The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults

Guner, Hacer; Acartürk, Cengiz

doi:10.1007/s10209-018-0642-4

Cited by 157 publications

(130 citation statements)

References 70 publications

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“…As such, the future social VR service system must be conceived to meet some functional requirements, including ensuring the safety and trust in social VR [57]. A significant positive correlation was also observed between UA and BI, consistent with previous studies on the technology acceptance model [54]. Although the participants considered that the current hardware was not light enough and that the database was not abundant, they thought that social VR represented the future trend, and that the technology will improve in the future.…”

Section: Discussionsupporting

confidence: 86%

“…PEnjoy: We increased the explanatory power of the model from 76.7 to 79.6% through the addition of PEnjoy dimensions, indicating that the elderly regard enjoyment as an important factor [52], and that technical staff should consider PEnjoy as an important indicator when developing VR applications [27,53]. PE: although the weight of PE was relatively small, it had a significant impact on the user's attitude of the elderly [54], which was consistent with Davis et al [55] and subsequent research on the acceptance of technology. This shows that activities which are helpful to daily life and hobbies will encourage the use of social VR [56].…”

Section: Discussionmentioning

confidence: 95%

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Acceptance and Influencing Factors of Social Virtual Reality in the Urban Elderly

Shao

Lee

2020

Sustainability

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In the stage of aging society and population aging, the social needs of the elderly are widely discussed by researchers. Especially driven by the demand of tele-medical treatment and tele-rehabilitation therapy, it is vital for the elderly to integrate into virtual communities by combining social virtual reality (VR) with different medical services and entertainment needs. In addition, affected by the COVID-19 epidemic, it is more difficult for people to have face-to-face contacts. With more remote consultation, entertainment and virtual social connectivity, the application of social VR is more urgent and valuable. However, there is little discussion on the acceptability and influencing factors of social VR among the elderly at present. Therefore, in order to get further data, we used (1) early stage semi-structured interviews and then (2) Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaires for investigation. One hundred fourteen elderly people aged 60–89 living in the metropolitan area of Taipei were taken as the subjects. To help them understand the situation and state of using social VR, these elderly people were asked to use a head-mounted display (HMD) to experience social VR games. The preliminary results showed that the elderly had obvious preference for entertainment (32.4%) and medical treatment (31.3%). The interview showed that this was related to the physiological condition or medical needs of the age range. In order to further understand how social VR would affect the social life of the elderly, we proposed the further demand structure of UTAUT Model based on the interview of both experts and the elderly. The model structures include (1) Performance Expectancy, (2) Perceived Enjoyment, (3) Social Influence, (4) User Attitude, (5) Behavioral Intention, and so on. These structures were applied to conduct interviews and questionnaires to find out the influence extent and relevance of the elderly on different structural needs, and suggestions were given accordingly. The results of the above interviews showed that (1) the elderly thought that the functions of entertaining and interacting of social VR could increase their social opportunities, and also meet medical needs (teleconference, cognitive decline, etc.), (2) the closeness of social relations (between family members, friends, doctors, and places), and also affect the relevance of Perceived Enjoyment (β = 0.77, p = 0.000 < 0.05). The results of these phenomena and interviews showed the interplay between the demand structures and their special relevance. They also indicated that as to social VR technologies, various demands and functional issues of the elderly need to be considered, and these demands would appear in the subtle usage, and different social VR interfaces and functions would emerge based on their special living ways and physical and psychological demands.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 95%

Acceptance and Influencing Factors of Social Virtual Reality in the Urban Elderly

Shao

Lee

2020

Sustainability

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“…First, our findings are in line with the technology acceptance model (TAM). The major components of TAM consist of external variables, perceived usefulness, perceived ease of use, attitude, behavioral intention, and actual use [35,36]. External variables would refer to factors such as cost and fraud [37], which have been discussed above.…”

Section: Discussionmentioning

confidence: 99%

Mobile and Connected Health Technology Needs for Older Adults Aging in Place: Cross-Sectional Survey Study

Wang

Coleman

et al. 2019

JMIR Aging

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Background An increasing number of mobile and wearable devices are available in the market. However, the extent to which these devices can be used to assist older adults to age in place remains unclear. Objective This study aimed to assess older adults’ perceptions of using mobile and connected health technologies. Methods Using a cross-sectional design, a total of 51 participants were recruited from a senior community center. Demographics and usage of mobile or wearable devices and online health communities were collected using a survey questionnaire. Descriptive statistics assessed usage of devices and online health communities. The Fisher exact test was used to examine the relationship between technology usage and having access to a smartphone. Results The sample was primarily comprised non-Hispanic white (35/51, 69%), educated (39/51, 76% any college), and female (36/51, 71%) participants, with an average age of 70 (SD 8) years. All participants were insured and nearly all lived at home (49/51, 94%). A total of 86% (44/51) of the participants had heard of wearable health devices, but only 18 out of 51 (35%) had ever used them. Over 80% (42/51) expressed interest in using such devices and were interested in tracking exercise and physical activity (46/51, 90%), sleep (38/51, 75%), blood pressure (34/51, 67%), diet (31/51, 61%), blood sugar (28/51, 55%), weight (26/51, 51%), and fall risk (23/51, 45%). The greatest concerns about using wearable devices were cost (31/51, 61%), safety (14/51, 28%), and privacy (13/51, 26%); one-fourth (12/51) reported having no concerns. They were mostly interested in sharing data from mobile and connected devices with their health care providers followed by family, online communities, friends, and no one. About 41% (21/51) of the older adults surveyed reported having ever heard of an online health community, and roughly 40% (20/51) of the participants reported being interested in joining such a community. Most participants reported having access to a smartphone (38/51, 74%), and those with such access were significantly more likely to show interest in using a wearable health device ( P <.001) and joining an online health community ( P =.05). Conclusions Our findings suggest that, although few older adults are currently using mobile and wearable devices and connected health technologies for managing health, they are open to this idea and are mostly interested in sharing such data with their health care providers. Further studies are warranted to explore strategies to balance the data sharing preference of older adults and how to best integrate mobile and wearable device data with clinical workflow for health care providers to promote healthy aging in place.

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“…ATT refers to the positive or negative feelings that individuals hold when taking a particular behavior [56,57]. PU1 as an antecedent variable of attitude toward behavior represents the convenience and security of renting [58], including for providing assistance with children's education, economic pressure, and residence flexibility. Compared with buying a house, renting a home can save costs and reduce financial burdens.…”

Section: Attitude Toward Behavior Subjective Norm and Behavioral Intmentioning

confidence: 99%

Understanding the Intention and Behavior of Renting Houses among the Young Generation: Evidence from Jinan, China

Zheng

Cheng

2019

Sustainability

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In the last decade, the rapid growth of China’s economy and population has generated a large demand for housing. Increasingly high prices have become the main obstacle for homeownership, especially for the young generation. In this study, we investigate the determinants of rental housing among the young generation in Chinese cities. A theoretical model and hypotheses were proposed by extending the theory of planned behavior (TPB). An empirical analysis was conducted via the structural equation model validation to reveal the following conclusions. (1) Attitude towards behavior (perceived usefulness and perceived usability) are the most important factors influencing renting behavior. (2) Mandatory policies and regulatory pressures promote renting behavior. (3) The government’s economic incentives have a significant impact on perceptual behavior control and indirectly affect behavioral intentions through perceptual behavior control. Based on the above conclusions, this study proposed recommendations for the government and businesses. This study contributed to existing theory and practice by providing useful insights into the influences on the young generation’s renting intentions. Furthermore, these findings provide the government with implications for facilitating the sustainability of the housing market.

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The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults

Cited by 157 publications

References 70 publications

Acceptance and Influencing Factors of Social Virtual Reality in the Urban Elderly

Acceptance and Influencing Factors of Social Virtual Reality in the Urban Elderly

Mobile and Connected Health Technology Needs for Older Adults Aging in Place: Cross-Sectional Survey Study

Understanding the Intention and Behavior of Renting Houses among the Young Generation: Evidence from Jinan, China

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