Direct and indirect effects of supply chain plasticity and AI capability on business performance: the moderating role of network embeddedness

Abstract

Purpose This study examines how artificial intelligence (AI) capability and supply chain (SC) plasticity jointly influence business performance, while considering the moderating role of network embeddedness. Design/methodology/approach Drawing on network theory and the dynamic capability (DC) view, the study develops and tests a conceptual model using survey data collected from 453 managers in manufacturing and service firms, analyzed with the PLS-SEM approach. Findings The findings show that SC plasticity is the strongest driver of business performance, enabling rapid reconfiguration of processes and networks while mediating the impact of AI capability. Relational embeddedness positively moderates the effect of AI capability on SC plasticity but weakens the link between SC plasticity and performance, reflecting the paradox of embeddedness. Structural embeddedness weakens the effect of AI capability on SC plasticity but shows no significant effect on the SC plasticity-performance relationship. Practical implications Managers should view SC plasticity as a strategic capability that converts digital resources into performance. While network embeddedness can strengthen adaptability, over-embeddedness may limit its benefits. Managers must also balance the benefits of embedded networks with the risks of over-embeddedness, ensuring flexibility in partnerships while leveraging trust-based ties for adaptability. Originality/value This study is among the first to link AI capability with SC plasticity and provides empirical evidence on the boundary conditions of digital transformation and adaptability in emerging economies.