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Assessing the technical efficiency of a set of observations requires that the associated data composed of inputs and outputs are perfectly known. If this is not the case, then biased estimates will likely be obtained. Data Envelopment Analysis (DEA) is one of the most extensively used mathematical models to estimate efficiency. It constructs a piecewise linear frontier against which all observations are compared. Since the frontier is empirically defined, any deviation resulting from low data quality (imperfect knowledge of data or IKD) may lead to efficiency under/overestimation. In this study, we model IKD and, then, apply the so-called Hit & Run procedure to randomly generate admissible observations, following some prespecified probability density functions. Sets used to model IKD limit the domain of data associated with each observation. Any point belonging to that domain is a candidate to figure out as the observation for efficiency assessment. Hence, this sampling procedure must run a sizable number of times (infinite, in theory) in such a way that it populates the whole sets. The DEA technique is used during the execution of each iteration to estimate bootstrapped efficiency scores for each observation. We use some scenarios to show that the proposed routine can outperform some of the available alternatives. We also explain how efficiency estimations can be used for statistical inference. An empirical case study based on the Portuguese public hospitals database (2013–2016) was addressed using the proposed method. [ABSTRACT FROM AUTHOR]

• External operational variables are associated with efficiency estimation. • Efficiency is higher in municipalities with lower levels of income per capita. • Efficiency is lower for municipalities with higher undifferentiated waste produced. • Adoption of zero waste strategy is related to higher efficiency in MSW service. • Privatization is not associated with significant improvement in performance results. This paper assesses the performance of waste services in the region of Tuscany in Italy. It adopts non-parametric techniques for this purpose. Furthermore, it investigates the influence of the operational environment on the estimated performance by using the robust order-m technique. Meaningful levels of inefficiency were found in the Italian waste services. A specification of the ownership of the operator entrusted with waste management by municipalities and the formal adoption of the zero-waste strategy are included as environmental variables. The study concludes that the influence of the operational environment is a determinant of performance. The income per capita is negatively associated with efficiency, while population density is positively associated. Furthermore, efficiency levels are lower for municipalities with higher mixed waste per capita produced. Improving good pro-environmental behaviours is likely to improve efficiency and, thus, tariffs. The empirical results support the idea that it is necessary to make relevant organizational decisions that imply higher costs to increase the refuse collection rate. An increase in the separate collection rate beyond 50% is associated with the reduction of the efficiency level reached. Efficiency analysis does not consider the additional costs and the opportunities for municipalities to get revenues by selling them as secondary raw materials. Besides, the adoption of a zero-waste strategy is related to higher efficiency in MSW service provision. Finally, results about the ownership issue support the idea that privatization is not necessarily associated with the performance improvement of the waste services. [ABSTRACT FROM AUTHOR]

Highlights • We assess the relevance of thirteen exogenous variables on European seaports' performance. • We integrate the Stochastic multicriteria acceptability analysis with the order-α model. • Seaports' location or management model have no central role on seaports' efficiency. • The efficiency of seaports does not depend on features like local richness, and performance indicators. Abstract Maritime transport is hugely important for the modern global economy. The increasing development of economic globalization has led to a considerable increase in intercontinental exchanges, stimulating the use of ports and shipping companies with a cheap and quick way to reach most of Europe, Asia, Africa or North America. This study aims to assess the relevance of thirteen exogenous variables, including GDP per capita, water depth, management model, and European directional division, on the performance of the seaports using the nonparametric robust output-oriented order-α model. The study also integrates the Stochastic multicriteria acceptability analysis with the order-α model to handle with cases of imperfect knowledge. This problem is not unusual in nonparametric benchmarking exercises and can have very serious effects on performance outcomes. Our findings suggest that none of the thirteen explanatory variables (either external or performance indicator) has a meaningful association with seaports' technical efficiency. This means that the efficiency of resources usage to deliver port services does not depend on features like localization, management model, local richness, and some performance indicators. [ABSTRACT FROM AUTHOR]

There are several ways of aggregating partial performance indicators into composites, each of them with advantages but also shortcomings and caveats. The objectivity demanded for policy-making and regulation of utilities leads many researchers to build their analyses over the so-called Benefit-of-Doubt (BoD), which, in turn, results from the well-known Data Envelopment Analysis (DEA). In a compensatory manner, these models construct piecewise linear frontiers containing the benchmarks, but they disregard increasing marginal products and the existence of non-concavity regions, thus underestimating efficiency. Multiplicative approaches have been proposed to solve these problems; one of these is the geometric (rather than linear) aggregation of variables. But still they fail to solve some problems like the correct treatment of undesirable variables, the existence of regulatory constraints, and the existence of imperfect knowledge of data. Therefore, this paper builds upon a geometric aggregation of performance indicators and proposes some strategies to solve the aforementioned shortcomings of the existing models. The new framework is exemplified and tested with the Portuguese urban solid waste management utilities. • Development of a multiplicative benchmarking model for composite indicators. • The model can receive several regulatory impositions. • The model is extended to receive imperfect knowledge of data. • The model was exemplified with data from the urban solid waste management sector. [ABSTRACT FROM AUTHOR]

To assess customer satisfaction determinants in a public pediatric inpatient service and propose some strategies to enhance the consumer and customer experience. We applied a Multiple Criteria Customer Satisfaction Analysis to estimate the value functions associated with each satisfaction (sub)criterion and determine the corresponding weights. We characterized satisfaction criteria (according to the Kano's model), estimated the customers' demanding nature and the potential improvements, and proposed strategic priorities and opportunities to enhance customer satisfaction. Strategies for satisfaction enhancement do not depend solely on the criteria with the lowest satisfaction levels and the estimated weights, each criterion's nature, the customers' demanding nature, and the technical margin for improvements. Areas deserving attention include clinical staff's communication skills, the non-clinical professionals' efficiency, availability, and kindness; food quality; visits' scheduling and quantity; and facilities' comfort. • We assess the customer satisfaction determinants in a pediatric inpatient service. • We applied a Multiple Criteria Customer Satisfaction Analysis to estimate utility. • Strategies do not depend solely on the criteria with the lowest satisfaction levels. • Areas deserving attention were identified and priorities were established. [ABSTRACT FROM AUTHOR]

In contrast to conventional data envelopment analysis (DEA), where a system is considered as a "black-box", network DEA acknowledges its internal structure to generate more enlightening results. It goes without saying that putting network DEA in practice is natural- and progressively rarer as the complexity of a system's structure increases. In particular, its employment in healthcare is no exception. Thus, we designed a slacks-based model to measure the efficiency of the Portuguese secondary healthcare providers bearing in mind their internal services. However, the absence of data regarding the connections between those services called for the additional use of a simulation method – the well-known Monte Carlo method –, modelled with the judgements of a decision-maker. This unprecedented application of static systems with a matrix-type structure found that two-thirds of those providers were inefficient and allowed the identification of target areas for future policy reforms in the Portuguese National Health Service. • Unprecedented matrix-type structure network data envelopment analysis application. • Efficiency measurement of the Portuguese public secondary healthcare providers. • Value modelling within a Monte Carlo simulation. • 2 in 3 Portuguese public hospitals are inefficient. [ABSTRACT FROM AUTHOR]

Depletion of natural resources needs quantification and efficiency analysis of the use of resources to improve sustainability. This paper evaluates the efficiency of Organization for Economic Co-Operation and Development (OECD) countries in terms of Water-Energy-Land-Food (WELF-Nexus) to ensure sustainability and environmental viability for both present and future generations. An input-output index system is built at a transnational level. Composite and intrinsic indicators are introduced to incorporate the interconnections and tradeoffs between sectors of the nexus and outcomes of the nexus. The nonparametric benchmarking order-α model, resulting from Data Envelopment Analysis (DEA) is used to estimate WELF-Nexus efficiency of each country, and to alleviate the intricacies of using composite and intrinsic indicators. To ensure resource generativity, an output target setting model that accommodates predefined input is proposed. Results show variation in performance among OECD members, with an annual average efficiency score of 68%, 69% and 78% in 2007, 2012, and 2016. Sensitivity analysis was performed to measure the effect of drought on WELF efficiency, a decrease of about 13% on average WELF efficiency was observed. Outputs improvement was recommended for each country: Lithuania (14%), Mexico (10%) and 11% for Hungary, Latvia, and Turkey due to their high inefficiency. The study provides a robust framework for policy making and shows that a win-win strategy for the nexus must be implemented to achieve WELF-Nexus efficiency, given the trade-offs within its sectors. Furthermore, it highlights that innovative-driven policies will enhance WELF-Nexus efficiency and sustainability. Unlabelled Image • Depletion of natural resources requires efficiency analysis. • We evaluate the efficiency of WELF-Nexus to ensure environmental viability. • We integrate the robust order-a DEA with an output target setting model. • Innovative-driven policies will enhance WELF-Nexus efficiency and sustainability. [ABSTRACT FROM AUTHOR]

The ongoing outbreak of SARS-CoV-2 has been deeply impacting health systems worldwide. In this context, it is pivotal to measure the efficiency of different nations' response to the pandemic, whose insights can be used by governments and health authorities worldwide to improve their national COVID-19 strategies. Hence, we propose a network Data Envelopment Analysis (DEA) to estimate the efficiencies of fifty-five countries in the current crisis, including the thirty-seven Organisation for Economic Co-operation and Development (OECD) member countries, six OECD prospective members, four OECD key partners, and eight other countries. The network DEA model is designed as a general series structure with five single-division stages – population, contagion, triage, hospitalisation, and intensive care unit admission –, and considers an output maximisation orientation, denoting a social perspective, and an input minimisation orientation, denoting a financial perspective. It includes inputs related to health costs, desirable and undesirable intermediate products related to the use of personal protective equipment and infected population, respectively, and desirable and undesirable outputs regarding COVID-19 recoveries and deaths, respectively. To the best of the authors' knowledge, this is the first study proposing a cross-country efficiency measurement using a network DEA within the context of the COVID-19 crisis. The study concludes that Estonia, Iceland, Latvia, Luxembourg, the Netherlands, and New Zealand are the countries exhibiting higher mean system efficiencies. Their national COVID-19 strategies should be studied, adapted, and used by countries exhibiting worse performances. In addition, the observation of countries with large populations presenting worse mean efficiency scores is statistically significant. [ABSTRACT FROM AUTHOR]

Health is one of the most fundamental human rights. In that sense, the creation of health systems attempted to provide the population with organisations, institutions, and resources to meet their needs. However, health inequalities are prevalent in all countries. Thus, evaluating health systems is vital to understand this issue. Accordingly, the aim of this work is to develop a multi-criteria decision analysis (MCDA) approach to innovatively rank nine of the European health systems with Beveridgian financing that would help determine the shortcomings of the Portuguese National Health Service and discern the operating "best practices", in a close interaction with the Portuguese Ministry of Health. First, the panel of decision-making actors was guided through the design of a cognitive map to promote their learning process and help them identify eleven fundamental points of view. Second, their operationalisation was facilitated by selecting acceptable descriptors of performance. Finally, an MCDA approach was proposed to evaluate the chosen health systems using an additive model, which included a sensitivity and a robustness analysis. In the end, the model was perceived by the panel as being trustworthy and reliable. This framework can be used for further MCDA modelling in similar applications based on participatory procedures. • Unique application of MCDA to rank Beveridgian health systems. • Cognitive mapping employed in problem structuring. • Multi-attribute value theory used in model structuring. • Participatory framework to assess the operating "best practices" in the area. [ABSTRACT FROM AUTHOR]