Information communication technologies (ICTs) are increasingly vital for older adults, helping manage health, finances, services, and social connections. However, older Asian Americans exhibit disparities in technology use compared to non-Hispanic Whites. During the COVID pandemic, those without digital access faced additional health disparities. Factors such as ethnicity, limited English proficiency (LEP), self-rated health, and subjective cognitive decline have been shown to influence ICT use among older adults. Additionally, ICT use is associated with reduced loneliness among older adults. However, these relationships have not been investigated in the context of older Asian Americans, despite their heightened relevance given that older Asian Americans disproportionately report LEP, poor health, and limited social and emotional support.The Technology Acceptance Model (TAM), focusing on perceived usefulness (PU) and ease of use (PEOU), is a key framework for studying technology adoption. While the TAM has been applied to various older adult demographic groups, it has not been applied to older Asian Americans. Integrating demographic and health factors into the TAM may improve predictions of ICT use and deepen understanding of the complex relationship between technology acceptance and loneliness among older Asian Americans.
This dissertation adapts the TAM, with the aim of increasing understanding of the factors that influence ICT acceptance and use among low-income, Asian American older adults. Additionally, we examine the relationship between technology acceptance and loneliness. In all three chapters, we conducted secondary cross-sectional analyses of surveys collected from primarily Asian American residents living in affordable senior housing communities that participated in the Lighthouse Project for Older Adults.
In Chapter 1, we examined the relationships of demographic factors, PU, PEOU, smartphone use (including experience and frequency), and broader ICT use (encompassing smartphones, tablets, and computers) using descriptive statistics, correlation analysis, and hierarchical regression analysis. We found that English proficiency was associated with smartphone and ICT use, as well as PEOU, but not PU. English proficiency also influenced the relationship between PEOU and smartphone use. Ethnicity was a predictor of PU, PEOU, and ICT use, with Chinese participants showing higher levels compared to Korean participants. However, ethnicity did not predict smartphone use when considering PU, PEOU, and other demographics. Younger and more educated individuals exhibited greater smartphone and ICT use. Education was positively linked to PEOU, while age and education did not predict PU. Male gender was associated with PEOU but not PU, smartphone use, or ICT use. Our regression analyses supported a TAM adapted for low-income older Asian Americans; PU and PEOU were key predictors of smartphone and ICT use, even when considering other demographic variables.
In Chapter 2, we examined how health-related factors, including self-rated health and subjective cognitive decline, influence ICT acceptance and use among older Asian Americans. We found that self-rated health was significantly associated with smartphone use, ICT use, PU, and PEOU, but these relationships became insignificant when adjusting for demographic factors and subjective cognitive decline. However, adding health covariates improved the TAM's predictive ability for smartphone and ICT use compared to Chapter 1's model. Notably, the interaction between subjective cognitive decline and age significantly improved model fit. As suggested by the scatterplots, we determined that smartphone and ICT use increased with subjective cognitive decline in the youngest age group (62-74) but decreased in older age groups (75-84 and 85+), particularly among the oldest group. Using contrast analysis, we found that smartphone and ICT use decreased significantly with age among participants who reported subjective cognitive decline, but not among those without.
In Chapter 3, by conducting exploratory factor analysis (EFA), Horn’s parallel analysis, and structural equation modeling (SEM), beginning with confirmatory factor analysis (CFA), we examine the operationalization of PU, PEOU, and ICT use from Chapters 1 and 2, and validate the adapted TAM, demonstrating significant positive associations between PU, PEOU, and ICT use. Using SEM, we found that ICT use was significantly negatively associated with loneliness, even when adjusting for gender, age, education, English proficiency, Asian ethnicity, and subjective cognitive decline. Additionally, we found that Asian ethnicity and subjective cognitive decline were individually associated with loneliness.
Overall, this dissertation confirms the TAM as a valuable framework for understanding technology adoption among low-income older Asian Americans. It enhances our understanding of the complex interplay between demographic and health factors and the acceptance and use of smartphones and ICTs within this population. The findings underscore the necessity of addressing language barriers and considering the impact of health-related factors, particularly subjective cognitive decline, to promote ICT acceptance and use. Moreover, the study highlights the potential of ICTs in reducing loneliness among low-income Asian American older adults by fostering increased social interaction and preserving cultural connections. Tailored interventions and support strategies should focus on enhancing PU and PEOU, such as demonstrating culturally relevant use cases for technology, improving usability for individuals with LEP, and providing digital literacy training.
