Bibliometric Analyses in Data-Driven Decision-Making
Description
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.
More details
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Persons
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.
Content
Preface xxiii
Acknowledgements xxix
Part 1: Introduction to Bibliometric Analysis and Methodologies 1
1 Introduction to Bibliometric Analysis and Methodologies 3
Gülay Demir, Prasenjit Chatterjee, Abhijit Saha and Seifedine Kadry
1.1 Introduction 4
1.1.1 Stages of Bibliometric Analysis 5
1.1.1.1 Preparation Phase Before Bibliometric Analysis 6
1.1.1.2 Application Phase of Bibliometric Analysis 14
1.2 Historical Development of Bibliometrics 21
1.3 Key Bibliometric Indicators 24
1.4 Bibliometric Data Sources 26
1.5 Methodologies in Bibliometric Analysis 29
1.6 Applications of Bibliometric Analysis 31
1.7 Challenges and Limitations 33
1.8 Future Directions in Bibliometrics 35
1.9 Conclusions 38
References 39
Part 2: Bibliometric Analysis in Logistics and Supply Chain 45
2 Multi-Criteria Decision-Making in Logistics and Supply Chain Management: A Bibliometric Analysis 47
Murat Kemal Keles and Askin Ozdagoglu
2.1 Introduction 48
2.2 Literature Review 51
2.3 Materials and Methods 52
2.4 Bibliometric Analysis Results of the Logistics/Supply Chain and MCDM 53
2.4.1 Performance Analysis 53
2.4.1.1 The General Overview of the Database 53
2.4.1.2 The Annual Publication and Citation Status 54
2.4.1.3 The Publication and Citation Status of Journals 55
2.4.1.4 The Most Relevant Affiliations 55
2.4.1.5 Authors' Status 57
2.4.1.6 The Most Productive Countries 57
2.4.1.7 Most Cited Document 59
2.4.2 Scientific Mapping Analysis 60
2.4.2.1 Thematic Map 60
2.4.2.2 Trend Topics 61
2.4.2.3 Keyword Analysis 62
2.5 Discussion 64
2.6 Conclusions 66
References 67
3 Digital Supply Chain: A Bibliometric Analysis 71
Rajeev Ranjan, Sonu Rajak, Prasenjit Chatterjee, Gulay Demir and Ernesto DR Santibanez Gonzalez
3.1 Introduction 72
3.2 Bibliometric Analysis 74
3.2.1 Research Gaps and Research Questions 75
3.3 Materials and Methods 76
3.4 Bibliometric Analysis of DSC 79
3.4.1 Performance Analysis 79
3.4.1.1 Overall Review of the Database 79
3.4.1.2 A Rise in Annual Publications 80
3.4.1.3 Average Annual Citations 81
3.4.1.4 Sankey Diagram 81
3.4.1.5 Most Cited and Most Published Journals 83
3.4.1.6 The Most Important Affiliations 83
3.4.1.7 Frequently Referenced Authors 84
3.4.1.8 The Most Productive Countries 84
3.4.1.9 Most Cited Document 86
3.4.2 Analysis of Science Mapping 88
3.4.2.1 Thematic Map 88
3.4.2.2 Trend Topics 89
3.4.2.3 Word Cloud 90
3.4.2.4 Collaborative Network of Co-Words in Publications on DSC 91
3.4.2.5 Conceptual Structure Map 92
3.5 Discussions 92
3.6 Conclusions 94
References 95
4 Agile Supply Chain Dynamics: A Bibliometric Analysis with a Technology-Barrier-Performance Framework 99
Vikrant Sharma and Prasenjit Chatterjee
4.1 Introduction 100
4.2 Literature Review 101
4.3 Methodology 103
4.4 Results 104
4.4.1 Descriptive Analysis 104
4.4.2 Sources 106
4.4.3 Authors 108
4.4.4 Main Research Country 110
4.4.5 Relationship 112
4.5 Mapping Results with VOSviewer Software 112
4.6 Conceptual Structure and Evolution of the Field 115
4.7 Discussion 122
4.7.1 Principal Findings 122
4.7.2 Technology, Enablers, Barriers, and Performance Indicators Framework 124
4.7.3 Future Direction for Agile Supply Chain 127
4.7.4 Limitation of Study 128
4.8 Conclusions 128
References 129
Part 3: Multi-Criteria Decision-Making (MCDM) and Bibliometric Analysis 137
5 Multi-Criteria Decision-Making Methods for Robot Selection: A Bibliometric Analysis of Research Trends 139
Rajeev Ranjan, Sonu Rajak and Prasenjit Chatterjee
5.1 Introduction 140
5.2 Bibliometric Analysis 142
5.3 Materials and Methods 143
5.4 Results 146
5.4.1 Performance Analysis 146
5.4.1.1 Database Overview 147
5.4.1.2 Annual Increase in Publications 147
5.4.1.3 Status of Average Annual Citations 148
5.4.1.4 Sankey Diagram 149
5.4.1.5 Most Cited Journals 149
5.4.1.6 The Most Relevant Affiliations 151
5.4.1.7 The Most Cited Authors 151
5.4.1.8 The Most Productive Nations 152
5.4.1.9 Most Cited Document 155
5.4.2 Science Mapping Analysis 155
5.4.2.1 Co-Occurrence Keywords Analysis 155
5.4.2.2 Thematic Analysis 158
5.4.2.3 Trend Topics 159
5.4.2.4 Scientific Landscape 160
5.4.2.5 Timeline Analysis 161
5.4.2.6 Citation Burst Analysis 163
5.5 Discussion 163
5.6 Conclusions, Managerial Implication, and Future Research Directions 165
References 166
6 Bibliometrics Analysis on Economics and MCDM 169
Yüksel Aydin
6.1 Introduction 170
6.2 Literature Review 171
6.3 Research Methodology 174
6.4 Bibliometric Analysis Results on Economics and MCDM 174
6.4.1 Performance Analysis 174
6.4.1.1 Main Information 174
6.4.1.2 Annual Status of Publications 175
6.4.1.3 Average Annual Citations 176
6.4.1.4 Magazines with the Most Publications 176
6.4.1.5 Most Important Universities 177
6.4.1.6 Most Important Authors 178
6.4.1.7 Most Productive Countries 179
6.4.1.8 Most Cited Article 180
6.4.2 Scientific Mapping Analysis 181
6.4.2.1 Thematic Map 181
6.4.2.2 Trend Topics 182
6.4.2.3 Keyword Analysis 183
6.5 Discussion 185
6.6 Conclusion 186
References 187
7 Material Selection by Multi-Criteria Decision-Making: A Bibliometric Analysis 191
Rajeev Ranjan, Sonu Rajak and Prasenjit Chatterjee
7.1 Introduction 192
7.2 A Brief Background of Multi-Criteria Decision-Making (mcdm) 192
7.2.1 Bibliometric Analysis 196
7.2.2 Research Gaps and Research Questions 197
7.3 Materials and Methods 198
7.4 Material Selection by MCDM Method's Bibliometric Analysis Findings 199
7.4.1 Performance Analysis 199
7.4.1.1 Overall Review of the Database 200
7.4.1.2 An Increase in Publications Per Year 201
7.4.1.3 Average Annual Citations 201
7.4.1.4 Sankey Diagram 203
7.4.1.5 Most Cited and Most Published Journals 203
7.4.1.6 The Most Important Affiliations 204
7.4.1.7 Frequently Referenced Authors 205
7.4.1.8 The Most Productive Countries 205
7.4.1.9 Most Cited Document 208
7.4.2 Analysis of Science Mapping 208
7.4.2.1 Thematic Map 209
7.4.2.2 Trend Topics 210
7.4.2.3 Keyword Co-Occurrence Analysis 212
7.4.2.4 Scientific Landscape 213
7.4.2.5 Timeline Analysis 214
7.4.2.6 Citation Burst Analysis 216
7.5 Discussions 216
7.6 Conclusions, Managerial Implication, and Future Research Directions 218
References 220
8 Evaluation Based on Distance from Average Solution (EDAS) Method: A Bibliometric Analysis 223
Rajeev Ranjan, Sonu Rajak, Prasenjit Chatterjee and Seifedine Kadry
8.1 Introduction 224
8.2 EDAS Method 226
8.2.1 Fundamentals of EDAS Method 226
8.2.2 Bibliometric Analysis 229
8.2.3 Research Gaps and Research Questions 230
8.3 Materials and Methods 232
8.4 Results of the EDAS Method Bibliometric Analysis 234
8.4.1 Performance Analysis 234
8.4.1.1 Overall Review of the Database 234
8.4.1.2 Annual Publication Increase 235
8.4.1.3 Average Annual Citations 235
8.4.1.4 Sankey Diagram 237
8.4.1.5 Most Cited and Most Published Journals 237
8.4.1.6 The Affiliations that Matter Most 238
8.4.1.7 Frequently Cited Authors 238
8.4.1.8 The Most Productive Countries 239
8.4.1.9 Most Cited Document 242
8.4.2 Analysis of Science Mapping 243
8.4.2.1 Thematic Map 243
8.4.2.2 Trend Topics 243
8.4.2.3 Keyword Co-Occurrence Analysis 245
8.4.2.4 Scientific Landscape 247
8.4.2.5 Timeline Analysis 248
8.4.2.6 Citation Burst Analysis 249
8.5 Discussions 250
8.6 Conclusions 251
References 252
9 Evolution of m-Polar Fuzzy Set as a Decision-Making Tool: A Bibliometric Review 257
Madan Jagtap and Prasad Karande
9.1 Introduction 258
9.1.1 Paper Organization 259
9.1.2 Research Methodology and Contributions of the Work 259
9.2 Literature Review 261
9.2.1 Different Types of Fuzzy Sets 261
9.3 Fuzzy Sets in Decision-Making 262
9.4 m-Polar Fuzzy Sets in Decision-Making 263
9.5 Comparison of m-Polar Fuzzy Set and Ordinary Fuzzy Sets in Decision-Making 265
9.6 Analysis of m-Polar FSs 265
9.6.1 Analysis Based on m-Polar FS Publications 265
9.6.2 Analysis Based on Journals 267
9.6.3 Analysis Based on Authors' Contributions 282
9.6.4 Analysis Based on Application of m-Polar Fuzzy Logic 284
9.6.5 Bibliometric Analysis for the m-Polar Fuzzy Set 286
9.6.5.1 Co-Author and Author Mapping for m-Polar Fuzzy Set 286
9.6.5.2 Bibliographic Coupling of Universities for the m-Polar Fuzzy Set 287
9.6.5.3 Bibliographic Coupling Countries for the m-Polar Fuzzy Set 287
9.6.5.4 Citation Documents Analysis for m-Polar Fuzzy Sets 288
9.6.5.5 Co-Occurrences of Author's Keywords Analysis for m-Polar Fuzzy Sets 289
9.6.5.6 Co-Authorship Organizations Analysis for the m-Polar Fuzzy Sets 290
9.7 Conclusion 290
References 291
10 Bibliometrics Analysis on Renewable Energy and Multi-Criteria Decision-Making 295
Rahim Arslan
10.1 Introduction 296
10.2 Literature Review 298
10.3 Methodology 299
10.3.1 Data Collection 299
10.3.1.1 Selection of Databases 299
10.3.1.2 Keyword Search 299
10.3.1.3 Time Interval 300
10.3.1.4 Visualization of Data 300
10.3.2 Data Acquisition 300
10.4 Bibliometric Indicators 300
10.4.1 Number of Publications and Citations 301
10.4.2 Evaluation According to the Amount of Citations 303
10.4.3 Author and Organization Analysis 303
10.4.4 Country Analysis 306
10.4.5 Journal Analysis 308
10.4.6 Keyword Analysis 309
10.4.7 Trend Analysis 316
10.5 Discussion 317
10.6 Research Gaps and Future Directions 318
References 319
Part 4: Bibliometric Analysis in Healthcare and Medicine 323
11 Gamification in Healthcare: Bibliometric Analysis on Gamification in Nursing Care 325
Askeri Çankaya
11.1 Introduction 326
11.1.1 Gamification in Healthcare 326
11.1.2 Gamification in Nursing Care 327
11.2 Material and Methods 329
11.3 Results 330
11.4 Discussions 334
11.5 Conclusions 336
References 336
12 Virtual Reality in Healthcare: A Bibliometric Analysis of Studies on Wound Care 339
Hatice Özsoy
12.1 Introduction 340
12.1.1 Virtual Reality in Nursing 340
12.1.2 Virtual Reality in Wound Care 341
12.1.3 Literature Review 341
12.2 Materials and Methods 342
12.2.1 Research Problem and Aim 342
12.2.2 Data Sources and Research Methods 343
12.3 Results 343
12.3.1 Main Information 343
12.3.2 Annual Scientific Production 344
12.3.3 The Most Productive Countries 344
12.3.4 Author Keywords 345
12.3.5 Most Productive and Cited Countries 347
12.3.6 Sankey Diagram 348
12.3.7 Country Collaboration Network 348
12.4 Discussion 349
12.5 Conclusion 350
References 350
13 Escape Room Method in Healthcare: A Bibliometric Analysis 355
Esra Özkan
13.1 Introduction 356
13.1.1 Escape Rooms in Healthcare 356
13.1.2 Escape Rooms in Clinical Nursing 357
13.1.3 The Theory of Escape Room 357
13.2 Material and Method 358
13.2.1 Data Extraction and Analysis Process 359
13.3 Results 360
13.4 Discussions 363
13.5 Conclusions 364
References 364
14 Bibliometric Analysis of Wavelet Transformation Applications in Biosignal and Medical Image Processing 367
S. N. Kumar, Linu Tess Antony and Jibil K. John
14.1 Introduction 368
14.2 Review of the Literature Using Bibliometric Analysis 370
14.3 Results and Discussion 372
14.4 Conclusion 386
References 387
Part 5: Artificial Intelligence and Machine Learning 389
15 Artificial Intelligence in Business Management: Current Developments and Future Perspectives Through Bibliometric Analysis 391
Zekiye Tamer
15.1 Introduction 392
15.2 Public Relations in Business Management and AI 393
15.3 Method 395
15.3.1 Bibliometric Analysis 395
15.3.2 Research Gaps and Research Questions 396
15.3.3 Findings 396
15.4 Conclusions 409
References 411
16 Decision Trees in Transportation Research: Bibliometric Analysis and Future Directions 413
Gülay Demir, Prasenjit Chatterjee and Abhijit Saha
16.1 Introduction 414
16.2 Literature Review 415
16.3 Fundamentals of Decision Trees 416
16.3.1 Types of Decision Trees 417
16.3.2 Applications of Decision Trees 418
16.3.3 Basic Components for Decision Trees 419
16.3.4 Working Principle of Decision Trees 419
16.3.5 Applications of Decision Trees in Transportation 420
16.3.6 Decision Tree Model Drawing 420
16.4 Research Methodology 421
16.4.1 Data Collection 422
16.4.2 Bibliometric Analysis Tools 422
16.4.3 Data Analysis 422
16.4.4 Bibliometric Indicators 423
16.4.4.1 Number of Publications and Citations 423
16.4.5 Number of Citations 425
16.4.6 Author and Organization Analysis 426
16.4.7 Country Analysis 428
16.4.8 Journal Analysis 429
16.4.9 Keyword Analysis 431
16.4.10 Trend Analysis 437
16.5 Discussion 439
16.6 Conclusions and Future Research Directions 441
References 441
17 Machine Learning in Climate Change from 2015 to 2024: A Bibliometric Analysis 447
Minh Thu Nguyen
17.1 Introduction 447
17.2 Research Methodology 449
17.3 Trend of Scientific Articles 450
17.3.1 Research Productivity in 2015-2024 Year 450
17.3.2 Publication of Scientific Journals 451
17.3.3 Output of Author 453
17.3.4 Distribution of Country 455
17.4 Conclusions 459
References 460
18 Neuro-Fuzzy Systems and Inference in the Evolution of Intelligent Systems: A Bibliometric Review 463
Sefer Darici
18.1 Introduction 464
18.2 Literature 465
18.3 Materials and Methods 466
18.4 Bibliometric Analysis Results of Neuro-Fuzzy Systems and Inference 467
18.4.1 Performance Analysis 467
18.4.1.1 Database Overview 467
18.4.1.2 Annual Publication and Citation Status 468
18.4.1.3 Publication and Citation Status of Journals 469
18.4.1.4 The Most Relevant Affiliations 471
18.4.1.5 Status of Authors 471
18.4.1.6 The Most Productive Countries 472
18.4.1.7 Most Cited Document 474
18.4.2 Scientific Mapping Analysis 475
18.4.2.1 Thematic Map 475
18.4.2.2 Trend Topics 476
18.4.2.3 Keyword Analysis 477
18.5 Discussion 479
18.6 Conclusions 482
References 482
19 Genetic Algorithms in Bioinformatics: A Bibliometric Review 487
Ahmet Turan Demir
19.1 Introduction 488
19.2 Method 490
19.2.1 Data Collection 490
19.2.2 Data Analysis 490
19.2.3 Software Tools 491
19.3 Results 491
19.3.1 General Information of Publications 491
19.3.2 Distribution of Publication and Citation Counts by Year 492
19.3.3 Most Cited Articles, Countries, and Journals 494
19.3.4 Most Productive Authors, Institutions, Journals, and Countries 496
19.3.5 Collaborative Networks and Collaboration Analyses 499
19.3.6 Thematic Analysis of Popular Research Topics 501
19.3.7 Trend Topics 503
19.4 Discussions 506
19.5 Future Directions and Implications 507
19.6 Conclusions 508
References 509
20 Swarm Intelligence in Two Decades: Bibliometric Analysis and Research Visualizations 513
Ayse Meriç Yazici
20.1 Introduction 513
20.2 Bibliometric Analysis 515
20.3 Methodology 515
20.4 Data Analysis and Findings 515
20.5 Theoretical and Practical Limitations 522
20.6 Future Recommendations 523
20.7 Conclusions 523
References 524
21 Bayesian Methods in Marketing: A Bibliometric Examination of Trends and Patterns 527
Bibin Xavier
21.1 Introduction 527
21.2 Methodology 529
21.3 Bibliometric Analysis 530
21.3.1 Annual Scientific Production 530
21.3.2 Average Citations Per Year 533
21.3.3 Most Relevant Sources 533
21.3.4 Most Local Cited Sources 533
21.3.5 Core Sources by Bradford's Law 535
21.3.6 Sources' Local Impact 539
21.3.7 Most Relevant Authors 541
21.3.8 Most Relevant Affiliations 541
21.3.9 Countries' Scientific Production 542
21.3.10 Most Global Cited Documents 543
21.3.11 Bibliographic Coupling 545
21.4 Discussion 545
21.5 Conclusions 548
References 548
22 Bibliometric Analysis of Search Engine Optimization-Based Decision-Making Strategies 551
Uzma Mumtaz, Anand Bharathi S., S. Rajamohan and Naseeb Ahmad
22.1 Introduction 552
22.1.1 Fundamentals of Search Engine Optimization (seo) 552
22.1.2 Introduction to Bibliometric Analysis in SEO Decision-Making 552
22.1.3 Objectives and Scope of the Chapter 553
22.1.4 Selection Criteria for Scopus Search 554
22.2 Insights through Research Publication Trends 554
22.2.1 Analysis of Annual Publication Trends in Informed SEO Decision-Making Strategies 554
22.2.2 Key Milestones and Landmark Publications 556
22.2.3 Trends in Publication Activity Over Time 556
22.2.4 Impact of External Factors on Publication Trends 557
22.3 Prominent Authors, Organizations, and Countries 557
22.3.1 Top Authors and Their Citation Impact 557
22.3.2 Leading Academic Institutions and Their Contributions 557
22.3.3 Global Distribution of Research Output and Impact 558
22.4 Most Influential Journals and Articles 559
22.4.1 Analysis of Most Influential Journals 559
22.4.2 Examination of Highly Cited Articles and their Impact 560
22.4.3 Key Insights from Influential Articles 562
22.5 Analysis of Co-Citations and Author Co-Citation Networks 562
22.5.1 Co-Citation Analysis: Identifying Frequently Cited References 562
22.5.2 Author Co-Citation Networks: Mapping Collaborative Partnerships 564
22.5.3 Insights from Co-Citation Analysis 565
22.6 Keyword Co-Occurrence Analysis 566
22.6.1 Top Keywords in Informed SEO Decision-Making Strategies 566
22.6.2 Analysis of Keyword Relationships and Frequency 566
22.6.3 Insights for Research and Practice 568
22.7 Thematic Analysis of Bibliometric Coupling 569
22.7.1 Identification of Theme Clusters in Informed SEO-Based Decision-Making Strategies 569
22.7.2 Overview of Key Research Themes and Findings 573
22.7.3 Implications for Future Research Directions 573
22.8 Challenges and Opportunities 574
22.8.1 Limitations and Pitfalls of Bibliometric Analysis in SEO 574
22.8.2 Opportunities for Further Research and Innovation 574
22.8.3 Addressing Challenges in Bibliometric Analysis for Informed SEO Decision-Making 575
22.9 Conclusions 575
22.9.1 Recap of Key Findings and Insights 575
22.9.2 Importance of Bibliometric Analysis in SEO Strategy 576
22.9.3 Future Outlook and Recommendations for Practitioners Insights through Research 576
Acknowledgment 576
References 577
Part 6: Technology, Sustainability, and Innovation 581
23 Bibliometric Insights into the Nexus of Digital HR, Innovation, and Sustainability: Toward a Smart Workforce 583
Shivakami Rajan and L. R. Niranjan
23.1 Introduction 584
23.1.1 Research Gap 584
23.1.2 Research Questions 584
23.1.3 Literature Review 585
23.1.4 Conceptual Framework 587
23.2 Methodology 588
23.2.1 Inclusion and Exclusion Criteria 589
23.2.2 Data Analysis 589
23.3 Results-Bibliometric Findings 589
23.3.1 Descriptive Details of Publications 589
23.3.2 Keyword Analyses 590
23.3.3 Authors 593
23.3.4 Findings 596
23.4 Discussions 600
23.5 Implications 603
23.5.1 Industry 603
23.5.2 Managerial 603
23.6 Future Directions 604
23.7 Conclusions 605
References 605
24 Delineation of Blockchain and Customer Experience: A Review and Thematic Analysis 615
Akshay Kumar Mishra and Sandeep Kumar Mohanty
24.1 Introduction 615
24.2 Conceptual Background: Blockchain and Customer Experience 617
24.3 Methodology 618
24.4 Bibliometric and Network Analysis 619
24.4.1 Performance Analysis 619
24.4.2 Influential Documents Analysis 621
24.4.3 Keywords and Network Analysis 623
24.4.4 Thematic Analysis 625
24.4.4.1 Blockchain, Artificial Intelligence, and Customer Experience 626
24.4.4.2 Blockchain and Competition 627
24.4.4.3 Blockchain and Motivation 628
24.4.4.4 Blockchain and Cost Reduction 628
24.5 Implications and Future Research Directions 629
24.6 Conclusion 632
References 633
25 A Post-Millennial Bibliometric Analysis of Algorithmic Fairness: Trends, Research Barriers, and Future Directions 639
Murat Atan, Mustafa Mehmet Bayar and Irmak Uzun Bayar
25.1 Introduction 640
25.1.1 Explainable AI and the Fairness Hype from a Data-Driven Decision-Making Perspective 640
25.1.2 Why Focus on the Post-Millennial Era? 641
25.2 Relevant Bibliometric Analyses 641
25.3 Materials and Methods 643
25.4 Bibliometric and Network Analysis 645
25.4.1 Sources 645
25.4.2 Annual Monitoring of Growth 648
25.4.3 Language Choices 650
25.4.4 Subject Areas 650
25.4.5 Keyword Analysis 653
25.4.6 Geographical Distribution of Publications and International Collaborations 656
25.4.7 Authorship 657
25.4.8 Institutional Performance 664
25.4.9 Research Barriers and Funding Impact 666
25.5 Conclusion 666
References 668
Index 673
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...
