User Acceptance of AR Assistance System in Maintenance: Toward a Human-Centered Industry

Abstract

Augmented reality (AR) technologies are key enablers of Industry 5.0, empowering the workforce and offering significant benefits in maintenance operations. However, challenges with user adoption and acceptance hinder industrial implementation. Therefore, this work proposes an improved framework based on the technology acceptance model (TAM), extended with technology, task, and user-related factors, to better understand and predict the acceptance of AR assistance systems (ARAS) in industrial maintenance settings. A user study was conducted to test the model empirically. With N = 57 participants, TAM’s main paths were confirmed. Among external factors, perceived risk negatively influences the intention to use, while augmentation quality positively impacts ease of use and usefulness (indirect). The results demonstrate the model’s explained variance (R2 = 43.2%) and high predictive capabilities. Contributions and implications are also presented for academics and practitioners aiming to enhance user adoption and implementation strategies of maintenance-based ARAS, thereby promoting a human-centered manufacturing.