This study aims to comprehensively evaluate the financial and economic performance of firms operating in the MANUFACTURING sector of the Istanbul Stock Exchange (BIST) and prominent in terms of R&D expenditure in the Turkishtime R&D 500 (2024) list. In R&D-intensive firms, performance is multidimensional and cannot be explained solely by periodic profitability indicators. Accordingly, the study, based on a multi-criteria decision-making (MCDM) approach, evaluates firms simultaneously under multiple criteria and produces an integrated ranking.

The methodological contribution of the study is the integration of the COCOFISO method, a novel consensus-based ranking approach, with picture fuzzy sets, which can explicitly represent uncertainty and indecision. While evaluations in classical MCDM approaches are often reduced to a single numerical value, picture fuzzy sets incorporate hesitation and opposing views in the decision-making environment by expressing each observation with three components. In this context, firstly, a sample was created by matching the firms selected from the Turkishtime R&D 500 (2024) list with KAP/BIST data; then, a set of criteria representing financial and economic performance was defined, and the benefit/cost aspects of the criteria were determined. The obtained numerical performance indicators were picture fuzzy values/numbers using the determined conversion scale; thus, each firm-criterion cell was represented by membership, uncertainty, and contrast components.

In the PF-COCOFISO procedure, the criterion performances of the firms are integrated through both additive and multiplicative evaluation components within the framework of COCOFISO's consensus logic; intermediate scores derived from different evaluation strategies are then combined into a final performance score to obtain the firms' rankings. This structure aims to produce more balanced and stable results without adhering to a single aggregation logic. In the final stage, the stability of the ranking results was tested through alternative weighting scenarios and sensitivity analyses, thereby supporting the method's reliability and the robustness of the findings.