Unlocking Digital Payoffs in Pharmaceutical Supply Chains: Organizational Learning Capability as a Boundary Condition

Abstract

Pharmaceutical supply chains operate under stringent regulatory regimes, temperature-sensitive logistics, and repeated threats of shortage and recall, conditions that render digital transformation key to reliability and efficiency of operations. This paper investigates whether supply chain digitalization enhances operational performance and whether organizational learning capability amplifies such impact in an emerging-economy setting. Grounded on the resource-based view and dynamic capabilities theory, we conceptualize supply chain digitalization as a portfolio of digital assets and process innovations and theorize organizational learning capability as a high-order capability for absorbing, assimilating, transforming, and applying knowledge encoded in digital traces. We test the model on survey data from the pharmaceutical supply chain sector of Jordan (n = 217) and estimate relationships by means of Partial Least Squares Structural Equation Modeling (PLS-SEM) with reflective indicators. The study found that supply chain digitalization positively and significantly impacts operational performance and that organizational learning capability positively moderates supply chain digitalization and operational performance interaction. Overall, results uncover digital investment paying faster, better, and more reliable speed, quality, and reliability when invested in learning architectures institutionalizing cross-functional knowledge flows and continuous improvement. The paper contributes by placing organizational learning capability in the role of a boundary-enforcing condition for realizing digitalization returns in regulated, high-reliability supply chains and by providing evidence from an under-investigated emerging-economy environment. Managerially, results highlight sequencing digitalization prompted by regulatory needs with operations redesign and investing in learning routines cultivating faith in data-driven and AI-based decision making.