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How do director networks improve digital innovation in China An explanation grounded in knowledge-based view

Qiang Lu et al.

International Journal of Technology Management2025https://doi.org/10.1504/ijtm.2025.143597article
AJG 2ABDC B
Weight
0.46

Abstract

The role of links in knowledge transmission, from the external to the internal, highlights the need to explore whether director networks can promote digital innovation. Based on knowledge-based view, this study assesses the influence of director networks on digital innovation. With the analysis of data (from 2012 to 2019) from Chinese manufacturing listed companies, this study finds the following results: 1) director network centrality has a positive effect on digital innovation; 2) director network structural holes positively impact digital innovation; 3) knowledge environment negatively moderates the relationship between director networks and digital innovation; 4) legal environment negatively moderates the relationship between director network centrality and digital innovation, but has no moderating effect on the relationship between director network structural holes and digital innovation. This study explores the relationship between director networks and digital innovation, providing both practical and theoretical significance to the field of digital innovation.

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https://doi.org/https://doi.org/10.1504/ijtm.2025.143597

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@article{qiang2025,
  title        = {{How do director networks improve digital innovation in China An explanation grounded in knowledge-based view}},
  author       = {Qiang Lu et al.},
  journal      = {International Journal of Technology Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijtm.2025.143597},
}

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How do director networks improve digital innovation in China An explanation grounded in knowledge-based view

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.