Mapping an innovation ecosystem using network clustering and community identification: a multi-layered framework
Guannan Xu, Weijie Hu, Yuanyuan Qiao, Yuan Zhou
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.