Skip to main content Skip to main navigation menu Skip to site footer
Articles
DOI: 10.14254/1795-6889.2022.18-1.2
Submitted: 2022-04-15
Published: 2022-06-30

Factors influencing the intention to use assistive technologies by older adults

Bialystok University of Technology, Poland
Bialystok University of Technology, Poland
assistive technologies gerontechnology technology acceptance model robot older adults

Abstract

Society is ageing at an unprecedented pace worldwide creating implications for the health and social care. Gerontechnology has been recognized as a solution that increases and supports the independency and well-being of older adults at home. This article aims to identify the most critical success factors effecting the adoption of an assistive gerontechnology by older adults in Poland. The object of the authors' interest was Rudy robot, an AI-enabled mobile solution helping users remain physically healthy, mentally sharp, and socially connected. The data was collected among Polish citizens using the CATI technique between November and December 2020. The number of returned questionnaires amounted to 824. The authors used Generalized Least Squares (GLS) of Structural Equation Modelling (GLS-SEM) to verify the hypotheses. The obtained results confirmed statistically significant relationships between the variables of perceived usefulness of Rudy robot and attitude reflecting the willingness to use this technology, as well as between perceived ease of use and perceived usefulness of robot. However, relationship between perceived ease of use and inclination to use this technology in the future was not statistically significant. The conducted research confirmed that the functionality of the analysed Rudy robot for older-adult care positively influences their intension to use it in the future for their own needs or family members. The obtained results confirmed usefulness of robots as assistive technology helping older adults.

Metrics

Metrics Loading ...

References

  1. Arthanat, S., Wilcox, J., & Macuch, M. (2019). Profiles and Predictors of Smart Home Technology Adoption by Older Adults. OTJR. Occupation, Participation, and Health, 39(4), 247–256. http://doi.org/10.1177/1539449218813906 DOI: https://doi.org/10.1177/1539449218813906
  2. American Association of Retired Persons. (2014). What makes a community livable? AARP PPI. Retrieved from https://www.aarp.org/livable-communities/info-2014/aarp-ppi-survey-what-makes-a-community-livable.html [20.04.2021].
  3. Barlow J (2006). Building an evidence base for successful telecare implementation – updated report of the Evidence Working Group of the Telecare Policy Collaborative chaired by James Barlow.
  4. Bechtold, U., Capari, L., & Gudowsky, N. (2017). Futures of ageing and technology – comparing different actors’ prospective views. Journal of Responsible Innovation, 4, 157-176. http:///doi.org/10.1080/23299460.2017.1360721 DOI: https://doi.org/10.1080/23299460.2017.1360721
  5. Bechtold, U., & Sotoudeh, M. (2013). Assistive technologies: Their development from a technology assessment perspective. Gerontology, 11, 521-533. http://doi.org/10.4017/gt.2013.11.4.015.00 DOI: https://doi.org/10.4017/gt.2013.11.4.015.00
  6. Beer, J. M. Prakash, A. Mitzner, T.L., & Rogers, W.A. (2011). Understanding Robot Acceptance Technical Report HFA-TR-1103 Atlanta, GA: Georgia Institute of Technology School of Psychology Human Factors and Aging Laboratory, Retrieved from https://smartech.gatech.edu/bitstream/handle/1853/39672/HFA-TR-1103-RobotAcceptance.pdf, [21.04.2021].
  7. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588–606. http://doi.org/10.1037/0033-2909.88.3.588 DOI: https://doi.org/10.1037/0033-2909.88.3.588
  8. Bentler, P.M., & Chou, C.P. (1987). Practical issues in structural modelling. Sociological Methods & Research, 16(1), 78-117. http:// doi.org/10.1177/0049124187016001004 DOI: https://doi.org/10.1177/0049124187016001004
  9. Bollen, K. A. (1986). Sample Size and Bentler and Bonett’s Nonnormed Fit Index. Psychometrika, 51, 375–377. https://doi.org/10.1007/BF02294061 DOI: https://doi.org/10.1007/BF02294061
  10. Bollen, K.A. (1989). Structural Equations with Latent Variables New York, NY: John Wiley and Sons. DOI: https://doi.org/10.1002/9781118619179
  11. Broekens, J., Heerink, M. & Rosendal, H. (2009). Assistive social robots in elderly care: A review. Gerontechnology, 8, 94–103. http://doi.org/10.4017/gt.2009.08.02.002.00 DOI: https://doi.org/10.4017/gt.2009.08.02.002.00
  12. Brooke, J., & Jackson D. (2020). Older people and COVID‐19: Isolation, risk and ageism, Journal of clinical nursing, 29(13-14), 2044-2046. http://doi.org/10.1111/jocn.15274 DOI: https://doi.org/10.1111/jocn.15274
  13. Browne, M. W., MacCallum, R.C., Kim, Ch-T., Andersen, B. L., & Glaser, R. (2002). When fit indices and residuals are incompatible. Psychological methods, 7(4), 403-421. https://doi.org/10.1037//1082-989x.7.4.403 DOI: https://doi.org/10.1037/1082-989X.7.4.403
  14. Caine, K.E., Fisk, A.D., & Rogers, W.A. (2006). Benefits and privacy concerns of a home equipped with a visual sensing system: a perspective from older adults. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50(2), 180–184. http:// doi.org/10.1177/154193120605000203 DOI: https://doi.org/10.1177/154193120605000203
  15. Cajita, M. I., Hodgson, N.A., Budhathoki, C., & Han, H. R. (2017). Intention to Use mHealth in Older Adults with Heart Failure. Journal of Cardiovascular Nursing, 32(6), E1–E7. http://doi.org/10.1097/JCN.0000000000000401 DOI: https://doi.org/10.1097/JCN.0000000000000401
  16. Cajita, M. I., Hodgson, N. A., Lam, K. W., Yoo, S., & Han, H. R. (2018). Facilitators of and Barriers to mHealth Adoption in Older Adults With Heart Failure. Computers, informatics, nursing: CIN, 36(8), 376–382. http:// doi.org/10.1097/CIN.0000000000000442 DOI: https://doi.org/10.1097/CIN.0000000000000442
  17. Carstensen, L.L. (1995). Evidence for a life-span theory of socioemotional selectivity. Current Directions in Psychological Science, 4(5), 151-156. https://www.jstor.org/stable/2018235.6 DOI: https://doi.org/10.1111/1467-8721.ep11512261
  18. Chen, K., & Chan, A.H.S. (2014a). Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics, 57(5), 635–652. http://doi.org/10.1080/ 00140139.2014.89585519 DOI: https://doi.org/10.1080/00140139.2014.895855
  19. Chen, K., & Chan A.H.S. (2014b). Predictors of gerontechnology acceptance by older Hong Kong Chinese. Technovation, 34(2), 126-135. https://doi.org/10.1016/j.technovation.2013.09.010 DOI: https://doi.org/10.1016/j.technovation.2013.09.010
  20. Chen, M., Lu, T., Chen, K., & Liu, C.E. (2011). A TAM-based study on senior citizens digital learning and user behavioral intention toward use of broadband network technology services provided via television. African Journal of Business Management, 5. 7099–7110. http:// doi.org/10.5897/AJBM10.1213
  21. Chu, L., Chen, H-W, Cheng, P-Y., Ho, P., Weng, I.-T., Yang, P-L., Chien, S-E., Tu, Y-C., Yang, C-C, Wang, T-M., Fung, H. H. & Yeh, S-L. (2019). Identifying Features that Enhance Older Adults’ Acceptance of Robots: A Mixed Methods Study. Gerontology, 65, 441-450. http:// doi.org/10.1159/000494881 DOI: https://doi.org/10.1159/000494881
  22. Cohen, C., Kampel, T., & Verloo, H. (2016). Acceptability of an intelligent wireless sensor system for the rapid detection of health issues: findings among home-dwelling older adults and their informal caregivers. Patient preference and adherence, 10, 1687–1695. http://doi.org/10.2147/PPA.S113805 DOI: https://doi.org/10.2147/PPA.S113805
  23. Conci, M., Pianesi, F., & Zancanaro, M. (2009) Useful, Social and Enjoyable: Mobile Phone Adoption by Older People. In: Gross T. et al. (eds) Human-Computer Interaction – INTERACT 2009. Lecture Notes in Computer Science, 5726. Springer, Berlin, Heidelberg. http://doi.org/10.1007/978-3-642-03655-2_7 DOI: https://doi.org/10.1007/978-3-642-03655-2_7
  24. Davis, F. D. (1985). A technology Acceptance model for empirically testing new and-user information systems: theory and results. Unpublished Doctoral dissertation, MIT Sloan School of Management, Cambridge, MA.
  25. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quartely, 13(3), 319–340. http://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008
  26. Ellis, R.D., & Allaire, J.C. (1999). Modeling computer interest in older adults: the role of age, education, computer knowledge, and computer anxiety. The Journal of the Human Factors and Ergonomics Society, 41(3), 345–355. http://doi.org/10.1518/001872099779610996 DOI: https://doi.org/10.1518/001872099779610996
  27. Ezer, N., Fisk, A. & Rogers, W. (2009). Attitudinal and intentional acceptance of domestic robots by younger and older adults. In C. Stephanidis (Eds.), Universal Access in Human-Computer Interaction Intelligent and Ubiquitous Interaction Environments. Berlin: Springer. DOI: https://doi.org/10.1007/978-3-642-02710-9_5
  28. European Commission (2021). The 2021 Ageing Report. Underlying Assumptions & Projection Methodologies, Retrieved from http:// https://ec.europa.eu › economy-finance › ip142_en [24.06.2021].
  29. European Parliament Technology Assessment (2019). Technologies in care for older people. Retrieved from https://eptanetwork.org/images/documents/minutes/EPTA_report_2019.pdf, [25.05.2021].
  30. Eurostat (2020), Ageing Europe - statistics on population developments, Retrieved from https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Ageing_Europe_-_statistics_on_population_developments [26.06.2021].
  31. Fornell, C., & Larcker, D. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. http://doi.org/10.2307/3151312 DOI: https://doi.org/10.1177/002224378101800104
  32. Graafmans, J., Taipale, V., & Charness, N. (eds.) (1998). Gerontechnology. A sustainable investment in the future. Amsterdam: IOS Press.
  33. Grenade, L., & Boldy, D. (2008). Social isolation and loneliness among older people: issues and future challenges in community and residential settings. Australian Health Review, 32, 468-478. http://doi.org/10.1071/AH080468 DOI: https://doi.org/10.1071/AH080468
  34. Ha, J., & Park, H.K. (2020). 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. Clinical Interventions in Aging, 2(15), 1873–1881. doi.org/10.2147/CIA.S268606 DOI: https://doi.org/10.2147/CIA.S268606
  35. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., & Tatham, R.L. (2013). Multivariate Data Analysis. Technometrics, 49, 103-104. DOI: https://doi.org/10.1198/tech.2007.s455
  36. Halicka, K. (2019). Gerontechnology—The assessment of one selected technology improving the quality of life of older adults. Engineering Management in Production and Services, 11, 43–51. http://doi.org/10.2478/emj-2019-0010 DOI: https://doi.org/10.2478/emj-2019-0010
  37. Halicka K., & Kacprzak D. (2021), Linear ordering of selected gerontechnologies using selected MCGDM methods, Technological and Economic Development of Economy, 27 (4), 921–947, https://doi.org/10.3846/tede.2021.15000 DOI: https://doi.org/10.3846/tede.2021.15000
  38. Hargittai, E. (2010). Digital na(t)ives? Variation in internet skills and uses among members of the “net generation. Sociological Inquiry, 80, 92–113. http://doi.org/10.1111/j.1475-682X.2009.00317.x DOI: https://doi.org/10.1111/j.1475-682X.2009.00317.x
  39. Haufe, M., Peek, S. T. M., & Luijkx, K. G. (2019). Matching gerontechnologies to independent-living seniors’ individual needs: Development of the GTM tool. BMC Health Services Research, 19, 26 http://doi.org/10.1186/s12913-018-3848-5 DOI: https://doi.org/10.1186/s12913-018-3848-5
  40. Hebesberger, D., Koertner, T., Gisinger, C., & Pripfl J. (2017). A long-term autonomous robot at a care hospital: A mixed methods study on social acceptance and experiences of staff and older adults, International Journal of Social Robotics, 9(3), 417–429. http://doi.org/10.1007/s12369-016-0391-6 DOI: https://doi.org/10.1007/s12369-016-0391-6
  41. Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2010). Assessing Acceptance of Assistive Social Agent Technology by Older Adults: The Almere Model. International Journal of Social Robotics, 2, 361–375. http://doi.org/10.1007/s12369-010-0068-5 DOI: https://doi.org/10.1007/s12369-010-0068-5
  42. Hensel, B. K., Demiris, G., & Courtney, K. L. (2006). Defining obtrusiveness in home telehealth technologies: a conceptual framework. Journal of the American Medical Informatics Association: JAMIA, 13(4), 428–431. http://doi.org/10.1197/jamia.M2026 DOI: https://doi.org/10.1197/jamia.M2026
  43. Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75–84. http://doi.org/10.1016/j.ijmedinf.2017.02.002 DOI: https://doi.org/10.1016/j.ijmedinf.2017.02.002
  44. Hu, Li‐tze, & Bentler, P. M (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives, Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55. http://doi.org/10.1080/10705519909540118 DOI: https://doi.org/10.1080/10705519909540118
  45. Iacobucci, D. (2020). Structural equations modeling: Fit Indices, sample size, and advanced topics. Journal of Consumer Psychology, 20, 90-98. http://doi.org/10.1016/j.jcps.2009.09.003 DOI: https://doi.org/10.1016/j.jcps.2009.09.003
  46. Jöreskog, K. G., & Sörbom, D. (1979). Advances in Factor Analysis and Structural Equation Models. Cambridge: Abt Books, MA.
  47. Karapanos, E. (2013). User Experience Over Time. Studies in Computational Intelligence, 436, 57-83. Berlin, Heidelber: Springer. http://doi.org/10.1007/978-3-642-31000-3_4 DOI: https://doi.org/10.1007/978-3-642-31000-3_4
  48. Ke, C., Lou, V.W.Q., Tan, K.C.K., Wai, M.Y., & Chan, L.L. (2020), Changes in technology acceptance among older people with dementia: the role of social robot engagement. International Journal of Medical Informatics, 141, 104241. https://doi.org/10.1016/j.ijmedinf.2020.104241 DOI: https://doi.org/10.1016/j.ijmedinf.2020.104241
  49. Khosravi, P., & Ghapanchi, A.H. (2016). Investigating the effectiveness of technologies applied to assist seniors: A systematic literature review. International Journal of Medical Informatics, 85(1), 17-26. https://doi.org/10.1016/j.ijmedinf.2015.05.014 DOI: https://doi.org/10.1016/j.ijmedinf.2015.05.014
  50. Kim, D.J. Ferrin D.L., & Rao, H.R. (2008). A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564. https://doi.org/10.1016/j.dss.2007.07.001 DOI: https://doi.org/10.1016/j.dss.2007.07.001
  51. Kim, E.H., Gellis, Z.D., Bradway, C.K., & Kenaley, B. (2019). Depression care services and telehealth technology use for homebound elderly in the United States. Aging Mental Health, 23, 1164–1173. https://doi.org/10.1080/13607863.2018.1481925 DOI: https://doi.org/10.1080/13607863.2018.1481925
  52. Langer, A., Ronit Feingold-Polaka, R., Mueller, O., Kellmeyer, P., & Levy-Tzedek, S. (2019), Trust in socially assistive robots: Considerations for use in rehabilitation. Neuroscience and Biobehavioral Reviews, 104, 231-239. https://doi.org/10.1016/j.neubiorev.2019.07.014 DOI: https://doi.org/10.1016/j.neubiorev.2019.07.014
  53. Li, J., Ma, Q., Chan, A.H.S., & Man, S.S. (2019). Health monitoring through wearable technologies for older adults: smart wearables acceptance model. Applied Ergonomics, 75, 162–169. https://doi.org/10.1016/j.apergo.2018.10.006 DOI: https://doi.org/10.1016/j.apergo.2018.10.006
  54. Lie, A.L.S. Lindsay, S., & Brittain, K. (2015). Technology and trust: older people's perspectives of a home monitoring system. Ageing and Society, 36(7), 1501-1525. https://doi.org/10.1017/S0144686X15000501 DOI: https://doi.org/10.1017/S0144686X15000501
  55. Ma, Q., Chan, A.H.S., & Teh, P.-L. (2020). Bridging the Digital Divide for Older Adults via Observational Training: Effects of Model Identity from a Generational Perspective. Sustainability, 12, 4555. doi.org/10.3390/su12114555 DOI: https://doi.org/10.3390/su12114555
  56. Ma, Q., Chan A-H. S., & Teh, P-L. (2021). Insights into Older Adults’ Technology Acceptance through Meta-Analysis. International Journal of Human–Computer Interaction, 37(11), 1049-1062, https://doi.org/10.1080/10447318.2020.1865005 DOI: https://doi.org/10.1080/10447318.2020.1865005
  57. Maskeliūnas, R., Damaševičius, R., & Segal, S. (2019). A Review of Internet of Things Technologies for Ambient Assisted Living Environments. Future Internet, 11, 259. https://doi.org/10.3390/fi11120259 DOI: https://doi.org/10.3390/fi11120259
  58. Mertens, A.W., Wille, M., Theis, S., Rasche, P., Finken, L., Schlick, C.M. (2015). Attitudes of Elderly People towards Assistive System: Influence of Amortization Barriers on the Adherence in Technically Assisted Rehabilitation and the Difusion of Health Technologies. In Proceedings of the 19th Triennial Congress of the IEA, Melbourne, Australia, 9–14 August 2015, 9, p. 14.
  59. Miskelly, F.G. (2001). Assistive technology in elderly care. Age Ageing, 30(6), 455–458. https://doi.org/10.1093/ageing/30.6.455 DOI: https://doi.org/10.1093/ageing/30.6.455
  60. Mitzner, T.L., Savla, J., Boot, W.R., Sharit, J., Charness, N., Czaja, S.J., & Rogers, W.A. (2019). Technology Adoption by Older Adults: Findings from the PRISM Trial. Gerontologist, 59, 34–44. https://doi.org/10.1093/geront/gny113 DOI: https://doi.org/10.1093/geront/gny113
  61. Mostaghel, R., & Oghazi, P. (2017). Elderly and technology tools: a fuzzyset qualitative comparative analysis. Quality & Quantity, 51, 1969–1982. https://doi.org/10.1007/s11135-016-0390-6 DOI: https://doi.org/10.1007/s11135-016-0390-6
  62. Naneva, S., Sarda Gou, M., Webb, T.L., & Prescott, T.J. (2020). A Systematic Review of Attitudes, Anxiety, Acceptance, and Trust Towards Social Robots. International Journal of Social Robotics, 12, 1179-1201. https://doi.org/10.1007/s12369-020-00659-4 DOI: https://doi.org/10.1007/s12369-020-00659-4
  63. Niehaves, B., & Plattfaut R. (2014). Internet adoption by the elderly: employing IS technology acceptance theories for understanding the age-related digital divide. European Journal of Information Systems, 23(6), 708–726. https://doi.org/10.1057/ejis.2013.19 DOI: https://doi.org/10.1057/ejis.2013.19
  64. Niefeld, M.R., & Kasper, J.D. (2005). Access to ambulatory medical and long-term care services among elderly Medicare and Medicaid beneficiaries: organizational, financial, and geographic barriers. Medical Care Research and Review, 62, 300-19. https://doi.org/10.1177/1077558705275418 DOI: https://doi.org/10.1177/1077558705275418
  65. Olson, K.E., O’Brien, M.A., Rogers, W.A., & Charness, N. (2011). Diffusion of technology: frequency of use for younger and older adults. Ageing International, 36(1), 123–145. https://doi.org/10.1007/s12126-010-9077-9 DOI: https://doi.org/10.1007/s12126-010-9077-9
  66. Pai, J.C., & Tu, F.M. (2011). The acceptance and use of customer relationship management (CRM) systems: An empirical study of distribution service industry in Taiwan. Expert Systems with Applications, 38, 579–584. https://doi.org/10.1016/j.eswa.2010.07.005 DOI: https://doi.org/10.1016/j.eswa.2010.07.005
  67. Pan, S., & Jordan-Marsh, M. (2010). Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective, Computers in Human Behavior, 26, 1111–1119. https://doi.org/10.1016/j.chb.2010.03.015
  68. Pal, D., Funilkul, S., Vanijja, V., & Papasratorn, B. (2018). Analyzing the Elderly Users’ Adoption of Smart-Home Services. IEEE Access 6, 51238–51252. https://doi.org/10.1109/ACCESS.2018.2869599 DOI: https://doi.org/10.1109/ACCESS.2018.2869599
  69. Pavlou, P.A. (2003). Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 69-103. https://www.jstor.org/stable/27751067 DOI: https://doi.org/10.1080/10864415.2003.11044275
  70. Pan, S., & Jordan-Marsh, M. (2010). Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Computers in Human Behavior, 26 (5), 1111–1119. https://doi.org/10.1016/j.chb.2010.03.015 DOI: https://doi.org/10.1016/j.chb.2010.03.015
  71. Peek, S.M., Wouters, E.M., van Hoof, J., Luijkx, K.G., Boeije, H.R., & Vrijhoef H.M. (2014). Factors influencing acceptance of technology for aging in place: A systematic review. International Journal of Medical Informatics, 83, 235–248. https://doi:10.1016/j.ijmedinf.2014.01.004 DOI: https://doi.org/10.1016/j.ijmedinf.2014.01.004
  72. Peek, S.T., Luijkx, K.G., Rijnaard, M.D., Nieboer, M.E., van der Voort, C.S., Aarts, S., van Hoof, J., Vrijhoef, H.J., & Wouters, E.J. (2016). Older Adults' Reasons for Using Technology while Aging in Place. Gerontology, 62(2), 226-37. https://doi:10.1159/000430949 DOI: https://doi.org/10.1159/000430949
  73. Peek, S.T.M., Luijkx, K.G., Vrijhoef, Nieboer, H. J.M., Aarts, M. E S., van der Voort, C. S., Rijnaard, M. D. & Wouters E. J. M. (2019). Understanding changes and stability in the long-term use of technologies by seniors who are aging in place: a dynamical framework. BMC Geriatrics, 19, 236. https://doi.org/10.1186/s12877-019-1241-9 DOI: https://doi.org/10.1186/s12877-019-1241-9
  74. Petermans, J., & Piau, A. (2017). Gerontechnology: Don’t miss the train, but which is the right carriage? European Geriatric Medicine, 8, 281-283. https://doi.org/10.1016/j.eurger.2017.06.011 DOI: https://doi.org/10.1016/j.eurger.2017.06.011
  75. Petrovčič, A., Peek, S., & Dolničar, V. (2019). Predictors of Seniors' Interest in Assistive Applications on Smartphones: Evidence from a Population-Based Survey in Slovenia. International Journal of Environmental Research and Public Health, 16(9), 1623. https://doi.org/10.3390/ijerph16091623 DOI: https://doi.org/10.3390/ijerph16091623
  76. Piau, A. Campo, E. Rumeau, P., Vellas, B., & Nourhashemi, F. (2014). Aging society and gerontechnology: A solution for an independent living?. The Journal of nutrition, health & aging, 18(1), 97-112. https://doi.org/10.1007/s12603-013-0356-5 DOI: https://doi.org/10.1007/s12603-013-0356-5
  77. Pilotto, A., D'Onofrio, G., Benelli, E., Zanesco, A., Cabello, A., Margelí, M.C., Wanche-Politis, S., Seferis, K., Sancarlo, D., & Kilias, D. (2011). Information and communication technology systems to improve quality of life and safety of Alzheimer's disease patients: a multicenter international survey. Journal of Alzheimer's Disease, 23, 131-41. https://doi.org/10.3233/JAD-2010-101164 DOI: https://doi.org/10.3233/JAD-2010-101164
  78. Pressler, K. A., & Ferraro, K.F. (2010). Assistive device use as a dynamic acquisition process in later life. Gerontologist 50(3), 371–381. https://doi.org/10.1093/geront/gnp170 DOI: https://doi.org/10.1093/geront/gnp170
  79. Renaud, K., & Van Biljon, J. (2008). Predicting Technology Acceptance and Adoption by the Elderly: A Qualitative Study. In Proceedings of the 2008 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries: Riding the Wave of Technology, Wilderness, South Africa, 6–8 October 2008, 210–219. DOI: https://doi.org/10.1145/1456659.1456684
  80. Quaosar, G.M.A.A., Hoque, M.R., & Bao, Y. (2018). Investigating factors a ecting elderly’s intention to use m-healthservices: An empirical study. Telemedicine and e-Health, 24(4), 309–314. https:// 10.1089/tmj.2017.0111 DOI: https://doi.org/10.1089/tmj.2017.0111
  81. Rogers, E.M. (2003). Diffusion of Innovations, ed 5. New York: Free Press.
  82. Richardson, S.J., Carroll, B.C., Close, J., Gordon, A.L., O’Brien, J., Quinn, T.J., Rochester, L., Sayer, A.A., Shenkin, S.D., van der Velde, N., Woo, J., & Witham, M.D. (2020). Research with older people in a world with COVID-19: identification of current and future priorities, challenges and opportunities, Age and Ageing, 49(6), 901–906, https://doi.org/10.1093/ageing/afaa149 DOI: https://doi.org/10.1093/ageing/afaa149
  83. Rogers, W.A., Mitzner, T.L., & Sanford J.A. (2014). A research framework to guide design of technologies for successful aging with disabilities. Gerontechnology, 13(2), 272–272. https://doi.org/10.4017/gt.2014.13.02.095.00 DOI: https://doi.org/10.4017/gt.2014.13.02.095.00
  84. Rossi, S., Conti, D., Garramone, F., Santangelo, G., Staffa, M., Varrasi, S., & Di Nuovo, A. (2020). The Role of Personality Factors and Empathy in the Acceptance and Performance of a Social Robot for Psychometric Evaluations. Robotics, 9(2), 39. https://doi.org/10.3390/robotics9020039 DOI: https://doi.org/10.3390/robotics9020039
  85. Rossi, S., Staffa, M., Bove, L., Capasso, R., & Ercolano, G. (2017). User’s Personality and Activity Influence on HRI Comfortable Distances. In Proceedings of the 9th International Conference on Social Robotics, ICSR, Tsukuba, Japan, 22–24 November 2017, Cham: Springer International Publishing, 167–177. DOI: https://doi.org/10.1007/978-3-319-70022-9_17
  86. Salem, M., Lakatos, G., Amirabdollahian, F., & Dautenhahn, K. (2015a). Towards Safe and Trustworthy Social Robots: Ethical Challenges and Practical Issues. In A. Tapus, E. André, J. C. Martin, F. Ferland, M. Ammi (Eds.) Social Robotics. ICSR 2015. Lecture Notes in Computer Science, 9388. Springer, Cham. https://doi.org/10.1007/978-3-319-25554-5_58 DOI: https://doi.org/10.1007/978-3-319-25554-5_58
  87. Salem, M., Lakatos, G., Amirabdollahian, F., & Dautenhahn, K. (2015b). Would you trust a (faulty) robot? Effects of error, task type and personality on human–robot cooperation and trust. In Proceedings of 10th annual ACM/IEEE international conference on human robot interaction. ACM, pp. 141-148. DOI: https://doi.org/10.1145/2696454.2696497
  88. Sanders, D., & Scott, P. (2020). Literature review: technological interventions and their impact on quality of life for people living with dementia. BMJ Health Care Informatics, 27, 100064. https://doi.org/10.1136/ bmjhci-2019-100064 DOI: https://doi.org/10.1136/bmjhci-2019-100064
  89. Schumacker, R. E. (2010). A beginner’s guide to structural equation modeling (3rd ed.). New York, NY: Routledge Academic.
  90. Schwaninger, I., Güldenpfennig, F., Weiss, A., & Fitzpatrick, G. (2021). What Do You Mean by Trust? Establishing Shared Meaning in Interdisciplinary Design for Assistive Technology. International Journal of Social Robotics. https://doi.org/10.1007/s12369-020-00742-w DOI: https://doi.org/10.1007/s12369-020-00742-w
  91. Sharma, R., Nah, F.F.H., Sharma, K., Katta, T.S.S.S., Pang, N., Yong, A. (2016). Smart Living for Elderly: Design and Human-Computer Interaction Considerations. In Human Aspects of IT for the Aged Population. Healthy and Active Aging. ITAP, 112-122. https://doi.org/10.1007/978-3-319-39949-2_11 DOI: https://doi.org/10.1007/978-3-319-39949-2_11
  92. Syed-Abdul, S., Malwade, S., Nursetyo, A.A., Sood, M., Bhatia, M., Barsasella, D., Liu, M.F., Chang, C.C., Srinivasan, K., Raja, R., & Yu-Chuan J.L. (2019). Virual reality among the elderly: A usefulness and acceptance study from Taiwan. BMC Geriatrics, 19(1), 223. https://doi.org/10.1007/s12369-020-00742-wdoi.org/10.1186/s12877-019-1218-8 DOI: https://doi.org/10.1186/s12877-019-1218-8
  93. Talukder, M.S., Sorwar, G., Bao, Y., Ahmed, J.U., & Palash, M.A.S. (2020). Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technological Forecasting and Social Change, 150. https://doi.org/10.1007/s12369-020-00742-wdoi.org/10.1016/j.techfore.2019.119793 DOI: https://doi.org/10.1016/j.techfore.2019.119793
  94. Tao, D., Shao, F., Liu, S., Wang, T., & Qu, X. (2016). Predicting Factors of Consumer Acceptance of Health Information Technologies: A Systematic Review. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 60(1), 598–602. https://doi.org/10.1007/s12369-020-00742-wdoi.org/10.1177/1541931213601137 DOI: https://doi.org/10.1177/1541931213601137
  95. Thordardottir, B., Malmgren Fange, A., Lethin, C., Rodriguez Gatta, D., & Chiatti, C. (2019). Acceptance and use of innovative assistive technologies among people with cognitive impairment and their caregivers: A systematic review. BioMed Research International, 1–18. https://doi.org/10.1155/2019/9196729 DOI: https://doi.org/10.1155/2019/9196729
  96. Tsai, L.T.; Portegijs, E., Rantakokko, M., Viljanen, A., Saajanaho, M., Eronen, J., & Rantanen, T. (2015). The association between objectively measured physical activity and life-space mobility among older people. Scandinavian Journal of Medicine & Science in Sports, 25(4), 368-e373. https://doi.org/10.1111/sms.12337 DOI: https://doi.org/10.1111/sms.12337
  97. Tsai, H-Y-S., Rikard, R. V., Cotten S. R. & Shillair, R. (2019). Senior technology exploration, learning, and acceptance (STELA) model: from exploration to use – a longitudinal randomized controlled trial, Educational Gerontology, 45(12), 728-743. https://doi.org/10.1080/03601277.2019.1690802 DOI: https://doi.org/10.1080/03601277.2019.1690802
  98. United Nations (2019), World Population Ageing, New York. Retrieved from https://digitallibrary.un.org/record/3846855, [20.05.2021].
  99. Venkatesh, V., Morris, M., Davis, G., & Davis F. (2003). User acceptance of information technology: toward a unified view. MIS Quartely, 27(3), 425–478. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540
  100. Venkatesh, V., Thong, J.Y.L., & Xu X. (2012). Consumer acceptance and use of informationtechnology: extending the unified theory of acceptance and use of technology, MIS Quarterly, 36, 157–178. https://doi.org/10.2307/41410412 DOI: https://doi.org/10.2307/41410412
  101. Wang, H., Tao, D., Yu, N., & Qu, X. (2020). Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF. International Journal of Medical Informatics, 139, 104156. https://doi.org/10.1016/j.ijmedinf.2020.104156 DOI: https://doi.org/10.1016/j.ijmedinf.2020.104156
  102. World Health Organization (2021a). Aging. Retrieved from https://www.who.int/health-topics/ageing#tab=tab_1, [18.05.2021].
  103. World Health Organization (2021b). Dacade of Healthy Aging: baseline report. Summary. Retrieved from https://www.who.int/publications/i/item/9789240017900, [19.05.2021].
  104. World Health Organization (2021c). Global report on ageism. Geneva. Retrieved from https://www.who.int/publications/i/item/9789240016866, [19.05.2021].
  105. World Health Organization (2021d). Older people & COVID-19. Retrieved from https://www.who.int/teams/social-determinants-of-health/demographic-change-and-healthy-ageing/covid-19 [22.04.2021].
  106. Wu, Y.H., Wrobel, J., Cornuet, M., Kerhervé, H., Damnée, S., & Rigaud, A.S. (2014). Acceptance of an assistive robot in older adults: A mixed-method study of human-robot interaction over a 1-month period in the living lab setting. Clinical interventions in aging, 9, 801–811. https://doi.org/10.2147/CIA.S56435 DOI: https://doi.org/10.2147/CIA.S56435
  107. Zhou, J., Zhang, B., Tan, R., Tseng, M.-L., & Zhang, Y. (2020). Exploring the Systematic Attributes Influencing Gerontechnology Adoption for Elderly Users Using a Meta-Analysis. Sustainability, 12, 2864. https://doi.org/10.3390/su12072864 DOI: https://doi.org/10.3390/su12072864

How to Cite

Ejdys, J., & Gulc, A. (2022). Factors influencing the intention to use assistive technologies by older adults. Human Technology, 18(1), 6–28. https://doi.org/10.14254/1795-6889.2022.18-1.2