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Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions

Author

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  • Ziaul Haque Munim
  • Mariia Dushenko
  • Veronica Jaramillo Jimenez
  • Mohammad Hassan Shakil
  • Marius Imset

Abstract

This study provides a bibliometric review of 279 studies on the applications of big data and artificial intelligence (AI) in the maritime industry, published in 214 academic outlets, authored by 842 scholars. We extracted bibliographical data from the Web of Science database and analysed it using the Bibliometrix tool in R software. Based on citation analysis metrics, we revealed the most influential articles, journals, authors and institutions. Using the bibliographic coupling methodology, we identified four underlying research clusters: (1) digital transformation in maritime industry, (2) applications of big data from AIS, (3) energy efficiency and (4) predictive analytics. We analysed these clusters in detail and extracted future research questions. Besides, we present research collaboration networks on the institution and author level.

Suggested Citation

  • Ziaul Haque Munim & Mariia Dushenko & Veronica Jaramillo Jimenez & Mohammad Hassan Shakil & Marius Imset, 2020. "Big data and artificial intelligence in the maritime industry: a bibliometric review and future research directions," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 577-597, July.
    • Handle: RePEc:taf:marpmg:v:47:y:2020:i:5:p:577-597
    DOI: 10.1080/03088839.2020.1788731
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