Metrics, Volume 2, Issue 4 (December 2025) – 3 articles

Star scientists are highly influential researchers who have made significant contributions to their field, gained widespread recognition, and often attracted substantial research funding. They are critical for the advancement of science and innovation and significantly influence the transfer of knowledge and technology to industry. Identifying potential star scientists before their performance becomes outstanding is important for recruitment, collaboration, networking, and research funding decisions. This study utilizes machine learning techniques and builds four different classifiers, i.e., random forest, support vector machines, naïve bayes, and logistic regression, to predict star scientists in the field of artificial intelligence while highlighting features related to their success. The analysis is based on publication data collected from Scopus from 2000 to 2019, incorporating a diverse set of features such as gender, ethnic diversity, and collaboration network structural properties. The random forest model achieved the best performance with an AUC of 0.75. Our results confirm that star scientists follow different patterns compared to their non-star counterparts in almost all the early-career features. We found that certain features, such as gender and ethnic diversity, play important roles in scientific collaboration and can significantly impact an author’s career development and success. The most important features in predicting star scientists in the field of artificial intelligence were the number of articles, betweenness centrality, research impact indicators, and weighted degree centrality. Our approach offers valuable insights for researchers, practitioners, and funding agencies interested in identifying and supporting talented researchers. Full article

The fashion industry, despite its global economic importance, is a major contributor to environmental degradation and social inequality. In response, sustainable fashion has emerged as a growing movement advocating ethical, ecological, and socially responsible practices. This study presents a comprehensive bibliometric analysis of 1134 peer-reviewed journal articles on sustainable fashion indexed in Scopus from 1986 to 2025. Results show an exponential rise in research output after 2015, with interdisciplinary contributions from social sciences, business, environmental science, and engineering. By applying performance analysis and science mapping techniques, the study identifies five major research themes: “Consumer Behavior,” “Design Ethics,” “Circular Economy,” “Innovation,” and “Digital Media.” The geographic distribution reveals strong outputs from both developed and emerging economies. This study provides an integrative overview of the intellectual landscape of sustainable fashion and serves as a roadmap for researchers, policymakers, and practitioners who are interested in the development of sustainable fashion. Full article

Digital transformation in higher education necessitates a central role for university faculty, yet there is a lack of comprehensive tools to measure their actual pedagogical use of technology. This study aims to refine the definition of a composite indicator to evaluate mobile technology adoption among social science university teachers. Using the results of the validated MOBILE-APP questionnaire, administered to a sample of N = 295 teachers from various social science degree programs, we employed multilevel structural equation modeling (SEM) to develop and implement a synthetic indicator for assessing mobile technology adoption levels among educators. The analysis of the considered factors (motivation, training, tools, and use) revealed differences in mobile technology adoption based on degree program, age, and previous experience. High motivation, training, use of institutional tools, and propensity for use promote the adoption of mobile technologies. Three levels of mobile technology adoption are identified and characterized. This synthetic indicator can be used both technically and socially to track the evolution of mobile technology adoption, enabling comparative analyses and longitudinal assessments that inform strategic decisions in training, infrastructure, and curriculum development. This research represents a step forward in the development of quantitative indicators and the assessment of research practices. Full article