Explainable AI and Algorithm Transparency on Employee Acceptance: The Mediating Role of Trust

Abstract—The rapid spread of artificial intelligence (AI) in companies has raised critical challenges regarding employee acceptance, particularly concerning trust and system understanding. This study investigates the impact of explainable AI (XAI) and algorithm transparency on employee acceptance, with trust acting as a mediating variable. Based on a user-centered AI approach, this study explores how different dimensions of AI design influence user perception and technology acceptance in the workplace. A quantitative approach was chosen, based on survey data from 200 employees with experience using AI systems. The data were analyzed using partial least squares structural equation modeling (SEM-PLS) to examine direct and indirect relationships between the variables. The results show that algorithm transparency has the strongest impact, significantly influencing both employee trust and acceptance. Furthermore, a significant indirect effect via trust is also observed. Trust also has a positive and significant impact on employee acceptance, thus confirming its role as a key psychological mechanism in AI adoption. In contrast, explainable AI shows a different pattern: It significantly influences employee acceptance but has no significant impact on trust, nor does it mediate trust. This suggests that explanations alone are insufficient to build trust, especially if employees perceive these explanations as irrelevant or incomprehensible. The study contributes to the research by highlighting that transparency plays a more important role than explainability in fostering trust and acceptance in AI systems. Specifically, the findings suggest that companies should prioritize transparent AI design, clear communication, and trust-building strategies to ensure successful AI implementation. The study also underscores the importance of user-centered AI for a sustainable and inclusive digital transformation. Keywords— Explainable AI; Algorithm Transparency; Trust; Employee Acceptance; SEM-PLS; Human-Centered AI