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

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

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.

Creating and consuming AI-generated content: ethical challenges of trust and transparency

This study explores the ethical and trust-related implications of artificial intelligence (AI) in strategic communication. It compares professional content creators (e.g. journalists, public relations (PR) practitioners and advertising practitioners) and content consumers, focusing on differences in AI knowledge, trust and attitudes toward responsible AI practices. A quantitative survey examined participants’ AI knowledge, trust and support for responsible AI practices. Comparative and correlational analyses were then conducted to explore the intersections of these variables. Greater AI knowledge correlated with higher trust and more positive perceptions of AI-generated content. However, creators, particularly those working in strategic communication, were less supportive of responsible AI practices. Consumers, despite lower AI knowledge, expressed stronger support for responsible AI practices. These findings reveal a gap between professional and public expectations regarding AI use and responsible AI practices. This study integrates creator and consumer perspectives within a single framework, empirically demonstrating differences in support for responsible AI practices and identifying trust as a central factor linking AI knowledge to positive evaluations of AI-assisted content across both audiences. By positioning trust and user knowledge as key mechanisms, the study advances theorization of the transparency dilemma as a conditional process rather than a uniform effect.

Duality of AI in Knowledge Management: Correlating Utility Perceptions with Implementation Challenges

This study investigates how organizational members concurrently perceive the benefits of artificial intelligence (AI) for knowledge management processes (KMPs) and the challenges involved in implementing AI within knowledge management systems (KMSs). Based on survey data from 378 respondents across diverse sectors and roles, the research employs validated instruments measuring perceptions of AI’s contribution to knowledge acquisition, documentation, sharing, and application, as well as perceived human, technological, financial, and ethical‑regulatory barriers. The results show a consistent positive relationship between perceived AI usefulness and perceived implementation barriers: individuals who attribute greater value to AI-enhanced knowledge processes also express heightened awareness of the complexities required to integrate AI into organizational systems. Knowledge documentation presents the strongest associations with all barrier categories, while knowledge sharing exhibits the weakest. Human‑related barriers emerge as the most pervasive across all processes, indicating the central role of employee readiness and organizational culture in shaping AI-enabled KM. These findings reveal a dual perception in which optimism regarding AI’s potential coexists with recognition of the organizational adjustments it demands. The study contributes to a more integrated understanding of AI adoption in KM, emphasizing that effective implementation requires aligning technological capabilities with human, cultural, and governance considerations.

Does AI strengthen business leadership decision making: literature review

AI does not replace the role of executive leaders; instead, it serves as a cognitive aid that frees up a leader's capacity from routine operational tasks, yet it still requires the contextual intuition and ethical governance of human leaders.

Does AI Strengthen Business Leardership Decision Making: Literature Review

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

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.