Aug 2026· International Trade, Politics and Development· pp. 1-20· 0 citations· 63 references
TL;DR
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, and suggest that trust is the main mechanism through which AI-related social and technical factors contribute to improved decision-making outcomes.
Abstract
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.
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.
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.
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.
The findings suggest that perceived AI trustworthiness is positively associated with responsible AI adoption and higher perceived decision efficacy, while decision complexity is an important boundary condition associated with the perceived efficacy of GAI in managerial decision processes.
Guangming Cao, Yanqing Duan, John S. Edwards· Journal of Business Ethics· 0 citations
A model develops and validates a model to examine how interface clarity, cognitive-technical alignment, algorithmic reliability, and decision explainability collectively influence behavioral intent to adopt AI-based decision support tools, and offers strong empirical support for applying CFT in AI adoption contexts.
A. Waqar, Khaled A. Alrasheed, Azlan Shah Ali et al.· Acta Psychologica· 1 citation
Assessing the effects of technology reliability (RL), credibility (CR) and technical competence (TEC) on HR professionals’ trust and, subsequently, their intent to deploy AI tools reveals that technology RL, CR and TEC each enhance trust in AI.
R. Arora, Neha Kumari Siradhana· South Asian Journal of Human...· 0 citations
The study concludes that Explainable AI is not only a technological enhancement but also a strategic tool for promoting employee trust and supporting effective digital transformation and recommends that organizations prioritize explainability, invest in AI literacy and training, and develop transparent AI governance frameworks to encourage successful adoption of AI-driven business process automation.
The rapid adoption of Artificial Intelligence (AI) in data driven decision making has increased the complexity of analytical models, creating significant communication barriers between technical experts and nontechnical stakeholders. Limited understanding of AI generated insights often reduces trust, delays decision making, and restricts the effective adoption of AI supported recommendations. As organizations increasingly rely on explainable and human centered AI systems, effective communication has become essential to ensuring that AI out- puts are accessible, transparent, and meaningful for diverse stakeholder groups. This study aims to identify the key challenges in communicating complex AI model results and to develop practical communication strategies that enhance human trust among nontechnical stakeholders. A qualitative research design was employed using case studies, open ended surveys, expert interviews, and focus group discussions involving data scientists and nontechnical decision makers from business organizations. The collected data were analyzed through thematic analysis to identify recurring communication barriers and effective explanatory practices. The findings reveal that technical jargon, cognitive overload, and limited contextual explanations are the primary factors reducing stakeholder trust in AI generated insights. Conversely, explainable AI communication supported by intuitive data visualization, contextual storytelling, simplified summaries, and audience centered messaging significantly improves understanding, transparency, and stakeholder confidence across organizational contexts. The study proposes a human centered communication framework that strengthens trust in AI assisted decision making while promoting more inclusive and responsible technology adoption. These findings contribute to explainable AI research by demonstrating how effective communication can bridge the gap between technical complexity and human understanding, thereby generating meaningful humanistic impacts in organizational decision making and sustainable organizational innovation.
Dwi Apriliasari, Bintang Nandana Henry, Alexander Williams· Journal of Orange Technology· 0 citations
Human factors play a more prominent role than AI certifications when it comes to trust-building in AI suggestions for ethical decision-making in the workplace, suggesting that overall, AI attitude may overshadow perceived certifier trustworthiness.
Natalie Martin, Tobias Kopp, Pascal Vetter et al.· Human Factors· 0 citations
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