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A machine learning approach towards reviewing the role of ‘Internet of Things’ in the shipping industry

Author

Listed:
  • Kelly Gerakoudi

    (The American College of Greece)

  • Georgios Kokosalakis

    (The American College of Greece)

  • Peter J. Stavroulakis

    (The American College of Greece
    University of Piraeus)

Abstract

The technology of the Internet of Things (IoT) represents a cornerstone of the fourth industrial revolution. We adopt a machine learning approach to examine the effect of IoT technology on shipping business operations. Text mining and the probabilistic latent Dirichlet allocation are applied for an unsupervised topic modelling analysis of two hundred and twenty-eight academic papers. Our findings reveal the potential of IoT to provide more efficient approaches to business operations and improve the quality of services, highlighting the value of instant and secure information flow among all parties involved. Problematic areas of the new technology are also identified, in reference to issues of standardization and interoperability. Relatively few studies have used machine learning techniques to elicit insights into the holistic effect of emerging IoT technology in the shipping industry. The research findings highlight the potential of IoT technology to transform shipping operations, offering useful and practical implications to academics and professionals.

Suggested Citation

  • Kelly Gerakoudi & Georgios Kokosalakis & Peter J. Stavroulakis, 2024. "A machine learning approach towards reviewing the role of ‘Internet of Things’ in the shipping industry," Journal of Shipping and Trade, Springer, vol. 9(1), pages 1-29, December.
    • Handle: RePEc:spr:josatr:v:9:y:2024:i:1:d:10.1186_s41072-024-00177-w
    DOI: 10.1186/s41072-024-00177-w
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    References listed on IDEAS

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