Eco-social nonlinearity and country sustainability capabilities as drivers of green market competitiveness: eco-marketing insights

Purpose This investigation aims to test the structural strength, directional effects, and threshold curvature of sustainability capabilities, where two objectives are emphasized: identifying feedback pathways within the partial least squares structural equation modeling models and verifying the quadratic turning point of social inclusion and governance strategies at the country and company levels, respectively. Design/methodology/approach In its most general design of the study, quadratic regression analysis of social inclusion and community impact on the outcome of ecosystem-biodiversity performance (R2 = 0.41; p < 0.01) at the company level was used in STATA to measure the curvature and significance of the turning point (1.56) within the mediation of governance and strategy variable (0.009). A large number of bootstrap outcomes at SMART-PLS show that the eco-social nonlinearity model enhances predictive accuracy, thus reducing the uncertainty of emissions-profitability dependence. Findings The results show that the integrated effect of ecological foundations, institutional eco-governance, and knowledge and innovation capacity is strongly positive and significant (p < 0.001), which is better than the single-dimension estimations and has achieved good cross-country validation results. Practical implications In an applied managerial context, nonlinear feedback from ecological performance, governance strength, and innovation capacity allows stronger eco-marketing alignment of sustainability messaging compared to traditional linear data; maximizing reliability and threshold-based triggers strengthens the interpretive capacity of each sustainability signal when compared to static eco-labels. Originality/value The originality of this research lies in its rigorous methodology and specific focus to reveal threshold-based, synergistic sustainability capabilities that reasonably describe and predict green market competitiveness.