从合作网络的角度来看,中国人工智能行业的探索和利用性创新表现:数据驱动的分析
Liping Zhang1, Hailin Li1, Wenhao Zhou1
1College of Business Administration, Huaqiao University, Quanzhou 362021, Fujian, China.
Entropy (Basel, Switzerland)
|June 26, 2025
概括
合作网络结构对企业的创新表现产生重大影响. 特定的网络特征和企业集群为增强人工智能领域的探索性和利用性创新提供了量身定制的战略.
科学领域:
- 创新管理 创新管理
- 网络科学 网络科学
- 人工智能行业 人工智能行业 人工智能行业
背景情况:
- 企业的竞争力取决于创新的表现.
- 了解协作网络结构是提高创新成果的关键.
- 中国的人工智能 (AI) 产业为研究组织间协作提供了一个充满活力的环境.
研究的目的:
- 研究组织间协作网络结构与企业的探索性和利用性创新绩效之间的复杂关系.
- 确定影响创新绩效的关键结构特征.
- 为人工智能行业内的公司提供战略见解.
主要方法:
- 分析了来自中国281家人工智能公司的14790项专利.
- 集群算法的应用.
- 使用信息和吉尼指数进行分类.
- 检查网络特征:中心度,接近中心度,局部聚类系数和结构洞.
主要成果:
- 四个结构特征 (中心度,接近中心度,局部聚类系数,结构漏洞) 显著影响创新绩效.
- 不同的公司集群表现出独特的特征组合,为发展战略提供信息.
- 企业可以采用各种战略途径,以根据其目标改善创新绩效.
结论:
- 网络结构是创新绩效的关键决定因素.
- 基于公司集群和网络特征的定制策略对于AI公司至关重要.
- 企业应该将创新策略与其具体的发展目标相协调,以获得最佳的结果.
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