Network Data Envelopment Analysis Research Articles

Multi-choice conic goal programming model-based network data envelopment analysis

In multi-stage processes, classical Data Envelopment Analysis (DEA) acts like a black box and measures the efficiency of decision-making units (DMUs) without considering the internal structure of the system. Unlike classical DEA, recent studies have shown that the overall system efficiency scores are more meaningful if researched using the Network DEA (NDEA) methodology. NDEA performs simultaneous efficiency evaluations of sub-processes and the entire system. Recently, the composition method integrated with multi-objective programming (MOP) has been preferred by many authors to alleviate the drawbacks of earlier methods such as decomposition, slack-based measure (SBM) and the system-centric approach. This study proposes a novel approach incorporating Multi-Choice Conic Goal Programming into the NDEA (MCCGP-NDEA). It provides a more accurate representation of the Pareto front by revealing potential Pareto optimal solutions which are overlooked by the composition methods. Due to its ability to modify stage weights based on the decision makers' (DMs) preferences, it is likely to gather more samples from the Pareto surface. Computational results on available benchmark problems confirm that the proposed MCCGP-NDEA is a viable alternative to existing methods.

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.

Relationship between recycling, circular economy and eco‐innovation in Europe. Application of a dynamic network data envelopment analysis model

AbstractMonitoring of European‐level circular economy (CE) efficiency requires more research than does currently exists in the body of literature on CE. In this regard, this research aims to answer two research questions. Firstly, to be efficient in the implementation of CE at a macroeconomic level, should there be a relationship between the efficiency of the three phases of CE (efficiency in recycling, efficiency in the implementation of CE and efficiency of eco‐innovation)? To answer this question, we use decoupling theory. Secondly, are the three phases of CE related from the point of view of dynamic efficiency? To answer this question, we use dynamic efficiency theory.In accordance with both of these theories, we establish a circular relationship between recycling, CE and eco‐innovation. We test this circular relationship through the application of the dynamic network data envelopment analysis (DNDEA) model of Tone and Tsutsui. The analysis is based on Eurostat data for the period of 2016 to 2022 for 27 European countries.This work contributes to the advancement of the research via (1) the development of a model for measuring the transversal and longitudinal efficiency of the CE and (2) the findings of positive and significant relationships between the efficiency ratios of the three fundamental phases (recycling, CE and eco‐innovation). This leads us to affirm that the countries studied that have managed to be efficient in recycling have also been in the implementation phase of CE and eco‐innovation.

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.

Dose digital transformation contribute to improving financing efficiency? Evidence and implications for energy enterprises in China

The high penetration of digitalization has emerged as a crucial driving force in enhancing financing efficiency across global enterprises. However, few studies have explored the potential effects of digital transformation on enterprises' financing efficiency, wherein the financing system is composed of fund procurement and application stages. Under such background, this study constructs a novel network DEA model with dominance probability and adjusted utility to analyze the multiple-dimensional performance of the financing system. Then an empirical econometric model is developed from the theoretical analysis and applied to Chinese energy enterprises for the period 2015-2021. The main findings are summarized as follows. First, our research demonstrates that digital transformation can significantly increase financing efficiency, with fund procurement identified as the lagging stage to efficiency improvement. Second, when considering the heterogeneous analysis, digital transformation is particularly effective in alleviating financing constraints and enhancing operating performance for clean energy enterprises. Finally, with regard to the mechanism's explorations, digital transformation increases enterprises' financing efficiency by reducing the ownership concentration and management expense rate. These findings provide an empirical foundation for the advancement of digital transformation, and also hold significant implications for narrowing financing efficiency gaps across energy enterprises.

Target setting for airlines: A DEA bargaining approach with network structure

AbstractThe objective of this study is to find out how airlines can reduce their inputs and provide appropriate passenger and cargo capacity, thereby increasing passenger and cargo traffic and reducing CO2 emissions. A network data envelopment analysis (DEA)–based Nash bargaining model is developed incorporating intermediate products and weakly disposable undesirable outputs and is applied to a dataset of 33 airline companies to obtain the bargaining solution. According to the results, airlines that are weak efficient or inefficient could establish their targets to attain efficiency through a fair and logical negotiation among the inputs, intermediate products, good outputs and bad output. Appropriate policies need to be implemented that would improve the airlines’ performance in terms of improvement in not only the efficiency of inputs and desirable outputs along with intermediate products but also with regard to environmental performance. This would ensure sustainable growth in airlines’ performance.

Comprehensive intuitionistic fuzzy network data envelopment analysis incorporating undesirable outputs and shared resources

The economic growth rate is intricately linked to the efficiency and effectiveness of the banking industry. A widely applicable mathematical technique for such assessments is Data Envelopment Analysis (DEA), which evaluates the relative efficiency of Decision-Making Units (DMUs) by comparing their inputs and outputs. Traditional DEA treats DMUs as black boxes, neglecting internal processes that contribute to inefficiencies in individual DMUs. Additionally, it assumes precise values for inputs and outputs that do not apply to real-world problems. This study introduces a comprehensive network series of two-stage DEA, incorporating shared inputs and intermediate measures, undesirable outputs, external inputs and outputs, initial inputs, and terminal outputs. The network two-stage DEA is extended to intuitionistic fuzzy circumstances to address uncertainty. In this extension, a non-linear intuitionistic fuzzy number, namely a parabolic intuitionistic fuzzy number, represents higher-order imprecise datasets. An illustrative example validates the proposed methodology, and comparisons with existing methods are conducted. Moreover, the methodology is applied to assess the efficiency of Indian public sector banks, demonstrating its applicability and showcasing the efficacy of the procedures and algorithms used. Decision-makers can make better choices using optimal efficiency values to gain insights into inputs, intermediate measures, and outputs.•The research study focused on a network two-stage DEA model, incorporating undesirable outputs and shared resources in the presence of uncertainty.•The methodology involves solving the network two-stage DEA model using parabolic intuitionistic fuzzy numbers.•The experimental analysis involves assessing the efficiency of Indian public sector banks.

A new concept of education-innovation-economy-environment sustainability system: a new framework of strategy-based network data envelopment analysis

A new concept of education-innovation-economy-environment sustainability system: a new framework of strategy-based network data envelopment analysis

Allocating the fixed cost as a complementary input in two-stage system: A DEA approach

The existing research on fixed cost allocation (FCA) in two-stage system data envelopment analysis (DEA) models typically regards the fixed cost as an additional input for each decision-making unit (DMU). However, these models overlook the prevalent real-world scenario where fixed costs act as complementary inputs in actual production processes. This study proposes a general two-stage network DEA model that incorporates the fixed cost as a complementary input to optimize the allocation scheme. First, we construct a functional relationship between the efficiency scores of DMUs and their allocated fixed costsusing a modified super-efficiency DEA model, which effectively classifies DMUs and solves infeasible solutions within the variable returns to scale (VRS) framework. Then, we propose a fair and unique allocation model based on fairness and efficiency maximization principles. Specifically, we allocate fixed costs based on the operational scale for inelastic DMUs while equitably increasing the efficiency scores for elastic DMUs. Finally, a numerical example and an empirical study on subsidy allocation among 30 provinces demonstrate the rationality and acceptability of our approach.

Dynamics, regional heterogeneity and robustness of fiscal poverty alleviation efficiency in China: Dynamic network DEA and Bootstrap resampling methods

This paper aims to tackle the issues of evaluating the dynamic performance of fiscal poverty alleviation in 22 Chinese provinces (regions) over 2016-2019. First, we open up the internal structure of the fiscal poverty alleviation system (FPAS) and clarify its input-output process as a two-stage series system consisting of the public investment process and poverty reduction process. On this basis, we construct dynamic network DEA models with and without carryover activities for measuring the period efficiency and overall efficiency of FPASs (i.e., FPAS efficiency) and the period efficiency and overall efficiency of evaluation indicators (i.e., indicator efficiency), and also prove the relationship between the two proposed models. Second, we combine the proposed dynamic network DEA models and Bootstrap resampling method to assess the robustness of FPAS efficiency for exploring the risk of returning to poverty for each FPAS. The results show that: (i) the carryover activities have some impact on the FPAS efficiency and indicator efficiency; (ii) the period efficiency shows an upward trend, and most of FPASs or evaluation indicators have been at high efficiency in the sample period, but there is still regional heterogeneity; (iii) there are some differences between the indicator efficiency and there are some inconsistencies between them and the FPAS efficiency; (iv) Bootstrap resampling results indicate that several FPASs have a great risk of returning to poverty.

Research on the Transformation Efficiency of Scientific and Technological Achievements of Minis-try-affiliated Universities Based on Network DEA

Previous studies on the transformation of scientific and technological achievements in universities were based on the black box model, and the results obtained were not comprehensive enough. This paper divides the transformation of scientific and technological achievements of universities into the knowledge research and development stage and the value transformation stage. The two-stage network DEA model is used to study the transformation efficiency of scientific and technological achievements of 55 subordinate universities based on the data from 2009 to 2017.The research in this paper finds that the overall efficiency of the transformation of scientific and technological achievements in Chinese universities is relatively stable, and the efficiency of the knowledge research and development stage is relatively high, but the efficiency of the value realization stage is seriously low, resulting in a low level of overall efficiency."985" engineering colleges and "double first-class" colleges have higher input and output efficiency, and cluster analysis is used to classify and summarize the technological transformation of each college. Finally, corresponding development suggestions are put forward.

Ascertaining the impact of e-service quality on e-loyalty for the e-commerce platform of liner shipping companies

Focusing on the e-commerce platforms provided by major global liner shipping companies (LSCs), this study explores how e-service quality (e-SQ) influences e-loyalty (e-LY) and establish benchmarks for individual customers. We initially employed structural equation modeling to develop a research framework, and then we transformed this framework into a network data envelopment analysis model. Our findings revealed that the e-SQ of LSCs has a significant positive impact on the e-satisfaction (e-SA) and e-LY of their customers. Additionally, we observed that trust plays a significant moderating role in the relationship between e-SA and e-LY. Through benchmark analysis, we found that for small-scale shippers, virtually no slack exists between their benchmark and actual perception. However, the slack increased as the scale of the shipper increased, implying that LSCs have provided an e-SQ that exceeds the expectations of their large-scale customers. Meanwhile, it’s crucial for LSCs to pay attention to specific aspects such as the architecture of e-commerce platforms and privacy protection because the slacks of these e-SQ items are relatively small. Furthermore, the large slacks in e-LY for large-scale shippers imply that their e-LY leaves much room for improvement compared with that of small-scale shippers. Overall, our study underscores the importance of maintaining or elevating e-SQ standards to reinforce e-LY while considering the mediating and moderating effects of e-SA and trust in the context of the e-commerce platforms provided by major LSCs.

Two-sided platform customer classification system based on network DEA: an application to an on-demand freight platform

Two-sided platforms include two parts of users, customers and suppliers, which are the key strategic resources in two-sided platforms. Through Customer Intelligence Classification (CIC) management provided in this study, a platform can distinguish different values of customers to allocate resources efficiently and maximise customer resource value and enterprise investment returns. Meanwhile platform can also distinguish collusive drivers through CIC. Network Data Envelopment Analysis (NDEA) is applied in this research to open the black box of a two-sided platform operation system by dividing it into two sub-processes: Marketing and Service (MS) sub-process and Value-Creating (VC) sub-process. Four categories of customers are classified through performance evaluated by NDEA from data of an on-demand freight platform Z from March to August in 2018. In addition, six influencing factors are discovered through Panel Regression Model. The main conclusions are: (1) Customer with high marketing service valuation and low value-creating valuation, needs proper operation methods to promote their order quality. (2) Customer with low marketing service valuation and high value-creating valuation, needs to take attention to collusion between customers and driver. (3) Shipping fee, client number, driver number and coupons per order should become customers’ Key Performance Indicators to evaluate their potential.

Are Brazilian Higher Education Institutions Efficient in Their Graduate Activities? A Two-Stage Dynamic Data-Envelopment-Analysis Cooperative Approach

Higher education evaluation presents itself as a worldwide trend. It aims to improve performance due to its importance for economic and personal growth. Graduate activities are essential for Brazilian research and innovation systems. However, previous studies have disregarded the importance of this educational level and have evaluated efficiency by jointly considering teaching and research or only undergraduate courses. Therefore, this study contributes to Brazilian reality by proving a national graduate activities efficiency evaluation that considers them as a two-stage system (formative and scientific production stages). The study provides three main methodological contributions by presenting a new centralized two-stage dynamic network data envelopment analysis (DNDEA) model with shared resources. Besides measuring efficiency, an efficiency decomposition based on a leader–follower assumption shows managers how much efficiency can alter when one of the stages needs to be prioritized. Finally, a new framework based on modified virtual inputs and outputs provides a bi-dimensional representation of the efficiency frontier. Results indicate the usefulness of the approach for ranking universities, and the need to improve scientific production, highlighting the negative impacts of COVID-19 on the formative process efficiency and showing no significant regional discrepancies regarding performance.

Supply chain performance evaluation using a network data envelopment analysis model with bias-corrected estimates

Supply chain performance evaluation using a network data envelopment analysis model with bias-corrected estimates

Enhancing urban emergency management: A two-stage network DEA analysis of Chinese cities

In order to scientifically evaluate the emergency management capabilities of major cities in China, this article conducts a comprehensive study on the input and output indicators of emergency response in each city. A two-stage network DEA model was used to construct an evaluation model that reflects the emergency management capacity of cities. A dataset containing emergency management data from 36 well-known cities in China was selected to effectively evaluate its performance, and the city that demonstrated the most effective input-output ratio in the field of emergency management was ultimately determined. The research results show that using a two-stage network DEA model as the foundation to construct an evaluation model that reflects urban emergency management capabilities can promote a wise combination of subjective and objective evaluations, and achieve scientific investment in urban emergency assets.

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.

Estimating most productive scale size decomposition in a fuzzy network data envelopment analysis model: assessing the sustainability and resilience of the supply chain

This paper estimates the Most Productive Scale Size (MPSS) in the NDEA model to appraise the sustainability and resilience of the supply chains. As the corresponding input and output criteria are not always accurately measurable, we also introduce the fuzzy version of our proposed NDEA model and apply the proposed model in a case study involving 10 Iranian supply chains of Companies Producing Soft Drinks (CPSDs). The considered-three-echelon supply chains include suppliers, manufacturers, and distributors. Mathematical analysis proves that the MPSS of the considered supply chain can be decomposed as the sum of the MPSS values of the individual stages. Thus, the supply chain is overall MPSS if and only if it is MPSS in every three stages. The results of this study reveal that the Behnoush supply chain is overall MPSS in all three stages, including supplier, manufacturer, and distributor, for any α ∈ {0.1, 0.5, 1}. A sensitivity analysis has been performed to measure the impact of each criterion on the entire supply chain performance. The sensitivity analysis results indicate that the social and resilience criteria significantly impact the performance and ranking of supply chains. Finally, we discuss how to improve the sustainability and resilience of non-MPSS supply chains.

Comparative Network Efficiency Analysis of the Airlines in Turkey After Deregulation

Deregulation has significantly developed the civil air transport industry in an ever-globalizing world. Even though deregulation has caused significant structural transformations in airline companies, the effect of deregulation effect on the production, marketing efficiency, and competitiveness of airline carriers worldwide, especially in Turkey, has not been fully revealed yet. Therefore, this study aims to analyze the efficiency of Turkish air carriers after the deregulation process in Turkish civil aviation by dividing the efficiency into production and market efficiency. Production and marketing efficiencies of airlines were estimated using the window network data envelopment analysis methodology. Efficiency analysis results showed production efficiency at 0.887, marketing efficiency at 0.764, and system efficiency at 0.796. Results also indicate that low-cost airlines have a higher production efficiency score (0.918) than full-service airlines (0.825). In comparison, the marketing efficiency of full-service airlines (0.879) is higher than that of low-cost carriers (0.708). The study determined that the system efficiency does not change according to the business model. The system efficiency score of the full-service provider airlines with a larger market share is higher and more balanced. The close and dynamic monitoring of the air transport market and the continuation of operations under a business model incorporating an appropriate marketing mix will increase the marketing efficiency of the airlines.

Allocating Benefits Due to Shared Resources Using Shapley Value and Nucleolus in Dynamic Network Data Envelopment Analysis

Shared resources are common among supply chain partners and also occur in multiple linked stages of an internal network. The sharing of these resources impacts the organization’s profits. This study is focused on the potential benefits of resource sharing on a three-stage network system and on the profit improvement allocation. Previous treatments concentrate on defining optimal proportions to allocate resources and disregard the impacts of allocations to promote cooperation and are limited to static evaluations. Data Envelopment Analysis performs the decision-making units (DMUs) efficiency measurement. Methodological advances have resulted in models that analyze their internal structure and temporal impacts on efficiency. We propose an integrated cooperative game and dynamic network DEA that considers known quantities of resources used in each stage and the time effects to optimize the system’s profit. Each DMU stage is a player, and we investigate performance before and after resource sharing. Using Shapley value and Nucleolus, it is possible to allocate the benefits obtained based on the marginal contributions of each stage, providing incentives to motivate and maintain cooperation. A numerical example is used to illustrate the method. The results confirm the identification of inefficient DMUs and that sharing resources allows for profit increase for all of them.