Modelling Human–AI Trust in Sociotechnical Systems: A Systems Perspective on Organizational Change

ABSTRACT China's service workplaces are rapidly integrating generative AI into customer‐facing routines, yet theory remains underspecified on how AI interface design cues translate into frontline employees' trust and evaluations of AI‐assisted service delivery. This paper develops a sociotechnical, employee‐centred framework to address this gap. Drawing on stimulus‐organism‐response (S‐O‐R) logic and human‐AI interaction scholarship, this conceptual article theorizes how two actionable interface design cues, AI anthropomorphism and AI responsiveness, operate as stimuli that shape employee trust (organism), which subsequently informs perceived AI‐assisted service quality (response). The model also frames employee‐AI collaboration as a planned organizational change process, clarifying how AI reconfigures work design, coordination and governance. The framework positions trust as the central mechanism linking interface design cues to employees' perceived quality of AI‐assisted service delivery. It further specifies China‐relevant boundary conditions, collectivist norms, time pressure, hierarchical coordination and constrained discretion, as moderators that shape the strength of the proposed relationships, generating testable propositions. The paper offers an integrated sociotechnical model that connects AI design choices to trust and service‐quality evaluations under algorithmic management, advancing an employee‐centred agenda for empirical validation in service organizations.