A Systems-Based Balanced Cost-Benefit Analysis of Corporate-Community Engagement in Resource Extraction: The CoSLIE Framework

This study develops and empirically calibrates the Community-Social Licence-Insurance Equilibrium (CoSLIE) Model, a dynamic, multi-theoretic framework that reconceptualises corporate-community engagement in resource-dependent economies through a systems-based lens. Synthesising stakeholder theory, legitimacy theory, and resource dependence theory, the model transcends static paradigms by embedding recursive feedback loops among Community Engagement (CE), Balanced Cost-Benefit perceptions (BCB), Social Licence to Operate (SLO), and Social Insurance (SI), with Local Governance Quality (LGQ) as an institutional moderator. Employing Partial Least Squares Structural Equation Modelling (PLS-SEM), Bayesian inference, Panel Vector Autoregression (PVAR), machine learning algorithms (GBM, SVR), and Lyapunov-based chaos diagnostics, the model is validated using primary data from 420 respondents across Ghana, South Africa, and Tanzania. The results reveal robust direct and mediated effects of CE on both SLO and SI, moderated significantly by LGQ. The dynamic simulations confirm bounded legitimacy growth trajectories, with high governance environments yielding rapid SLO convergence, while low-LGQ contexts exhibit chaotic volatility. Monte Carlo simulations indicate consistently positive Net Present Values (NPVs), underscoring the financial resilience of structured engagement strategies. Social Network Analysis further highlights the community as the central node in legitimacy diffusion. The CoSLIE Model thus offers a mathematically rigorous, empirically validated, and ethically reflexive decision-support tool, equipping firms, regulators, and communities to optimise stakeholder investments under uncertainty. By translating intangible legitimacy constructs into quantifiable economic indicators, CoSLIE advances a new frontier in ESG-aligned stakeholder governance and policy-responsive social sustainability analytics.