Mapping an innovation ecosystem using network clustering and community identification: a multi-layered framework

Guannan Xu, Weijie Hu, Yuanyuan Qiao and Yuan Zhou ()
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Guannan Xu: Beijing University of Posts and Telecommunications
Weijie Hu: Beijing University of Posts and Telecommunications
Yuanyuan Qiao: Beijing University of Posts and Telecommunications
Yuan Zhou: Tsinghua University

Scientometrics, 2020, vol. 124, issue 3, No 15, 2057-2081

Abstract: Abstract The existing literature on innovation ecosystem overlooks the differences between knowledge ecosystems and business ecosystems, and mostly focuses on a single-layer analysis of the ecosystem. Also, ecosystem mapping studies involve either whole-network analysis at the macro-level or ego-network analysis at the micro-level, while few studies have investigated network community analysis at the meso-level. Therefore, this paper proposes a framework of Multi-layered Innovation Ecosystem Mapping (MIEM) to explore both knowledge and business ecosystems, thereby extending the analysis to the network communities. Based on multi-source heterogeneous data and machine learning, MIEM includes four steps in conducting the analysis: define the research scope and collect data; construct whole networks; identify communities; and recognize strategic roles. In particular, Newman topological clustering is adopted to identify network communities, and a strategic-role matrix is used to analyze the roles in a community. Based on this framework, a case study of numerical-control machine tool ecosystem mapping is conducted using patents and value-added tax invoice data.

Keywords: Innovation ecosystem; Machine learning; Network community identification; Multi-source heterogeneous data; Topological clustering; Multi-layered framework (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s11192-020-03543-0

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