The Acceptance of Health Information Systems by Senior Citizens: A Technology Acceptance Model
https://doi.org/10.51176/1997-9967-2025-2-37-53
Abstract
With the accelerating ageing of the population, there is an increasing need for older citizens to adapt to using digital healthcare solutions, including Health Information Systems (hereinafter – HIS), as an important element of affordable medicine. The primary purpose of this study is to examine the use and acceptance of HIS among senior citizens in Turkey who are actively employed or capable of working, using the Technology Acceptance Model (hereinafter – TAM) as the theoretical framework. A quantitative research design was applied, including survey data from 221 elderly individuals and a comparative dataset from 50 middleaged and 56 elderly participants. The results showed that self-efficacy (β = 0.73, p < 0.001) and facilitating conditions (β = 0.77, p < 0.001) significantly predicted perceived ease of use, which in turn was significantly related to perceived usefulness (β = 0.73, p < 0.001). However, neither perceived usefulness nor perceived ease of use significantly affected attitude or behavioral intention among elderly participants. T-tests revealed no statistically significant differences in HIS acceptance between middle-aged (33–40) and elderly (65–76) groups across all factors (p > 0.05). The analysis results indicated that the physical, motor and cognitive skills of elderly individuals who are active in working life or able to work are in better condition than their peers. Accordingly, the usage and acceptance levels of HIS among middle-aged and elderly individuals are almost at the same level. However, it has been determined that some improvements will improve the usage level.
About the Authors
Şeyma YahşiTurkey
Master
06760, Çubuk, Ankara
I. T. Medeni
Turkey
PhD, Professor
06760, Çubuk, Ankara
T. D. Medeni
Turkey
PhD, Professor
06760, Çubuk, Ankara
Mehmet S. Güzel
Turkey
PhD, Professor
06760, Çubuk, Ankara
References
1. Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227247. https://doi.org/10.2307/249577
2. Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557-582. https://doi.org/10.1111/j.1540-5915.1997.tb01322.x
3. Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information systems research, 9(2), 204-215. https://doi.org/10.1287/isre.9.2.204
4. Agarwal, R., Sands, D. Z., & Schneider, J. D. (2010). Quantifying the economic impact of communication inefficiencies in US hospitals. Journal of Healthcare Management, 55(4), 265-282. https://doi.org/10.1590/1982-7849rac2019170396
5. Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological bulletin, 84(5), 888. https://doi.org/10.1037/0033-2909.84.5.888
6. Albarracin, D., & Ajzen, I. (2007). Predicting and changing behavior: A reasoned action approach. Prediction and Change of Health Behavior: Applying the Reasoned Action Approach. Lawrence Erlbaum Associates, Mahwah, NJ, 3-21. https://doi.org/10.4324/9780203937082
7. Alpert, J. M., Sharma, B., Cenko, E., Zapata, R., Karnati, Y., Fillingim, R. B., Gill, T. M., Marsiske, M., Ranka, S., & Manini, T. M. (2024). Identifying barriers and facilitators for using a smartwatch to monitor health among older adults. Educational Gerontology, 50(4), 282-295. https://doi.org/10.1080/03601277.2023.2260970
8. Alsswey, A., Umar, I. N. B., & Bervell, B. (2018). Investigating the acceptance of mobile health application user interface cultural-based design to assist Arab elderly users. International Journal of Advanced Computer Science and Applications, 9(8). https://doi.org/10.14569/IJACSA.2018.090819
9. Anderson, A. A. (1996). Predictors of computer anxiety and performance in information systems. Computers in Human Behavior, 12(1), 61-77. https://doi.org/10.1037/0003-066X.51.4.355
10. Anderson, C. L., & Agarwal, R. (2011). The digitization of healthcare: boundary risks, emotion, and consumer willingness to disclose personal health information. Information systems research, 22(3), 469-490. https://doi.org/10.1287/isre.1100.0335
11. Aranha, M., Shemie, J., James, K., Deasy, C., & Heavin, C. (2024). Behavioural intention of mobile health adoption: A study of older adults presenting to the emergency department. Smart Health, 31, 100435. https://doi.org/10.1016/j.smhl.2023.100435
12. Ayabakan, S., Bardhan, I., Zheng, Z. E., & Kirksey, K. (2017). The impact of health information sharing on duplicate testing. MIS Quarterly, 41(4), 1083–1103. https://doi.org/10.25300/MISQ/2017/41.4.04
13. Bandura, A. (1992). Social cognitive theory of social referencing. In Social referencing and the social construction of reality in infancy (pp. 175-208). Springer. https://doi.org/10.1111/1467-839X.00024
14. Bandura, A., Freeman, W. H., & Lightsey, R. (1999). Self-efficacy: The exercise of control. Springer. https://doi.org/10.1891/0889-8391.13.2.158
15. Benbasat, I., & Dexter, A. S. (1986). An investigation of the effectiveness of color and graphical information presentation under varying time constraints. MIS quarterly, 10(1), 59-83. https://doi.org/10.2307/248881
16. Bozionelos, N. (2004). Socio-economic background and computer use: The role of computer anxiety and computer experience in their relationship. International Journal of Human-Computer Studies, 61(5), 725-746. https://doi.org/10.1348/0963179041752682
17. Bozkurt, A., Hamutoğlu, N.B., Kaban, A.L., Taşçı, G., & Aykul, M. (2021). Dijital bilgi çağı: Dijital toplum, dijital dönüşüm, dijital eğitim ve dijital yeterlilikler. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 7(2), 35-63. https://doi.org/10.51948/auad.911584
18. Brown, S., & Venkatesh, V. (2005). Model of Adoption of Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle. MIS quarterly, 29, 399-436. https://doi.org/10.2307/25148690
19. Chau, P. Y., & Hu, P. J. (2002). Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of management information systems, 18(4), 191-229. https://doi.org/10.1080/07421222.2002.11045699
20. Chau, P. Y., & Hu, P. J. H. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision sciences, 32(4), 699-719. https://doi.org/10.1111/j.1540-5915.2001.tb00978.x
21. Chen, K., & Chan, A. H. S. (2014). Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics, 57(5), 635-652. https://doi.org/10.1080/00140139.2014.895855
22. Chin, W. W., & Todd, P. A. (1995). On the use, usefulness, and ease of use of structural equation modeling in MIS research: A note of caution. MIS quarterly, 19(2), 237-246. http://dx.doi.org/10.2307/249690
23. Chua, S. L., Chen, D.-T., & Wong, A. F. (1999). Computer anxiety and its correlates: a meta-analysis. Computers in Human Behavior, 15(5), 609-623. https://doi.org/10.1016/S0747-5632(99)00039-4
24. Davis, F., & Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS quarterly, 13, 319. https://doi.org/10.2307/249008
25. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003. http://dx.doi.org/10.1287/mnsc.35.8.982
26. Drag, L. L., & Bieliauskas, L. A. (2010). Contemporary review 2009: cognitive aging. Journal of geriatric psychiatry and neurology, 23(2), 75-93. https://doi.org/10.1177/0891988709358590
27. Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Psychological Bulletin, 84(5), 888–918. https://doi.org/10.1037/0033-2909.84.5.888
28. Gardner, R. L., Cooper, E., Haskell, J., Harris, D. A., Poplau, S., Kroth, P. J., & Linzer, M. (2019). Physician stress and burnout: the impact of health information technology. Journal of the American Medical Informatics Association, 26(2), 106-114. https://doi.org/10.1093/jamia/ocy145
29. Ghasemaghaei, M., Hassanein, K., & Benbasat, I. (2019). Assessing the design choices for online recommendation agents for older adults: older does not always mean simpler information technology. MIS quarterly, 43(1), 329–346. https://doi.org/10.25300/misq/2019/13947
30. Godoe, P., & Johansen, T. (2012). Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept. Journal of European psychology students, 3(1), 38-52. http://dx.doi.org/10.5334/jeps.aq
31. Guner, H., & Acarturk, C. (2020). The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults. Universal Access in the Information Society, 19, 311–330. https://doi.org/10.1007/s10209-018-0642-4
32. Guo, X., Sun, Y., Wang, N., Peng, Z., & Yan, Z. (2013). The dark side of elderly acceptance of preventive mobile health services in China. Electronic Markets, 23, 49-61. http://dx.doi.org/10.1007/s12525-012-0112-4
33. Ha, J. Y., & Park, H. (2020). Effects of a Person-Centered Nursing Intervention for Frailty among Prefrail Community-Dwelling Older Adults. International Journal of Environmental Research and Public Health, 17(18), 6660. https://doi.org/10.3390/ijerph17186660
34. Heart, T., & Kalderon, E. (2013). Older adults: are they ready to adopt health-related ICT? International journal of medical informatics, 82(11), e209-e231. https://doi.org/10.1016/j.ijmedinf.2011.03.002
35. Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159-172. https://doi.org/10.1016/j.jbi.2009.07.002
36. Hong, S.-J., Lui, C. S. M., Hahn, J., Moon, J. Y., & Kim, T. G. (2013). How old are you really? Cognitive age in technology acceptance. Decision support systems, 56, 122-130. https://doi.org/10.1016/j.dss.2013.05.008
37. 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. https://doi.org/10.1016/j.ijmedinf.2017.02.002
38. Hsiao, C.-H., & Tang, K.-Y. (2015). Examining a model of mobile healthcare technology acceptance by the elderly in Taiwan. Journal of Global Information Technology Management, 18(4), 292-311. https://doi.org/10.1016/j.tele.2015.08.014
39. Hu, P. J., Chau, P. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91112. https://doi.org/10.1080/07421222.1999.11518247
40. Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. (1997). Personal computing acceptance factors in small firms: a structural equation model. MIS quarterly, 21(3), 279-305. http://dx.doi.org/10.2307/249498
41. Ismail, N. I., Abdullah, N. H., & Shamsuddin, A. (2015). Adoption of hospital information system (HIS) in Malaysian public hospitals. Procedia-Social and Behavioral Sciences, 172, 336-343. https://doi.org/10.1016/j.sbspro.2015.01.373
42. Khatun, F., Palas, M. J. U., & Ray, P. K. (2017). Using the unified theory of acceptance and use of technology model to analyze cloud-based mHealth service for primary care. Digital Medicine, 3(2), 69. http://dx.doi.org/10.4103/digm.digm_21_17
43. Lam, J. C., & Lee, M. K. (2006). Digital inclusiveness--Longitudinal study of Internet adoption by older adults. Journal of Management Information Systems, 22(4), 177-206. http://dx.doi.org/10.2753/MIS07421222220407
44. Lee, B., Chen, Y., & Hewitt, L. (2011). Age differences in constraints encountered by seniors in their use of computers and the internet. Computers in Human Behavior, 27(3), 1231-1237. https://doi.org/10.1016/j.chb.2011.01.003
45. Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12(1), 50. https://doi.org/10.17705/1CAIS.01250
46. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204. http://dx.doi.org/10.1016/S0378-7206(01)00143-4
47. Li, H., Wu, J., Gao, Y., & Shi, Y. (2016). Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective. International journal of medical informatics, 88, 8-17. https://doi.org/10.1016/j.ijmedinf.2015.12.010
48. Li, Q., & Luximon, Y. (2018). Understanding older adults’ post-adoption usage behavior and perceptions of mobile technology. International Journal of Design, 12(3), 93–105. https://doi.org/10.57698/ijdesign.2018.12.3.2869
49. Ma, Q., Chan, A. H., & Chen, K. (2016). Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Applied ergonomics, 54, 62-71. https://doi.org/10.1016/j.apergo.2015.11.015
50. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191. https://doi.org/10.1287/isre.2.3.173
51. McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334–359. https://doi.org/10.1287/isre.13.3.334.81
52. Mendi, O. (2012). E-dönüşüm sürecinde sağlık bilişimi uygulamalarının yeri ve hastaların e-sağlık uygulamaları kapsamındaki tutumlarını belirlemeye yönelik bir araştırma [Unpublished doctoral dissertation].
53. Nayak, L.U., Priest, L., & White, A.P. (2010). An application of the technology acceptance model to the level of Internet usage by older adults. Universal Access in the Information Society, 9, 367-374. https://doi.org/10.1007/s10209-009-0178-8
54. Ngafeeson, M. N. (2013). Understanding user resistance to information technology: Toward a comprehensive model in health information technology. [Unpublished doctoral dissertation].
55. Nguyen, M., Fujioka, J., Wentlandt, K., Onabajo, N., Wong, I., Bhatia, R. S., Bhattacharyya, O., & Stamenova, V. (2020). Using the technology acceptance model to explore health provider and administrator perceptions of the usefulness and ease of using technology in palliative care. BMC Palliative Care, 19(1), 138. https://doi.org/10.1186/s12904-020-00644-8
56. 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, 708726. https://doi.org/10.1057/ejis.2013.19
57. Selwyn, N. (2004). The information aged: A qualitative study of older adults’ use of information and communications technology. Journal of Aging Studies, 18(4), 369–384. https://doi.org/10.1016/j.jaging.2004.06.008
58. Smith, A. (2014). Older adults and technology use. Pew Research Center. https://www.pewresearch.org/internet/2014/04/03/older-adults-and-technology-use/
59. Srivastava, A., Ayyalasomayajula, S., Bao, C., Ayabakan, S., & Delen, D. (2022). Relationship between electronic health records strategy and user satisfaction: a longitudinal study using clinicians’ online reviews. Journal of the American Medical Informatics Association, 29(9), 1577-1583. https://doi.org/10.1093/jamia/ocac082
60. Subramanian, G.H. (1994). A Replication of Perceived Usefulness and Perceived Ease of Use Measurement*. Decision Sciences, 25, 863-874. https://doi.org/10.1111/j.1540-5915.1994.tb01873.x
61. Tat, H. C. (2018). Sağlık sektöründe hastane bilgi sistemi kullanımının teknoloji kabul modeli ile incelenmesi [Yüksek lisans tezi, Akdeniz Üniversitesi, Sosyal Bilimler Enstitüsü, İşletme Anabilim Dalı]. Akdeniz Üniversitesi Açık Erişim Sistemi. https://acikerisim.akdeniz.edu.tr/handle/123456789/3857
62. Taylor, S., & Todd, P. (1995). Assessing IT Usage: The Role of Prior Experience. MIS Quarterly, 19(4), 561–570. https://doi.org/10.2307/249633
63. Ticehurst, G., & Veal, A. (2000). Business research methods: A managerial approach. New South Wales: Longman.
64. Venkatesh, V. (1999). Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation. MIS Quarterly, 23(2), 239-260. https://doi.org/10.1111/j.1540-5915.1994.tb01873.x
65. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x
66. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
67. Vrhovec, S., & Rupnik, R. (2011). A model for resistance management in IT projects and programs. Electrotech Review, 78, 73-78.
68. Vroman, K. G., Arthanat, S., & Lysack, C. (2015). “Who over 65 is online?” Older adults’ dispositions toward information communication technology. Computers in Human Behavior, 43, 156–166. https://doi.org/10.1016/j.chb.2014.10.018
69. Wagner, N., Hassanein, K., & Head, M. (2010). Computer use by older adults: A multi-disciplinary review. Computers in Human Behavior, 26(5), 870-882. https://doi.org/10.1016/j.chb.2010.03.029
70. Wu, J.-H., Wang, S.-C., & Lin, L.-M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International Journal of Medical Informatics, 76(1), 66–77. https://doi.org/10.1016/j.ijmedinf.2006.06.006
71. Yetkin, H. (2021). Sağlık Bilişim Sistemleri Kapsamında Elektronik Reçete Uygulamasına Yönelik Hekimlerin Görüşlerinin İncelenmesi Necmettin Erbakan University. [Unpublished doctoral dissertation].
72. Zallman, L., Finnegan, K., Roll, D., Todaro, M., Oneiz, R., & Sayah, A. (2018). Impact of medical scribes in primary care on productivity, face-to-face time, and patient comfort. The Journal of the American Board of Family Medicine, 31(4), 612–619. https://doi.org/10.3122/jabfm.2018.04.170325
73. Zhang, Z. H., Jhaveri, D. J., Marshall, V. M., Bauer, D. C., Edson, J., Narayanan, R. K., Robinson, G. J., Lundberg, A. E., Bartlett, P. F., & Wray, N. R. (2014). A comparative study of techniques for differential expression analysis on RNA-Seq data. PloS ONE, 9(8), e103207. https://doi.org/10.1371/journal.pone.0103207
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For citations:
Yahşi Ş., Medeni I.T., Medeni T.D., Güzel M.S. The Acceptance of Health Information Systems by Senior Citizens: A Technology Acceptance Model. Economy: strategy and practice. 2025;20(2):37-53. https://doi.org/10.51176/1997-9967-2025-2-37-53