Determinants of intention with remote health management service among urban older adults: A Unified Theory of Acceptance and Use of Technology perspective

Abstract

BackgroundAlthough older adults health management systems have been shown to have a significant impact on health levels, there remains the problem of low use rate, frequency of use, and acceptance by the older adults. This study aims to explore the significant factors which serve as determinants of behavioral intention to use the technology, which in turn promotes actual use.MethodsThis study took a total of 402 urban older adults over 60 years to explore the impact of the use behavior toward remote health management (RHM) through an online questionnaire. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), the author adds four dimensions: perceived risk, perceived value, perceived interactivity and individual innovation, constructed an extended structural equation model of acceptance and use of technology, and analyzed the variable path relationship.ResultsIn this study, the factor loading is between 0.61 and 0.98; the overall Cronbach's Alpha coefficients are >0.7; The composite reliability ranges from 0.59 to 0.91; the average variance extraction ranges from 0.51 to 0.85, which shows the good reliability, validity, and discriminant validity of the constructed model. The influencing factors of the behavioral intention of the older adults to accept the health management system are: effort expectation, social influences, perceived value, performance expectation, perceived interactivity and perceived risk. Effort expectation has a significant positive impact on performance expectation. Individual innovation positively impacts performance expectation and perceived interactivity. Perceived interactivity and behavioral intention have a significant positive effect on the use behavior of the older adults, while the facilitating conditions have little effect on the use behavior.ConclusionsThis paper constructs and verifies the extended model based on UTAUT, fully explores the potential factors affecting the use intention of the older adult users. According to the research findings, some suggestions are proposed from the aspects of effort expectation, performance expectation, perceived interaction and perceived value to improve the use intention and user experience of Internet-based health management services in older adults.

Association between the perceived value of adopting new behaviors and depressive symptoms among older adults

“…Ma et al researched the effect of novel and environmentally friendly foods on consumer acceptance and found that PV had a significantly positive effect on attitude, and attitude also had a positive effect on purchase intention 46 . In Li et al’s study, PV positively affected the behavioral intention of older adults to use remote health management services 47 . These studies showed a positive association between PV, intention, and behavior.…”

“…Values guide, motivate, and influence attitudes and behaviors 45 . Although direct comparisons cannot be made, owing to the lack of reports examining the association between PV and actual engagement in health behaviors, consumer science research has focused on the relationship between PV and attitudes and intentions 22 , 46 , 47 . Ma et al researched the effect of novel and environmentally friendly foods on consumer acceptance and found that PV had a significantly positive effect on attitude, and attitude also had a positive effect on purchase intention 46 .…”

Investigating the intention of purchasing private pension scheme based on an integrated FBM-UTAUT model: The case of China

“…In general, performance expectancy is often considered to be a stronger predictor, as individuals are more likely to adopt and use technology that they believe will improve their performance and meet their goals. Studies have also shown that performance expectancy can also have a positive influence on effort expectancy ( Sung et al, 2015 ; Fedorko et al, 2021 ; Li et al, 2023 ), as individuals may be more willing to put in the effort to learn and use a technology if they believe it will have a positive impact on their performance. However, effort expectancy can also play an important role.…”

“…However, the effects of SI and FC on BI can vary depending on the specific context. For example, Li et al (2023) reported a statistically nonsignificant relationship between FC and BI in a remote health management service context and Batucan et al (2022) found that SI has no significant impact on BI in an online learning system context.…”

Application and Challenges of the Technology Acceptance Model in Elderly Healthcare: Insights from ChatGPT

“…The TAM has served as a valuable framework for assessing the elderly's perception and acceptance of various healthcare technologies. In the realm of telehealth, TAM can be employed to gauge how elderly individuals perceive and embrace remote consultations, monitoring, and virtual care technologies [24,28,34,[37][38][39][40][41][42][43][44][81][82][83][84]. Furthermore, TAM has been proven useful in understanding the factors influencing the adoption of wearable health devices among the elderly, including smartwatches and fitness trackers designed to monitor vital signs and activity levels [28,45,46].…”