Artificial Intelligence as a Catalyst for New Quality Productivity: Evidence from Chinese Companies

This paper aims to explore the pathways through which artificial intelligence (hereinafter – AI) contributes to enhancing new quality productivity, based on empirical analysis of Chinese listed enterprises. The analysis covers panel data from 12,880 observations for 2013-2022, excluding companies from the financial and construction sectors. Based on the data of Chinese A-share listed enterprises, this study systematically explores the driving mechanism and practice path of AI technology on enterprises’ new quality productivity. By constructing AI technology application indicators through machine learning and text analysis methods, combined with the heterogeneity perspective (enterprise attributes, industry characteristics, and regional policies), the empirical test finds that AI technology significantly enhances enterprises’ new quality productivity. Its core paths include intelligent supply chain management, digital innovation efficacy enhancement, and information asymmetry alleviation. The results show that AI has a statistically significant positive effect on new quality productivity (coefficient = 1.18, p < 0.01). In addition, it was found that the key channels of impact are digital innovations (effect = 0.465, p < 0.01), supply chain efficiency (effect = 0.121, p < 0.01) and reduction of information asymmetry (effect = -0.053, p < 0.01). Heterogeneity analysis shows that the empowering effect of AI is particularly significant in SOEs, labor-intensive industries, hightech manufacturing industries and regions with high government financial support. This study provides theoretical and empirical evidence for differentiated policy-making and emphasizes that AI technology needs to be combined with organizational characteristics and the external environment to accelerate the sustainable development of new quality productivity.

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