Marine and Offshore Engineering Studies
Open access
Abstract
Abstract
The ship recycling industry experiences frequent accidents with severe consequences attributed to human and organizational factors. Existing human factors frameworks, including the Human Factors Analysis and Classification System (HFACS), provide comprehensive taxonomies for accident analysis, but their complexity and granularity pose challenges in low-resource, high-risk contexts such as ship dismantling yards. This paper presents the development of HFACS-SR (HFACS for Ship Recycling), a simplified human factors taxonomy tailored to the ship recycling sector, retaining the systemic insight of HFACS while enhancing usability for practitioners in shipbreaking yards. A participatory card-sorting study with 14 industry experts (safety officers, academics, regulators, supervisors, and an NGO representative) used 117 human factor codes derived from the SHIELD taxonomy, developed under the EU SAFEMODE project for aviation and maritime safety occurrences. Participants sorted the codes via KardSort, rated their relevance to ship recycling, and suggested modifications. Quantitative tiering identified 50 high-consensus codes. Qualitative refinement merged overlapping codes, promoted practitioner-identified gaps, and retained threshold-adjacent codes on evidential grounds, reducing the taxonomy from 117 to 53 codes across five levels. A new External Influences level captures regulatory, economic, and political pressures unique to ship recycling and absent from conventional HFACS. Inter-rater agreement was substantial (Fleiss' κ = 0.69). Plain language renaming further enhanced frontline usability. HFACS-SR preserves the multi-layered systemic perspective of HFACS while improving applicability in ship recycling, offering practitioners a clearer, context-specific framework for accident investigation and safety management.
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@article{Akpadiaha2026Human,
title = {A Human Factors Taxonomy for Ship Recycling: Developing and Validating HFACS-SR for a High-Risk, Low-Resource Industry Through Card Sorting},
author = {Ini Sunday Akpadiaha and Emediong Christopher Umana and Mmenyene Michael Udoh},
journal = {Preprints.org},
year = {2026},
doi = {10.20944/preprints202607.0560.v1},
url = {https://doi.org/10.20944/preprints202607.0560.v1}
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