Towards an understanding of the consequences of technology-driven decision support for maritime navigation

Doctoral thesis, 2022

The maritime industry is undergoing a transformation driven by digitalization and connectivity. There is speculation that in the next two decades the maritime industry will witness changes far exceeding those experienced over the past 100 years. While change is inevitable in the maritime domain, technological developments do not guarantee navigational safety, efficiency, or improved seaway traffic management. The International Maritime Organization (IMO) has adopted the Maritime Autonomous Surface Ships (MASS) concept to define autonomy on a scale from Degrees 1 through 4.  Investigations into the impact of MASS on various aspects of the maritime sociotechnical system is currently ongoing by academic and industry stakeholders. However, the early adoption of MASS (Degree 1), which is classified as a crewed ship with decision support, remains largely unexplored. Decision support systems are intended to support operator decision-making and improve operator performance. In practice they can cause unintended changes throughout other elements of the maritime sociotechnical system. In the maritime industry, the human is seldom put first in technology design which paradoxically introduces human-automation challenges related to technology acceptance, use, trust, reliance, and risk. The co-existence of humans and automation, as it pertains to navigation and navigational assistance, is explored throughout this thesis.

The aims of this thesis are (1) to understand how decision support will impact navigation and navigational assistance from the operator’s perspective and (2) to explore a framework to help reduce the gaps between the design and use of decision support technologies. This thesis advocates for a human-centric approach to automation design and development while exploring the broader impacts upon the maritime sociotechnical system. This work considers three different projects and four individual data collection efforts during 2017-2022. This research took place in Gothenburg, Sweden, and Warsash, UK and includes data from 65 Bridge Officers (navigators) and 16 Vessel Traffic Service (VTS) operators. Two testbeds were used to conduct the research in several full mission bridge simulators, and a virtual reality environment. A mixed methods approach, with a heavier focus on qualitative data, was adopted to understand the research problem. Methodological tools included literature reviews, observations, questionnaires, ship maneuvering data, collective interviews, think-aloud protocol, and consultation with subject matter experts. The data analysis included thematic analysis, subject matter expert consultation, and descriptive statistics. 

The results show that operators perceive that decision support will impact their work, but not necessarily as expected. The operators’ positive and negative perceptions are discussed within the frameworks of human-automation interaction, decision-making, and systems thinking. The results point towards gaps in work as it is intended to be done and work as it is done in the user’s context. A user-driven design framework is proposed which allows for a systematic, flexible, and iterative design process capable of testing new technologies while involving all stakeholders. These results have led to the identification of several research gaps in relation to the overall preparedness of the shipping industry to manage the evolution toward smarter ships. This thesis will discuss these findings and advocate for human-centered automation within the quickly evolving maritime industry.