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Forum"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study aims to provide a comprehensive and data-driven analysis of the evolution, intellectual structure, and thematic dynamics of possibilistic risk research over the period 1975\u20132025. A dataset of 461 documents retrieved from the Web of Science Core Collection was analyzed using bibliometric science-mapping techniques implemented in the Bibliometrix R package. To complement the structural analysis, Latent Dirichlet Allocation (LDA) and Structural Topic Modeling (STM) were applied to titles and abstracts. Topic selection was based on a combination of perplexity, UMass coherence, and qualitative interpretability of topics, while STM incorporated publication year to capture temporal dynamics. The results reveal a steady growth of scientific production and an increasingly internationalized research landscape. Bibliometric and network analyses highlight a well-defined intellectual core rooted in possibility theory and fuzzy uncertainty, alongside strong expansion into applied domains. Topic modeling identifies eight major themes, showing a clear shift from foundational theoretical work toward optimization, computational methods, and application-oriented research, particularly in supply chains, decision-making, and complex systems. Temporal analysis confirms the declining dominance of purely theoretical topics and the rise of interdisciplinary and application-driven research. The findings demonstrate that possibilistic risk research has evolved into a mature and methodologically integrated field, combining strong theoretical foundations with growing practical relevance. By integrating bibliometric and topic-modeling approaches, this study provides a unified and robust perspective on the field\u2019s development and underscores the increasing role of optimization, data-driven methods, and interdisciplinary applications in shaping future research directions.<\/jats:p>","DOI":"10.1007\/s43069-026-00660-z","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T06:56:53Z","timestamp":1781247413000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Evolution of Possibilistic Risk Research: A Bibliometric and Topic-Modeling Perspective"],"prefix":"10.1007","volume":"7","author":[{"given":"Irina","family":"Georgescu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jani","family":"Kinnunen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,12]]},"reference":[{"issue":"4","key":"660_CR1","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1016\/j.joi.2017.08.007","volume":"11","author":"M Aria","year":"2017","unstructured":"Aria M, Cuccurullo C (2017) Bibliometrix: an R-tool for comprehensive science mapping analysis. 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