AI Trustworthness in Managerial Decision-Making: Ethics, Transparency, and Explainability as Key Drivers

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

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.

Ethical Decision-Making in the Workplace: Which Factors Influence Decision-Makers' Willingness to Revise Their Decisions due to AI-Generated Suggestions?

ObjectiveTo examine how moral intensity and perceived trustworthiness of an AI certifier influence individuals' willingness to change their decision due to AI-generated suggestions.BackgroundAI-supported decision-making is increasingly used in professional contexts and situations with a high moral intensity. Yet it is unclear how different factors influence the willingness to base decisions on AI-generated suggestions in such situations. Perceived trustworthiness in the AI system is key for effective and ethically sound deployment. Investigating the interplay of these factors is critical for understanding AI-supported ethical decision-making and designing AI systems that support responsible decision-making in morally complex workplace environments.MethodWe conducted a 2 × 2 vignette-based online experiment with a representative US sample (n = 546), manipulating moral intensity and AI certifier trustworthiness. Participants made an initial workplace decision (whom to let go), received a deviating AI suggestion, and then made a second decision. Effects of independent and control variables on decision changes were analyzed using hierarchical logistic regression and χ2-tests.ResultsDecision makers were more likely to change decisions in high moral intensity scenarios due to AI suggestions. Certifier trustworthiness had no significant effect, whereas a positive general attitude towards AI increased the likelihood of changing decisions, suggesting that overall, AI attitude may overshadow perceived certifier trustworthiness.ConclusionHuman 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. Further research is needed to clarify how these factors interact with perceived certifier trustworthiness and other contextual factors.ApplicationEncouraging reliance on AI-based recommendations via certifications alone is challenging. Organizations should focus on their employees' general attitudes towards AI to support AI-based ethical decision-making in the workplace. AI systems could play a significant supporting role particularly in decision situations with high moral intensity.

Managerial attitudes toward AI: evaluating transparency, sense of control and fairness perceptions

Little research has explored the relationship between artificial intelligence (AI) and marketing performance, particularly regarding managers’ perceptions and attitudes toward AI. This paper aims to investigate the impact of AI fairness and transparency on business-to-business (B2B) marketing managers’ attitudes toward AI. From a marketing manager’s human-oriented perspective, this study proposes three scientific contributions. The research presents a conceptual framework for examining marketing professionals’ attitudes toward AI in marketing. Two studies involving 233 marketing managers provide insights into the drivers of AI adoption. Industrial managers view AI primarily as a tool to enhance efficiency, improve lead generation and support complex decision-making processes, demonstrating a pragmatic and utility-focused attitude. B2B firms should leverage AI for lead qualification, predictive analytics and account-based marketing, allowing businesses to benefit from its potential. AI competence must be considered a core competency for all involved organizations to ensure the creative deployment of AI. This study shows how attitudes toward AI and control over it affect its expected performance in B2B marketing, contributing to a deeper understanding of AI’s role in this context.

AI Adoption in HRM: Exploring Trust Through Lens of Reliability, Credibility and Technical Competence

Instilling trust in artificial intelligence (AI) is indispensable for professionals to feel confident in relying upon automated systems for talent management decisions, and AI-powered HR solutions are expeditiously innovating human resource management (HRM) processes. Over the past 5 years, investments in AI-driven HR operations have increased extensively, indicating a shift from traditional HR technologies to more advanced, intelligent systems. This study examines how the trust factor influences the adoption of AI in HRM by 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. The data were compiled through a structured questionnaire administered to 530 ITeS companies in Delhi NCR, India, and the responses were analysed using partial least squares structural equation modelling. The findings reveal that technology RL, CR and TEC each enhance trust in AI. This trust, in turn, substantially contributes to the adoption of AI in HRM. By emphasising the central role of trust, these results provide actionable insights for HR professionals, researchers and technology developers seeking to enable effective AI integration in HR practices. Building on these findings, organisations can more confidently implement AI solutions, knowing that fostering trust is key to maximising their impact in HR.

Human-Ai Interface: Assessing The Role Of Explainable Ai (Xai) In Mitigating Knowledge Worker Resistance And Fostering Trust During Ai-Driven Business Process Automation

Enhancing trust and decision-making in AI-driven construction: Applying cognitive fit theory to interface clarity and alignment.

As artificial intelligence (AI) systems increasingly support decision-making in the construction sector, understanding the cognitive mechanisms behind user adoption is essential. Based on Cognitive Fit Theory (CFT), the following research 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. Data were gathered from 206 construction professionals utilizing a structured questionnaire and assessed utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM). Results assure that interface clarity and cognitive alignment greatly evolved perceived algorithmic reliability, which then strongly predicts behavioral intent. Decision explainability perception was discovered to mitigate the association among observed reliability and adoption intent, indicating that transparent AI reasoning strengthens the trust-intention link. Furthermore, perceived algorithmic reliability mediates the influence of both interface clarity and cognitive alignment on behavioral intent. The study offers strong empirical support for applying CFT in AI adoption contexts, especially in high-risk, complex environments such as construction. These insights inform the design of cognitively aligned AI interfaces to foster trust, enhance interpretability, and promote sustainable adoption of intelligent systems. Implications for AI interface design, construction technology implementation, and future research in human-AI interaction are discussed.