The role of psychological basic need satisfaction in seafarers’ interaction with energy-efficiency decision support systems and preferences for automation types

Abstract

This dissertation investigates Basic Psychological Need satisfaction and Preferences for Automation Types in maritime energy-efficient operations, by focusing on seafarers’ interactions with decision-support systems (DSS) for energy-efficient route planning. Given the need to reduce CO₂ emissions in the shipping industry, operational measures like energy-efficient route planning are essential. However, high workloads, safety demands, and conflicting stakeholder goals challenge effective implementation. DSS can potentially support seafarers in overcoming these barriers, but previous research highlights obstacles to adoption, particularly mismatches between technical systems and onboard realities or scepticism towards automation. This dissertation addresses these challenges from an engineering psychology perspective by systematically (1) describing route planning tasks and decision-making, (2) applying Basic Psychological Needs theory to analyse seafarers’ satisfaction of needs both at work and in technology usage, and (3) developing a scale to assess preferences for automation types. The dissertation comprises five publications, each contributing multiple empirical insights. The synopsis accompanying these articles gives a comprehensive background on energy efficiency in the maritime industry, task analysis, Basic Psychological Needs and human-automation interaction, before discussing implications of the research. Article 1 provides an introduction to the research landscape, presenting a systematic literature review on human factors related to onboard energy efficiency. Although not a core dissertation contribution, the review shows prior research focused mainly on stakeholder perspectives, with limited attention to seafarers and specific system properties supporting onboard operations. Article 2 builds on this foundation with a hierarchical task analysis of energy-efficient route planning, informed by guidelines and expert input (N = 3). An online study (N = 65) used this analysis to have seafarers rate tasks on subjective value, success expectancy, and cost, identifying tasks like tidal and weather routing as high-value but costly or of lower success expectancy. The study also assessed Basic Psychological Need at work satisfaction, revealing lower autonomy satisfaction than competence or relatedness, and preferences for automated Information Acquisition and Analysis but human decision selection. Post hoc analysis of interviews conducted in a simulator study (N = 22) for Article 3 further used the Critical Decision Method to explore seafarers’ decision-making in route planning, highlighting safety, regulatory adherence, practical experience, and transparency as priorities. The detailed task analysis supported the external validity of the experimental studies, guiding autonomy-supportive DSS design and a differentiated analysis of autonomy facets to explore the autonomy-automation preference relationship. Article 3 presents an experimental study using a high-fidelity ship-bridge simulator, where seafarers (N = 22) evaluated usability, user experience, and Basic Psychological Need in technology usage satisfaction with a route planning DSS versus a digital charting tool. The DSS performed similarly or better across most metrics, though autonomy satisfaction was lower. Thematic analysis of post-task interviews emphasised transparency and flexibility as crucial for user autonomy, steering the dissertation toward autonomy-supportive DSS feature development. Article 4 builds on these insights through a simulator study with experienced seafarers (N = 18) and an international online study (N = 48). Comparing a charting tool, a “standard” DSS, and a DSS with route adjustability (an autonomy-support feature), results showed that while most metrics improved between the charting tool and the standard DSS, only the DSS with route adjustability significantly enhanced autonomy in technology usage satisfaction and trust. Replication of the correlation between autonomy at work and decision selection preferences from Article 2 were not confirmed; however, lower autonomy satisfaction at work was confirmed. Thematic analysis of simulator study interviews further differentiated facets of autonomy in technology use, using the Dimensions of Autonomy in Human-Algorithm Interaction model, which suggested algorithm comprehensiveness, usability, user empowerment, and collaborative workflows could potential be leveraged to enhance autonomy. This article demonstrates how human-centred design can identify and address Basic Psychological Need frustrations in technology use. Article 5 details the development and validation of the Preference for Automation Types Scale (PATS), used in Articles 2 through 5. Based on the Model of Types and Levels of Automation, PATS differentiates preferences for automation types. Validation studies across three samples, including seafarers, students using generative AI for essay writing (N = 107) or a DSS for vacation planning (N = 126), demonstrated the PATS’ dimensionality, reliability, and construct validity. The scale effectively assessed preferences as a human vs. automation dichotomy while distinguishing specific automation types across contexts, making it a valuable tool for aligning a system’s automation with users’ preferences. The General Discussion integrates findings from all studies, addressing theoretical implications for engineering psychology and human factors research. It underscores the need for autonomy-supportive technology, especially where autonomy needs at work are frustrated, and highlights that traditional user experience and usability measures are insufficient for evaluating complex automation systems. The PATS’ broader implications are explored as a preference measure, which could assess user inclinations for or against specific automation features even where overall trust is adequate, enabling cross-context comparisons in areas with varied automation demands, such as transportation and healthcare. Practical recommendations for the maritime industry include DSS design principles like transparency (e.g. clear communication of algorithmic decisions) and adaptability through adjustable automation. Additionally, international maritime policies should promote human-centred design by standardising usability testing and establishing transparency standards. In conclusion, this dissertation contributes to engineering psychology research on Basic Psychological Needs in technology usage and human-automation interaction. It provides a comprehensive framework for human-centred DSS design, offering insights applicable to other safety-critical domains and supporting the broader goal of mitigating climate change through enhanced energy-efficient operations in the shipping industry.