External vs. internal: revealing the interactive effects of two collaboration networks on radical innovation performance

Purpose External and internal collaboration networks are crucial factors that encourage enterprises to engage in radical innovation activities. Exploring the complex mechanisms through which the characteristics of external and internal collaboration networks affect radical innovation performance is essential for innovative development by enterprises. Design/methodology/approach From the perspective of interaction between enterprises' external collaboration networks and internal inventor collaboration networks, this study examines the combination of key influencing factors and multiple improvement pathways for the radical innovation performance of Chinese artificial intelligence (AI) enterprises in heterogeneous collaborative contexts using machine learning methods, such as a K-means clustering algorithm and a classification and regression tree algorithm. Findings Based on the heterogeneity of collaboration network characteristics, Chinese AI enterprises can be divided into three types: externally oriented, internally extensive and internally cohesive. Hence, the characteristics of their external and internal collaboration networks and radical innovation performance differ significantly. The results reveal that the characteristics of both external and internal collaboration networks jointly influence enterprises' radical innovation performance, and the characteristics of internal collaboration networks play a more crucial role. Additionally, external and internal collaboration networks have complex nonlinear effects on enterprises' radical innovation performance through different combinations of characteristics. Originality/value This study reveals diverse pathways through which Chinese AI enterprises can enhance radical innovation performance in different collaborative contexts, offering insights into how to optimize the configuration of external and internal collaboration networks to achieve the strategic objectives of high-level innovation.

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Publication Details

Journal
European Journal of Innovation Management
Published
2026-08-25
DOI
https://doi.org/10.1108/ejim-03-2026-0282
Primary Topic
Innovation and Knowledge Management
Type
article
Field-Weighted Citation Impact
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External vs. internal: revealing the interactive effects of two collaboration networks on radical innovation performance

Yenchun Jim Wu, Hanhui Qiu, Jinyi Chen, Liping Zhang et al.
European Journal of Innovation Management
Innovation and Knowledge Management
article

External vs. internal: revealing the interactive effects of two collaboration networks on radical innovation performance

Yenchun Jim Wu, Hanhui Qiu, Jinyi Chen, Liping Zhang, Minghong Lin
article en

Abstract

Purpose External and internal collaboration networks are crucial factors that encourage enterprises to engage in radical innovation activities. Exploring the complex mechanisms through which the characteristics of external and internal collaboration networks affect radical innovation performance is essential for innovative development by enterprises. Design/methodology/approach From the perspective of interaction between enterprises' external collaboration networks and internal inventor collaboration networks, this study examines the combination of key influencing factors and multiple improvement pathways for the radical innovation performance of Chinese artificial intelligence (AI) enterprises in heterogeneous collaborative contexts using machine learning methods, such as a K-means clustering algorithm and a classification and regression tree algorithm. Findings Based on the heterogeneity of collaboration network characteristics, Chinese AI enterprises can be divided into three types: externally oriented, internally extensive and internally cohesive. Hence, the characteristics of their external and internal collaboration networks and radical innovation performance differ significantly. The results reveal that the characteristics of both external and internal collaboration networks jointly influence enterprises' radical innovation performance, and the characteristics of internal collaboration networks play a more crucial role. Additionally, external and internal collaboration networks have complex nonlinear effects on enterprises' radical innovation performance through different combinations of characteristics. Originality/value This study reveals diverse pathways through which Chinese AI enterprises can enhance radical innovation performance in different collaborative contexts, offering insights into how to optimize the configuration of external and internal collaboration networks to achieve the strategic objectives of high-level innovation.

European Journal of Innovation Management
Huaqiao University (CN), National Taiwan Normal University (TW), Ming Chuan University (TW)
Industry, innovation and infrastructure
Openalex Percentile: Top 6%
Innovation and Knowledge Management
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