科研管理

科研管理 ›› 2025, Vol. 46 ›› Issue (7): 13-23.DOI: 10.19571/j.cnki.1000-2995.2025.07.002

• 论文 • 上一篇    下一篇

创新网络内生因素对结构韧性的影响研究——以上海人工智能产业为例

杨伟1,2，李福玲1，张晓泉3   

  1. 1.杭州电子科技大学管理学院，浙江 杭州310018；
    2.杭州电子科技大学数据科学与智能决策实验中心，浙江 杭州310018；
    3.杭州电子科技大学图书馆，浙江 杭州310018

  • 收稿日期:2023-11-20 修回日期:2024-12-27 出版日期:2025-07-20 发布日期:2025-07-14
  • 通讯作者: 张晓泉
  • 基金资助:
    国家自然科学基金面上项目：“科技自立自强背景下数字产业创新生态系统韧性演进机理演进”（72174051, 2021.01—2015.12）；浙江省哲学社会科学规划领军人才培育专项课题：“数字创新生态系统韧性形成机制与提升策略研究”（23QNYC10ZD，2023.01—2026.12）。

The impact of endogenous factors of innovation networks on structural resilience: Taking Shanghai′s artificial intelligence industry as an example

Yang Wei1,2, Li Fuling1, Zhang Xiaoquan3   

  1. 1. School of Management, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China;
     2. Experimental Center of Data Science and Intelligent DecisionMaking, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China;
    3. Library of Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China
  • Received:2023-11-20 Revised:2024-12-27 Online:2025-07-20 Published:2025-07-14

摘要：     提升创新网络的结构韧性是人工智能产业高质量发展面临的重要理论与现实问题。本文以上海人工智能产业为例，通过新闻文本挖掘构建合作创新网络，进而使用指数随机图模型揭示网络内生因素对结构韧性的影响，研究发现：上海人工智能产业创新网络结构韧性演化可分为缓慢增长、高速增长和保持平稳三个主要阶段，网络异配性不断提高是创新网络从同配核心网络转变为韧性网络的关键；中介性结构负向影响人工智能产业创新网络结构韧性；扩张性结构总体负向影响人工智能产业创新网络结构韧性，但其中三星结构正向影响结构韧性。本研究弥补了人工智能产业创新网络韧性研究的不足，有助于深化创新网络韧性的理论认识，对丰富创新网络研究的数据来源也具有积极的启示意义。

关键词: 人工智能产业, 创新网络, 结构韧性, 指数随机图模型

Abstract:    Improving the structural resilience of innovation networks is an important theoretical and practical issue facing the high-quality development of the AI industry. This paper adopted the AI industry in Shanghai as an example, constructed a cooperative innovation network through text mining, and then used the ERGM to reveal the impact of endogenous factors of the innovation network on its structural resilience. The research found that: (1) The evolution of the structural resilience of the innovation network in Shanghai′s AI industry can be divided into three main stages: slow growth, rapid growth, and maintaining stability. The continuous improvement of network heterogeneity is the key driver for the transformation of the innovation network from an assortative core network to a resilient network; (2) The intermediary structure negatively affects the structural resilience of the artificial intelligence industry′s innovation network; (3) The expansive structure negatively affects the structural resilience of the AI industry′s innovation network in general, but the three-star structure positively affects its structural resilience. This study has addressed the gap in research on the resilience of innovation networks in the artificial intelligence industry, and it will help to deepen the theoretical understanding of innovation network resilience, thus having positive implications for enriching the data sources of innovation network research.

Key words: artificial intelligence industry, innovation network, structural resilience, exponential random graph model (ERGM)