Building trust and reducing perceived complexity in AI adoption: effects on decision-making quality in maritime organisations

Purpose This study investigates how sociotechnical factors shape artificial intelligence (AI) trust and how it influences perceived complexity reduction and decision-making quality in maritime organisations. It addresses the limited understanding of how trust enables maritime professionals to improve AI-supported decision-making quality in high-risk operational environments. Design/methodology/approach Drawing on sociotechnical systems theory and Luhmann's systemic trust theory, the study develops a structural model linking AI familiarity, AI beliefs, AI system quality, AI system transparency, AI trust, perceived complexity reduction and decision-making quality. Data were collected from 106 maritime professionals in Singapore and examined through structural equation modelling. Findings The results indicate that AI system quality, AI system transparency and AI familiarity significantly enhance AI trust, while AI beliefs have a non-significant effect. AI trust is strongly associated with both perceived complexity reduction and decision-making quality. In contrast, perceived complexity reduction shows a weaker positive effect on decision-making quality. These findings suggest that trust is the main mechanism through which AI-related social and technical factors contribute to improved decision-making outcomes. Originality/value This study integrates sociotechnical systems theory and Luhmann's systemic trust theory to explain AI adoption in maritime organisations. It advances existing research by positioning AI trust as the central link between sociotechnical factors, perceived complexity reduction and decision-making quality in the maritime context.