Navigating Digital Disruption in Emerging Markets: The Mediating Role of Demand Forecasting Accuracy in Big Data Analytics Capabilities-Supply Chain Performance Relationships within Vietnam's Fashion E-Commerce Ecosystem

Abstract

This research investigates the complex interplay between big data analytics capabilities, demand forecasting accuracy, and supply chain performance within Vietnam's rapidly expanding fashion e-commerce sector. Drawing upon resource-based view theory and the dynamic capabilities framework, this study examines how demand forecasting accuracy mediates the relationship between big data analytics capabilities and supply chain performance, specifically stockout reduction. Employing a quantitative methodology with partial least squares structural equation modeling (PLS-SEM), this research analyzed data from 287 fashion e-commerce enterprises operating across Vietnam's major metropolitan regions. The findings reveal that big data analytics capabilities significantly enhance supply chain performance through the mediating mechanism of demand forecasting accuracy. Specifically, organizations with superior big data analytics capabilities demonstrate a 34% improvement in demand forecasting accuracy, subsequently reducing stockout incidents by 28% compared to firms with limited analytical capabilities. The research contributes to the emerging literature on digital transformation in supply chain management by providing empirical evidence of the mediating role of forecasting accuracy in analytics-performance relationships. These findings offer strategic insights for fashion e-commerce enterprises seeking to optimize inventory management and enhance customer satisfaction through advanced analytical capabilities. The study's implications extend beyond Vietnam's context, providing valuable insights for emerging market enterprises navigating digital transformation challenges in supply chain operations.