{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:40:45Z","timestamp":1782232845826,"version":"3.54.5"},"reference-count":136,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCP"],"abstract":"<jats:p>Older adults in the U.S. are interested in maintaining independence, aging at home longer, and staying active. Their substantial size, market share, and household wealth sparked the interest of investors and developers in remote monitoring, smart homes, ambient-assisted living, tracking, applications, and sensors via the IoT. This study used the unified theory of acceptance and use of technology extended (UTAUT2). The overarching research question was: \u201cTo what extent do performance, effort, influence, conditions, motivation, price, and habit affect older adults\u2019 behavioral intent to use IoT technologies in their homes?\u201d The research methodology for this study was a nonexperimental correlation of the variables that affect older adults\u2019 intention to use IoT-enabled technologies in their homes. The population was adults 60 plus years in northern Virginia. The sample consisted of 316 respondents. The seven predictors cumulatively influenced older adults\u2019 behavioral intent to use IoT-enabled technologies, F(7, 308) = 133.50, p &lt; 0.001, R2 = 0.75. The significant predictors of behavioral intention to use IoT technologies were performance expectancy (B = 0.244, t(308) = 4.427, p &lt; 0.001), social influence (B = 0.138, t(308) = 3.4775, p = 0.001), facilitating conditions (B = 0.184, t(308) = 2.999, p = 0.003), hedonic motivation (B = 0.153, t(308) = 2.694, p = 0.007), price value (B = 0.140, t(308) = 3.099, p = 0.002), and habit (B = 0.378, t(308) = 8.696, p &lt; 0.001). Effort expectancy was insignificant (B = \u22120.026, t(308) = \u22120.409, p = 0.683). This study filled the gap in research on older adults\u2019 acceptance of IoT by focusing specifically on that population. The findings help reduce the risk of solutions driven by technological and organizational requirements rather than the older adults\u2019 unique needs and requirements. The study revealed that older adults may be susceptible to undue influence to adopt IoT solutions. These socioeconomic dimensions of the UTAUT2 are essential to the information technology field because the actualizing of IoT-enabled technologies in private homes depends on older adults\u2019 participation and adoption. This research is beneficial to IoT developers, implementers, cybersecurity researchers, healthcare providers, caregivers, and managers of in-home care providers regarding adding IoT technologies in their homes.<\/jats:p>","DOI":"10.3390\/jcp2030034","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T23:28:41Z","timestamp":1660865321000},"page":"662-699","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Synopsis of \u201cThe Impact of Motivation, Price, and Habit on Intention to Use IoT-Enabled Technology: A Correlational Study\u201d"],"prefix":"10.3390","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4891-9054","authenticated-orcid":false,"given":"Christina L.","family":"Phibbs","sequence":"first","affiliation":[{"name":"Cyber Solutions Innovation Center, The MITRE Corporation, McLean, VA 22102-7539, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6763-6714","authenticated-orcid":false,"given":"Shawon S. M.","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Hawaii-Hilo, Hilo, HI 96720, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1109\/ACCESS.2015.2437951","article-title":"The internet of things for health care: A comprehensive survey","volume":"3","author":"Daehan","year":"2015","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s12938-018-0567-4","article-title":"Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes","volume":"17","author":"Biagetti","year":"2018","journal-title":"BioMed. Eng. 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