Formation Drivers and Evolutionary Dynamics of Industry-University-Research Collaborative Networks in China’s Clean Energy Industry
Robust industry-university-research (I-U-R) collaborative networks are essential for accelerating innovation in the clean energy industry (CEI). This study employs the exponential random graph model to investigate how the network structural, node, and edge attributes drive the formation of I-U-R col...
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MDPI AG
2025-03-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/13/3/173 |
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| author | Qiezeng Yuan Heng Chen Chang Liu |
| author_facet | Qiezeng Yuan Heng Chen Chang Liu |
| author_sort | Qiezeng Yuan |
| collection | DOAJ |
| description | Robust industry-university-research (I-U-R) collaborative networks are essential for accelerating innovation in the clean energy industry (CEI). This study employs the exponential random graph model to investigate how the network structural, node, and edge attributes drive the formation of I-U-R collaborative networks in China’s CEI, using a dataset of 5276 I-U-R collaborative patents. Key findings reveal the following: (1) convergence structures and transitive closure differentially drive network formation across the growth and maturity phases; (2) the nodes with strong R&D capabilities consistently drive network formation, though enterprises increasingly exert a negative effect and universities show a weakening positive effect; (3) multi-dimensional proximity displays temporal dynamics—geographic proximity follows an inverted U-curve, social proximity is U-shaped, and technological proximity gradually weakens; (4) node types exhibit heterogeneous moderating effects. Enterprises negatively moderate R&D capability during growth and maturity periods, weakening the technological proximity across all periods, and social and geographic proximity in maturity. Universities positively moderate the R&D capability but show period-specific effects on proximity: weakening social proximity in the sprouting stage, geographic proximity in the growth stage, and shifting their moderation of technological proximity from positive (growth) to negative (maturity). These findings deepen the understanding of how the I-U-R collaborative networks in China’s CEI format, contributing to the collaborative innovation theory through insights into the dynamic roles of node types. |
| format | Article |
| id | doaj-art-69d35b4eb7f34e2c9ad4c1cbdc068224 |
| institution | DOAJ |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-69d35b4eb7f34e2c9ad4c1cbdc0682242025-08-20T02:43:03ZengMDPI AGSystems2079-89542025-03-0113317310.3390/systems13030173Formation Drivers and Evolutionary Dynamics of Industry-University-Research Collaborative Networks in China’s Clean Energy IndustryQiezeng Yuan0Heng Chen1Chang Liu2School of Economics and Management, Harbin Engineering University, Nantong Street, Harbin 150001, ChinaSchool of Economics and Management, Harbin Engineering University, Nantong Street, Harbin 150001, ChinaSchool of Economics and Management, Harbin Engineering University, Nantong Street, Harbin 150001, ChinaRobust industry-university-research (I-U-R) collaborative networks are essential for accelerating innovation in the clean energy industry (CEI). This study employs the exponential random graph model to investigate how the network structural, node, and edge attributes drive the formation of I-U-R collaborative networks in China’s CEI, using a dataset of 5276 I-U-R collaborative patents. Key findings reveal the following: (1) convergence structures and transitive closure differentially drive network formation across the growth and maturity phases; (2) the nodes with strong R&D capabilities consistently drive network formation, though enterprises increasingly exert a negative effect and universities show a weakening positive effect; (3) multi-dimensional proximity displays temporal dynamics—geographic proximity follows an inverted U-curve, social proximity is U-shaped, and technological proximity gradually weakens; (4) node types exhibit heterogeneous moderating effects. Enterprises negatively moderate R&D capability during growth and maturity periods, weakening the technological proximity across all periods, and social and geographic proximity in maturity. Universities positively moderate the R&D capability but show period-specific effects on proximity: weakening social proximity in the sprouting stage, geographic proximity in the growth stage, and shifting their moderation of technological proximity from positive (growth) to negative (maturity). These findings deepen the understanding of how the I-U-R collaborative networks in China’s CEI format, contributing to the collaborative innovation theory through insights into the dynamic roles of node types.https://www.mdpi.com/2079-8954/13/3/173clean energy industryindustry-university-researchcollaborative networknode typeheterogeneity |
| spellingShingle | Qiezeng Yuan Heng Chen Chang Liu Formation Drivers and Evolutionary Dynamics of Industry-University-Research Collaborative Networks in China’s Clean Energy Industry Systems clean energy industry industry-university-research collaborative network node type heterogeneity |
| title | Formation Drivers and Evolutionary Dynamics of Industry-University-Research Collaborative Networks in China’s Clean Energy Industry |
| title_full | Formation Drivers and Evolutionary Dynamics of Industry-University-Research Collaborative Networks in China’s Clean Energy Industry |
| title_fullStr | Formation Drivers and Evolutionary Dynamics of Industry-University-Research Collaborative Networks in China’s Clean Energy Industry |
| title_full_unstemmed | Formation Drivers and Evolutionary Dynamics of Industry-University-Research Collaborative Networks in China’s Clean Energy Industry |
| title_short | Formation Drivers and Evolutionary Dynamics of Industry-University-Research Collaborative Networks in China’s Clean Energy Industry |
| title_sort | formation drivers and evolutionary dynamics of industry university research collaborative networks in china s clean energy industry |
| topic | clean energy industry industry-university-research collaborative network node type heterogeneity |
| url | https://www.mdpi.com/2079-8954/13/3/173 |
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