A decision support framework for optimizing career path prediction and vocational mobility of college graduates

Abstract

Vocational mobility (VM) is one of the most definitive and determinative factors in career advancement and flexibility, especially for college graduates starting their careers in competitive job markets. Previous strategies for modelling career paths cannot incorporate uncertainty and variability into decisions and consequently tend to provide imprecise assessments. To address these shortcomings, this article introduces an effective decision support system based on the interval-valued spherical fuzzy MARCOS (IVSF-MARCOS) method, integrated with multi-criteria group decision-making (MCGDM). This will enable the model to systematically combine different and disparate expert judgments, allowing it to deal with imprecise, vague, or incomplete information in complex decisions involving the environment. The judgments of five decision-makers are used to assess fifteen career options based on ten factors, including potential income, employment security, advancement opportunities, and market saturation levels. The proposed model has fewer uncertainties and higher levels of precision and accuracy in handling the findings, unlike traditional models of decision-making. The study’s practical implications are presented in the form of a ranking of career fields relevant to individuals and market needs. With the help of research that utilizes the IVSF-MARCOS method as an integral part of a larger study conducted within an MCGDM framework, this study contributes to the theory of career path prediction and VM by proposing a new decision-support process capable of managing uncertainty and group evaluation.