Emerging Research Trends Research Articles

A comprehensive bibliometric study of cyberloafing: trends, patterns and future directions

Purpose This study aims to provide a comprehensive bibliometric analysis of cyberloafing by identifying key trends, distinctive works and prominent contributors in the area. Design/methodology/approach The authors conducted a bibliometric analysis of 398 documents (between 2001 and 2024) indexed in Scopus. The Biblioshiny through R studio and VOS Viewer was used to aid the investigation. Findings The study used bibliometric techniques like citation and keyword analysis to analyse existing literature, identify influential publications, key terms and emerging research trends. Additionally, bibliographic coupling enabled the classification of learning outcomes (LO) into five clusters, and Theory–Context–Characteristics–Methodology (TCCM) provides a structured approach to identifying future research directions by categorising them into Theory, Context, Characteristics and Methodology to ensure comprehensive and systematic academic exploration. Practical implications The research seeks to identify gaps, emerging themes, and the intellectual trajectory of cyberloafing, offering a valuable resource for researchers and policymakers to understand its antecedents, attitudes and implications for organisations. Originality/value This study provides a comprehensive novel bibliometric analysis of cyberloafing research from 2001 to 2024, highlighting thematic mapping and cluster analysis and using TCCM for knowledge advancement and future research pathways.

Integrating Artificial Intelligence and Precision Therapeutics for Advancing the Diagnosis and Treatment of Age-Related Macular Degeneration.

Age-related macular degeneration (AMD) is a multifactorial retinal disease influenced by complex molecular mechanisms, including genetic susceptibility, inflammation, oxidative stress, and metabolic dysregulation. While substantial progress has been made in understanding its pathogenesis, the full molecular underpinnings of AMD remain unclear, impeding the effectiveness of current therapeutic strategies. This study provides an in-depth exploration of the molecular interactions involved in AMD progression, particularly focusing on genetic predispositions (such as CFH, ARMS2/HTRA1, and APOE), inflammatory pathways (including complement system dysregulation and cytokine responses), lipid metabolism (e.g., cholesterol homeostasis and drusen formation), and angiogenesis (VEGF signaling). Through a systematic review and bibliometric analysis of literature published between 2015 and 2025, the study identifies emerging research trends, existing gaps, and promising future therapeutic directions. It further investigates innovative precision medicine approaches, including gene editing (CRISPR), RNA therapeutics (siRNA, antisense oligonucleotides), immunomodulatory therapies, and nanotechnology-based drug delivery systems. Additionally, the study examines the role of metabolic disorders such as diabetes and dyslipidemia in AMD progression, highlighting the influence of systemic health factors on disease onset. Finally, the potential of artificial intelligence (AI) in enhancing AMD management through biomarker-based risk stratification, predictive modeling, and personalized treatment optimization is assessed. By mapping the intricate molecular networks underlying AMD and evaluating novel therapeutic strategies, this research aims to contribute to the development of more effective, individualized treatment protocols for patients with AMD.

Financial and corporate governance determinants of dividend policy in family firms: A bibliometric analysis and future research agenda

This study conducts a systematic literature review of 63 peer-reviewed articles published between January 2008 and December 2023, sourced from the Scopus database, to examine the key determinants shaping dividend policies in family firms. The analysis reveals that financial factors, particularly profitability, leverage, and growth opportunities, serve as primary drivers of dividend decisions. In addition to these, non-financial determinants related to corporate governance, such as board independence, CEO duality, gender diversity, and board size, significantly influence dividend policy by mitigating agency conflicts and aligning the interests of controlling and minority shareholders. By integrating both financial and non-financial perspectives, the study offers a comprehensive understanding of the multifaceted mechanisms that govern dividend strategies in family-owned enterprises. Furthermore, a bibliometric analysis was performed using VOSviewer and RStudio to visualize the intellectual structure of the field, identify emerging research trends, and map scholarly collaborations. The findings provide critical insights for academics, practitioners, and policymakers seeking to strengthen governance frameworks and enhance financial decision-making within family business contexts.

The published role of artificial intelligence in drug discovery and development: a bibliometric and social network analysis from 1990 to 2023

Today, drug discovery and development is one of the fields where Artificial Intelligence (AI) is used extensively. Therefore, this study aims to systematically analyze the scientific literature on the application of AI in drug discovery and development to understand the evolution, trends, and key contributors within this rapidly growing field. By leveraging various bibliometric indicators and visualization techniques, we seek to explore the growth patterns, influential authors and institutions, collaboration networks, and emerging research trends within this domain. Bibliometric and network analysis methods (co-occurrence, co-authorship, and collaboration, etc.) were used to achieve this goal. Bibliometric visualization tools such as Bibliometrix R package software, VOSviewer, and Litmaps were used for comprehensive data analysis. Scientific publications on AI in drug discovery and development were retrieved from the Web of Science Core Collection (WoS CC) database covering 1990–2023. In addition to visualization programs, the InCites database was also used for analysis and visualization. A total of 4059 scientific publications written by 13,932 authors and published in 1071 journals were included in the analysis. The results reveal that the most prolific authors are Ekins (n = 67), Schneider (n = 52), Hou Tj (n = 43), and Cao Ds (n = 34), while the most active institutions are the “Chinese Academy of Science” and “University of California.” The leading scientific journals are “Journal of Chemical Information and Modelling,” “Briefings in Bioinformatics,” and “Journal of Cheminformatics.” The most frequently used author keywords include “protein folding,” “QSAR,” “gene expression data,” “coronavirus,” and “genome rearrangement.” The average number of citations per scientific publication is 28.62, indicating a high impact of research in this field. A significant increase in publications was observed after 2014, with a peak in 2022, followed by a slight decline. International collaboration accounts for 28.06% of the publications, with the USA and China leading in both productivity and influence. The study also identifies key funding organizations, such as the National Natural Science Foundation of China (NSFC) and the United States Department of Health & Human Services, which have significantly supported advancements in this field. In conclusion, this study highlights the transformative role of AI in drug discovery and development, showcasing its potential to accelerate innovation and improve efficiency. The findings provide valuable insights into the current state of research, emerging trends, and future directions, offering a roadmap for researchers, industry professionals, and policymakers to further explore and leverage AI technologies in this domain.Scientific contributionThis study provides a comprehensive bibliometric analysis of 4,059 scientific publications (1990–2023) to map the evolution, trends, and key contributors in AI-driven drug discovery, identifying prolific authors (e.g., Ekins, Schneider), leading institutions (e.g., Chinese Academy of Sciences, University of California), and high-impact journals (Journal of Chemical Information and Modelling). It reveals critical collaboration patterns (28.06% international co-authorships), dominant funding sources (e.g., NSFC, NIH), and emerging research hotspots (e.g., protein folding, QSAR, coronavirus), while highlighting the transformative role of deep learning post-2014. By synthesizing these insights, the study offers a strategic roadmap for researchers and policymakers to optimize AI applications in drug development, addressing both current challenges and future opportunities in the field.

Mapping the intersection of HIV and Alzheimer's disease: a bibliometric analysis of emerging research trends.

HIV and Alzheimer's disease (AD) are significant global health challenges with overlapping neuroinflammatory and protein aggregation mechanisms. Understanding their intersection is critical for advancing therapeutic strategies, particularly in aging populations. This study aims to provide a comprehensive bibliometric analysis of research trends at the intersection of HIV and AD, identify emerging themes, and highlight key contributors in this interdisciplinary field. Using the Web of Science Core Collection, we retrieved 4,856 articles and reviews published between 1994 and 2025. Bibliometric analysis was conducted with VOSviewer, CiteSpace, and R software to examine publication trends, international collaboration, institutional contributions, journal dynamics, author networks, and thematic evolution. The analysis reveals a 14.18% annual growth rate in publications, with the U.S. leading in productivity, followed by China, Germany, and Japan. Key institutions include the NIH and the University of California System, while journals such as Journal of Biological Chemistry and PLOS ONE show significant growth. Prominent authors include Masliah, Eliezer, and Heaton, RK. Research highlights the overlap between HIV-associated neurocognitive disorders (HAND) and AD, emphasizing shared mechanisms like neuroinflammation, protein aggregation, and blood-brain barrier disruption. Recent advances focus on cerebrospinal fluid biomarkers, oxidative stress, and the impact of antiretroviral therapy (ART) on neurological outcomes. Studies increasingly explore the role of advanced methodologies, including machine learning, in elucidating shared mechanisms such as neuroinflammation, endoplasmic reticulum stress, and protein misfolding. This bibliometric analysis underscores the dynamic and rapidly evolving research landscape at the intersection of HIV and AD, driven by collaborative efforts and technological advancements. Future research should prioritize longitudinal studies, mechanistic insights, and translational applications to address unanswered questions in this critical field.

Fertility-sparing treatment for patients with endometrial cancer: a bibliometric analysis from 2000 to 2024.

The rising global incidence of endometrial cancer (EC), particularly among younger patients, has established fertility-sparing treatment as a critical focus in gynecological and reproductive medicine. Despite its clinical significance, comprehensive bibliometric analyses in this field remain limited. This study conducted the most extensive bibliometric analysis to date, encompassing 506 publications on fertility-sparing EC treatments published between January 1, 2000, and December 22, 2024. Utilizing CiteSpace, JepaC, and VOSviewer, we systematically evaluated contributions across regions, institutions, journals, authors, and keywords to identify emerging research trends. China and the United States emerged as leading contributors, collectively accounting for 44.3% of publications. Fudan University and Cha University were identified as the most active institutions. Author Chen Xiaojun demonstrated the highest publication output, while Seok J and Seong SJ ranked as the most frequently cited researchers. Keyword analysis revealed five dominant research themes: "progestin," "reproductive outcomes," "age," "prognostic factors," and "myometrial invasion." This analysis delineates the evolving landscape of fertility-sparing EC research, highlighting persistent emphasis on hormonal therapies and prognostic determinants. The findings underscore the need for standardized treatment protocols and long-term fertility outcome studies. By mapping research trajectories and visualizing knowledge networks, this study provides actionable insights to guide future investigations in reproductive oncology.

Public Service Management: An Emerging Research Trend

Public Service Management: An Emerging Research Trend

Mapping research frontiers in microplastics-induced oxidative stress: a bibliometric analysis (2010–2024)

Introduction: Microplastics exposure is increasingly recognized as a significant contributor to oxidative stress, prompting growing concerns about potential human health impacts. This study aimed to systematically analyze emerging research trends and key thematic areas related to oxidative stress induced by microplastics, providing insights that could inform effective antioxidant-based interventions.Methods: A total of 1,820 relevant articles published between 2010 and 2024 were retrieved from the Web of Science Core Collection. Bibliometric techniques, including Latent Dirichlet Allocation (LDA), keyword co-occurrence analysis, and temporal regression modeling, were employed to analyze thematic structures, keyword relationships, and research development patterns.Results: Analysis revealed a notable increase in research volume over the study period, particularly highlighting health risk assessments and molecular mechanisms as focal points. Oxidative stress was identified as a central mediator linking inflammation, gut microbiota alterations, and apoptotic pathways. Clustering analyses demonstrated significant interdisciplinary connections, underscoring complex interactions across multiple biological systems. Additionally, temporal analysis indicated a recent surge in studies emphasizing health risk evaluations and potential antioxidant interventions.Discussion: The findings emphasize oxidative stress as a crucial mechanism in microplastics-induced toxicity, suggesting dietary antioxidants, such as zinc and N-acetylcysteine, could mitigate associated health risks. This study provides valuable perspectives to guide future research directions and public health strategies aimed at addressing microplastics exposure.

Application and Development of Nanotechnology in Traditional Chinese Acupuncture in Recent 20 Years: A Comprehensive Review.

Acupuncture, rooted in Traditional Chinese Medicine (TCM), serves as a complementary and alternative therapeutic modality that involves the insertion of needles into specific body points to stimulate and treat a variety of conditions. Over time, acupuncture has garnered global acceptance, progressively addressing an increasing spectrum of medical disorders. The integration of nanotechnology holds considerable potential to improve drug delivery efficacy and broaden the scope of acupuncture's applications, providing novel opportunities for investigating the underlying principles of meridians and acupoints. A bibliometric analysis of the application of nanotechnology in acupuncture over the past two decades (2004-2024) highlights emerging research trends and focal points in the field. This article provides an overview of the history, biological mechanisms, preparation methods, and clinical research status of nanoacupuncture, with a focus on analyzing the applications of nanoacupuncture technology. Nanotechnology enables the visualization and imaging of meridians and acupoints, facilitating a deeper understanding of the physiological mechanisms behind acupuncture's therapeutic effects. Modified acupuncture needles incorporating nanotechnology can serve dual purposes: functioning as sensors for real-time monitoring of various physiological parameters, thereby supporting disease diagnosis and enhancing therapeutic efficacy through specialized interventions. Furthermore, nanotechnology-enhanced acupuncture needles can act as platforms for targeted drug delivery working in tandem with responsive nanoparticles for disease treatment. Despite its promising potential and clinical applicability, nanoacupuncture faces inherent limitations and challenges that must be addressed.

Retraction notice to "Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review" [Heliyon 10 (2024) e36743

Retraction notice to "Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review" [Heliyon 10 (2024) e36743

Marine algal polysaccharides: Multifunctional bioactive ingredients for cosmetic formulations.

Marine algal polysaccharides: Multifunctional bioactive ingredients for cosmetic formulations.

Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations

The integration of renewable energy systems into modern buildings is essential for enhancing energy efficiency, reducing carbon footprints, and advancing intelligent energy management. However, optimizing RES operations within building energy management systems introduces significant complexity, requiring advanced control strategies. One significant branch of modern control algorithms concerns reinforcement learning, a data-driven strategy capable of dynamically managing renewable energy sources and other energy subsystems under uncertainty and real-time constraints. The current review systematically examines RL-based control strategies applied in BEMS frameworks integrating RES technologies between 2015 and 2025, classifying them by algorithmic approach and evaluating the role of multi-agent and hybrid methods in improving real-time adaptability and occupant comfort. Following a thorough explanation of a rigorous selection process—which targeted the most impactful peer-reviewed publications from the last decade, the paper presents the mathematical concepts of RL and multi-agent RL, along with detailed summaries and summary tables of the integrated works to facilitate quick reference to key findings. For evaluation, the paper examines and outlines the different attributes in the field considering the following: methodologies of RL; agent types; value-action networks; reward functions; baseline control approaches; RES types; BEMS types; and building typologies. Grounded on the findings presented in the evaluation section, the paper offers a structured synthesis of emerging research trends and future directions, identifying the strengths and limitations of RL in energy management.

Bibliometric Analysis of Financial Inclusion Research

Financial inclusion has emerged as a crucial factor in promoting economic development, reducing poverty, and fostering financial stability. This study employs a bibliometric analysis to explore the intellectual landscape, key research themes, and trends in financial inclusion research using data exclusively from the Scopus database. Utilizing VOSviewer, the study examines keyword co-occurrence, co-authorship networks, and geographic distribution to identify influential scholars, institutions, and countries contributing to the field. The findings reveal an evolution from traditional microfinance approaches toward digital financial services, financial technology (FinTech), and sustainability-linked financial inclusion initiatives. The analysis highlights strong international research collaborations, with India, China, and the United Kingdom playing significant roles in shaping financial inclusion discourse. Moreover, emerging research trends suggest an increasing focus on digital finance, environmental sustainability, and gender disparities in financial access. Despite progress, persistent challenges such as financial literacy gaps, regulatory barriers, and regional inequalities remain critical concerns. This study contributes to the understanding of financial inclusion research trends and provides insights for policymakers, financial institutions, and researchers to design more inclusive and effective financial policies. Future research should integrate interdisciplinary approaches and advanced data analytics to address existing gaps and enhance financial accessibility globally.

Citation Count Modelling: The Role of Statistical Distributions

The statistical distributions develop more accurate models of research impact, such as citation counts and h-index. The statistical models play a crucial role in scientometric analysis and citation mapping. The distributions help identify typical citation pattern, outliers, anomalies.These enable comparison across fields, authors, and papers. The probability distributions aid in predicting future citation counts and emerging research trends as well as areas of research. These indicate papers with immoderately high impact and metrics of research quality, such as citation-based metrics. By applying statistical distributions, scientometric analysis can improve accuracy, reveal underlying citation behaviour and how citations accumulate over time, trends, predict research phenomena. Advance theory development helps contribute to progress of theories of research behaviour and impact. This paper intends to gain insights into research impact, quality and citation flow that support the advancement of scientific research. Main objectives of this study are to understand and describe the patterns of citation counts and to predict the number of citations a paper or author will receive.

Bibliometric Insight into Financial Sustainability Publication Trends

Objectives: This study addresses the fragmented literature on financial sustainability by reviewing research conducted over the past five years. The objective is to bridge the knowledge gap by compiling an annotated bibliography of recent financial sustainability publications, focusing on the developments from 2018 to 2023. The paper identifies key topics and emerging research trends in this field.Methodology: The research employs bibliometric analysis and VOSviewer to analyze data from the Scopus database. It aims to identify four distinct research clusters related to financial sustainability by examining recent publications. The study also assesses the research methods used in the literature, including archival, experimental, and survey methods.Findings: The bibliometric analysis reveals four distinct research clusters that have emerged in the past decade. These clusters include topics such as co-creation, digitization of cultural heritage, financial management in the digital era, international digital platforms, ethical issues, organizational forms, social innovation, work integration social enterprises (WISE), cryptocurrencies, and financial regulation. The study also highlights the predominance of archival, experimental, and survey research methods over the past six years.Conclusion: This research provides a comprehensive overview of the evolving landscape of financial sustainability, offering valuable insights for students and academics. It summarizes key trends, methodologies, and emerging topics in financial sustainability research, contributing to a better understanding of the field and guiding future studies.

Augmented Reality in Higher Education: A Bibliometric Analysis of Research Trends and Evolution from 1997 to 2024

Augmented reality (AR) is a technology that superimposes digital content onto the physical world, blending virtual components like images, videos, and 3D models with real environments. In the realm of higher education, AR-based learning presents significant benefits. This research seeks to examine and illustrate studies on AR in the context of higher education. Data were obtained from the Scopus database, focusing on AR-related publications, which were analyzed and visualized using Biblioshiny. The article selection process in bibliometric analysis followed the PRISMA diagram. The analysis examined various bibliographic factors, including countries, institutions, journals, authors, publications, and frequently co-occurring keywords. A total of 832 articles meeting the inclusion criteria were analyzed, covering the period from 1997 to 2024. Research in this field has expanded significantly, with a notable rise in publications since 2015. The article by Akçayir M, published by Elsevier in the Educational Research Review journal in 2017, has the highest total citations, with 1309 citations. The United States and China emerged as the leading contributors to this area of study. The university with the most publications is led by the University of Bologna in Italy. Key research themes in AR for higher education include education, computer science, and technology. This article also highlights emerging research trends and the evolution of AR concepts in the educational domain, with a particular focus on higher education.

Mapping Machine Learning Trends in Chemistry Research using LLM with Multi-Turn Prompting

A review of research in the field of chemistry that incorporates machine learning is essential to identify recent developments and explore its potential applications. Published research articles provide an opportunity to analyze emerging research trends. The use of natural language processing (NLP) technology not only accelerates text data analysis but also enhances accuracy in understanding the content and context of scientific articles. Previously, trend analysis in ophthalmology research had been conducted using Zero-Shot Learning. In this study, an analysis of chemistry-related articles focusing on machine learning was carried out using a multi-turn prompting technique. The process began with data collection through web scraping of abstracts containing the keywords "machine learning" and "chemistry." The retrieved data was then tabulated and analyzed using a Large Language Model (LLM) with a Multi-Turn Prompting approach, where general prompts were initially used, followed by deeper exploration based on previous responses. Additionally, statistical descriptive analysis was performed using targeted prompts. Analysis of 200 article abstracts identified seven key terms related to the use of machine learning in chemistry: chemical (138 articles), protein (119 articles), drug (107 articles), structure (100 articles), molecular (96 articles), chemistry (91 articles), and quantum (84 articles). Furthermore, three dominant research topics were found in the intersection of chemistry and machine learning: protein and molecular structure, quantum chemistry, and drug discovery. The number of articles on machine learning in chemistry began to rise in 2012 and saw a significant increase in 2019. The findings suggest that there are still many opportunities for developing machine learning applications in chemistry, particularly in quantum chemistry. This field only began to gain attention in 2013, and the number of published articles remains relatively low each year, indicating that it is still in the early stages of exploration.

Mapping the knowledge domain of Natural Capital and Sustainability: A bibliometric analysis using the Scopus database for future research direction

Mapping the knowledge domain of Natural Capital and Sustainability: A bibliometric analysis using the Scopus database for future research direction

Key contributors and trends in circulating tumor DNA research in lung cancer: A bibliometric analysis

Lung cancer is one of the leading causes of cancer-related mortality, and liquid biopsy, particularly the detection of circulating tumor DNA (ctDNA), offers a promising non-invasive alternative for diagnosis. Despite significant research on ctDNA in lung cancer, a comprehensive bibliometric analysis on this topic is lacking in the literature. This study systematically reviews ctDNA research trends in lung cancer using bibliometric methods to identify leading contributors, emerging themes, and underexplored areas for future research. We conducted a search of the Web of Science Core Collection database for ctDNA-related lung cancer publications up to 2023. The bibliometric analysis was performed using VOSviewers, CiteSpace, and the R package “bibliometrix.” The results revealed a total of 2862 publications on ctDNA in lung cancer, comprising 1998 articles and 864 reviews. Between 2021 and 2023, the number of publications stabilized, with an average of approximately 360 publications per year. The countries with the highest number of published papers were China and the United States. The University of Texas MD Anderson Cancer Center was the leading institution in terms of publication output. Among journals, Cancers published the highest number of papers, while Clinical Cancer Research had the highest citation impact. Lanman RB was the leading author by publication count, and Newman was the most co-cited author. Current research on ctDNA in lung cancer primarily focuses on areas such as minimal residual disease, prognosis and recurrence monitoring, adjuvant therapy decision-making, epidermal growth factor receptor and targeted therapy, and immunotherapy. This bibliometric analysis highlights the impact of ctDNA in lung cancer, revealing key contributors and emerging research trends.

A Comprehensive Review on Cellulose Nanofibers, Nanomaterials, and Composites: Manufacturing, Properties, and Applications.

Cellulose nanofibers (CNFs), cellulose nanomaterials (CNMs), and cellulose-based composites represent a convergence of material science, sustainability, and advanced engineering, paving the way for innovative and eco-friendly materials. This paper presents a comprehensive review of these materials, encompassing their extraction, preparation methods, properties, applications, and future directions. The manufacturing of CNFs and CNMs leverages diverse techniques-chemical, mechanical, and enzymatic-with each offering distinct advantages in tailoring material characteristics to meet specific needs. Strategies for functionalization and surface modification are detailed, highlighting their role in enhancing the properties of CNFs and composites while addressing challenges in scaling production to industrial levels. The structural, mechanical, thermal, optical, electrical, and biocompatibility properties of CNFs, CNMs, and their composites are explored, underscoring their versatility for applications across various industries. Cellulose-based composites, in particular, demonstrate exceptional tunable properties for specific uses, although achieving uniform dispersion remains a key technical hurdle. These materials have applications in packaging, automotive, aerospace, biomedical devices, energy storage, and environmental remediation. Emerging research trends emphasize the integration of CNFs and CNMs with advanced manufacturing technologies, promoting sustainable practices and life cycle considerations while advancing their commercialization potential. This rapidly evolving field holds immense promise for addressing global challenges by creating high-performance, and sustainable materials. This review is crucial in advancing the understanding of cellulose nanofibers, nanomaterials, and cellulose-based composites, providing valuable insights that will drive the development of sustainable, high-performance materials for a wide range of applications, ultimately addressing key global challenges.