A Novel Decision-Making Algorithm Enhancing Career Planning and Employment Guidance on Circular T-Spherical Fuzzy CRITIC WASPAS Method

Abstract

This study aims to improve decision-making in vocational training, career planning, and employment guidance by using the algorithm of the multi-attributes group decision-making (MAGDM) approach based on the circular T-spherical fuzzy (CTSF) framework. The criteria importance through the intercriteria correlation (CRITIC) method is used to assign importance to evaluation attributes based on their variation and relationship with each other, while the weighted aggregated sum product assessment (WASPAS) method is applied to rank different strategic alternatives and identify the most effective solution. The CTSF set (CTSFS) is an enhanced version of the traditional fuzzy extension that incorporates the radius and circular behavior of its grades. This enhancement enables CTSF to convey vagueness, interdependent relationships, and ambiguity more precisely than typical fuzzy sets (FS) and their extensions. We created CTSF weighted (CTSFWA) and CTSF weighted geometric (CTSFWG) to accomplish this, combining CTSF value data into a singleton set. A few fundamental properties are also examined, including idempotency, boundedness, and monotonicity. The model is experimented with the help of a hypothetical case study that is aimed at enhancing employment outcomes, and five alternatives are assessed in comparison to five important attributes. The findings indicate that the fifth option, i.e., “Digital Career Guidance Platforms,” is the most ideal strategy of career planning and employment guidance. Sensitivity and comparative analyses are also conducted to confirm the reliability and stability of the proposed approach. The findings indicate that the model is practical, reliable, and useful for supporting strategic decisions in workforce development and employment planning.