Abstract
Student entrepreneurship has become a strategic priority for higher education policy, yet the empirical literature relies on symmetric, variance-based methods such as multiple regression and partial least squares structural equation modelling (PLS-SEM), which assume linearity, additivity and a single dominant route to entrepreneurial intention. Two features of student entrepreneurship are difficult to reconcile with these assumptions: equifinality, because different combinations of antecedents can lead to an equally high intention, and causal asymmetry, because the conditions that explain a high intention are not the mirror image of those explaining a low one. This study develops a two-stage framework that addresses both. In Stage 1, seven motivating criteria are weighted under uncertainty with a spherical fuzzy expert-weighting procedure anchored in Analytic Hierarchy Process (AHP) consistency screening; the spherical fuzzy representation records membership, non-membership and hesitancy degrees and thus suits imprecise judgements. In Stage 2, calibrated illustrative case-profile data are analysed with fuzzy-set Qualitative Comparative Analysis (fsQCA), which identifies the criterion combinations jointly sufficient for a high intention and, through the negated outcome, makes the asymmetry explicit; the weights provide an interpretive layer for contextualising the configurations. The framework is demonstrated on an illustrative, literature-informed dataset of ten cases with notional expert judgements, alongside a deployment design for at least 60 nascent or intending student entrepreneurs. The contribution is threefold: a parsimonious, expert-weighted criteria set; a reusable algorithm connecting fuzzy multi-criteria weighting with set-theoretic analysis of the outcome and its negation; and a transparent research-design template for emerging-economy contexts. All numerical results are therefore illustrative.