Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/10752
Title: Social virtual reality as a solution for aging services and gerontology: Understanding the older adults’ acceptance of virtual reality applications
Authors: Prof. LAU Kung Wong, Nick 
Issue Date: 2024
Source: Advances in Gerontology, 2024, vol. 14, pp. 161-170.
Journal: Advances in Gerontology 
Abstract: A demographic shift towards an ageing population necessitates a comprehensive examination of the quality of life of the older adults and a new development on aging service as well as gerontology. Current research has shown that virtual reality has the potential to enhance the well-being of them, including those with social isolation. Technology-mediated communication is vital for social connections in society. Virtual technologies enhance social engagement through social virtual reality. These platforms unite users in simulated virtual reality settings, enabling socialization. Contrary to misconceptions, the older adults have demonstrated high levels of engagement with virtual reality. The older adults have elicited positive responses, engagement and enjoyment. This research aims to enhance the well-being of the older adults by understanding their attitudes toward using social virtual reality for their social life. By investigating the older adults’ acceptance of using these applications, all the factors were tested and correlations were found from the Technology Acceptance Model. The findings also reveal that the “Use of Context” strongly influences “Perceived Ease of Use.” Social virtual reality eliminates the need for face-to-face chatting and enables virtual communication and activities to take place among friends. However, there is a comparatively low recognition rate of adoption among the participating elderly. Interaction designers and academics should emphasize the need for human-computer interaction in these applications.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/10752
ISSN: 2079-0570
2079-0589
DOI: 10.1134/S207905702460068X
Appears in Collections:Applied Data Science - Publication

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