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Classifying research leadership in infantile hemangioma studies: A bibliometric analysis using Kano diagrams and
Meng-Ju Li1,2, Feng-Jie Lai3,4
1Department of Pediatrics, National Taiwan University Hsin-Chu Hospital, Hsinchu, Taiwan.
Background:
Infantile hemangioma (IH) is a common benign vascular tumor in pediatric patients, characterized by endothelial cell proliferation and a typical course of rapid growth, gradual involution, and eventual regression. Despite extensive research on IH, leadership classification in publications remains underexplored. This study introduces a novel bibliometric approach by integrating Kano diagrams and absolute advantage coefficients (AACs) to classify leaders within IH research.
Methods:
A total of 2817 articles and reviews published from 2015 to 2024 were retrieved from the Web of Science Core Collection. The analysis consisted of 2 parts: summarizing the overall publication AAC and classifying leaders across the top 10 metadata entities. An online R-based bibliometric module was used, incorporating the follower-leading clustering algorithm to visualize Kano-based leadership classifications. Leadership was categorized as super (AAC ≥ 0.8), quasi-super (≥0.7), strong (≥0.6), moderate (≥0.5), and weak (<0.5). The top 20 entities for each metadata category were visualized.
Results:
The United States led publications, followed by China, with a weak advantage (AAC = 0.23). Other leading entities included Shanghai Jiao Tong University (China, strong, 0.60), the Dermatology department (moderate, 0.54), author Xiaoxi Lin (China, weak, 0.45), the Pediatric Dermatology journal (quasi-super, 0.70), the Pediatrics research area (weak, 0.49), and the keyword "CHILDREN" (weak, 0.44). Notably, Dr Xiaoxi Lin also achieved a super leadership rating (AAC = 0.88) based on individual publication profile analysis.
Conclusion:
The Kano integrated with AAC approach effectively identifies and classifies research leadership in publications of IH, offering a novel lens to evaluate dominance and influence across metadata entities in bibliometric studies.
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