Search

Summarization: We propose a holistic evaluation framework that banks could adopt when screening corporate entities. The framework is based on the development of a composite indicator of social, environmental and financial performance. This integrated view of evaluation is conceptually aligned with ISO standards and empirical proposals of academics and market practitioners in defining inclusive performance. We complement those proposals with a methodological framework that permits incorporation of a plethora of viewpoints in the evaluation process, reflecting the expectations of the various stakeholders in the environment of a bank. We further enhance the evaluation process with analytics of a more detailed hierarchical view of performance, fine-tuning of the evaluation process, and provide implications and suggestions to the senior executive team of a bank’s clients. Presented on: European Journal of Operational Research

The assessment of the teacher performance is a subject of criticism due to the lack of a well-establish methodology. This study develops an overall score to measure the dimensions that encompass the academic activities. To that end, a Benefit-of-the-doubt model is proposed. The advantage of this technique is the flexibility in the weights, so that the model selects for each teacher the most favourable set of weights. Furthermore, the paper proposes the barycentric coordinate system as a method to classify the teachers in clusters depending on their contribution to the dimensions. A specific pie chart has been proposed as an efficient way to report the contribution of the teachers to the dimensions and the overall teacher performance.

Since 2012, driven by the desire to propose a subjective well-being index (SWBI) complementary to the traditional measures, with high time and space frequency, our team evaluates, analysing Twitter data, a composite index that captures various aspects and dimensions of individual and collective life. The SWBI is a multidimensional indicator whose components were inspired by the dimensions adopted for the Happy Planet Index provided by the New Economic Foundation. In detail, it consists of eight dimensions that describe three different areas: personal well-being, social well-being and well-being at work. The Italian subjective well-being index ($$\hbox {SWBI}_{{ITA}}$$), that we display here, audits the Italian subjective well-being revealed by tweets acquired via the public Twitter API, written in the Italian language, and posted from Italy from January 2012 to December 2017. Around 1–5% of the data includes geo-referenced information, which allows us to provide an index at local level. The Twitter data analysis is carried on with a human supervised sentiment analysis method, the Integrated Sentiment Analysis (iSA) algorithm. In this work, after a weighting procedure adopted to partially overcome the selection bias caused by the use of data from social network, we describe the $$\hbox {SWBI}_{{ITA}}$$ dimensions in the considered period at the regional level. Moreover, for some dimensions, for which a similar currently available measure provided by Italian official statistics exists, comparisons are proposed emphasizing novelties, similarities and differences.

In the analysis of well-being, according to the “beyond GDP” approach, it is important to measure well-being and how it is distributed across territories and among the different groups that make up society. This paper focuses on job quality, which is an important aspect of life that contributes to well-being. More precisely, the social environment at workplace is measured by a composite indicator, and its distribution across European countries is investigated. A comparison of different aggregation approaches for the construction of the composite indicator is provided, and the measure is examined by means of influence and robustness analyses.

This research presents a framework through which a new Higher Education Access and Deprivation (HEAD) composite index was created to assist UK universities in efficiently recruiting and supporting students from areas with traditionally low engagement rates in higher education. The index was designed to be easily adaptable by university staff to suit their own work priorities and/or an institution’s strategic requirements by utilising open socio-demographic data and construction techniques that require minimal technical statistical skills. Although Cornwall was used as the study area in this research, this index has been designed such that it can be readily applied elsewhere. Two differently weighted models were created using the framework – one with equal weights and the other based on the frequency the constituent indicators appeared in the reviewed academic literature. Both models were mapped across Cornwall, identifying areas of deprivation at a finer resolution than under the current widely used Participation of Local Areas (POLAR) methodology. The weighted model performed marginally better than both the equal weighted model and the current POLAR methodology when validated against external data, and additionally it worked well in both rural and urban environments leading to it being selected as the new HEAD index. The HEAD index maps smaller scale areas of deprivation than previously available, and by enabling users to investigate the underlying socio-demographic characteristics of an area, it also allows universities to create interventions, support, and policies that best targets the students they aim to recruit and teach.

Natural resources are gradually coming under continuous and increasing pressure due to anthropogenic interventions and climate variabilities. The result of these pressures is reflected in the sustainability of natural resources. Significant scientific efforts during the recent years focus on mitigating the effects of these pressures and on increasing the sustainability of natural resources. Hence, there is a need to develop specific indices and indicators that will reveal the areas having the highest risks. The Water and Land Resources Degradation Index (WLDI) was developed for this purpose. WLDI consists of eleven indicators and its outcome results from the spatiotemporal performance of these indicators. The WLDI is based on the Standardized Drought Vulnerability Index (SDVI) and the Environmentally Sensitive Areas Index (ESAI). The WLDI is applied for the period from October 1983 to September 1996, considering Greece as a study area. The results of the application of this index reveal the areas with the highest risks, especially in the agricultural sector, with less than the needed water quantities due to extensive periods of droughts. This index could be used by scientists, but also by policy makers, to better and more sustainably manage environmental pressures.

This article aims to improve one of the newest energy transition measures—the World Economic Forum WEF Energy Transition Index (ETI) and find its driving forces. This paper proposes a new approach to correct the ETI structure, i.e., sensitivity analysis, which allows assessing the accuracy of variable weights. Moreover, the novelty of the paper is the use the spatial error models to estimate determinants of the energy transition on different continents. The results show that ETI is unbalanced and includes many variables of marginal importance for the shape of the final ranking. The variables with the highest weights in ETI did not turn out to be its most important determinants, which means that they differentiate the analysed countries well; nonetheless, they do not have sufficient properties of approximating the values of the ETI components. The most important components of ETI (with the highest information load) belong to the CO2 emissions per capita, the innovative business environment, household electricity prices, or renewable capacity buildout. Moreover, we identified the clustering of both ETI and its two main pillars in Europe, which is not observed in America and Asia. The identified positive spatial effects showing that European countries need much deeper cooperation to reach a successful energy transition.

Composite indicators (CIs), i.e., combinations of many indicators in a unique synthetizing measure, are useful for disentangling multisector phenomena. Prominent questions concern indicators’ weighting, which implies time-consuming activities and should be properly justified. Landscape fragmentation (LF), the subdivision of habitats in smaller and more isolated patches, has been studied through the composite index of landscape fragmentation (CILF). It was originally proposed by us as an unweighted combination of three LF indicators for the study of the phenomenon in Sardinia, Italy. In this paper, we aim at presenting a weighted release of the CILF and at developing the Hamletian question of whether weighting is worthwhile or not. We focus on the sensitivity of the composite to different algorithms combining three weighting patterns (equalization, extraction by principal component analysis, and expert judgment) and three indicators aggregation rules (weighted average mean, weighted geometric mean, and weighted generalized geometric mean). The exercise provides the reader with meaningful results. Higher sensitivity values signal that the effort of weighting leads to more informative composites. Otherwise, high robustness does not mean that weighting was not worthwhile. Weighting per se can be beneficial for more acceptable and viable decisional processes.

Hospitals consume most of the health systems' financial resources. In Portugal, for instance, public hospitals represent more than half of the National Health Service debt and are decisive in their financial insufficiency. Although profit is not the primary goal of hospitals, it is essential to guarantee their financial sustainability to ensure users' health care and the necessary resources. An analysis of the existing literature shows that researches focus mainly on the hospital's technical efficiency. The literature has paid little or even no attention to the use of composite indicators in hospital benchmarking studies. This study uses the Benefit of Doubt methodology alongside recent data about Portuguese public hospitals (2013-2017) to understand the factors that contribute to low performance and high indebtedness levels. Our results suggest that hospitals perform better in terms of access (average score: 0.982). The group of criteria with the lowest performance was efficiency and productivity (average score: 0.919), suggesting resources waste. Financial performance is, in general, higher than quality, raising social concerns about the way that public hospitals have been managed. Findings bring relevant implications. For example, the way hospitals are currently financed should consider efficiency, productivity, quality, and access. Regulators should ensure that minimum performance levels are fulfilled, applying preventive and corrective measures to avoid future low-performance levels. We suggest that hospital managers introduce satisfaction inquiries to improve quality. These improvements can attract more patients in the medium- or long-term; thus, our results are useful to citizens to make a better choice.

Beyond 5G networks will be fundamental towards enabling sustainable mobile communication networks. One of the most challenging scenarios will be met in ultra-dense networks that are deployed in densely populated areas. In this particular case, mobile network operators should benefit from new assessment metrics and data science tools to ensure an effective management of their networks. In fact, incorporating architectures allowing a cognitive network management framework could simplify processes and enhance the network&rsquo, s performance. In this paper, we propose the use of composite indicators based on key performance indicators both as a tool for a cognitive management of mobile communications networks, as well as a metric which could successfully integrate more advanced user-centric measurements. Composite indicators can successfully synthesize and integrate large amounts of data, incorporating in a single index different metrics selected as triggers for autonomous decisions. The paper motivates and describes the use of this methodology, which is applied successfully in other areas with the aim of ranking metrics to simplify complex realities. A use case that is based on a universal mobile telecommunications system network is analyzed, due to technology simplicity and scalability, as well as the availability of key performance indicators. The use case focuses on analyzing the fairness of a network over different coverage areas as a fundamental metric in the operation and management of the networks. To this end, several ranking and visualization strategies are presented, providing examples of how to extract insights from the proposed composite indicator.

PurposePortugal has been experiencing a continuous growth in tourism activity, with hospitality industry as one of the main tourism sectors. Therefore, the assessment of hotel companies’ performance is very important to assist decision processes. The purpose of this paper is to assess the financial performance (FP) of 570 hotel companies operating hotel units in Portugal in 2017. To explore the question of brand affiliation, a comparison was made between hotel companies with similar stars rating and market orientation. In addition, this paper intends to fill a gap in literature studying the Portuguese reality on the subject of brand affiliation.Design/methodology/approachThe present study uses a methodology based on data envelopment analysis (DEA) to assess the overall performance for each company, which further decomposed into the within-group performance and the technological gap. The performance of the hotel company is assessed through the aggregation of multiple financial indicators using the composite indicator (CI) derived from the DEA model. A bivariate analysis based on the Tobit regression to test the robustness of brand effect on FP of hotel companies (HC) was also included.FindingsThe empirical results show that branded companies, on average, have significantly better overall FP than non-branded companies. On the one hand, the brand effect tends to improve the within-group FP of HCs and the brand presents a statistically significant positive effect on the FP. On the other hand, the best practices are observed in both branded and non-branded companies.Practical implicationsThe results of this study illustrate that, globally, the better FP of the branded companies is because of their individual relative companies’ performance and a better model of operation given by the brand effect. Brand affiliation will generally allow for a better FP and essentially a better profitability for invested equity, a higher return on sales and a higher value added per employee.Originality/valueThe study provides important theoretical and practical contributions that can assist the strategic decision of the HCs in choosing to operate independently or to adopt brand affiliation. Also, it is innovative because the FP of branded and non-branded HCs is measured not using a set of individual financial ratios but through a single CI that aggregates those financial ratios, using a DEA model.

Objetivou-se analisar em nível nacional os fatores associados à disponibilidade de serviços odontológicos especializados nos municípios brasileiros. É um estudo ecológico, com amostra de 776 municípios que participaram do 1º ciclo do Programa de Melhoria do Acesso e da Qualidade dos Centros de Especialidades Odontológicas (PMAQ-CEO) realizado em 2014. As variáveis dependentes do estudo consistiram em um coeficiente criado com base nas variáveis de número de profissionais e carga horária semanal de cirurgiões-dentistas atuando nas especialidades mínimas por 10 mil habitantes. Realizou-se uma análise fatorial exploratória para a criação de um escore que mensura o desempenho dos municípios quanto à disponibilidade de serviços odontológicos especializados. A fim de avaliar os fatores associados ao desempenho dos municípios utilizou-se o teste do qui-quadrado de Pearson, tendo como variáveis independentes os seguintes indicadores municipais categorizados em tercis: renda per capita, Índice de Desenvolvimento Humano Municipal (IDH-M), população residente, despesa total com saúde por habitante e equipes de saúde bucal por 10 mil habitantes. O maior desempenho quanto à disponibilidade de serviços especializados em saúde bucal esteve associado a municípios com menor porte populacional (67,3%; IC: 61,6-73,0; p < 0,001), com menor IDH-M (41,9%; IC: 35,8-48,0; p < 0,001) e com menor renda per capita (41,2%; IC: 35,2-47,3; p < 0,001) e maior média de equipes de saúde bucal por 10 mil habitantes (50,6%; IC: 46,0-58,4; p < 0,001). Os resultados demonstram impactos positivos da implantação da Política Nacional de Saúde Bucal no país, como o cumprimento do objetivo de ampliação da oferta dos serviços de atenção secundária sob o princípio da equidade na assistência. The study aimed to analyze factors associated with the availability of specialized dental services in Brazilian municipalities. This was an ecological study with a sample of 776 municipalities that participated in the first cycle of the Program to Improve Access and Quality of Dental Specialization Centers (PMAQ-CEO, in Portuguese) survey held in 2014. The study’s dependent variables consisted of a coefficient created with variables on the number of professionals and the workweek of dentists in the minimum set of specialties, per 10,000 inhabitants. Exploratory factor analysis was performed to create a score for the municipalities’ performance with the availability of specialized dental services. Factors associated with the municipalities’ performance were assessed with Pearson’s chi-square test, with the following municipal indicators as independent variables, categorized in tertiles: per capita income, Municipal Human Development Index (HDI-M), resident population, total health spending per inhabitant, and Oral Health Teams per 10,000 inhabitants. Higher performance with the availability of specialized oral health services was associated with municipalities having smaller populations (67.3%; CI: 61.6-73.0; p < 0.001), lower HDI-M (41.9%; CI: 35.8-48.0; p < 0.001), lower per capita income (41.2%; CI: 35.2-47.3; p < 0.001), and higher mean number of oral health teams per 10,000 inhabitants (50.6%; CI: 46.0-58.4; p < 0.001). The results show positive impacts from the implementation of the National Oral Health Policy in Brazil, meeting the goal of expanding the supply of secondary care services according to the principle of equity in care. El objetivo fue analizar nacionalmente los factores asociados con la disponibilidad de servicios odontológicos especializados en municipios brasileños. Se trata de un estudio ecológico, con una muestra de 776 municipios que participaron en el 1er ciclo del Programa para Mejorar el Acceso y la Calidad de los Centros de Especialización Odontológicas (PMAQ-CEO, en Portugués), realizado en 2014. Las variables dependientes del estudio consistieron en un coeficiente creado a partir de las variables de número de profesionales y carga horaria semanal de cirujanos-dentistas, ejerciendo en las especialidades mínimas por 10.000 habitantes. Se realizó un análisis factorial exploratorio para la creación de una puntuación que midiera el desempeño de los municipios, respecto a la disponibilidad de servicios odontológicos especializados. Con el fin de evaluar los factores asociados con el desempeño de los municipios, se utilizó el test de chi-quadrado de Pearson, teniendo como variables independientes los siguientes indicadores municipales categorizados en terciles: renta per cápita, Índice de Desarrollo Humano Municipal (IDH-M), población residente, gasto total por habitante en salud y equipos de salud bucal por 10.000 habitantes. El mayor desempeño respecto a la disponibilidad de servicios especializados en salud bucal estuvo asociado a municipios con menor tamaño poblacional (67,3%; IC: 61,6-73,0; p < 0,001), con menor IDH-M (41,9%; IC: 35,8-48,0; p < 0,001) y con menor renta per cápita (41,2%; IC: 35,2-47,3; p < 0,001) y mayor media de equipos de salud bucal por 10.000 habitantes (50,6%; IC: 46,0-58,4; p < 0,001). Los resultados demuestran impactos positivos de la implantación de la Política Nacional de Salud Bucal en el país, como el cumplimiento del objetivo de ampliación de la oferta de los servicios de atención secundaria, basados en el principio de la equidad en la asistencia.

Composite indicators are a very useful tool for conveying summary information on the overall performance of institutions and facilitating decision-making. Increasingly, there is a demand for indicators that allow performance to be assessed after the implementation of a strategy. This has several difficulties, and in this paper, we address three of them: how to evaluate at different points in time, how to estimate the weighting of the criteria and how to normalize the data. Our proposal is based on multicriteria techniques, using a recent method, uwTOPSIS, and is applied to data collected from 2975 students enrolled in the first year of science and engineering at the Industrial University of Santander (Colombia) from the first semester of 2016 to the first semester of 2019. In the paper, we show that our proposal makes it possible to measure and evaluate the academic performance of students at two points in time, and this allows the University to know whether its student support policy has been successful and to what degree it has been effective. Due to the large amount of data handled, data management has been done using R programming language, and model implementation has been done with Python.

This study aims to build a list of composite indicators by information that enable the assessment of philanthropic higher education organizations&rsquo, (PHEOs) social responsibility based on the interests of their stakeholders. A list of 88 social responsibility indicators was built based on a literature review and stakeholder interest to serve as a basis for the composite indicators. In order to identify and validate the indicators, field research was carried out. Stakeholders from Brazil and the United Kingdom scored them from one (not important) to five (very important) for each indicator identified in the literature review. With 540 valid answers, they suggested inclusions and exclusions according to their interests. Next, a correlation analysis was performed to identify and eliminate redundant indicators. The principal component analysis extracted the composite indicators. The results point to 11 principal components that are configured as composite indicators to evaluate the performance of PHEOs social responsibility considering the stakeholder&rsquo, s interests, a factor that differentiates this research from the literature revision done. Some of the composite indicators are close to the social responsibility categories reviewed in the literature. However, others show more specific and in-depth interests, especially regarding the stakeholders themselves. These composite indicators help managers establish disclosure policies whenever they are focused on seeking legitimacy in the social context of PHEOs. It also contributes to the advancement of theoretical knowledge, presenting composite indicators, from the stakeholder&rsquo, s perspective, for the disclosure of social responsibility of PHEOs.

Promoting sustainable agricultural systems, maintaining biodiversity, and establishing measures to counteract climate change are the clear objectives of the future Common Agricultural Policy (CAP); however, there is still uncertainty regarding its implementation. At the level of the European Union, the justification for the CAP appears increasingly linked to the translation of policy objectives into results-based measures. Accordingly, in this paper, we estimate a composite indicator to track changes at the farm level and assess the impacts of agri-environment climate schemes (AECSs) on the change in management intensity. As AECS payments are tailored to compensate for the costs and income forgone due to participation in environmentally friendly measures, we assume that any environmental benefits increase with the amount of payment received. We estimate the effects of differential payments on management intensity by applying a generalised propensity score approach to a case study comprising all AECSs implemented in the Veneto region (North-eastern Italy). We find that AECSs have a significant effect on the composite indicator of extensification, but the provision of environmental good differs among the varying levels of payments. Our results contribute in-depth reflections and a proposal for measuring the cost-effectiveness of AECSs to the ongoing debate on the post-2020 CAP regarding AECSs.

The assessment and management of landscape fragmentation (LF), i.e., the subdivision of the habitat into smaller and more isolated patches, can benefit from the adoption of a composite indicator explaining, in a unique measure, the various concerns involved. However, the use of composite indicators may be affected by lack of data, subjectivity in algorithm design, and oversimplification connected to reduction to just one index. In these cases, the findings obtained might not provide the researcher with reliable information. In this paper, we design and apply the Composite Indicator of Landscape Fragmentation (CILF), a metric resuming three indicators concerning the effect on LF of transport and mobility infrastructures, human settlements, and patch density per se. The application concerns the measurement of LF spatial pattern and dynamics from 2003 to 2008 of 51 landscape units in the island of Sardinia (Italy). We considered a complete spatial data set, chose the generalized geometric mean as aggregation algorithm, and verified its robustness via sensitivity analysis of the results. We found that, in 2003 and 2008, the CILF spatial pattern shows higher values in coastal areas and has varied randomly, i.e., without a consistent tendency to converge to, or diverge from, a mean value. Overall, we demonstrate that the CILF is a powerful instrument for monitoring LF in Sardinia and advocate that it can be further implemented, following the same methodological framework, by extending the pool of indicators considered and assessing a weighted version of the composite indicator.

The performance of secondary schools is usually assessed based on students’ results on national exams at the end of secondary education. This research uses data on academic achievements by first-year univer- sity students to benchmark secondary schools on their ability to lead students to success in higher edu- cation. The analysis is conducted using data of University of Porto and Catholic University of Porto, Portu- gal, for a three-year period, corresponding to more than 10.0 0 0 students from 65 degrees, for which the school of origin is known. A number of variables representing students’ success in Higher education were constructed for each school in our sample and aggregated through a Benefit of the Doubt indicator. Re- sults suggest that the schools’ ranking based on schools’ ability to prepare students for university success is quite different from the ranking based on results on national exams. Given these findings, we propose complementing schools’ performance assessments (traditionally based on national exam results or indi- cators of value added) with indicators that account for the preparation of students for success in future challenges, which is indisputably a key objective of secondary education. We propose a composite indi- cator for the analysis of these complementary aims as well, and results show that frontier units indeed exhibit trade offs between traditional measures of performance and our new measure of performance.

Small island spaces are confronted with significant disadvantages that ultimately result in strong economic vulnerability. The conventional literature emphasizes the main role of agriculture in generating structural vulnerability. Specifically, the higher the weight of agriculture compared to other sectors is, the more structurally vulnerable an economy is. However, the recent food crises revealed that the economic dependence on agriculture is not a problem on its own, but the issue is rather the efficiency of this sector along with the orientation of domestic production towards diversification and food self-sufficiency. In this paper, we thus propose a new structural economic vulnerability indicator based on the assumption that promoting the local agriculture could boost development. We insert the share of the agriculture sector in the GDP proxied by imported food dependency into the 'official' economic vulnerability index. Moreover, for robustness, the proposed indicator is obtained based on an endogenous weighting system derived from data envelopment analysis. Finally, our simulations for a sample of 131 developing economies point out that considering food dependency reduces dramatically the exposure of small island economies to structural vulnerability.

The choice of the normalization method is one of the steps for constructing a composite indicator for metric data (see, e.g. Nardo et al., 2008, pp. 19-21). Normalization methods lead to different rankings of the set of objects based on composite indicator values. In the article 18 normalization methods and 5 aggregation measures (composite indicators) were taken into account. In the first step the groups of normalization methods, leading to identical rankings of the set of objects, were identified. The considerations included in Table 3 reduce this number to 10 normalization methods. Next, the article discusses the procedure which allows separating groups of normalization methods leading to similar rankings of the set of objects separately for each composite indicator formula. The proposal, based on Kendall’s tau correlation coefficient (Kendall, 1955) and cluster analysis, can reduce the problem of choosing the normalization method. Based on the suggested research procedure the simulation results for five composite indicators and ten normalization methods were presented. Moreover, the proposed approach was illustrated by an empirical example. Based on the analysis of the dendrograms three groups of normalization methods were separated. The biggest differences in the results of linear ordering refer to methods n2, n9a against the other normalization methods.

The multifunctional Ecosystem Service supply analysis at the spatial level is often the output of a weighted sum of layers in a Geographic Information System (GIS). This procedure is weak in detecting and representing the relationships between the input layers. Nonetheless, composite indicators produced by overlaying techniques are quite common in applied research and their discrepancies are underestimated in the scientific community, thus affecting the quality of resulting composite maps. In this work, we empirically test the effectiveness of multivariate statistics to obtain reliable composite Ecosystem Maps in the Turin metropolitan area (north-west Italy). We apply the Principal Component Analysis (PCA, using Matlab and ESRI ArcGis) to seven Ecosystem Service models (Habitat Quality, Carbon Sequestration, Water Yield, Nutrient Retention, Sediment Retention, Crop Production and Crop Pollination) and we evaluate how much the resulting composite map differs from the traditional GIS overlay. In doing this, the spectral analysis (with eigenvectors and eigenvalues) of the covariance matrix of the normalized layers confirms the heuristic arguments about the dependence between Ecosystem Services. We show that the PCA method can provide valuable results in landscape Green Network design, avoiding the limits of standard overlaying procedures. Finally, smoothing and classification techniques, applied to PCA estimates, can further improve the approach and encourage its use in various ecological indicators.

Summarization: In recent years, corporate governance (CG) has become a central point of interest in corporate finance. Among others issues, the participation of women as executives in corporate management has received much attention. The academic research in this field is controversial. We investigate the relationship between boardroom gender diversity and firm financial performance, using a sample of 76 U.S.A’s major listed companies between 2006 and 2013. A novel CG performance indicator is constructed using a multi-attribute approach based on the principles of efficiency measurement, separating the effect of women’s administrative participation from other CG attributes. At the second stage, a panel regression analysis is performed to explore the relationship between CG and women’s participation on the risk-adjusted performance of the firms. Presented on: Information Systems and Operational Research

In the last decades, policy discussions have increasingly considered participation in arts and cultural activities as a vehicle to reach broader social policy goals, such as social inclusion or active citizenship. However, convincing empirical evidence on the social impacts of arts and culture is still scarce. In particular, little attention has been given to the impact that cultural participation may have on individuals’ engagement in civil society, especially in Europe. In order to address this lack of evidence, this paper explores the connection between cultural participation and civic participation in Italy using Likert-scale data collected in the ISTAT “Aspetti della Vita Quotidiana” Survey (2014). To do so, two composite indicators—the participation in cultural life indicator and the participation in civic life indicator—have been built using an approach based on partial order theory and the concept of Average Rank. The effect of cultural participation on civic engagement has been estimated using a quantile regression model which controls for potentially cofounding factors such as education, income, age and gender. Results do confirm that participation in arts and cultural activities is highly correlated with participation in civic life. It is particularly worth noticing that at higher levels of civic participation, cultural participation has a positive and strong effect on civic engagement of people having a low educational level.

Organized crime impacts on societies, and it is one of the main threats to global justice. Assessment of the presence and activities of organized crime is therefore crucial for the design of effective policies and actions. Several methodological approaches have been proposed to address the two main challenges implied by this effort: (1) the complexity of defining what organized crime is; and (2) the collection and generation of reliable data to estimate it. Moreover, organized crime groups increasingly engage in illicit activities that extend beyond their traditional territories and the borders of a single state. This expansion is facilitated by the ever-growing economic and social connections among people and countries. This poses serious threats to countries and their citizens by generating direct and indirect economic damage, affecting social structures, and hindering the development and stability of states. This paper discusses the latest methodological advances in measurement of OC within a country or a region. It then describes a strategy for assessing OC presence based on the estimation of the transnational illicit markets and the role of countries in those illicit networks. Examples from the current research studies are provided.

In the analysis of the difference in the distribution and profiles of the equitable and sustainable well-being, the territorial dimension is a fundamental reading-key for local policies since it allows the areas of advantage or relative deprivation to emerge more accurately. Specifically, in Italy the provincial level coincides with the administrative area of metropolitan cities, which are the subject of growing attention from European and national policies. The BES 2018 report by Italian National Institute of Statistics (ISTAT) has confirmed that from 2015 an improvement in many areas of well-being has been marked, even if territorial differences remain stable both in levels and dynamics. These differences appear in some cases as real structural differences between the North and South of Italy. Then, the measures of equitable and sustainable well-being in the territories allow, in various degrees, to deepen and specify this situation employing synthetic measures of well-being. In this work, we propose a statistical methodology focused on the simultaneous partial least squares structural equation modeling and simultaneous K-means clustering to obtain a composite indicator of Italian well-being and at the same time a classification of Italian territorial micro-areas by means of the just updated provincial data about BES 2018. In this way, the territorial differences of well-being can be more reliably and more exactly defined on the basis of the relationships among all elementary indicators and domains proposed in the analysis of well-being by ISTAT.

The aim of this work is to verify a theoretical representation of the multidimensional concept of territorial risk, through a study of Italian provinces. The concept is referred to the exposure of the territorial system to the risk of an exogenous event, as a combination of factors of fragility and resilience. In the proposed framework, elements of the organization of a territorial system had been identified, in its economic, social and environmental dimensions, mostly affecting his exposure to the adverse impact generated by a disturbing event and the ability to answer and to regenerate. According to this scheme of representation, the territorial risk is the result of the combination of these factors that influence the probability of a local system to undergo negative changes as a result of the occurrence of the event. The evaluation of vulnerability and resilience of a system, by adopting a holistic reading of the phenomenon, involves the identification of systemic components and attributes such as openness to the external environment, structural diversification, availability of resources, structural dependence/independence, functional redundancy, adaptability strategy. In the paper the authors propose the construction of an indicator system and composite indices for monitoring, through multivariate statistical techniques, the factors of fragility and ability to recovery. The proposed system is applied to a study of the economic, social and environmental vulnerability and resilience of the Italian provinces.

In recent years, building disaster resilient communities has become a primary objective of crisis management institutions across the globe, as a resilient community is likely to suffer fewer losses and recover more quickly when faced with an adverse event. However, in order to strengthen a community's resilience, one needs to first establish a baseline, an initial measure that can be used to compare communities and to track changes over time. This article presents such a baseline, a community resilience index, for Norway. Following the approach outlined by the Baseline Resilience Indicators for Communities (BRIC), the article constructs a hierarchical index, using 47 indicators divided into six subdomains, to describe the resilience capacities of the Norwegian municipalities. The results show considerable variations in the relative levels of resilience. Most markedly, there seems to be a north-south divide; i.e., many of the northern municipalities having lower levels of overall resilience and many of the southern municipalities having higher levels of overall resilience. These initial observations are further analysed by deconstructing the overall index into its components to identify driving forces behind the overall resilience score. To validate the results, the resilience scores are compared to previously established vulnerability metrics. The resilience and vulnerability metrics are then used to identify potential low-risk (high resilience, low vulnerability) and high-risk (low resilience, high vulnerability) areas across Norway.

Producción Científica, There is wide consensus regarding the capacity of the creative economy as a factor in economic development and sustainability in regions in the medium term, and it is an issue that has aroused interest in measuring the creative potential of areas by estimating composite indicators. Most works posit indicators by countries or cities and normally employ a single aggregation method. This work proposes the construction of a synthetic indicator of creativity with regional disaggregation for a wide sample of European countries, comparing different aggregation methods under the hypothesis of the benefit of the doubt (BoD). We use Eurostat data and also analyse spatial dependence processes. Findings point to an inequality in the regional distribution of creativity in Europe and this reveals creative gap models in areas, suggesting that creativity triggers greater spatial disparities wherever it is found. The principal creative clusters are located in regions in central and northern Europe, particularly in regions with national capitals. The regional distribution of the indicator evidences positive spatial dependence, thereby revealing the existence of spatial externalities that provide feedback for the concentration process and, therefore, a widening of the disparities., Junta de Castilla y León (project VA012G19)

Supplemental Digital Content is available in the text, Evidence-based guidelines for the correct management of cancer patients are developed on the idea that timely care can improve health prognoses and quality of life. The aim of this paper is to evaluate the adherence of clinical pathways to clinical guidelines provided at the hospital level, for colorectal cancer care. By using a retrospective observational study, we proposed a method for associating each patient to a healthcare provider and modeling adherence as a latent construct governed by a set of 10 influential indicators. These indicators measure the adherence to specific guidelines for diagnosis, surgical treatment, chemotherapy, and follow-up. The model used was that of the item response theory (IRT). When evaluating providers, the IRT allows for a comparison of indicators in terms of their discriminating ability and difficulty, and in terms of their adherence to guidelines. The IRT results were compared with non-latent methods: numerator-based weight and denominator-based weight. A strong degree of coherence of the indicators in measuring adherence, and a high level of overall agreement between latent and non-latent methods were noted. The IRT approach demonstrated similar providers’ evaluations between endoscopy and histological assessment indicators. The greatest discriminating ability among providers could be attributed to all diagnostic exams, while the lowest was associated with follow-up endoscopies. The most difficult indicator to achieve was fecal occult blood test, while follow-up imaging was the easiest. In a decision-making framework, valuable indications can be derived from the use of IRT models rather than weighting methods. Using IRTs, we were able to highlight the principal indicators in terms of strength of discrimination, and to isolate those that merely duplicated information.

A survey has been performed in collaboration with the National Association of the Municipalities of Italy (Veneto region) to study the role of the municipalities with regard to the local area, to reconceptualize the profile of public directors and to study municipal directors as far as requirements in terms of competencies for skills and training are concerned. In the paper the importance of municipal director requirements for skills and training is assessed using composite indicators. The analysis showed that the most important skill requirements refer to internal aspects of the organization, whereas the most important training requirements refer also to external aspects. The analysis has been repeated on the strata of municipal directors obtained by considering municipality population, director sex, length of service, education and opinions about municipal mission. It is shown that the various strata of directors are rather concordant in assessing the importance of skill and training requirements. Municipal directors are aware of Italian public administration now being oriented toward new public management where they are assessed according to the results they produce. The robustness of the results has been addressed using uncertainty analysis.

This study aims to construct an original interval-based composite indicator of the gender diversity considering different assumptions on the development of the composite indicator. In this way the composite indicator built can be considered more robust than a classical version of the same indicator. Composite indicators are a very important tool to analyse and evaluate policies and sectors. The problem in using composite indicators is that the results which can be obtained can be dependent to the assumptions given on their construction. In this sense we have already considered an initial approach in the construction of composite indicator (Paoloni et al. (2016) Towards a new architecture of knowledge: Big data, culture and creativity. Proceedings, 15–17 June 2016 Dresden Germany (pp. 1944–1958)). We take into account different assumptions, and we are able to construct an interval-based composite indicator. In this way we can consider a value which is useful for the comparison (the chosen assumption), a centre of the composite indicator, and the range which is related to the variability due to the different assumptions. Our work contributes to the existing literature on composite indicators in gender studies. In particular our work is addressed on providing a composite indicator of gender diversity for the listed European companies. This study is useful to policy purposes because it helps the process of decision-making which can be based on the different rankings which are derived from the interval-based composite indicators.

The aim of this paper is to understand and evaluate agricultural sustainability in the Stavropol Territory by means of a composite indicator. In particular, the paper applies principal component analyses to calculate a composite sustainability index by integration of selected economic, social and environmental indicators. The results demonstrate the utility of analyzing several indicators in conjunction. The results also may indicate which variables influence development of regional agriculture. This information is important in order to design agricultural support policy and to implement an increase the sustainability of the agriculture sector. JEL Code: O13, Q11

The assessment of the sustainability of agricultural systems is multidimensional in nature and requires holistic measures using indicators with different measurements and units reflecting social, economic, and environmental aspects. To simplify the assessment process, various indicators have different units, and measurements are grouped under broad indicator heads, and normalization and/or transformation processes are carried out in order to aggregate them. In this study, a total of 50 indicators from agricultural sustainability categories of productivity, stability, efficiency, durability, compatibility, and equity are employed to investigate which normalization technique is the most suitable for further mathematical analysis for developing a final composite indicator. To understand the consistency and quality of normalization measurement techniques and compare the benefits and drawbacks of the various selected normalization processes, the indicators of agricultural sustainability are considered. Each of the different techniques for normalization has advantages and drawbacks. This study shows that the proportionate normalization and hybrid aggregation rules of the arithmetic mean and the geometric mean are appropriate for the selected data set, and that this technique has a wider applicability for developing composite indicators for agricultural sustainability assessment.

We evaluate modelling solutions built composing different modelling approaches. Modelling of soil temperature was the case study, evaluating modelling solutions against a multi-year, multi-location database of field recorded data. Widely used simulation packages were re-implemented using advanced techniques, and nine new modelling solutions were built hybridizing across the original ones. Multi-metric indices were developed for modelling solution evaluation.The hybrid solution implementing the Parton's approach (surface temperature) and the SWAT (temperature along the soil profile) led to the best compromise between agreement and robustness under the explored conditions. Differently, choices of soil water redistribution models caused a modest variability with respect to the simulation of soil temperature. The methodology presented allows providing clear indications about model choice and should be considered useful practice in model development.The model libraries used to run the analysis are freely available for download, and they allow for further extension of the composition exercise. We develop nine new modelling solutions (MSs) for soil temperature simulation.New MSs were composed by hybridization of models from known modelling packages.Software component libraries allowed the model composition study.The evaluation of MSs was done on a large dataset via a multi-metric procedure.This is a proof of concept for fine-granularity model composition and MS evaluation.

The need to reduce the emissions of greenhouse gases in energy generation has led to international and national bodies recommending and encouraging a series of policies and measures to boost so-called green energy generation and achieve sustainability in the energy sector. In order to assess the energy sustainability path of countries worldwide, we first propose a dynamic analysis of an energy sustainability index (ESI) based on the methodology of composite indicators built on a large dataset of 136 heterogeneous countries and 38 variables observed in 3 years (2000, 2005 and 2011). Second, to isolate the influence of socio-demographic and economic characteristics on the energy sustainability of countries, we perform quantile regression with clustered data. Our findings demonstrate that major changes are required in developing countries, where development is pursued mainly through the use of energy sources based on fossil fuels. To ensure access to sustainable energy, policy makers need to consider and improve the key factors according to the ESI’s quantiles. In particular, human development variables (i.e., life expectancy and the percentage of completed secondary schooling attained in the population) and the variables related to civil rights and institutions (e.g., percentage of women in national parliaments) are crucial in promoting energy sustainability. By contrast, high unemployment rates, high congestion costs and the continued use of highly polluting energy sources have the effect of reducing energy sustainability.

The aim of the paper is to discuss the possible use of Combined Heat and Power (CHP) plants and to highlight some abnormal effects, generated by the available support mechanisms, both in sizing and management and to propose guidelines for defining optimal operational strategies of CHP power plants. Some composite indicators are proposed to give a more comprehensive assessment of the performances of a plant, for the design and optimization phase and for a possible approach to support mechanisms. A new composite indicator is introduced in order to assess the benefits of different scenarios. The method is tested with reference to a case study: a medium size district heating system, powered by a CHP plant supported by conventional auxiliary boilers. Data coming from a real plant equipped with a remote monitoring system are analyzed. Operating data of a typical month are used in order to test the approach for the reference system. The paper shows how the use of the defined composite indicators can modify in a meaningful way the operating strategy of the CHP, increasing a lot the share of thermal energy produced with the CHP unit with respect to the conventional boilers.

In places like China, an ageing population coupled with changes in living standards and increases in disposable income, imply a shift of the demand for health-related goods and services which is likely to affect the whole organization of the industries that supply such goods and services at the global level. One of the industries most likely to be affected is the pharmaceutical sector. In the early 2000s China was already the second largest global producer of pharmaceutical ingredients. The pharmaceutical sector has become one of the most important industries promoted by the Chinese government and Five-Year Plan of China’s Strategic Emerging Sectors, mergers and acquisition (M&A) activity has been the key strategy to restructure the sector and increase its competitiveness. This paper firstly provides an updated picture of the evolution of M&As in the pharmaceutical sector, compared to other sectors, in China in the period 2005–2013. Secondly, we develop a composite indicator to measure the industrial performance of all Chinese industrial sectors over time, which allows us to assess the performance of the pharmaceutical industry compared to that of other sectors of the Chinese economy. Finally, we develop and estimate an empirical model that tests the relationship between the number of M&A in a sector and its performance, with a particular focus on the pharmaceutical case. The results offer some initial evidence of positive effects from the process of restructuring of the pharmaceutical sector in China.

New emphases on livability and sustainability are creating demands for measuring and applying these concepts in transportation policy and planning. However, livability and sustainability are complex, multidimensional concepts that require careful measurement if they are to be applied meaningfully in plan evaluation and benchmarking. This paper provides a framework for constructing and applying quantitative livability and sustainability indicators. In addition to critically reviewing principles of constructing indicators describing a multidimensional concept such as livability or sustainability, we also discuss methods for capturing local context, a critical feature for transportation planning. Specifically, we review methods for incorporating diverse stakeholder perspectives into indicator construction and spatial analytic tools for geographic entities and relationships. We also discuss spatial decision support systems and the Geodesign concept for organizing these tools and technologies as well as integrating livability indicators into the overall planning process.

This article analyzes agricultural sustainability in the context of land degradation, rural poverty and social inequality, taking China’s Loess Hills as an example. The analysis attempts to understand the multi-dimensionality of sustainability at the farm level and its relationship with physical-socio-economic-infrastructural-technological framework conditions in the context of the land set-aside program viz. the Grain for Green Project (GGP). We developed composite indices of sustainability and its environmental, economic and social dimensions using a principal component analysis (PCA)-based weighting scheme. Regression analyses were conducted to examine the relationship between the estimated sustainability indicators and the variables representing framework conditions of knowledge, demographics, resource endowment and production techniques. The stated analysis was conducted on a dataset collected by means of household surveys in 2014 in valleys and flood plain areas in Yanhe Township. Findings reveal hidden correlations among the indicators of environmental, economic, and social pillars of sustainability. The ratio of land under the conservation program to actual farmland emerged as a key determinant of overall agricultural sustainability and its social dimension, which reaches the maximum when the ratio is around 0.56 and 0.64, respectively. The results also show that there is need to balance off-farm and on-farm income diversification as well as highlight the role of women in ensuring the sustainability of farming households. The core achievement of the article is the definition of the thresholds for the land set-aside program and the identification of major determinants of agricultural sustainability in the rural Chinese context in particular and in rural farming communities in general.

The PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) method is applied to five different types of agricultural systems in coastal Bangladesh in order to rank the alternatives from most to least suitable according to a range of sustainability indicators. More specifically, composite indicators from six sustainability categories&mdash, productivity, stability, efficiency, durability, compatibility, and equity&mdash, are used for this assessment. The case study demonstrates that PROMETHEE constitutes a flexible MCDA (Multi-Criteria Decision Analysis) tool to investigate the sustainability of agricultural systems, rank the different alternative systems, and provide valuable insights.

In recent decades, the concept of sustainability has become increasingly prominent in agricultural policy debates. This has led more and more stakeholders to pay attention to the questions of monitoring and evaluation of agricultural practices, and raised the question of appropriate indicators to assess sustainability aspects of given practices. We provide here a review of indicators of sustainability for agriculture. We describe sustainability indicators used in the literature following the typology based on the three sustainability pillars: environmental, economic and social. The literature review shows that the environmental pillar has undergone an ‘indicator explosion’, due to the multitude of themes covered and the attention given by society to this dimension of sustainability. By contrast, economic indicators target a relatively small number of themes. Social indicators typically cover two main themes: sustainability relating to the farming community and sustainability relating to society as a whole. The measurement of these social indicators is challenging as they are often qualitative and may therefore be considered subjective. Careful attention should be given to the choice of indicators, since the data measured will infl uence the calculation of that indicator and therefore the outcome of the analysis. It should first be decided whether individual or composite indicators are preferable, and whether single indicators or a set of indicators should be used. Also, sustainability assessments should be validated, credible and reproducible. Several selection criteria are provided in the literature, such as representativeness, transferability, adaptability and measurability at an acceptable cost.

International audience; Mauritania is vulnerable to climate change due to its location along the Sahel, the desert climate of the Sahara, and its socioeconomic characteristics. To support the identification of climate change adaptation measures in Brakna and Assaba in Mauritania, a spatial assessment of vulnerability to climate change targeting the livelihood sectors of agriculture and pastoralism was carried out. Based on the identification of relevant drivers of climate change vulnerability in a broad consultation process among national and local stakeholders, a variety of geospatial indicators were identified and integrated in the assessments based on a standardized vulnerability assessment approach. In this paper, we provide a reflection on the methodology applied and identify lessons to be learnt on data quality, spatial scales, aggregation and visualizations. The primary conclusion is that users of the assessment results and stakeholders need to be engaged in the entire assessment process in order to reflect local characteristics more fully, and to ensure that the results are reflected in informed decision-making.

In this paper we present a novel approach for construction of a leading indicator for household demand for debt, i.e. consumer credit demand index. In order to derive the proper set of indicators we first present a theoretical background for the dimensions of the index. With all dimensions of the index at hand we search for indicators in the State of the Households Survey and consumer finance survey. We later aggregate the dimensions into a single index with generalized mean which does not allow for full compensability between dimensions. Finally, we check the leading properties of the index.

City rankings that aim to measure the environmental sustainability of European cities may contribute to the evaluation and development of environmental policy of European cities. The objective of this study is to identify and evaluate the methodological characteristics of these city rankings. First, a methodology was developed to systematically identify methodological characteristics of city rankings within different steps of the ranking development process. Second, six city rankings (European Energy Award, European Green Capital Award, European Green City Index, European Soot-free City Ranking, RES Champions League, Urban Ecosystem Europe) were examined. Official websites and any methodological documents found on those websites were content analyzed using the developed methodology. Interviews with representatives of the city rankings were conducted to acquire any additional information. Results showed that the city rankings varied greatly with respect to their methodological characteristics and that all city rankings had methodological weaknesses. Developers of city rankings are advised to use the methodology developed in this study to find methodological weaknesses and improve their ranking. In addition, developers ought to be more transparent about the methodological characteristics of their city rankings. End-users of city rankings are advised to use the developed methodology to identify and evaluate the methodological characteristics of city rankings before deciding to act on ranking results. (c) 2014 Elsevier Ltd. All rights reserved.

Innovative bioenergy projects show a growing diversity in biomass pathways, transformation technologies and end-products, leading to complex new processes. Existing energy-based indicators are not designed to include multiple impacts and are too constrained to assess the sustainability of these processes. Alternatively, indicators based on exergy, a measure of “qualitative energy”, could allow a more holistic view. Exergy is increasingly applied in analyses of both technical and biological processes. But sustainability assessments including exergy calculations, are not very common and are not generally applicable to all types of impact. Hence it is important to frame the use of exergy for inclusion in a sustainability assessment. This paper reviews the potentials and the limitations of exergy calculations, and presents solutions for coherent aggregation with other metrics. The resulting approach is illustrated in a case study. Within the context of sustainability assessment of bioenergy, exergy is a suitable metric for the impacts that require an ecocentric interpretation, and it allows aggregation on a physical basis. The use of exergy is limited to a measurement of material and energy exchanges with the sun, biosphere and lithosphere. Exchanges involving services or human choices are to be measured in different metrics. This combination provides a more inclusive and objective sustainability assessment, especially compared to standard energy- or carbon-based indicators. Future applications of this approach in different situations are required to clarify the potential of exergy-based indicators in a sustainability context.

This study investigated the relative performance of (car) drivers at the individual level, using data from a driving simulator, in order to identify the best drivers within the sample and to gain insight into the most problematic behavior of each driver. To this end, 38 participants varying in age and gender were enrolled to take part in a particular simulator scenario (i.e., curve taking) and their speed, acceleration and lateral position – the three most important driving performance indicators based on literature review – were monitored at various points (before, during and after the curve). As a widely accepted tool for performance monitoring, benchmarking and policy analysis, the concept of composite indicators (CIs), which combines single indicators into one index score, was employed, and the technique of data envelopment analysis – an optimization model for measuring the relative performance of a set of decision making units, or drivers in this study – was used for the index construction. Based on the results, all drivers were ranked, and best performers were distinguished from underperforming drivers. Moreover, by analyzing the weights allocated to each indicator from the model, the most problematic parameter (e.g., lateral position) and point along the curve (e.g., at curve end) were identified for each driver, leading to specific driver improvement recommendations (e.g., training programs).

This study investigates the relative performance of older drivers at the individual level, based on specific measures of functional and driving abilities. To do so, 55 participants aged 70 years and above completed tests of an assessment battery of psychological and physical aspects as well as knowledge of road signs; moreover, they took a driving simulator test in which specific driving situations that are known to cause difficulties for older drivers were included. To evaluate the overall performance of each driver, all the above information was combined by using the concept of composite indicators, and the technique of data envelopment analysis, which is an optimization model for measuring the relative performance of a set of units, or drivers in this study, was employed. The model output distinguishes the best-performers from those underperforming drivers, and helps in guiding future development of training interventions tailored to each individual by specifically targeting those functions that are (mostly) impaired.

Data envelopment analysis (DEA) as a performance evaluation methodology has lately received considerable attention in the construction of composite indicators (CIs) due to its prominent advantages over other traditional methods. In this chapter, we present the extension of the basic DEA-based CI model by incorporating fuzzy ranking approach for modeling qualitative data. By interpreting the qualitative indicator data as fuzzy numerical values, a fuzzy DEA-based CI model is developed, and it is applied to construct a composite alcohol performance indicator for road safety evaluation of a set of European countries. Comparisons of the results with the ones from the imprecise DEA-based CI model show the effectiveness of the proposed model in capturing the uncertainties associated with human thinking, and further imply the reliability of using this approach for modeling both quantitative and qualitative data in the context of CI construction.

In this paper, a method of artificial neural networks (NN) is proposed as an alternative tool for the one-step-ahead prediction of composite indicators (CIs) of Lithuania’s economy. CI is composed of widely used social and economic indicators. The NN is applied for forecasting CI during the financial crisis and later periods (2008–2010) on the basis of data of earlier years (1998–2007). In this work, the Extreme Learning Machine (ELM) algorithm is combined with locally weighted regression. The analysis shows that the prediction error of a testing sample is statistically smaller compared to Levenberg–Marquardt or ELM methods.

A careful evaluation of student satisfaction is central for tertiary education institutions because help them to pinpoint their strengths and identify areas for improvement. University student satisfaction is not directly measurable because it depends upon many different aspects. It may be studied through a composite indicator based on other variables (partial indicators) that are directly observed. In this paper we consider student socialization which is an important aspect of students’ educational experience even if it is not as widely considered as assessments of skills development and ratings of various aspects of courses and programs. More precisely we consider student relations with the other people that act within the university context: teachers, teacher assistants, non teaching staff, other students, citizens of the town that hosts the university. To grasp the complexity of university experience, it is not enough to know the degree to which students are satisfied, it is important to understand the factors that contribute to student satisfaction. To this end we propose a simple algorithm to find the most important partial aspects underlying the student satisfaction socialization issue. An application to students of the University of Padova is discussed. The method is compared to PCA, and it is shown that is much simpler to be used, requires milder assumptions and is always readily comprehended.

International audience; In this paper we propose to use an aggregation rule derived from the Group Decision Theory, and based on the ranks of a set of individual indicators, for building a family of composite indicators for the security of energy supply. This family of composite indicators depends on a parameter associated with the risk-averse level of the decision maker, which can hence vary continuously from risk-prone to risk-averse. It represents a valuable and objective instrument to evaluate the degree of security of energy supply of different countries without entering into controversial matters related to the choice of the weights. This methodology can be used to aggregate either ranks or normalized values of the individual indicators. We apply it to a set of individual indicators, accounting for different dimensions of the security of supply and derived from the outputs of an energy system model. We study the evolution in time of the countries' performances, as well as the propagation of the uncertainties associated with the individual indicators to the composite. We also studied the robustness of such composite indicators with respect to the risk-averse level.