Exploring predictors of technology adoption among older adults

dc.contributor.advisor Peter Martin
dc.contributor.author Heinz, Melinda
dc.contributor.department Human Development and Family Studies
dc.date 2018-08-11T17:59:37.000
dc.date.accessioned 2020-06-30T02:47:43Z
dc.date.available 2020-06-30T02:47:43Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2013
dc.date.embargo 2015-07-30
dc.date.issued 2013-01-01
dc.description.abstract <p>The purpose of this study was to investigate predictors of older adult technology adoption through a mixed methods perspective. One hundred and seventy-six older adults responded to a quantitative survey assessing their technology adoption. Four participants were selected for qualitative interviews. The mean age of participants was 74.71 years old that included an age range of 65-96 year old participants. The majority of older adults lived independently, and no participants lived in care facilities. In the quantitative phase, structural equation modeling in Mplus was used to evaluate the fit of a technology adoption model using personality, self-efficacy, perceptions of technology, and attitudes of technology as predictors. Noteworthy findings indicated the model showed a good fit predicting technology adoption. Education, perceived usefulness, and attitudes toward using technology were positively associated with technology adoption. Participant age was negatively associated with technology adoption, indicating younger older adults were significantly more likely to adopt technology. Greater levels of agreeableness predicted greater levels of perceived usefulness and self-efficacy. Additionally, a significant indirect effect was obtained from perceived usefulness via attitudes toward using technology to technology adoption. This finding indicated that greater levels of perceived usefulness influenced more positive attitudes toward technology which in turn predicted greater levels of technology adoption. The qualitative phase indicated three themes specifically highlighting the importance of 1) earlier life experiences (e.g., workplace experiences), 2) personal preferences (e.g., choices regarding keeping up with technology), and 3) societal perspectives (e.g., concern for human interaction) on technology adoption. A revised theoretical model of technology adoption is suggested, tying together the quantitative and qualitative findings of this research study. Lastly, future research should consider implementing lifelong learning opportunities teaching older adults the usefulness of technology and giving them a chance to interact with technology in a supportive environment.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/13155/
dc.identifier.articleid 4162
dc.identifier.contextkey 4250804
dc.identifier.doi https://doi.org/10.31274/etd-180810-3401
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/13155
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/27344
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/13155/Heinz_iastate_0097E_13483.pdf|||Fri Jan 14 19:45:54 UTC 2022
dc.subject.disciplines Family, Life Course, and Society
dc.title Exploring predictors of technology adoption among older adults
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication aa55ac20-60f6-41d8-a7d1-c7bf09de0440
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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