Abstract:

The article focuses on the detection of latent variables for modeling maritime transport security using text mining in scientific publications. The aim of the article is to identify non-measurable and unobservable variables that are important for the security of maritime transport. The auxiliary goals are the systematization and clustering of latent variables into similar thematic groups (modeling issues), as well as the extraction and identification of associations in the context of measuring the security of maritime transport.

The study conducted a narrative literature review and then used text mining to detect associations between terms. Research shows that maritime transport security issues are rooted in the issues of digital transformation, digitization, information, blue economy, and development. 11 thematic groups relevant to modeling or measuring maritime transport security were identified. Research shows that the issue of maritime transport security is embedded in the issues of digital transformation, information, blue economy and development. In the context of the need to measure the security of maritime transport, latent variables were identified, such as: security threats, port security and code. In addition, as a result of the research, it was noticed that, on the one hand, it is important to separate the activities within the port from the activities specific to maritime transport itself. Second, it is important to identify security threats in order to define measures. Thirdly, measurement is associated with certain procedures, code – ‘programming language’ as an institution of security standards in maritime transport.