The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults

Abstract

To become an information society, it is required that the citizens have access to information and communication technologies (ICT) in appropriate ways. ICT plays a major role to improve inclusion of various parts of the society (such as children, disabled citizens, and elderly) into daily life. According to reports of WHO, the world population is getting older. This urges the need for a systematic investigation of ICT needs of elderly citizens and potential problems being faced during the course of interaction with ICT interfaces. The present study focuses on the use and acceptance of ICT by elderly citizens in comparison to younger adults by providing data from citizens living in Turkey. It reports data collected from 232 elderly participants (60–96 years old) and 235 younger adults (19–40 years old). The findings of the study show that, both elderly and younger adults confirm the technology acceptance model (TAM) in a similar way. This was accompanied by elderly citizens’ need for assistance, encouragement and friendlier interface designs. The present study aims to contribute towards increasing awareness about the needs and expectations of elderly citizens and inspire further research on ICT use of the elderly population.

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References

  1. 1.

    Abdullah, F., Ward, R.: Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Comput. Hum. Behav. 56 (2016), 238–256 (2016)

    Article  Google Scholar 

  2. 2.

    Abdullah, F., Ward, R., Ahmed, E.: Investigating the influence of the most commonly used external variables of TAM on students’ perceived ease of use (PEOU) and perceived usefulness (PU) of e-portfolios. Comput. Hum. Behav. 63, 75–90 (2016)

    Article  Google Scholar 

  3. 3.

    Agudo-Peregrina, A.F., Hernández-García, A., Pascual-Miguel, P.J.: Behavioral intention, use behavior and the acceptance of electronic learning systems: differences between higher education and lifelong learning. Comput. Hum. Behav. 34, 301–314 (2014)

    Article  Google Scholar 

  4. 4.

    Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process 50(2), 179–211 (1991)

    Article  Google Scholar 

  5. 5.

    Al-Gahtani, S.S.: Empirical investigation of e-learning acceptance and assimilation: a structural equation model. Appl. Comput. Inform. 12(1), 27–50 (2016)

    MathSciNet  Article  Google Scholar 

  6. 6.

    Bagozzi, R.P.: The legacy of the technology acceptance model and a proposal for a paradigm shift. J. Assoc. Inform. Syst. 8(4), 244–254 (2007)

    Google Scholar 

  7. 7.

    Benbasat, I., Barki, H.: Quo vadis TAM? J. Assoc. Inform. Syst. 8(4), 211–218 (2007)

    Google Scholar 

  8. 8.

    Braun, M.T.: Obstacles to social networking website use among elderly people. Comput. Hum. Behav. 29(3), 673–680 (2013)

    Article  Google Scholar 

  9. 9.

    Bruder, C., Blessing, L., Wandke, H.: Adaptive training interfaces for less-experienced, elderly users of electronic devices. Behav. Inf. Technol. 33(1), 4–15 (2014)

    Article  Google Scholar 

  10. 10.

    Burton-Jones, A., Hubona, G.S.: The mediation of external variables in the technology acceptance model. Inf. Manag. 43(6), 706–717 (2006)

    Article  Google Scholar 

  11. 11.

    Chang, M.K., Cheung, W.: Determinants of the intention to use internet/WWW at work: a confirmatory study. Inf. Manag. 39(1), 1–14 (2001)

    Article  Google Scholar 

  12. 12.

    Chan, K., Chen, A.H.S.: Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics 57(5), 635–652 (2014)

    Article  Google Scholar 

  13. 13.

    Chaumon, M.-E.B., Michel, C., Barnard, F.T., Croisile, B.: Can ICT improve the quality of life of elderly adults living in residential home care units? From actual impacts to hidden artefacts. Behav. Inf. Technol. 33(6), 574–590 (2014)

    Article  Google Scholar 

  14. 14.

    Chung, J.E., Park, N., Wang, H., Fulk, J., McLaughlin, M.: Age differences in perceptions of online community participation among non-users: An extension of the technology acceptance model. Comput. Hum. Behav. 26(6), 1674–1684 (2010)

    Article  Google Scholar 

  15. 15.

    Czaja, S.J., Charness, N., Fisk, A.D., Hertzog, C., Nair, S.N., Rogers, W.A., Sharit, J.: Factors predicting the use of technology: findings from the center for research and education on aging and technology enhancement (CREATE). Psychol. Aging 21(2), 333–352 (2006)

    Article  Google Scholar 

  16. 16.

    Czaja, S.J., Lee, C.C.: The impact of aging on access to technology. Univ. Access Inf. Soc. 5, 341–349 (2007)

    Article  Google Scholar 

  17. 17.

    Davis, F.D.: Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Qarterly 13(3), 319–340 (1989)

    Article  Google Scholar 

  18. 18.

    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1003 (1989)

    Article  Google Scholar 

  19. 19.

    Dogruel, L., Joeckel, S., Bowman, N.D.: The use and acceptance of new media entertainment technology by elderly users: development of an expanded technology acceptance model. Behav. Inf. Technol. 34(11), 1052–1063 (2015)

    Article  Google Scholar 

  20. 20.

    Duriau, V.J., Reger, R.K., Pfarrer, M.D.: A content analysis of the content analysis literature in organization studies: research themes, data sources, and methodological refinements. Organ. Res. Methods 10, 5–34 (2007)

    Article  Google Scholar 

  21. 21.

    Fishbein, M., Ajzen, I.: Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley, Reading, MA (1975)

    Google Scholar 

  22. 22.

    Fischer, S.H., David, D., Crotty, B.H., Dierks, M., Safran, C.: Acceptance and use of health information technology by community-dwelling elderly. Int. J. Med. Inform. 83(9), 624–635 (2014)

    Article  Google Scholar 

  23. 23.

    Gefen, D., Straub, D.: A practical guide to factorial validity using PLS-graph: tutorial and annotated example. Commun. Assoc. Inf. Syst. 16(5), 91–109 (2005)

    Google Scholar 

  24. 24.

    Gefen, D., Straub, D., Boudreau, M.C.: Structural equation modeling and regression: guidelines for research practice. Commun. Assoc. Inf. Syst. 4(7), 2–74 (2000)

    Google Scholar 

  25. 25.

    González, A., Ramírez, M.P., Viadel, V.: Attitudes of the elderly toward information and communications technologies. Educ. Gerontol. 38(9), 585–594 (2012)

    Article  Google Scholar 

  26. 26.

    Hair, J.F., Sarstedt, M., Pieper, T.M., Ringle, C.M.: The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications. Long Range Plan. 45 (2012), 320–340 (2012)

    Article  Google Scholar 

  27. 27.

    Hanson, V.L.: (2010). Influencing technology adoption by elderly people. Interact. Comput., Volume 22, Issue 6, November 2010, Pages 502–509

    Article  Google Scholar 

  28. 28.

    Hanson, V.L.: Technology skill and age: what will be the same 20 years from now? Univ. Access Inf. Soc. 10(4), 443–452 (2011)

    Article  Google Scholar 

  29. 29.

    Hargittai, E., Piper, A.M., Morris, M.R.: (2018). From internet access to internet skills: digital inequality among older adults. Univ. Access Inf. Soc. https://doi.org/10.1007/s10209-018-0617-5

    Article  Google Scholar 

  30. 30.

    Heerink, M., Kröse, B., Evers, V., Wielinga, B.:Assessing acceptance of assistive social agent technology by older adults: the almere model Int. J. Soc. Robot. 2(4), 361–375 (2010)

    Article  Google Scholar 

  31. 31.

    Hoque, R., Sorwar, G.: Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int. J. Med. Inform. 101(9), 75–84 (2017)

    Article  Google Scholar 

  32. 32.

    Joo, Y.J., Lim, K.Y., Kim, E.K.: Online university students’ satisfaction and persistence: examining perceived level of presence, usefulness and ease of use as predictors in a structural model. Comput. Educ. 57(2), 2011, pp. 1654–1664 (2011)

    Article  Google Scholar 

  33. 33.

    Karahasanovi, A., Brandtzæg, P.B., Heim, J., Luders, M., Vermeir, L., Pierson, J., Lievens, B., Vanattenhoven, J., Jans, G.: Co-creation and user-generated content—elderly people. Comput. Hum. Behav. 25(3), 655–678 (2009)

    Article  Google Scholar 

  34. 34.

    Kim, Y.J., Chun, J.U., Song, J.: Investigating the role of attitude in technology acceptance from an attitude strength perspective. Int. J. Inf. Manag. 29(1), 67–77 (2009)

    Article  Google Scholar 

  35. 35.

    Lee, Y., Kozar, K.A., Larsen, K.R.T.: The technology acceptance model: past, present, and future. Commun. Assoc. Inf. Syst. 12(50), 752–780 (2003)

    Google Scholar 

  36. 36.

    Lefever, S., Dal, M., Matthíasdóttir, A.: Online data collection in academic research: advantages and limitations. Br. J. Educ. Technol. 38(4), 574–582 (2006)

    Article  Google Scholar 

  37. 37.

    Lian, J.W., Yen, D.C.: Online shopping drivers and barriers for elderly people: age and gender differences. Comput. Hum. Behav. 37 (2014), 133–143 (2014)

    Article  Google Scholar 

  38. 38.

    Lu, J., Yao, J.E., Yu, C.S.: Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. J. Strateg. Inf. Syst. 14(3), 245–268 (2005)

    Article  Google Scholar 

  39. 39.

    Ma, Q., Chan, A.H.S., Chen, K.: Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Appl. Ergon. 54 (2016), 62–71 (2016)

    Article  Google Scholar 

  40. 40.

    Macedo, I.M.: Predicting the acceptance and use of information and communication technology by older adults: an empirical examination of the revised UTAUT2. Comput. Hum. Behav. 75, 935–948 (2017)

    Article  Google Scholar 

  41. 41.

    Magsamen-Conrad, K., Upadhyaya, S., Joa, C.Y., Dowd, J.: Bridging the divide: using UTAUT to predict multigenerational tablet adoption practices. Comput. Hum. Behav. 50 (2015), 186–196 (2015)

    Article  Google Scholar 

  42. 42.

    Marquié, J.C., Jourdan-Boddaert, L., Huet, N.: Do older adults underestimate their actual computer knowledge? Behav. Inf. Technol. 21(4), 273–280 (2002)

    Article  Google Scholar 

  43. 43.

    McCloskey, D.W.: The importance of ease of use, usefulness, and trust to online consumers: an examination of the technology acceptance model with older consumers. J. Organ. End User Comput. 18(3), 47–65 (2006)

    Article  Google Scholar 

  44. 44.

    Mitzner, T.L., Boron, J.B., Fausset, C.B., Adams, A.E., Charness, N., Czaja, S.J., et al.: Older adults talk technology: technology use and attitudes. Comput. Hum. Behav. 26(6), 1710–1721 (2010)

    Article  Google Scholar 

  45. 45.

    Mynatt, E.D., Rogers, W.A.: Developing technology to support the functional independence of elderly people. Ageing Int. 27(1), 24–41 (2002)

    Article  Google Scholar 

  46. 46.

    Nayak, L.U.S., Priest, L., White, A.P.: An application of the technology acceptance model to the level of Internet use by elderly people. Univ. Access Inf. Soc. 9(4), 367–374 (2010)

    Article  Google Scholar 

  47. 47.

    Niehaves, B., Plattfaut, R.: Internet adoption by the elderly: employing IS technology acceptance theories for understanding the age-related digital divide. Eur. J. Inf. Syst. 23(6), 708–772 (2014)

    Article  Google Scholar 

  48. 48.

    Pan, S., Jordan-Marsh, M.: Internet use intention and adoption among Chinese elderly people: from the expanded technology acceptance model perspective. Comput. Hum. Behav. 26(5), 1111–1119 (2010)

    Article  Google Scholar 

  49. 49.

    Park, Y.: An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Educ. Technol. Soc. 12(3), 150–162 (2009)

    Google Scholar 

  50. 50.

    Park, Y., Son, H., Kim, C.: Investigating the determinants of construction professionals’ acceptance of web-based training: an extension of the technology acceptance model. Autom. Constr. 22, 377–386 (2012)

    Article  Google Scholar 

  51. 51.

    Park, J., Han, S.H., Kim, H.K., Cho, Y., Park, W.: Developing elements of user experience for mobile phones and services: survey, interview, and observation approaches. Hum. Factors Ergon. Manuf. Serv. Ind. 23(4), 279–293 (2013)

    Article  Google Scholar 

  52. 52.

    Patsoule, E., Koutsabasis, P.: Redesigning websites for older adults: a case study. Behav. Inf. Technol. 33(6), 561–573 (2014)

    Article  Google Scholar 

  53. 53.

    Peek, S.T.M., Wouters, E.J.M., Hoof, J., Luijkx, K.G., Boeije, H.R., Vrijhoef, H.J.M.: Factors influencing acceptance of technology for aging in place: a systematic review. Int. J. Med. Inform. 83(4), 235–248 (2014)

    Article  Google Scholar 

  54. 54.

    Petrovčič, A., Slavec, A., Dolničar, V.: The ten shades of silver: segmentation of older adults in the mobile phone market. Int. J. Hum. Comput. Interact. 34(9), 845–860 (2018)

    Article  Google Scholar 

  55. 55.

    Phang, W.C., Sutanto, J., Kankanhalli, A., Li, Y., Tan, B.C.Y., Teo, H.H.: Senior citizens’ acceptance of information systems: a study in the context of e-Government services. IEEE Trans. Eng. Manag. 53(4), 555–569 (2006)

    Article  Google Scholar 

  56. 56.

    Porter, C.E., Donthu, N.: Using the technology acceptance model to explain how attitudes determine Internet use: the role of perceived access barriers and demographics. J. Bus. Res. 59(9), 999–1007 (2006)

    Article  Google Scholar 

  57. 57.

    Preusse, K.C., Mitzner, T.L., Fausset, C.B., Rogers, W.A.: Older adults’ acceptance of activity trackers. J. Appl. Gerontol. 36(2), 127–155 (2017)

    Article  Google Scholar 

  58. 58.

    Purnomo, S.H., Lee, Y.: E-learning adoption in the banking workplace in Indonesia: an empirical study. Inf. Dev. 29(2), 138–153 (2013) 2013

    Article  Google Scholar 

  59. 59.

    Ramón-Jerónimo, M., Peral-Peral, B., Arenas-Gaitán, J.: Elderly persons and internet use. Soc. Sci. Comput. Rev. 31(4), 389–403 (2013)

    Article  Google Scholar 

  60. 60.

    Riva, G., Teruzzi, T., Anolli, L.: The use of the internet in psychological research: comparison of online and offline questionnaires. Cyber Psychol. Behav. 6(1), 73–80 (2003)

    Article  Google Scholar 

  61. 61.

    Roldán, J., Sánchez-Franco, M.J.: Variance-based structural equation modeling: guidelines for using partial least squares in information systems research. Research methodologies, innovations and philosophies in software systems engineering and information systems, Chapter 10, pp. 193–221 (2012)

    Google Scholar 

  62. 62.

    Ryu, M.H., Kim, S., Lee, E.: Understanding the factors affecting online elderly user’s participation in video UCC services. Comput. Hum. Behav. 25(3), 619–632 (2009)

    Article  Google Scholar 

  63. 63.

    Sarstedt, M., Ringle, C.M., Smith, D., Reams, R., Hair, J.F.: Partial least squares structural equation modeling (PLS-SEM): a useful tool for family business researchers. J. Fam. Bus. Strategy 5(1), 105–115 (2014)

    Article  Google Scholar 

  64. 64.

    Streukens, S., Leroi-Werelds, S.: Bootstrapping and PLS-SEM: a step-by-step guide to get more out of your bootstrap results. Eur. Manag. J. 34(6), 2016, Pages 618–632 (2016)

    Article  Google Scholar 

  65. 65.

    Subasi, O., Leitner, M., Hoeller, N., Geven, A., Tscheligi, M.: Designing accessible experiences for older users: user requirement analysis for a railway ticketing portal. Univ. Access Inf. Soc. 10, 391–402 (2011)

    Article  Google Scholar 

  66. 66.

    Tabachnick, B.G., Fidell, L.S.: Using multivariate statistics, 5th edn. Pearson, Boston (2013) (2007)

    Google Scholar 

  67. 67.

    Teo, T.: Modelling technology acceptance in education: a study of pre-service teachers. Comput. Educ. 52(2), 2009, pp. 302–312 (2009)

    Article  Google Scholar 

  68. 68.

    TurkStat: Elderly statistics 2016. Turkish Statistics Institute, March 2017 (2017)

    Google Scholar 

  69. 69.

    Vassli, L.T., Farshchian, B.A.: Acceptance of health-related ICT among elderly people living in the community: a systematic review of qualitative evidence. Int. J. Hum. Comput. Interact. 34(2), 99–116 (2018)

    Article  Google Scholar 

  70. 70.

    Venkatesh, V.: Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf. Syst. Res. 11(4), 342–365 (2000)

    Article  Google Scholar 

  71. 71.

    Venkatesh, V., Bala, H.: (2008). Technology acceptance model 3 and a research agenda on interventions. Decis. Sci., 39, pp. 273–315

    Article  Google Scholar 

  72. 72.

    Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46(2), 186–204 (2000)

    Article  Google Scholar 

  73. 73.

    Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. Q. Manag. Inf. Syst. 27(3), 425–478 (2003)

    Article  Google Scholar 

  74. 74.

    Venkatesh, V., Thong, J.Y.L., Xu, X.: Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36(1), 157–178 (2012)

    Article  Google Scholar 

  75. 75.

    Venkatesh, V., Thong, J.Y., Xu, X.: Unified theory of acceptance and use of technology: a synthesis and the road ahead. J. Assoc. Inf. Syst. 17, 328–376 (2016) No. 5 (2016

    Google Scholar 

  76. 76.

    Vijayasarathy, L.R.: Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Inf. Manag. 41(6), 2004, pp. 747–762 (2004)

    Article  Google Scholar 

  77. 77.

    Wang, L., Rau, P.L.P., Salvendy, G.: Older adults’ acceptance of information technology. Educ. Gerontol. 37(12), 1081–1099 (2011)

    Article  Google Scholar 

  78. 78.

    World Bank.: Population ages 65 and above. DataBank: World development indicators retrieved from September 8, 2017 (2017). https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS?contextual=default&end=2016&start=2000&year_high_desc=true

  79. 79.

    World Health Organization: World report on ageing and health. WHO Library Cataloguing-in-Publication Data, Luxemburg (2015)

    Google Scholar 

  80. 80.

    Yang, H., Yoo, Y.: It’s all about attitude: revisiting the technology acceptance model. Decis. Support Syst. 38(1), 2004, pp. 19–31 (2004)

    Article  Google Scholar 

  81. 81.

    Yousafzai, S.Y., Foxall, G.R., Pallister, J.G.: Technology acceptance: a meta-analysis of the TAM: part 2. J. Model. Manag. 2(3), 281–304 (2007)

    Article  Google Scholar 

  82. 82.

    Zhou, J., Rau, P.-L.P., Salvendy, G.: Age-related difference in the use of mobile phones. Univ. Access Inf. Soc. 13, 401–413 (2013)

    Article  Google Scholar 

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Guner, H., Acarturk, C. The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults. Univ Access Inf Soc 19, 311–330 (2020). https://doi.org/10.1007/s10209-018-0642-4

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Keywords

  • Senior citizens
  • Technology acceptance model (TAM)
  • Acceptance of ICT
  • Elderly population
  • Accessibility
  • Structural equation modeling (SEM)