Facilitating Gerontechnology Adoption: Observational Learning with Live Models

Peng, Lu; Ma, Qisheng; Yu, Rita W. L.; Chan, Alan H. S.; Teh, Pei Lee; So, Ka Kit

doi:10.1007/978-3-319-92252-2_27

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…28 A variety of technology education and training programs would improve self-efficacy and motivate elderly users. 29 In addition, PEU was strongly negatively correlated with support; in other words, when PEU was low, the demand for support was high. 30 This indicates that older people are more likely to adopt gerontechnology if there is enough environmental support to make it easier to use.…”

Section: Discussionmentioning

confidence: 99%

<p>Factors Affecting the Acceptability of Technology in Health Care Among Older Korean Adults with Multiple Chronic Conditions: A Cross-Sectional Study Adopting the Senior Technology Acceptance Model</p>

Ha¹,

Park²

2020

CIA

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Purpose Older adults experience challenges employing technology in their health-care management due to changes in cognitive and physical functions. This study aimed to investigate the acceptance of technology among older Korean adults with multiple chronic health conditions and examine factors associated with technology acceptance, adopting the senior technology acceptance model (STAM). Patients and Methods In total, 226 community-dwelling older adults with more than two chronic conditions participated in this study. We conducted a survey that covered demographics, gerontechnology self-efficacy, gerontechnology anxiety, facilitating conditions, self-reported health conditions, cognitive ability, social relationships, attitude toward life and satisfaction, physical functioning, and technology acceptance. Results Older Korean adults with multiple chronic health conditions scored moderately high for technology acceptance (25.36±5.28). There were significant differences in technology acceptance according to age (r=−0.241), cognitive ability (r=0.225), gerontechnology self-efficacy (r=0.323), and facilitating conditions (r=0.288). Only age and education were significant factors predicting technology acceptance (Adjusted R 2 =0.151, p<0.001). Conclusion Although older Korean adults with multiple chronic conditions displayed good technology acceptance, their age and education level predicted the level of acceptance. Given that some components of the STAM model have social and cultural relevance, it is necessary to conduct research across various cultures to better understand technology acceptance by older adults.

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Section: Discussionmentioning

confidence: 99%