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Keyword standardization and restructuring: the impact on analysing network-based science maps in innovation management research

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Abstract

Content analysis with the use of keywords experiences a take-off period in science mapping. Within the family of co-word analyses, analyzing the keyword content is commonly preceded by computer-assisted preprocessing, which may leave substantial noise and bias in the structure of the network set up. Despite these flaws, only few articles have attempted to go beyond conventional keyword standardization steps, although leveraging expert knowledge holds the promise to reduce the tradeoff between interpretability and representativeness in scaled bibliometric studies. We propose systematic manual preprocessing, an algorithmic keyword standardization and restructuring (KSR) procedure, and the paper is a validation study of the method. The innovation management (IM) disciplinary area is used to demonstrate the extent to which the quality and interpretability of bibliometric networks change and improve if in-depth keyword standardization and restructuring is implemented. For the demonstration, two networks of more than 5000 articles were set up and analyzed using identical steps, keyword preprocessing being the only difference. The impact of the KSR procedure on the clusterings is considerable and interpretation is greatly affected. Recommendations have been compiled for researchers, who would like to build keyword-based science maps to analyze content.

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Notes

  1. Namely, bibliographic coupling and co-citation analyses.

  2. For building science maps, the other three tools are co-author, bibliographic coupling and co-citation analyses. Computer-assisted textual analysis comprises a diverse set of methods, the overview of which is beyond the scope of this article. Word frequency counts, wordclouds, sentiment analysis, topic modelling, text classification, Natural Language Processing etc. are illustrative examples of textual analysis. These methods are used in practice-oriented works as well as analysis and academic research.

  3. Organization is a so-called polysemic term.

  4. Three search strings were combined for the management and business domains in Web of Science. 1. (innovation* AND keyword* AND “*network*” AND (“author*” OR “article*” OR “researcher*” OR “scholar*” OR “scientist*”)), 2. (innovation AND (“keyword* content analys*” OR (keyword* AND (cooccur* OR co-occur)) OR (coword OR co-word))), 3. (innovation* AND keyword* AND (cooccur* OR co-occur* OR coword OR co-word)). In relevant articles keyword cooccurence must have been analysed in a network approach.

  5. Such as smart city, the systemic view of entrepreneurship supply chains and business models.

  6. The search profile in Table A3 of the Appendices can be used to download the metadata from the Web of Science database.

  7. The latter is also a valid approach. Maintaining focus on content analysis, hence the effort to remove insignificant content representations from the network. Cosine similarity ranges from 0 to 1, where 1 indicates the strongest similarity.

  8. The principle of omitting the most frequent terms is analogous with the Term Frequency-Inverse Document Frequency (TF/IDF) approach. In our case, at the first clustering step, the global semantic context has already been set and at the second level a more granular semantic structure or context emerges.

  9. Papers not retained by the KSR method, were assigned a separate cluster.

  10. Papers not retained by any of the keyword similarity methods—the one used for RAW and KSR for the RESTRUCT—were assigned a separate cluster.

  11. Most of the dropout occurs when the characteristic keywords, whose presence would have adverse impact on delineating second level communities, are omitted to enhance discriminatory power of the clustering. This ommission is common practice in setting up co-occurrence science maps. For details, please consult Tables A4a, A4b, A5a and A5b in the Appendices.

  12. For details, please consult Figures A3a and A3b in the Appendices.

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Acknowledgements

The authors would like to thank Philippe Mouricou for the comments and suggestions to the working paper and the anonymous reviewers for their work and constructive criticism of the submitted manuscript.

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  1. Dept. of Strategy, Entrepreneurship and International Business, ESSCA School of Management, Budapest, Hungary

    Balázs Borsi

  2. Dept. of Science Policy and Scientometrics, Library and Information Centre of the Hungarian Academy of Sciences, Budapest, Hungary

    Zsófia Vida & Sándor Soós

  3. Institute of Research on Adult Education and Knowledge Management, Eötvös Loránd University, Budapest, Hungary

    Sándor Soós

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Borsi, B., Vida, Z. & Soós, S. Keyword standardization and restructuring: the impact on analysing network-based science maps in innovation management research. Scientometrics 130, 593–617 (2025). https://doi.org/10.1007/s11192-025-05232-2

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