Decoding digital burnout in the maritime workforce: An explainable ai approach to digital ageing and deprivation

BackgroundThe rapid digitalisation in the maritime sector has introduced new psychological risks; however, the non-linear pathways of digital burnout remain under-researched.ObjectiveThis study aims to investigate the multifaceted nature of digital burnout among seafarers by identifying key demographic drivers and latent risk profiles.MethodsA hybrid analytical framework was employed, integrating traditional ANOVA with K-Means clustering and explainable artificial intelligence (XAI) tools, specifically SHAP and Decision Tree classifiers, to analyse survey data from Turkish seafarers.ResultsWhile ANOVA identifies age, income, and experience as significant drivers, machine learning reveals complex risk pathways. K-Means clustering identified three distinct profiles, with the high-risk group (Cluster 0) exhibiting critical Digital Ageing (45.12) and Emotional Exhaustion (21.90) scores. Decision Tree analysis indicates medium income as the primary root node for burnout stratification. SHAP analysis pinpoints mid-to-late career experience (16-20 years) and being a Deck Officer as the strongest catalysts for exhaustion, whereas younger age (21-25) and lower experience (6-10 years) act as protective buffers.ConclusionsDigital burnout in the maritime sector is a spectrum condition driven by professional hierarchy and career midpoint pressures, rather than mere technology use. Findings underscore the necessity for human-centred digital policies and targeted resilience initiatives to safeguard the global maritime workforce's mental well-being.