Metrics

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

Rural areas are facing major challenges and profound changes that directly affect the quality of life of rural populations. In this context, new ideas and opportunities are emerging, where social innovation initiatives are leading to solutions that attempt to revitalize the social fabric of rural areas. Considering this, the aim is to conduct a productivity measurement and a bibliometric analysis that examines the research landscapes of social innovations in rural areas. With a comprehensive analysis of 178 publications, this study examines main authors, countries, journals, research areas, and key themes in the field. The results show the relevance of principal areas such as agriculture, digitalization, and forestry. Alongside these, new organizational models are being developed, such as rural hubs, living labs, and community cooperatives. Future research could explore the role of these organizations in rural areas in greater depth. Full article

This paper presents a flexible approach to a multipurpose, heterogeneous archive and data management system model that merges the robustness of legacy grid-based technologies with modern cloud and edge computing paradigms. It leverages innovations driven by big data, IoT, AI, and machine learning to create an adaptive data storage and processing framework. In today’s digital age, where data are the new intangible gold, the “gold rush” lies in managing and storing massive datasets effectively—especially when these data serve governmental or commercial purposes, raising concerns about privacy and data misuse by third-party aggregators. Astronomical data, in particular, require this same thoughtful approach. Scientific discovery increasingly depends on efficient extraction and processing of large datasets. Distributed archival models, unlike centralized warehouses, offer scalability by allowing data to be accessed and processed across locations via cloud services. Incorporating edge computing further enables real-time access with reduced latency. Major astronomical projects must also avoid common single points of failure (SPOFs), often resulting from suboptimal technological choices driven by collaboration politics or In-Kind Contributions (IKCs). These missteps can hinder innovation and long-term project success. The principal goal of this work is to outline best practices in archival and data management projects—from policy development and task planning to use-case definition and implementation. Only after these steps can a coherent selection of hardware, software, or virtual environments be made. The proposed model—CTAARCHS (Cloud-based Technologies for Astronomical Archiving Research Contents and Handling Systems)—is an open-source, multidisciplinary platform supporting big data needs in astronomy. It promotes broad institutional collaboration, offering code repositories and sample data for immediate use. Full article

The higher education sector is increasingly being reshaped and reimagined in the era of Generative Artificial Intelligence (GenAI). For instance, the promise of GenAI to innovate pedagogical approaches in the way teaching and learning (T&L) occur across universities has been increasingly recognised. It is in this context that the question of how literature on the GenAI and Pedagogy (GenAIP) nexus has evolved in recent years has the potential to generate insights that inform and shape T&L policies and practices. However, the systematic analysis of scholarly literature on the GenAIP nexus has remained under the radar. This study responds to this gap and draws on PRISMA for the Scoping Review (PRISMA-ScR) method to carry out a Bibliometric Scoping Review of the GenAIP nexus. It examines scholarly research outputs (n = 310) published between 2023 and 2025 that are available on the Scopus database with two research objectives: (i) to ascertain research trends, thematic emphasis, prominent authors, countries and outlets, and (ii) to map various pedagogical approaches. Beyond revealing that authors from developing economies have produced significantly fewer research outputs than those from developed economies, the analysis highlights an urgent need for appropriate GenAI policies and curriculum redesign. It also documents 40 distinct pedagogical approaches reported in the literature. In light of the growing academic integrity challenges posed by GenAI, this article discusses three key implications for the higher education sector and future research: (i) redesigning courses and assessments to foster AI literacy, (ii) developing fit-for-purpose academic integrity policies, and (iii) delivering AI-focused professional development for academic staff. Full article

In recent decades, there has been an increase in the number of retractions across the biomedical and social sciences. A high rate of retractions undermines the integrity of scholarly journals and threatens the credibility of scientific disciplines. It is unknown how common retractions are within the field of social work. The aim of this study was to determine the prevalence of retractions among ten leading social work journals. This cross-sectional study employed three search strategies. First, each journal’s website was searched using the keywords “retracted” and “retraction”. The same procedure was employed, for each journal, using Google Scholar’s advanced search function. Finally, the Retraction Watch Database was queried using the name of each journal. None of the 196 results produced from these search strategies resulted in the identification of a single retracted article. Reasons for this absence are explored and recommendations to enhance the integrity of social work research and journals are discussed. Full article

In the ongoing ‘data revolution’, the ubiquity of digital data in society underlines a transformative era. This is mirrored in the sciences, where ‘big data’ has emerged as a major research field. This article significantly extends previous scientometric analyses by tracing the field’s conceptual emergence and evolution across a 30-year period (1993–2022). Bibliometric analysis is based on 17 data categories that co-constitute the conceptual network of ‘big data’ research. Using Scopus, the search query resulted in 70,163 articles and 315,235 author keywords. These are analysed aggregately regarding co-occurrences of the 17 data categories and co-occurrences of data categories with author keywords, and regarding their disciplinary distributions and interdisciplinary reach. Temporal analysis reveals two major development phases: 1993–2012 and 2013–2022. The study demonstrates: (1) the rapid expansion of the research field concentrated on seven main data categories; (2) the consolidation of keyword (co-)occurrences on ‘machine learning’, ‘deep learning’, ‘artificial intelligence’ and ‘cloud computing’; and (3) significant interdisciplinarity across four main subject areas. Scholars can use the findings to combine data categories and author keywords in ways that align scholarly work with specific thematic and disciplinary interests. The findings could also inform research funding, especially concerning opportunities for cross-disciplinary research. Full article

Directed Energy Deposition is an additive manufacturing subgroup that uses a laser beam to melt the wire or powder to create a melt pool. In the current study, a scientometric analysis has been carried out to analyze the contribution of countries, publication type analysis, distribution of publications over the years, keywords analysis, author analysis, cited journal, categories, institutes of publication, and report the practical implications. Firstly, the database was extracted from the Web of Science and then post-processed with CiteSpace 6.2.R4 and VOSviewer 1.6.20 software. Afterward, the associated results had been extracted and reported. It was found that China is the leader according to publication, followed by the USA and Germany, which mostly published their achievements in article and proceeding paper formats, which are increasing annually. According to the keywords, additive manufacturing, Laser Metal Deposition, and fabrication are the most commonly used. Based on the CiteSapce and VOSviewer results, Lin, Xin and Huang, Weidong are the authors with the highest publication rates. In addition, Additive Manufacturing, Materials & Design, and Materials Science and Engineering: A are the most cited journals, and regarding the categories, materials science, multidisciplinary, applied physics, and manufacturing engineering are the most commonly used DED processes. Northwestern Polytechnical University, Fraunhofer Gesellschaft, and the United States Department of Energy (DOE) have performed the most research in the field of DED. Full article

Simulation has become a pivotal tool in the design, analysis, and optimization of the built environment, and has been widely adopted by professionals in architecture, engineering, and urban planning. These techniques enable stakeholders to test hypotheses, evaluate design alternatives, and predict performance outcomes prior to construction. Applications span energy consumption, airflow, thermal comfort, lighting, structural behavior, and human interactions within buildings and urban contexts. This study maps the scientific landscape of simulation research in the built environment through a bibliometric analysis of 12,220 publications indexed in Scopus. Using VOSviewer 1.6.20, it conducted citation and keyword co-occurrence analyses to identify key research themes, leading countries and journals, and central publications in the field. The analysis revealed seven primary thematic clusters: (1) human-focused simulation, (2) building-scale energy performance simulation, (3) urban-scale energy performance simulation, (4) sustainable design and simulation, (5) indoor environmental quality simulation, (6) building aerodynamics simulation, and (7) computing in building simulation. By synthesizing these trends and domains, this study provides an overview of the field, facilitating greater accessibility to the simulation literature and informing future interdisciplinary research and practice in the built environment. Full article

Advances in technology and changes in the way people entertain themselves have made video games a cultural agent on a par with more traditional games, including language learning. In addition, the use of video games in education is becoming increasingly common and numerous benefits associated with their use have been discovered. The aim of this article is to analyze the search trends in studies dealing with the use of video games in language learning. To this end, a bibliometric analysis was carried out by applying the traditional laws of bibliometrics (Price’s law, Bradford’s law of concentration, Lotka’s law, Zipf’s law and h-index) to documents published in journals indexed in the Core Collection of the Web of Science (WoS). Annual publications between 2009 and 2022 show an exponential growth R2 = 86%. The journals with the most publications are Computer assisted language learning (Taylor & Francis) and Computers and Education (Elsevier). Jie Chi-Yang and Gwo Jen-Hwan were the most cited authors. The United States and Taiwan were the countries with the highest scientific output. The use of video games in language learning has been of particular interest in recent years, with benefits found for students who use them in their classes, although more research is needed to establish criteria and requirements for each video game for its intended purpose. Full article

A longstanding ambiguity surrounds the operationalization of consciousness in artificial systems, complicated by the philosophical and cultural weight of subjective experience. This work examines whether cognitive architectures may be designed to support a functionally explicit form of artificial consciousness, focusing not on the replication of phenomenology, but rather on measurable, technically realizable introspective mechanisms. Drawing on a critical review of foundational and contemporary literature, this study articulates a conceptual and methodological shift: from investigating the experiential perspective of agents (“what it is like to be a bat”) to analyzing the informational, self-regulatory, and adaptive structures that enable purposive behavior. The approach combines theoretical analysis with a comparative review of major cognitive architectures, evaluating their capacity to implement access consciousness and internal monitoring. Findings indicate that several state-of-the-art systems already display core features associated with functional consciousness—such as self-explanation, context-sensitive adaptation, and performance evaluation—without invoking subjective states. These results support the thesis that cognitive engineering may progress more effectively by focusing on operational definitions of consciousness that are amenable to implementation and empirical validation. In conclusion, this perspective enables the development of artificial agents capable of autonomous reasoning and self-assessment, grounded in technical clarity rather than speculative constructs. Full article

This paper explores the link between the results of the UK’s Research Evaluation Exercises (REEs) and university decisions on which Units of Assessment (UOA) to submit to in future REEs. How the raw data from REEs can be converted into two novel measurements of research performance—an internal and an external measurement—is explained. Data on two UOAs, Business and Management Studies (BMS) and Economics and Econometrics (E&E), from five consecutive REEs undertaken in the United Kingdom (UK) between 1992 and 2014, was then used to assess whether and how the results of one REE were related to UOA submissions in the next. The findings reveal that both the internal and external assessments of performance were associated with changes in the probability of resubmission to the same UOA in the next REE, with the external comparisons being particularly important. It also appears that while one instance of poor performance might be tolerated by a university, repeated poor performance was associated with a heightened risk of withdrawal from both the BMS and E&E UOAs in the next REE. In addition, holding research performance constant, universities were significantly more likely to withdraw from the E&E UOA than the BMS UOA. New (post-1992) universities were also more likely to continue to submit to a UOA in the next REE than pre-1992 institutions. There is also some evidence that the quality of submissions to the BMS UOA is catching up with that of submissions to the E&E UOA. The somewhat worrying implications of these findings for the health of the Economics discipline in UK universities are assessed. Full article

Quantum computing promises to offer significant computational advantages over classical computing, leveraging principles such as superposition and entanglement. This necessitates effective metrics and measurement techniques for evaluating quantum systems, aiding in their development and performance optimization. However, due to fundamental differences in computing paradigms and current immaturity of quantum software abstractions, classical software and hardware metrics may not directly apply to quantum computing, where the distinction between software and hardware can still be somewhat indiscernible compared to classical computing. This paper provides a comprehensive review of existing quantum software and hardware metrics in the scientific literature, highlighting key challenges in the field. Additionally, it investigates the application of Functional Size Measurement (FSM), based on the COSMIC ISO 19761 FSM Method, to measure quantum software. Three FSM approaches are analyzed by applying them to Shor’s and Grover’s algorithms, with measurement results compared to assess their effectiveness. A comparative analysis highlights the strengths and limitations of each approach, emphasizing the need for further refinement. The insights from this study contribute to the advancement of quantum metrics, especially software metrics and measurement, paving the way for the development of a unified and standardized approach to quantum software measurement and assessment. Full article

Many publications on early house and techno music have the character of documentation and include (auto-)biographical statements from contemporaries of the scene. This literature has led to many statements, hypotheses, and conclusions. The weaknesses of such sources are their selective and subjective nature, and the danger of unclear memories, romanticization, and constructive memory. Consequently, a validation through content-based, quantitative music analyses is desirable. For this purpose, the HOuse and Techno music from Germany and AMErica (HOTGAME) corpus was built. Metrics from the field of data quality control show that the corpus is representative and explanatory for house and techno music from Germany and the United States of America between 1984 and 1994. HOTGAME can serve as a reliable source for the analysis of early house and techno music using big data methods, like inferential statistics and machine learning. Full article

In this research, we aim to synthesize the complex issue of students’ and science teachers’ conceptions of the nature of science (NOS). We identified the conceptions of ninety-five pre-service science teachers (PSTcs) enrolled in the Qualifying Master’s Program in Teaching Science at the secondary level in Quebec (Canada) about the NOS, particularly relative to the development of science through history and approaches to constructing scientific knowledge, especially regarding the relationship between observation, hypothesis, experiment, measure and theory. To this end, we constructed a multiple-choice questionnaire (MCQ) comprising 11 statements to characterize their conceptions. The qualitative data analysis underscores the intricate nature of scientific knowledge construction. The PSTcs identified are as follows: 1. Scientific theories today correspond to improving ancient theories; 2. Science progresses by accumulation; 3. Science advancement results from improving current theories thanks to experimentation; 4. The observation is a pure observation that is preconceived; 5. We must experiment with scientific equipment in a laboratory to disprove a theory; and 6. Experiments precede scientific theory. These conceptions are crucial not only for developing training programs that help pre-service science teachers (PSTs) to study the concepts of science prescribed in the curriculum within the history and epistemology of science, but also to underscore the urgency and importance of addressing these conceptions. Full article

Scatterplots can reveal important data relationships, but their visual complexity can make pattern identification challenging. Systematic analytical approaches help structure interpretation by dividing scatterplots into meaningful regions. This paper introduces the region partitioning framework (RCF), a systematic method for dividing scatterplots into interpretable regions using k × k grids, in order to enhance visual data analysis and quantify structural changes through transformation metrics. RCF partitions the x and y dimensions into k × k grids (e.g., 4 × 4 or 16 regions), balancing granularity and readability. Each partition is labeled using an R(p, q) notation, where p and q indicate the position along each axis. Two perspectives are supported: the absolute mode, based on raw values (e.g., “very short, narrow”), and the relative mode, based on min–max normalization (e.g., “short relative to population”). I propose a set of transformation metrics—density, net flow, relative change ratio, and redistribution index—to quantify how data structures change between modes. The framework is demonstrated using both the Iris dataset and a subset of the airquality dataset, showing how RCF captures clustering behavior, reveals outlier effects, and exposes normalization-induced redistributions. Full article

In an era of evolving scholarly ecosystems, machine learning (ML) and artificial intelligence (AI) have become pivotal in advancing research impact analysis. Despite their transformative potential, the fragmented body of literature in this domain necessitates consolidation to provide a comprehensive understanding of their applications in multidimensional impact assessment. This study bridges this gap by employing bibliometric methodologies, including co-authorship analysis, citation burst detection, and advanced topic modelling using BERTopic, to analyse a curated corpus of 1608 scholarly articles. Guided by three core research questions, this study investigates how ML and AI enhance research impact evaluation, identifies dominant methodologies, and outlines future research directions. The findings underscore the transformative potential of ML and AI to augment traditional bibliometric indicators by uncovering latent patterns in collaboration networks, institutional influence, and knowledge dissemination. In particular, the scalability and semantic depth of BERTopic in thematic extraction, combined with the visualisation capabilities of tools such as CiteSpace and VOSviewer, provide novel insights into the dynamic interplay of scholarly contributions across dimensions. Theoretically, this research extends the scientometric discourse by integrating advanced computational techniques and reconfiguring established paradigms for assessing research contributions. Practically, it provides actionable insights for researchers, institutions, and policymakers, enabling enhanced strategic decision-making and visibility of impactful research. By proposing a robust, data-driven framework, this study lays the groundwork for holistic and equitable research impact evaluation, addressing its academic, societal, and economic dimensions. Full article

This study aims to provide a comprehensive knowledge mapping and extensive analysis of NeuroIS research, elucidating global trends and directions within this field from January 2007 to January 2024. A visual analysis of 256 research articles sourced from the Scopus database is conducted. The knowledge mapping, utilizing CiteSpace (CiteSpace 3.6 R1) and VOSviewer (VOSviewer 1.6.19), illustrates the current research landscape, encompassing collaboration networks, co-citation networks, references exhibiting citation bursts, and keyword analysis. The findings highlight the United States and Germany as leading nations in the exploration of NeuroIS, with the Karlsruher Institut für Technologie in Germany identified as a prominent institution in this domain. René Riedl, Pierre-Majorique Léger, Marc T. P. Adam, and Christof Weinhardt emerge as the most prolific authors in the field. Noteworthy themes that have garnered attention in recent years include customer experience, information systems, and information processing. Document analysis reveals that the study by Dimoka et al. in 2012 is the most cited work, providing a comprehensive overview of global NeuroIS research. Analysis of the document co-citation network identifies electroencephalography (EEG) in the context of technostress, the social impact of information in security alerts, and user experience in human–computer interaction as key areas of focus. René Riedl is recognized as the most cited researcher, while MIS Quarterly is distinguished as the leading journal in this field. Twelve NeuroIS papers exhibit high citation counts, with significant activity noted in 2021 and 2022. The timeline delineates the evolution of topics such as neuroscience, fMRI, cognitive neuroscience, social media, trust, eye tracking, and human–computer interaction. This study pioneers the examination of the current research status of NeuroIS through bibliometric analysis and the latest available data. It advocates for enhanced collaborations among scholars and institutions to improve information systems management and foster the development of NeuroIS. The study underscores the importance of ongoing research and cooperation in NeuroIS to deepen our understanding of how neuroscience can inform information systems design and management, thereby enhancing human–technology interaction. By identifying key trends, influential authors, and prominent themes, this analysis lays the groundwork for further exploration and innovation in this interdisciplinary domain. As technology continues to advance and our reliance on information systems intensifies, the insights derived from NeuroIS research can provide valuable perspectives on enhancing user experiences, optimizing information processing, and applying neuroscientific principles to develop more effective IT artifacts. Through sustained collaboration and knowledge sharing, the NeuroIS community can drive progress and shape the future of information systems management in an increasingly dynamic digital landscape. Full article

The agri-food sector is expanding, driven by growing global demand. At the same time, it faces the challenge of increasing its efficiency and adopting sustainable practices. This study aimed to map scientific production in this field, identifying trends, emerging themes, critical gaps, and future directions for research. A bibliometric analysis was conducted with 5141 papers published between 1977 and 2024, extracted from the Scopus and Web of Science databases. We applied keyword co-occurrence analysis, thematic analysis, thematic evolution, and three-field graphs using the metrics betweenness centrality, closeness centrality, and PageRank. The results revealed a significant growth in publications in the agri-food sector, especially after 2012, emphasizing the high centrality and relevance of themes such as sustainability, agri-food, and agriculture. Topics such as bioactive compounds, blockchain, and traceability were identified as areas of growing interest, and the circular economy stood out as an emerging topic. Italy, Spain, and France lead in scientific production and international collaboration. The most prominent journals were Sustainability, the Journal of Cleaner Production, and Agriculture and Human Values. Research in the sector is expanding, focusing on sustainability, the circular economy, and bioactive compounds. International collaborations and high-impact journals are pillars for advances in the sector. Full article