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Big data of innovation literature at the firm level: a review based on social network and text mining techniques

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  • O. Lerena
  • F. Barletta
  • F. Fiorentin
  • D. Suárez
  • G. Yoguel
Abstract
This paper aims to provide a state-of-the-art-review of the literature on the innovation process at the firm level (IFL), based on Social Network Analysis and Text Mining techniques. As opposed to the ‘black box’ vision, we conceive innovation as a process that emerges from formal and informal R&D efforts. Based on search results on academic publishing, we built a corpus of 13,132 contributions, published between 1970 and 2018. A bibliographic-coupling analysis was then performed, which allowed us to detect eight thematic communities: i) Collaborative innovation, ii) Business model, iii) Knowledge management, iv) Innovation capabilities, v) Firm performance, vi) Networks of innovators, vii) R&D studies, and viii) Eco-innovation. Each of them is subsequently analyzed with text mining and tested using term-based clustering. Our analysis reveals the existence of multiple and heterogeneous dimensions of the IFL that are partially addressed by the literature. Findings open up new questions about the content of the communities and the existence of bridges between them.

Suggested Citation

  • O. Lerena & F. Barletta & F. Fiorentin & D. Suárez & G. Yoguel, 2021. "Big data of innovation literature at the firm level: a review based on social network and text mining techniques," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 30(2), pages 134-150, February.
    • Handle: RePEc:taf:ecinnt:v:30:y:2021:i:2:p:134-150
    DOI: 10.1080/10438599.2019.1684646
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