Research on the Impact of Artificial Intelligence Applications on the Commercial Credit of Manufacturing Enterprises— Based on panel data from listed companies in Guangdong and Guangxi
DOI:
https://doi.org/10.66069/ojspub.25360705Keywords:
Artificial intelligence, Manufacturing, Corporate credit, Mediating effect, HeterogeneityAbstract
In the context of the development of the digital economy and new productive forces, artificial intelligence has become a key driving force for the digital transformation of manufacturing, reshaping the information flow mechanism in the supply chain and the enterprise credit system. Using panel data of A-share listed manufacturing enterprises in Guangdong and Guangxi regions, this study analyzed the annual observation data of 6,059 enterprises from 2011 to 2024 to systematically examine the impact of AI applications on enterprise credit performance, its transmission mechanism, and differentiated stratification characteristics. Benchmark regression analysis and robust multiple tests indicated that the application of artificial intelligence significantly enhanced the credit financing capacity of manufacturing enterprises. Mechanism analysis shows that total factor productivity plays a positive partial mediating role; Agency costs do not show a significant mediating effect; Supply chain efficiency, on the other hand, shows a masking effect. Heterogeneity analysis showed that the positive impact of AI on credit performance was statistically significant only in state-owned enterprises, high-tech manufacturers, and listed companies in Guangdong Province, but lacked statistical significance for private enterprises, traditional heavy industries, and local enterprises in Guangxi. To mitigate the endogenous bias caused by bidirectional causality and the absence of variables, this study used the provincial annual average of artificial intelligence (AI) as an instrumental variable in the two-stage least squares regression analysis, while maintaining strict analytical rigor. The findings enrich the microeconomic empirical evidence on the interaction mechanism between digital technology and supply chain credit, and provide empirical support and policy recommendations for local governments in Guangdong Province and Guangxi Zhuang Autonomous Region to formulate customized digital policies, establish cross-provincial AI credit collaboration platforms, and optimize the intelligent transformation paths of manufacturing enterprises.
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