https://hdl.handle.net/10481/13757 Sat, 11 Apr 2026 20:11:21 GMT 2026-04-11T20:11:21Z https://hdl.handle.net/10481/112583 Predicting the performance of industrial enterprises through ICT-enabled processes: The interplay of staff capacity and organizational commitment Blanco Encomienda, Francisco javier; Al Jasimee, Khalid Hasan https://hdl.handle.net/10481/112583 https://hdl.handle.net/10481/112177 A practical guide to proper estimation and inference of the Gini index by avoiding often encountered methodological pitfalls Muñoz Rosas, Juan Francisco; Pavía, José Manuel; Álvarez Verdejo, Encarnación The Gini index is the most widely-used measure of inequality. Unfortunately, its computation is subject to error. Researchers and practitioners often fall into common methodological pitfalls, leading to inaccurate estimates and inferences, and ultimately hindering efforts to reduce inequality and improve societal quality of life. This paper clarifies the challenges of non-parametric estimation of the Gini index more comprehensively than previous contributions, and offers robust methodological recommendations to ensure accurate estimates. Additionally, we reference a free, easy-to-use R package which, together with the clear methodological insights, enhances the real-world applicability of our findings. First, we investigate the impact of common methodological pitfalls on point estimates, providing a complete review for both infinite and finite populations. We then examine variance estimation and the performance of confidence intervals. Among other issues, the findings reveal that, when a popular regression-based variance estimator is used, the variance of the Gini index is seriously underestimated in distributions with high skewness and inequality, as often observed in real-world applications. Jackknife variance estimates and jackknife intervals, based on studentized quantiles, prove to be the most accurate approaches. The analysis employs variables with varying degrees of skewness and inequality (as both characteristics influence the potential for bias), thereby encompassing most of the situations found in empirical research. This research is part of the project PID2022-136235NB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU, and has also been supported by Ministerio de Ciencia e Innovación [grant number PID2021-128228NB-I00] and the Generalitat Valenciana [grant number CIAICO/2023/031]. https://hdl.handle.net/10481/112177 https://hdl.handle.net/10481/111633 Exploring brand attitude in e-commerce using the S-O-R model: the role of information quantity, information quality and source credibility Blanco Encomienda, Francisco Javier; Rosillo-Díaz, Elena https://hdl.handle.net/10481/111633 https://hdl.handle.net/10481/111106 Explainable classifier with adaptive optimisation for medical data Trillo Vílchez, José Ramón; Moral Ávila, María José Del; Tapia García, Juan Miguel; García Cabello, Julia; Cabrerizo Lorite, Francisco Javier Artificial Intelligence (AI) has become increasingly important in critical domains such as medicine, where accurate and interpretable decision-making is essential. However, many high-performing AI models operate as “black boxes”, limiting transparency and making it difficult for clinicians to understand or verify predictions. To address this challenge, we present an eXplainable Artificial Intelligence (XAI) framework that integrates a fuzzy rule-based classifier with genetic algorithms and 2-tuple linguistic representations. The method incrementally generates general fuzzy rules, introduces fuzzy exception rules to capture atypical cases, and applies rule selection and parameter tuning to enhance both accuracy and interpretability. Experiments on nine medical datasets demonstrate that our approach achieves competitive or superior accuracy compared to state-of-the-art algorithms, while requiring fewer rules. These results show that the method not only improves predictive performance but also provides clear, human-readable explanations for each decision, thereby increasing trust and facilitating its application in medical practice. This work has been supported by the grant PID2022-139297OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU. Moreover, it is part of the project C-ING-165-UGR23, co-funded by the Regional Ministry of University, Research and Innovation and by the European Union under the Andalusia ERDF Program 2021-2027. Funding for open access publishing: Universidad de Granada/CBUA. https://hdl.handle.net/10481/111106 https://hdl.handle.net/10481/110602 Labor Force Heterogeneity and Wage Polarization: Italy and Spain Addabo, Tindara; García Fernández, Rosa María; Llorca Rodríguez, Carmen María; Maccagnan, Anna Purpose: The purpose of this paper is to assess the change in the Italian and Spanish wage polarization degree in a time of economic crisis, taking into account the factors affecting labor-force heterogeneity. Gender differences in the evolution of social fractures are considered by carrying out the analysis separately for males and females. Design/methodology/approach: The approach by Palacios-González and García-Fernández (2012) on polarization is applied to the micro data provided by the EU Living Conditions Surveys (2007, 2010 and 2012). According to Palacios-González and García-Fernández's approach, polarization is generated by two tendencies that contribute to the generation of social tension: the homogeneity or cohesion within group and the heterogeneity between groups. The following labor force characteristics are considered: gender, level of education, type of contract, occupational status and job status. Findings: The results for Italy reveal a higher increase of polarization for women than for men from the perspective of the type of contract. In Spain, the wage polarization of women also increases more intensively compared to men from the perspectives of level of education, job status and occupational status, while in Italy the reduction of the wage polarization index by level of education can be related, above all, to an increase in overqualification of women. Originality/Value: While the empirical literature on polarization has made considerable investigation into employment and job polarization, this paper explores the rather less explored matter of wage polarization. Furthermore, particular attention is paid to the impact on polarization of the Great Recession. https://hdl.handle.net/10481/110602