Empowering Users in The Age of GenAI: The Role of Explainability in AI Decision Self-Efficacy
Author(s)
Date Issued
2025
Type
conferenceObject
Abstract
This study examines the role of explainability in shaping user interactions with Generative AI systems. Drawing on prior frameworks, the research investigates how causability influences explainability and how explainability affects user perceptions of transparency, fairness, and accountability. It is further explored how these perceptions shape trust, engagement, and, ultimately, AI decision self-efficacy. Additionally, it analyses how AI literacy moderates the relationship between user perceptions and trust. Employing a quantitative research design with PLS-SEM analysis, data will be collected from professionals who use GenAI tools. By extending existing research on Explainable AI to the emerging field of Generative AI, this study contributes to understanding how system features and individual differences influence psychological outcomes in human-AI interaction. The findings will offer insights for designing more transparent AI systems and developing interventions to enhance users' confidence in making AI-supported decisions.
Conference(s)
WOA XXVI - Workshop dei Docenti e dei Ricercatori di Organizzazione Aziendale, Navigating Organizational Change in Times of Uncertainty