文献类型：期刊文献

英文题名：When Do Structural Holes Yield Breakthrough Innovation? An Inverted U-Shape Bounded by Collaboration-Layer Centralities

作者：Li, Shugang[1];Dong, Jinxian[1];Yu, Zhaoxu[2];Wen, Zhifang[1];Sun, Mengsi[1];Ye, Xinyi[1]

机构：[1]Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China;[2]East China Univ Sci & Technol, Dept Automat, Shanghai 200231, Peoples R China

年份：2026

卷号：14

期号：7

外文期刊名：SYSTEMS

收录：;EI(收录号：20263121204193);Scopus(收录号：2-s2.0-105045984669);WOS:【SSCI(收录号:WOS:001833099000001)】；

基金：This research received no external funding.

语种：英文

外文关键词：breakthrough innovation; Technology Innovation Composite Network; cross-layer integration mechanism; knowledge-network structural holes; collaboration-network centrality

摘要：Breakthrough innovation-central to industrial competitiveness and the ongoing clean-energy transition-remains persistently constrained by information homogenization and weak cross-domain integration in single-layer innovation networks. Technology Innovation Composite Networks (TICNs) have therefore been advocated as dual-layer platforms coupling knowledge and collaboration networks, yet the cross-layer mechanism through which they generate breakthrough outputs has not been specified. This paper specifies and tests how knowledge-layer structural holes open access to heterogeneous information that must cross into the collaboration layer to be recombined into breakthroughs. Two distinct boundaries shape the outcome. Inventors' finite cognitive processing capacity makes integration returns decay along an inverted U-shape; separately, excessive degree and closeness centrality drive the collaboration layer into homogenization and localization, narrowing the range of structural holes it can productively absorb and shifting the breakthrough peak toward lower structural-hole levels. Together, they delineate an optimal cross-layer integration zone. Using panel data on 10,681 patents, 948 inventors, and 5631 inventor-year observations from new energy (2004-2018), a fixed-effects negative binomial model confirms the inverted U-shape and the steepening, peak-shifting moderations of degree and closeness centrality; a Lind-Mehlum test places the turning point inside the observed data range, and negative binomial (robust SE), Poisson and zero-inflated Poisson specifications-together with a stricter top-1% breakthrough threshold-yield consistent results. The study moves multilayer network research from structural description toward mechanism-level identification and offers actionable network-design guidance.