Factors Influencing Caregivers’ Intention to Use Transfer-care Robots

Few studies have examined the acceptance of transfer-care robots across diverse caregiver roles and practice settings, with most research limited to single professional groups or institutional environments. This mixed-methods study integrates nurses, personal care assistants, and family caregivers from hospitals, long-term care facilities, and home care to capture a holistic perspective on technology adoption across the continuum of care. Employing a sequential explanatory design guided by Davis’ Technology Acceptance Model, we conducted a quantitative survey (n=224) followed by focus group interviews (n=15). Quantitative findings revealed that job relevance was the strongest predictor of intention to use ( β =0.50, P <.001), with perceived usefulness and self-efficacy also being significant (adj. R² =.81). Thematic analysis identified 4 themes—physical burden of transfers, urgent need for robotic assistance, desire for safe and efficient robots, and aspirations for transformative technology—which contextualized these predictors by highlighting how TCRs can mitigate physical strain and fall risks. The findings highlight the necessity of informatics strategies prioritizing user-centered design and workflow alignment. These results can inform efforts to enhance caregiving environments and support the framework for successful implementation of robotic assistance in diverse health care settings.