Research Reveals How Collaboration Networks Shape Corporate Innovation Performance

Innovation performance in corporations is strongly influenced by how firms are embedded within collaboration networks, according to new research published in Frontiers of Engineering Management. The study, which analyzed 22 years of global patent data from the 3D printing industry, provides clarity on why previous research has reported inconsistent results regarding the relationship between network embeddedness and innovation output.

Researchers from Beijing University of Posts and Telecommunications, Tsinghua University, the Higher School of Economics in Russia, and collaborators published their findings in 2025 with DOI:10.1007/s42524-025-4188-x. The study examined data from 6,109 organizations over more than two decades, constructing collaboration networks based on co-patenting activities and identifying innovation communities through topological clustering methods.

The research distinguishes between two types of embeddedness that have previously been conflated in innovation studies. Within-community embeddedness represents ties to peers inside the same innovation community, while cross-community embeddedness reflects connections that bridge multiple communities. Both types were found to significantly enhance innovation output, though through different mechanisms.

Within-community connections provide trusted access to shared knowledge, allowing faster resource integration and reducing coordination costs. Cross-community ties offer diverse expertise and non-redundant information, broadening innovation perspectives. The study's analytical framework, detailed in the original research available at https://doi.org/10.1007/s42524-025-4188-x, reveals how these network structures jointly shape firms' innovation performance.

Perhaps most significantly, the research identifies collaboration complementarity as a pivotal contingency factor. When complementarity between partners is high, firms gain more from within-community relational embeddedness, while the innovation benefits of cross-community collaboration weaken due to integration complexity and resource absorption costs. This finding helps reconcile contradictory results in past literature and provides new evidence for how network structure drives innovation outcomes.

The authors note that innovation is not only about forming partnerships but about forming the right partnerships in the right network positions. Dense internal ties accelerate trust and knowledge transfer, while cross-community ties introduce novel perspectives. However, high complementarity does not always guarantee more innovation—it amplifies internal collaboration benefits but increases coordination costs across communities.

These findings have significant implications for business strategy and industrial policy. Companies embedded deeply in innovation communities may strengthen internal ties to leverage complementarities, while selectively bridging external communities to maintain diversity of ideas. The research suggests firms should manage their collaboration portfolios strategically rather than expanding cooperation blindly.

Policymakers can use these insights to guide industrial cluster development, promote cross-sector collaboration, and design incentive mechanisms for innovation-driven industries. The analytical framework is also applicable to emerging domains such as artificial intelligence, new materials, and biomanufacturing. Optimizing embeddedness and collaboration patterns can accelerate technological progress and enhance competitiveness in global innovation ecosystems.

The study was supported by multiple funding sources including the National Natural Science Foundation of China, the National Social Science Foundation of China, the Beijing Social Science Foundation, and the HSE University Basic Research Program. The research contributes to understanding how digitalization and globalization accelerate knowledge exchange across firms, fostering open innovation ecosystems where organizations collaborate beyond geographical boundaries.