Abstract
This study introduces MAK approach to investigate intellectual structure of fields which combines text-net analysis (TNA), latent dirichlet allocation (LDA), and co-citation analysis. Researchers have previously deployed co-citation analysis to reveal the intellectual structure of fields. However, in these applications, the research has two technical limitations—small representativeness in datasets analyzed and the primary consideration for dated documents—towards the co-citation analysis. These limitations impede the formation of a larger picture in the structure. The present study seeks to eliminate these limitations by utilizing TNA and LDA methods as topic modeling approaches for 38,368 journal articles as references with 125,154 appearances in 2680 articles published between 1980 and 2019 in the Strategic Management Journal (SMJ). We suggest researchers should embrace MAK approach as complementary approach to research, with its focus on the intellectual structures of the field. We provide a workflow to show potential research applications and address advantages and limitations associated with the two new methods.
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Appendix: Labels used in the TISA analysis with their meaning, clusters and citation numbers in the SM intellectual structure
Appendix: Labels used in the TISA analysis with their meaning, clusters and citation numbers in the SM intellectual structure
Cluster 1—Red, Cluster 2—Green, Cluster 3—Blue, Cluster 4—Yellow, Cluster 5—Purple in Fig. 8 Ordered by cluster number.
Labels | Cited Articles | Clusters | Citation Number |
|---|---|---|---|
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Köseoglu, M.A. Identifying the intellectual structure of fields: introduction of the MAK approach. Scientometrics 125, 2169–2197 (2020). https://doi.org/10.1007/s11192-020-03719-8
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DOI: https://doi.org/10.1007/s11192-020-03719-8