Purpose Board diversity has become a central issue in corporate governance due to its potential role in fostering innovation and long-term strategic thinking. This paper aims to evaluate the influence of board composition – specifically ownership structure, board structure, and demographic diversity – on innovation outcomes in Latin America’s metal mining sector. This study introduces a fuzzy inference system (FIS) to assess the marginal impact of these governance variables on innovation capacity. Design/methodology/approach This research applies a Mamdani-type fuzzy logic model to a sample of 12 listed metal mining firms in Latin America. The study draws on corporate governance theory and upper echelons theory to structure three blocks of input variables: ownership (institutional and promoter participation), board structure (size and independence) and demographic diversity (gender and age). Each block is modeled using fuzzy rules and linguistic variables to capture the complexity and interdependence of governance attributes under uncertainty. Findings The findings are exploratory and should be interpreted as patterns observed within this specific sample. The results suggest that promoter ownership contributes more significantly to innovation than institutional investors. Smaller boards are associated with higher innovation scores than larger or highly independent boards. Gender diversity demonstrates a meaningful correlation with innovation, while age diversity yields inconclusive results. The fuzzy model enables the ranking of firms based on innovation scores, reflecting governance maturity levels and their alignment with innovation strategies. Originality/value This study offers an original contribution by applying fuzzy logic to board-level governance variables, a method not commonly used in the governance-innovation literature. It addresses the analytical gap in evaluating ambiguous and non-linear relationships between governance composition and innovation, particularly in high-capital, low-frequency sectors such as mining. This approach may support firms and policymakers in designing governance frameworks that enhance innovation under conditions of limited data and contextual variability.
Mendiola et al. (Wed,) studied this question.