A DATA-DRIVEN MULTI-CRITERIA DECISION SUPPORT MODEL FOR ANALYZING SCIENTIFIC DEVELOPMENT OF OECD COUNTRIES - BAYAZİT BEDİRHANOĞLU, Şule (Journal of process management and new technologies, 2026)
In the global science ecosystem, countries' academic outputs are a key determinant of their strategic development level. The aim of this study is to examine the development dynamics of higher education systems over a period of approximately thirty years using integrated decision analytics. The scientific dynamics of OECD countries between 1996 and 2024 were analyzed using integrated LODECI and RAWEC methods based on the SCIMAGO database. The study compares long-term trends with current data from 2024 from a comparative perspective. The criterion weighting analysis conducted with LODECI determined that the Citations criterion is the most important determinant of academic competitiveness. This finding shows that the positioning of higher education institutions at the national and international levels is shaped in the context of scientific impact and productivity, beyond mere quantity. Countries were ranked according to their performance by integrating the weights into the RAWEC method. This study, which synthesizes innovative MCDM methods with long-term data, offers a unique methodological framework for analyzing countries' scientific development.