中国企业如何在全球价值链中实现创新?——基于学习机制视角的分析
How do Chinese firms achieve innovation in the global value chains?An analysis based on the learning mechanism perspective
冯晓华 1李欣禹 2张雪妍2
作者信息
- 1. 湖北大学商学院,武汉 430062;湖北省开放经济研究中心,武汉 430062
- 2. 湖北大学商学院,武汉 430062
- 折叠
摘要
通过文献梳理提炼出 193 个中国企业在全球价值链中的学习案例,定量分析中国企业在全球价值链中的学习机制,考察中国企业如何在全球价值链中实现创新.研究发现:中国企业在全球价值链中的学习以外部机制为主,其中使用频率最高的是海外并购和内部研发,集群学习、产学研合作也是中国企业较为偏好的学习机制;中国企业的全球价值链内部学习机制主要以面对面学习为主,对使用国际标准、劳动培训和转移狭窄任务三种学习机制的选择偏好相似;高技术企业倾向于选择内部研发和海外并购等学习机制,中低技术企业则更多依赖集群式学习机制.建议通过拓展全球价值链学习机制深度、融入全球创新网络,夯实内部研发学习机制、发展新质生产力,完善科技创新体系、提升企业全球价值链学习效果,有效提升中国企业在全球价值链中的创新能力.
Abstract
Based on 193 learning cases of Chinese firms in the Global Value Chains(GVCs)that were extracted from relevant literature,this paper quantitatively analyzes Chinese firms'learning mechanisms in the GVCs and investigates how Chinese firms achieve innovation.The results show that the learning of Chinese firms in the GVCs is mainly based on external mechanisms,with overseas acquisitions and internal R&D being the most frequently used methods.Meanwhile,cluster learning and industry-university-research cooperation are also the preferred learning mechanisms of Chinese firms.The learning mechanism in the GVCs used by Chinese firms is mainly face to face learning,with similar preferences for the use of international standards,labor training and transfer of narrow tasks.In addition,high-tech firms tend to choose internal R&D and overseas acquisitions,while medium-and low-tech firms rely more on cluster learning.Chinese firms are recommended to expand the depth of learning mechanisms in the GVCs to integrate into global innovation networks,consolidate the internal R&D learning mechanisms to foster new quality productive forces,improve scientific and technological innovation system to enhance the effectiveness of learning in the GVCs,and effectively enhance the innovation ability in the GVCs.
关键词
学习机制/全球价值链/创新/企业/中国Key words
learning mechanisms/the global value chains/innovation/firm/China引用本文复制引用
基金项目
国家社会科学基金一般项目(20BJL140)
出版年
2024
NETL
NSTL
万方数据