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Knowledge structure of technology licensing based on co-keywords network: A review and future directions

Qing Li, Huaige Zhang and Xianpei Hong

International Review of Economics & Finance, 2020, vol. 66, issue C, 154-165

Abstract: A wide array of scholars study technology licensing from different aspects but there is no review literature to summarize the research results. To conclude these literatures and reveal the intellectual structure which can let us clearly predict potential future research, we select 106 keywords from 3761 literatures available in the core collection of web of science in 2005–2016, and adopt k-core of social network analysis (SNA), and Multi-dimensional scaling (MDS) to analyze the co-occurrence words matrix and map the intellectual structure of technology licensing by the software Ucinet and Netdraw. Under hierarchical analysis and content analysis, the results show technology licensing research can be divided into four layers including nucleus layer, middle layer, detail layer and marginal layer, and there are four main topics contained basic theory, spectrum band licensing, environment, and education and technology. According to the map, we infer the possibility of adopting multi methods to develop technology licensing research and clearly predict potential future research.

Keywords: Technology licensing; Social network analysis; Multi-dimensional scaling; Hierarchical analysis; Literature review (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:66:y:2020:i:c:p:154-165

DOI: 10.1016/j.iref.2019.11.007

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