In a paper published in the journal PLOS ONE, researchers investigated the adoption of smart home devices among elderly individuals in China. Employing the technology acceptance model (TAM) alongside additional variables, the study distributed 236 questionnaires to older adults aged 60 and above, focusing on user characteristics, family dynamics, usage experiences, and intentions regarding smart home services.
Study: Insights into Smart Home Adoption Among China's Elderly. Image Credit: Andrew Angelov/Shutterstock
Utilizing structural equation modeling (SEM) and factor analysis, the study identified six dimensions of influencing factors and determined the weights of 13 specific factors within these dimensions. These findings offered valuable insights for designing and implementing smart home technologies tailored to the needs of elderly users, thereby enhancing their well-being and autonomy.
Related Work
Previous research in smart home technology for older adults has focused on addressing specific challenges like fall detection and emergency calls, which are vital for elderly care. However, there needs to be more understanding of the broader impact of smart home devices on older adults' quality of life, safety, and convenience.
Although researchers have applied classic theories like TAM and the Unified Theory of Technology Acceptance and Usage (UTAUT) to understand older adults' acceptance of emerging technologies, there remains a need to explore studies on this demographic's acceptance of smart home devices, particularly in China. Quantitative research in this field is still developing, urging a more comprehensive exploration of factors influencing senior users' acceptance and usage of smart homes in China.
Survey Design Methodology
The methodology employed in this study entailed designing and implementing a questionnaire survey to investigate the desire for smart home devices among the older population. Recognizing the multifaceted nature of individual variations in this desire, physiological, psychological, cognitive, sociological, and family situations were considered.
The questionnaire consisted of four parts: the first section gathered socio-demographic data, including age, gender, health status, income, and education level, which are crucial determinants of smart home device adoption. The second part focused on the home situation of seniors, particularly their family structure and living arrangements, acknowledging the preference for home care over institutional care among older adults. The third section assessed respondents' experiences with smart home products, filtering out those who needed experience for further analysis. The fourth part explored their views and acceptance of smart home services using a Likert scale method.
The study participants were seniors aged 60 and above, encompassing different age categories within this demographic. Considering the cognitive changes associated with aging and their impact on human-computer interaction, the study focused on elderly individuals with experience using smart homes capable of independent or assisted human-computer interactions. The questionnaire survey was conducted in school communities and nearby urban areas, using a convenience sampling method to select respondents randomly. Participants were either asked to complete the questionnaire themselves or completed it on their behalf by primary caregivers or interviewers, ensuring inclusivity.
Researchers conducted initial data entry and verified the validity and reliability of the questionnaire. The study commenced on March 10, 2023, and concluded on March 20, 2023, yielding 236 completed questionnaires. After researchers removed invalid samples based on predefined criteria, researchers obtained 200 valid questionnaires, resulting in an effective recovery rate of 84.75%. They conducted subsequent analysis using statistical package for the social sciences (SPSS) 26.0 software for reliability assessment and analysis of moment structures (AMOS) 26.0 software for SEM and factor analysis, ensuring the accuracy and consistency of the findings. The significance threshold was set at α = 0.05 to maintain the rigor of the analysis process.
Researchers' Significant Findings
The descriptive statistical analysis of demographic variables from 200 valid samples reveals key insights into the demographics of the older population's desire for smart home devices. The study notes a predominance of females, consistent with national demographics reflecting higher life expectancy among females. The age distribution indicates a majority between 60 and 74 years old, highlighting the relevance of age in smart home adoption.
Furthermore, participants' educational background is significant, with over half having at least a junior high school education, suggesting a correlation between education level and smart home device comprehension and operation. Additionally, the analysis delves into factors such as family structure, self-care capacity, and familiarity with smart home products, shedding light on the multifaceted nature of older adults' preferences and needs in adopting such technologies.
The questionnaire data's reliability and validity analysis underscore the research findings' robustness. With high Cronbach's α coefficients indicating strong reliability and favorable results from the Kaiser-Meyer-Olkin test and Bartlett's test of sphericity, the research data proves suitable for further analysis.
Confirmatory factor analysis further validates the measurement model, with standardized loading coefficients indicating a strong relationship between variables. The analysis ensures the accuracy and consistency of the study's conceptual framework, providing a solid foundation for hypothesis testing and subsequent discussions.
The hypothesis testing analysis reveals the factors shaping the adoption of smart home devices among older users. Researchers highlight the significance of perceived usefulness, ease of use, intergenerational support, and perceived value in driving adoption. However, perceived risk poses a significant barrier, negatively impacting usefulness and ease of use. Addressing usability, enhancing value, and mitigating risks is crucial for promoting adoption and improving older adults' quality of life and independence.
Conclusion
In summary, the study investigated the factors influencing the adoption of smart home technology among older people in the context of population aging. By applying the TAM with additional variables, researchers identified key factors such as perceived usefulness, perceived ease of use, intergenerational technical support, perceived value, and perceived risk.
The findings underscored the importance of these factors in shaping older consumers' attitudes towards smart home technologies, providing valuable insights for industry stakeholders and policymakers to develop accessible and user-friendly solutions tailored to the needs of aging populations.
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