Unlocking AI’s Radical Innovation Potential: The Contingent Roles of Digital Foundation and Government Subsidy

Over the past decade, artificial intelligence (AI) has been increasingly used in firm innovation. While AI has contributed to innovation improvement, direct evidence of its effectiveness in radical innovation is limited. This study fills this gap by empirically investigating the impact of AI on radical innovation and how this relationship is shaped by digital foundation and government subsidy from the perspectives of technological synergy and the external institutional environment. Using panel data from Chinese A-share listed firms from 2007 to 2023, this study empirically tests hypotheses through regression analyses. The findings reveal that AI adoption significantly promotes radical innovation, and this relationship is moderated by the characteristics of a firm’s digital foundation (i.e., degree and rate) as well as government subsidy. Specifically, a high degree of digital foundation hinders AI-driven radical innovation, while a fast rate enhances it. In addition, government subsidy strengthens the positive impact of AI adoption on radical innovation. A heterogeneity analysis further shows that both the timing (early vs. late) and pace (fast vs. slow) of AI adoption exert nuanced impacts: firms that adopt AI later and at a slower pace tend to achieve greater gains in radical innovation. This study advances research on radical innovation in the era of intelligence and provides managerial implications regarding the interplay of AI with internal digital foundation and external government subsidy.

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Publication Details

Journal
Systems
Published
2025-08-15
DOI
https://doi.org/10.3390/systems13080702
Citations
2
Primary Topic
Innovation Policy and R&D
Type
article
Field-Weighted Citation Impact
6.26

Funders

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article

Unlocking AI’s Radical Innovation Potential: The Contingent Roles of Digital Foundation and Government Subsidy

Zongjun Wang, Xiaocun Song, Jinrong Huang, Xian Zhang
2 citations
Systems
Innovation Policy and R&D
6.26
article

Unlocking AI’s Radical Innovation Potential: The Contingent Roles of Digital Foundation and Government Subsidy

Zongjun Wang, Xiaocun Song, Jinrong Huang, Xian Zhang
article en
2 citations

Abstract

Over the past decade, artificial intelligence (AI) has been increasingly used in firm innovation. While AI has contributed to innovation improvement, direct evidence of its effectiveness in radical innovation is limited. This study fills this gap by empirically investigating the impact of AI on radical innovation and how this relationship is shaped by digital foundation and government subsidy from the perspectives of technological synergy and the external institutional environment. Using panel data from Chinese A-share listed firms from 2007 to 2023, this study empirically tests hypotheses through regression analyses. The findings reveal that AI adoption significantly promotes radical innovation, and this relationship is moderated by the characteristics of a firm’s digital foundation (i.e., degree and rate) as well as government subsidy. Specifically, a high degree of digital foundation hinders AI-driven radical innovation, while a fast rate enhances it. In addition, government subsidy strengthens the positive impact of AI adoption on radical innovation. A heterogeneity analysis further shows that both the timing (early vs. late) and pace (fast vs. slow) of AI adoption exert nuanced impacts: firms that adopt AI later and at a slower pace tend to achieve greater gains in radical innovation. This study advances research on radical innovation in the era of intelligence and provides managerial implications regarding the interplay of AI with internal digital foundation and external government subsidy.

SystemsVol. 13(8)
Huazhong University of Science and Technology (CN), Hunan Agricultural University (CN)
Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 4%
Innovation Policy and R&D
6.26
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Unlocking AI’s Radical Innovation Potential: The Contingent Roles of Digital Foundation and Government Subsidy — Zongjun Wang, Xiaocun Song, et al. · Systems (2025) | TGRS Research Map | TGRS