Performance evaluation for health‐care sectors using a dynamic network data envelopment analysis approach

PurposeThe purpose of this study is twofold. First, this research estimates banks' efficiencies from the perspectives of resource utilization and investment after incorporating risk measures as an exogenous input in the investment-efficiency stage. Second, the current study examines the relationship between intellectual capital (IC) and banks' efficiencies.Design/methodology/approachFirst, this study uses a dynamic network data envelopment analysis approach in investigating the efficiencies of 24 Taiwanese banks in 2007–2018 from two perspectives. Second, this research utilizes various regression techniques, namely, ordinary least squares (OLS), robust least squares and truncated regression, to gauge the impact of IC on banks' efficiencies. Typically, IC is determined based on a monetary value-based measure and value-added intellectual coefficient (VAICTM).FindingsResource-utilization (investment) efficiencies were observed as 0.941 (0.964), thereby contributing to the mean overall efficiency of the sample banks at 0.952. However, the related efficiency changes decline over the sample period, thereby suggesting that the average banks' efficiencies hardly increase. Regression analyses show a significantly positive relationship between IC and banks' overall resource-utilization and investment efficiencies.Research limitations/implicationsOverall, this study suggests that researchers should consider risks when estimating banks' efficiencies owing to their connection to banks' investment performance. From banks' dynamic two-stage efficiencies, this study demonstrated that investments in IC will bring improved future economic benefits.Originality/valueDifferent from prior studies, this study improves banks' efficiency evaluation models by incorporating risk measures and assuming weighted periods for the 2007–2008 global financial crisis. Moreover, the use of monetary value-based measure of IC provides consistent results as the commonly-used VAICTM does.

Reviews the state of Vietnam’s innovation system and proposes an agenda for improving it by (1) assessing the country’s current innovation capacity; (2) examining the demand side and how the need for knowledge and the absorptive capacity of firms can find improvement; (3) considering how the supply of knowledge and skills from higher education and research can rise; and (4) adopting the goal of creating an innovation-led economy that includes restructuring state-owned enterprises (SOEs) to create a level playing field, strengthening microeconomic foundations to enhance private sector competitiveness and leveraging external trade opportunities for growth. Vietnam’s economic aspirations require it to raise its growth rate, but since 1990, it has grown mainly by expanding the labor force and both foreign direct investment (FDI) and domestic capital. Demographic shifts and limit of capital mobilization have now closed that option, forcing future growth to rely on a labor force working smarter with higher quality capital resources.

Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach

Data envelopment analysis (DEA) is a tool for measuring the relative efficiency of decision-making units (DMUs) that perform similar tasks. One of the uses of the DEA is the establishment of benchmarks for inefficient DMUs. These benchmarks play the role of target for inefficient DMUs. However, traditional DEA (TDEA) models fail to rank the efficient DMUs, and as a result, they cannot determine a benchmark for the inefficient DMUs. To overcome this shortcoming, this paper proposes virtual network DEA (NDEA) approach. The proposed method not only ranks the whole inefficient and efficient DMUs but also establishes a virtual benchmark called ideal DMU. The case study demonstrates the applicability of the proposed model.

This study evaluates the operational and revenue generation efficiency of English Premier League (EPL) clubs from 2014/15 to 2023/24 using a novel Dynamic Network Data Envelopment Analysis (DNDEA) model under the Variable Returns to Scale (VRS) assumption. By integrating dynamic and network structures, the model decomposes club performance into sequential stages, specifically operational conversion and revenue generation, and traces the intertemporal transmission of economic, sporting, and social factors. Unlike traditional static DEA models that treat efficiency as time-invariant and “black-boxed”, the DNDEA model provides a temporal-diagnostic lens that detect shifts in efficiency trajectories and inter-stage feedback across seasons. Results reveal two enduring archetypes: high-performing clubs that achieve competitive outcomes with limited financial inputs through effective resource management, and financial giants that excel in revenue generation but struggle to translate investments into consistent sporting success. Dynamic analysis shows system efficiency peaked in 2014/15 before declining sharply in 2015/16 and 2018/19, respectively, driven by imbalances between escalating financial investments and stagnating on-field performance. Operational inefficiencies in Stage 1 (resource conversion) were more critical than those in Stage 2 (revenue generation), underscoring challenges in aligning short-term investments with long-term sustainability. The study advances sports analytics by providing a holistic framework for evaluating football club efficiency, emphasizing actionable strategies such as optimizing talent acquisition, prioritizing dual-return investments, and leveraging fan engagement.

The main aim of this research is to present an innovative method known as fuzzy network data envelopment analysis (FNDEA) in order to assess the performance of network decision-making units (DMUs) that possess a two-stage structure while taking into account the uncertainty of data. To attain this goal, we utilize various methodologies including the non-cooperative game (leader-follower) NDEA method, the concept of Z-number, credibility theory, and chance-constrained programming (CCP) to develop a model for the fuzzy NDEA approach. The FNDEA approach offers several advantages, such as the linearity of the presented FNDEA models, the ability to rank two-stage DMUs in situations of ambiguity, the provision of a unique efficiency decomposition method in an uncertain environment, and the capability to handle Z-information. To demonstrate the applicability and effectiveness of the proposed approach, we implement the Z-number network data envelopment analysis (ZNDEA) approach in assessing the performance of Iranian private insurance companies. The results of this implementation reveal that the proposed ZNDEA method is suitable and effective for measuring and ranking insurance companies in situations where data ambiguity is present.

This paper proposes a novel network data envelopment analysis (NDEA) approach to measure both technical efficiency and service effectiveness of railway transportation services. In particular, railway transportation systems consume varying amounts of shared inputs to produce outputs. The proposed NDEA model represents both the non-storable feature of transportation service and production technologies in a unified framework in the presence of shared inputs. Hence, the proposed model not only measures the overall efficiency of the railways, but also estimates passenger and freight technical efficiency, service effectiveness, and technical effectiveness, simultaneously. A case study validates the proposed model.

BackgroundIn recent years, measuring and evaluating the efficiency of health systems has been explored in the context of seeking resources to ensure the sustainability of ‘countries’ health and social systems and addressing various crises in the health sector. The study aims to quantify and compare the efficiency of OECD health systems in 2000, 2008, and 2016. The contribution to research in the field of efficiency in the healthcare system can be seen in the application of Dynamic Network Data Envelopment Analysis (DNDEA), which help us to analyse not only the overall efficiency of the healthcare system but analyse the overall efficiency as the result of the efficiencies of individual interconnected areas (public and medical care area). By applying the DNDEA model, we can realise the analysis not only within one year, but we can find out if the measures and improvements taken in the healthcare sector have a positive impact on its efficiency in a later period (eight-year interval).MethodsThe analysis focuses on assessing the efficiency of the health systems of OECD countries over three periods: 2000, 2008, and 2016. Data for this study were derived from the existing OECD database, which provides aggregated data on OECD countries on a comparable basis. In this way, it was possible to compare different countries whose national health statistics may have their characteristics. The input-oriented Dynamic Network Data Envelopment Analysis model was used for data processing. The efficiency of OECD health systems has been analysed and evaluated comprehensively and also separately in two divisions: public health sub-division and medical care sub-division. The analysis combines the application of conventional and unconventional methods of measuring efficiency in the health sector.ResultsThe results for the public health sub-division, medical care sub-division and overall health system for OECD countries under the assumption of constant returns to scale indicate that the average overall efficiency was 0.8801 in 2000, 0.8807 in 2008 and 0.8472 in 2016. The results of the input-oriented model with the assumption of constant returns to scale point to the overall average efficiency of health systems at the level of 0.8693 during the period. According to the Malmquist Index results, the OECD countries improved the efficiency over the years, with performance improvements of 19% in the public health division and 8% in the medical care division.ConclusionsThe results of the study are beneficial for health policymakers to assess and compare health systems in countries and to develop strategic national and regional health plans. Similarly, the result will support the development of international benchmarks in this area. The issue of health efficiency is an intriguing one that could be usefully explored in further research. A greater focus on combining non-parametric and parametric models could produce interesting findings for further research. The consistency in the publication and updating of the data on health statistics would help us establish a greater degree of accuracy.

Dynamic network data envelopment analysis model with fuzzy inputs and outputs: An application for Iranian Airlines

Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation

Background: Gains in bank efficiency improvement have widely been regarded as one of the most effective and efficient means of ensuring sustainability of a financial system. Aim: This article proposes a relative dynamic two-stage network data envelopment analysis model for measurement of bank efficiency based on the slack-based measure. Setting: Twenty-seven banks in Ghana during the period of 2009–2014. Methods: By considering simultaneous processes within the framework of two-stage data envelopment analysis, the slack-based measure approach identifies the sources of inefficiency in the banks. Results: In the empirical analysis, non-performing loans are an undesirable output in one production process, which should also be treated as a carry-over factor; that is to say, some non-performing loans from the preceding year can be collected in the current year. The carry-over factors should be used to indicate the presence of performance gaps that exist in the banks. The proposed model was used to measure the efficiency of the 27 banks in Ghana during the period of 2009–2014. We also present useful suggestions for improvement in bank efficiency based on the empirical results. Conclusion: The 27 main commercial banks in Ghana are far from efficient. For all banks, the efficiency score in the second stage is much higher than that of the first stage. That means more attention should be paid to the first stage of production in order to increase the banks’ efficiency.

No abstract available.

The objective of this paper is to assess the sustainability of supply chains by proposing a dynamic network data envelopment analysis (DNDEA) model in the presence of interval data, due to the fact that in many real-world applications, the condition of convexity in the production technology might be violated. To prevent this issue, a DNDEA model based on the free disposal hull (FDH) approach is developed. For the first time, this paper develops a DNDEA version of the free disposal hull (FDH) model in the context of the SCOR framework. It is also shown that this model always presents a finite efficiency score for assessing the sustainability of supply chains. Moreover, using this model, real benchmarks can be calculated to improve the sustainability of unsustainable supply chains. A case study in print industry is given. The results validate our proposed model.

Assessing the performance of water supply operations in Chinese water companies: A union dynamic multi-activity network data envelopment analysis

Evaluating the efficiency of major container shipping companies: A framework of dynamic network DEA with shared inputs