Uncovering the Knowledge Networks in Innovation Research:
A Topic Modeling Approach
DOI:
https://doi.org/10.4013/base.2025.221.02Keywords:
Knowledge; Interorganizational Networks; Innovation; Topic Modelling; Latent Dirichlet Allocation (LDA).Abstract
Over the years, research on knowledge and innovation networks has been conducted in various directions and from various perspectives. With the volume of published studies, especially in the last decade, the challenges of understanding the field as a whole have increased. The aim of this study was to identify research topics on knowledge and innovation networks using topic modeling. We derived 50 research topics by applying the Latent Dirichlet Allocation (LDA) model, which is the most popular topic modeling algorithm in scientific studies. Our sample consisted of the abstracts of 6,746 articles on networks, knowledge, and innovation, extracted from Scopus and Web of Science, and published from 1985 to 2021. From these data, we explored topic trends over the years, identifying 21 hot topics, 21 cold topics, and 8 steady topics that could help drive future studies on knowledge and innovation networks.
Downloads
Published
Issue
Section
License
I grant the journal BASE the first publication of my article, licensed under Creative Commons Attribution license (which allows sharing of work, recognition of authorship and initial publication in this journal).
I confirm that my article is not being submitted to another publication and has not been published in its entirely on another journal. I take full responsibility for its originality and I will also claim responsibility for charges from claims by third parties concerning the authorship of the article.
I also agree that the manuscript will be submitted according to the journal’s publication rules described above.