Bibliometric Analyses in Data-Driven Decision-Making (eBook)

Prasenjit Chatterjee, Abhijit Saha, Seifedine Kadry, Gulay Demir (Herausgeber)

eBook Download: EPUB

2025
965 Seiten
Wiley-Scrivener (Verlag)
978-1-394-30254-3 (ISBN)

Infos

The book provides essential insights and practical tools needed to effectively navigate the evolving landscape of scholarly research, helping enhance the understanding of publication trends, citation impacts, and collaboration networks across multiple fields.

Bibliometric Analyses in Data-Driven Decision-Making offers a comprehensive guide to researchers, academics, and practitioners interested in utilizing bibliometric analysis to understand and navigate the dynamic landscape of the increasingly vital field of data-driven decision-making and its applications across many areas. It provides insights into growth, impact, and trends within the field, using bibliometric tools and methodologies. This volume adopts a pragmatic approach, balancing theoretical concepts with practical applications of data-driven decision-making models through the perspectives of bibliometric analyses using real-world examples, case studies, and step-by-step guides.

The reader will find the book:

• Gives practical guidance on conducting bibliometric analyses across a range of applications for data-driven decision-making;
• Illustrates the application of bibliometric tools in the field with real-world case studies;
• Provides in-depth coverage of various bibliometric indicators and metrics;
• Explores emerging trends and challenges in bibliometric analysis;
• Provides a comprehensive overview of software and tools available for bibliometric research.

Audience

Librarians and Information professionals involved in research management, knowledge discovery, and the evaluation of scholarly communication, as well as professionals in industries reliant on cutting-edge research and development, technology assessment, and innovation. Also, a range of researchers and scholars seeking how to apply bibliometric analysis to assess the impact of their work, and advanced insights into bibliometric metrics, collaboration networks, and research trends.

Prasenjit Chatterjee, PhD is a post-doctoral fellow in the Department of Applied Data Science, Noroff University College, and a professor of mechanical engineering and Dean of Research and Consultancy at the MCKV Institute of Engineering, India. He has published over 135 research papers in various international journals and conferences, authored and edited more than 43 books on intelligent decision-making, fuzzy computing, supply chain management, optimization techniques, risk management, and sustainability modelling.

Abhijit Saha, PhD is an assistant professor in the Department of Computing Technologies at SRM Institute of Science and Technology, Tamil Nadu, India with over ten years of teaching and research experience. He has published over 50 research articles in international journals and serves on the editorial boards of three international journals. His research interests encompass fuzzy set theory, soft set theory, aggregation operators, optimization, and decision-making.

Seifedine Kadry, PhD is a professor in the Department of Computer Science and Mathematics at the Lebanese American University and the Department of Applied Data Science at Noroff University College. He has published over 100 peer-reviewed papers and authored several books. His research interests include data science, system prognostics, stochastic systems, smart learning, social network analysis, and e-systems.

Gülay Demir, PhD is affiliated with Sivas Cumhuriyet University Vocational School of Health Services. She has over 75 publications, primarily in graduate-level textbooks. Her research interests include fuzzy logic, multi-criteria analysis, mathematical statistics, statistical analysis, data analysis, non-parametric statistics, statistical calculation, and parametric statistics.

Textauszug

Preface

Bibliometrics is used to map the latest state of scientific knowledge that is desired to be investigated in a field of interest. In this analysis, it is essential to determine the scientific performance of authors, articles, journals, institutions, and countries by analyzing keywords and citations. Bibliometric analysis is a statistical technique used to study the latest trends in a particular field, in which the quality and quantity of publications in the scientific literature are evaluated.

This book offers a comprehensive examination of bibliometric analysis across various domains, organized into six distinct parts. It begins with an overview of bibliometric analysis and methodologies, laying the foundation for the detailed discussions that follow. Subsequent parts explore bibliometric analysis in logistics and supply chain management, healthcare and medicine, and its integration with multi-criteria decision-making (MCDM). The book also addresses advancements in artificial intelligence (AI) and machine learning and concludes with a focus on technology, sustainability, and innovation. Each parts provides a thorough analysis of the literature and emerging trends within these fields.

Chapter 1 provides an overview of bibliometric analysis as a quantitative method for assessing knowledge production, diffusion, and impact through metrics like citation counts and publication frequency. It discusses the growing importance of bibliometric studies from 2020 to 2024, emphasizing their role in research management and policy-making. Chapter 2 reviews MCDM research in logistics and supply chain management using bibliometric analysis, highlighting significant growth and diversification in MCDM applications from 2001 to 2024. Chapter 3 examines the evolution of digital supply chains (DSCs) through a comprehensive decade-long bibliometric analysis, drawing from 469 unique articles in the Scopus and Web of Science (WoS) databases (2000-2024), and identifying key research trends and influential publications.

Chapter 4 provides an in-depth bibliometric review of agile supply chain research from 1995 to 2023, highlighting its evolution and emerging trends. Using Bibliometrix R Package, VOSviewer, and SciMAT, the study identifies key articles, authors, institutions, and countries. Chapter 5 presents a bibliometric analysis of robot selection problems using MCDM methods, leveraging Biblioshiny, R Studio, VOSviewer, and CiteSpace software for the analyses. Chapter 6 delivers a comprehensive bibliometric analysis of MCDM in economics, employing the R Biblioshiny application and VOSviewer software. Chapter 7 offers a bibliometric analysis of material selection using MCDM methods, utilizing Biblioshiny, R, VOSviewer, and CiteSpace, analyzing 252 articles from Scopus (2000-2024).

Chapter 8 presents a comprehensive bibliometric analysis of the Evaluation-based on Distance from Average Solution (EDAS) method, examining 201 articles from the Scopus database published between January 2015 and April 2024. Using R programming, VOSviewer, and CiteSpace, the analysis reveals China as the leading contributor, with India emerging as a strong player in international collaborations. This study offers insights into the evolution, key contributors, and future advancements in MCDM research related to the EDAS method. Chapter 9 reviews the development and application of multipolar (m-polar) fuzzy sets as advanced tools for decision-making in environments characterized by vagueness and uncertainty. It highlights the limitations of traditional fuzzy sets in complex scenarios and demonstrates how m-polar fuzzy sets address these shortcomings, enhancing decision-making processes. The chapter also examines the benefits and drawbacks of m-polar fuzzy sets compared to conventional fuzzy sets and explores their evolution through a review of relevant publications, emphasizing their superior performance in various decision-making applications.

Chapter 10 provides a bibliometric analysis of renewable energy and MCDM, exploring research trends, key publications, and gaps. Analyzing 739 publications indexed in Scopus from 2004 to April 2024, the study reveals growing global interest, with significant contributions from China, India, and Turkey, while highlighting areas needing further research, such as integration challenges and optimization of hybrid systems. Chapter 11 examines the use of gamification in healthcare, specifically nursing care, through a bibliometric analysis of 51 articles published between 2011 and 2024. The study shows a sharp increase in publications from 2020 onwards, identifying key trends and keywords like “gamification,” “education,” and “nursing,” and emphasizes gamification’s potential to enhance patient engagement. Chapter 12 presents a bibliometric analysis of virtual reality (VR) in wound care, underscoring its growing role in nursing education and clinical practice. Analyzing 26 articles, the study identifies key themes, authors, and keywords such as “virtual reality,” “pain,” and “distraction,” and highlights the effectiveness of VR in enhancing clinical skills, with the Journal of Burn Care & Research as a major publication source.

Chapter 13 provides a bibliometric analysis of escape room studies in nursing, focusing on their impact on stress management and learning. Analyzing 52 studies published since 2018, the research highlights the Journal for Nurses in Professional Development as the leading publication source and identifies “Can You Escape? Creating an Escape Room to Facilitate Active Learning” as the most cited paper. The study underscores the potential of escape rooms to enhance nursing education and recommends further clinical research to evaluate their effectiveness. Chapter 14 presents a bibliometric analysis of wavelet transform and its applications in biosignal and medical image processing. The study, which examines publications across major scientific databases, reveals a steady increase in research activity, identifying key themes, collaborative networks, and influential publications, illustrating the growing importance and impact of wavelet transform in these biomedical fields.

Chapter 15 explores the advancements and impact of AI in business administration through a bibliometric analysis of 1,997 articles published from 2013 to 2023. Using Biblioshiny and VOSviewer, the study reveals a surge in AI-related publications, with China leading in research contributions and the USA excelling in international collaborations. Chapter 16 provides a bibliometric analysis of decision trees in transportation research, examining 1,107 publications from 2004 to 2024. The study shows a 20.18% annual growth in publications, reflecting the increasing significance of decision trees in this field, with key research areas including machine learning, data analysis, and intelligent transportation systems. The chapter identifies research gaps and offers recommendations, such as exploring advanced machine learning techniques and optimizing autonomous vehicle systems to drive innovation in transportation research. Chapter 17 analyzes the bibliometric and spatial distribution of machine learning research related to climate change, based on 4,406 Scopusindexed articles from 2015 to 2024. The study reveals a dramatic increase in publications, from 528 in 2021 to 1,239 in 2024, with Science of the Total Environment as the leading journal. The chapter also maps global research productivity, highlighting China’s dominance and offering insights into the evolving trends and spatial distribution of research in this field.

Chapter 18 investigates the development and impact of neuro-fuzzy systems in AI and information processing by analyzing 687 papers indexed in Scopus from 2000 to 2024. Key findings highlight prominent journals such as Expert Systems with Applications and IEEE Transactions on Fuzzy Systems. The chapter also identifies major trends, including neuro-fuzzy system development, inference algorithms, and AI integration, offering insights into current research and future directions. Chapter 19 examines the evolution and impact of bioinformatics and genetic algorithms research from 2004 to 2024 through bibliometric analysis. The study reveals a notable increase in publications, especially since 2015, with significant contributions from the USA, China, and India. Major research areas include genomic studies, machine learning, and data analytics, showcasing international collaborations and providing insights for future research directions and potential partnerships in these fields.

Chapter 20 explores the evolution and trends in Swarm Intelligence research from 2001 to 2024 through bibliometric analysis and visualizations. Using OpenAlex and VOSviewer, the study assesses publication trends, subject headings, authorship, affiliations, and country contributions. The findings reveal the growth of research in this field over time, offering valuable insights into current trends and identifying opportunities for future advancements. Chapter 21 provides a bibliometric analysis of Bayesian methods in marketing from 1994 to 2024, highlighting key trends, influential works, and collaborative networks. The study shows that while Bayesian methods offer significant advantages in managing uncertainty and adapting to market dynamics, their adoption in marketing research remains limited. Future research directions include integrating big data, developing dynamic models, exploring hierarchical approaches, and...

Produkt-Details

• Erscheint lt. Verlag: 11.8.2025
• Reihe/Serie: Sustainable Computing and Optimization
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Schlagworte: Author productivity • bibliographic databases • Bibliography Coupling • Bibliometric Software • Citation analysis • citation index • Citation Mapping • Citation Metrics • citation patterns • co-authorship • Data sources • emerging themes • Impact Analysis • Mapping Research Fields • network analysis
• ISBN-10: 1-394-30254-1 / 1394302541
• ISBN-13: 978-1-394-30254-3 / 9781394302543