Organization-level factors associated with the adoption of care robots in long-term care providers: insights from a 3-year pooled cross-sectional study in Japan

Care robots are expected to be useful in long-term care (LTC) settings to resolve several challenges associated with aging populations. However, their use is still limited and little is known about the factors associated with their adoption. This study aimed to identify the factors associated with the adoption of care robots, using a quantitative analysis of a large sample at the organizational level in Japan. We analyzed residential service providers using 3 years of pooled cross-sectional data from the Fact-Finding Survey on Long-term Care Work in Japan. In this survey, care robots were categorized as robots for “Transfer support,” “Mobility assistance,” “Toiletry support,” “Monitoring and Communication,” “Bathing support,” and “Support for LTC workers.” We defined “adoption of care robots” as LTC providers having adopted at least one of the care robots. To identify the characteristics of LTC providers associated with the adoption of care robots, we conducted a multivariable logistic regression comparing providers with and without care robots. Of the 4,688 LTC providers, 1,250 (26.7%) adopted care robots. The characteristics of LTC providers that were found to be associated with the adoption of care robots were: information and communication technology (ICT) equipment adoption (adjusted odds ratio [aOR] 3.115, 95% confidence interval [CI] 2.453, 3.956), younger average age of care workers (aOR 0.958, 95% CI 0.945, 0.971), large number of employees (aOR 1.008, 95% CI 1.006, 1.010) and appointment of employment management supervisor (aOR 1.611, 95% CI 1.400, 1.853). The primary results were consistent across robot categories. Our findings suggest that care robot adoption is highly compatible with ICT equipment adoption, particularly among younger workers, and among large-scale providers and those with a high awareness of improving employment management indicated by the appointment of an employment management supervisor. To promote adoption, we suggest staff technology-literacy training (especially for senior employees), targeted financial incentives for small providers, and management-led integration of robots within employment-management improvements. This study identified the factors associated with the adoption of care robots at the organizational level and is expected to contribute to realizing the implementation of care robots.