Technostress in older healthcare workers: Latent profiles and implications for care robot acceptance

Background: The adoption of care technologies in long-term care settings is ongoing; while the aging of the care workforce continues, particularly in South Korea. This study aims to identify technostress types among older care workers in Korea, including their influencing factors, and examine differences in acceptance of transfer-assistive care robots. Methods: The study analyzed data from a 2023 survey on 336 care workers aged 50 years and above. Latent profile analysis was conducted on five technostress dimensions: overload, invasion, complexity, privacy, and inclusion. The Three-Step method and the Bolck-Croon-Hagenaars command were used to examine the antecedents of profile membership and differences in technology acceptance variables, including self-efficacy, anxiety, attitude, perceived ease of use, perceived usefulness, and intention to use robots. Results: Four technostress profiles were identified: low technostress, high complexity and inclusion, moderate technostress, and high technostress with low inclusion. Age, education, income, job position, type of facility, work experience, and digital competence were all significantly associated with technostress profile membership. The low technostress group demonstrated the most favorable outcomes across all technology acceptance variables, whereas the high complexity and inclusion group exhibited the least favorable outcomes, characterized by the highest levels of anxiety and lowest levels of self-efficacy, perceived usefulness, and behavioral intention to use. Conclusions: Technostress profiles significantly influenced older healthcare workers' acceptance of transfer-assistive robots. Tailored interventions to decrease technostress-particularly related to complexity and inclusion pressures-may enhance technology adoption and improve care delivery in healthcare settings.