Scientometric Analysis Research Articles

Generative AI, Research Ethics, and Higher Education Research: Insights from a Scientometric Analysis

In the digital age, the intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration of this multifaceted relationship. This study aims to quantify and characterize the current research trends and critically assess the discourse on ethical AI applications within HE. Employing a mixed-methods design, we integrated quantitative data from the Web of Science, Scopus, and the Lens databases with qualitative insights from selected studies to perform scientometric and content analyses, yielding a nuanced landscape of AI utilization in HE. Our results identified vital research areas through citation bursts, keyword co-occurrence, and thematic clusters. We provided a conceptual model for ethical AI integration in HE, encapsulating dichotomous perspectives on AI’s role in education. Three thematic clusters were identified: ethical frameworks and policy development, academic integrity and content creation, and student interaction with AI. The study concludes that, while AI offers substantial benefits for educational advancement, it also brings challenges that necessitate vigilant governance to uphold academic integrity and ethical standards. The implications extend to policymakers, educators, and AI developers, highlighting the need for ethical guidelines, AI literacy, and human-centered AI tools.

Urgent needs for second life using and recycling design of wasted electric vehicles (EVs) lithium-ion battery: a scientometric analysis

Currently, lithium-ion batteries are increasingly widely used and generate waste due to the rapid development of the EV industry. Meanwhile, how to reuse “second life” and recycle “extracting of valuable metals” of these wasted EVBs has been a hot research topic. The 4810 relevant articles from SCI and SSCI Scopus databases were obtained. Scientometric analysis about second life using and recycling methodologies of wasted EVBs was conducted by VOSviewer, Pajek, and Netdraw. According to analytical results, the research of second life using and recycling mythologies has been growing and the expected achievement will continue to increase. China, Germany, the USA, Italy, and the UK are the most active countries in this field. Tsinghua University in China, “Fraunhofer ISI, Karlsruhe” in Germany, and “Polytechnic di Torino” in Italy are the most productive single and collaborative institutions. The journals SAE technical papers and World Electric Vehicle Journal have the highest publication and citations than other journals. Chinese author “Li Y” has the highest number of 36 publications, and his papers were cited 589 times by other authors. By analyzing the co-occurrence and keywords, energy analysis, second life (stationary using, small industry), and treatment methods, (hydrometallurgy and pyrometallurgical, electrochemical, bio-metallurgical) were the hot research topics. The S-curve from the article indicates hydrometallurgical and bio-metallurgical methods are attached with great potential in the near future. Further, different treatment methodologies are observed especially advanced techniques in hydrometallurgical, and spent medium bioleaching techniques in bio-metallurgical are good, economically cheap, has low CO2 emission, environmentally friendly, and has high recovery rate. Finally, this research provides information on second life use and top recycling methodology opportunities for future research direction for researchers and decision-makers who are interested in this research.

A scientometric analysis of ESG criteria implementation in the construction industry

This article examines the evolution of Environmental, Social, and Governance (ESG) principles, originally rooted in finance, analysing their status and future trends. Through scientometric analysis, it explores the global ESG research landscape, with a focus on the construction industry. The study utilizes Scopus as bibliographic database and VOSviewer and Biblioshiny as processing tools. This comprehensive exploration seeks to deepen the understanding of ESG principles and their integration into the construction sector, including real estate and infrastructure.

Exploring the Climate Change-AI Nexus: A Bibliometric and Scientometric Study1

Abstract The intersection between artificial intelligence (AI) and climate change mitigation has received increased attention as a result of pressing global environmental concerns and technological advancements. This paper will cover key scientific literature about the development and implementation of AI in tackling climate change challenge. Methodologically, bibliometric and scientometric analyses, including cooccurrence and co-citation analysis, were employed to systematically examine research trends and patterns. The major research question focuses on tracing the evolution of AI research in relation to climate change, investigating important drivers of interest and identifying influential works and research groups. The data find a considerable increase in AI and climate change papers after 2015, demonstrating a growing appreciation for AI’s potential to provide novel solutions. Implications of this trend include the use of AI technologies for environmental monitoring, prediction, and policy makers. Moreover, interdisciplinary collaborations on AI and climate change between experts from various fields shown the importance of global climate issues. This paper provides a new idea for the study on AI in climate change, highlighting opportunities for further exploration and collaboration.

The scientific legacy of professor Y.V. Krykavskyi: development of logistics theory in Ukraine and future perspectives

The article presents the first attempt at scientometric analysis of the scientific legacy of the prominent Ukrainian scholar, Doctor of Economic Sciences, Professor Yevhen Vasylovych Krykavskyi. The main stages of his scientific work and its impact on the development of the general theory and practice of logistic science in Ukraine are identified. It is demonstrated that Y.V. Krykavskyi himself formulated the theoretical foundations of logistics as a separate direction of scientific research in the field of economic sciences, established the basic conceptual framework, founded a scientific school of marketing and logistics, and trained a large number of candidates and doctors of sciences who continue and deepen the theoretical developments of the great scientist, as well as expand the spheres of practical application of logistic concepts, tools, and methods, and supply chain management in new economic conditions.

Financial Investment Valuation Models for Photovoltaic and Energy Storage Projects: Trends and Challenges

Energy production through non-conventional renewable sources allows progress towards meeting the Sustainable Development Objectives and constitutes abundant and reliable sources when combined with storage systems. From a financial viewpoint, renewable energy production projects withstand significant challenges such as competition, irreversibility of investments, high uncertainty levels, and considerable investment amounts. These facts make their financial valuation fundamental for all the agents involved. Using the Web of Science (WoS) and Scopus databases, a scientometric analysis was carried out to understand the methods that have been used in the financial appraisal of photovoltaic energy generation projects with storage systems. The present research project was developed from 268 studies published between 2013 and 2023; tools such as Bibliometrix 4.1.3, VOSViewer 1.6.19, and Tree of Science 0.0.1a9 were used. Two main findings stand out: (i) the most used methods in the literature are the traditional ones, and within them, the levelized cost of energy has been used with greater frequency; and (ii) there is an interest in analyzing the investments of these systems for residences within the framework of distributed energy generation. Two gaps were found in the literature: (i) the studies that were carried out have not comprehensively incorporated the financial challenges faced by these investments; and (ii) the evaluation of these projects has not been addressed from the perspective of a utility-based power generator.

Climate Change and Cultural Heritage: A Global Mapping of the UNESCO Thematic Indicators in Conjunction with Advanced Technologies for Cultural Sustainability

This study investigates the relationship between cultural heritage and climate change, assessing the global implementation of advanced technologies in line with UNESCO’s Thematic Indicators for Cultural Sustainability. Few studies have been conducted on this topic; hence, theoretical background examines the keywords related to cultural heritage preservation, conservation, restoration, climate change mitigation, and adaptation, as well as the intersection of culture and climate change. It also analyses the definitions provided by leading global organizations and explores the use of advanced technologies in protecting cultural heritage. The research methodology is based on an analytical method consisting of a bibliometric assessment and a scientometric assessment. The bibliometric and scientometric analyses map occurrences, frequencies, and intercorrelations of these keywords with UNESCO Thematic Indicators and advanced technology utilization. The findings reveal a predominance of conservation-related Thematic Indicators, suggesting a conservative approach to cultural sustainability, particularly for environmental resilience, wealth, and livelihoods. In terms of advanced technologies, laser scanning and photogrammetry are used for both conservation and restoration purposes, while chromatography and virtual tours are mainly applied to conservation and preservation practices. Otherwise, infrared thermography, X-ray imaging, and online platforms are used, respectively, for heritage preservation, restoration, and conservation. On the other hand, ground-penetrating radar and remote sensing exhibit fewer connections to heritage protection. The mapping of culture and climate change also highlights the importance of conservation in responding to changing climate conditions. Climate adaptation is closely linked to both conservation and preservation efforts, highlighting the critical role of cultural heritage in fostering climate resilience.

Cartography and Neural Networks: A Scientometric Analysis Based on CiteSpace

Propelled by emerging technologies such as artificial intelligence and deep learning, the essence and scope of cartography have significantly expanded. The rapid progress in neuroscience has raised high expectations for related disciplines, furnishing theoretical support for revealing and deepening the essence of maps. In this study, CiteSpace was used to examine the confluence of cartography and neural networks over the past decade (2013–2023), thus revealing the prevailing research trends and cutting-edge investigations in the field of machine learning and its application in mapping. In addition, this analysis included the systematic categorization of knowledge clusters arising from the fusion of cartography and neural networks, which was followed by the discernment of pivotal clusters in the field of knowledge mapping. Crucially, this study diligently identified the critical studies (milestones) that have made significant contributions to the development of these elucidated clusters. Timeline analysis was used to track these studies’ origins, evolution, and current status. Finally, we constructed collaborative networks among the contributing authors, journals, institutions, and countries. This mapping aids in identifying and visualizing the primary contributing factors of the evolution of knowledge mapping encompassing cartography and neural networks, thus facilitating interdisciplinary and multidisciplinary research and investigations.

A Visual Survey of Tunnel Boring Machine (TBM) Performance in Tunneling Excavation: Mainstream Direction, Brief Review and Future Prospects

This study employs scientometric analysis to investigate the current trajectory of research on tunnel boring machine (TBM) performance and collaborative efforts. Utilizing software tools like Pajek 5.16 and VOSviewer 1.6.18, it scrutinizes literature from 2000 to 2021 sourced from the Web of Science (WOS). The findings illuminate TBM research as an interdisciplinary and intersectoral field attracting increasing national and institutional attention. Notable contributions from China, Iran, the United States, Turkey, and Australia underscore the global significance of TBM research. The recent upsurge in annual publications, primarily driven by Chinese research initiatives, reflects a renewed vigor in TBM exploration. Additionally, the paper presents a succinct evaluation of TBM advantages and drawbacks compared to conventional drill and blast methods, discussing key considerations in excavation methodology selection. Moreover, the study comprehensively reviews TBM performance prediction models, categorizing them into theoretical, empirical, and artificial intelligence-driven approaches. Finally, rooted in metaverse theory, the discourse delves into the immersive learning model and the architecture of a TBM metaverse. In the future, the immersive training and learning model diagram can be employed in scenarios such as employee training and the promotion of safety knowledge. Additionally, the TBM metaverse architecture can simulate, monitor, diagnose, predict, and control the organization, management, and service processes and behaviors of TBMs. This will enhance efficient collaboration across various aspects of the project production cycle. This forward-looking perspective anticipates future trends in TBM technology, emphasizing societal impact and enhancement of economic benefits.

Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions

Over the last decade, the digital twin (DT) concept has effectively revolutionized conventional bridge monitoring and management. Despite their overall success, current bridge DTs encounter conceptual ambiguities, hindering their inherent potential for practical implementation. Moreover, intelligent decision support models have not been properly considered as a component of the bridge DTs framework to enhance the reliability of decisions for asset maintenance. Therefore, this paper conducts a scientometric analysis and a comprehensive state-of-the-art review, exploring current bridge DT research trends and architectures and introducing an enhanced conceptual framework for bridge DTs. To this end, more than 480 research publications have been reviewed, compared, and analyzed. The research result encompasses the redevelopment of a multilayer DT framework, fostering its implementation in the full lifecycle of bridge infrastructure while exploring the potential integration of decision support systems and data fusion from advanced technologies to improve the overall efficiency of implementing DT technology in bridges.

Scientometric Analysis of Socioemotional Wealth, Innovation, and Family Businesses: Dynamics of Their Interrelationship

This study presents a scientometric analysis of the interrelationship between socioemotional wealth, innovation, and family businesses, based on 298 articles published between 1975 and 2022. Grounded in the theory of socioemotional wealth, it investigates how emotional and social resources within family businesses influence innovation and, consequently, business success. The methodology includes statistical and graphical analysis to identify publication trends, collaboration networks, and key themes. Results reveal a significant increase in recent academic production, highlighting the growing relevance of these concepts. Four thematic clusters are identified, emphasizing the importance of socioemotional wealth and innovation in the management of family businesses, providing a solid foundation for future research and business practices.

A scientometric analysis of drone-based structural health monitoring and new technologies

Critical global challenges, such as climate change and the insufficient availability of resources, mean that it is a pivotal time to make cities more intelligent, efficient, and sustainable in a drive towards a net-zero carbon future. This requires intelligent, interactive, and responsive structural health monitoring (SHM) to assure the longevity and safety of ageing infrastructure. Drones have the potential to revolutionise SHM. Drone-based SHM (as a potential fly-by technique) involves equipping drones with various sensors, or using inbuilt sensors, to capture data and images of structures from different angles and perspectives. The data is then processed and analysed to facilitate accurate assessment of the structure’s health and early diagnosis of damage. Although the use of fly-by is relatively new, the speedy advances in various technologies that could be integrated with it, such as computer vision with artificial intelligence, deep learning, and links to digital twins, put these systems on the verge of a potential breakthrough. This paper provides an overview of fly-by SHM technique using both scientometric and qualitative systematic literature review processes, in order to provide a distinct understanding of the state of the art of research. As an original contribution, our research identified four main clusters of research within the field of fly-by SHM: (1) the application of UAV-enabled vision-based monitoring; (2) the integration of drones, advanced sensor technologies, and artificial intelligence; (3) drone-based SHM integrating modal analysis, energy harvesting, and deep learning; and (4) automation and robotics in drone-based SHM. The paper highlights the integration of new technologies such as artificial intelligence, machine learning, and sensors with the fly-by technique for SHM, identifies the gaps in current fly-by SHM research, and suggests new directions for research.

A scientometric analysis of technostress in education from 1991 to 2022

A scientometric analysis of technostress in education from 1991 to 2022

Behavioral Patterns of Social Media Users toward Policy: A Scientometric Analysis

This research explores the behavioural patterns of social media users in response to policies, bridging a gap in the literature by providing in-depth insights into the interaction between user behaviour and public policies relating to social media. An important contribution of this research lies in its holistic approach to understanding how social media users respond to various public policies and their impact on their behaviour. This research aims to explore the influence of the interaction between social media and public policy, understanding how user behaviour on these platforms affects responses to government policies. This study uses qualitative research methods with a scientometric approach. The main results underline the crucial role of social media not only as a communication channel but also as a significant influence tool in shaping public opinion and policy. These studies show that social media has a dual potential, which can support professional and community development, as well as influence government policies and actions through social interaction and information dissemination. However, negative impacts are also evident, such as in the spread of fake news that can significantly alter political views. The practical implications of this research emphasise the need for proactive and systematic government strategies in using social media for the dissemination of accurate information and combating fake news, thereby improving the effectiveness of policy communication and responsiveness to public needs.

Explosion of research on psychopathology and social media use after COVID-19: A scientometric study.

Despite advances in research on psychopathology and social media use, no comprehensive review has examined published papers on this type of research and considered how it was affected by the coronavirus disease 2019 (COVID-19) outbreak. To explore the status of research on psychopathology and social media use before and after the COVID-19 outbreak. We used Bibliometrix (an R software package) to conduct a scientometric analysis of 4588 relevant studies drawn from the Web of Science Core Collection, PubMed, and Scopus databases. Such research output was scarce before COVID-19, but exploded after the pandemic with the publication of a number of high-impact articles. Key authors and institutions, located primarily in developed countries, maintained their core positions, largely uninfluenced by COVID-19; however, research production and collaboration in developing countries increased significantly after COVID-19. Through the analysis of keywords, we identified commonly used methods in this field, together with specific populations, psychopathological conditions, and clinical treatments. Researchers have devoted increasing attention to gender differences in psychopathological states and linked COVID-19 strongly to depression, with depression detection becoming a new trend. Developments in research on psychopathology and social media use are unbalanced and uncoordinated across countries/regions, and more in-depth clinical studies should be conducted in the future. After COVID-19, there was an increased level of concern about mental health issues and a changing emphasis on social media use and the impact of public health emergencies.

A scientometric analysis of multicriteria decision-making research

ABSTRACT Multi-criteria decision-making (MCDM) is an important decision-making technique for incorporation into Decision Support Systems (DSS). This paper uses bibliographic techniques to characterise the MCDM research field. This research identifies the historical origin of MCDM in both Operations Research/Management Science (OR/MS) and Computer Science. This research identifies the increasing importance of environmental and medical applications in addition to traditional engineering and business applications. It also examines the core literature and the extent to which newer papers are cited.

Scientometric analysis of textile sensors

Textile sensors have been widely used in many applications such as healthcare, sports, and entertainment. Focusing on the scientific evolution in the research field of textile sensors, this paper presents a comprehensive review on the academic publications, research hotspots, and major research directions in the field based on scientometric analysis. Totally 3,820 papers published between the year 1991 and 2023 were retrieved and investigated from the Web of Science Core Collection. The software CiteSpace and VOSviewer were adopted to perform scientometric analysis on the collaboration between countries, regions, and institutions. In addition, the citations and co-citations of authors, the source journals, the references, and the co-occurrence of keywords were analyzed. Furthermore, five main research directions in the field of textile sensors were also summarized in this paper, and the most promising and potentially profitable research direction could be health monitoring. The analysis result shows that the research hotspots in recent years include the development of triboelectric nanogenerator, the manufacture of transparent skin-like sensors and optical fiber sensors, the process of data collected from wearable textile sensors, and the design of new materials such as nanocomposites for textile sensors.

Deep brain stimulation in Parkinson's disease: A scientometric and bibliometric analysis, trends, and research hotspots.

Parkinson disease (PD), a prevalent neurodegenerative ailment in the elderly, relies mainly on pharmacotherapy, yet deep brain stimulation (DBS) emerges as a vital remedy for refractory cases. This study performs a bibliometric analysis on DBS in PD, delving into research trends and study impact to offer comprehensive insights for researchers, clinicians, and policymakers, illuminating the current state and evolutionary trajectory of research in this domain. A systematic search on March 13, 2023, in the Scopus database utilized keywords like "Parkinson disease," "PD," "Parkinsonism," "Deep brain stimulation," and "DBS." The top 1000 highly cited publications on DBS in PD underwent scientometric analysis via VOS Viewer and R Studio's Bibliometrix package, covering publication characteristics, co-authorship, keyword co-occurrence, thematic clustering, and trend topics. The bibliometric analysis spanned 1984 to 2021, involving 1000 cited articles from 202 sources. The average number of citations per document were 140.9, with 31,854 references. "Movement Disorders" led in publications (n = 98), followed by "Brain" (n = 78) and "Neurology" (n = 65). The University of Oxford featured prominently. Thematic keyword clustering identified 9 core research areas, such as neuropsychological function and motor circuit electrophysiology. The shift from historical neurosurgical procedures to contemporary focuses like "beta oscillations" and "neuroethics" was evident. The bibliometric analysis emphasizes UK and US dominance, outlining 9 key research areas pivotal for reshaping Parkinson treatment. A discernible shift from invasive neurosurgery to DBS is observed. The call for personalized DBS, integration with NIBS, and exploration of innovative avenues marks the trajectory for future research.

Partner violence in healthcare settings on breastfeeding mothers: a scientometric analysis

Background Partner violence is a significant issue affecting women’s health, particularly their breastfeeding practices in healthcare settings. Despite a considerable body of research on this topic, there is a need for a systematic overview of the existing literature to explore its thematic and structural developments. Methods This scientometric analysis covers a period of 45 years (1977-2022) and identifies 415 peer-reviewed articles published in English before 2023 from the Scopus database. The articles were included if they engaged with themes related to partner violence, healthcare settings, breastfeeding, and mothers, using predefined search terms and filters. Bibliometric data were decoded and visualized using VOSviewer and RStudio to analyze and visualize the scientific dialogue, trends, patterns, and networks in the literature regarding partner violence and its impact on breastfeeding mothers in healthcare settings. Results The study revealed a concentrated exploration of themes related to violence, healthcare provision, mental health, and women’s reproductive health. The co-occurrence network and thematic mapping illuminated seven pivotal clusters, revealing a complex narrative of interconnected research areas. Niche and emerging themes such as “Depression,” “HIV,” and “Covid-19” were identified, highlighting the evolving landscape of the research domain. Conclusions This analysis provides a panoramic overview of the existing literature, revealing intricate thematic structures and collaboration networks within the research on partner violence and breastfeeding mothers in healthcare settings. The study not only sheds light on the historical and current states of research but also pinpoints potential future directions, thereby assisting scholars and policymakers in navigating and prioritizing subsequent investigative endeavors.

A scientometric analysis of bibliotherapy: mapping the research landscape

PurposeBibliotherapy, a therapeutic approach that uses books and reading materials to promote psychological well-being and personal growth, has become more prevalent in recent years. This scientometric study aims to provide a comprehensive view of the bibliotherapy research landscape by highlighting its evolution, trends, and noteworthy contributions using Biblioshiny and VOSviewer.Design/methodology/approachThe academic literature on bibliotherapy is evaluated in-depth in this study utilizing scientometric techniques, including citation and co-citation analysis. A thorough search of the Scopus database revealed 1,703 papers between 1942 and 2023 that dealt with bibliotherapy. For data analysis, the renowned applications Biblioshiny and VOSViewer are employed.FindingsThe study reveals that the output of publications has fluctuated, reflecting scholarly interest in this discipline. The distribution of research across various countries, organizations and academic subjects is investigated further to highlight the diverse and global extent of bibliotherapy research. By analyzing co-citation networks and locating pertinent publications and authors, this scientometric method analyzes the intellectual structure of bibliotherapy research.Research limitations/implicationsBibliometric analysis enriches the theoretical understanding of bibliotherapy by unveiling the networks, influential works and existing gaps in the literature, thus guiding a more informed and collaborative approach to future research and practice in the domain.Practical implicationsEmploying bibliometric analysis in bibliotherapy can refine practices and training programs, ensuring they are evidence-based and practical, enhancing the quality of therapeutic services provided to individuals.Originality/valueIt is a valuable resource for academics, practitioners and policymakers interested in the field since it offers a thorough and current assessment of the bibliotherapy research landscape. The findings of this study have the potential to steer future research, guide the development of bibliotherapeutic interventions supported by evidence and enhance the use of bibliotherapy as a therapeutic modality.