Over recent decades, machinery has become more complex and costly to maintain, while reliability demands have increased. Predictive maintenance (PdM) offers a cost-effective solution by using sensor data to monitor equipment health, estimate remaining life, and optimize maintenance schedules. Despite its technical promise, organizations often face challenges in employee acceptance of PdM systems. This dissertation explores when and why employees accept or reject PdM decision-support systems, using a human-centric Industry 5.0 perspective that prioritizes employee needs in technology design.\n\nChapters 2 and 3 identify key factors influencing acceptance, such as trust, control, decision-making clarity, and the balance between job demands and resources. Chapters 4 and 5 examine how forecast formats and false alarms affect trust and adherence to PdM advice. Findings show that interval forecasts are preferred over point forecasts when accuracy is emphasized, and that including failure probabilities can reduce the negative impact of false alarms.\n\nOverall, the dissertation provides behavioral and psychological insights into PdM adoption, emphasizing the importance of designing systems that align with employee expectations and work realities to foster trust and integration into decision-making.