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Revisiting the Paradoxes of Knowledge Diversity and Network Structure for Team Innovation: A Machine-Learning Inductive Study

Published online by Cambridge University Press:  18 December 2025

Xin Gao ,
Jar-der Luo ,
Song Wang  and
Peter Ping Li
Xin Gao
Affiliation:
Xiamen University, China
Jar-der Luo*
Affiliation:
Tsinghua University, China
Song Wang*
Affiliation:
Zhejiang University, China
Peter Ping Li
Affiliation:
Copenhagen Business School, Denmark Dongbei University of Finance and Economics, China
*
Corresponding author: Jar-der Luo; Email: jdluo@mail.tsinghua.edu.cn
Song Wang; Email: wasofei@zju.edu.cn
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Abstract

Team innovation is nurtured by the combination of team members’ diverse knowledge and collaborative teamwork. Previous research predominantly assumed a linear interaction between knowledge diversity and network density in predicting team innovation. A pivotal question arises: How do varying levels of knowledge diversity and network density interact to influence team innovation? To address this complex question, we conducted a machine-learning inductive study, leveraging its ability to uncover curvilinear interactive patterns between knowledge diversity and network density in fostering team innovation. We collected comprehensive, multisource data from 1,883 teams within a prominent high-technology firm in China over a four-year period from 2014 to 2017. The results indicate that knowledge diversity and network density exhibit a curvilinear interactive effect on team innovation. The two factors reinforce each other in the initial stage and foster peak innovation with an optimal balance at a medium-to-high level. Beyond this threshold, however, the two factors begin to restrain each other’s effectiveness. Consistent with the perspective of yin-yang balancing, this study deepens our understanding of the paradoxical joint effects of knowledge diversity and network density on team innovation.

中文摘要

中文摘要

团队创新依赖于团队成员多样化的知识与协作配合的结合。以往研究在预测团队创新时, 多假设知识多样性与网络密度之间存在线性互动关系。然而, 一个关键问题随之而来: 不同水平的知识多样性与网络密度是如何相互作用, 从而影响团队创新的? 为探讨这一复杂问题, 我们采用了大数据分析与机器学习预测模型的归纳方法, 以识别知识多样性与网络密度之间在促进团队创新方面的曲线型交互模式的能力。我们收集了来自中国一家知名高科技企业的 1,883 个团队在 2014 年至 2017 年间的多来源综合数据。研究结果表明, 知识多样性与网络密度在团队创新上呈现曲线型交互效应: 在初始阶段, 两者相互强化, 并在中高水平达到最佳平衡时促进创新峰值; 但一旦超过这一阈值, 两者便开始相互抑制效果。本研究加深了我们对知识多样性与网络密度在团队创新上悖论式联合作用的理解。

Keywords

algorithmic inductionknowledge diversitymachine learningnetwork densityteam innovationyin-yang balancing算法归纳知识多样性机器学习网络密度团队创新阴阳平衡

Information

Type
Article
Information
Management and Organization Review , Volume 21 , Special Issue 4: Special Issue on ‘New Technology and OB/HRM in China’ , August 2025 , pp. 794 - 822
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Association for Chinese Management Research.

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Footnotes

*

He has equal contribution as the first author.

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