Q-Multi-Criteria Consensus Model: Synthesizing cognitive priority structures from multi-actor disagreement

<title>Abstract</title> <p>Traditional multi-criteria decision-making (MCDM) aggregates preferences over predefined alternatives, yet many managerial problems are not tasks of choosing among options but of establishing shared priorities, where the object of analysis is an endogenous cognitive structure – what stakeholders collectively treat as important. This paper introduces the Q-Multi-Criteria Consensus Model (Q-MCCM), a hybrid framework that shifts decision analytics from ranking external alternatives to constructing consensus-weighted knowledge structures from subjective disagreement. Q-MCCM combines forced-distribution Q-sorts (Q methodology) with a behavioral extension of the Best–Worst Method that replaces manual pairwise judgments with an algorithmic Vector Propagation mapping derived from normalized Q-sorts. The method reduces cognitive load while preserving ordinal consistency by construction and yields a complete and reproducible workflow from ranked data to consensus weights and robust priority rankings. A data-generating process study demonstrates the approach, showing high cross-method rank convergence (ρ &gt; 0.9 across fuzzy TOPSIS, VIKOR, and F-PROMETHEE integrations) and revealing a 'strategic consensus' interaction effect in which subject groups shared expert panel priorities. Q-MCCM provides a reproducible, consensus-based instrument for institutional priority setting, curriculum design, and strategic capability assessment, enabling governance in environments where alternatives are ill-defined, but priorities must be negotiated.</p>