Investigating older adults users' willingness to adopt wearable devices by integrating the technology acceptance model (UTAUT2) and the Technology Readiness Index theory

Abstract

Objective: With the continuous advancement of wearable technology, smart wearable devices are increasingly recognized for their value in health monitoring, assessment, and intervention for the older adults, thus promoting intelligent older adults care. This study, based on the theoretical framework of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Technology Readiness Index (TRI) model, aims to identify and explore the key factors influencing older adults consumers' willingness to adopt smart wearable devices and their impact mechanisms. Method: A questionnaire survey was conducted to collect valid data from 389 older adults respondents. Empirical analysis validated the model's applicability and explored the key factors influencing acceptance. Results: Factors influencing the use of smart wearable devices by the older adults include performance expectancy (beta = 0.152, p < 0.001), effort expectancy (beta = 0.154, p < 0.001), social influence (beta = 0.135, p < 0.05), facilitating conditions (beta = 0.126, p < 0.05), hedonic motivation (beta = 0.166, p < 0.001), price value (beta = 0.182, p < 0.001), and digital health literacy (beta = 0.189, p < 0.001). Additionally, optimism (beta = 0.208, p < 0.001), innovativeness (beta = 0.218, p < 0.001), and discomfort (beta = -0.245, p < 0.001) significantly positively influenced performance expectancy, while optimism (beta = 0.282, p < 0.001), innovativeness (beta = 0.144, p < 0.01), discomfort (beta = -0.239, p < 0.001), and insecurity (beta = -0.117, p < 0.05) significantly positively influenced effort expectancy. Insecurity did not significantly influence performance expectancy. Performance expectancy and effort expectancy partially mediated the relationship between personality traits (optimism, innovativeness, discomfort, and insecurity) and behavioral intention. Digital health literacy significantly negatively moderated the relationship between performance expectancy and behavioral intention, as well as between effort expectancy and behavioral intention. Discussion: The study confirms that integrating the UTAUT2 model and TRI theory effectively explains the acceptance of smart wearable devices among older adults consumers, emphasizing the importance of enhancing digital health literacy in the design and promotion of smart health devices. The findings provide guidance for developers, increasing the acceptance and usage rate of these devices among the older adults.

Objective: With the continuous advancement of wearable technology, smart wearable devices are increasingly recognized for their value in health monitoring, assessment, and intervention for the older adults, thus promoting intelligent older adults care. This study, based on the theoretical framework of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Technology Readiness Index (TRI) model, aims to identify and explore the key factors influencing older adults consumers’ willingness to adopt smart wearable devices and their impact mechanisms. Method: A questionnaire survey was conducted to collect valid data from 389 older adults respondents. Empirical analysis validated the model’s applicability and explored the key factors influencing acceptance. Results: Factors influencing the use of smart wearable devices by the older adults include performance expectancy (β = 0.152, p < 0.001), effort expectancy (β = 0.154, p < 0.001), social influence (β = 0.135, p < 0.05), facilitating conditions (β = 0.126, p < 0.05), hedonic motivation (β = 0.166, p < 0.001), price value (β = 0.182, p < 0.001), and digital health literacy (β = 0.189, p < 0.001). Additionally, optimism (β = 0.208, p < 0.001), innovativeness (β = 0.218, p < 0.001), and discomfort (β = −0.245, p < 0.001) significantly positively influenced performance expectancy, while optimism (β = 0.282, p < 0.001), innovativeness (β = 0.144, p < 0.01), discomfort (β = −0.239, p < 0.001), and insecurity (β = −0.117, p < 0.05) significantly positively influenced effort expectancy. Insecurity did not significantly influence performance expectancy. Performance expectancy and effort expectancy partially mediated the relationship between personality traits (optimism, innovativeness, discomfort, and insecurity) and behavioral intention. Digital health literacy significantly negatively moderated the relationship between performance expectancy and behavioral intention, as well as between effort expectancy and behavioral intention. Discussion: The study confirms that integrating the UTAUT2 model and TRI theory effectively explains the acceptance of smart wearable devices among older adults consumers, emphasizing the importance of enhancing digital health literacy in the design and promotion of smart health devices. The findings provide guidance for developers, increasing the acceptance and usage rate of these devices among the older adults.