Based on the provincial panel data of China from 2000 to 2023, we systematically examined the impact effect and internal mechanism of artificial intelligence (AI) innovation on the alternate with industrial upgrading. The empirical results showed that AI innovation has a significant promoting effect on the alternate with industrial upgrading, and this conclusion remains valid after a series of robustness tests and endogeneity treatments. This study advances the literature in three major ways: First, we integrated a nonlinear threshold framework with a multi-dimensional moderation analysis, enabling a more nuanced understanding of how AI innovation affects industrial chain modernization under heterogeneous institutional and factor conditions. Second, we empirically identified the industrialization level as a partial mediating mechanism, thereby clarifying the internal transmission path from AI innovation to structural upgrading. Third, by uncovering a negative moderating role of enterprise productivity, the study revealed an 'efficiency trap' that challenges the conventional assumption that higher productivity always enhances technological returns.
Citation: Qingsheng Zhu, Kexu Zhang. The driving mechanism and challenges of artificial intelligence innovation to the alternate with industrial upgrading from multiple perspectives[J]. Data Science in Finance and Economics, 2026, 6(1): 121-146. doi: 10.3934/DSFE.2026005
Based on the provincial panel data of China from 2000 to 2023, we systematically examined the impact effect and internal mechanism of artificial intelligence (AI) innovation on the alternate with industrial upgrading. The empirical results showed that AI innovation has a significant promoting effect on the alternate with industrial upgrading, and this conclusion remains valid after a series of robustness tests and endogeneity treatments. This study advances the literature in three major ways: First, we integrated a nonlinear threshold framework with a multi-dimensional moderation analysis, enabling a more nuanced understanding of how AI innovation affects industrial chain modernization under heterogeneous institutional and factor conditions. Second, we empirically identified the industrialization level as a partial mediating mechanism, thereby clarifying the internal transmission path from AI innovation to structural upgrading. Third, by uncovering a negative moderating role of enterprise productivity, the study revealed an 'efficiency trap' that challenges the conventional assumption that higher productivity always enhances technological returns.
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Qingsheng Zhu, Kexu Zhang. The driving mechanism and challenges of artificial intelligence innovation to the alternate with industrial upgrading from multiple perspectives[J]. Data Science in Finance and Economics, 2026, 6(1): 121-146. doi: 10.3934/DSFE.2026005
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