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Achieving carbon peaking and carbon neutrality goals is a fundamental requirement for advancing high-quality development in China. Carbon emission efficiency (CEE) indicates the proportion of optimal emissions to actual emissions in the production system, providing a basis for reasonable carbon emission reductions. To facilitate the realisation of China's goals, this paper analyses CEE and the carbon emission reduction potential in China from 2001 to 2020. Considering the heterogeneity of both regions and industries, this paper proposes a three-hierarchy meta-frontier directional distance function (DDF) method to measure CEE under convex and non-convex assumptions of production possibility set (PPS). The empirical results show that the assumption of convex or non-convex axiom on the innermost PPS has a great impact on efficiency distribution, and the distribution of technology gap rate (TGR) is more affected by the selection of convex or non-convex assumption compared with the distribution of CEE. Currently, China's carbon emission efficiency remains low, mostly due to management inefficiency. The primary industry has more advanced emission reduction technologies than the other two industries. The eastern region has a large potential for carbon emission reduction because of its high carbon emission base, despite its high efficiency. [ABSTRACT FROM AUTHOR]

This study investigates the existence of solutions for the key plant capacity utilisation (PCU) concepts using general nonparametric technologies. This is done via a theoretical review of existing and some new PCU concepts. Focusing on short-run and long-run output-oriented, attainable output-oriented, and input-oriented PCU notions, we first investigate the existence of solutions at the firm level. Under mild axioms, this question regarding the existence of solutions for these PCU concepts at the firm level is affirmatively answered under variable and constant returns to scale as well as under convex and nonconvex assumptions. However, short-run and long-run output-oriented and attainable output-oriented PCU concepts may not be implementable depending on certain conditions. There are no such reservations for the input-oriented PCU. Then, for this same range of PCU concepts, we explore the more difficult question as to the existence of solutions at the industry level. The output-oriented and attainable output-oriented PCU exist at the industry level under strict conditions: existence and attainability are interwoven at this level. The industry input-oriented PCU is always feasible at the industry model. This theoretical review is supplemented by a semi-systematic empirical review, and an empirical application. We conclude that input-oriented PCU is clearly the best concept. [ABSTRACT FROM AUTHOR]

Numerous studies have focused on digital twins (DTs) and their applications. However, a common method for evaluating the performance of DTs is lacking. Digital twins performance assessment mechanism is crucial when improving or monitoring any process or system in a business, such a strategy could serve as a guide to help researchers and practitioners create more effective digital twins. Guided by these challenges, we propose a structured method for measuring the performance and flexibility of digital twins, and our study provides a procedure for quantitative calculation of the performance of DTs based on four key performance indicators (KPIs). Moreover, we propose a new KPI, DTflex, for determining the flexibility of digital twins. We evaluated the performance of DTflex as a KPI to demonstrate its use as a simple tool for designers and practitioners to quickly compare different DT methodologies, which might provide valuable input for the iterative improvement of DTs to increase their efficiency in future applications. The knowledge provided along with the method proposed in this paper is expected to help researchers and practitioners working in different fields to compare DT methodologies and DT performance which will lead to higher efficiency DT solutions and increased re-usage of existing DTs. [ABSTRACT FROM AUTHOR]

To formulate multi-tier supply chain management strategies with global impact, understanding the roles of various supply chain partners in disseminating sustainability along the supply chain is crucial. We analyze the influence of different supply chain positions on sustainability and financial outcomes of Turkish manufacturing companies listed in İstanbul Stock Exchange (BIST). We propose a sustainability measure, and rank firms according to their sustainability practices in relation to their financial outcomes using data envelopment analysis (DEA). We estimate Seemingly Unrelated Regression (SUR) equations to control for the common financial shocks that can affect sustainability and financial outcomes simultaneously. Our analysis reveals that first-tier and sub-supplier (second-tier and lower-tiers) firms' financial performance is substantially better compared to Original Equipment Manufacturer (OEM) firms while their sustainability outcomes stay behind. We find that this latter effect is more profound for first-tier firms. Although the supplier firms generally comply with the OEM's product standards, our results show that they are not aligned with the OEM in terms of the environmental and social impact of their operations. Given the crucial role of first-tier suppliers in sustainability dissemination along the supply chain, policies should be formulated in this industry amid these characteristics of the sustainability dissemination. [ABSTRACT FROM AUTHOR]

Despite the potential cost reduction and environmental friendliness of inland lock waterways, there is a noticeable lack of scientific research on their efficiency and performance. Moreover, empirical insights into the impact of waterway locks on logistics systems are insufficient. Similarly, despite extensive research on data envelopment analysis (DEA) in various logistics contexts, the efficiency of waterway locks remains underexplored. This study addresses these gaps by evaluating inland waterway transposition efficiency based on operational aspects and handling capacity. In this study, data from nine primary inland waterway locks in an emerging economy were examined, comparing their relative efficiency by input-output relationships. Efficiency indices, performance objectives, benchmarks, composite efficiency, and separability conditions were calculated and analyzed. Our results highlight external factors like vessel numbers and volume as efficiency influencers. The study uncovers strategies to enhance efficiency, mainly through operational improvements or leveraging performance indices from other units. Empirical findings present opportunities to optimize maritime logistics operations, especially in countries with expansive territories. This is a novel research that introduces efficiency analysis models for inland waterway locks and offers actionable insights for an underexplored research area. Lastly, the article offers promising research opportunities for maritime logistics systems' performance. [ABSTRACT FROM AUTHOR]

The aim of this to study is to examine how non-performing loans on the balance sheets of Japanese banks affect their performance by adopting a material balance principle. The paper outlines how the material balance conditions can be applied when modelling banks' production process in the presence of non-performing loans. The paper utilizes the generalized weak G-disposability principle which accounts for the heterogeneity among banks' input quality. We test how an input-oriented model (non-performing loans are treated as an input), the weak disposability assumption and the adopted material balance approach, affect banks' performance levels. We apply our test on a sample of Japanese banks over the period 2013 to 2019. Our findings indicate that the input-oriented model and the material balance estimator even if they present similar distributions, they account differently the effect of non-performing loans' fluctuations over the examined period. In addition, the results under the weak disposability assumption are found to be different compared to the material balance measures and less sensitive to banks' non-performing loans variation levels. We also provide evidence that the generalized weak G-disposability assumption captures better banks' performance fluctuations that has been caused by the restructuring of the Japanese banking industry. [ABSTRACT FROM AUTHOR]

Along with the popularity of Olympic Games, evaluating and ranking participating nations has become one of the hottest issues in recent years. Data envelopment analysis (DEA) is extensively used for participating nations' performance evaluation, while the prior literature mainly addresses the maximum efficiency by ignoring the weights diversity, which might lead to unreasonable and biased results. By considering the fixed-sum medals constraint, this paper proposes an integrated approach of stochastic multicriteria acceptability analysis (SMAA) and DEA for evaluating the participating nations. The proposed approach addresses the weights diversity and multiple equilibrium efficient frontiers by investigating the overall feasible space, and two strategies are considered to guarantee the fairness concern for calculating the efficiencies. Besides, the proposed approach provides both rank acceptability and holistic acceptability index to obtain the complete ranking from the best to the worst and to get the relative ranking relationship in the overall feasible space. The proposed approach can provide more reasonable performance measurements since it explores different rank positions and improves the whole satisfaction of all participating nations. Finally, the proposed approach is applied to empirically analyze the participating nations in the Winter Olympic Games from 2010 to 2022 and some valuable analytical results are provided. [ABSTRACT FROM AUTHOR]

The determination of reasonable improvement direction is the key difficulty of directional distance functions in data envelopment analysis target setting method, but there is no consensus direction determination method at present. This paper introduces the concept of minimizing the distance between decision-making units (DMUs) and production frontier for constructing a new endogenous mechanism of improvement direction, and the corresponding target setting method is constructed to setting target for DMUs, which is used to minimize the total projection distance between DMUs and their targets. In addition, the free direction and unit invariant of improvement direction are discussed for meeting the diverse needs of decision makers. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis is applied to a sample of IT projects. The application is based on a discussion concerning the choice of inputs and outputs in the case when the units being evaluated are projects. This discussion takes into account various understandings of project success and thus project desired outputs, as well as the ambiguity and a strong situation dependence concerning the choice of project inputs, understood as variables that should be minimized. The sample consists of over 80 IT projects implemented in Poland. The applied DEA model has permitted to identify the projects that clearly stand out from the rest of the sample as less efficient. Their analysis made it possible to draw conclusions on the choice of inputs and outputs. A set of indications on the context-specific choice of inputs and outputs were formulated. [ABSTRACT FROM AUTHOR]

Developing China's high-tech industry is important to make the country innovation-oriented. This requires optimising the allocation of innovation resources and improving innovation efficiency. However, few studies have investigated this topic and the realisation path for the high-tech industry. This study develops an input-oriented inverse data envelopment analysis (DEA) model with frontier changes to analyse the optimisation of resource allocation in China's high-tech industry during 2019–2025. With this method, decision makers can scientifically analyse the specific amount of resource investment. We also construct an analysis framework from short- and long-term perspectives. The results show that the excessive input of research and development (R&D) personnel and unbalanced allocation of capital resources are the main barriers to the development of high-tech industries in the short term, and in the mid- and long terms, the demands for investment in talent and capital will continue to increase. Improvement directions for promoting the development of China's high-tech industry are discussed. Finally, we present valuable information for policymaking to promote progress in high-tech industries in different regions. [ABSTRACT FROM AUTHOR]

The paper proposes a joint use of two global sensitivity analysis methods to evaluate the influence of model inputs on spatially explicit outputs. The analysis is conducted on spatial outputs of geodiversity assessment. First, the screening method is used to identify inputs with low importance and interactions, which can be discarded in later analysis. A simplified model is compared with a complete model using the mean and variance calculated on spatial outputs to evaluate outcome uncertainty. Finally, the variance decomposition method computes two sensitivity index maps per input. The results are mapped using bivariate classification, allowing for the simultaneous identification of regions with higher geodiversity scores and their corresponding variability. The research reveals that the streamlined approach preserves the overall patterns of outcome uncertainty and sensitivity observed in an original model while simultaneously reducing the number of outcome maps to interpret. Additionally, bivariate mapping proves efficient in concurrently depicting spatial sensitivity and interaction effects. The analytic efficiencies afforded by the presented approach and bivariate mapping enhance the operational knowledge of spatially explicit uncertainty and sensitivity analysis by simplifying the computational procedure and offering an alternative way of visualizing and interpreting input factors driving the sensitivity of model output. KEY POLICY HIGHLIGHTS: Spatially explicit uncertainty and sensitivity analysis extends the geodiversity assessment methods and comprises the next step in improving the accuracy of geodiversity assessments for land management decisions. An integrated uncertainty and sensitivity analysis helps focus on high-priority areas (high sensitivity and low uncertainty) to improve assessment reliability. The uncertainty and sensitivity analysis in models with spatially explicit output results depicted on maps can help reduce field research expenses by identifying a particular area or a specific natural component that warrants better-quality data. Bivariate maps employed to visualize the results of spatially explicit uncertainty and sensitivity analysis can potentially reduce the cognitive effort of visually switching between multiple single-variable (theme) maps. [ABSTRACT FROM AUTHOR]

The outbreak and subsequent COVID-19 pandemic became an urgent public hygiene crisis and concern for international society. The significance of evaluating a healthcare system's performance when dealing with public hygiene hazards generally reflects the preparation and reaction level of a country. Data envelopment analysis (DEA) can assess efficiency by comparing outputs produced by inputs in each decision-making unit (DMU). This research thus employs a multiplicative DEA approach relative to a log-linear technology to construct a network DEA model that measures overall efficiency in a two-stage network structure. The stage efficiencies via a weighted geometric mean aggregate into overall efficiency. We decompose the weighted geometric mean efficiency through means of using the general two-stage structure as a numerical example. Some interesting findings about the change in overall and stage efficiencies appear. First, a variation in the weight of stage efficiency does not change the stage efficiency scores. Second, the stage efficiency scores for the most part remain unchanged under different weights of stage efficiency. Finally, we apply the proposed network DEA model in multiplicative form to evaluate the efficiency of COVID-19 treatment in OECD countries. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic posed unheralded challenges to people, business, government at all levels (federal, provincial, regional), and society at large. In addition to the direct consequences of taking care of infected people, which in some countries led to a virtual collapse of the healthcare system, the pandemic strained eldercare, employment, economic growth, and exacerbated mental health and social problems. During the first year of the pandemic, researchers' and policy makers' main focus was on 'flattening the curve,' and on predictive modeling of infections and deaths. In this paper we present a non-parametric data-driven descriptive analysis based on Data Envelopment Analysis to assess COVID-19 in ten Canadian provinces over the two year period 2020 to 2021. The objective is to derive worst- and best-case intra-provincial benchmarks to assess if and to what extent the situation could have been worse respectively better. To take account for any indirect socio-economic impact our analysis incorporates official monthly unemployment rates and a stringency index reflecting the level of social policy restrictions imposed by the provincial governments. A major contribution of the model framework is that it provides a mechanism for measuring the impact of the two main strategies in curbing the pandemic, namely vaccination and social policy restrictions. As a robustness check, the bench-mark results are compared against bias-corrected efficiency measures. [ABSTRACT FROM AUTHOR]

The logistics performance index (LPI) is a comprehensive and comparable composite index developed by the World Bank to assess a country's logistics and trade facilitation environment. However, the existing LPI relies on externally assigned weights. To enhance LPI scores, this study adopts an effectiveness-based hierarchical data envelopment analysis method that internally allocates objective weights based on the dataset. Such endogenous weight information can provide additional valuable insights for countries to prioritize and strategize efforts to enhance their performance in the future. The results of this study indicate that focusing on improving the policy category yields greater benefits than improving the service category in terms of ranking national logistics performance. Furthermore, this study finds that logistics performance is influenced by income levels and geographical area. Income levels impact the regulatory and trade facilitation environment, with varying income levels leading to different priority policy areas. Geographical location also plays a crucial role in regional economic integration and trade facilitation. A favorable geographical location reduces costs and time while enhancing supply chain predictability and reliability. It is hoped that this study serves as a valuable resource for countries in identifying optimization strategies to improve their logistics performance. [ABSTRACT FROM AUTHOR]

Generic data envelopment analysis (DEA) models are based on deterministic input and output. However, input and output vectors are often interrupted by random factors, such as measurement errors and data noise, in real economic situations. This study proposes a new chance-constrained network DEA model based on the modified directional distance function (DDF) and enhanced Russell measure (ERM) model for assessing government management and culture-led urban regeneration. In addition to exploring the randomness of data, this study integrates the advantages of both ERM and DDF and considers the inefficiency level from a non-oriented viewpoint, the direction vector, and each input and output simultaneously. Each input and output of the two production stages can use non-radials to measure efficiency. Results show that the urban-rural gap has gradually widened since 2015. To validate the legitimacy of the model, this study utilizes the bootstrapping method to verify the results of the stochastic network DEA model and the conventional two-stage network DEA approach. This study also considers different alpha values as basis for comparison to confirm whether the results obtained differ by uncertainty level. [ABSTRACT FROM AUTHOR]

The sharing economy affords new opportunities to the global tourism industry, accelerating the development of tourism at a striking rate. This paper examines productivity of the bed and breakfast (B&B) industry and how to improve the performance of the B&B industry in a sharing economy. A modified global Malmquist index using slack-based measures is developed to measure productivity, and benchmark selection models using technology forecasting are proposed to improve future performance. The future technology is forecasted by averaging the frontier shifts (technology changes) in previous periods. This study challenges the implicit assumption that the technology will be stationary in traditional benchmarking, and suggests that how the information of frontier shifts derived from Malmquist index can be used to forecast future technology in a rational manner. The empirical results inform that there is a productivity growth in the B&B industry in a sharing economy, and the main driver of the growth is technology progress rather than performance improvement. In particular, a counterintuitive but interesting result is that the B&B industry experienced a slight productivity growth after the outbreak of COVID-19. The benchmarks in 2021 and 2022 are forecasted to improve the performance of the B&B industry. [ABSTRACT FROM AUTHOR]

This study combines the general equilibrium efficient frontier data envelopment analysis (GEEFDEA) approach, which handles fixed-sum CO2 emissions, with the meta-frontier Malmquist-Luenberger productivity index (MMLPI). We use this novel technique to measure the CO2 emission performance (CEP) of 30 of China's provincial-level thermal power industries during 2006–2020, both statically and dynamically, and reveal the drivers of CEP changes. Our proposed approach considers the fixed-sum feature of CO2 emissions and also captures the impact of technology heterogeneity. The empirical results show that the CEP of China's thermal power industry was good during the sample period, showing average annual increases of 2.42%, mainly due to technical efficiency gains and technological progress. The CEP of most provinces grew at various rates, between 0.23% and 8.38%; and the drivers of CEP changes differ between provinces. Policy implications are discussed, such as the need to strengthen technology innovation and optimize production processes to improve CEP. [ABSTRACT FROM AUTHOR]

This study explores the critical determinants of efficiency in European Higher Education Institutions (HEIs). The contemporary landscape presents a wide range of challenges and opportunities, necessitating unprecedented levels of efficiency. Our primary objective is to unravel the fundamental elements that drive the efficiency of European HEIs. To achieve this, we employ a novel methodological approach, integrating Data Envelopment Analysis (DEA) with a Machine Learning technique, specifically Feature Selection. This combination sheds new light on areas previously less explored in DEA, including the intricate interplay among variables. Our empirical investigation contributes to the academic discourse by presenting a comprehensive, multi-country analysis from a multifaceted viewpoint. We assess the efficiency levels of European universities, highlighting the influence of factors such as student fee funding, Ph.D. program intensity, and international mobility on achieving high efficiency scores. The findings of this study have some implications for policy-making, suggesting strategies to enhance the efficiency levels of HEIs. [ABSTRACT FROM AUTHOR]

AbstractMultinational firms establish foreign affiliates to reduce production and transaction costs and open markets. This study was conducted to determine whether foreign affiliates are efficient in production—the efficiency of foreign affiliates established by Korean multinationals through foreign direct investment from 2007 to 2018. DEA efficiency was measured by dividing it into pure, technical, and scale efficiency. An empirical analysis using the panel Tobit and Probit models examined the factors affecting efficiency decisions. It was confirmed that the total number of foreign affiliates’ employees significantly affects their efficiency. In addition, it was analyzed that establishments with traditional FDI motives, such as taking advantage of low wages and promoting exports, harm the efficiency of foreign affiliates. [ABSTRACT FROM AUTHOR]

This paper addresses the question whether energy consumption variables and the disaggregation of output matter in the context of efficiency analysis of container terminals. While it is obvious that the energy consumption of refrigerated cargo is higher than the energy consumption of non-refrigerated cargo, this work investigates whether those differences show in an overall efficiency analysis of terminals. This would point to a potentially important input for efficiency measures, to be considered in future productivity and efficiency analysis of terminals. Starting with a discussion on theoretical concepts and variable selection for measuring the energy dimension of terminal efficiency, this is the first paper that applies data envelopment analysis (DEA) comparing results with and without energy consumption, as well as differentiating productive outputs (dry and reefer container handling). The results reveal how the output disaggregation leads to substantially different efficiency scores and are a first step to show the relevance of output disaggregation and the inclusion of the energy variables as inputs in container terminal efficiency studies. [ABSTRACT FROM AUTHOR]

While interests in Industry 4.0 technologies such as cloud computing and the Internet of Things (IoT) are growing, an ongoing challenge is to properly evaluate the performance of the providers of such technologies. Methods have been developed and applied to assess the efficiency of cloud service providers (CSPs) for Industry 4.0. However, most existing methods suffer from such shortcomings as subjective weights and computing complexity, rendering it difficult to succinctly differentiate the performance of CSPs in a competitive market. Besides, the literature has not taken into account many different types of data such as ratios, integers, and undesirable factors that can affect the performance of CSPs. Most importantly, most existing studies do not consider the sustainability of CSPs in evaluating their performance. To address the above issues, we present a comprehensive analytical method based on data envelopment analysis (DEA) to gauge the sustainability of CSPs for Industry 4.0. Validating the usefulness of our method using a real-life dataset, we provide not only a viable means with sound academic underpinning but also significant managerial insights for practitioners to assess the sustainability of CSPs for Industry 4.0. [ABSTRACT FROM AUTHOR]

The aim of this work is to develop a model for evaluating the efficiency of the Circular Economy (CE) in Europe. In this context, we have developed a dynamic efficiency model of the CE, the Dynamic Network Data Envelopment Analysis (DNDEA) model (Tone and Tsutsui 2014), with the purpose of it being utilized to determine the degree of progress of the policies on the CE, the degree of compliance with those policies, as well as to determine the factors that are associated with a greater or lesser efficiency. The results obtained indicate that the factors which have most affected the dynamic inefficiency have been: the Imports from the EU countries, the Exports to the non-EU countries, and the Gross Value Added. The patents that are related to Waste Management, Recycling, and Persons Employed in the CE sector have been the factors that have added the greatest efficiency. The contribution of this work lies in the development of a new model for the measurement of the dynamic efficiency of the CE at the macroeconomic level in Europe, while considering the relationships between the results and the resources that are allocated to the CE, and establishing a period of implementation and execution. [ABSTRACT FROM AUTHOR]

This paper introduces cooperative game theory into the data envelopment analysis (DEA) method for building a novel fixed cost allocation (FCA) approach from group and individual consciousness. We start by acknowledging the divergences of decision-making units (DMUs) and propose an asymmetrical Nash bargaining DEA game, where DMUs' bargaining powers are defined through the functions of cross-efficiency and operation scale. However, the asymmetrical Nash bargaining solution is proposed from an individual perspective, without providing the theoretical support of full cooperation. To narrow this gap, we further introduce the core concept of a coalitional cooperative game into the built asymmetrical Nash bargaining DEA game, resulting in the asymmetrical core-Nash bargaining DEA game. The asymmetrical core-Nash bargaining solution is based on the common weights, with the properties of stability, fairness, Pareto-efficiency, and invariance to affine transformation. Furthermore, our proposed approach inherently ensures the uniqueness of the fixed cost scheme. Finally, numerical analysis and case study are provided to show the concrete application of the new method. [ABSTRACT FROM AUTHOR]

Human resource allocation is a critical component of project management, especially for projects that rely on the knowledge and expertise of skilled personnel. Data Envelopment Analysis (DEA) is a suitable method for this allocation since teams working for different tasks of a project can be considered as Decision Making Units (DMUs) that produce valuable outputs using different types and levels of personnel (inputs). We use Centralized DEA models for efficient allocation of human resources of the overall project among several teams. Using the software teams of a project undertaken in a large technological defense company as DMUs, we define appropriate outputs that measure effort, importance of work, and schedule performance. Factors relevant to the importance of work are aggregated using Rank Order Centroid weights according to expert evaluations. We differentiate between the permanent personnel of the company by their expertise level, and also subcontractors as different inputs. We define staffing policies with varying flexibilities that can be applied in different time periods, and suggest the transfer or removal of some personnel. Our results demonstrate that a centralized view on human resource allocation can bring noteworthy savings, especially in the long term when there is higher flexibility in staffing decisions. [ABSTRACT FROM AUTHOR]

As increasing disbursements (such as costs and fees) and risk (such as portfolio risk and leverage risk) may affect managed fund performance, analysts go beyond risk-adjusted return measures for performance appraisal. A methodology that assesses performance in a multidimensional framework is data envelopment analysis (DEA). A variant of DEA is inverse DEA. In this paper, inverse DEA is applied to determine output (investment income and benefit payments) targets for a given fund to perform at a desired efficiency level when increase in disbursements and risk at known levels is envisaged. An output-oriented inverse DEA model assuming variable returns to scale is formulated with theoretical underpinning. The proposed modelling framework ensures that, when an input augmented fund with estimated output targets is included in the observed fund set, the frontier of best performance established with the observed fund set does not change. The inverse DEA model is applied to a sample of Australian superannuation funds to demonstrate how a given fund may obtain pathways to improve its performance under different input-augmentation scenarios. As different pathways suggest feasible output targets, fund managers may find them valuable in forward planning. [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]

Innovation, which constitutes an important factor for growth, strongly diversifies regions' level of development. To compare regional innovativeness of EU territories, we employ the data envelopment analysis (DEA) framework. We analyse the relative efficiency of regions using a standard DEA for the country-level, a spatial DEA for regional analysis and the Malmquist indexes to measure the innovation productivity change over time. In our research, the inputs are human capital and R&D expenditure, while the outputs include the number of patents and GDP. We combine the results of these methods with spatial statistics to pinpoint the spatio-temporal patterns of innovation efficiency across European regions. We confirm validity of the assumed complex spatial structure of innovation efficiency. Our study indicates that innovation efficiency at the country-level is dissimilar, or even partially contradictory, to NUTS 2 outcomes due to high regional dispersion within state borers and similarities among neighbouring regions from different countries. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis is among the most frequently used methods to create composite indices in sustainability measurement. The present study aims to contribute toward the use of this methodology thanks to its ability either to seek an individual weighting scheme for each destination or to use a common set of weights for all the alternatives. The first alternative is similar to the CCR model, while the other is comprised of two parts: one that maximizes the total efficiency of the units and the other that minimizes the minimum efficiency. The study was conducted in Cuba using both parts, objective and subjective indicators attained from the regional proposal known as the Caribbean Zone of Sustainable Tourism. The results demonstrate the reliability of the selected indicators to measure tourism sustainability, the feasibility of the proposal for being combined with other approaches in supporting the decision-making process, and the great explanatory power of the outcomes. [ABSTRACT FROM AUTHOR]

This study examines the impacts of political competition on eco-efficiency. We first develop a theoretical model in which local government officials compete against each other to maximise their own political score. We find that after an initial stage of decline, eco-efficiency eventually turns upwards, once environmental performance becomes a meaningful component of local government officials' annual assessment. Eco-efficiency also exhibits a pattern of convergence. Lastly, the level of political competition is found to be negatively correlated with eco-efficiency. For the empirical analysis, we use a data envelopment analysis (DEA) model to compute the eco-efficiency level for 191 Chinese cities from 2003 to 2015. Our empirical evidence presents a 'U'-shape pattern in the trend of eco-efficiency and identifies two peer effects that work in opposite directions: the incentivising effect arising from higher performing neighbours, and the disincentivising effect when a city outperforms its competitors. Both peer effects lead to convergence in eco-efficiency, and our spatial econometric modeling analysis suggests that the net peer effect is significantly positive. We also find evidence of political competition reducing eco-efficiency, as predicted in the theoretical model. Our findings are robust to alternative measures of eco-efficiency. [ABSTRACT FROM AUTHOR]

This study proposes a two-stage modeling framework to evaluate the efficiency of firm performance based on sustainability (environmental, social, and governance (ESG)), operational and financial measures. Diverging from previous studies, this study conceptualizes firm performance as two serially linked stages, with operational performance as the first stage and market performance as the second stage. Acknowledging that ESG activities may reflect firm value and that these activities depend on a firm's operational performance, ESG scores are modeled simultaneously as an output of the operational performance stage and as an input for the market stage. Under this performance appraisal framework, two-stage data envelopment analysis models capturing overall and stage-level efficiency are constructed. The application of the proposed modeling framework is demonstrated using a sample of large Saudi listed firms as a case study of an emerging market. The main conclusion is that ESG legislation, regulations, and disclosure guidelines should be well defined in emerging markets to increase the overall efficiency of listed firms' performance. [ABSTRACT FROM AUTHOR]

This paper extends the literature on economic consequences of corruption. We simultaneously consider corruption's impacts on output, production efficiency, and environmental performance in an eco-efficiency framework. Pollution is considered as undesirable output in the production process. We construct an eco-efficiency measure from a non-oriented data envelopment analysis model that incorporates a non-convex metafrontier, super-efficiency, and undesirable outputs into a directional distance function. By using panel data from 30 Chinese provinces from 1998 to 2016 in a spatial analysis, we find that corruption, measured by filed-cases of corruption-related crimes, has a significantly negative direct impact on eco-efficiency. The regional spillover effect is not significant for provinces in the same geographical region, but is marginally significant for provinces sharing a common border. [ABSTRACT FROM AUTHOR]

Our objective in this paper is to evaluate the benefits of bus transit. To obtain the more accurate and highly discrimination results, this study establishes an evaluation indicator system from a stakeholder perspective and proposes a novel combined method consisting of dynamic network data envelopment analysis, cross-efficiency evaluation, and Shannon entropy aggregation method. An empirical study of bus transit systems in 33 key Chinese cities from 2016 to 2019 is provided. The results show that the benefit of bus transit in most key cities was ineffective. In contrast to production efficiency, focusing on improving service effectiveness is more conducive to improving the benefits of bus transit. Small cities have higher benefits than large cities. Meanwhile, the benefits of bus transit vary geographically. This evaluation outcome provides a more appropriate basis for decision-making related to the benefits of public transit services, as well as for operational and management studies. [ABSTRACT FROM AUTHOR]

Advanced analytics offers new means by which to increase efficiency. However, real-world applications of advanced analytics in manufacturing are scarce. One reason is that the management task of selecting advanced analytics technologies (AATs) for application areas in manufacturing is not well understood. In practice, choosing AATs is difficult because a myriad of potential techniques (e.g. diagnostic, predictive, and prescriptive) are suitable for different areas in the value chain (e.g. planning, scheduling, or quality assurance). It is thus challenging for managers to identify AATs that yield economic benefit. We propose a multi-criteria decision model that managers can use to select efficient AATs tailored to company-specific needs. Based on a data envelopment analysis, our model evaluates the efficiency of each AAT with respect to cost drivers and performance across common application areas in manufacturing. The effectiveness of our decision model is demonstrated by applying it to two manufacturing companies. For each company, a customized portfolio of efficient AATs is derived for a sample of use cases. Thereby, we aid management decision-making concerning the efficient allocation of corporate resources. Our decision model not only facilitates optimal financial allocation for operations in the short-term but also guides long-term strategic investments in AATs. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to find the determinants of productivity growth and its evolution over time for 27 North Atlantic Treaty Organization (NATO) countries over the period 2010–2017, with respect to the Global Peace Index. To this end, a production function was estimated for each year using Data Envelopment Analysis (DEA) methodology while the components of efficiency change, technical change, scale efficiency change and productivity change were calculated using the Malmquist Index for determining the change in productivity over time. According to the results obtained, the countries show a variation in productivity with a rising trend of 2%; Albania, Belgium, Croatia, Czech Republic, France, Germany, Greece, Montenegro, Slovenia, Norway and the United Kingdom being the countries where the rise was more intense. Moreover, relative productivity fell in 11 other countries in the period under review. In particular, with regard to efficiency change, the countries improved on average by 0.2%, with Portugal showing the greatest rise and France the smallest. Regarding technical change, it rose on average by 5.2%. Finally, scale efficiency fell on average by 3.3%, with Romania being the most disadvantaged and Portugal the most favoured. [ABSTRACT FROM AUTHOR]

The objective of this study was to propose an index based on the IPAT model to quantify the performance of countries concerning their emissions, also taking into account the level of climate technology and the level of inequality. In this study, two quantitative techniques were applied to a sample of 100 countries and 21 indicators. The main results were: (a) a 1% increase in social inequality contributes to an increase in emissions levels by 0.105; (b) the 1% increase in climate techs contributes to reducing emissions levels by 0.141; (c) The proposed IPA index shows that only Estonia and Slovenia are the best performers, mainly in overcoming the inequality barrier, while South Africa and Nigeria are the worst performers in reducing their emissions. The results provide evidence that climate policies should contain social targets to improve country performance and should use a representative value to identify their effectiveness. [ABSTRACT FROM AUTHOR]

Administrative capacity depends on organisational and human factors that can enhance or diminish the performance of local governments. To analyse the effect of these factors, efficiency scores of municipal governments were calculated and regressed against organisational and staff characteristics, using double bootstrap data envelopment analysis, on a sample of 1470 Mexican municipalities. Of the variables related to staff characteristics, the proportion of employees with college education, and remunerations, have a positive and statistically significant effect on efficiency. Of the organisational characteristics of local governments, the number of computers per employee was the main variable explaining the efficiency of municipal governments. [ABSTRACT FROM AUTHOR]

Healthcare organizations must operate efficiently to provide affordable care without compromising their sustainability and competitiveness. The purpose of this is to measure the profit efficiency of select Indian Corporate Hospitals during the COVID-19 pandemic. Further, the study attempts to analyze the productivity changes in select corporate hospitals during COVID-19 and postimplementation of the national insurance policy in India. The study used the Data envelopment analysis (DEA) technique to measure the efficiency of corporate hospitals. It utilizes Constant Return to Scale (CRS), and Variable Return to Scale (VRS) to estimate profit inefficiency. DEA-based Malmquist Productivity Index (MPI) was used to examine the productivity change during the last three years since the outbreak of COVID-19. The DEA-MPI model is iterated for panel data for Financial Year 2018–2021. with year-wise employee benefits and operating expenses as the input parameters and year-wise net sales as the output parameter. The study reveals surprising results and requires critical analysis. Out of select hospitals, 80% of hospitals were found inefficient using CRS while 70% of the hospitals were found inefficient using VRS. The findings of this study are useful for healthcare administrators and managers to highlight improvement programs for efficiency maximization. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between internal corporate social responsibility (ICSR) and firm efficiency. Our research employed a two-stage analysis of 33,413 firm-year observations from between 2008 and 2020. First, we measured the level of firm efficiency using data envelopment analysis (DEA). Second, we used panel regression to investigate the impact of investments made by firms into ICSR on their efficiency. Our results showed that such investments into ICSR (e.g. on employee development) increased firm efficiency during the study period. [ABSTRACT FROM AUTHOR]

In this paper, we empirically analyse the redistributive efficiency of fiscal policy considering its degree of decentralization for a sample of thirty-five countries over the 2000–2016 period. To do this, we follow a two-stage process where we first employ a bootstrap Data Envelopment Analysis (DEA) method to estimate the redistributive efficiency of cash transfers and direct taxes at the central and subnational levels. We obtain evidence that redistributive efficiency has diminished after the Great Recession (2007–2008), and is lower in the Southern European countries, and higher in almost the rest of Europe and the Latin American countries. Second, a bootstrap truncated regression analysis is used to identify the contextual factors that might explain redistributive efficiency variation across countries and time, supporting the argument that good governance is a requirement for decentralization to be efficient in promoting policy outcomes. [ABSTRACT FROM AUTHOR]

The traditional approach decomposing profit inefficiency into the sum of its technical and its allocative components identifies first the frontier projection of each firm based on the exogenous choice of a specific technical measure, e.g., based on slacks, directional, etc. However, in real life situations, firms and organizations are interested in benchmarking themselves against competitors representing the largest feasible profit improvement given market prices. Resorting to the recently defined general direct approach decomposition of profit inefficiency, which decomposes profit loss into the profit technical gap existing between the firm and its frontier projection, and a remaining profit allocative gap, we introduce a decomposition that endogenizes the technical component. This is achieved by securing technical inefficiency reductions that, simultaneously, search to maximize the profit of the projected benchmark. The proposal defines a new measure of technical inefficiency that corresponds to a monetized version of the weighted additive model. We also present a normalized version of the reversed decomposition that is units’ invariant through the definition of a suitable normalization factor. [ABSTRACT FROM AUTHOR]

This research aims to aid original equipment manufacturers (OEMs) to model, analyze, evaluate, and benchmark potential design and manufacturing suppliers based on respective product engineering teams' efficiencies. The product engineering efficiency in this study is modeled in terms of product engineering-related attributes such as commercial lead time, number of parts, number of green features, number of end products developed, and so forth. Essentially, these parameters capture more complex interactions than simple traditional supplier selection criteria such as cost, quality, delivery, and flexibility. Due to the presence of information uncertainty in terms of bounds related to the suppliers' related parameters, a number of data envelopment analysis (DEA) efficiency measurement models have been deployed. The proposed decision support system is novel because it models both the self-assessment type and cross-efficiency type using DEA such that maximum discrimination can be achieved amongst suppliers in the presence of interval data. The study is demonstrated for ten different sheet-metal cabin suppliers. Comparison with some well-known, relevant methods is also carried out to illustrate the validity of the proposed method. The research can specifically help supply chain managers to align the evaluation of potential suppliers with their firm's commercial considerations in the presence of information uncertainty. [ABSTRACT FROM AUTHOR]

This study evaluates the quality of the digital platform economy at the global scale by employing a network model rooted in nonparametric linear techniques (data envelopment analysis) on a sample of 116 countries for 2019. The proposed model is in accordance with the geographic diversity (country heterogeneity) and the multilayered structure characterizing the interactions between system participants: governments, digital platforms, platform-dependent firms, and end users. The core findings indicate that the configuration of countries' platform economy is very heterogeneous, which suggests that an informed, tailor-made approach to policy might produce more effective outcomes. Policies aimed at enhancing the digital platform economy should emerge from the analysis of its main factors if the development of a strategy seeking qualitative improvements in the system is the desired goal. [ABSTRACT FROM AUTHOR]

With a two-stage analysis framework, this paper investigates how information communication technology can improve the sustainable performance of smart tourism destinations. It analyzes the impact of online attention (in the form of activity on a search engine) and the level of the digital economy on sustainable performance of smart tourism destinations, measured using an advanced data envelopment analysis model. The empirical results from 50 key smart tourism destinations in China show that: (1) the average value of smart destination performance from 2008 to 2019 is 0.74; (2) both online attention and digital economy have significant positive impacts on sustainable performance; (3) digital economy positively moderates the impact of online attention on sustainable performance; (4) undesirable outputs (air pollution) and tourist satisfaction are two factors that can be considered in measuring the sustainable performance of a destination. [ABSTRACT FROM AUTHOR]

Rail freight is one of the most important pillars for the decarbonising of the transport system in Europe. To ensure the decarbonisation effect of rail freight, a substantial modal split on rail freight is needed, with the aim of strengthening the competitiveness of rail freight and improving the quality of service on the rail freight corridors (RFCs). With the aim of monitoring and evaluating the development of RFC performance, the Data Envelopment Analysis model (DEA) was applied in this study. The evaluation of efficiency was carried out for eight RFCs in 2020 and 2021 using performance indicators considered with Pearson correlation analysis. The results of the efficiency of the RFCs showed that seven of the studied corridors are not operated efficiently, while the correlation analysis showed a significant dependency between the selected indicators. Nevertheless, the efficiency of the corridors and the overall performance of rail freight can be improved by integrating innovative technologies. Therefore, this study also investigated how digital automatic couplers (DACs) and intelligent video gates (IVGs) affect the efficiency of RFCs. The results show that the inclusion of indicators related to DAC and IVG tasks has a positive impact on improving the efficiency of RFCs. [ABSTRACT FROM AUTHOR]

Despite various supervised methods and algorithms, prediction methods do not necessarily provide the optimal values of the outputs. In this paper, an approach is proposed based on the integration of the clustering algorithm and a new mathematical programming model for predicting the efficient and optimal level of hospital outputs. In the first stage, units are evaluated using data envelopment analysis (DEA) and then, high-efficiency units are selected. In the second stage, the selected units are clustered based on the inputs of the units using the fuzzy C-means algorithm. For each unit to be estimated, the corresponding cluster is found and the closest unit to the target unit is determined. Finally, using the proposed new mathematical programming model, the optimal values are estimated based on the more efficient unit. A case study on Iranian hospitals illustrates the implementation of algorithms and methods and shows the abilities of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a robust tool for ranking MHE prior to selecting the best one. The proposed ranking methodology is based on a new key performance indicator, identified as market weight scheme (MWS). The MWS of each MHE reflects the market's importance of its performance criteria and it is built through an ingenious integration of the most resonated appreciative (MRA) model with the ordered weighted averaging (OWA) operator. The flexibility of MRA is exploited to customize the weight profiles of the data envelopment analysis (DEA) peer-evaluations, whereas the OWA aggregation process is run to produce the associated MWS for a pre-set decision maker's (DM) subjective judgment towards the MHE market stability. Thus, the novelty of the present study is threefold. (1) The MHE is selected from a collective market perspective rather than individualistic manufacturer's viewpoint. (2) The ranking process is based on the aggregation of weight profiles instead of cross-efficiency (CE) scores, as traditionally done in DEA-CE approaches. (3) The DM's subjectivism is integrated into the assessment process through an optimism measure that mimics the market stability. The application of the proposed methodology on a sample of 27 robots asserts its robustness as a selection tool. [ABSTRACT FROM AUTHOR]

This paper explores the foundations for developing incentives for influencing units operating within centrally managed organizations. We begin by laying out the theory of managerial control in principal-agent contexts and draw from the incentive mechanisms developed in the related field of economic regulation. In particular, we highlight issues, differences and similarities in three recently proposed approaches under these circumstances, not only to compare them, but more importantly to motivate and arrive at requirements that should be met by incentivization systems in centrally managed multi-unit organizations. The stipulated requirements are not intended to be exhaustive but rather aim at defining conceptual foundations for further discussions and encouraging avenues for future research in this field. Our investigations are supported by graphical examples and an analysis of empirical data from banking. [ABSTRACT FROM AUTHOR]

Primary Health Care (PHC) is intended to provide a comprehensive response to the patients' health needs. Considering the increasing demand to optimise the use of the available resources while keeping a high-quality standard, it is essential to investigate the health services' performance. We propose a methodology based on the Data Envelopment Analysis (DEA) technique, examining the volume of activities performed and considering the impact of these activities by using a quality indicator directly related to care outcomes. To perform a longitudinal analysis and observe the changes in quality separately, a Quality-Adjusted Malmquist Productivity Index is implemented. The methodology is illustrated by analysing the performance and the evolution in productivity and quality of the primary care services delivered by the municipalities of the Brazilian state of Santa Catarina from 2008 to 2014. Our findings reinforce the view that adequate planning and structuring of PHC services must consider not only the volume of activity but also the impact of the services delivered. [ABSTRACT FROM AUTHOR]

This paper aims to propose a method to measure the operational efficiency of the movie theater businesses. The method is based on a bootstrap data envelopment approach to test the statistical significance of the performance difference between companies in the movie theater industry. The proposed method is utilized to investigate the operational efficiency of the three major U.S. movie theater chains. This article utilizes data from each company's individual financial filings to estimate their bias-corrected efficiency scores. An aggregate report of the efficiency scores shows that the movie theater industry is experiencing a significant decline in both scale and technical efficiencies. The efficiency assessment of the three movie theater chains reports statistically significant differences. Based on the constant returns to scale assumption, the efficiency scores are different among the three movie theater chains. Closely, two significant differences are identified when considering the variable returns to scale assumption. The kernel density of the efficiency scores is estimated as well. Results suggest that the movie theater chains are in imperative need of developing an innovative strategic plan to tackle poor attendance and weak managerial practices. Other contributions and managerial issues are also discussed. [ABSTRACT FROM AUTHOR]

The main purpose of this article is to identify the main characteristics of Lean Six Sigma projects executed by efficient companies. To do so, this article explores a large empirical database, with 112,421 projects, executed from 2001 to 2018 in 57 strategic business units from a heavy machinery producer, which is recognised as a worldwide reference in the use of Lean Six Sigma methodology. The research was conducted in two stages, first, the data was analysed with a Network Data Envelopment Analysis (DEA) model, which provided an efficiency ranking formed by the strategic business units. In the second stage, a Classification Tree (CHAID) method was used to identify the common characteristics of the projects accomplished by the most efficient ones. The findings show that these strategic business units are characterised by projects executed in departments closely related to senior management, especially Corporate Governance and Business Strategy. In addition, their projects usually focus on people, innovation, or cost reduction and are structured more frequently with the DMAIC cycle. These findings broaden the understanding of the criteria that should be considered in Lean Six Sigma project selection and prioritisation, by providing new insights (not identified in previous studies) to decision-makers and researchers, from projects associated with the efficient performance of Lean Six Sigma, which can be particularly valuable considering the multiple projects conducted in this initiative, that frequently compete for resources. [ABSTRACT FROM AUTHOR]