, Volume 113, Issue 2, pp 1177–1197 | Cite as

How to identify metaknowledge trends and features in a certain research field? Evidences from innovation and entrepreneurial ecosystem

  • Chao Zhang
  • Jiancheng GuanEmail author


Identifying the trends and features of metaknowledge will help scholars track knowledge through topics. This paper designs a new methodology to make it in a certain field. The proposed novel design performs well in the interdisciplinary domain where there are plenty noisy data and conflicting findings. This study applies this research design to a typical interdisciplinary domain, i.e. innovation and entrepreneurial ecosystems. To identify the scope of research and rationalize data collection process, this paper makes a definition of innovation and entrepreneurial ecosystems based on previous researches. Next, we design two data filtering procedures, which can handle the noisy data and provide the datasets for sequence analyses. Then, we adopt the co-citation analysis and network meta-analysis to clarify the trends and features of multiple metaknowledges. Finally, we draw conclusions about emerging trends, mainstream and hotspots, current situation, future challenges or other features of metaknowledge. We also integrate some conflicting findings, which provide more accurate evidences for the field. Evidences show that this novel research design is an effective tool for analyzing metaknowledges and also suitable for other fields.


Metaknowledge Co-citation Network meta-analysis Innovation ecosystem Entrepreneurial ecosystem 



This study is supported by the Grants from National Natural Science Foundation of China (Nos. 71673261 and 71373254) and from The Research Team of Natural Science Foundation of Guangdong Province in China (2016A030312005). The authors are very grateful for the valuable comments and suggestions from two anonymous reviewers and the Editor-in-Chief of the Journal Scientometrics, which significantly improved the quality of the paper.


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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  1. 1.School of Economics & ManagementUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.School of Business AdministrationSouth China University of TechnologyGuangzhouChina

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