Trust Under Techno-Pressure: A Mixed-Methods Study of Human–Artificial Intelligence Collaboration in the Garment Factories

Abstract

The rapid adoption of artificial intelligence (AI) in labor-intensive manufacturing raises concerns about how trust between humans and AI develops under production pressure. This study examines the erosion and consequences of human–AI trust in garment factories, where workers must quickly adapt to AI-driven systems in highly monitored environments. Drawing on the Swift Trust Theory and the Job Demands–Resources model, we propose a framework that considers relationships among constructs, such as compressed trust formation, trust fragility, sacrificial compliance, perceived organizational support, and workplace techno-pressure. We employed a two-phase mixed-methods design. An exploratory qualitative study informed construct development, followed by a quantitative study for scale validation and hypothesis testing. Results show that compressed trust formation is positively associated with trust fragility, and both are positively linked to sacrificial compliance. Trust fragility partially mediates the relationship between compressed trust formation and sacrificial compliance. Perceived organizational support weakens the relationship between compressed trust formation and trust fragility, whereas workplace techno-pressure strengthens the relationship between trust fragility and sacrificial compliance. The findings suggest that trust formed rapidly under techno-pressure can enable short-term coordination but remains structurally fragile and may convert into self-sacrificial work behaviors. The study extends Swift Trust Theory to human–AI collaboration and embeds trust dynamics within the Job Demands–Resources model, highlighting how organizational support and techno-pressure management shape whether digital transformation supports sustainable or harmful forms of adaptation.