An intelligent group decision algorithm to analyze the promotion of female leisure sports behavior in colleges and universities

As far as we know, we have not observed any other publication before us that has employed T-spherical fuzzy aggregated arithmetic and geometric operators to evaluate strategies of encouraging leisure sports behavior of females. The current study addresses that gap by proposing a powerful, parameter adaptive decision framework that fits different levels of uncertainties and institutional environments. Using T-spherical fuzzy sets (TSFS), this study offers an intelligent group decision-making method to examine and improve female leisure sports behavior promotion in colleges and universities. This study tackles the problems of uncertainty and ambiguity in decision-making processes by integrating TSFS, which successfully captures the imprecision inherent in human perspectives in recognition of the growing significance of leisure sports for promoting physical and mental well-being. Five different approaches are compared to assess eight essential characteristics, including Facilities' accessibility (FA), security and safety (SS), sports facilities' quality (SFQ), social and cultural acceptance (SCA), initiatives and rewards (IR), awareness of health and wellbeing (AHW), community and peer assistance (CPA), Time management and academic integration (TMAI), to encourage female students to participate in leisure sports. To analyze input from experts and produce accurate rankings of the options, the suggested approach makes use of the T-spherical fuzzy aggregated arithmetic weighted average (TSFAAWA) and T-spherical fuzzy aggregated geometric weighted average (TSFAAWG) operators. These operators provide increased flexibility by allowing decision-makers to modify parameter values for maximizing outcomes under various circumstances thanks to their embedded parameters, t, and U. The algorithm scores the options, evaluates performance, and determines the best course of action for encouraging female participation in recreational sports in higher education. Furthermore, by altering the parameters, the sensitivity of the suggested method is examined, guaranteeing the robustness and dependability of the rankings. A comparison with current approaches demonstrates how much more accurate and flexible the proposed model is. This study presents an adaptable strategy that may be applied to other uncertain and multi-attribute decision-making scenarios and offers a thorough framework for decision-making in the educational sector.