Investigating Cognition, Attitudes, and Acceptance of Smart Elderly Care Systems among Chinese Nurses

Abstract

Aim/Purpose: This study aimed to investigate Chinese elderly care nursing staff’s cognition, attitudes, and behavioral intentions toward adopting smart elderly care systems in response to the challenges posed by rapid population aging. This study was based on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The research integrated perceived usefulness, ease of use, security, performance expectancy, and organizational support into a unified structural framework, seeking to identify key determinants influencing technology adoption and inter-institutional variations within elderly care settings. The findings are expected to provide both theoretical insights and practical guidance for policymakers and institutional leaders to enhance training, optimize resource allocation, and promote sustainable implementation of smart elderly care systems in China.

Introduction/Background: China’s rapidly aging population has increased the demand for high-quality senior care, encouraging research into smart elderly care systems that combine the Internet of Things, big data, and digital technology. While such systems can improve efficiency and service accessibility, their effectiveness is highly dependent on the elderly care nurse staff’s acceptance and ability. Existing research has rarely focused on nursing staff in elderly care facilities. To fill this gap, this study investigated Chinese nurses’ cognition, attitudes, and adoption intentions for smart elderly care systems, offering evidence to promote effective technology integration and policy development.

Methodology: In this study, a cross-sectional quantitative design was employed to investigate factors that influence Chinese nursing staff’s adoption of smart elderly care systems. The survey focused on frontline providers of eldercare in China, including registered nurses, enrolled nurses, and assistant nurses, as well as eldercare aides and care workers. Participants were drawn from long-term care facilities, nursing homes, community-based eldercare institutions, and geriatric or rehabilitation wards within hospitals. The researcher designed an online survey link with a QR code and shared it with the nursing institutions, the elderly community, and the elderly healthcare institutions in different cities in China. Since the survey was online, no provinces or cities restricted it. After data screening, 537 valid responses were obtained. SmartPLS 4.0 was employed to conduct a series of analytical procedures for the structural model, including the assessment of reliability and validity, the analysis of correlation relationships among variables, and the estimation of path coefficients.

Findings: The study revealed several key findings. First, perceived usefulness, ease of use, security, performance expectancy, and organizational support positively influenced nursing staff’s attitudes toward smart elderly care systems. Second, attitude significantly influenced adoption intention, which highlights its mediating role. However, perceived organizational support did not exert a direct effect on adoption intention. Moreover, attitude mediated relationships between five antecedent variables and adoption intentions. These results aligned with prior studies and confirmed that cognitive, perceptual, and organizational factors collectively shape nurses’ attitudes and behavioral intentions toward adopting smart elderly care technologies in practice.

Contribution/Impact on Society: This study enriches theoretical understanding by contextualizing TAM and UTAUT within China’s elderly care sector, emphasizing the attitudinal mechanisms underlying nurses’ adoption of smart elderly care systems. It demonstrated that organizational support indirectly influences adoption through attitudes rather than directly shaping behavioral intentions, which highlights the need to adapt organizational behavior theories to resource-limited care environments. The findings also underscored the centrality of perceived security in technology acceptance within data-sensitive healthcare settings, offering empirical evidence to guide equitable, secure, and sustainable digital transformation in elderly care services.

Recommendations: To foster effective adoption, policymakers should establish unified technical standards, enhance data protection legislation, and reduce urban–rural disparities through targeted subsidies and infrastructure development. Elderly care institutions should strengthen organizational support via continuous training, accessible technical assistance, and incentive programs that foster trust and engagement. At the operational level, integrating smart systems into daily routines, improving perceived usefulness and security, and enhancing user-friendliness can increase adoption sustainability. Emphasizing data protection education would further build confidence among nursing staff, ensuring effective and enduring implementation of smart elderly care technologies.

Research Limitations: This study had several limitations. The quantitative, self-reported design may not have fully captured actual behaviors and could have introduced social desirability bias. The cross-sectional approach limits understanding of long-term adoption trends, while the model excluded contextual factors such as cultural norms, workforce diversity, and patient influences. Moreover, as the research was conducted within China’s elderly care context, the findings may not be directly generalizable to other cultural or institutional settings.

Future Research: Future studies should adopt mixed-method approaches, integrating qualitative interviews or observations to explore deeper behavioral insights. Longitudinal research is recommended to assess adoption dynamics over time. Expanding comparative analyses across diverse cultural, institutional, and policy environments would enrich the understanding of contextual influences and broaden the applicability of the findings.