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Background : The efficiency of medical services directly impacts the economic burden of healthcare, making it crucial to analyze the input-output efficiency of various types of medical institutions. However, while hospitals had been extensively analyzed for their efficiency, other types of medical institutions had received limited attention in this regard. Methods : In this study, we employed data envelopment analysis (DEA) methods based on time series and internal benchmarks to autonomously assess the efficiency of 18 distinct categories of healthcare facilities in China over the past decade. The verification was conducted through the utilization of the critical incident technique (CIT). Additionally, we utilized the Delphi process (AHP) method to evaluate suppliers of medical consumables, implemented a multi-population genetic algorithm for managing these consumethod and analytic hierarchymables efficiently, and applied stakeholder theory to manage medical personnel efficiency. Results : Our findings indicated that medical institutions capable of providing clinical services exhibited higher levels of efficiency compared to those unable to do so. Multiple indicators suggested redundancy within these institutions. Notably, comprehensive benefit evaluation revealed that clinical laboratory had performed poorly over the past decade. We selected an inefficient medical institution for intervention in reagent management and the work efficiency of medical staff. After implementing the Delphi method and multi-population genetic algorithm for consumable replenishment, the reagent cost was reduced by 40%, 39% and 31% respectively in each of the three experimental groups, compared to the control group. By applying stakeholder theory and process reengineering methods, we were able to shorten quality control management time for medical staff in the experimental group by 41 min per day, reduce clinical service time by 25 min per day, and extend rest time by 70 min per day, while the quality indicators were all meeting the targets. Conclusion: By employing various mathematical models as described above, we were able to reduce costs associated with medical consumables and enhance medical personnel work efficiency without compromising quality objectives. [ABSTRACT FROM AUTHOR]

In order to clarify the production and organization mode of family farm of flue-cured tobacco which is suitable for the present situation of mountainous rural areas, based on the dependence of farms on social services and the differences in the functions of farmers, the family farms of flue-cured tobacco in the mountainous areas of Ankang were divided into four types. They were pattern 1(investment management family farm), pattern 2(investment management family farm), pattern 3(investment management family farm), and pattern 4(investment family farm). The typical investigation method was used to conduct the distribution tracing investigation, DEA, descriptive statistics and analysis of variance were used to compare management efficiency, input structure and production efficiency of the four models. The results showed that:The higher the dependence on social services, the lower the operating net profit margin and production efficiency. From mode 1 to mode 4, the operating net profit margin is 26. 14%, 21. 29%, 14. 75%, 9. 51%, and the comprehensive technical efficiency is 0. 78, 0. 53, 0. 36, 0. 21, respectively, the net income of unit area is 18 082. 82, 13 570. 52, 10 084. 05, 4 740. 42 yuan in turn. The input redundancy and output deficiency of each mode are calculated by Frontier Analysis, which provides a path to improve production efficiency. Mode 2 was the best mode of production organization. Mode 3 was the future direction of development. The important measures to improve the production efficiency of flue-cured tobacco farms in mountainous areas were to establish the benefit link mechanism with the professional managers and leaders of the farms, and to sign the stable supply and marketing agreement with the suppliers, carry out classified differentiation management for ordinary workers. [ABSTRACT FROM AUTHOR]

Today's market with more frequent but smaller deliveries complicates the realization of logistics processes and activities. Customers expect products within 24 h or 48 h. This poses a logistics challenge for companies in fulfilling the expected time window. In these situations, companies can either perform self-distribution or they can outsource it. When outsourcing, companies must define key performance indicators (KPIs) to measure the efficiency of the distribution process. One such KPI is On-Time In-Full (OTIF). OTIF fulfillment is increasingly becoming a fundamental requirement not only in distribution but also throughout the entire supply chain. Two essential prerequisites for successful outsourcing collaboration are ensuring that orders are delivered on time and in full. A review of the existing literature revealed that no studies simultaneously address this complex issue. This was exactly the main motive for developing a model that simultaneously takes into account the utilization of logistics resources on one hand, and OTIF on the other when calculating the efficiency. The model developed in this paper was tested on the data of a company operating in the Serbian market. The results showed that seven of the company's warehouses are inefficient. A two-stage matrix analysis with the three most important indicators (operational efficiency, on-time delivery, and in-full delivery) was performed based on the obtained results. Other than the obvious scientific contributions, the proposed model can help companies increase their resource utilization efficiency as well as OTIF fulfillment. [ABSTRACT FROM AUTHOR]

Greek wines excel in quality and exports, but the viticultural sector faces significant challenges from complex supply chains, shifting European policies, and the growing need for sustainability amidst climate change and economic pressures. External environmental costs could affect significantly the decision-making process of farmers, reflecting a broader evaluation of sustainability in viticulture. This study evaluates the economic and environmental impacts of organic, integrated, and conventional viticulture management systems in Drama, Greece using a life cycle (LC) approach and data envelopment analysis (DEA) to determine efficiency, quantify environmental impacts in monetary terms, and incorporate these costs into the analysis. Organic management systems have lower energy consumption and emissions compared to integrated and conventional systems, with organic systems ranging from 4546 to 6573 kWh/ha in energy use and 1358 to 1795 kg CO2 eq./ha in emissions, while integrated and conventional systems range from 9157 to 12,109 kWh/ha and 2961 to 3661 kg CO2 eq./ha. The DEA analysis reveals that most organic systems perform efficiently when accounting for environmental costs, whereas conventional systems face significant efficiency declines, with only a few maintaining optimal performance. Policy-supported transitions based on the provider gets principle are crucial for balancing economic and environmental goals in viticulture, as the integration of shadow prices significantly impacts efficiency. [ABSTRACT FROM AUTHOR]

Vaccination is seen as the most promising one among the efforts to stop COVID-19 and the U.S. government has given great importance to vaccination. However, which states have performed well in administering COVID-19 vaccines and which have not is an open significant question. Another important question is what makes a state more successful than others when evaluating vaccination performance. To answer both of these questions, we proposed a hybrid method that consists of Data Envelopment Analysis and Ensemble ML Methods. DEA was employed to find the vaccine efficiency of the states using the data aggregated from counties. ML techniques are then applied for the vaccine efficiency prediction and understanding the significance of the variables in the prediction. Our findings revealed that there are considerable differences between U.S. States' performance and only 16 of the states were efficient in terms of their vaccination performance. Furthermore, Light GBM, Random Forest and XGBoost models provided the best results among the five ensemble machine learning methods that were applied. Therefore, an information fusion-based sensitivity analysis method was used to combine the results of each ML technique and ascertain the relative significance of the factors in the prediction of the efficiency. As the findings for factors affecting vaccination performance, percentage of vaccine doses delivered and COVID-19 deaths were found to be the major influential factors on the prediction of the efficiency and percentage of fully vaccinated people, the number of healthcare employees and human development index followed these variables. [ABSTRACT FROM AUTHOR]

Following the industrial revolution, factors such as accelerated industrial processes, increased population, urbanization, and deforestation contributed to a variety of environmental issues, particularly global warming. This study proposes a new Data Envelopment Analysis model to assess the environmental activities of developed and developing countries that have signed the Kyoto Protocol. Using the proposed model, Decision Making Units (DMUs) with similar performance were clustered by creating efficient boundaries, and the efficiency improvement of the effective DMUs in each cluster on each other, as well as the efficiency improvement on the ineffective DMUs in the entire data set, were examined. The final efficiency score of the proposed model is calculated by adding the initial efficiency score, which expresses the initial position of the DMUs, and the efficiency improvement over all DMUs. Finally, the full ranking is obtained by sorting the clusters from smallest to largest, as well as the final efficiency scores of the DMUs within each cluster, from largest to smallest. During the study's implementation phase, the environmental effectiveness of 38 countries that signed the Kyoto Protocol and whose data can be accessed based on determined input/output was assessed using the proposed model. The undesirable input/output problem encountered in environmental effectiveness evaluation was addressed by determining a monotonically decreasing function. The ranking obtained with the proposed model was compared to three different models found in the literature. The proposed model defined seven clusters, and Sweden, Switzerland, Iceland and Norway were in the 1st cluster as the DMUs with the highest efficiency scores. [ABSTRACT FROM AUTHOR]

Background: Providing high-quality healthcare services at low or no cost, especially in a densely populated country like India, is an enduring challenge. Thus, the efficacy of government-run hospitals and healthcare instructions has become critical for developing and developing countries. Aim: The current study aimed to investigate the relative performances under variable ownership patterns and scrutinise whether the differences in performances are significant or not to explore determinants of the performance of the hospitals. Methods: The study has been conducted in East and West Bardhaman, West Bengal, India. Data envelopment analysis measures hospitals' performance under variable ownership patterns. The Mann-Whitney U Test is employed to examine whether the performance differences among these hospitals are significant. Finally, Censored Tobit Analysis is used to gain insight into the determinants of their performance. Hospitals are categorized according to their ownership pattern: government hospitals owned by the state government, public hospitals owned by public sector undertakings, and private authorities. Results: Considering the input-output variables, relative performances have been measured. The hospitals under government ownership show the best performance, followed by public hospitals owned by public sector undertakings and private authorities. This performance level has significant determinants like the size of the hospital, bed occupancy rate, and the management and authority of the hospitals. Conclusion: In a developing or underdeveloped nation, all healthcare service providers need to be efficient enough to attain the health of the masses. This study has revealed that the vision of 'health for all' can be reached through the mission of 'healthcare inclusion' strategy by including all hospitals on the supply side regardless of their motive, ownership pattern, or other phenomenon. [ABSTRACT FROM AUTHOR]

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Copyright of Food Science & Technology & Economy is the property of Grain Science & Technology & Economy Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In metropolitan subway operations, the implementation of express train services is crucial for facilitating the efficient movement of large passenger volumes. However, preliminary feasibility studies for express train operations often rely on outdated standards from the 1990s, which fail to accurately reflect contemporary needs and conditions. To address these limitations, this study employs a Graph Convolutional Network (GCN) utilizing data from subway stations, user reviews, and the interrelationships between stations. This approach aims to provide a more current and comprehensive method for selecting express stop stations, incorporating user feedback. Additionally, the study evaluates efficiency using Data Envelopment Analysis (DEA). The findings demonstrate a significant increase in efficiency on the subway lines between Japan and New York, thereby validating the potential of using review data in the selection of express stop stations. This paper presents a novel set of criteria for effective stop selection that aligns with user needs, promoting more efficient budget allocation and broader public support. [ABSTRACT FROM AUTHOR]

Indonesia is the fourth largest coffee-producing country in the world after Brazil, Vietnam, and Colombia (Richardson et al., 2023). Seeing this opportunity is widely used by business people in making coffee shops (Fahmi & Savira, 2023). This study aims to see the level of efficiency produced by coffee shops with data envelopment analysis (DEA) techniques. This analysis uses input variables, namely labor costs, raw material costs, overall capital, and the amount of labor, and output variables, namely gross profit, and net profit. In DEA using variable return scale (VRS) assumptions where the addition of inputs is not necessarily offset by the output. The sample used was 67 coffee shops in Soloraya. The results of the analysis show that 22 coffee shops have experienced a 100 percent efficiency level while 45 coffee shops have not experienced a 100 percent efficiency level, meaning that those that have not reached 100 percent need improvement in the use of inputs and improved output targets and also benchmarking to make adjustments to their business activities. Managers find it easier to make decisions about whether the business will be saved or increased targets so DEA techniques are more suitable for business people to use in making a decision. [ABSTRACT FROM AUTHOR]

Modernizing agricultural economies requires the infusion of knowledge and industrialization, necessitating the bridging of the "digital divide" and "talent gap" between urban and rural areas. Public libraries, as key knowledge dissemination institutions, play a crucial role in this process. This study aimed to explore how the development of such institutions can align with agricultural economic growth. Using China as a case study, where the Rural Revitalization Strategy has been implemented in recent years, including the extensive construction of rural public libraries and other infrastructure, we empirically analyzed the correlation between county-level public libraries and agricultural economic development from 2012 to 2019. The results show that the number of county-level public libraries and their assets, collection sizes, e-books, and professional staff have a significant positive impact on agricultural economics, while non-professional staff and facilities have a negative impact. It is recommended that future rural public library development should focus on enhancing professional standards and advancing digitalization and mobile internet integration, while being cautious about expanding the physical scale and staffing. This study fills a gap in the research on the correlation between rural public libraries and agricultural economics, and the methodology employed has a certain degree of general applicability. However, the applicability of the conclusions may be limited by China's unique national conditions. [ABSTRACT FROM AUTHOR]

Port efficiency has different meanings for various stakeholders. This paper examines the existing studies on the efficiency types and analysis perspectives. Most studies focused on traditional port efficiencies (i.e., the technical, allocative and economic efficiency). Some extended these traditional types by considering ships' time in port and the negative environmental impacts. The majority of studies assessed port efficiency from the perspective of port authorities, managers, and operators. Few did it from the perspective of the users and the public. Even fewer noticed the possible conflicts between the port service level and its profitability. This highlights the need for port efficiency analyses to balance the interests of different stakeholders. Frontier methods are popular in port efficiency evaluation, but publications extending or combining different methods are still very rare. Several issues are identified, including the consistencies between decision-making units and the selection of input/output variables, the nature of port services, the extension and combination of these methods, and the impacts of new developments and occurrences, which can be the direction of further studies. • 301 studies on port efficiency analysis are reviewed from types and perspectives. • Majority assessed port traditional efficiency from the perspective of port. • Analysing efficiency from different perspectives needs more attentions. • Frontier methods are widely used, new approaches with combined methods are rare. [ABSTRACT FROM AUTHOR]

This study introduces an integrated method for managing process risks in a business process reengineering (BPR) project using robust data envelopment analysis (RDEA) and machine learning (ML). The goal is to prioritize risks based on three standard factors of PFMEA (severity, occurrence and detection (S-O-D)) and incorporating two additional factors (breakdown cost and breakdown duration) seen as undesirable outputs. The model also accounts for the effect of uncertainty on expert-estimated values by applying disturbance percentages in the linear PFMEA-RDEA model. A machine-learning model is proposed to predict new values if partial or total modifications have been made to the processes. The approach was implemented in an automotive sector company, and the results showed the impact of uncertainty on values by comparing different approaches, such as RPN, PFMEA-DEA and PFMEA-RDEA. A new reduced risk categorization was achieved, which allowed for decision makers to focus on the necessary actions for reengineering. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to assess the efficiency of climate change competitiveness via a case study on EU regions by using the data envelopment analysis (DEA) model and Shannon entropy. First, on the same premise as similar composite indicators, we develop a DEA model to assess the relative performance of the regions in climate change competitiveness. Then, we extend our calculations with a DEA-like model and Shannon entropy to derive global estimates of a new competitiveness index by using common weights. Results show that the proposed DEA-Entropy model enables the construction of a regional climate change competitiveness index among all regions via a set of common weights. The proposed model's common weight structure demonstrates more discriminative power compared to the weights obtained through pure DEA or DEA-like methods. In order to validate the proposed DEA-Entropy model, it was applied to 120 EU regions. The results are meaningful for the regions to improve their competitiveness. [ABSTRACT FROM AUTHOR]

Cooperatives offer significant opportunities for economic empowerment of people in difficult economic climates. However, concerns exist in literature on the efficiency level of these organisations in relation to members' deposits and financial-related factors. This study analysed the influence of members' deposits and related financial factors on the efficiency level of cooperative organisations. Data Envelopment Analysis (DEA) and the Tobit regression model were used to analyse the secondary data collected from 99 cooperative organisations over 7 years. Empirical results indicate a low level of efficiency below 50%. Further evidence from Tobit regression identified the significant effect of loan (β = 2.05, t = 3.41, p < 0.01), interest rate (β = 1.07, t = 2.16, p < 0.05), surplus (p < 0.05), expenses (p < 0.01), cooperative age (p < 0.01) and saving deposits (p < 0.10) on cooperative efficiency. The efficiency level of cooperatives is low, and the contribution of deposits is also negative, but potential to raise the level of efficiency exists with organisational management of loans and interest rates. Increasing cooperative efficiency is imperative to achieve the expected economic support from organisations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had an unimaginable effect on global economies and threatens the efficiency and growth trajectory of the banking sector. The pandemic has disrupted the primary operations of the banking sector, as banks were compelled to close physical branches to adhere to social distancing guidelines. Given the importance of the banking sector to economic development, understanding the consequences of COVID-19 on efficiency and growth trajectory of this sector is crucial for ensuring its sustainability. By drawing data from annual reports of 13 publicly quoted banks in Nigeria from 2019q1 to 2021q2 (pre and post COVID-19), this study investigates the impact of COVID-19 pandemic on efficiency and growth trajectory of the banking sector. The data obtained were mainly analysed using DEA and LGCM. The DEA results show an upward trend in the average scores of technical efficiency, pure technical efficiency, and scale efficiency in both periods. The LGCM results reveal a statistically insignificant difference in banks' growth trajectory (rate of change) in both periods. Overall, our analyses suggest that the outbreak of COVID-19 does not threaten the efficiency and growth trajectory of the banking sector. However, the insignificant difference between the pre and post nationwide lockdown suggests that there is an insignificant improvement in the efficiency of the banking sector during the post nationwide lockdown period. These findings offer practical implications for banks, regulators, and policymakers regarding the unprecedented uncertainty caused by the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

As a powerful mathematical tool for tackling uncertain decision problems, three-way decision has garnered substantial attention since its inception. However, real-world decision problems are inherently complex, and decision-makers often exhibit characteristics of incomplete rationality. Traditional three-way decision models, which rely on functions or relationships, face challenges when confronted with multi-output problems. In response to this challenge, this paper introduces an intuitionistic fuzzy three-way decision model grounded in data envelopment analysis (DEA). Initially, we propose an input–output correlation degree that integrates hesitancy information and serves as a procedural indicator for benefit scores. Subsequently, the traditional DEA is extended to accommodate the intuitionistic fuzzy environment and utilized to construct a comprehensive loss function. Furthermore, a novel intuitionistic fuzzy three-way decision model is developed, incorporating three dimensions: optimism, neutrality, and pessimism, and corresponding decision rules and algorithms are provided. Finally, the effectiveness of the proposed model is rigorously validated through a series of experiments and comparative analyses. The model offers a pioneering approach to address uncertain multi-input–output decision problems, effectively integrating decision-maker’s risk preferences within an intuitionistic fuzzy environment. [ABSTRACT FROM AUTHOR]

Currently, much attention is paid to digital transformation in all areas, including the public sphere. The latest studies show that it is necessary for the public sector to monitor the efficiency and satisfaction with the services provided. However, there are significant gaps in research in this area, including in Slovakia. This research proposes and applies the measurement of efficiency using the DEA method in the context of e-Government, provides a comparison of the roles of states in the use of public electronic services in the EU, and applies the method of measuring satisfaction using the American Customer Satisfaction Index, focused on the central state portal in Slovakia. The main methods that were used to fulfil the objectives of the work were data envelopment analysis, "DEA", and the American Customer Satisfaction Index, "ACSI". Other methods used include the Mann–Whitney U test, the chi-squared test, and Sperm correlation analysis. From the results of the work, it is possible to conclude that ACSI can be applied within Slovakia. Furthermore, the results show a strong correlation between perceived quality and satisfaction, which is 0.855. Overall satisfaction with the central state portal of public electronic services reached 61.7%. We conclude that it would be appropriate and possible to use ACSI as part of DEA measurement. [ABSTRACT FROM AUTHOR]

The core objective of Sustainable Development Goal (SDG) 12 is to do more and better with less, increasing net well-being gains from economic activities by reducing resource use, degradation, and pollution along the whole lifecycle while simultaneously improving quality of life. This paper quantifies the level of achievement of sustainable consumption and production in terms of efficiency scores. Due to data envelopment analysis, it is possible to monitor progress towards SDG 12, identifying the best performers to take as examples and the fields in which there is room for improvement. Although interesting differences emerge in countries' efficiency, the results show that the best performers are OECD members, partners, and accession candidates. This underscores the crucial role of the OECD in advancing national sustainable development objectives. [ABSTRACT FROM AUTHOR]

Academic literature has made an effort to demonstrate the positive effects of privatisation reforms on government performance and economic growth. However, there is no sufficient evidence to support the benefits of privatisation in terms of government spending efficiency. This study analyses the correlation between privatisation and government spending efficiency. Our empirical results do not support a positive effect of privatisation on government spending efficiency. These findings are relevant because they suggest that, although privatisation has been usually seen as a tool to balance public finances, it does not mean that government spending efficiency will be higher after privatising State‐owned enterprises. [ABSTRACT FROM AUTHOR]

In this paper, we develop different types of strategic alliance and business cooperation between different decision-making units (DMUs) based on ratio-based data envelopment analysis (DEA-R) models. In this regard, two DMUs as partners share some of their input components in order to improve their performance in the partnership process. This is possible when a DMU has a surplus of resources in an input component, while another DMU has a shortage of resources in this component, and this adjustment of resources is done in a way to improve the performance of both DMUs. In the following, we present the inverse DEA-R models in order to determine the optimal level of some input and output components from two DMUs in the process of partnerships as partners, in order to reach the target efficiency level for each of these DMUs. Considering that DEA-R models have a larger space than weights based on the ratio of input to output components (and vice versa), therefore we expect the presented DEA-R models to have a higher discriminating power in the selection of weights compared to data envelopment analysis (DEA) models, then DEA-R models avoid pseudo-inefficiency and efficiency underestimation issues and achieve potential gains for DMUs in the process of partnerships in a competitive market. We present an application of the proposed approach to the data set of 70 banks operating in the European Union and show that these banks can improve their performance based on the optimal strategy presented. [ABSTRACT FROM AUTHOR]

Sugarcane is an essential commodity in Indonesia. However, climate change negatively affects the sugarcane production efficiency. This study aims to measure the technical efficiency of sugarcane farming using a bootstrap data envelopment analysis (DEA) approach, compare the efficiency of irrigated and rain-fed sugarcane farming, and determine the factors affecting the technical efficiency of sugarcane farming in East Java, Indonesia. Primary data were collected from 451 sugarcane farmers during the 2020–2021 planting season. The results indicate that the “bias-corrected” technical efficiency scores of the single- and double-bootstrap approach (0.624 and 0.561) were lower than that of conventional DEA (0.714). The efficiency score of irrigated sugarcane farms (0.593) was higher than that of rain-fed farms (0.529). Moreover, the farmers’ age, household size, dependency, farming experience, training, subsidies, crop diversification, and access to irrigation impacted sugarcane farming’s technical efficiency. Improvement of 1% in training, irrigation access, and subsidies increased the technical efficiency by 0.034, 0.032, and 0.030, respectively. This strategy is expected to enhance the productivity and technical efficiency and reduce the poverty in rural households in East Java.

Amaç: Bu çalışmada OECD ülkelerinin COVID-19 pandemisiyle mücadelelerinin ilk bir yıllık sürecindeki kaynak verimliliklerinin aylık ve dönem boyu zaman dilimleri açısından karşılaştırmalı olarak incelenmesi amaçlanmıştır.Yöntem: Araştırmada Veri Zaflama Analizi (VZA) kullanılmıştır. VZA ile ülkelerin aylık ve dönem boyu zaman aralıklarına ilişkin etkinlik skorları elde edilmiştir. Daha sonra ülkelerin verimlilik sıralamalarını belirlemek amacıyla süper etkinlik analizi yapılmış ve ülkelerin ele alınan zaman dilimlerindeki kendi aralarındaki verimlilik sıraları elde edilmiştir.Bulgular: Ülkelerin COVID-19 pandemisiyle mücadele etkinlikleri zaman içerisinde değişmiş, bazı ülkelerin süreç boyunca başarısız olduğu görülmüştür. ABD, Kolombiya ve Yeni Zelanda’nın süreç boyunca en başarılı ülkeler olduğu görülmüştür.Özgünlük: Bu çalışma, OECD ülkelerinin COVID-19 pandemisiyle mücadele verimliliğini karşılaştırırken etkili olacağı düşünülen birçok değişkeni ele alması, belirli zaman aralıklarıyla incelemesi ve sadece verimliliklerinin değil, verimlilik sıralamalarının da belirlenmesi açısından literatürdeki diğer çalışmalardan ayrışmaktadır.

Cooperatives offer significant opportunities for economic empowerment of people in difficult economic climates. However, concerns exist in literature on the efficiency level of these organisations in relation to members’ deposits and financial-related factors. This study analysed the influence of members’ deposits and related financial factors on the efficiency level of cooperative organisations.

This paper extends the concept of methodological crosschecking by examining whether bank efficiencies computed by the two frontier techniques, stochastic frontier analysis (SFA) and data envelopment analysis (DEA), are consistent. The study used a panel of 220 unbalanced observations from 27 Ghanaian banks between 2007 and 2016 to estimate cost and technical efficiencies and check for consistency using five criteria: efficiency distribution, ranking, ability to identify best or worst banks, stability of efficiencies, and relationship with accounting ratios. The results suggest that there is no consistency in the way parametric and non-parametric techniques rank or identify the best or worst banks. Also, there exists a weak relationship between the efficiency scores generated by both SFA and DEA and the non-frontier accounting ratios of Ghanaian banks. This suggests that the latter may contain some exogenous variables that make them weak measures of efficiency and should be used with caution, especially for bank supervision. However, the SFA approach yielded efficiency scores that were comparatively more stable over time. Therefore, the study concludes that the SFA approach is more practical and thus more appealing for regulatory purposes in Ghana due to the relatively consistent efficiency scores under the SFA approach compared to those under the DEA.

We use a DEA analysis to compare the efficiency of the railways of Western and Eastern Europe and find while most railways of Western Europe are on the efficiency frontier, those of Eastern Europe are typically a long way from it. One explanation may be that the reform process only started much later in Eastern Europe than in the West, although it appears that Eastern Europe has largely caught up. Secondly, Eastern Europe suffered a significant loss of traffic after the end of the communist regime, and this may still be resulting in an excess of labour and assets. There is some evidence that this remains the case for labour and freight vehicles. Although the excess of freight vehicles may be largely vehicles out of service, they still contribute to the poor efficiency scores for Eastern Europe. It is also the case that the countries of Eastern Europe suffer some disadvantages in terms of population density. However, we believe that two aspects of policy play an important role in the poorer performance of Eastern European countries compared with Western. Firstly, is the poorer infrastructure quality associated with lower levels of investment. This shows up as less use of electric traction and slower train speeds, resulting in lower productivity of staff and assets. Despite the efforts of the European Commission to overcome this problem, there is still a long way to go. Secondly, is the strong use of public service obligations to maintain high levels of service with relatively low load factors. If it is desired to raise the efficiency of Eastern European railways, governments in Eastern Europe will need to consider whether they are specifying excessively high levels of service.

This paper analyses the waste management efficiency of European Union countries using a flexible nonparametric methodology known as directional data envelopment analysis (DEA). The study evaluates performance at the macro (country) level, considering waste generated as input, landfilled and incinerated waste as bad output and recycled waste as output. The analysis incorporates the heterogeneity and specificities of each country, with respect to social and economic sustainability, establishing specific and realistic targets for each country to achieve efficiency. The research introduces a flexible and innovative method for assessing waste management efficiency and provides new empirical evidence on European waste management, considering economic and social sustainability. The results reveal a significant disparity among European countries in both waste generation and waste recycling. Countries are categorised into five groups according to their level of efficiency, and Central European nations are observed to exhibit generally better performance. A pragmatic approach, based on clear collaboration among countries, could optimise the unique waste management characteristics of individual nations to enhance the overall efficiency of the European waste management system, contributing to a circular economy and sustainable development. [ABSTRACT FROM AUTHOR]

The purpose of the study is to assess the effectiveness of public-private partnership (PPP) projects during the COVID-19 pandemic, taking into account the specificities of the industry. The empirical research base included the main characteristics (contract price, federal district of the project, customer, general contractor, project start period, project start year, project deadlines, price reduction during project implementation, application security, contract security and type of activity) of 144 regional investment projects. The research methodology included mathematical modeling using the DEA (Data Envelopment Analysis) data convolution method, on the basis of which an efficiency index was assigned to each PPP project from 0 to 1. The study concluded that prior to the COVID-19 pandemic, regional investment projects received more funding; after the start of the COVID-19 pandemic, the implementation time of regional investment projects has increased, the contract value more often changes downwards during the project implementation; and the average support for a project application has increased. The efficiency of implementation of regional investment projects, calculated using the DEA method, was reduced. The results obtained will be useful to private investors and government authorities when implementing joint projects to improve their efficiency.

This study introduces an integrated method for managing process risks in a business process reengineering (BPR) project using robust data envelopment analysis (RDEA) and machine learning (ML). The goal is to prioritize risks based on three standard factors of PFMEA (severity, occurrence and detection (S-O-D)) and incorporating two additional factors (breakdown cost and breakdown duration) seen as undesirable outputs. The model also accounts for the effect of uncertainty on expert-estimated values by applying disturbance percentages in the linear PFMEA-RDEA model. A machine-learning model is proposed to predict new values if partial or total modifications have been made to the processes. The approach was implemented in an automotive sector company, and the results showed the impact of uncertainty on values by comparing different approaches, such as RPN, PFMEA-DEA and PFMEA-RDEA. A new reduced risk categorization was achieved, which allowed for decision makers to focus on the necessary actions for reengineering.

Performance evaluation is essential for an industry's ability to operate successfully. Efficiency management is becoming crucial and important to enhancing the sustainability of the industry. The industry needs to measure its performance for the following reasons: (i) develop the economy and its operation efficiency in a sustainable way; (ii) supply data for decision-making units (DMUs); and (iii) optimize output using the fewest resources. Performance measurement indicators relate to the performance of the industry itself, such as: added value/cost of production factors, indirect taxes, number of workers, input costs, number of companies, added value/market prices, and production index. The purpose of this research is to measure the performance of large and medium-sized industries (LMIs) in Indonesia. LMIs have a strategic role as the main engine and driver of the economy. Measuring LMI performance is very necessary so that LMI can grow and develop sustainably. Data envelopment analysis (DEA) is a method for measuring performance. DEA is a non-parametric linear programming technique and used to determine comparisons between DMUs with different inputs and outputs. The research results indicate that there are three DMU classification categories, namely: Category 1 (ES = 1), Category 2 (ES = 0.9986-0.9998), and Category 3 (ES = 0.9971-0.9974). The percentages for each category are 50%, 37.5%, and 12.5%.

Abstract Technical progress has a tremendous potential to reduce carbon dioxide emissions by reducing energy consumption, a major concern across production units. However, the existing empirical literature concerning technical efficiency and carbon intensity is scanty. Thus, this paper examines the relationship between technical efficiency and carbon intensity for the organized manufacturing sector of two states, Maharashtra and Odisha, and the all-India level from 2001 to 2018. The paper uses data envelopment analysis to estimate technical efficiency scores. It applies the 2006 Intergovernmental Panel on Climate Change Tier 1 methodology for estimating carbon intensity for each 3-digit manufacturing industry in all three sample cases. The study has used static panel regression and fractional logit regression techniques to examine the deterministic relationship between technical efficiency and carbon intensity. The result shows that technical efficiency is highly sensitive to carbon intensity in the Indian manufacturing industries. The findings also addressed that the size of the industries also reduces the technical performance of manufacturing units. This paper also confirmed that increased profit could boost the Indian manufacturing industries’ technical efficiency. Thus, this study addresses that carbon intensity as a proxy for the manufacturing sector’s potential to affect climate change plays a crucial role in explaining the technical efficiency variations across industries. Thus, it calls for better policies aimed at reducing the emissions of industries specifically to achieve sustainable growth for the Indian manufacturing sector.

Data envelopment analysis (DEA) is one of the widely used methods to measure the efficiency scores of decision making units (DMUs). Conventional DEA is unable to consider both uncertainty in data and decision makers' (DMs) judgments in the evaluations. This study, to address the shortcomings of the conventional DEA, proposes a new best worst method (BWM)- robust credibility DEA (BWM-RCDEA) model to estimate the efficiency scores of DMUs considering DMs' preferences and uncertain data, simultaneously. First, to handle uncertainty in input and output variables, fuzzy credibility model has been applied. Additionally, uncertainty in constructing fuzzy sets is modeled using robust optimization with fuzzy perturbation degree. In this paper, two new types of RCDEA models are proposed: RCDEA model with exact perturbation in fuzzy inputs and outputs and RCDEA model with fuzzy perturbation in fuzzy inputs and outputs. In addition, to deal with flexibility of weights and incorporating DMs' judgement into the RCDEA model, a bi-objective BWM-RCDEA model is introduced. Finally, the proposed bi-objective model is solved using min–max approach. To illustrate the usefulness and capability of the proposed model, efficiency scores of 39 distribution companies in Iran is investigated and results are analyzed and discussed. Finally, based on the results, recommendations have been made for policy makers. [ABSTRACT FROM AUTHOR]

Context: Improving the pure technical efficiency in manufacturing innovation is crucial to achieving sustainable development in the manufacturing industry. Objective: We aimed to explore the impact of design innovation factors on the pure technical efficiency of manufacturing innovation from 2011 to 2021, with industrial enterprises above the designated size in Zhejiang Province, China, taken as the research object. Method: The super-efficiency DEA model was used to calculate the pure technical efficiency of manufacturing innovation. Literature research, combined with the Pearson correlation coefficient, was employed to obtain five design innovation factors, including the number of policy regulations, growth rate of designers, number of design enterprises, number of patents granted, and number of design awards at the provincial level or above. Results: Based on the Tobit model, the influence of design innovation factors on the pure technical efficiency in manufacturing innovation was analyzed and demonstrated. Except for the number of policy regulations and growth rate of designers, the other three factors had a significant positive impact on the pure technical efficiency of manufacturing innovation. In general, design innovation exerts positive effects on the growth of pure technical efficiency. Conclusions: The results of this study provide helpful insights into the promotion of sustainable development in the manufacturing industry through design innovation. [ABSTRACT FROM AUTHOR]

The cultural industry has been recognized as an indispensable component of sustainable economic development. Urbanization often represents a country's level of economic development. While China is advancing its new urbanization strategy, it is also vigorously promoting cultural revitalization plans. This study employs a global Data Envelopment Analysis (DEA) model and Tobit regression analysis to examine the correlation between China's cultural industry and urbanization. The results indicate that although the overall economic efficiency of China's cultural industry is continuously improving, the returns to scale in many provinces are declining. Changes brought about by new urbanization, such as increases in per capita GDP, per capita income, and enterprise scale, have significant positive impacts on the cultural industry. However, the rising urban population ratio has a significant negative impact on the cultural industry. This study suggests that the current new urbanization in China faces issues of oversimplification and excessive advancement. It recommends adjusting relevant policies to allow sufficient time and space for the cultural industry to absorb the benefits brought by urbanization. Given China's specific national conditions, the conclusions of this study may not necessarily apply to other regions. However, the global DEA-Tobit combination method used in this study aligns more closely with reality and achieves a higher degree of fit, thus possessing a certain level of universality. [ABSTRACT FROM AUTHOR]

Over the past few decades, microfinance institutions have attracted the interest of governments and academics alike, given their unique nature of being financial institutions with a dual mission of promoting social development and reducing poverty. However, concerns have been raised about their effectiveness in achieving these goals while remaining financially sustainable. In this study, we attempt to examine the factors that have the greatest impact on the social, financial, and overall efficiency of microfinance institutions in African regions. We adopt a two-step approach: First, we assess the efficiency scores of 95 microfinance institutions in Africa between 2005 and 2018 using a data envelopment analysis (DEA) approach. We then regress their efficiency scores on a set of determinant variables, capturing the microfinance institutions' characteristics. Our findings suggest that a majority of institutions prioritize profitability over social outreach. Furthermore, the panel data regression indicates that factors such as profitability, equity capitalization, types of loans, and low gross domestic product (GDP) have a positive influence on microfinance institutions' efficiency. Conversely, variables including their risk portfolio, grants, microfinance institution status (Non-Governmental Organization (NGO), cooperative, etc.), operational area, political environment, and size exert a negative impact on efficiency. Through this study, we seek to enhance our understanding of microfinance institutions and to identify the factors that impact their operational efficiency, thereby reinforcing their crucial role in advancing financial inclusion, empowering marginalized communities, and fostering inclusive economic growth. [ABSTRACT FROM AUTHOR]

A Comprehensive Set of Asymptotic Properties for a Meaningful Aggregation of Malmquist Indices The Malmquist productivity index (MPI) has become one of the most widely used tools for analyzing dynamic performance of decision-making units. Whereas accounting for economic weights of individual units in aggregations of indices is emphasized in the literature, statistical theory for constructing confidence intervals and performing hypothesis tests based on weighted aggregation of the MPI are still unavailable. In "Statistical Inference for Aggregation of Malmquist Productivity Indices," Pham, Simar, and Zelenyuk use a novel approach (based on the uniform delta method) to develop new asymptotic theory (including new central limit theorems) for aggregate MPIs as the basis for the statistical inference and test. They also verify the finite-sample performance of their approach via extensive Monte Carlo experiments and provide an illustration using real-world data. The Malmquist productivity index (MPI) has gained popularity among studies on the dynamic change of productivity of decision-making units (DMUs). In practice, this index is frequently reported at aggregate levels (e.g., public and private firms) in the form of simple, equally weighted arithmetic or geometric means of individual MPIs. A number of studies emphasize that it is necessary to account for the relative importance of individual DMUs in the aggregations of indices in general and of the MPI in particular. Whereas more suitable aggregations of MPIs have been introduced in the literature, their statistical properties have not been revealed yet, preventing applied researchers from making essential statistical inferences, such as confidence intervals and hypothesis testing. In this paper, we fill this gap by developing a full asymptotic theory for an appealing aggregation of MPIs. On the basis of this, meaningful statistical inferences are proposed, their finite-sample performances are verified via extensive Monte Carlo experiments, and the importance of the proposed theoretical developments is illustrated with an empirical application to real data. Funding: M. Pham acknowledges support from an Australian Government Research Training Program Scholarship. V. Zelenyuk acknowledges financial support from the University of Queensland and the Australian Research Council [Grant FT170100401]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2424. [ABSTRACT FROM AUTHOR]

Objective: The aim of the research is to assess the research and development efficiency of the institutes of the Polish Academy of Sciences (PAS) in 2019 and to identify external factors that have a significant impact on it. Research Design & Methods: A two-stage procedure in the field of DEA methodology was used. In the first stage, the efficiency of PAS institutes was estimated using alternative BCC and SBM models. In the second stage, a Tobit model was used to isolate external factors significantly influencing efficiency. Given the data available, two types of research and development (R&D) efficiency were analysed: publishing efficiency and combined publishing and implementation efficiency. Findings: A significant share of institutes are highly inefficient (nearly a half of the units in terms of publishing efficiency and a third in terms of combined efficiency). The fields in which a given scientific unit conducts research which significantly affect publication efficiency differ from those that significantly affect combined efficiency. Both types of efficiency are significantly negatively affected by the increase in the scientific category of the unit. Implications/Recommendations: The source of high research and development inefficiency among a significant number of institutes is the fact that they also generate other outputs than those considered in the work. The majority of institutes do not apply to the R&D sphere. The significant negative impact of the increase in the scientific category on R&D efficiency indicates that future focus should prioritise the quality of publications over their quantity. Contribution: The efficiency of the research and development activities of the PAS institutes was assessed after the introduction of the last reform of Poland's system of science and higher education in 2018 (such studies have yet to be carried out). It is also important to use a two--stage approach within the DEA methodology in order to isolate external factors that significantly influence this efficiency. [ABSTRACT FROM AUTHOR]

The sheep and goat farms are one of the main breeding in many Italian regions located in mountainous and disadvantaged rural areas. One of the main Italian Protected Designation Origin cheese as Pecorino Romano is made by sheep milk from many regions. The main purpose of this research was to asses by a quantitative approach the technical efficiency in sheep and goat farms part of the Farm Accountancy Data Network (FADN) in all Italian regions since 2008 to 2020. By the Multi-direction Efficiency Analysis (MEA) it has been possible to overcome one of the main bottlenecks of the technical efficiency estimation analysing which is the excess in each input able to impact to the technical efficiency. Main results have pointed out that the Italian sheep and goat farms have a higher technical efficiency compared to other studies carried out in other European countries. Land capital, labour and assets have been the main inputs able to impact to the technical efficiency. Furthermore, an important role of subsidies to the sheep and goat Italian farms exists. [ABSTRACT FROM AUTHOR]

Accepted by: Prof. Ali Emrouznejad Non-parametric data envelopment analysis (DEA) is susceptible to the curse of dimensionality, a challenge that can be mitigated through the use of the multi-criteria decision-making (MCDM) method. Conversely, DEA can overcome the limitations of the MCDM method by defining the weights of the decision-making unit to calculate the data envelop. This study addresses this issue by introducing a novel model, the Trigonometric Envelopment Analysis for Ideal Solutions (TEA-IS). TEA-IS combines DEA and the Technique for Order Performance by Similarity to Ideal Solution approaches. The proposed method is employed to assess the efficiency and performance of 367 Chinese banks over a 19-year period using various financial variables. The TEA-IS model leverages machine learning techniques to predict positive or negative outcomes for Chinese banks, taking into account various influencing factors. Our results indicate that TEA-IS scores demonstrate superior discriminatory power and reliability compared with non-parametric and MCDM methods. Furthermore, our findings reveal the presence of synergy amongst Chinese banks and illustrate a pattern of volatility in the Chinese banking industry's performance. Notably, performance improved from 2000 to 2005, declined during the period from 2006 to 2013 and subsequently experienced a recovery until 2018. The majority of Chinese banks in the sample are categorized as medium performers with lower synergy levels. Additionally, the study underscores the positive impact of bank listing and age on bank performance, suggesting that regional banks outperform domestic ones. [ABSTRACT FROM AUTHOR]

Fisheries sector provides livelihood to about 25 million fishers and fish farmers at the primary level and almost twice the number along the value chain. The purse-seiner and trawlers are major means of large-scale harvest of marine fishery resources along Karnataka coast. Gangolli landings centre is one of the key marine fish landing centre of Udupi district of Karnataka. The present study evaluated the economic performance of both single day trawler and purse-seiner operated from the landing centre. The key economic indices like Benefit-cost (BC) ratio, operating ratio (OR), net present value (NPV), internal rate of return (IRR) and cost efficiency were estimated to evaluate the economic performance. The BC ratios for purse-seiner were estimated at 1.20, whereas it was 1.12 for the single day trawlers. The respective operating ratio were 0.56 and 0.67. The estimated IRR for the purse-seiner was 37.18%, a slightly higher IRR was observed for single day trawler (39.01%). The cost efficiencies for purse-seiner were 0.847 and 0.939 at constant and variable return to scale, respectively. Similar cost efficiencies were also observed for the single day trawlers. It was observed that the mechanized fishing sector is operating under narrow profit margin. The declining revenue due to decrease in quantum of high valued fishes in catches and escalating fuel prices have been identified as the major factor leading to deteriorating economics of the mechanized sector in the region. [ABSTRACT FROM AUTHOR]

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In Older to improve the operational efficiency of Chinese textile and clothing enterprises, 54 textile and clothing listed enterprises in China from 2018 to 20 were suspected as the research objects. Based on the data envelopment analysis ( DEA ) theory, the etteuation index system of operational efficiency in the digital transformation context was constructed, and DEA related models were used to analyze the operational efficiency of enterprises from both static and dynamic perspectives. The static analysis results show that the average comprehensive efficiency of the sample enterprises from 2018 to 2022 is between 0. 700 and 0. 800, and there is still a large room for improvement in the future; The number of enterprises with comprehensive efficiency is 8-14, indicating that the operational efficiency of most enterprises is in an invalid state of DEA. The number of companies with increasing returns to scale increased from 4 to 11, indicating that the number of enterprises with decreasing input-output ratio increased. The results of dynamic analysis show that the average value of total factor productivity index is greater than 1. The number of enterprises with total factor productivity index greater than 1 accounted for 61. 1%, indicating that the operation efficiency of most enterprises was in a state of progress. The technical progress decreased continuously from 1. 019 to 0. 968, indicating that the technological progress rate of enterprises decreased. In the future, enterprises should pay attention to the investment of digital talents and technologies, improve the operation system and improve the operation efficiency. [ABSTRACT FROM AUTHOR]

This paper empirically links the efficiency and performance assessment of the general government, proxied by efficiency scores, to the trust in government. Government spending efficiency scores are first computed via data envelopment analysis (DEA). Then, relying on panel data and instrumental variable approaches, we estimate the effect of public sector efficiency on citizens trust on national governments. The sample covers 36 OECD countries between 2007 and 2019. We find that the more efficient countries in terms of government spending are Australia, Chile, Ireland, New Zealand, South Korea, Switzerland. Secondly, our main finding is that better public sector spending efficiency is positively associated with citizens' higher trust in governments. In general, political economy variables and the existence of fiscal rules do not seem to significantly affect our measure of trust. The results hold using alternative proxies for public sector efficiency, alternative measures for trust, specifications with different control variables and different empirical approaches (instrumental variables). [ABSTRACT FROM AUTHOR]

Technical progress has a tremendous potential to reduce carbon dioxide emissions by reducing energy consumption, a major concern across production units. However, the existing empirical literature concerning technical efficiency and carbon intensity is scanty. Thus, this paper examines the relationship between technical efficiency and carbon intensity for the organized manufacturing sector of two states, Maharashtra and Odisha, and the all-India level from 2001 to 2018. The paper uses data envelopment analysis to estimate technical efficiency scores. It applies the 2006 Intergovernmental Panel on Climate Change Tier 1 methodology for estimating carbon intensity for each 3-digit manufacturing industry in all three sample cases. The study has used static panel regression and fractional logit regression techniques to examine the deterministic relationship between technical efficiency and carbon intensity. The result shows that technical efficiency is highly sensitive to carbon intensity in the Indian manufacturing industries. The findings also addressed that the size of the industries also reduces the technical performance of manufacturing units. This paper also confirmed that increased profit could boost the Indian manufacturing industries' technical efficiency. Thus, this study addresses that carbon intensity as a proxy for the manufacturing sector's potential to affect climate change plays a crucial role in explaining the technical efficiency variations across industries. Thus, it calls for better policies aimed at reducing the emissions of industries specifically to achieve sustainable growth for the Indian manufacturing sector. [ABSTRACT FROM AUTHOR]

The study introduces the Five-Year Plan as an assignable Chinese experience for attaining comprehensive development to provide policy reference for goal-based development related to sustainability. Before illustrating a dynamic localized implementation path of merging the UN Development Strategy and China's national planning policies, the paper uses quantitative comparative analysis to depict an overview of this dynamic. Two key features, goal integration and development efficiency, were then built to characterize China's multidimensional development goal implementation capacity. Then, we implemented a high-dimensional fixed-effects model to examine how different stages of development impacted the likelihood of achieving our goals. [ABSTRACT FROM AUTHOR]

The Russian invasion of Ukraine has generated a new impetus towards an enhanced European defence pillar, while many EU members have announced significant increases in their respective defence budgets over the coming years. Expectedly, a large percentage of the capital and technological inputs required to support the efforts towards a common European defence and the increased defence needs of many EU members will be procured from the European Defence Technological and Industrial Base (EDTIB). The European defence companies are among the major suppliers of conventional arms in the world. Their cumulative share of world arms exports is the second largest globally. This paper uses a Benefit-of-the-Doubt (BoD) model via Data Envelopment Analysis (DEA) to comparatively evaluate the sales performance of major European defence industries vis-à-vis other major international arms producers. The sample used in the analysis contains 72 firms and spans the period 2016–2020. The findings indicate that on the whole US defence manufacturers have a dominant presence among the biggest global producers and outrank many major European companies. These findings offer valuable policy recommendations. [ABSTRACT FROM AUTHOR]

Industry 4.0 seeks waste reduction via the optimization of production systems integrating technology and process. In addition to evaluating existing methods and technologies, academia also develops new ones. This research proposes a new hybrid artificial intelligence (AI) solution for production system efficiency optimization that combines data envelopment analysis (DEA), machine learning (ML)-based simulation and genetic algorithms (GAs) using real-world sensor data from a thermoelectric power plant. In the proposed method, DEA is employed to identify the production system's efficient frontier, which is used to build an ML model that predicts production efficiency through simulation. A genetic algorithm is then utilized to propose those settings that result in optimized production efficiency. Although the possibility of combining DEA-ML and ML-GA has been discussed in the literature, no research was found that combines these three methods for production efficiency optimization. The proposed solution was tested and validated using real-world data. The benefits of the hybrid AI solution were measured by comparing its predicted efficiency with the efficiencies achieved by running production with conventional control-loops based control systems. The results show that considerable efficiency improvement can be achieved using the proposed hybrid AI solution. [ABSTRACT FROM AUTHOR]