Social assistive robots (SARs) are considered to have the potential to benefit older adults in nursing homes, but their acceptance among older adults remains unclear. This study investigated older adults’ acceptance of SARs in Chinese nursing homes using an extended Almere model incorporating individual innovation theory and technophobia theory. A cross-sectional survey of 664 older adults from 11 institutions revealed relatively low acceptance (mean score 2.86 ± 0.96). Structural equation modeling revealed key determinants of older adults’ acceptance of SARs. Perceived usefulness, perceived ease of use, perceived enjoyment, and individual innovation directly and positively influenced their acceptance of SARs. Perceived adaptability, social presence, and perceived sociability had indirect positive effects on acceptance, while technological anxiety exerted an indirect negative effect. Tailoring SAR attributes to better meet older adults’ needs may enhance acceptance.
AjibadeP. (2018). Technology acceptance model limitations and criticisms: Exploring the practical applications and use in technology-related studies, mixed-method, and qualitative researches. Library Philosophy and Practice, 9.
2.
AjzenI.FishbeinM. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261–277. https://doi.org/10.1037/h0076477
3.
AkritidiD.GallosP.KoufiV.MalamateniouF. (2022). Using an extended technology acceptance model to evaluate digital health services. In Advances in informatics, management and technology in healthcare (pp. 530–533). IOS Press.
4.
Al-MaroofR. S.AlhumaidK.AlhamadA. Q.AburayyaA.SalloumS. (2021). User acceptance of smart watch for medical purposes: An empirical study. Future Internet, 13(5), 127. https://doi.org/10.3390/fi13050127
5.
AlmarzouqiA.AburayyaA.SalloumS. A. (2022). Determinants predicting the electronic medical record adoption in healthcare: A SEM-artificial neural network approach. PLoS One, 17(8), Article e0272735. https://doi.org/10.1371/journal.pone.0272735
6.
AndtfolkM.NyholmL.EideH.RauhalaA.FagerströmL. (2022). Attitudes toward the use of humanoid robots in healthcare—A cross-sectional study. AI & SOCIETY, 37(4), 1739–1748. https://doi.org/10.1007/s00146-021-01271-4
7.
BackonjaU.HallA. K.PainterI.KnealeL.LazarA.CakmakM.ThompsonH. J.DemirisG. (2018). Comfort and attitudes towards robots among young, middle aged, and older adults: A cross sectional study. Journal of Nursing Scholarship: An Official Publication of Sigma Theta Tau International Honor Society of Nursing, 50(6), 623–633. https://doi.org/10.1111/jnu.12430
8.
BroadbentE.StaffordR.MacDonaldB. (2009). Acceptance of healthcare robots for the older population: Review and future directions. International Journal of Social Robotics, 1(4), 319–330. https://doi.org/10.1007/s12369-009-0030-6
9.
CaiY.SongW.LiJ.JingY.LiangC.ZhangL.ZhangX.ZhangW.LiuB.AnY.LiJ.TangB.PeiS.WuX.LiuY.ZhuangC. L.YingY.DouX.ChenY.LiuG. H. (2022). The landscape of aging. Science China. Life Sciences, 65(12), 2354–2454. https://doi.org/10.1007/s11427-022-2161-3
10.
ChangS.-F.DuW.-F.WangJ.LinM.-Y.PengD. (2021). Using AI to implement O2O home health care program. E3S web of conferences.
ChiuC. J.HsiehS.LiC. W. (2021). Needs and preferences of middle-aged and older adults in Taiwan for companion robots and pets: Survey study. Journal of Medical Internet Research, 23(6), Article e23471. https://doi.org/10.2196/23471
13.
DautenhahnK. (2007). Socially intelligent robots: Dimensions of human–robot interaction. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 362(1480), 679–704. https://doi.org/10.1098/rstb.2006.2004
14.
DuX. (2021). Problems and countermeasures of medical-nursing integrated services in Chongqings private pension agencies: A qualitative study. Open Access Library Journal, 8(01), 1.
15.
Etemad-SajadiR.SturmanM. C. (2022). How to increase the customer experience by the usage of remote control robot concierge solutions. International Journal of Social Robotics, 14(2), 429–440. https://doi.org/10.1007/s12369-021-00800-x
16.
FanW.LiuJ.ZhuS.PardalosP. M. (2020). Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research, 294(1), 567–592. https://doi.org/10.1007/s10479-018-2818-y
17.
FeldingS. A.KohW. Q.TeupenS.BudakK. B.Laporte UribeF.RoesM. (2023). A scoping review using the Almere model to understand factors facilitating and hindering the acceptance of social robots in nursing homes. International Journal of Social Robotics, 15(7), 1115–1153. https://doi.org/10.1007/s12369-023-01012-1
18.
HeerinkM. (2011). Exploring the influence of age, gender, education and computer experience on robot acceptance by older adults. In Proceedings of the 6th international conference on Human-robot interaction. IEEE.
19.
GuoX.ShenH.WenQ.LiuS.YangY.ZhangH. (2023). Research on layout model and construction planning of aged care institutions for disabled elders in China: Based on Nanjing city data. BMC Geriatr, 23(1), 237.
20.
HeY.HeQ.LiuQ. (2022). Technology Acceptance in Socially Assistive Robots: Scoping Review of Models, Measurement, and Influencing Factors.Journal of Healthcare Engineering, 2022, 6334732. https://doi.org/10.1155/2022/6334732
21.
HeerinkM.KröseB.EversV.WielingaB. (2010b). Assessing acceptance of assistive social agent technology by older adults: The almere model. Springer.
22.
HeerinkM.KröseB.EversV.WielingaB. (2010a). Assessing acceptance of assistive social agent technology by older adults: The almere model. International Journal of Social Robotics, 2(4), 361–375. https://doi.org/10.1007/s12369-010-0068-5
23.
HoqueR.SorwarG. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101(1), 75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002
24.
Hunan Provincial Bureau of Statistics. (2022). Hunan statistical yearbook. China Statistics Press.
25.
JansenC. P.DiegelmannM.SchnabelE. L.WahlH. W.HauerK. (2017). Life-space and movement behavior in nursing home residents: Results of a new sensor-based assessment and associated factors. BMC Geriatrics, 17(1), 36. https://doi.org/10.1186/s12877-017-0430-7
26.
JoH.ParkD.-H. (2023). Affordance, usefulness, enjoyment, and aesthetics in sustaining virtual reality engagement. Scientific Reports, 13(1), Article 15097. https://doi.org/10.1038/s41598-023-42113-1
27.
KhasawnehO. Y. (2018a). Technophobia without boarders: The influence of technophobia and emotional intelligence on technology acceptance and the moderating influence of organizational climate. Computers in Human Behavior, 88, 210–218. https://doi.org/10.1016/j.chb.2018.07.007
KhoslaR.NguyenK.ChuM. T. (2017). Human robot engagement and acceptability in residential aged care. International Journal of Human–Computer Interaction, 33(6), 510–522. https://doi.org/10.1080/10447318.2016.1275435
30.
LeeY.KozarK. A.LarsenKaiRT. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12(1), 50.
31.
LiW.GuiJ.LuoX.YangJ.ZhangT.TangQ. (2023). Determinants of intention with remote health management service among urban older adults: A unified theory of acceptance and use of technology perspective. Frontiers in Public Health, 11, Article 1117518. https://doi.org/10.3389/fpubh.2023.1117518
32.
LuoC.YangC.YuanR.LiuQ.LiP.HeY. (2024). Barriers and facilitators to technology acceptance of socially assistive robots in older adults-A qualitative study based on the capability, opportunity, and motivation behavior model (COM-B) and stakeholder perspectives. Geriatric Nursing (New York, N.Y.), 58, 162–170. https://doi.org/10.1016/j.gerinurse.2024.05.025
33.
MarsaR.YounesiS. J.BarekatiS.RamshiniM.GhyasiH. (2020). A comparative study on stress, anxiety and depression between nursing-home elderly residents and home-dwelling elderly people. Salmand, 15(2), 176–187. https://doi.org/10.32598/sija.13.10.500
34.
MoxleyJ.SharitJ.CzajaS. J. (2022). The factors influencing older adults’ decisions surrounding adoption of technology: Quantitative experimental study. JMIR Aging, 5(4), Article e39890. https://doi.org/10.2196/39890
35.
ObayashiK.MasuyamaS., (2020). Pilot and feasibility study on elderly support services using communicative robots and monitoring sensors integrated with cloud robotics. Clinical Therapeutics, 42(2), 364–371. https://doi.org/10.1016/j.clinthera.2020.01.001
36.
OsiceanuM.-E. (2015). Psychological implications of modern technologies: “technofobia” versus “technophilia”. Procedia - Social and Behavioral Sciences, 180, 1137–1144. https://doi.org/10.1016/j.sbspro.2015.02.229
37.
PengM. Y. P.YanX. (2022). Exploring the influence of determinants on behavior intention to use of multiple media kiosks through technology readiness and acceptance model. Frontiers in Psychology, 13, Article 852394. https://doi.org/10.3389/fpsyg.2022.852394
38.
PiasekJ.Wieczorowska-TobisK. (2018). Acceptance and long-term use of a social robot by elderly users in a domestic environment. In 2018 11th international conference on human system interaction (HSI). IEEE.
39.
PuL. H.MoyleW.JonesC.TodorovicM. (2019). The effectiveness of social robots for older adults: A systematic review and meta-analysis of randomized controlled studies. The Gerontologist, 59(1), E37–E51. https://doi.org/10.1093/geront/gny046
40.
QiM.CuiJ.LiX.HanY. (2021). Perceived factors influencing the public intention to use e-consultation: Analysis of web-based survey data. Journal of Medical Internet Research, 23(1), Article e21834. https://doi.org/10.2196/21834
41.
RigaudA. S.DacunhaS.HarzoC.LenoirH.SfeirI.PiccoliM.PinoM. (2024). Implementation of socially assistive robots in geriatric care institutions: Healthcare professionals perspectives and identification of facilitating factors and barriers. Journal of Rehabilitation and Assistive Technologies Engineering, 11, Article 20556683241284765. https://doi.org/10.1177/20556683241284765
42.
RogersE. M. (1983). Diffusion of innavations. The Free Press.
43.
RossiS., et al. (2018). Psychometric evaluation supported by a social robot: Personality factors and technology acceptance In: 2018 27th IEEE international symposium on robot and human interactive communication (RO-MAN). IEEE.
44.
SimK.-B.ByunK. S.HarashimaF. (2006). Internet-based teleoperation of an intelligent robot with optimal two-layer fuzzy controller. IEEE Transactions on Industrial Electronics, 53(4), 1362–1372. https://doi.org/10.1109/tie.2006.878295
45.
SinnemaL.AlimardaniM. (2019). The attitude of elderly and young adults towards a humanoid robot as a facilitator for social interaction In: International conference on social robotics. Springer.
46.
SunE. H., (2022). Validity and reliability of the Chinese version of the technophobia scale in older adults. Chinese Journal of Nurisng, 57(03). 380–384.
47.
TaoD., (2009). Intention to use and actual use of electronic information resources: Further exploring technology acceptance model (TAM). AMIA Annual Symposium proceedings. AMIA Symposium, 629–633.
48.
TsertsidisA.KolkowskaE.HedströmK. (2019). Factors influencing seniors’ acceptance of technology for ageing in place in the post-implementation stage: A literature review. International Journal of Medical Informatics, 129, 324–333. https://doi.org/10.1016/j.ijmedinf.2019.06.027
49.
VenkateshThongXu (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
50.
WangH.GuanY.HuR.BraggF.YuM.ZhongJ. (2022). Willingness for community-based and institutional eldercare among older adults: A cross-sectional study in Zhejiang, China. BMJ Open, 12(4), Article e055426. https://doi.org/10.1136/bmjopen-2021-055426
51.
WernerK. (2012). Evaluation of human robot interaction factors of a socially assistive robot together with older people. In 2012 sixth international conference on complex, intelligent, and software intensive systems. IEEE.
52.
WongK. P.TehP. L.LimW. M.LeeS. W. H. (2025). Enhancing older adults’ lives through positive aging perception, quality-of-life enhancement, and social support to drive acceptance and readiness toward indoor assistive technology: Cross-sectional study. JMIR Aging, 8(1), Article e59665. https://doi.org/10.2196/59665
53.
YangK.ForneyJ. C. (2013). The moderating role of consumer technology anxiety in mobile shopping adoption: Differential effects of facilitating conditions and social influences. Journal of Electronic Commerce Research, 14(4), 334.
54.
YangQ. X. (2021). Study on Influencing Factors of Elderly’s Acceptance of Health Management System Based on UTAUT Model. Master’s thesis. https://doi.org/10.27026/d.cnki.ggszc.2021.000210(In Chinese).
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
