Data Envelopment Analysis Window Research Articles

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.

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.

Investigating the Efficiency of Combined Cycle Power Plants with the Data Envelopment Analysis Process (with an Emphasis on Air Pollution)

Performance management is one of the most significant strategies in promoting the efficiency of organizations and is highly sensitive. It is important to check the efficiency of combined cycle power plants because of their major contribution to power generation and air pollution. This study was conducted to evaluate the economic and environmental efficiency of Combined Cycle Power Plants (CCPPs). The inputs and outputs required to evaluate the performance of the power plants were determined according to expert opinions. Then, the 7-year statistics and information of the Qom, Neishabour, Shahid Rajaee, Yazd and Kerman power plants were collected as the desired CCPPs in Iran. The Window Data Envelopment Analysis (WDEA) method was used for evaluating the efficiency of the power plants. The 3-year window length showed that most of the power plants were efficient. Only the Rajaee, Neishabour and Yazd power plants were ineffective in some years. The mean efficiency of the power plants in the 4-year window length showed that the plants were efficient from 2008 to 2010.

Evaluation of Regional Water Resources Management Performance and Analysis of the Influencing Factors: A Case Study in China

Water resources management (WRM) is a global strategic issue. Effectively evaluating WRM performance and analyzing its influencing factors have strong practical significance for improving regional WRM performance. Data envelopment analysis (DEA) is one of the most popular models to measure the efficiency of decision-making units (DMUs). Taking regional WRM performance as efficiency DMUs, this paper proposes the indicator system to quantify WRM performance from the perspectives of resource endowments, economic and technological development, and ecosystem protection. The WRM performance of 31 provincial administrative regions in mainland China from 2013 to 2019 are measured, and the temporal and spatial changes are explored using the window DEA model and the Malmquist index model. Finally, the Tobit model is used to quantitatively analyze the affecting factors. The results indicate that: (1) in 2015 and 2019, WRM performance of Sichuan, Shaanxi, Xinjiang, Inner Mongolia, and Guangxi were slightly higher than that in other years, and the areas with more room for WRM performance improvements were areas with abundant water resources; (2) from 2013 to 2019, China’s overall WRM performance showed a slow rise, and there were obvious fluctuations in 2018; (3) the improvement in WRM performance was primarily the result of an improvement to comprehensive technical efficiency changes (EC), whereas technological progress changes (TC) have largely restricted China’s WRM performance; (4) the main influencing factors of WRM performance in China were as follows: industrial development level, water resources utilization rate, and urban sewage discharge per capita.

Stability of insurance efficiency during the Covid-19 pandemic: A comparative study between Islamic and conventional insurance in Indonesia

Purpose – The purpose of this study is to measure and compare The Stability of the efficiency of insurance companies, particularly during the COVID-19 pandemic, at 70 insurance institutions (both sharia and conventional) in Indonesia over Five years, from 2016 to 2020.Methodology – Non-parametric approach, Data Envelopment Analysis (DEA), then extended by Window DEA analysis was employed as the research method.Findings – This study found that COVID-19 has little effect on Insurance efficiency. COVID-19 has a detrimental impact on the efficiency of conventional insurance but does not affect Sharia insurance. This study also found that most Indonesian Insurances are considered the worst performers in terms of efficiency and efficiency stability during the study period. Most of the insurances are included in quadrant IV (low efficiency and high stability). Furthermore, according to the window DEA analysis, the most relatively stable value of Indonesian Insurances is Sinarmas Conventional InsuranceOriginality – The study that focuses on measuring the efficiency of sharia insurances compared to the efficiency of conventional insurances in Indonesia during the COVID-19 outbreak has never been conducted. This study was the first to measure and compare the efficiency of Indonesian Insurances using the Window DEA analysis.

Decomposition of efficiency in the global seed industry: A nonparametric approach

We analyzed the efficiency levels of nine of the largest commercial seed-producing firms globally for the period 2008–2015 and assessed if there is a relationship between firm size and efficiency. We employed the nonparametric technique of data envelopment analysis (DEA) using an input-oriented model with balanced panel data. We accounted for the assumption of time invariance of the frontier by using the DEA windows analysis technique. Aggregate mean overall technical efficiency increased by 0.8%. We decomposed these results to pure technical efficiency and scale efficiency, and found no meaningful relationship between firm size (assets) and efficiency.

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.

INTERTEMPORAL EFFICIENCY AND PRODUCTIVITY CHANGING ON TELECOMMUNICATION INDUSTRY (TI) IN ASEAN-5

The study aims to measure the technical and intertemporal efficiency and find the primary source of productivity change on top three telecommunication firms in each country of ASEAN-5 (Indonesia, Malaysia, Thailand, Philippines, and Singapore) from 2010 to 2016. Data Envelopment Analysis (DEA) bootstrapping with 2000 iterations, DEA window, and Malmquist index are applied to calculate technical efficiency, intertemporal efficiency, and productivity change. The estimation results elucidate that, on average, the technical efficiency of firms is relatively low. On the opposite, the intertemporal efficiency results indicate that the mean efficiency score of each window is high. However, the LDW and LDP tend to be high, showing that the efficiency scores fluctuate. The Malmquist index calculation yields that technological progress possesses a significant contribution to productivity change.Keywords: Technical Efficiency, Intertemporal Efficiency, Productivity Change, Telecommunication Industry, ASEAN-5 JEL Classifications: L8, F6, O5, O1, O3

Window data envelopment analysis approach: A review and bibliometric analysis

AbstractWindow Data Envelopment Analysis (WDEA) is a popular, effective, and applicable methods for dynamic performance assessment of peer decision making units (DMUs). WDEA is a non‐parametric panel method that operates based on the principle of moving averages and establishes efficiency measures by treating each DMU in different periods as a separate DMU. By applying the WDEA approach, a decision‐maker (DM) can measure the efficiency of different DMUs in different periods through a sequence of overlapping windows. Also, WDEA can increase the discrimination power by increasing the number of DMUs when a limited number of DMUs is available. Given the advantages of the WDEA approach and its applications in real‐world problems, this paper surveys and analyses 387 WDEA papers published from 1985 to 2020. The paper also recommends some suggestions, guidelines, and opportunities for future research. Notably, the findings show the applicability and efficacy of WDEA in the literature.

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.

Ranking EU Agricultural Sectors under the Prism of Alternative Widths on Window DEA

Providing food security while preserving natural resources and reducing the use of production factors (land, labor, and capital), is a critical challenge for EU agricultural sectors in the 21st century. Sustainable Development goals adoption has increased the need for eliminating Greenhouse gas emissions across all EU sectors, while production should remain stable or even increase. For this purpose, Window Data Envelopment Analysis (DEA) has been selected as a benchmarking technique, in order to assess input use efficiency of agricultural sectors of EU countries for the 2005–2019 period. Moreover, three-year projections (until 2022) have been calculated in order to acquire future efficiency scores. Emphasis has been given on the selection of alternative window widths, examining their influence on calculating efficiency scores for both projected and actual dataset. From a methodological point of view, this paper aims to highlight the assumption of zero technological change within Window DEA frames and present their differences. At the same time, results indicate that Estonia (1.000), the Netherlands (0.999) and Slovenia (0.999) are the most efficient countries in terms of input use efficiency, while Finland, UK, and Hungary (0.670, 0.755 and 0.771) score the least. Countries of central Europe (Hungary, Czech Rep., Croatia, Slovakia, and Austria) should redesign their agricultural strategies, so as to achieve the nine objectives of the upcoming CAP (2021–2027).

Cross-border mergers and acquisitions: Impact on marketing capability and firm performance

Emerging country firms have been increasingly engaging in cross-border mergers and acquisitions, and these acquirers predominantly acquire firms from developed countries. The motivation for such acquisitions is to achieve market access but also to benefit from transfers of cross-border managerial skills and knowledge. The performance of such acquisitions has started to receive some research attention, particularly financial performance, but the transfers to other areas such as marketing have not yet been explored. This article addresses this gap by studying the experience of 34 acquirers from emerging countries which acquired firms in developed countries. This study uses two-stage window data envelopment analysis (DEA) and Tobit regression to investigate the impact of these acquisitions on the marketing capability and overall firm performance of the acquiring firms. The results show that the marketing capability of the acquiring firms did improve in the post-merger years and this improvement can be partly attributed to the acquisition. The findings also show that the overall performance of the acquiring firms improved following acquisition, but this is a continuation of superior performance from the pre-merger years rather than a synergistic gain from the acquisitions.

Trends in Indian Private Sector Bank Efficiency: Non-Stochastic Frontier DEA Window Analysis Approach

The study examines the efficiency of private sector banks in India with the help of Window DEA (Data Envelopment Analysis) for a period from 2005 to 2017. With a window of three years, the period was divided into 11 windows. The study outcomes show that 59.9% of all private sector banks in India operate at more than 0.9 level of efficiency, and there are only three occasions when banks were operating at the efficiency value between 0.6 to 0.7. Further, the consistency in the efficiency scores of the banks has also been analyzed using an efficiency mapping matrix, and the mean efficiency score of the bank in each window is studied. The score of standard deviation was interpreted accordingly for these banks. Banks that are showing the highest efficiency scores also have a higher variance of efficiency scores. There was no bank identified in the matrix that promises high-efficiency ratings with low variability. The study concludes that the analysis of the efficiency mapping matrix indicates that, as a DMU escalates in the efficiency scores, the standard deviation reflecting the risk in overall efficiency scores also tends to rise. The findings complement the concept of higher risk to higher return or greater efficiency.

A thorough investigation of solar-powered hydrogen potential and accurate location planning for big cities: A case study

In recent years, hydrogen has constituted a clean energy carrier that can be gained by the use of renewable electricity. The most preliminary stage in the process of renewable hydrogen generation is to find the best place for exploiting the most energy. Thus, this study seeks to optimize the process of location selection for the construction of a solar power station. This evaluation is performed on 12 cities of Isfahan in Iran. After ascertaining 11 criteria of key importance, Window Data Envelopment Analysis (WDEA) Method is used to prioritize the cities according to the data for a period of 11 years. Consequently, the most promising site is technically and economically scrutinized as to hydrogen production using solar electricity. Results pertaining to the first part of the study showed that the city of Natanz was efficient over the entire studied period. Considering 4 cases of different performance rates, annual electricity generation using solar panel model X21-345 and hydrogen production using an alkaline electrolyzer were estimated for the city. The estimations indicated that hydrogen production under the worst and the best cases would be 2.22 kg and 5.55 leading to energy efficiency of between 2.5% and 7.1%, respectively. Finally, economic assessment proved promising results in which Levelized Cost of Electricity (LCOE) would be between 0.5317 and 1.6272 $/kWh and Levelized Cost of Hydrogen (LCOH) would vary from 0.7911 to 1.6778 $/kg.

Dynamic energy efficiency assessment of dairy farming system in Iran: Application of window data envelopment analysis

Livestock production systems, such as dairy farming, are one of the most important contributors to resource use and if not managed well, it can be environmentally detrimental. Iranian livestock sector faces a variety of the challenges such as high costs of energy and environmental legislations as well as an increasing demand for dairy products to respond the growing population. This paper aims to contribute to the discussion on technical efficiency as a key indicator of energy use within dairy farming systems. A Window Data Envelopment Analysis (W-DEA) with energy use as inputs and milk production as output was modelled with data from 25 provinces during the last 22 years (1994–2016) in Iran. In addition, the Slack-Based Model (SBM) was used to compare the radial DEA model with non-radial SBM, both in a dynamic environment (window analysis). The average efficiency score of Iranian dairy farming production system was estimated at approximately 0.85. Through the years, three provinces including Zanjan, Ardabil and Hormozgan had the highest technical efficiencies. Window analysis represented that provinces are distinctive in terms of their technical efficiencies and energy consumption over the years. Applying the SBM model improved the accuracy of the estimated efficiency scores compared to the radial (DEA) model. Further analysis represented a significant difference between the technical efficiency of different milk production levels. Provinces that produced higher volumes of milk had lower technical efficiencies. Based on the results it can be concluded that there is a substantial space for upgrading the technical efficiency of dairy farming in Iran by improving resource use efficiency which leads to an optimized energy consumption. It is recommended to reform Iranian livestock farming policies by applying mechanized systems, optimal strategies for water, electricity and fossil fuel consumption, use of renewable energy and better feed management while enhancing milk productivity and technical efficiency. In this respect, it is suggested that policy makers consider different indicators such as energy use efficiency and environmental impacts when allocating subsidies and resources to different provinces and farms.

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.