Background: Considering that over 90 percent of the world trade is transported by means of ocean transportation, the dangers in the maritime industry potentially affect many stakeholders. It is because of this, that overall safety became an increasingly important topic in the maritime sector. Enhancing safety onboard is not only important for the vessel’s crew, but also in terms of financial and fiscal drivers from the industry – often ships are chartered on the strength of their safety performance. Due to a changing view – from technical to human failures – the safety of seafarers slowly found its way to the spotlights. One may safely argue that, once proved feasible, a method of achieving acceptable crew safety performance outcomes in a cost-efficient way will be easily adopted by ship owning companies. To achieve cost-effectiveness however, one should not only look at crew safety performance outcomes but also be aware of the determinants contributing to these outcomes so that investments can be strategically made. Purpose: This study focuses on the latter issue and attempts to define the variables that influence the distribution of injury – in terms of severity – for a ship owning company operating in the ‘safety aware’ offshore sector. Hereby, the main goal was to develop a model explaining the distribution of injuries among crew members, so that investment post may be defined on the road to cost-efficient crew safety management. Methods: The study mainly uses quantitative methods. An in-depth company study was conducted, analyzing data provided by a Swiss based ship owning company with its main office located in The Netherlands. The database was provided by the company. Four injury levels were included in the study as dependent variables: first aid (n = 736), medical treatment (n = 98), restricted work (n = 13) and lost time injury (n = 23). Since the injury levels are ascending from first aid case – minor injury – to lost time injury – major injury, an ordinal logistic regression model was used in the first attempt. Later, a generalized ordinal logistic regression model was added to study the individual effects of the included independent variables. Results: Concluding evidence was found for eight of the eleven formulated hypotheses. A larger number of construction sites, cold weather conditions, and operating in areas near developing or less than developed countries had a negative impact on crew safety performance. Strict safety regulations had a positive effect. The more recent years were associated with a higher distribution of severe injuries. The more exposed construction crew appeared to be less prone to severe injuries, whereas reported injuries for the technical crew were relatively severe. Implications for practice: It is in reducing the gap between science and practice that the most valuable solutions for improving crew safety performance may be found. One should keep this in mind when conducting further research in this field with the ultimate purpose of saving lives and always pursue to translate scientific findings into workable solutions for practice.

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Acciaro, M. (Michele)
Maritime Economics and Logistics
Erasmus School of Economics

Korte, C.E. (Lieke) de. (2014, September 5). Crew Safety in Shipping. Maritime Economics and Logistics. Retrieved from