人工智能技术应用赋能企业突破性创新影响机制研究OACHSSCD

This paper examines how artificial intelligence(AI)applications foster breakthrough innovation in Chinese manufacturing firms in the context of China's"AI+"initiative and the policy emphasis on moving AI from"1 to N"in large-scale industrial deployment.Although prior studies link AI to productivity,labor reallocation,and supply-chain outcomes,three issues limit cumulative knowledge on breakthrough innovation.First,many em-pirical proxies(AI patents or industrial-robot penetration)conflate AI"inputs"with firm-level AI applications,and cannot distinguish heterogeneity across application types.Second,breakthrough innovation is often measured u-sing invention patent counts or citations,which primarily reflect innovation volume and may misclassify incremental inventions as breakthroughs.Third,the moderating role of executive risk governance capability remains inconclu-sive,partly because it is difficult to operationalize and quantify.Clarifying these issues matters for understanding how manufacturing firms transition from innovation"quantity accumulation"to"quality leapfrogging",while main-taining compliance and managing privacy,ethical,and regulatory risks. To address these challenges,this paper assembles an unbalanced panel of China's A-share listed manufactur-ing firms from 2015 to 2024 and develops two key measures.This paper quantifies AI applications via annual-report text analysis rather than patents,robots,or surveys.After standard text preprocessing,this paper expands an AI keyword dictionary using Word2Vec with reference to Stanford HAI's The 2025 AI Index Report,then classifies AI applications into five types—textual,interactive,functional,analytical,and visual AI—and measures each as log(keyword frequency+1).This paper measures breakthrough innovation more strictly using a patent-citation net-work approach. This paper finds that AI applications significantly increase breakthrough innovation,and the effect is positive across all five AI types,with textual AI exhibiting the largest marginal impact.Mechanism evidence indicates that AI promotes breakthroughs by optimizing labor skill structure,enhancing knowledge diversity,and improving man-agerial efficiency.Executive risk governance capability positively moderates the AI application-innovation relation-ship,and privacy regulatory risk governance has a stronger moderating effect than environmental regulatory risk gov-ernance.The positive effects are stronger for non-state-owned enterprises,firms in regions with stronger computing infrastructure,firms with higher supply-chain resilience,and capital-intensive firms. Based on these findings,this paper recommends differentiated"AI+"guidance by application type,parallel investments in workforce upskilling and human-AI collaboration,platforms for cross-domain knowledge sharing to sustain diversity and recombination,and deeper AI-enabled process reengineering to raise managerial efficiency.This paper suggests institutionalizing executive risk governance,especially privacy and data governance,to provide the compliance and data foundations necessary for continuous AI iteration and sustained breakthrough innovation.