A Dynamic Capabilities Framework for Supply Chain Resilience in the Nigerian Oil and Gas Sector: Theory Development and Empirical Validation

This dissertation develops and empirically validates a dynamic capabilities framework for supply chain resilience in the Nigerian oil and gas sector. Existing resilience frameworks, largely developed in advanced economy manufacturing contexts, inadequately account for the intersection of resource curse dynamics, insurgency-related disruptions, and regulatory volatility characteristic of Nigeria's upstream and downstream supply chains. The study synthesises dynamic capabilities theory, resource dependence theory, and complex adaptive systems theory to construct an original conceptual framework proposing that sensing, seizing, and reconfiguring capabilities mediate the relationship between disruption exposure and supply chain resilience outcomes. Using a longitudinal mixed-methods design, quantitative surveys were administered to 240 supply chain executives across 40 oil and gas firms in Rivers, Delta, Akwa Ibom, and Bayelsa States over two data collection waves, supplemented by 25 in-depth executive interviews. Partial least squares structural equation modelling with second-order constructs is employed for quantitative validation. Qualitative data are analysed through theory-building grounded coding. The study introduces the concept of contextual resilience reconfiguration, an original theoretical contribution addressing how Nigerian oil and gas firms adapt their supply chains under simultaneous commercial, security, and regulatory disruption. Findings are expected to provide an empirically validated framework with superior predictive validity over generic resilience models in this context. The dissertation makes original contributions to supply chain resilience theory, dynamic capabilities in extractive industry contexts, and applied logistics scholarship in the Niger Delta region. Keywords: dynamic capabilities, supply chain resilience, oil and gas, Nigeria, theory development.