Mapping the intellectual structure of the literature on leveraging social network for knowledge management: a bibliometric review

Kavita Verma & Kapil Malhotra

International Journal of Knowledge Management Studies2026https://doi.org/10.1504/ijkms.2026.152494article
AJG 1ABDC C
Weight
0.50

What the paper says

Social networks facilitate knowledge sharing, collaboration, and innovation by connecting experts and ideas. This study aims to conduct an extensive bibliometric review to ascertain the intellectual structure of social network and knowledge management (SN KM) studies. The study employs performance analysis and science mapping analysis on 532 documents retrieved from Scopus database between 1992 to 2024. Biblioshiny in RStudio package merged with VOSviewer was used to analyse and visualise data. The study analysed co-occurrence keywords, citation analysis, collaboration among countries and authors, and influential affiliations and documents. Bibliographic coupling and keyword analysis have been conducted to identify present research landscape and future roadmap. The results indicate a significant increase in publications over the past decade, highlighting most prolific authors, documents, countries, institutions, and journals in this field. The USA, UK, and China were noted as the most contributive countries leading international collaborations, where findings contradict Lotka's and Bradford's Laws.

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https://doi.org/https://doi.org/10.1504/ijkms.2026.152494

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@article{kavita2026,
  title        = {{Mapping the intellectual structure of the literature on leveraging social network for knowledge management: a bibliometric review}},
  author       = {Kavita Verma & Kapil Malhotra},
  journal      = {International Journal of Knowledge Management Studies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijkms.2026.152494},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.