Evaluating Corporate Governance in Complex Environments: A Fuzzy Decision-Support Model

Corporate governance is a critical pillar in organizational accountability, transparency, and risk mitigation, especially in the domains of corporate finance, law, and regulatory compliance. With increasing complexity and subjectivity in governance mechanisms, conventional evaluation models often fall short in handling ambiguity and multidimensional indicators. Traditional governance scoring methods lack flexibility to accommodate qualitative judgments and often yield inconsistent results, especially when integrating behavioral or ethical criteria. This study utilizes expert-elicited survey data from 30 governance professionals across five mid-to-large Indian enterprises, covering indicators such as board independence, stakeholder engagement, audit strength, and CSR practices. The proposed methodology combines Fuzzy Analytical Hierarchy Process (Fuzzy AHP) for criteria weighting, a Fuzzy Inference System (FIS) for rule-based decision modeling, and Fuzzy TOPSIS for ranking governance performance relative to an ideal benchmark. The novelty of the paper lies in its multi-layered fuzzy architecture that integrates both compliance metrics and stakeholder-centric variables, allowing for a context-sensitive and interpretable governance assessment. Performance was measured using rank consistency, sensitivity analysis, and Pearson correlation against expert panel evaluations. Results demonstrate that the fuzzy model outperforms traditional crisp methods with a 12.4% improvement in decision reliability and strong alignment (r = 0.87) with known governance quality ratings, offering a robust and participatory framework for governance evaluation in complex business environments.