRI Foundation, and Baznas because they have the highest efficiency and stability. This study has filled the gap in previous studies that measure the efficiency of zakat institutions with the standard DEA approach. Zakat institution managers can identify potential improvements in input and output variables to achieve maximum level of efficiency.

DETERMINATION OF EFFICIENCIES OF INFORMATION TECHNOLOGY SECTOR COMPANIES TRADED ON BORSA ISTANBUL IN TERMS OF FINANCIAL PERFORMANCES UNDER THE EFFECT OF COVID-19 PANDEMIC BY USING DEA WINDOW ANALYSIS

Window analysis is a time-dependent variant of Data Envelopment Analysis (DEA) and it is based on the moving average method. It is used for showing the trend of Decision Making Units’ (DMUs') performance over various times. The efficiency changes of 13 companies operating in Borsa Istanbul (BIST) Information Technology (IT) sector for quarterly periods between 2019:03 and 2021:06 are evaluated by DEA Window Analysis. The input-oriented CCR model and the input-oriented BCC Windows analysis models are applied on data set. When the CCR model and BCC model window analysis results are evaluated together, one of the first two periods with the highest average efficiency for both of them is 2021:06, and the period with the lowest average efficiency for both of them is 2019:09. DESPC company has the highest average efficiency for both models.

Evaluating the technical efficiency of Saudi Arabian telecommunications companies using window DEA and AHP techniques

PurposeThis study aims to evaluate the efficiency of the three telecommunications companies in Saudi Arabia: Saudi Telecom Company (STC), Mobily and Zain over the period of 2010–2019. This evaluation is a step toward improving the performance of the Saudi telecommunications sector.Design/methodology/approachThree multicriteria decision-making (MCDM) techniques were used to calculate technical efficiency. These techniques include the traditional data envelopment analysis (DEA), window DEA and analytical hierarchy process (AHP). The three inputs used were total assets, operating expenses and capital expenditures, whereas the two outputs were sales revenue and total stockholders’ equity.FindingsSTC was ranked first using the three techniques, followed by Zain, and then Mobily. According to the DEA window analysis, these three companies were all efficient only in 2012. The efficiency was high in the initial years, 2010–2013, when it was above 0.90, and it dropped below 0.90 in the subsequent years, 2014–2019. In addition, the efficiency of STC remained high, with an average of 0.990. However, the average efficiencies of Zain and Mobily during this period were 0.807 and 0.804, respectively.Originality/valueThis is the first study to use the three MCDM techniques to evaluate the performance of telecommunications providers. The results show that window DEA is better than the other two techniques at evaluating performance over time, as it has a higher discrimination power than either the traditional DEA or AHP.

Efficiency Analysis of Oil Refineries Using DEA Window Analysis, Cluster Analysis, and Malmquist Productivity Index

Oil and gas refineries play a key role in the economies of countries by providing energy to various industrial sectors. A lack of an integrated efficiency analysis procedure, in many industries, could significantly impact the planning of sustainable industrial structures and operations. It also can influence company performance and competitiveness, and, eventually, negatively compromise the fuel supply process. All these problems taken together might negatively impact the environment and sustainable practices. Studies of efficiency in the oil industry can help to reduce its environmental and social impact and to achieve long-term green transition goals. In this work, the data envelopment analysis (DEA) method was used to present improvement goals for production units, based on efficiency indexes. Furthermore, the DEA window analysis model, integrated with the Malmquist index and cluster analysis, was used to evaluate efficiency and the factors that explain the differences between refineries in a number of timeframes. A numerical analysis was carried out with data collected from 12 Brazilian oil refineries between 2012 and 2020, using DEA window analysis, cluster analysis, and the Malmquist index.

STABILITY OF ISLAMIC BANK EFFICIENCY IN INDONESIA AND MALAYSIA: HAS COVID-19 MADE ANY DIFFERENCE?

This study examines whether COVID-19 has affected the stability of Islamic banking efficiency in Indonesia and Malaysia using the Data Envelopment Window Analysis (DEWA) using data from 2017 to 2022. The findings indicate that Islamic banks in Malaysia exhibit a higher level of efficiency and stability as compared to their counterparts in Indonesia across all three efficiency measures, namely Technical Efficiency (TE), Pure Technical Efficiency (PTE), and Scale Efficiency (SE). In addition, the findings show that Islamic banks in both countries have stable efficiency performance over the period under investigation and hence the Covid-19 pandemic has not affected their efficiency levels. From the analysis, we further note that financing and labor costs are the main sources of inefficiency in Malaysian Islamic banks while fixed assets and operating revenue contribute the most to Indonesian Islamic banks’ inefficiency.

A Proposed DEA Window Analysis for Assessing Efficiency from Asymmetry Dynamic Data

Nowadays, one of the main challenges facing production management is how to enhance the performance of manufacturing processes by utilizing asymmetry input and output data. This research, therefore, developed a framework for window analysis in data envelopment analysis (DEA) for evaluating the overall technical efficiencies from asymmetry dynamic input and output data. The framework was applied to assess the technical (TE), managerial (PTE), and scale (SE) efficiencies of a blowing machine under three fuzzy input variables (planned production quantity, number of defectives, and idle time) and a fuzzy output variable (actual or target production quantity). The efficiency measures were then evaluated for all DMUs at low (L), middle (M), and high (H) data levels. The obtained optimal fuzzy efficiencies were then transformed into a single crisp optimal efficiency. The results showed that all seven DMUs of the blowing machine were technically inefficient. The input and output slacks were estimated and utilized to determine the necessary improvement actions. Improvement results revealed that the optimal TE, PTE, and SE were significantly improved, which may result in significant savings in production and quality costs. In conclusion, the proposed framework is effective in improving the efficiency of the blowing process and can be utilized for efficiency assessment in a wide range of applications.

Combining common-weights DEA window with the Malmquist index: A case of China’s iron and steel industry

In the conventional data envelopment analysis (DEA) window analysis, a decision-making unit (DMU) in each window is treated as different units in each period so that the evaluation for one unit is performed on different scales over time. This paper proposes a novel window analysis based on common weight across time (CWAT), which evaluates each unit in each window by its common scale independent of time. The model for obtaining common weights is described as linear programming. And the paper suggests the Malmquist productivity index (MPI) on CWAT, CWAT MPI, to analyze productivity change by inheriting the result of window analysis. The numerical experiments are illustrated to examine the validity of CWAT and MPI, and the result shows that the proposed method provides a new evaluation scale compared to previous studies. The proposed model is applied to evaluate the performance of China 45 iron and steel enterprises during 2009–2017. The energy and environmental efficiency are calculated using CWAT, and CWAT MPI analyzes the productivity change.

How Much Technically Efficient Is The South Asian Region? An Intra-Regional Comparison Among Its Affiliates

This study presents an analysis of the intra-regional performance of South Asian economies in terms of their productive efficiency during the period 1985–2019. A window data envelopment analysis (dea) based technique with bootstrapping is used to model the production frontier for seven South Asian economies to estimate their efficiency levels. The results show substantial differences in technical efficiency levels across South Asian economies and thus, the existence of vast potential to catch up with the best practices is revealed among these economies. On an average, the extent of technical inefficiency in south asia is about 46.92% per annum after adjusting for statistical bias. This is indicative of a huge gap in laggard and best practices within region. Both managerial and scale components of efficiency performance are equally responsible for observed production inefficiency in south asia. The existence of managerial inefficiency reflects failure of policy planners among affiliate countries to catch-up the best practices within South Asian region. However, the observed scale inefficiency (SIE) is because of the dominance of scale economies over diseconomies that urges need of enlarging cooperation among member nations. JEL Codes: F02, D240, C610

A novel stochastic semi-parametric frontier-based three-stage DEA window model to evaluate China's industrial green economic efficiency

Traditional three-stage data envelopment analysis (DEA) models do not consider the problem of functional form and multicollinearity. This study develops a new stochastic semi-parametric frontier-based three-stage DEA model. The frontier incorporates the effects of both external environmental factors and statistical noise on efficiency. We adopt the StoNED (stochastic non-smooth envelopment of data) approach and use the quasi-likelihood estimation method to estimate the parameters of inefficiency term and stochastic noise. We conduct Monte Carlo experiments to examine the performance of the new frontier under different circumstances. Our results show that the new frontier provides a more realistic and accuracy estimator for efficiency measures. An empirical analysis is used to evaluate green economic efficiency (GEE) in China. We empirically compare different models and the results show that external environmental factors cause significant differences. We provide each provincial average GEE evaluated by the improved QLE-StoNED model, which are outperforms compared with other recently developed estimators. And a gradient difference emerges in the GEE among the eastern, central and western areas of China. The results also offer practical implications for the harmonious development of industrial production and a green economy in China.

Is China’s Fishing Capacity Management Sufficient? Quantitative Assessment of China’s Efforts toward Fishing Capacity Management and Proposals for Improvement

As the country with the world’s largest fishing capacity, China suffers from the depletion of living marine resources, mainly caused by overexploitation, the effects of which also distinctly influence global sustainable utilization of marine resources and maritime order. The country has implemented a series of measures to control its national fishing capacity, notably since the late 1980s, and strongly expressed its responsibility and commitment to sustainable resource utilization in the 13th Five-Year Plan (2016–2020). This study quantitatively assesses the effect of China’s fishing capacity management efforts by examining changes in the fishing capacity of marine capture fisheries by region from 1979 to 2019 in Chinese waters, based on window data envelopment analysis (DEA), and further proposes directions for policy adjustment. The results suggest that China’s coastal and offshore fisheries are still subject to overcapacity of approximately 20%, despite major corrective efforts invested in past decades. This implies that China needs to further improve its fishing capacity management and set clear objectives, as determined by the availability of resources and regional features. Global fisheries management’s focus is shifting from input to output control, but overcapacity still threatens the sustainable use of fisheries resources in China and poses obstacles to other systems.

The Efficiency of Indonesia and Malaysia Sharia Bank in the Shadow of Covid-19 Pandemic: DEA Window Analysis

The Pandemic conditions can lead to various problems that can trigger financial distress and inefficiency, especially in the banking sector. In recent decades, there has been a rapid increase in sharia banking, and the importance of this sector to the economies of several countries. The assessment of sharia bank's efficiency will become important as efficiency reflects the banks' performance. This study aims to analyze the stability of sharia bank efficiency in Indonesia and Malaysia using the Data Envelopment Analysis (DEA) dan DEA Window method. This study uses 2015 to 2020 as a research period, especially to look at the stability efficiency of Indonesia and Malaysian Sharia Bank in the Covid-19 pandemic era. The result showed that Indonesia is better than Malaysia in terms of efficiency stability. However, both show a similar pattern, where there is a decrease in the average level of efficiency during the Covid-19 pandemic. From the perspective of efficiency stability analysis through several summary statistics such as its Standard Deviation (SD), Long Distance per Period (LDP), and Long Distance per Year (LDY), the sharia bank with the most relatively stable value of efficiency scores is Maybank Islamic Berhad, then followed by BRI Syariah and BSM.

Dynamically evaluating the comprehensive efficiency of technological innovation and low-carbon economy in China's industrial sectors

Technological innovation and low-carbon economy are significant for the high-quality development of China's industrial sectors. However, few scholars combine the two stages closely and discuss their coordinated development. This paper establishes an evaluation index system of technological innovation and low-carbon economy in China's industrial sectors. The technological innovation efficiency, low-carbon economy efficiency, and comprehensive efficiency of technological innovation and low-carbon economy are dynamically investigated by the two-stage data envelopment analysis (DEA) model and the DEA window analysis with 35 subsectors panel data during 1996–2018. The inter-industrial differences in the technological innovation efficiency and low-carbon economy efficiency are considered, and the influencing factors of the comprehensive efficiency of technological innovation and low-carbon economy are studied by the bootstrap truncation regression. The results show that: (1) The development of the technological innovation and low-carbon economy is uncoordinated, and the low-carbon economy efficiency needs improvement; (2) There is heterogeneous of the technological innovation efficiency and low-carbon economy efficiency in the 35 subsectors; (3) The density of science and technology institutions, and the average enterprises scale are positive to the comprehensive efficiency of technological innovation and low-carbon economy, while excessive reliance on technology introduction has a negative impaction. The corresponding suggestions are provided for promoting technological innovation efficiency and low-carbon economy efficiency of industrial sectors.

Dynamic analysis of macroeconomic performance of the Balkan countries

Abstract The paper presents the application of a non-parametric data envelopment analysis (DEA) technique for measuring the macroeconomic performance of the Balkan countries. In this context, for the period of 2006–2018, a dynamic DEA Window model was applied based on selected macroeconomic indicators as input and output variables. For a more comprehensive and objective analysis, the DEA Window analysis is complemented by a Malmquist productivity index that provides a more complete picture of the observed entities' performance and shows a trend of change from period to period. The results showed that in the observed period, Albania and to a large extent Montenegro, especially after the end of the global financial crisis, had the highest average efficiency, that is, they used the available resources effectively to increase the GDP growth rates. The EU Member States, Greece and Croatia, in particular, achieved the highest growth in overall productivity over the observed period, and this growth was largely due to a change in technical efficiency.

A Survey of DEA Window Analysis Applications

This article aims to review, analyze, and classify the published research applications of the Data Envelopment Analysis (DEA) window analysis technique. The number of filtered articles included in the study is 109, retrieved from 79 journals in the web of science (WoS) database during the period 1996–2019. The papers are classified into 15 application areas: energy and environment, transportation, banking, tourism, manufacturing, healthcare, power, agriculture, education, finance, petroleum, sport, communication, water, and miscellaneous. Moreover, we present descriptive statistics related to the growth of publications over time, the journals publishing the articles, keyword terms used, length of articles, and authorship analysis (including institutional and country affiliations). To the best of the authors knowledge, this is the first survey reviewing the literature of the DEA window analysis applications in the 15 areas mentioned in the paper.

Effect of cluster initiatives and natural clusters on business performance

PurposeThis study aims to determine how much company participation in a type of cluster affects its economic performance.Design/methodology/approachThis study includes companies operating in seven industries (automotive, engineering, textiles, information technology (IT) services, furniture, packaging and nanotechnology) in the Czech Republic. The companies are divided into three groups: members of institutionalized cluster, operating in the same region (natural clusters) and operating in other regions. Data envelopment window analysis is used to measure their performance for 2009–2019.FindingsResults show that the effect of clustering differs among industries. Companies in three industries (automotive, engineering, nanotechnology) reveal a positive impact of the cluster initiative on performance growth. Two industries (textile, packaging) with companies operating in a natural cluster show better performance than those in an institutionalized cluster. Moreover, the IT services and the furniture industries show no positive effect of clustering on corporate performance.Research limitations/implicationsThis research includes 686 companies from seven industries and monitored for 11 years. On the one hand, the sample includes a relatively high number of companies overall; but on the other hand, the sample is relatively small, especially for nonclustered companies. The reason is the lack of available financial statements for small companies.Practical implicationsFrom the perspective of practical cluster policy, the authors can recommend that monitoring the performance of member companies in clusters must be one of the criteria for evaluating the success of a cluster, such as cluster initiatives.Originality/valueThis study distinguishes between long-standing natural clusters in a given industry and institutionalized ones that have emerged because of a top-down initiative. An original database is created for clustered and nonclustered companies in seven industries, covering the entire Czech Republic.

Generalized robust window data envelopment analysis approach for dynamic performance measurement under uncertain panel data

Generalized robust window data envelopment analysis approach for dynamic performance measurement under uncertain panel data

Do mergers and acquisitions improve bank efficiency? Evidence from North Macedonia

This article focuses on the efficiency of commercial banks in North Macedonia in the period from 2007 to 2020. The main goal is to assess the relative efficiency of the banking sector as a whole, and more importantly, to evaluate and analyse the impact of mergers and acquisition (M&A) transactions on banks’ efficiency. For this reason, the leading nonparametric methodology Data Envelopment Analysis (DEA) has been employed, i.e., the window DEA model with two input and two output variables on a sample of 13 commercial banks. Based on balanced panel data from the banking sector of North Macedonia, the obtained results show a constant fall of efficiency of the whole banking sector, from 83.33% in 2007 to 70.06% in 2011 and 66.36% in 2020. The results of the M&A transactions case study analysis show that, in general, M&A transactions decrease banks' efficiency, i.e., they do not result in an efficiency increase. The contribution of the study is that it comprises the first study analysing the effects of banks' M&A on bank efficiency in the region of South-Eastern Europe with the application of DEA and thus, it provides valuable information for investors, bank management, M&A analysts, academic members and regulatory bodies.

DYNAMIC MODEL OF PERFORMANCE MEASUREMENT OF MIDDLE EAST AIRLINES

Over the past decades, the rapid development of the airline industry has occurred in the Middle East and particularly in the United Arab Emirates. The need for the development of the aviation sector has emerged due to the strategically important position that Middle East occupies as the central point between the eastern and western hemispheres. Its major airlines, so called Middle East Big Three (MEB3) - Emirates, Etihad and Qatar Airways, have become dominant in the region, as well as across the globe, primarily thanks to the high quality of their services and globally recognized brand. The focus of the assessment are the airlines’ non-financial performance indicators, such as: the number of carried passengers and cargo, number of employees, serving destinations, fleet size, etc. The analysis covered a period of twelve years, starting from 2005 until 2016, which was characterized by the expansion of these airlines. For each observed company, the efficiency was first separately measured using the Data Envelopment Analysis (DEA) method, followed by the dynamic analysis and measurement of their efficiencies using the DEA Window analysis. In both cases, Emirates Airlines had the best performance.

Dynamic Performance Assessment of Hospitals by Applying Credibility-Based Fuzzy Window Data Envelopment Analysis.

The goal of the current research is to propose the credibility-based fuzzy window data envelopment analysis (CFWDEA) approach as a novel method for the dynamic performance evaluation of hospitals during different periods under data ambiguity and linguistic variables. To reach this goal, a data envelopment analysis (DEA) method, a window analysis technique, a possibilistic programming approach, credibility theory, and chance-constrained programming (CCP) are employed. In addition, the applicability and efficacy of the proposed CFWDEA approach are illustrated utilizing a real data set to evaluate the performance of hospitals in the USA. It should be explained that three inputs including the number of beds, labor-related expenses, patient care supplies, and other expenses as well as three outputs including the number of outpatient department visits, the number of inpatient department admissions, and overall patient satisfaction level, are considered for the dynamic performance appraisal of hospitals. The experimental results show the usefulness of the CFWDEA method for the evaluation and ranking of hospitals in the presence of fuzzy data, linguistic variables, and epistemic uncertainty.