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Profiling academic-industrial collaborations in bibliometric-enhanced topic networks: A case study on digitalization research

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  • Chen, Hongshu
  • Jin, Qianqian
  • Wang, Ximeng
  • Xiong, Fei

Abstract

Collaborations between industry and academia provide a key pathway for innovation and serve as a stimulus for basic and applied research. The collaborative innovations of the two communities are embedded in both the collaborative networks of these organizations and the knowledge networks established by coupling among knowledge elements in the collaborative content. However, existing studies on academic-industrial collaborations have mainly been concerned with analyzing these interactions at the institutional level. To fill the gap of profiling collaborative content and to inspire related studies, this paper provides a bibliometric-enhanced method of mapping topic networks and measuring the semantic structures of academic-industrial collaboration. Via this method, topics can be extracted, vectorized, and correlated to construct a bibliometric-enhanced topic network as a representation of the collaborative content generated by these partnerships. Examining the structural properties of the topic network can provide comprehensive insights for future academic-industrial research collaborations. To showcase these insights, we conducted a case study involving both articles and patents in the field of digitalization. As the case study shows, the method provided in this paper can serve as a tool for cooperative research planning, innovation management, and problem-solving in a given target area of research.

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  • Chen, Hongshu & Jin, Qianqian & Wang, Ximeng & Xiong, Fei, 2022. "Profiling academic-industrial collaborations in bibliometric-enhanced topic networks: A case study on digitalization research," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:tefoso:v:175:y:2022:i:c:s0040162521008337
    DOI: 10.1016/j.techfore.2021.121402
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