Search Results (202)

This systematic literature review investigates the integration of artificial intelligence (AI) and the Internet of Things (IoT) in marketing, with a focus on their application in enhancing consumer engagement, personalization, and strategic decision-making. Using the Scopus database and a refined keyword search strategy, the study identified 223,671 initial records, which were narrowed down to 121 peer-reviewed academic articles after applying strict inclusion and exclusion criteria. Thematic analysis revealed that foundational technologies—such as machine learning, big data, and deep learning—dominate the field, while marketing strategy, decision systems, and customer experience emerge as central application areas. Co-citation and keyword network analyses indicate a technocentric and interdisciplinary knowledge structure, but also expose significant gaps in research related to ethics, regulation, consumer trust, and small business contexts. The review highlights opportunities for future research in underexplored areas such as sentiment analysis, sustainability, and human–AI interaction. For practitioners, the findings underscore the strategic importance of AI and IoT in driving personalized, data-driven marketing, while emphasizing the need for ethical transparency and regulatory alignment. Limitations include reliance on a single database, potential exclusion of relevant studies due to keyword constraints, and a focus on peer-reviewed journal articles only. This review addresses key gaps in the literature by offering a focused synthesis of current research and proposing directions for more balanced and responsible innovation in AI-enabled marketing. Full article

The application of artificial intelligence (AI) in monitoring and managing ocean waste reveals considerable promise for improving sustainable strategies to combat marine pollution. This study performs a bibliometric analysis to examine research trends, knowledge frameworks, and future directions in AI-driven sustainable ocean waste management. This study delineates key research themes, prominent journals, influential authors, and leading nations contributing to the field by analysing scientific publications from major databases. Research from citation networks, keyword analysis, and co-authorship patterns highlights significant topics such as AI algorithms for waste detection, machine learning models for predictive mapping of pollution hotspots, and the application of autonomous drones and underwater robots in real-time waste management. The findings indicate a growing global focus on utilising AI to enhance environmental monitoring, optimise waste reduction methods, and support policy development for sustainable marine ecosystems. This bibliometric study provides a comprehensive analysis of the current knowledge landscape, identifies research gaps, and underscores the importance of AI as a crucial enabler for sustainable ocean waste management, offering vital insights for researchers, industry leaders, and environmental policymakers dedicated to preserving ocean health. Full article

Objectives: This study aimed to analyze review articles on soccer performance analysis (PA) using a bibliometric approach to address three main research questions: (a) How has the publication of review articles related to soccer PA evolved over time? (b) Which authors and journals have been the most influential in this field and what are the collaboration networks between them? (c) What are the dominant topics, methodological issues, and gaps in the relevant literature? Methods: A systematic search was conducted in the Scopus database on 24 January 2025, using a comprehensive Boolean expression to identify relevant review articles. The extracted data were analyzed using VOSviewer (1.6.20.0). We employed bibliometric performance analysis, science mapping, and clustering techniques including co-authorship, co-occurrence, bibliographic coupling, and co-citation analyses. Results: The study included 314 review articles, demonstrating an exponential increase in publications since 2017. The most influential journal was Sports Medicine, while the leading authors included F. M. Clemente and H. Sarmento. Co-occurrence analysis revealed five thematic clusters covering physical performance, nutrition, coaching strategies, tactical analysis, and emerging data-driven approaches. Notably, significant gaps in the literature were identified in the areas of set pieces, key performance indicators, and contextual variables. Conclusions: This bibliometric analysis, creating a comprehensive map of the review articles on soccer PA, highlights the diverse and interdisciplinary nature of soccer PA research, identifies gaps in the literature, and offers practical considerations for researchers and journal editors aiming to advance the field of soccer PA. Full article

(1) Background: The literature indicates that pesticide use and exposure can lead to neurodegenerative and carcinogenic effects in living organisms. Additionally, pesticides have been reported to influence lipid metabolism. Based on this, the objective of this analysis was to identify the most relevant authors, countries, institutions, and journals addressing the relationship between pesticides and lipoproteins; (2) Methods: The analysis was conducted using the Web of Science database and bibliometric tools, including Bibliometrix/Biblioshiny and VOSViewer software; (3) Results: A total of 72 publications from 1977 to 2014 were identified, spanning 49 sources, 3453 references, and 390 authors. The journal Pesticide Biochemistry and Physiology stood out, with seven articles and an h-index of 5. The most relevant author was Samira Salihovic. China was the top country in terms of scientific output on this topic. The United Kingdom and Spain were notable for their international collaborations. Additionally, Duk Hee Lee and Monica P. Lind were found to have the highest co-citation relationship; (4) Conclusions: This analysis highlights the relatively small number of publications on pesticides and lipoproteins between 1977 and 2024, despite growing interest in the field due to its health implications. Expanding collaborations between developed and emerging countries is essential for advancing knowledge in this critical area. Full article

Background/Objectives: The increasing application of artificial intelligence (AI) and machine learning (ML) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI and ML in health and medicine. Methods: We used the Scopus database for searching and extracted articles published between 2000 and 2024. Then, we generated information about productivity, citations, collaboration, most impactful research topics, emerging research topics, and author keywords using Microsoft Excel 365 and VOSviewer software (version 1.6.20). Results: We retrieved a total of 22,113 research articles, with a notable surge in research activity in recent years. Core journals were Scientific Reports and IEEE Access, and core institutions included Harvard Medical School and the Ministry of Education of the People’s Republic of China, while core countries comprised the United States, China, India, the United Kingdom, and Saudi Arabia. Citation trends indicated substantial growth and recognition of AI’s and ML impact on health and medicine. Frequent author keywords identified key research hotspots, including specific diseases like Alzheimer’s disease, Parkinson’s diseases, COVID-19, and diabetes. The author keyword analysis identified “deep learning”, “convolutional neural network”, and “classification” as dominant research themes. Conclusions: AI’s transformative potential in AI and ML in health and medicine holds promise for improving global health outcomes. Full article

This study conducts a bibliometric and altmetric analysis of women’s entrepreneurship, examining research trends, challenges, and contributions to sustainable development. By analyzing 6729 journal articles from the Web of Science (1975–2024), this study employs citation analysis, co-citation analysis, and co-word analysis, alongside altmetric indicators, to assess research impact. Visualization tools such as VOSViewer (version 16.20) were used to map the scholarly landscape. Key findings highlight critical themes, including gender disparities, financial barriers, socio-cultural challenges, and digital transformation, underscoring the crucial role of women entrepreneurs in achieving the United Nations’ Sustainable Development Goals (SDGs). This study identifies major research clusters, leading authors, and institutions that shape the field, while also revealing regional disparities in research output. The growing prevalence of open-access research has enhanced knowledge dissemination, yet access to financial and technological resources remains a challenge for women entrepreneurs. This study emphasizes the need for targeted policies to improve access to finance, digital tools, and entrepreneurial education, fostering an enabling ecosystem for women-led businesses. By integrating traditional bibliometric techniques with altmetrics, this research provides a comprehensive assessment of global trends in women’s entrepreneurship, offering valuable insights for academics, policymakers, and practitioners. It contributes to the literature on gender and entrepreneurship by identifying emerging themes and gaps, advocating for inclusive policies and enhanced support mechanisms to drive sustainable economic growth. Full article

This study analyzes the impact of different Open Access (OA) publication models in engineering, comparing journals that charge Article Processing Charges (APCs) with those operating under the Diamond OA model. A total of 757 engineering OA journals, comprising 504 APC-based and 253 Diamond OA journals, were examined using bibliometric data from 2020 to 2023. The analysis focused on four key metrics: CiteScore, total citations, number of published articles, and the percentage of cited articles. The results indicate that APC-based journals dominate the upper quartiles (Q1 and Q2) regarding absolute citation counts, primarily driven by high-volume mega-journals such as IEEE Access. However, Diamond OA journals exhibit a higher proportion of cited articles (88.8% compared to 83.4% in APC-based journals) within the top 10% category. Despite their benefits in providing cost-free dissemination, Diamond OA journals account for only 8.4% of the 3012 active engineering journals indexed in Scopus, highlighting sustainability and visibility challenges. The findings suggest that, while APC-based journals achieve higher absolute citation counts, editorial reputation and visibility strategies significantly influence citation performance. This study contributes to the ongoing discussion on the financial sustainability and equity of OA publishing in engineering. Full article

Under the dual pressures of climate change and rapid urbanization, a comprehensive analysis of vegetation’s spatiotemporal patterns and their driving forces plays a pivotal role for addressing global ecological challenges. However, systematic bibliometric analyses in this field remain limited. This study involved a comprehensive bibliometric analysis of 18,270 related publications from 1989 to 2024 retrieved from the Web of Science SCI-Expanded database, elucidating research trends, methodologies, and key thematic areas. Utilizing the bibliometrix and biblioshiny tools, the results reveal an annual average growth rate of 17.62% in the number of published research articles, indicating rapid research expansion. Climate change emerged as the core driving force, with high-frequency keywords such as “vegetation”, “dynamics”, and “variability”. China (18,687 papers), the United States (14,502 papers), and Germany (3394 papers) are the leading contributors in this domain, with China showing the fastest growth in research output, albeit with relatively lower average citation rates. Core journals, including Remote Sensing of Environment and Global Change Biology, have played pivotal roles in advancing vegetation dynamics research, with remote sensing techniques dominating the field. The study highlights a shift in vegetation dynamics research from single-variable analyses (e.g., temperature, precipitation) to multi-scale and multidimensional approaches around 2010. Regional studies, such as those focusing on the Loess Plateau, are gaining importance, while advancements in remote sensing and machine learning technologies have enhanced the precision and scalability of research. This study provides a comprehensive summary of the current state and development trends in vegetation spatiotemporal dynamics and their driving forces, offering valuable insights for future research in the field. Full article

Background: Research on orchids has experienced substantial growth since the early 20th century, reflecting their ecological and evolutionary significance. Methods: This paper provides a comprehensive bibliometric analysis of orchid-related literature published between 1902 and 2024, based on data retrieved from the Web of Science Core Collection™ (WoS). Results: The primary goal is to assess the global research landscape of orchids by identifying key authors, institutions, and journals, as well as major research themes in the field. A thorough analysis of publication trends, citation frequencies, and keyword co-occurrence networks was conducted to uncover significant research hotspots. The findings indicate that orchid research has evolved from foundational topics such as taxonomy and classification to more intricate subjects, including conservation strategies, orchid-pollinator dynamics, and the role of orchids in ecosystem functions. Additionally, biotechnology-related research is emerging as a dominant trend. This study also highlights that China has the highest publication output, while collaboration between the United States and Europe continues to grow. The co-word analysis of keywords suggests that future research is likely to continue to focus on orchid conservation, the impacts of climate change, pollination biology, and symbiotic relationships with mycorrhizal fungi. Conclusions: This review offers valuable insights for researchers and conservationists, helping to identify future research priorities and strategies for the preservation and sustainable use of orchids. Full article

The association with minerals is the most critical factor influencing the stability of organic carbon in soil. It is essential to gain an in-depth understanding of the research progress and future development trends regarding the impact of soil minerals on organic carbon stability both domestically and internationally and to identify current key issues; a total of 1834 research papers from the Web of Science Core Collection database were selected as the data source. These papers were examined using CiteSpace, HistCite, VOSviewer, and Origin 9.1 tools. The analysis categorized and visualized data based on countries, institutions, journals, disciplines, and keywords, utilizing indices like the number of articles and total/average citation frequency. The results indicate that the number of publications on the study of soil minerals and their impact on organic carbon stability has been increasing from 2013 to 2023. China and the United States have significantly led in the number of publications in this field. However, research collaborations among countries also exhibit significant regional characteristics. The Chinese Academy of Sciences (CAS) has the most publications. Yet, its average frequency of local citations is only 0.81 per paper, significantly lower than the top ten average of 2.23 per paper. The journal with the highest number of articles in this field is Nature Communications, with 248 articles. The main research hotspots focus on aspects such as the adsorption of organic carbon by minerals, catalytic transformation, and redox reactions. Future research should build on this foundation to focus more on detailed mechanisms, particularly the long-term effects of different environmental factors and time scales on soil carbon stability. Full article

Over the last 25 years, research on geoparks has moved from basic research to comprehensive multidisciplinary studies related to the creation and development of geoparks, integrating the principle of sustainability. This research focuses on exploring geoparks as the core subject. The aim of this study is to synthesize the heterogeneous body of knowledge about geoparks in an exhaustive way by leveraging a multi-database bibliometric approach. The methodology applied is based on quantitative bibliometric analysis using R, including its application for non-coders and ensuring reliability with the PRISMA Statement framework. Ten databases were taken as the sources of research papers: Web of Science, Scopus, PubMed, Nature Journals, SpringerLink, Taylor & Francis, Wiley Journals, IEEE Xplore, and CABI. The method we used has limitations, providing a restricted number of trends aligned and scaled to the database boundary conditions used in analysis. The main goals of quantitative bibliometric analysis are as follows: (1) The impact of data integration—Evaluating how merging the data from the ten databases improves research coverage. (2) Global research trends—Identifying the evolution of geopark-related studies over time. (3) Three-year forecast—Predicting the upcoming research directions using a polynomial regression model. (4) Academic performance—Assessing geographical distribution, citation impact, and productivity using bibliometric laws. (5) Conceptual contribution—Identifying the key research themes that drive future studies and potential areas for exploration. Among these, we highlighted the key elements. The integration of the ten databases provides 63% greater insight into scientific research compared to that of the Web of Science (WoS) database. Geographically, the scientific output spans 102 countries, with China leading in production over the last two decades. The most impactful paper has accumulated 768 citations, while Ruben D.A. and Wu Fandong emerge as the most prolific authors. According to the bibliometric law, the core source of scientific output is Geoheritage. The future research directions are expected to address global challenges, particularly natural disasters in alignment with the Sustainable Development Goals (SDGs). Additionally, GIS-based subtopics leveraging advanced technologies for analyzing, mapping, and promoting geological resources represent a promising area for further exploration. The projections indicate that by the end of 2026, scientific production in this field could reach 5226 published papers, underscoring the growing significance of geopark research and interdisciplinary advancements. Full article

Biomass valorization and biofuel production are essential strategies for converting renewable organic materials into sustainable energy, addressing the urgent need for alternatives to fossil fuels. This study conducts a bibliometric analysis of 1657 publications from the Scopus database, covering the period from 2010 to December 2024, revealing significant trends in this field. The number of published articles reached 318 in 2024, peaking at 163 articles in 2020, which garnered 7302 citations, indicating robust global interest. China leads the research landscape with 550 publications and 17,577 citations, followed by the United States with 160 publications and 9359 citations. Key journals include “ACS Sustainable Chemistry and Engineering” and “Chemsuschem”, highlighting important contributions to the field. The analysis identifies four clusters: the role of lignin and heterogeneous catalysis in biomass conversion; pyrolysis and waste valorization techniques; key chemical intermediates from biomass; and advanced reactions like hydrogenolysis for enhancing product value. The study emphasizes the need for innovative strategies and interdisciplinary collaboration to improve biomass utilization and biofuel production. Future research should focus on developing integrated systems that promote a circular economy, optimize reaction conditions, and explore new biomass feedstocks. Overall, this research significantly advances the understanding of biomass conversion processes and supports the transition to renewable energy solutions, underscoring the critical role of biomass in achieving sustainability goals. Full article

Objectives: This bibliometric analysis investigates recent research trends in biologics and small molecules for treating inflammatory bowel disease (IBD) based on literature from the past decade. Methods: This cross-sectional study involved analyzing data retrieved from the Web of Science Core Collection (WoSCC) database to examine the evolution and thematic trends of biological agents and small-molecular drugs for IBD conducted between 1 January 2014, and 20 September 2024. VOSviewer software was utilized to assess co-authorship, co-occurrence, co-citation, and network visualization, followed by a further discussion on significant sub-themes. Results: From 2014 to 20 September 2024, the annual number of global publications increased by 23%, reflecting an acceleration in research activity. The journal “Inflammatory Bowel Diseases” published the highest number of manuscripts (579 publications) and garnered the most citations (13,632 citations), followed by the “Journal of Crohn’s & Colitis” (480 publications) and “Alimentary Pharmacology & Therapeutics” (250 publications). The United States led in productivity with 1943 publications and 66,320 citations, with UC San Diego (291) and authors Sandborn and Vermeire (180) topping the list. The co-occurrence cluster analysis of the top 100 keywords resulted in the formation of six distinct clusters: Disease Mechanisms, Drug Development, Surgical Interventions, Therapeutic Drug Monitoring (TDM), Immunological Targets, and Emerging Therapies. Burst terms (TNF-α inhibitors, JAK inhibitors, and trough-level optimization) highlight trends toward personalized biologics and small-molecule regimens. Conclusions: The bibliometric analysis indicates that IBD therapeutic research and clinical applications focus on biologics and small molecules, with research trends leaning toward precise therapy conversion or the combination in non-responders. Future work will assess monotherapy, the combination, and conversion therapies and investigate new drugs targeting inflammatory pathways. Full article

The influence of scientific papers is measured by their citations. Although predicting the papers’ citation impact based on non-content factors has garnered extensive attention, the influence of such factors is rarely compared. In this article, we compare the influence of non-content factors on the citation counts of academic publications across three fields, i.e., math, computer science, and management. We consider different methods in this study, including three machine learning approaches, namely, XGBoost, Gradient Boosting Decision Tree, and Random Forest, along with statistical techniques such as linear regression and quantile analysis. Our findings reveal that no matter the field or analytical method applied, author prestige and the number of references consistently stand out as the most influential factors, while the breadth of categories covered by the paper has minimal impact. In mathematics, the first citation date and article length are almost equally important as author prestige, while the number of authors and the journal impact factor are crucial for computer science papers. In management, the number of collaborating countries is relatively influential with respect to the paper’s citations. The results of the quantile regression indicate that at higher quantile levels, the impact of author prestige and the number of authors on the papers’ citation impact are more pronounced across all three disciplines, while the journal impact factor and paper length have the greatest influence at low and medium quantile levels. Our findings indicate that the reliance of academic citations on author prestige and journal impact factors not only highlights the unequal distribution of resources within the current academic system but also further exacerbates citation inequality. Full article

This paper presents a quantitative vision of the study of artificial intelligence risk assessment in business based on a bibliometric analysis of the most relevant publications. The main goal is to determine whether the risk assessment of artificial intelligence systems used in businesses is really a subject of increasing interest and to identify the most influential and productive sources of scientific research in this area. Data were collected from the Web of Science Core Collection, one of the most complete and prestigious databases. Regarding the temporal evolution of publications and citations this study evidences, this research subject shows rapid growth in the number of publications (at a compound annual rate of 31.20% from 2018 to 2024 inclusive), showing its high attraction for researchers, responding to the need to implement systematic risk assessment processes in the organizations using AI to mitigate potential harms, ensure compliance with regulations, and enhance artificial intelligence systems’ trust and adoption. Especially after the surge of large language models like ChatGPT or Gemini, AI is revolutionizing the dynamics of human–computer interaction using natural language, video, and audio. However, as the scientific community initiates rigorous studies on AI risk assessment within organizational contexts, it is imperative to consider critical issues such as data privacy, ethics, bias, and hallucinations to ensure the successful integration and interaction of AI systems with human operators. Furthermore, this paper constitutes a starting point, including for any researcher who wants to be introduced to this topic, indicating new challenges that should be dealt by researchers interested in AI and hot topics, in addition to the most relevant literature, authors, and journals about this research subject. Full article