THE INFLUENCE OF FIRM KNOWLEDGE CHARACTERISTICS ON TECHNOLOGICAL INNOVATION: A MULTILEVEL NETWORK STRUCTURE PERSPECTIVE

Lei Zou, Xi Xi
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Abstract

This study describes collaboration between firms and different organizations as a process of knowledge sharing, integrating inter-organizational collaboration networks and knowledge networks into a unified framework. It analyzes the bilayer structure of collaborative innovation networks, namely, the firm-knowledge bipartite network. The study investigates the impact of firm knowledge characteristics (diversity and uniqueness) on breakthrough and incremental innovation within collaborative innovation networks. The research employs patent data from Chinese A-share high-tech manufacturing companies listed from 2000 to 2018 and verifies the related hypotheses using a negative binomial regression model. The results suggest that, within collaborative innovation networks, firm knowledge diversity has a negative impact on breakthrough innovation but a positive impact on incremental innovation. On the other hand, firm knowledge uniqueness plays a positive role in breakthrough innovation but a negative role in incremental innovation. This study extends the understanding of firm technological innovation by considering both breakthrough and incremental innovation as distinct behaviors, providing a new perspective on the mechanisms underlying different innovation behaviors. By adopting a multilevel network structure approach to collaborative innovation, it contributes to a deeper understanding of internal and external factors influencing firm innovation behavior and expands the application scope of social network analysis.
企业知识特征对技术创新的影响:多层次网络结构视角
本研究将企业与不同组织之间的合作描述为一个知识共享的过程,将组织间合作网络和知识网络整合到一个统一的框架中。它分析了协作创新网络的双层结构,即企业-知识双向网络。研究探讨了企业知识特征(多样性和独特性)对协同创新网络中突破性创新和渐进性创新的影响。研究采用2000年至2018年中国A股高科技制造业上市公司的专利数据,并利用负二项回归模型验证了相关假设。结果表明,在协同创新网络中,企业知识多样性对突破性创新有负面影响,但对渐进性创新有正面影响。另一方面,企业知识的独特性对突破性创新起积极作用,但对增量创新起消极作用。本研究将突破性创新和增量创新视为不同的行为,从而扩展了对企业技术创新的理解,为研究不同创新行为的内在机制提供了新的视角。通过采用多层次网络结构方法来研究协同创新,有助于加深对影响企业创新行为的内外部因素的理解,并拓展了社会网络分析的应用范围。
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