The role of big data analytics strategy in enhancing firm performance: A process-driven approach through supply chain efficiency

This study examines how big data analytics (BDA) enhances supply chain performance by analyzing two key process pathways: decision-making and forecasting. Using data collected from 400 supply chain managers across various industries, the study applies structural equation modelling (SEM) using SmartPLS to test the relationships among BDA capabilities, supply chain efficiency (SCE), and firm-level performance outcomes. The results show that while BDA improves both forecasting and decision-making processes (DMP), only decision-making consistently drives higher SCE. Forecasting, though technically enhanced by BDA, fails to translate into efficiency gains due to volatility and uncertainty inherent in supply chain environments. SCE is found to fully mediate the relationship between BDA-enabled decision-making and operational and financial performance. This study extends and contributes to Chatterjee et al. (2023) by highlighting process-specific mechanisms in BDA adoption. The findings suggest that organizations should adopt a decision-centric BDA strategy, emphasizing real-time, data-driven choices over predictive modelling alone. This study contributes theoretically by highlighting process-specific mechanisms and offers practical guidance for optimizing BDA investments.