ABSTRACT In today’s competitive business, accurate evaluation of suppliers and relevant optimal order allocation to them is quite important and challenging. To overcome its underlying difficulties, this research proposes a hybrid approach for evaluating suppliers, forecasting demands, and allocating orders. In its implementation process, it initially applies Data Envelopment Analysis method based on Z-numbers to obtain more accurate, transparent and reliable data on suppliers. It then uses numerous machine learning algorithms to forecast future demands for various relevant items. Finally, it employs a multi-objective optimization model to minimize total supply costs, while maximizing order allocation to efficient suppliers and minimizing the number of suppliers to whom orders are placed subject to constraints of authorized delay in order delivery, capacity and demand satisfaction. The good accuracy of its generated results is related to its innovative part of using different machine-learning algorithms and integrating supply chain operation for more accurate evaluation of suppliers and relevant optimal order allocation to them. Its multi-purpose approach integrates supply chain decision-making regarding procurement costs while focusing on efficient and collaborative suppliers with strong, sustainable network connections. Its fuzzy programming approach has also played an effective role in the optimization process of its nonlinear multi-objective model.

In Bangladesh, sub-district hospitals (SDHs) are the first referral point for inpatient primary healthcare (PHC) services of the public providers in both rural and municipal corporation areas. These facilities also provide both outpatient and emergency healthcare services to the population at a minimum user fee. The efficient use of resources in primary-level healthcare facilities is essential for delivering quality healthcare services. Therefore, our aim was to estimate the technical efficiency (TE) of the SDHs in Bangladesh. We used an output-oriented data envelopment analysis (DEA) method to estimate the variable returns to scale (VRS) and constant returns to scale (CRS) TE of a total of 423 SDHs using data from the Local Health Bulletin -2017. To measure TE, we used workforce and inpatient beds as inputs and the number of inpatients and outpatients served by the hospitals in a month as output. We applied the Simar and Wilson model to find how the other internal and external characteristics of these hospitals influenced estimated TE score. We compared our DEA results with stochastic frontier analysis (SFA) and performed sensitivity analysis. The average VRS and CRS TE of the SDHs were estimated to be 58.9% and 53.4%, respectively. Of the 423 SDHs, 15 were fully efficient in CRS, 30 were in VRS and 60 were scale efficient, while the rest operated below the efficiency frontier. The population density per bed, ratios of bed occupancy, ratios of beds to physicians, ratios of physicians to nurses, and administrative division had a significant positive influence, while lengths of stay and ratios of beds to nurses had a significant negative influence on the SDHs efficiency scores. The mean TE demonstrated that the SDHs, on an average, could improve their output by 42% using the existing level of input mix. The results were consistent in the sensitivity analysis. The average TE of the SDHs was half of the best score, suggesting there is scope for overall improvement among the inefficient SDHs by learning from the efficient SDHs. The Ministry of Health and Family Welfare (MOHFW) of Bangladesh allocates resources to SDHs based on the number of beds rather than based on an assessment of needs. The MOHFW could improve its monitoring system to investigate why some facilities are performing well using similar resources while others do not and adjust the allocation system to take into account the quantity and quality of care.

This paper proposes a two-stage framework integrating Data Envelopment Analysis (DEA) and fuzzy multi-criteria decision-making methods to evaluate the performance of logistics firms in Vietnam. In the first stage, DEA models (CCR, BCC, and SBM) are employed to measure relative efficiency and identify benchmark firms among 15 leading logistics companies. In the second stage, FAHP–FTOPSIS is used to incorporate qualitative and sustainability-oriented criteria and to provide a comprehensive ranking of the efficient firms. The results indicate that a considerable proportion of firms operate below the efficiency frontier, implying substantial opportunities for resource optimization. Environmental and technological dimensions are found to be the most influential factors, while companies implementing green distribution strategies and strong data security practices consistently achieve higher rankings. Sensitivity analysis confirms the robustness and stability of the proposed framework. This study contributes by bridging operational efficiency assessment with broader strategic and sustainability considerations, overcoming the limitations of single-method evaluations used in prior research. The integrated DEA–FAHP–FTOPSIS approach offers managers a practical tool to diagnose weaknesses, prioritize improvement actions, and benchmark against top performers. In addition, it offers policymakers valuable insights to support digital transformation and green logistics initiatives in developing economy contexts.

County areas hold critical strategic significance in terms of population, gross domestic product, and their role within the National Fitness Strategy and National Fitness Public Service System.Analyzing the efficiency of county areas National Fitness public services in Yunnan Province can provide evidence-based support for advancing the development of a higher-level public service system for national fitness across Southwest China. Using a three-stage Data envelopment analysis (DEA) method, this study evaluates the efficiency of public fitness services at the county-level administrative division in Yunnan Province for the year 2023. The analysis encompasses 129 county-level administrative divisions, comprising 17 municipal districts and 112 county areas within Yunnan Province. Research indicates that environmental factors such as regional GDP, sports and fitness infrastructure, local industrial structure, and population density significantly influence the efficiency of national fitness services. After excluding these variables in the second-stage stochastic frontier analysis (SFA) regression analysis, the Technical Efficiency (TE) of national fitness services in Yunnan Province’s municipal districts and county areas reached 0.919 and 0.824 respectively in 2023. Adjusted Scale Efficiency (SE) for municipal districts and county area generally exceeded Pure Technical Efficiency (PTE), with significant PTE disparities between the municipal districts and county area. Additionally, 47.06% of municipal districts and 72.32% of county area exhibited Increasing Returns to Scale (IRS). Accordingly, efforts should be made to continuously improve the county area supervision management system and investment mechanisms, broaden service types to achieve integrated development, and fully leverage IRS to promote the dissemination of regional and ethnic traditional cultures.

The proposed research will analyze how investor sentiment affects accounting conservatism and will also explore whether the relationship between investor sentiment and accounting conservatism is moderated by the managerial ability of an emerging market setting. It relies on the behavioral finance and agency theory to give empirical evidence based on Iraqi listed non-financial firms, which is a relatively low market efficiency environment with heterogeneous governance practices. The accounting conservatism is gauged using panel data over a multi-year period using the asymmetric timeliness of earnings, and investor sentiment is also gauged with a composite index that is formed using market-based indicators indicating the trading behavior and expectations of investors. Managerial skill is measured in an efficiency-based measure which is derived through data envelopment analysis method. The hypotheses of the hypotheses of the study are analyzed using multiple regression analysis and all the statistical analysis is performed with the SPSS software package. The outcomes indicate that there is a strong negative correlation between investor sentiment and accounting conservatism, which implies that an increase in investor optimism is related to a decrease in the financial reporting conservatism. This result can be viewed as a behavioral perspective according to which the sentiment-based market pressures affect managerial reporting decisions. More to the point, the research concludes that the managerial capability has a moderating effect on this relationship. The relationship between investor sentiment and managerial ability is positive and statistically significant, which indicates that high-ability managers mitigate the negative impact of the investor sentiment on accounting conservativity and can maintain disciplined reporting better in volatile market sentiment. The results can be used in the accounting and finance literature, as they combine behavioral market variables with managerial ability views and extrapolate the previous evidence to a new emerging market that is still understudied. Practically, the findings indicate the significance of managerial competence as an internal system of governance, which improves the quality of financial reporting. The research has useful implications on regulators, investors, and policymakers who would like to enhance the credibility of the reporting and investor confidence in the emerging capital markets.

A multiscale risk operation and decision making model for inter-basin water diversion project under climate variability.

The high demand for clean water in Surabaya has driven the optimisation of the water treatment process at three water treatment plants (WTPs). The primary challenge is determining the optimal dosage for coagulation and flocculation, which play a crucial role. DEA was used to assess the optimum coagulant dosage. The DEA calculates the coagulant dosage efficiency score and energy consumption in every WTP unit, with a threshold score of 1. The DEAP 2.1 software performs the assessment simulation. When the WTP score is 1, it is considered efficient in saving coagulants, and an energy A score below 1 indicates inefficiency in conserving those resources. The inputs are energy requirements and coagulant doses between 2018 and 2022. The outputs are water quality parameters such as turbidity, total dissolved solids (TDS), and colour. The highest efficiency performance was obtained by WTP 2 (46%), followed by WTP 3 (43%) and WTP 1 (29%). Overall, the recommendations for energy efficiency and coagulant dosage efficiency vary. The energy efficiency improvement recommendations are pump monitoring, maintenance of the electromotor, and supervision of the main assets. On the other hand, maintaining optimal coagulant requirements, selecting the right coagulant, and administering flocculants effectively are key.

Enhancing operational efficiency serves as a critical foundation for the high-quality development of China’s tourism industry. However, existing studies have shown limited attention to evaluating the operational efficiency at the firm-level. To this end, this study applies the Slack-Based Measure Data Envelopment Analysis (SBM-DEA) method to measure the operational efficiency of 13 tourism listed firms in China from 2017 to 2023. The findings reveal that the overall operational efficiency of tourism firms remains at a low level, suggesting substantial potential for improvement. From the perspective of heterogeneity, firms operating comprehensive cultural tourism perform better than those operating natural scenery tourism, and firms located in the eastern region demonstrate higher efficiency levels than their counterparts in the central and western regions. In addition, the efficiency gap among firms narrows following the pandemic. The input and output improvement analysis reveals a significant excess in employee investment and the deficiency in tourists received. Based on these findings, this study also provides some suggestions for improving operational efficiency.

The efficiency of large industrial sectors plays a critical role in promoting high‐quality economic growth and enhancing total factor productivity. Data envelopment analysis (DEA) is widely used for such evaluations due to its flexibility and nonparametric structure; however, its core mechanism, which allows each decision‐making unit (DMU) to select optimal weights, often yields extreme and unreasonable weighting schemes that distort efficiency scores and weaken discriminatory power. This study addresses this foundational challenge through three key contributions. First, it introduces the weight extremity function (WEF), a mathematical construct that quantifies the degree of weight concentration within any given weighting scheme. Second, it develops a novel DEA model that incorporates WEF‐based constraints, effectively preventing DMUs from adopting unreasonable weight distributions while preserving the method’s inherent flexibility. The proposed framework transforms the resulting nonlinear programming problem into an equivalent linear formulation, ensuring computational tractability. Third, to eliminate subjective parameter selection, the model employs a volume‐under‐the‐surface calculation method to derive efficiency scores, relying solely on objective statistical properties of the data. An empirical study of China’s large‐scale industrial sectors across 31 provinces (2019–2022) demonstrated the enhanced discrimination capability and evaluation consistency of the proposed approach. The findings reveal significant geographical disparities in industrial efficiency across China, with the proposed model providing more nuanced and robust rankings than existing alternatives.

Manufacturing is the foundation of a country. Since the 21st century, China's manufacturing industry has entered a new stage of rapid growth and faces higher development requirements. Therefore, to better evaluate manufacturing enterprises' innovation efficiency, this paper innovatively constructs a systematic enterprise performance evaluation and classification model. Firstly, a preliminary efficiency assessment of the enterprise based on the generalized data envelopment analysis method(GDEA) was to obtain the relative efficiency value. Subsequently, grey relational analysis was used to quantify the degree of correlation between each output indicator and the efficiency value, screen out the key influencing factors, and achieve feature selection. On this basis, a secondary GDEA analysis was implemented to optimize the efficiency assessment results and enhance the ability to identify sample heterogeneity. Next, the efficiency evaluation results are labeled and divided to construct a binary classification dataset. The K-nearest neighbor, support vector machine, and logistic regression model are used for training, respectively, and the parameters are optimized through grid search. Finally, the AdaBoost ensemble learning method is utilized to conduct a weighted fusion of multiple base classifiers, construct a strong learner, and accurately identify high-efficiency enterprises. The results show that: (1) The efficiency evaluation platform is based on the grey correlation degree, and the AdaBoost integrated model has better evaluation and prediction capabilities than the traditional GDEA model. (2) Classify and predict the future development of the research enterprises and conduct specific strategic analysis for enterprises of different classifications.

Regional Development Banks (BPD) have a strategic role in encouraging regional economic growth through the financial intermediation function. However, the effectiveness of these functions still faces various challenges, such as low productive credit disbursement and high dependence on local government funds. This study aims to measure the level of efficiency of the intermediation function of Regional Development Banks (BPD) in Indonesia during the period 2020–2024 using the Data Envelopment Analysis (DEA) method. The data used included 27 BPDs with input variables (Total Assets, Third-Party Funds, Labor Expenses) and outputs (Loans Provided, Net Interest Income). The results of the study show that the average efficiency of BPD intermediation is 94.54%, with a downward trend in 2024 to 92.2%. Only three BPDs have been consistently efficient for five years, namely the Central Kalimantan BPD, the West Java and Banten BPD (BJB), and the South and West Sulawesi BPD (Sulselbar). These findings indicate the need to optimize the use of inputs and increase credit distribution to achieve maximum efficiency. The implications of the study include policy recommendations in the form of BPD consolidation and strengthening of intermediation strategies.

This study aims to measure the relative efficiency of eight Algerian university centers (Aflou, Mila, Elbayadh, Barika, Naama, Tindouf, Maghnia, Tipaza) during the 2023–2024 academic year. Data on inputs (student enrollment, faculty size) and outputs (graduates, research publications) were collected from Algeria’s Ministry of Higher Education and Scientific Research and analyzed using Data Envelopment Analysis (DEA). The CCR and BCC models under input- and output-oriented frameworks revealed that 75% of centers achieved full efficiency (score=1), while 25% (notably Tipaza and Elbayadh) exhibited inefficiencies requiring 15–33% input reductions or output increases. Critically, smaller centers (Aflou, Tindouf) outperformed larger institutions despite 40% lower budgets, debunking the “bigger is better” paradigm. The study identifies three evidence-based reforms: decentralized resource reallocation (redirecting 22% of budgets from inefficient to efficient centers), dynamic enrollment caps, and research-output incentives, potentially saving 1.2 billion DA annually. Future research should implement longitudinal DEA tracking to measure reform impacts, integrate labor market outcomes (graduate employment rates), and conduct comparative studies across North African universities. By proving that strategic resource optimization, not budget expansion, drives sustainable development, this work provides a replicable model for Global South nations aligning higher education with national development visions like Algeria’s 2030 agenda.

This study aims to analyze the efficiency of digital and conventional banks in Indonesia using the Data Envelopment Analysis (DEA) method and examine the effects of bank size, digitalization, and credit risk on efficiency. The data covers eight digital banks and eight conventional banks during the 2024–2025 period, with input variables: labor costs, total assets, and operational costs; and output variables: interest income, third-party funds (TPF), and total disbursed credit. The analysis results show that digital banks have a higher average efficiency level (0.91) than conventional banks (0.83). Bank size has a significant positive effect on efficiency, while digitalization and credit risk (NPL) have a negative effect. These findings emphasize the importance of business scale and risk management in maintaining banking efficiency in the digital era.

This study has a twofold. First, it aimed to measure the efficiency of the insurance sector in the Kingdom of Saudi Arabia during the period 1995-2022. Efficiency was measured using the Data Envelopment Analysis (DEA) method, employing three inputs: general and administrative expenses, underwriting costs, and direct written premiums, and one output represented by the insurance operations surplus. The study also relied on the Malmquist Index to calculate and determine the nature of changes in the total factor productivity (TFP) of insurance companies. The results showed that only one company, Inaya Saudi Arabia Cooperative Insurance, achieved the optimal level of efficiency. Furthermore, the Malmquist Index results indicated a slight decrease in overall productivity, accompanied by a decline in pure technical efficiency and a reduction in scale efficiency. The Second objective of the research was the study of the impact of insurance sector efficiency on economic growth using the Autoregressive Distributed Lag (ARDL) model. Three models were estimated, considering three indicators of efficiency: insurance sector efficiency under constant returns to scale (CRS), under variable returns to scale (VRS), and the Malmquist Productivity Index. The findings revealed a long-run relationship between the efficiency of the insurance sector and economic growth. In the short run, the Error Correction Model (ECM) showed that efficiency has a negative and significant effect in both the CRS and VRS models, while its effect was positive and significant in the productivity index model. The causal relationships between insurance sector efficiency and economic growth were apprehended using the Toda-Yamamoto method. The results indicated a bidirectional causal relationship between insurance sector efficiency and economic growth in the constant returns to scale (CRS) model. In contrast, the variable returns to scale (VRS) model showed no causal relationship. The Malmquist Index model demonstrated the broadest connection with economic variables and the largest number of causal relationships directed toward economic growth among the models. Our results would have several prominent implications for policymakers when designing strategies to improve the performance and efficiency of the insurance sector, consistent with the economic diversification strategy outlined by the Kingdom in its Vision 2030.

This study analyzes the impact of financing risk and bank size on the efficiency of Islamic Commercial Banks (BUS) in Indonesia from 2018 to 2024. Efficiency is measured using the Data Envelopment Analysis (DEA) method with an intermediation approach, while panel data regression is used for hypothesis testing. The Common Effect Model (CEM) is identified as the best fit. Results show that financing risk (NPF) negatively affects efficiency, while bank size (TASET) has a positive effect. These findings highlight the importance of effective risk management and asset scale optimization in improving the operational efficiency of Islamic banks.

Each maritime country produces annual reports on its maritime safety policy. The annual report details the implementation of established policies, plans, and regulations concerning the supervision and protection of rights and interests at sea. By analyzing the Annual Reports for the Republic of Croatia from 2017 to 2024, maritime traffic and activities at sea were examined. The data include the number of available inspection vessels, the nautical miles traveled, fuel consumption, and similar metrics. All this information is related to the total number of inspected vessels, which is a key performance indicator for maritime traffic control. The aim of the analysis is to determine the correlation between fuel consumption, distance traveled, number of voyages, and number of inspected vessels over eight consecutive years. Data Envelopment Analysis (DEA) is used to assess the relationship between inputs and outputs to identify which years were efficient. Additionally, the multi-criteria decision-making method PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) is used to interpret and validate the DEA results, particularly the efficiency ranking. The proposed DEA–PROMETHEE hybrid model enables decision-makers to better understand DEA results, especially when efficiency scores are very similar. In terms of practical applications, the results based on the DEA input and output analysis, extended with the PROMETHEE method, show that the optimized use of available resources contributes to increased overall maritime safety.

Urban public transportation, a crucial element of urban life, is one of the key indicators determining a city's level of development. The urban public transportation sector, a standard solution to many problems experienced in metropolitan cities, such as traffic congestion, air pollution, noise, and excessive energy consumption, makes it imperative to use resources best and improve service quality. This study aims to evaluate the efficiency of public transportation systems in eight metropolitan municipalities in Türkiye using Data Envelopment Analysis (DEA). Furthermore, the entropy method assessed the relative importance of each input and output variable. The findings guide municipalities and contribute to the literature. It demonstrates that inefficient municipalities can identify potential areas for improvement by examining the practices of their benchmark, efficient municipalities. This study provides an empirical assessment of the bus systems of eight metropolitan municipalities in Türkiye using DEA and Entropy weighting methods, offering actionable insights for optimizing resource utilization, enhancing public transport service quality, and informing the development of sustainable urban mobility policies. As a result, these findings directly contribute to municipalities increasing the efficiency of their public transport systems and improving sustainable acceleration mobility policies.

ABSTRACT Enterprises that reduce their consumption of raw materials and production of pollution should observe the resulting high degree of environmental efficiency, which should translate positively into their financial performance. Our study compares the environmental efficiency of companies listed on the regulated markets of the European Union (EU) and its impact on their market value from the point of view of natural resource management. We use the non‐parametric Data Envelopment Analysis method and the Ohlson Valuation Model to assess the impact of environmental efficiency on companies' market value. The research sample includes public companies from the 27 EU Member States listed on their respective stock exchanges during the period 2014–2023. The data come from the LSEG database. The research shows that there is a positive relationship between environmental efficiency and market value. Companies' environmental performance may, therefore, be an important factor for investors. The surveyed companies cannot, however, be considered environmentally efficient.

The study aims to identify the peculiarities of applying the two-step method of Data Envelopment Analysis (DEA) to analyze the effectiveness of scientific and journalistic activities of technical higher education institutions (HEIs) in Ukraine. The first step is to assess the effectiveness of scientific and journalistic activities of technical higher education institutions of Ukraine using the VRS-input-oriented model, which contains scientometric indicators. According to the results of the study, in 2024, 34% of technical higher education institutions of Ukraine had the maximum relative net technical efficiency of scientific and journalistic activities. The average relative net technical efficiency of technical HEIs of Ukraine in 2024 was 0.82. Based on the estimated efficiency values, the ranking of technical higher education institutions for 2024 was compiled. At the second step, using the methods of one-factor analysis of variance and the Mann-Whitney U-test, a study was conducted to determine the impact of the scale of the HEI on the efficiency of scientific and publishing activities. It was found that the scale of the studied higher education institutions does not affect this efficiency.

Against the backdrop of reforms in China's drug review and approval system, the process of technological structure adjustment, and accession to the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) international mutual recognition agreements, China's pharmaceutical innovation "going global" model has gained opportunities for rapid development. Pharmaceutical innovation going global primarily encompasses three approaches: product export, technology licensing (license-out), and data cross-border compliance. From 2011 to 2023, sales of new products and product exports in China's pharmaceutical manufacturing industry maintained continuous growth. This study employs the Data Envelopment Analysis (DEA) method, with sales revenue from new product exports and patent applications as innovation output indicators, to analyze regional innovation "going global" efficiency. The results indicate that 7 regions—including the eastern region, Beijing, Tianjin, Shanghai, and Zhejiang—exhibit optimal scale efficiency, while the eastern region and three provinces (Jiangsu, Shandong, and Guangdong) show diminishing returns to scale. Except for Beijing, Tianjin, and provinces in the eastern region, most regions experience technical slack, export slack, and patent slack. Using a Panel Data model to examine the path of regional innovation "going global" technological structure adjustment, the findings reveal significant heterogeneity in the impact of technological transformation on pharmaceutical industry innovation "going global" following ICH accession. Notably, technology introduction exerts an inhibitory effect on pharmaceutical industry innovation "going global". Additionally, ICH international standards demonstrate provincial heterogeneity, with eastern provinces being more likely to benefit from ICH agreements. Therefore, it is imperative to actively participate in international regulatory coordination, advance technological structure adjustment for innovation "going global", and enhance technical support for international standard review and inspection, thereby promoting the healthy and sustainable development of the pharmaceutical innovation "going global" model.