What does this research mean for the field?

Both external and internal collaboration networks jointly and nonlinearly influence an enterprise's radical innovation performance, with internal networks playing a more crucial role. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To investigate the interactive effects and configurational pathways of external and internal collaboration networks on radical innovation performance in artificial intelligence enterprises.

August 27, 2026European Journal of Innovation Management

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

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Authors

LZLiping Zhang MLMinghong Lin HQHanhui Qiu

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Overview

Machine learning analysis reveals interactive effects of collaboration networks on radical innovation in AI firms, indicating internal teamwork is the primary driver of breakthrough performance.

Key Points

To investigate the interactive effects and configurational pathways of external and internal collaboration networks on radical innovation performance in artificial intelligence enterprises.
Applied machine learning methodologies, including K-means clustering and classification and regression tree (CART) algorithms, to analyze heterogeneous collaboration configurations among Chinese AI enterprises.
Classified enterprises into three distinct collaborative types: externally oriented, internally extensive, and internally cohesive.
Determined that internal collaboration networks exert a more dominant influence on radical innovation performance than external networks, exhibiting complex nonlinear interactive effects.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a8fea3310c91c1e9262255e https://doi.org/10.1108/ejim-03-2026-0282

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August 27, 2026European Journal of Innovation Management

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

View Full Paper

Ask AI

Bookmark

Authors

LZLiping Zhang MLMinghong Lin HQHanhui Qiu

Discussion

Member takes

Overview

Machine learning analysis reveals interactive effects of collaboration networks on radical innovation in AI firms, indicating internal teamwork is the primary driver of breakthrough performance.

Key Points

To investigate the interactive effects and configurational pathways of external and internal collaboration networks on radical innovation performance in artificial intelligence enterprises.
Applied machine learning methodologies, including K-means clustering and classification and regression tree (CART) algorithms, to analyze heterogeneous collaboration configurations among Chinese AI enterprises.
Classified enterprises into three distinct collaborative types: externally oriented, internally extensive, and internally cohesive.
Determined that internal collaboration networks exert a more dominant influence on radical innovation performance than external networks, exhibiting complex nonlinear interactive effects.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a8fea3310c91c1e9262255e https://doi.org/10.1108/ejim-03-2026-0282

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