Factors Influencing Caregivers' Intention to Use Transfer-care Robots: A Sequential Explanatory Mixed-methods Study

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.