R&D team network configurations, knowledge diversity and breakthrough innovation: a combined effect framework

Wenhao Zhou, Hailin Li
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Abstract

PurposeThis study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.Design/methodology/approachBased on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.FindingsIt has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.Originality/valuePrevious studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.
研发团队网络配置、知识多样性和突破性创新:综合效应框架
目的本研究旨在提出一个综合效应框架,以探讨研发(R&D)团队网络、知识多样性和突破性技术创新之间的关系。与传统的线性净效应不同,本文基于机器学习方法,探讨了企业内部三种可能的团队配置类型及其突破性创新驱动机制。设计/方法/途径本研究基于中国生物制药行业 2,337 家企业的专利申请数据构建研发团队网络,采用 K-Means 方法,以组间差异最大为原则,探讨研发团队的配置类型。此外,研究还利用决策树模型(DT)挖掘了不同团队网络配置因素、知识多样性与突破性创新之间的条件组合关系。研究发现,在生物制药制造业中,企业研发团队配置主要存在紧密协作型、知识扩张型和规模导向型三种类型,反映了企业在技术创新、网络关系、知识资源和人力资本等方面的三种资源投资偏好。研究结果凸显了紧密协作团队中知识多样性与团队网络特征之间的挤出效应和互补效应。在知识扩张型和规模导向型团队中,低知识多样性和高团队结构洞(SHs)是实现突破性创新的最佳团队配置条件。 原创性/价值以往的研究主要关注外部协作网络与企业创新之间的关系。此外,传统的回归方法主要描述变量之间的线性净效应,忽视了技术突破是一个综合概念,需要多种因素的共同作用。针对这一缺陷,本文提出了研发团队与企业突破创新之间的组合效应框架,进一步完善了社会网络理论,拓展了数据挖掘方法在创新管理领域的适用性。
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