Data Envelopment Analysis Window Analysis 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.

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.

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.

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.

Seaport efficiency and competitiveness in European seaports

The measurement and assessment of seaport efficiency and competitiveness have been the subject of many academic works in port economics. This study aimed to benchmark and measure the efficiency and competitiveness of the largest 30 container ports in Europe over the period 2005–2018. The technical efficiency of the sample container ports was estimated by the DEA (Data Envelopment Analysis) window analysis. Then, the obtained efficiency scores were introduced as explanatory variables with 8 other variables to explain the competitiveness of these ports using the Principal Component Analysis (PCA). Our findings suggest that handling costs is considered to be the most influential factor for competitiveness. Moreover, our estimates showed that efficiency is negatively correlated with the third axis of competitiveness which strengthens the fact that the Northern European seaports are found to be more competitive and less efficient. This may explain the over-investment and excessive use of inputs phenomena in order to provide customers with a higher quality of service.

Measuring the performance of the Indian banking industry: data envelopment window analysis approach

PurposeThis study aims to empirically evaluate the performance of commercial banks operating in India.Design/methodology/approachThe efficiency of the commercial banks is evaluated using the data envelopment analysis (DEA) approach. We measure the technical, pure technical and scale efficiency of the sampled conventional banks using the input-oriented model. We employed an extended DEA window analysis approach based on a panel sample of 47 banks in the Indian scenario. The period of study is from 2009 to 2018.FindingsThe results obtained from CRS and VRS measures envisage that Indian banks have failed to manage their inputs efficiently and convert them into outputs. It implies that Indian banks do not operate at an optimum level. Moreover, the results show that public banks exhibit superior efficiency scores followed by private and foreign banks. Apart from the aggregate sector level, we also investigate the performance of Indian banks at the individual level for in-depth analysis. The individual bank-level analysis reports that the public sector banks (PSBs) are the most efficient followed by foreign banks, whereas, the least efficient are the private banks.Research limitations/implicationsThe findings of our study have implications for government, financial institutions and policymakers to access the verve and flexibility of the Indian banking system. The government should consider restructuring inefficient banks to enhance overall performance. This can be considered by improvement in managerial efficiency, efficient allocation of scarce resources and appropriate scale of operation. However, the findings of the study should be interpreted in light of the period of study for the banks being operational (as we filter out banks that ceased to exist) in India and empirical methods employed. The results may vary if alternative measures are used.Originality/valueThe present paper investigates the efficiency of the Indian banking sector employing the Data Envelopment Window Analysis (DEWA) technique. To the best of our knowledge, the present study is perhaps the first one to employ the DEWA measure on the Indian banking industry to gauge their performance over time.

COMPARISON OF HEALTHCARE SYSTEM PERFORMANCES IN OECD COUNTRIES

Health spending is increasing every day around the world. Because of this, efficient use of resources becomes more important. The aim of this study was to compare the health efficiencies of the Organization for Economic Co-operation and Development (OECD) countries. In order to consider the trend of efficiency of the countries in the observed period (2014-2018), window analysis is chosen as the most appropriate input oriented data envelopment analysis (DEA) technique. The DEA window method was chosen since it leads to increased discrimination on findings and it enables year-by-year comparisons of the results. Input and output variables used in the study were determined by examining other studies in the literature. In this respect, the input variables were identified as the number of physicians per thousand people, the number of nurses per thousand people, the number of hospital beds per thousand people, health spendings (% of GDP); and output variables were expected life expectancy at birth, and rate of surviving infants. According to the results of the DEA Window analysis, only Mexico was found to be efficient. Other countries with an efficiency score of more than 90% are Turkey (0.999), Japan (0.991), Korea (0.974), Luxembourg (0.937). On the other hand; Austria (0.591), Switzerland (0.545) and Germany (0.511) were the last countries in the efficiency score ranking. In these countries, which produce high health output, their inputs are also high, so they are at the end of the ranking of efficiency scores.

Long-Term Economic Sustainability of Humanitarian Logistics-A Multi-Level and Time-Series Data Envelopment Analysis.

Although resources are scarce and outputs incorporate the potential to save human lives, efficiency measurement endeavors with data envelopment analysis (DEA) methods are not yet commonplace in the research and practice of non-government organizations (NGO) and states involved in humanitarian logistics. We present a boot-strapped DEA window analysis and Malmquist index application as a methodological state of the art for a multi-input and multi-output efficiency analysis and discuss specific adaptions to typical core challenges in humanitarian logistics. A characteristic feature of humanitarian operations is the fact that a multitude of organizations are involved on at least two levels, national and supra-national, as well as in two sectors, private NGO and government agencies. This is modeled and implemented in an international empirical analysis: First, a comprehensive dataset from the 34 least developed countries in Africa from 2002 to 2015 is applied for the first time in such a DEA Malmquist index efficiency analysis setting regarding the national state actor level. Second, an analysis of different sections in a Rohingya refugee camp in Bangladesh is analyzed based on a bootstrapped DEA with window analysis application for 2017, 2018, and 2019 quarter data regarding the private NGO level of operations in humanitarian logistics.

Assessing Renewable Energy Production Capabilities Using DEA Window and Fuzzy TOPSIS Model

Climate change and air pollution are among the key drivers of energy transition worldwide. The adoption of renewable resources can act as a peacemaker and give stability regarding the damaging effects of fossil fuels challenging public health as well as the tension made between countries in global prices of oil and gas. Understanding the potential and capabilities to produce renewable energy resources is a crucial pre-requisite for countries to utilize them and to scale up clean and stable sources of electricity generation. This paper presents a hybrid methodology that combines the data envelopment analysis (DEA) Window model, and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) in order to evaluate the capabilities of 42 countries in terms of renewable energy production potential. Based on three inputs (population, total energy consumption, and total renewable energy capacity) and two outputs (gross domestic product and total energy production), DEA window analysis chose the list of potential countries, including Norway, United Kingdom, Kuwait, Australia, Netherlands, United Arab Emirates, United States, Japan, Colombia, and Italy. Following that, the FTOPSIS model pointed out the top three countries (United States, Japan, and Australia) that have the greatest capabilities in producing renewable energies based on five main criteria, which are available resources, energy security, technological infrastructure, economic stability, and social acceptance. This paper aims to offer an evaluation method for countries to understand their potential of renewable energy production in designing stimulus packages for a cleaner energy future, thereby accelerating sustainable development.

Evaluating environmental performance using data envelopment analysis: The case of European countries

Environmental efficiency is considered to be a critical pillar of sustainable development; consequently, improving environmental performance has aroused attention at both the local and global level. This study evaluated the environmental performance of European Union (EU) countries using the data envelopment analysis (DEA) approach and the global Malmquist-Luenberger index. Although literature using the DEA approach to measure environmental performance exists, this study’s main contribution was considering different types of undesirable outputs and using long-term panel data on EU countries. In particular, the DEA window analysis technique was applied to evaluate the environmental performance of 27 EU countries in cross-sectional and time-varying data during the period 2000–2017. Three DEA models were examined; labor, capital, and energy were used as common inputs with a combination of different outputs, including gross domestic product, carbon dioxide and particulate matter emissions, and waste. The empirical results revealed that the trends in the environmental performance of the entire EU and its individual countries were similar under all examined models. Environmental performance was indeed negatively affected by the financial crisis of 2007–2008; this impact was mainly observed in eastern EU countries. Furthermore, both economic and environmental variables significantly influenced countries’ overall efficiency. The analysis using the global Malmquist-Luenberger index elucidated that overall, EU countries experienced the efficiency improvement during the study period, although fluctuations were observed. These results enable countries and policymakers to understand their environmental performance, identify strengths and weaknesses, and set targets for further improvement based on the current best practices of comparable peers.

Evaluating the performance of Chinese fossil fuel power plants by data environment analysis: An application of three intermediate approaches in a time horizon

To our knowledge, the previous studies did not answer the following questions: how much does window analysis differ from the original Data Envelopment Analysis (DEA) approaches and how to develop DEA window analysis? To address these questions, we propose three intermediate approaches (cross-sectional approach, window efficiency analysis and window index approach) and statistically compare their performances. The underlying concern is that we need to examine whether a metrological bias exists in our study. The concern is important in guiding policy issues such as energy policy and climate change. Empirically, the proposed approaches are applied to measure the unified efficiency of fossil fuel power plants at a provincial level in China from 2007 to 2017, since these plants are important energy consumers and CO2 emitters. We obtain two important implications. First, considerable differences were confirmed among the three approaches, thus implying an existence of the methodological bias. Second, Beijing (the capital of China) outperformed the other provinces in terms of their measures, since the government has paid serious attention to air pollution emitted from fossil fuel power plants. The governmental regulation in Beijing is a good example of pollution prevention in China. It is expected that the success on Beijing will be disseminated to the other provinces.

Evolution of the Efficiency of Nationwide Commercial Banks in China Based on an SBM-Undesirable Model and DEA Window Analysis

In recent years, Chinese economic development has slowed down and competition in the financial industry has become increasingly fierce. The purpose of this paper is to study the efficiency characteristics of China’s banking industry in the new environment and provide suggestions for banks to improve efficiency. This paper uses a data envelopment analysis (DEA) SBM-undesirable model and window analysis to measure the technical efficiency of 13 nationwide commercial banks in China during the period from 2008 to 2017. Furthermore, the convergence characteristics of bank technical efficiency are examined. The empirical results show that state-owned banks were more efficient than joint stock banks before 2012. After 2012, state-owned banks were less efficient than joint stock banks. Finally, this paper explores the influential factors of technical efficiency. Noninterest income ratio, net interest margin, growth rate of total investment in fixed assets, and consumer price index have a significant positive impact on bank efficiency. The cost-to-income ratio has a significant negative impact on bank efficiency. Further research using the threshold model shows that noninterest income ratio has a threshold effect on bank efficiency.

Efficiency evaluation and dynamic evolution of China’s regional green economy: A method based on the Super-PEBM model and DEA window analysis

Given the current circumstances of increasingly serious resource consumption and environmental pollution, the development of China’s green economy will profoundly impact the nation’s future economic prosperity and even global economic development. Based on panel data from 2008 to 2017, this paper measures the green economic efficiency (GEE) of Chinese regions and analyzes its dynamic evolution using time series, based on a novel data envelopment analysis (DEA) model. Using environmental DEA techniques, this paper introduces the Super-PEBM (EBM based on pearson correlation coefficient) model, which is developed from the Epsilon based measure (EBM) model, and combines Super-PEBM with the window analysis method. Firstly, environmental DEA technology is used to analyze GEE at the regional level in China. Secondly, based on panel data, the window analysis method is used to analyze the regional differences of China’s regional GEE. Finally, the development trend of China’s regional GEE in different time windows is obtained by combining the Super-PEBM model and window analysis. The empirical analysis results using panel data for regions in China from 2008 to 2017 show that: (1) the overall GEE of China is slowly increasing, and the regional differences are still significant, and (2) the improvement of GEE can help to reduce regional differences.