| 供应链数字化与企业关键核心技术创新能力 |
| The impact of supply chain digitalization on enterprise's key core technology innovation capability: causal inference based on double Machine Learning |
| 摘要点击 89 全文点击 0 投稿时间：2024-04-15 修订日期：2025-07-26 |
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| 中文关键词 供应链数字化; 企业关键核心技术创新能力; 技术创新; 双重机器学习 |
| 英文关键词 supply chain digitization; enterprise's core technology innovation capability; technology innovation; double machine learning |
| 基金项目 |
| 投稿方向 |
| 作者 | 单位 | 邮编 | | 徐妍* | 长安大学 | 710064 | | 毕梦潇 | 长安大学 | |
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| 中文摘要 |
| 促进数字技术与供应链的深度融合, 构建智慧化、数字化的供应链, 有助于缓解我国在关键核心技术面临的“卡脖子”难题. 为此, 本文选取2011-2022年企业发明专利等数据, 结合资源基础理论与动态能力理论, 采用双重机器学习模型分析供应链数字化对企业关键核心技术创新能力的影响. 研究结果表明, 供应链数字化显著提升了企业关键核心技术创新能力. 机制研究表明, 供应链数字化能够通过增强企业感知能力、捕捉能力以及重构能力, 从而提升企业关键核心技术创新能力. 此外, 本文还从供应链数字化的外部环境以及内外部特征出发, 探讨了其对企业关键核心技术创新能力的异质性影响. 本文采用具有独特优势的双重机器学习模型, 不仅为该领域的理论研究进行有益的补充, 也为模型的应用研究提供参考价值. |
| 英文摘要 |
| Promoting the deep integration of digital technology and supply chains, and constructing intelligent and digital supply chains, can help alleviate the "bottleneck" challenges faced by China in critical core technologies. To this end, this paper selected data on enterprise invention patents from 2011 to 2022, combining resource-based theory and dynamic capability theory, and employed a dual machine learning model to analyze the impact of supply chain digitalization on enterprise's key core technology innovation capabilities. The research results indicate that supply chain digitalization significantly enhances enterprise's key core technology innovation capabilities. Mechanism analysis reveals that supply chain digitalization improves enterprise's sensing, seizing, and reconfiguring capabilities, thereby boosting their key core technology innovation capabilities. Additionally, this paper explores the heterogeneous effects of supply chain digitalization on enterprise's key core technology innovation capabilities, considering both external environments and internal and external characteristics. The use of a dual machine learning model with unique advantages not only provides a valuable contribution to theoretical research in this field but also offers reference value for applied research on modeling. |
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