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Synthesizing Knowledge through A Data Analytics-Based Systematic Literature Review Protocol

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Listed:
  • Rachael Ruizhu Xiong

    (Kansas State University)

  • Charles Zhechao Liu

    (University of Texas at San Antonio)

  • Kim-Kwang Raymond Choo

    (University of Texas at San Antonio)

Abstract

Systematic literature reviews (SLR) are commonly undertaken by researchers to stay informed of the latest development in a particular topic, but this manual process is demanding and can only locate and analyze a limited number of articles. We propose a data analytic-based SLR protocol and a set of semi-automated tools to leverage the latest advances in data analytics and facilitate a more effective, objective, and comprehensive SLR process. Our protocol incorporates scraping tools to collect articles from seven bibliographic databases, and text analytics, social network analysis, natural language processing, citation analysis, and main path analysis to analyze a large number of articles. To demonstrate its utility of, we apply the protocol on the topic of “information diffusion in social networks”. The results reveal 11 latent topics under this broad domain along with the most critical articles for each topic, and the connections among the associated 1,229 articles and their references.

Suggested Citation

  • Rachael Ruizhu Xiong & Charles Zhechao Liu & Kim-Kwang Raymond Choo, 2025. "Synthesizing Knowledge through A Data Analytics-Based Systematic Literature Review Protocol," Information Systems Frontiers, Springer, vol. 27(1), pages 235-258, February.
    • Handle: RePEc:spr:infosf:v:27:y:2025:i:1:d:10.1007_s10796-023-10432-3
    DOI: 10.1007/s10796-023-10432-3
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    References listed on IDEAS

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