The 21st century has marked the dawn of an aging population. China’s aging process ranks first worldwide. The country has recognized the gravity of this demographic shift and implemented strategies to address it at the national level. A vast majority of elderly Chinese individuals (approximately 90%) aspire to age in their own homes. Smart homes, endowed with cutting-edge digital technologies, such as AI, the Internet of Things, and big data, hold vast potential for enabling this vision. However, acceptance of smart home products and services among elderly individuals in China remains low. The main reason is that the related products and services fail to effectively alleviate the perceived risk of this population in the R&D process of related products and services, and there is a lack of effective measurement methods. To holistically assess the potential obstacles faced by elderly individuals using smart home products and services, this study targeted individuals aged 45–60 years in China. This study aimed to develop a comprehensive perceived risk scale specific to smart homes for this demographic. Initially, this study identified key risk dimensions and corresponding measurement items through a rigorous literature review, user interviews, and expert consultations. Subsequently, it ensured the reliability and validity of each dimension and its corresponding observation variables through preliminary research, exploratory factor analysis, and confirmatory factor analysis. This approach allowed for a comprehensive understanding of the challenges faced by future elderly individuals when utilizing smart home products and services, thus enabling the development of more effective solutions. The scale encompassed ten factors and seventy measurement items, including Privacy and Security Risk (seven items), Physical Risk (seven items), Technological Risk (nine items), Performance Risk (seven items), Service Risk (nine items), Financial Risk (five items), Psychological Risk (seven items), Industry and Market Risk (six items), Social Support Risk (six items), and Policy and Legal risk (seven items). The measurement scale developed in this study represents a groundbreaking first attempt to create a systematic scale for assessing the perceived risks associated with smart homes for the elderly in China. It not only enables professionals, businesses, and manufacturers to avoid or reduce barriers in the R&D process of related products and services, facilitating smart home industry growth and enhancing user adoption, but also serves as a universal reference for the potential obstacles that digital technology may encounter in addressing aging-related issues, which has significant theoretical value and practical importance.

Introduction: Digitalization and aging are two defining characteristics of contemporary social transformation. As smart integrated devices become increasingly embedded in everyday life, understanding how elderly individuals interact with these technologies is essential for promoting digital inclusion and social integration. Although previous research has examined digital engagement among older adults, the specific behavioral and contextual factors that shape their usage of smart devices in a digital society remain underexplored. This study seeks to clarify how these factors operate and how they may be leveraged to support the well-being of the aging population.Methods: This study utilized data from the 2020 China Longitudinal Aging Social Survey (CLASS), focusing on elderly individuals living in urban areas. A combination of logistic regression analysis, Lasso regression, and robustness tests was employed to identify the key predictors of smart integrated device usage. The analysis examined a range of variables including demographic characteristics, health status, education, income, internet usage experience, and family structure. Comparisons were made across groups to assess how these factors influence usage behavior, and Lasso regression was used to identify the most robust predictors.Results: The analysis revealed that elderly individuals are more likely to use smart devices if they are male, older, married, in poorer health, more highly educated, have lower income, have fewer children, and have previous experience using the internet. Among these, internet usage experience emerged as the most significant and consistent predictor across all models, as identified by the Lasso regression. Furthermore, the purposes for which the elderly use the internet, such as communication, information, or entertainment. These patterns were found to be stable even after controlling for potential confounding variables.Discussion: The findings challenge traditional assumptions about fixed sensory or cognitive hierarchies in aging populations and instead suggest that smart device usage among the elderly is shaped by a dynamic interplay of motivational, ability-based, and environmental factors. Drawing on Behavioral Design Theory, the study interprets these patterns through three lenses: motivation (e.g., health monitoring and social interaction), ability (e.g., physical and cognitive usability of devices), and triggers (e.g., technical and emotional support from family members). Under this framework, digital engagement appears to be highly context-dependent, with adaptive resource allocation and social support playing a crucial role in determining whether and how elderly individuals use smart technologies. The results emphasize the importance of designing inclusive digital environments and policies that respond to the nuanced needs and experiences of the aging population.