International Center for Electronic Commerce, School of Business, Hanyang University, Seoul, Republic of Korea.
Front Public Health. 2024 Sep 25;12:1449594. doi: 10.3389/fpubh.2024.1449594. eCollection 2024.
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.
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.
Factors influencing the use of smart wearable devices by the older adults include performance expectancy ( = 0.152, < 0.001), effort expectancy ( = 0.154, < 0.001), social influence ( = 0.135, < 0.05), facilitating conditions ( = 0.126, < 0.05), hedonic motivation ( = 0.166, < 0.001), price value ( = 0.182, < 0.001), and digital health literacy ( = 0.189, < 0.001). Additionally, optimism ( = 0.208, < 0.001), innovativeness ( = 0.218, < 0.001), and discomfort ( = -0.245, < 0.001) significantly positively influenced performance expectancy, while optimism ( = 0.282, < 0.001), innovativeness ( = 0.144, < 0.01), discomfort ( = -0.239, < 0.001), and insecurity ( = -0.117, < 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.
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.
随着可穿戴技术的不断进步，智能可穿戴设备在老年人健康监测、评估和干预方面的价值日益受到认可，从而促进了老年人的智能护理。本研究基于接受和使用技术的统一理论 2 （UTAUT2）和技术准备指数（TRI）模型的理论框架，旨在确定和探索影响老年人消费者采用智能可穿戴设备的关键因素及其影响机制。
通过问卷调查收集了 389 名老年人受访者的有效数据。实证分析验证了模型的适用性，并探讨了影响接受度的关键因素。
影响老年人使用智能可穿戴设备的因素包括绩效预期（ = 0.152， < 0.001）、努力预期（ = 0.154， < 0.001）、社会影响（ = 0.135， < 0.05）、促成条件（ = 0.126， < 0.05）、享乐动机（ = 0.166， < 0.001）、价格价值（ = 0.182， < 0.001）和数字健康素养（ = 0.189， < 0.001）。此外，乐观（ = 0.208， < 0.001）、创新（ = 0.218， < 0.001）和不适（ = -0.245， < 0.001）显著正向影响绩效预期，而乐观（ = 0.282， < 0.001）、创新（ = 0.144， < 0.01）、不适（ = -0.239， < 0.001）和不安全感（ = -0.117， < 0.05）显著正向影响努力预期。不安全感对绩效预期没有显著影响。绩效预期和努力预期部分中介了人格特质（乐观、创新、不适和不安全感）与行为意向之间的关系。数字健康素养显著负向调节绩效预期与行为意向之间的关系，以及努力预期与行为意向之间的关系。
本研究证实，将 UTAUT2 模型和 TRI 理论相结合，有效地解释了老年人消费者对智能可穿戴设备的接受程度，强调了在设计和推广智能健康设备时增强数字健康素养的重要性。研究结果为开发者提供了指导，提高了老年人对这些设备的接受度和使用率。
