Research on the Economic Effects of Generative AI on Enterprise Digital Transformation and Data Factor Marketization
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Abstract
As a new generation of general-purpose technology, generative artificial intelligence is reshaping the internal logic of enterprise digital transformation and the institutional environment for data factor marketization. Existing research has insufficiently examined the quantitative linkage mechanisms between the two. Based on panel data from Chinese Ashare listed enterprises from 2018 to 2023, this study uses the Levinsohn-Petrin method to measure enterprise total factor productivity, constructs a two-way fixed effects model to test the effect of generative AI application on enterprise efficiency, and introduces a data factor marketization index constructed by the entropy-weight TOPSIS method as a moderating variable. Endogeneity is addressed using historical communication infrastructure as an instrumental variable. The results show that generative AI significantly improves total factor productivity, and that data factor marketization positively moderates this relationship. The effect is stronger among private enterprises and in provinces with advanced digital-economy foundations. From an engineering infrastructure perspective, edge-cloud networks, electromagnetic communication links, and data transmission platforms are important foundations for AI deployment and data circulation. The conclusions support data factor reform and responsible generative AI governance.
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