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Supply Chain Risk in the Baltic Sea: Victim Phenomenology Machine Learning Model
Abstract
The key objective of this study is to generate a model identifying how decision makers perceive cybercrime risk in logistics supply chain operations taking place in the Baltic Sea Region of the North Atlantic Sea. A mixed-methods approach was applied, using a novel replication-logic case study research design featuring the pragmatist-iterative ideology supplemented with machine learning (ML) to assist with thematic coding. The snowball purposive sampling technique was used to collect online data from confidential informants who worked for large organizations in the Baltic Sea region, after they were cyber-attacked. In the final model, six phenomenological topics across three victim vulnerability keywords were found to be significant. The findings have implications for cybersecurity policy formulation in Europe and globally, where cybercrimes are increasing mostly due to the Russia-Ukraine situation.
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