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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Guannan Xu & Weijie Hu & Yuanyuan Qiao & Yuan Zhou, 2020. "Mapping an innovation ecosystem using network clustering and community identification: a multi-layered framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2057-2081, September.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:3:d:10.1007_s11192-020-03543-0
    DOI: 10.1007/s11192-020-03543-0
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