AI-driven innovation management and total factor productivity: empirical evidence from the equipment manufacturing sector

Purpose In the context of global economic digitalization and the rapid advancement of AI technology, the equipment manufacturing industry faces two key challenges: enhancing productivity and transitioning to high-end manufacturing. In this context, this study aims to explore the impact of AI technology adoption on total factor productivity (TFP) of equipment manufacturing firms and the intermediary and moderating channels. Design/methodology/approach This study examines the impact of AI technology on the TFP of equipment manufacturing enterprises in China by employing fixed effects, mediation, and moderation models. Findings The results show that artificial intelligence technology significantly improves the total factor productivity of equipment manufacturing enterprises, and the robustness test results support this finding. A firm’s market competitiveness mediates this process, whereas changes in the quality of internal control moderate this effect. In addition, the impact of AI technology on TFP varies with the size and ownership structure of equipment manufacturing firms. Originality/value The findings offer both theoretical and empirical support to policymakers for promoting the high-quality development of equipment manufacturing firms through AI technology at the micro level.