Network Data Envelopment Analysis Approach Research Articles

Performance evaluation of the global airline industry under the impact of the COVID-19 pandemic: A dynamic network data envelopment analysis approach

The COVID-19 pandemic posed unprecedented challenges to the airline industry, necessitating a focus on maintaining high efficiency for profitability. This study assesses the efficiency of 26 international airlines from 2019 to 2022 using a dynamic network data envelopment analysis (DNDEA) methodology. The model accounts for the dynamic effect between two consecutive periods and incorporates an internal structure to evaluate airline performance across multiple dimensions. It enables the assessment of overall, period-specific, and stage-specific efficiencies. The findings reveal that while overall efficiency is moderately high on average, no airline achieved full efficiency during the pandemic. Efficiency decreased notably from 2019 to 2020, with a partial recovery but not a return to pre-pandemic levels by 2022. Operational performance remains satisfactory and stable, while service and financial performance exhibit lower efficiency, especially among low-cost airlines compared to full-service counterparts. Additionally, the study explores airlines' environmental impact by considering greenhouse gas emissions. Comparative analysis with a dynamic DEA model without internal structure highlights theoretical contributions, and the study offers managerial insights for airline leaders and policymakers.

Discretionary investment managers evaluation in pension fund: Shared input dynamic network DEA approach

Investment trust corporations (ITCs) constitute one of the financial industry's subsectors, and their evaluations of fund management performance play a crucial role. With the recent increase in the number of ITCs and their assets, how the Public Service Pension Fund management board (PSPFMB) of Taiwan makes investment decisions regarding multiple companies has become a critical issue. Using the shared input dynamic network data envelopment analysis approach (SDNDEA), we empirically estimate the performance efficiency scores of the funds of 34 ITCs and analyze the performance of the manufacturing structures of their internal networks. Our results indicate that the PSPFMB should consider the effects of being entrusted with decisions on efficiency. Employing K-means clustering techniques, we investigate the operational characteristics of each group and identify the differences among them. Moreover, this research identifies significant externalities related to ownership structures, indicating that the PSPFMB should consider the effects of such structures on efficiency in the context of entrusting decisions. Thus, the PSPFMB can make optimal investment decisions based on our ITC evaluation and selection model and help pension funds achieve stable long-term investment benefits.

Scientific and technological innovation efficiency in Chinese provincial higher education institutions: a three-division network DEA approach

Scientific and technological (S&T) innovation in higher education institutions (HEIs) has become a major driving force behind China’s development. The Chinese government has attached great importance to S&T innovation in HEIs, creating the need for efficiency evaluation that can test the effectiveness of S&T policies and provide decision support for S&T innovation. The operational process of S&T innovation in HEIs can be decomposed into three divisions: research and development (R&D), technology transfer (TT), and technical service (TS). Furthermore, the intermediate products produced by the R&D division may be wasted in the TT division. Based on the internal structure and the utilization of intermediate products, this paper proposes a three-division network DEA to explore the efficiencies of S&T innovation in Chinese provincial HEIs. Based on empirical data from 2011 to 2020, the main findings are as follows. (1) The efficiencies of the S&T innovation in HEIs can be improved when the R&D and TT divisions cooperate to avoid wasting intermediate products. (2) The TT division is not only the main source of the overall inefficiency but also the main cause of the efficiency differences among areas. (3) The TT efficiencies of four areas tend to converge to the same steady state.

A State-Level Analysis of Mexican Education and Its Impact on Regional, Economic, and Social Development: Two-Stage Network DEA Approach

Education has been considered a cornerstone for human and economic development. Although there is a national educational strategy in most countries, various implementations are at the state level. This paper studies academic efficiency at the primary and secondary levels and the human development dimensions –long and healthy life, being knowledgeable, and enjoying a decent standard of life– at the state level. For this purpose, a network data envelopment analysis (NDEA) with two stages was proposed. The first stage studies the educational process efficiency, while the second evaluates its impact in the form of the human development index. The study found significant differences between the evaluated states in the education stage, where the lowest efficiencies are mainly in the southwest of Mexico. The results also indicate that better education quality leads to greater regional, economic, and social development at the state level. This study contributes to the NDEA applications on the understanding of the impact that education has in improving the development of the regions holistically.

Efficiency evaluation and productivity analysis of complex electric power systems in China: A directional slacks-based network data envelopment analysis approach

The paper introduces a robust methodology for evaluating complex electricity system efficiency, making several contributions. It systematically decomposes the electric power system into various processes, including power generation, transmission, distribution, and power consumption, with parallel sub-processes for production and domestic power consumption. Employing data envelopment analysis (DEA), the paper develops a network directional slacks-based (DSB) model to evaluate the whole system and the internal processes. The model is theoretically proven to be feasible, providing efficiency scores and identifying inefficiencies in the system and its processes. A noteworthy addition is the introduction of the DSB green Luenberger productivity index and its decomposition, offering an analysis of productivity changes in electricity systems. Empirical analysis covering 30 Chinese provinces from 2013 to 2019 uncovers the performance variations among provinces and processes, pointing out inefficiencies and changes in technology and efficiency over time. The empirical findings aim to support efforts in enhancing the overall efficiency of China’s interprovincial power system.

A shared-input integer network DEA approach linking football teams’ wages and market values

This paper proposes a new approach, based on an SBI, shared-input NDEA model, to assess the sport and value creation performance of football clubs that are playing in national and international competitions. The novel features of the proposed approach include the relationship between the different sub-processes that make the team transform their salaries into sporting results and these into market value; the computation of inefficiency scores for the participation in the national and international competitions as well as the inefficiency of the value creation stage; and the computation of target intervals for the sports results of the clubs. The proposed approach has been applied to 22 teams, from six major European football leagues that played the UEFA Champions league in the 2021/2022 season.

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.

Measuring an efficiency aggregation of medical diagnostic laboratories: A window NDEA approach

The efficiency evaluation of laboratories, as one of the most significant areas of healthcare, plays a key role in the quality of laboratory management. The classic data envelopment analysis (DEA) models have overlooked intermediate products, internal interactions and dealt with analyzing the network within the “Black Box” mode. This results in the loss of important information, and at times, a considerable modification occurs in efficiency results. This article evaluated the efficiency of some selected medical diagnostic laboratories in the city of Tehran according to the network data envelopment analysis (NDEA) approach. We considered a four-stage structure with additional inputs and undesirable outputs. We obtain the labs’ performance over a period of 6 months in 2022 by the NDEA window analysis process. To this aim, a four-stage structure model of three chief medical diagnostic laboratory processes as the pre-test, the test, and the post-test is designed. We considered sustainability criteria (economic, social, and environmental) to appraise the performance of laboratories, thus helping to improve the social, economic, and environmental problems of medical diagnostic laboratories. By using the Delphi viewpoint, the criteria for efficiency evaluation are achieved. The results showed that laboratory unit No. 22 maintained the highest average overall efficiency, since the high accuracy of this unit’s laboratory results had led to many physicians recommending this unit to their patients. We found that the only laboratory unit No. 20 had a decreasing trend, as it is located in an area that abounds with administrative and educational centers. At the beginning of the exam period, then the summer holidays, and finally the wave of end-of-summer trips, a decline occurs in efficiency over the period of six months.

Sustainability in the evaluation of urban rail transit considering both firms and passengers perspectives: A dynamic network data envelopment analysis approach

This paper divides the operation of urban rail transit into sustainable production stage and sustainable consumption stage from the perspective of firms and passengers, and includes service input, service output, service consumption in a unified model. A dynamic network epsilon based measure model is proposed to measure the production efficiency, service effectiveness and operational effectiveness of urban rail transit respectively, which is used to evaluate the operational sustainability of urban rail transit. Taking 23 urban rail transit systems in China from 2015 to 2021 as an example, the research results show that urban rail transit production efficiency is superior to service effectiveness, but most urban rail transit operational effectiveness is not high, and a large amount of input redundancy in the current urban rail transit sustainable consumption stage comes from vehicle speed. At the same time, the perennial shortage of passengers and passenger miles is the main reason leading to the low service effectiveness. In addition, there is heterogeneity in urban rail transit service effectiveness among different city sizes, and there is a significant positive correlation between operational effectiveness and service effectiveness. Therefore, improving service effectiveness is the key to ensure sustainable urban rail transit operation.

Understanding the Efficiency in Generating Human Development in Sub-Saharan Africa: A Two-Stage Network DEA Approach

Understanding the Efficiency in Generating Human Development in Sub-Saharan Africa: A Two-Stage Network DEA Approach

Environmental efficiency evaluation of China's power system considering factor's spatial dependence: A dynamic spatial network DEA approach

Evaluating and improving the environmental efficiency of the power system is the key to achieving sustainable development. However, the classic DEA approach is susceptible to producing biased results due to its inability to address spatial dependence across factors. Considering the spatial dependence of inputs and outputs, this paper proposes a dynamic spatial network DEA model with slacks-based measure (SNSBM) to examine the environmental efficiency of China's power systems, with a focus on the undesirable outputs of CO2, NOx, and SO2. The investigation incorporates the Moran's I test to account for the spatial interactions of patent counts, coal consumption, CO2 emissions, and renewable power generation. Compared to the classic network DEA, the SNSBM results reveal distinct spatial characteristics and a heightened level of differentiation, which highlights the importance of eliminating spatial dependence. From a dynamic perspective, environmental efficiency shows a visible surge in transmission and distribution divisions after 2015, confirming the success of China's market-oriented reform. In addition, CO2 is the largest contributor to the environmental inefficiency of the power systems, followed by NOx and SO2. This study highlights the importance of spatial dependence and provides many insightful policy recommendations for improving the environmental efficiency of China's power systems.

Efficiency analysis of Chinese universities with shared inputs: An aggregated two-stage network DEA approach

Evaluating the resource utilization efficiency of a country's universities is a prerequisite for implementing a balanced development strategy. Universities often share some resources such as the teachers and fixed assets used for teaching and research. Data envelopment analysis (DEA), as a non-parametric method for performance evaluation of decision-making units (DMUs), has been used for measuring the operating efficiency of universities with shared inputs based on radial measures under multiplier DEA models. However, few existing studies consider non-radial measures and different perspectives of external and internal evaluations. From both external and internal perspectives, we propose aggregated two-stage DEA models to measure the efficiency scores of 52 Chinese universities in 2014. The main results indicate that: (1) both internal and external evaluations yield relatively high average operating efficiency, with around 53 % of universities being efficient. The allocation ratios of shared inputs for the same university differ according to the internal and external evaluations. (2) Heterogeneity analysis reveals that 985 universities are less efficient than non-985 universities, and comprehensive universities operate more efficiently than science and engineering universities. (3) There are efficiency differences between 985 and non-985 universities in the teaching stage. Science and engineering as well as comprehensive universities also differ in the teaching efficiency.

Assessing renewable energy efficiency to identify improvement strategies: A network data envelopment analysis approach

China has been analyzing, assessing, and enhancing renewable energy efficiency as crucial steps in mitigating global energy depletion and climate change. Most studies evaluating renewable energy efficiency (REE) use a single-stage data envelopment analysis (DEA) model, and thereby, ignore the interactions among economic, social, and environmental subsystems. In contrast, this study develops a matrix-type structure network DEA model that considers the influence of undesirable outputs and mutual interactions among these three subsystems to measure REE in China's 30 provinces. Examining the 2005 to 2019 timeframe, the study shows that: 1) the changes in China's REE reflect a significant “V” shape during this timeframe, with 2013 marking the boundary, owing to an improvement in the efficiency of the environmental subsystem; 2) REE improved in 15 provinces, decreased in 13, and barely changed in the other two; 3) REE tended to converge between 2005 and 2011 and diverge between 2011 and 2019; and 4) for further REE improvement, total fixed asset investments, fossil energy imports, and household waste collected and transported need to decrease by 26 %, at least; while GDP per capita, excellent air quality, and years of education need to increase by >4 %. The expansion of the advanced provinces contributes to REE improvement for international economies and industrial communities. It will accelerate the free movement of production inputs, facilitating international energy saving and sustainable development.

A dynamic multi-stage network data envelopment analysis approach for evaluating performance of wheat farms

PurposeBecause of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions.Design/methodology/approachA dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods.FindingsThe results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method.Originality/valueTo the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.

Performance evaluation of compulsory education system in Taiwan: A modified dynamic network data envelopment analysis approach

The purpose of this study is to examine changes in the efficiency of Taiwan's compulsory education system between 2011 and 2020, identify the priority school sub-system for improving the system, and determine the input/output inefficiencies responsible for the sub-system inefficiencies. To achieve these objectives, a modified dynamic network data envelopment analysis model is used to assess the efficiency of the compulsory education systems and their internal primary and middle school sub-systems regulated by local governments. The findings indicate that both primary and middle school efficiency are crucial for evaluating the compulsory education system efficiency, and teacher and staff excesses in most local government's compulsory education systems are the primary sources of inefficiency. Therefore, the prompt adjustment of teacher and staff allocation based on student numbers is the primary approach to enhancing system efficiency.

Comprehensive evaluation of passenger road transportation through dynamic network data envelopment analysis: A case study of state road transport undertakings in India

AbstractState road transport undertakings (STUs) in India are public utility services that link diverse terrains and remote and hilly areas throughout the country and play a vital role in enhancing public mobility. These organizations are motivated to work through a sense of social responsibility. For example, they operate in low‐revenue‐generating regions to serve the public. Therefore, they require regular performance monitoring and constant efforts toward course corrections. Existing literature on the performance evaluation of STUs ignores the internal structures of the passenger transportation process and treats the process as a “black‐box.” Consequently, it fails to identify the precise cause of inefficiencies, making course corrections in a multistage transportation process difficult. To close this research gap and comprehensively evaluate the performance of STUs, considering their internal structure and based on long‐term optimization, this study employs a slacks‐based dynamic network data envelopment analysis (DNDEA) approach to deconstruct India's publicly owned passenger road transportation process into operations and revenue divisions, subjected to a multiperiod performance evaluation from 2014–2015 to 2017–2018. Moreover, we utilize panel data regression to explore other influential factors not included in the DNDEA, explaining the efficiency variation across STUs. This study is the first to explore the internal structure of STUs in India and perform a comprehensive evaluation of STUs, considering their internal divisions and multiple‐period long‐term optimization. The DNDEA results highlight evidence of capital misallocation in India's public road transport sector. While operational inefficiency can be attributed to improper utilization of resources, price regulation is the major source of inefficiency in the revenue division. Fixed effects panel data regression revealed that STUs with higher bus utilization and passenger lead are more efficient. The contribution of this study lies in its innovative approach of using DNDEA to analyze India's passenger road transportation sector and its inner constructs, providing insights into the efficiency scores of STUs and their dynamic changes in one model. Managers and policymakers can use the findings of this study to revise and frame policies targeted at improving STU efficiency.

COVID-19 crisis and the efficiency of Indian banks: Have they weathered the storm?

The purpose of this study is to determine whether Indian banks were able to weather the COVID-19 storm. We estimate banks’ deposits-generating and operating efficiencies using a two-stage directional distance function-based network data envelopment analysis (DDF-NDEA) approach and seek to capture the immediate impact of COVID-19 on these efficiency measures by comparing their magnitudes in the pre-pandemic (2014/15–2019/20), just 1-year prior to the pandemic (2019/20), and during the pandemic year (2020/21) periods. The study looks at whether the impact of the COVID-19 pandemic was uniform across ownership types and size classes. The empirical findings suggest that the Indian banking system was resilient and withstood the immediate impact of the COVID-19 pandemic. During the study period, however, the large and medium-sized banks experienced some efficiency losses. By and large, regardless of bank group, banks have shown resilience to the shock of the global health pandemic and improvements in efficiency.

A chance-constrained network DEA approach based on enhanced Russell-based directional distance measure to evaluate public sector performance: a case study of OECD countries

A chance-constrained network DEA approach based on enhanced Russell-based directional distance measure to evaluate public sector performance: a case study of OECD countries

Efficiency and Fiscal Performance of Indian States: An Empirical Analysis Using Network DEA

The purpose of this empirical study is to evaluate and explain the fiscal performance of Indian states from 2009-10 to 2014-15 using a network DEA approach. While previous research has compared India's fiscal and developmental performance at the sub-national level, this study departs from the extant literature by evaluating state-wise performance at a disaggregated level. The states are first compared based on their tax mobilization and then evaluated in terms of development spending and overall financial performance. Censored regression analysis is also used to explore the impact of outstanding liabilities on GDP ratio, Gross Capital Formation, and GDP growth rate. The results indicate a positive association between efficiency scores and GDP growth rate and log of Gross Capital Formation. However, the linkage between efficiency and the outstanding liabilities ratio is negative. These findings suggest the need for a balanced approach to government spending to avoid the recurrence of the debt crisis in the future.

A fuzzy rough network data envelopment analysis approach for evaluating the sustainability of supply chains: a case study in the pasta industry

ABSTRACT Evaluation of the sustainability of supply chains is a complex decision-making problem. One of the techniques, which are used for assessing the sustainability of supply chains is data envelopment analysis (DEA). Conventional DEA models consider decision making units (DMUs) as black boxes that consume a set of inputs to produce a set of outputs and do not take into consideration the internal interactions of DMUs. Also, they assume inputs and outputs are crisp. In this research, the network DEA (NDEA) model for assessing the sustainability of supply chains in the presence of fuzzy rough data is developed. The main contribution of this paper is to develop a novel NDEA model in the existence of internal and external uncertainties. To validate the proposed model, a case study for evaluating the sustainability of the supply chain in the pasta industry is presented.