The influence of AI adoption on decision making, forecasting, and performance: Evidence from solar and telecommunication sectors in Pakistan and China

This study investigates how Artificial Intelligence (AI) adoption builds sustainable supply chain performance in the renewable energy and telecommunications sectors, focusing on the mediating mechanisms of decision-making and forecasting. Using a comparative design, data was collected from 214 companies in Pakistan and China. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), integrating the Technology-Organization-Environment (TOE) framework and Resource-Based View (RBV). Results confirm a fully mediated pathway, AI adoption is positively associated with decision making and forecasting capabilities, which are positively associated with operational and financial performance, and are associated stronger supply chain performance. A key finding is that these capabilities exert no direct effect, revealing that AI's sustainability impact is realized indirectly through core business outcomes. The comparative analysis highlights contextual influence, with China's mature ecosystem fostering deep AI integration and Pakistan's emerging context showing reliance on external tools. This study provides a novel, integrated TOE-RBV model that unpacks the "black box" of AI's impact on sustainability. It offers unique empirical evidence from contrasting emerging economies, demonstrating that the path to AI-driven sustainable supply chains is mediated and context-dependent. For managers, embedding AI into core processes is critical for sustainability gains. For policymakers, strategies must be context-specific, focusing on foundational digital infrastructure in emerging economies and advanced innovation ecosystems in mature ones.