Navigating Crisis: A Resource-Based and Dynamic Capability Approach to Turnaround Strategies in the Pharmaceutical Sector

This study examines how pharmaceutical manufacturing companies in emerging markets reconfigure internal resources and dynamic capabilities to navigate crises. The study identifies internal and external antecedents of organizational decline based on the Resource-Based View (RBV) and Dynamic Capabilities (DC) frameworks. It evaluates the effectiveness of turnaround strategies under varying levels of competitive intensity. A sequential multi-method design combined Random Forest (RF), a machine learning method for detecting nonlinear interactions and ranking predictor importance, with Structural Equation Modeling (SEM) to validate hypothesized causal relationships. Data were collected from pharmaceutical firms in Jordan, and the analysis proceeded in three phases: identifying drivers of decline, modeling strategic responses, and testing mediation and moderation effects. Findings reveal that external pressures, particularly economic downturns and supply chain disruptions, are the primary sources of decline, with internal factors such as labor constraints and operational inefficiencies also significant. Both categories of antecedents shaped firms’ recovery actions. Organizational agility emerged as a key enabler of effective responses, particularly in highly competitive environments. Turnaround strategies mediated the relationship between crisis antecedents and firm performance, while competitive intensity moderated these relationships. The study contributes a theoretically grounded and empirically validated framework for organizational recovery in high-risk, resource-constrained environments. It advances disaster risk research by integrating RBV and DC perspectives and applying RF and SEM methodologies. Practically, the study provides guidance for managers and policymakers in pharmaceuticals and other critical industries on realigning strategies and building agility to withstand systemic disruptions and prolonged uncertainty.