Intelligent Manufacturing, Digital Technology Innovation, and New Quality Productive Forces in the Manufacturing Industry: An Empirical Study Based on Multi-period DID
Year: 2025
Type: article
Abstract: Intelligent manufacturing has become an important indicator for measuring the core competitiveness of a country’s manufacturing industry. Using panel data from A-share listed manufacturing enterprises in China from 2012 to 2023, this paper views the implementation of intelligent manufacturing pilot policies as a quasi-natural experiment, and conducts an empirical analysis employing a mul-ti-period difference-in-differences model to systematically examine the impact of intelligent manufacturing pilot policies on the new quality productive forces of the manufacturing industry and its underlying mechanisms. The em-pirical results show that intelligent manufacturing pilot policies have a positive effect on promoting new quality productive forces in the manufacturing indus-try, and this conclusion remains robust after various tests. Mechanism analysis reveals that intelligent manufacturing pilot policies effectively enhance the level of dig-ital technological innovation in manufacturing, thereby assisting in im-proving new quality productive forces. Group analysis based on enterprise size, owner-ship nature, and geographical location shows that the impact of intelli-gent man-ufacturing pilot policies on new quality productive forces in manu-facturing is heterogeneous, with a greater promoting effect on new quality pro-ductive forces in large-scale manufacturing enterprises, non-state-owned en-terprises, and northern regions. Therefore, government departments should continue to opti-mize relevant policies and enhance enterprises’ digital techno-logical innovation capabilities, ultimately achieving high-quality development of the manufactur-ing industry.
Source: Economics & Business Management
DOI: 10.63313/ebm.9088
Authors: Ai Li, Junming Pan
Institution: Southwest Petroleum University
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4413382589