Digital-green synergistic transformation of manufacturing firms: configurational pathways and AI enabling effects

Purpose This study investigates the configurational pathways underpinning digital-green synergistic transformation (DGT) and examines the enabling role of artificial intelligence (AI), thereby advancing understanding of how manufacturing firms navigate the dual imperatives of green development and digital upgrading. Design/methodology/approach Using 9,336 firm-year observations from 2,407 Chinese manufacturing firms over 2013–2023, this study draws on the Technology-Organization-Environment (TOE) framework and employs dynamic QCA to identify pathways and panel regressions to examine AI’s enabling effects and heterogeneity. Findings Results indicate that no single factor constitutes a necessary condition for DGT. We identify three distinct configurational pathways to high DGT: “endogenous dual-innovation driven” (SC1), “policy-driven and green-led” (SC2) and “policy-driven and digital-led” (SC3). AI significantly promotes DGT by enabling these pathways, with the largest estimated enabling effect observed for SC1, followed by SC3 and SC2. Heterogeneity analysis reveals that the enabling effect of AI is more pronounced in state-owned enterprises, firms with high market attention and those with high industrial chain integration. Originality/value This study contributes to the literature at the intersection of digitalization and greening by elucidating how multiple technological, organizational and environmental factors form configurational pathways to DGT. It further demonstrates AI’s enabling role in these pathways, providing insights into how firms can embed AI to achieve their strategic goals. By revealing how AI’s effects vary across firm, market and industry contexts, this study advances a contextualized understanding of AI-enabled transformation.