Data-Driven Operations Management and Supply Chain Innovation in the Retail Industry

Abstract

This study examines how data-driven operations management capabilities shape supply chain innovation performance among retail firms operating in volatile, digitally mediated markets. Despite enthusiasm for analytics, artificial intelligence, and real-time visibility tools, retailers still struggle to convert data investments into measurable innovation outcomes, and the mechanisms linking specific data-driven capabilities to innovation remain fragmented across the literature. Drawing on the resource-based view and dynamic capabilities theory, this research proposes and empirically tests a model linking three data-driven capabilities, namely data analytics capability, real-time supply chain visibility, and artificial intelligence/machine learning adoption, to supply chain innovation performance, with data-driven decision-making culture as a mediating mechanism. A quantitative, cross-sectional survey design was employed, collecting responses from 215 operations and supply chain managers in retail organizations, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that all three capabilities exert significant positive effects on supply chain innovation performance, with data-driven decision-making culture partially mediating these relationships. Real-time visibility emerged as the strongest direct predictor, followed by AI/ML adoption and data analytics capability. These findings extend supply chain innovation theory by clarifying the differentiated contribution of distinct data-driven capabilities and offer practical guidance for retail operations managers seeking to prioritize digital investments that generate innovation returns rather than incremental efficiency gains alone.