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Purpose: This paper aims to identify different combinations of causal conditions (celebrity attributes) that explain our outcome: destination image. More specifically, three main research questions guide our work: (1) Which attributes should an outstanding sportsperson have to enhance the image of his/her country as a destination image? (2) Are these the same for different product categories? (3) Do tourists and residents differ? Design/methodology/approach: To this end, the fuzzy-set Qualitative Comparative Analysis (fsQCA) was used with a sample of 187 participants (105 tourists and 82 residents). Findings: Results show that some attributes of a sports celebrity are more critical than others in enhancing destination image. Those attributes of sports celebrities appearing in the intermediate and parsimonious analysis should be prioritized. This is the case of trustworthiness. Second, experience is a peripheral requirement (only appeared in the intermediate analysis). Third, attractiveness is unnecessary and an even and undesired attribute in many solutions. Fourth, when comparing tourists and residents, both groups value the role of football players, while residents also appreciate the role of marathon runners. Tennis players are the less relevant sports celebrities to build Spain's destination image. Originality/value: First, a new statistical analysis in the marketing discipline, QCA, has been used. The use of qualitative approaches to investigate destination images has been scarce. Second, the study of the role of sports celebrity endorsement on brand–place attachment has yet to be investigated. Third, studies about the role of residents in the image of a tourism destination/city are scarce. Tourists and residents must be investigated because they can benefit from sports celebrities' activities. [ABSTRACT FROM AUTHOR]

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In the search for solutions for Integrated Water Resources Management (IWRM), decision-makers frequently face governance-related challenges such as the fragmentation of institutions, the interconnection with other essential services and overlapping competences. In many cases, solutions to these challenges involve strengthening coordination between different actors and sectors. Today different digital tools and resources are available to support related decision-making processes. These tools can (1) offer a context-specific assessment of the water governance system ("Diagnosis") and (2) provide, in some cases, context-specific recommendations to tackle identified deficits ("Therapy"). In this paper, we aim to advance the understanding of the potential of digital tools for water governance and management assessments. After reviewing relevant tools, we introduce the Diagnostic Water Governance Tool (DWGT) that builds on a clear diagnosis-therapy-logic. After presenting the empirical basis and functional logic of the DWGT, we test the DWGT in two case studies: the Guadalquivir river basin in Spain and the Emscher river basin in Germany. We find that the DWGT provides context-specific, evidence-based instrument recommendations that can enhance water governance and management, albeit accompanying suitability evaluations by governance experts remain critical. We conclude by discussing the limitations of digital tools for water governance and management assessments and sketch out fields of future research. • Most water governance online tools do not allow for case-specific assessments. • Diagnostic Water Governance Tool identifies context-specific coordination deficits. • The Tool gives case-sensitive recommendations for governance instruments. • Fuzzy set Qualitative Comparative Analysis provides sound methodological framework. • The Tool's analysis can supplement but not replace expert evaluations. [ABSTRACT FROM AUTHOR]

Introduction. Confinement has had a great emotional impact on the population, especially in terms of stress. Factors such as the presence of previous mental or physical illness, resilience or emotional intelligence may influence the occurrence or increase of stress. The aim was to assess predictors of stress by comparing two statistical methodologies (one linear and one non-linear). Method. 802 Spaniards (65.50% women) who completed the questionnaires autonomously after signing the informed consent form participated. Stress (PSPP), COVID-19 threat perception (BIPQ-5), resilience (CD-RISC-10) and emotional intelligence (TMMS-24) were assessed. Descriptive statistics, hierarchical regression (HRM) and fuzzy set comparative qualitative analysis (fsQCA) were conducted. Results. Data obtained by HRM showed that the presence of previous mental illness, low resilience and emotional clarity, high emotional alertness and COVID-19 threat perception predicted 51% of the variance in stress. On the other hand, the results of the QCA showed that different combinations of these variables explained 71% of high stress and 56% of low stress. Pointing out how the presence of previous mental illness, high resilience, high emotional clarity and repair, low emotional alertness and low COVID-19 threat perception play a key role in explaining stress. Conclusions. These aspects will help to promote personal resources to buffer stress in confinement situations. [ABSTRACT FROM AUTHOR]

Higher education is currently undergoing a revolution with the widespread use of electronic tools in classrooms. Simultaneously, personal opinions are increasingly stronger and more expressive resulting in electronic Word-of-Mouth (eWOM). This article presents two studies. The first study exposes students' opinions about higher education and digital technology by a qualitative analysis. It also confirms that students use constantly their electronic devices to keep informed and updated. The second study focus on eWOM effects and follows a quantitative approach on two samples of students from two different countries by using the Fuzzy Set Qualitative Comparative Analysis. It supports the influences of eWOM on learning performance, likeability concern, student-instructor interaction, and responsiveness. It also shows that cultural differences may explain differences between those links. This research provides actual insights on awareness about digital technology in higher education. [ABSTRACT FROM AUTHOR]

The need of Non-profit Organizations (NPOs) of generating trust and credibility, to their stakeholders by an efficient management of their resources, lead them to openly show that they develop adequate good governance practices. But this is not a simple task and few research has been done on measuring methods of good governance in this field; without achieving an agreement about the best procedure. This paper aims at facilitating the measurement of good governance practices in NPOs by a fuzzy linguistic consensus-based group multi-criteria decision-making (MCGDM) model that will provide agreed and easy-understanding weights for a list of indicators proposed by the stakeholders and entities in such good governance practices. To do that, a linguistic 2-tuple BWM method with a consensus reaching process (CRP) will be developed and then applied to a real-world case in Spain, in which a group of experts from significant Spanish NPOs will assess the list of indicators proposed by the most representative entities (the alliance between the non-governmental organizations (NGO) Platform for Social Action, and the NGO Coordinator for Development (CONGDE) to obtain a prioritization of such indicators for measuring the good governance practices in Spanish NPOs. [ABSTRACT FROM AUTHOR]

The covid-19 pandemic has had catastrophic effects on the global economy. The olive oil sector, whose producers were already undergoing their own crisis, has also clearly been affected by this situation. Despite Spain's world leadership in olive oil production, the added value that this product can generate has not been maximized. Falling prices and supply saturation are the problems that have been most highlighted and to which covid-19 has added. Faced with such a situation, the digitalization of the olive mill has gone from being a recommendation to an essential factor in avoiding the paralysis of its activity. In this context, the objective of this study is to detect the organizational factors that have been associated with a better adaptation of the organizations to the current complex situation. To this end we have made use of the fuzzy sets Qualitative Comparative Analysis technique which makes it possible to contrast different variables in order to explain a result, overcoming the limitations of more traditional techniques. The results obtained reveal that the cooperative form, the degree of innovation, the training of the top managers and the flexibility and size of the organization are factors that contribute to a better adaptation of the olive mill to the situation caused by covid-19. [ABSTRACT FROM AUTHOR]

In a globalized world, the banking sector has been forced to advance not only in financial performance, but also in non-financial performance, especially in sustainability criteria. For this purpose, multicriteria decision methods are especially suited to evaluate efficiency and to make a stable ranking of the most outstanding banks in the Spanish financial system. However, we are aware of the difficulties involved due to the inherent uncertainty and subjectivity of this process. For this reason, the use of fuzzy models is proposed, especially intuitionistic fuzzy numbers combined with the Analytic Hierarchy Process and the TOPSIS. The combination of financial criteria based on the CAMELS rating system with non-financial sustainability criteria makes it possible to order the Spanish banking system based on global efficiency. The most relevant contributions are: first, the use of intuitionistic fuzzy numbers in the performance evaluation process, whereby the quality of the information available can be quantified; and the most important one, a simplification of the process in the implementation of the intuitionistic fuzzy TOPSIS. Finally, through a sensibility analysis, it is possible to isolate the relevance of the sustainability process to obtain the global performance evaluation. [ABSTRACT FROM AUTHOR]

This paper contributes to the expanding literature on VIKOR, a well-known multi-criteria decision-making technique. It is abundantly used to find the compromise solution regarding some significant criteria under different models. The general VIKOR methodology implements the principle that a recommended response must be a feasible solution which is nearest to positive ideal solution having maximum group utility and minimum individual regret. In this article, we propose a new strategy to address multi-criteria group decision-making problems named complex Pythagorean fuzzy VIKOR (CPF-VIKOR) method. It is designed to handle a great deal of vagueness and hesitation which are often present in human decisions. The CPF-VIKOR method allows the linguistic terms to express individual opinions of experts about the performance of alternatives and the weights of the criteria. We combine the individual judgments of experts with the help of complex Pythagorean fuzzy weighted averaging operator. Further, we compute the ranking measure with the help of group utility and regret measures by adjusting the weight of strategy of maximum group utility within the unit interval. We sort the alternatives in an ascending order relative to group utility measure and individual regret measure. Moreover, we demonstrate our proposed method with the help of a flow chart and two numerical examples: one for the selection of the most beneficial renewable energy project in Spain and another for the selection of the most suitable logistic village location in Turkey. Finally, we present the comparative analysis of our proposed method with the CPF-TOPSIS method to prove its validity. [ABSTRACT FROM AUTHOR]

The construction of large renewable energy projects is characterized by the great uncertainties associated with their administrative complexity and their constructive characteristics. For proper management, it is necessary to undertake a thorough project risk assessment prior to construction. The work presented in this paper is based on a hierarchical risk structure identified by a group of experts, from which a Probabilistic Fuzzy Sets with Analysis Hierarchy Process (PFSAHP) was applied. This probabilistic analysis approach used expert opinion based on the Monte Carlo Method that allows for extracting more information from the original data. In addition, the coherence of the experts’ opinions is assessed using a novel parameter known as Confidence Level, which allows for adjusting the opinions of experts and weighting their judgments regarding impact and probability according to their coherence. This model has the advantage of offering a risk analysis in the early stages of the management of renewable energy projects in which there is no detailed information. This model is also more accurate than the classic fuzzy methodology when working with complete distribution functions, whilst it avoids the loss of information that results from the traditional mathematical operations with Fuzzy numbers. To test the model, it was applied to a 250 MW photovoltaic solar plant construction project located in southeast of Spain (Region of Murcia). As a result of the application of the proposed method, risk rankings are obtained with respect to the cost, the time, the scope and from a general point of view of the project. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to present a model, which assesses the wide range of data offered in non-financial reports, and enables benchmarking of these data between different organizations.Design/methodology/approach This work uses aspects of fuzzy logic and qualitative comparative analysis to build fuzzy sets, which form the basis of the benchmarking tool.Findings The model presented permits the identification of both negative and positive aspects of an organization's CSR actions, and shows where improvements can be made by highlighting the standards reached by others.Originality/value The model offers a benchmarking tool that allows analysis of non-financial reporting, something missing from the field of CSR until now. Additionally, it offers a new approach where data sets are constructed to measure environmental/social impact in function of each unit of economic value generated. This approach aligns social/environmental and economic performance, thereby emphasizing the interconnectivity of a company's financial, social and environmental bottom lines. [ABSTRACT FROM AUTHOR]

Spanish primary and secondary school curricula comprise several contents, learning outcomes and assessment criteria directly related with probability and approximate calculus. Some of them refer to situations modeled by the students, which entail not only uncertainty but also imprecision. For this reason, different techniques including fuzzy logic and fuzzy sets theory could be applied when dealing with this kind of situations in the classroom. Several teaching situations handling imprecise concepts in primary and secondary schools are suggested from a theoretical point of view. These more exible ways of reasoning could be combined with the traditional probability approach, allowing to tackle more general problems and not only those involving exact calculations or specific numerical assignments. Moreover, this type of approaches will provide the students with tools to manage imprecision as a mathematical tool in their personal life. [ABSTRACT FROM AUTHOR]

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn. [ABSTRACT FROM AUTHOR]

Abstract: Comprehensive health risk assessment based on aggregate exposure and cumulative risk calculations requires a better understanding of exposure variables and uncertainty associated with them. Although there are many sources of uncertainty in system models, two basic kinds of parametric uncertainty are fundamentally different from each other: natural/stochastic and epistemic uncertainties. However, conventional methods such as standard Monte Carlo Sampling (MCS), which assumes vagueness as random property, may not be suitable for this type of uncertainty analysis. An improved systematic uncertainty and variability analysis can provide insight into the level of confidence in model estimates, and it can aid in assessing how various possible model estimates should be weighed. The main goal of the present study was to introduce Fuzzy Latin Hypercube Sampling (FLHS), a hybrid approach for incorporating epistemic and stochastic uncertainties separately. An important property of this technique is its ability to merge inexact generated data of the LHS approach to increase the quality of information. The FLHS technique ensures that the entire range of each variable is sampled with proper incorporation of uncertainty and variability. A fuzzified statistical summary of the model results produces a detailed sensitivity analysis, which relates the effects of variability and uncertainty of input variables to model predictions. The feasibility of the method has been tested with a case study, analyzing total variance in the calculation of incremental lifetime risks due to polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) for the residents living in the surroundings of a municipal solid waste incinerator (MSWI) in the Basque Country, Spain. [Copyright &y& Elsevier]

Sentinel-2 (S2) multi-spectral instrument (MSI) images are used in an automated approach built on fuzzy set theory and a region growing (RG) algorithm to identify areas affected by fires in Mediterranean regions. S2 spectral bands and their post- and pre-fire date (Δpost-pre) difference are interpreted as evidence of burn through soft constraints of membership functions defined from statistics of burned/unburned training regions; evidence of burn brought by the S2 spectral bands (partial evidence) is integrated using ordered weighted averaging (OWA) operators that provide synthetic score layers of likelihood of burn (global evidence of burn) that are combined in an RG algorithm. The algorithm is defined over a training site located in Italy, Vesuvius National Park, where membership functions are defined and OWA and RG algorithms are first tested. Over this site, validation is carried out by comparison with reference fire perimeters derived from supervised classification of very high-resolution (VHR) PlanetScope images leading to more than satisfactory results with Dice coefficient > 0.84, commission error < 0.22 and omission error < 0.15. The algorithm is tested for exportability over five sites in Portugal (1), Spain (2) and Greece (2) to evaluate the performance by comparison with fire reference perimeters derived from the Copernicus Emergency Management Service (EMS) database. In these sites, we estimate commission error < 0.15, omission error < 0.1 and Dice coefficient > 0.9 with accuracy in some cases greater than values obtained in the training site. Regression analysis confirmed the satisfactory accuracy levels achieved over all sites. The algorithm proposed offers the advantages of being least dependent on a priori/supervised selection for input bands (by building on the integration of redundant partial burn evidence) and for criteria/threshold to obtain segmentation into burned/unburned areas. [ABSTRACT FROM AUTHOR]

In this paper, we address one of the most important topics in the field of Social Networks Analysis: the community detection problem with additional information. That additional information is modeled by a fuzzy measure that represents the risk of polarization. Particularly, we are interested in dealing with the problem of taking into account the polarization of nodes in the community detection problem. Adding this type of information to the community detection problem makes it more realistic, as a community is more likely to be defined if the corresponding elements are willing to maintain a peaceful dialogue. The polarization capacity is modeled by a fuzzy measure based on the J D J p o l measure of polarization related to two poles. We also present an efficient algorithm for finding groups whose elements are no polarized. Hereafter, we work in a real case. It is a network obtained from Twitter, concerning the political position against the Spanish government taken by several influential users. We analyze how the partitions obtained change when some additional information related to how polarized that society is, is added to the problem. [ABSTRACT FROM AUTHOR]

Introduction. Presents the first bibliometric study on the evolution of the fuzzy sets theory field. It is specially focused on the research carried out by the Spanish comunity. Method. The CoPalRed software, for network analysis, and the co-word analysis technique are used. Analysis. Bibliometric maps showing the main associations among the main concepts in the field are provided for the periods 1965-1993, 1994-1998, 1999-2003 and 2004-2008. Results. The bibliometric maps obtained provide insight into the structure of the fuzzy sets theory research in the Spanish community, visualize the research subfields, and show the existing relationships between those subfields. Furthermore, we compare the Spanish community with other countries (the USA and Canada; the UK and Germany; and Japan and Peoples Republic of China). Conclusions. As a result of the analysis, a complete study of the evolution of the Spanish fuzzy sets community and an analysis of its international importance are presented. [ABSTRACT FROM AUTHOR]