School of Information, Beijing Wuzi University, Beijing, China.
School of Public Policy and Management, Tsinghua University, Beijing, China.
PLoS One. 2023 Nov 10;18(11):e0293429. doi: 10.1371/journal.pone.0293429. eCollection 2023.
Based on the social network theory, this study utilizes knowledge absorption capacity as the mediating variable and technology turbulence as the moderating variable; furthermore, it focuses on China's intelligent manufacturing industry data to explore the effect of the intelligent manufacturing enterprise innovation network on technology innovation performance and the regulating mechanism of technology turbulence. Based on the patent data obtained from Derwent Database (survey period: 2016-2020), the empirical analysis indicates the following: (1) Network relationship, network location, and network density are significantly and positively correlated with technology innovation performance; however, network size exerts no significant effect on technology innovation performance. (2) Network relationship strength, network location, and network density exert significantly positive effects on the two dimensions of knowledge absorption capacity, namely the In-degree and the Out-degree. Network size exerts no significant effect on knowledge absorption capacity. (3) Knowledge absorption capacity exerts a partial mediating effect on the relationship between innovation network and technology innovation performance. (4) The three dimensions of innovation network that exert a significant effect on technology innovation performance are positively correlated with the interaction terms of technology turbulence, which indicates that the interaction terms, namely innovation network and technology turbulence, exert a positive impact on technology innovation performance through knowledge absorption capacity, and that the moderating effect of technology turbulence exerts a role through knowledge absorption capacity. Finally, this study postulates implementations and policy proposals for enhancing the innovation performance of intelligent manufacturing enterprises.
基于社会网络理论,本研究将知识吸收能力作为中介变量,技术动荡作为调节变量;并聚焦于中国智能制造产业数据,旨在探讨智能制造企业创新网络对技术创新绩效的影响,以及技术动荡的调节机制。基于德温特数据库(调查期间:2016-2020 年)获取的专利数据,实证分析表明:(1)网络关系、网络位置和网络密度与技术创新绩效呈显著正相关;但网络规模对技术创新绩效没有显著影响。(2)网络关系强度、网络位置和网络密度对知识吸收能力的两个维度(入度和出度)均具有显著正向影响。网络规模对知识吸收能力没有显著影响。(3)知识吸收能力对创新网络与技术创新绩效的关系具有部分中介效应。(4)对技术创新绩效具有显著影响的创新网络的三个维度与技术动荡的交互项呈正相关,这表明创新网络和技术动荡的交互项通过知识吸收能力对技术创新绩效产生积极影响,技术动荡的调节作用是通过知识吸收能力发挥的。最后,本研究提出了增强智能制造企业创新绩效的实施和政策建议。