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Older people's intention to use medical apps during the COVID-19 pandemic in China: an application of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and the Technology of Acceptance Model (TAM)

Published online by Cambridge University Press:  17 October 2022

Yin Ma Open the ORCID record for Yin Ma [Opens in a new window]  and
Muyuan Luo Open the ORCID record for Muyuan Luo [Opens in a new window]
Yin Ma
Affiliation:
School of Philosophy and Sociology, Lanzhou University, Lanzhou, China
Muyuan Luo*
Affiliation:
Research Institute of Social Development, Southwestern University of Finance and Economics, Chengdu, China
*
*Corresponding author. Email: luo_muyuan@sina.com
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Abstract

Previous studies on older adults' intention to adopt medical apps during irregular circumstances like the COVID-19 outbreak are still in its infancy. In order to fill this knowledge gap, we developed a theoretical framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model and the Technology of Acceptance Model (TAM) to explain Chinese older people's willingness to use medical apps during the COVID-19 pandemic. We collected 1,318 online questionnaires during the first wave of the pandemic in China in early 2020. We employed structural equation modelling to analyse the data, and the results show that (a) attitudes towards using apps influence older people's intention to use apps significantly; (b) only two factors, perceived usefulness and facilitating conditions, which were proposed in the UTAUT model, significantly predicted the older adults' intention to use apps, but not others; and (c) perceived usefulness, perceived ease of use, subjective norm and facilitating conditions all significantly impact attitudes towards using apps. Further mediation analysis found that attitudes towards using apps significantly mediated the paths suggested in the original UTAUT model. Due to the online survey method we used, older people who do not use the internet were excluded from our sampling process. However, our timely research contributes to the existing literature by showcasing older people's usage of eHealth technology in public health emergencies. It also builds on the broader discussions on technology use by combining the TAM and the UTAUT model, highlighting the vital role of people's attitude towards using technology in shaping their intention to use it.

Keywords

Technology of Acceptance Model (TAM)COVID-19ChinaUnified Theory of Acceptance and Use of Technology (UTAUT) modelmedical appsenior citizens

Information

Type
Article
Information
Ageing & Society , Volume 44 , Issue 7 , July 2024 , pp. 1515 - 1532
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Adams, DA, Nelson, RR and Todd, PA (1992) Perceived usefulness, ease of use, and usage of information technology: a replication. MIS Quarterly 16, 227–247.10.2307/249577CrossRefGoogle Scholar
Agustina, P and Indriati, F (2018) The influence of UTAUT factors on E-retention with E-satisfaction as mediating variable in E-learning. Hasanuddin Economics and Business Review 2, 19–33.Google Scholar
Ajzen, I (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes 50, 179–211.Google Scholar
Ajzen, I and Fishbein, M (2005) The influence of attitudes on behavior. In Albarracin, D, Johnson, BT and Zanna, MP (eds), The Handbook of Attitudes. Mahwah, NJ: Erlbaum, pp. 173–121.Google Scholar
Alam, MZ, Hoque, MR, Hu, W and Barua, Z (2020) Factors influencing the adoption of mHealth services in a developing country: a patient-centric study. International Journal of Information Management 50, 128–143.10.1016/j.ijinfomgt.2019.04.016CrossRefGoogle Scholar
Anderson, JC and Gerbing, DW (1988) Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin 103, 411–423.10.1037/0033-2909.103.3.411CrossRefGoogle Scholar
Askari, M, Klaver, NS, Van Gestel, TJ and Van de Klundert, J (2020) Intention to use medical apps among older people in the Netherlands: cross-sectional study. Journal of Medical Internet Research 22, e18080.10.2196/18080CrossRefGoogle ScholarPubMed
Beijing Youth Daily (2021) Accelerate reform and promote the high-quality development of China's Internet. Beijing Youth Daily. Available at http://www.xinhuanet.com/tech/2021-02/04/c_1127061225.htm.Google Scholar
Brown, SA, Venkatesh, V and Hoehle, H (2015) Technology adoption decisions in the household: a seven-model comparison. Journal of the Association for Information Science and Technology 66, 1933–1949.10.1002/asi.23305CrossRefGoogle Scholar
Byambasuren, O, Beller, E, Hoffmann, T and Glasziou, P (2020) Barriers to and facilitators of the prescription of mHealth apps in Australian general practice: qualitative study. JMIR mHealth and uHealth 8, e17447.10.2196/17447CrossRefGoogle ScholarPubMed
Chen, K (2017) An Encounter Between the Aged and New Media: AnaIyzing the Media Life Under the Perspective of Positive Aging Based on the Research of New Media Classroom in Hefei. Hefei, China: Anhui University.Google Scholar
Cheng, YM (2011) Antecedents and consequences of e-learning acceptance. Information Systems Journal 21, 269–299.10.1111/j.1365-2575.2010.00356.xCrossRefGoogle Scholar
Cheng, D, Liu, G, Qian, C and Song, YF (2008) Customer Acceptance of Internet Banking: Integrating Trust and Quality with UTAUT Model. Available at https://doi.org/10.1109/soli.2008.4686425.CrossRefGoogle Scholar
China Development (2020) Expert View: The Optimal Allocation of Emergency Medical Resources from the Perspective of Fighting COVID-19. Available at http://special.chinadevelopment.com.cn/2020zt/xgfyjjz/yqzj/2020/02/1617312.shtml.Google Scholar
Cialdini, RB, Kallgren, CA and Reno, RR (1991) A focus theory of normative conduct: a theoretical refinement and reevaluation of the role of norms in human behavior. Advances in Experimental Social Psychology 24, 201–234.10.1016/S0065-2601(08)60330-5CrossRefGoogle Scholar
Cole JI, Suman M, Schramm P and Zhou L (2017) The 2017 Digital Future Project: Surveying the Digital Future, Year Fifteen. Los Angeles, CA: Center for the Digital Future at USC Annenberg.Google Scholar
Cole JI, Suman M, Schramm P and Zhou L (2018) The 2018 Digital Future Project: Surveying the Digital Future, Year Sixteen. Los Angeles, CA: Center for the Digital Future at USC Annenberg.Google Scholar
Compeau, D, Higgins, CA and Huff, S (1999) Social cognitive theory and individual reactions to computing technology: a longitudinal study. MIS Quarterly 23, 145–158.10.2307/249749CrossRefGoogle Scholar
Davis, FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13, 319–340.10.2307/249008CrossRefGoogle Scholar
de Veer, AJE, Peeters, JM, Brabers, AEM, Schellevis, FG, Rademakers, J and Francke, AL (2015) Determinants of the intention to use e-Health by community dwelling older people. BMC Health Services Research 15, 103.10.1186/s12913-015-0765-8CrossRefGoogle ScholarPubMed
Díaz-Giménezab, J and Díaz-Saavedrac, J (2009) Retirement in Spain. Review of Economic Dynamics 12, 147–167.10.1016/j.red.2008.06.001CrossRefGoogle Scholar
Fisk, M, Livingstone, A and Pit, SW (2020) Telehealth in the context of COVID-19: changing perspectives in Australia, the United Kingdom, and the United States. Journal of Medical Internet Research 22, e19264.10.2196/19264CrossRefGoogle ScholarPubMed
Fornell, C and Larcker, DF (1981) Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18, 39–50.10.1177/002224378101800104CrossRefGoogle Scholar
Guo, XT, Sun, YQ, Wang, N, Peng, ZY and Yan, ZY (2013) The dark side of elderly acceptance of preventive mobile health services in China. Electronic Markets 23, 49–61.10.1007/s12525-012-0112-4CrossRefGoogle Scholar
Guo, C, Keane, M and Ellis, K (2019) Impacts of mobile use on third agers in China. In Xu, X (ed.), Impacts of Mobile Use and Experience on Contemporary Society. Singapore: Mobile Studies International, pp. 20–36.Google Scholar
Hair, JF, Ringle, CM and Sarstedt, M (2011) PLS-SEM: indeed a silver bullet. Journal of Marketing Theory and Practice 19, 139–152.10.2753/MTP1069-6679190202CrossRefGoogle Scholar
Hair, JF, Hollingsworth, CL, Randolph, AB and Chong, AYL (2017 a) An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management and Data Systems 117, 442–458.10.1108/IMDS-04-2016-0130CrossRefGoogle Scholar
Hair, JF, Hult, GTM, Ringle, C and Sarstedt, M (2017 b) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd Edn. London: Sage.Google Scholar
Hair, JF, Risher, JJ, Sarstedt, M and Ringle, CM (2019 a) When to use and how to report the results of PLS-SEM. European Business Review 31, 2–24.Google Scholar
Hair, JF, Sarstedt, M and Ringle, CM (2019 b) Rethinking some of the rethinking of partial least squares. European Journal of Marketing 53, 566–584.10.1108/EJM-10-2018-0665CrossRefGoogle Scholar
Han, Y, He, Y, Lyu, J, Yu, C, Bian, M and Lee, L (2020) Aging in China: perspectives on public health. Global Health Journal 4, 11–17.10.1016/j.glohj.2020.01.002CrossRefGoogle Scholar
He, K, Zhang, JB and Zeng, YM (2020) Households’ willingness to pay for energy utilization of crop straw in rural China: based on an improved UTAUT model. Energy Policy 140, 111373.10.1016/j.enpol.2020.111373CrossRefGoogle Scholar
Holtz, B and Krein, S (2011) Understanding nurse perceptions of a newly implemented electronic medical record system. Journal of Technology in Human Services 29, 247–262.10.1080/15228835.2011.639931CrossRefGoogle Scholar
Hoque, R and 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, 75–84.10.1016/j.ijmedinf.2017.02.002CrossRefGoogle ScholarPubMed
Hussain, A, Wenbi, R, da Silva, AL, Nadher, M and Mudhish, M (2015) Health and emergency-care platform for the elderly and disabled people in the Smart City. Journal of Systems and Software 110, 253–263.10.1016/j.jss.2015.08.041CrossRefGoogle Scholar
Huxhold, O, Hees, E and Webster, NJ (2020) Towards bridging the grey digital divide: changes in internet access and its predictors from 2002 to 2014 in Germany. European Journal of Ageing 17, 271–280.Google ScholarPubMed
Hwang, HG, Dutta, B and Chang, H (2019) The differing effect of gender and clinical specialty on physicians’ intention to use electronic medical record. Methods of Information in Medicine 58, e58–e71.Google ScholarPubMed
Isaac, O, Abdullah, Z, Aldholay, AH and Ameen, AA (2019) Antecedents and outcomes of internet usage within organisations in Yemen: an extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Asia Pacific Management Review 24, 335–354.10.1016/j.apmrv.2018.12.003CrossRefGoogle Scholar
Karuri, J, Waiganjo, P and Manya, A (2013) Adoption of health information systems by health workers in developing countries – contextualizing UTAUT. IST-Africa Conference & Exhibitions, 29–31 May 2013, IEEE, Nairobi, Kenya, pp. 1–8.Google Scholar
Khalilzadeh, J, Ozturk, AB and Bilgihan, A (2017) Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior 70, 460–474.10.1016/j.chb.2017.01.001CrossRefGoogle Scholar
Lan, Z, Liu, H and Yang, C (2020) Investigating influencing factors of Chinese elderly users’ intention to adopt mHealth based on the UTAUT2 model. BIBE2020: Proceedings of the Fourth International Conference on Biological Information and Biomedical Engineering, Association for Computing Machinery, New York, NY, USA. Article 16, pp. 1–5. doi: 10.1145/3403782.3403798.CrossRefGoogle Scholar
Lian, JW (2015) Critical factors for cloud based e-invoice service adoption in Taiwan: An empirical study. International Journal of Information Management 35, 98–109.10.1016/j.ijinfomgt.2014.10.005CrossRefGoogle Scholar
Li, M, Wang, ZZ and Wang, LJ (2017) The influence factors of the elderly using gerontechnology in China: based on the survey data of Beijing, Nanjing and Xianyang. Population and Development 23, 84–92.Google Scholar
Ma, Y, Ruangkanjanases, A and Chen, SC (2019) Investigating the impact of critical factors on continuance intention towards cross-border shopping websites. Sustainability 11, 5914.10.3390/su11215914CrossRefGoogle Scholar
MacKinnon, DP, Lockwood, CM and Williams, J (2004) Confidence limits for the indirect effect: distribution of the product and resampling methods. Multivariate Behavioral Research 39, 99–128.10.1207/s15327906mbr3901_4CrossRefGoogle ScholarPubMed
Manson, A and Lee, R (2011) Population aging and the generational economy: key findings. In Lee, R and Manson, A (eds), Population Aging and Generational Economy Project: A Global Perspective. Cheltenham, UK: Edward Elgar, pp. 3–31.Google Scholar
Mohlman, J and Basch, CH (2021) Health-related correlates of demonstrated smartphone expertise in community-dwelling older adults. Journal of Applied Gerontology 40, 510–518.Google ScholarPubMed
Montesdioca, GPZ and Macada, ACG (2015) Measuring user satisfaction with information security practices. Computers and Security 48, 267–280.Google Scholar
Moon, JW and Kim, YG (2001) Extending the TAM for a world-wide-web context. Information and Management 38, 217–230.10.1016/S0378-7206(00)00061-6CrossRefGoogle Scholar
Moore, GC and Benbasat, I (1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research 2, 192–222.10.1287/isre.2.3.192CrossRefGoogle Scholar
Nagarajan, NR, Teixeira, AAC and Silva, ST (2021) Ageing population: identifying the determinants of ageing in the least developed countries. Population Research and Policy Review 40, 187–210.Google Scholar
Nassuora, A (2013) Students acceptance of mobile learning for higher education in Saudi Arabia. International Journal of Learning Management Systems 1, 1–9.Google Scholar
Nistor, N, Lerche, T, Weinberger, A, Ceobanu, C and Heymann, O (2014) Towards the integration of culture into the Unified Theory of Acceptance and Use of Technology. British Journal of Educational Technology 45, 36–55.Google Scholar
Nuq, PA and Aubert, B (2013) Towards a better understanding of the intention to use eHealth services by medical professionals: the case of developing countries. International Journal of Healthcare Management 6, 217–236.Google Scholar
Or, CKL and Karsh, BT (2009) A systematic review of patient acceptance of consumer health information technology. Journal of the American Medical Informatics Association 16, 550–560.10.1197/jamia.M2888CrossRefGoogle ScholarPubMed
Parker, SJ, Jessel, S, Richardson, JE and Reid, MC (2013) Older adults are mobile too! Identifying the barriers and facilitators to older adults’ use of mHealth for pain management. BMC Geriatrics 13, 43.Google ScholarPubMed
People's Daily Online (2020) Ali released the ‘Digital Life Report for the Elderly’: don't let the elderly fall behind in the digital age. People's Daily Online. Available at https://baijiahao.baidu.com/s?id=1681342225381525125andwfr=spiderandfor=pc.Google Scholar
Research2Guidance (2015) Mhealth App Market Sizing 2015–2020. Available at https://research2guidance.com/product/mhealth-app-market-sizing-2015-2020/.Google Scholar
Ringle, CM, Wende, S and Becker, J-M (2015) SmartPLS 3. Bönningstedt, Germany: SmartPLS. Available at http://www.smartpls.com.Google Scholar
Rudnicka, E, Napierala, P, Podfigurna, A, Meczekalski, B, Smolarczyk, R, Grymowicz, M (2020) The World Health Organization (WHO) approach to healthy ageing. Maturitas 139, 6–11.Google ScholarPubMed
Sarstedt, M, Ringle, CM, Cheah, JH, Ting, HR, Moisescu, OI and Radomir, L (2020) Structural model robustness checks in PLS-SEM. Tourism Economics 26, 531–554.10.1177/1354816618823921CrossRefGoogle Scholar
Schwab, K (2016) The Fourth Industrial Revolution. Geneva: World Economic Forum.Google Scholar
Serbern, DF (2014) The Examination of Factors Influencing Social Media Usage by African American Small Business Owners Using the UTAUT Model. Minneapolis, MN: Capella University.Google Scholar
Shao, Z (2015) The New Urban Area Development. Berlin: Springer.10.1007/978-3-662-44958-5CrossRefGoogle Scholar
Straub, D, Keil, M and Brenner, W (1997) Testing the technology acceptance model across cultures: a three country study. Information and Management 33, 1–11.Google Scholar
Takian, A, Kiani, MM and Khanjankhani, K (2020) COVID-19 and the need to prioritize health equity and social determinants of health. International Journal of Public Health 65, 521–523.10.1007/s00038-020-01398-zCrossRefGoogle ScholarPubMed
Tencent (2018) Mobile Internet Report for the Elderly Users. Available at https://www.sohu.com/a/231859559_300488.Google Scholar
Thomas, TD, Thakur, D, Jackman, G-A, Thomas, M, Gajraj, R and Allen, C (2014) Measurement invariance of the UTAUT constructs in the Caribbean. International Journal of Education and Development Using Information and Communication Technology 10, 102–127.Google Scholar
Vancea, M and Solé-Casals, J (2016) Population aging in the European Information Societies: towards a comprehensive research agenda in eHealth innovations for elderly. Aging and Disease 7, 526–539.10.14336/AD.2015.1214CrossRefGoogle ScholarPubMed
van der Heijden, H (2003) Factors influencing the usage of websites: the case of a generic portal in The Netherlands. Information and Management 40, 541–549.Google Scholar
Venkatesh, V, Morris, MG, Davis, GB and Davis, FD (2003) User acceptance of information technology: toward a unified view. MIS Quarterly 27, 425–478.10.2307/30036540CrossRefGoogle Scholar
Vicente, MR (2021) ICT for healthy and active aging: the elderly as first and last movers. Telecommunications Policy 46, 102262.10.1016/j.telpol.2021.102262CrossRefGoogle Scholar
Weaver, CK, Zorn, T and Richardson, M (2010) Goods not wanted: older people's narratives of computer use rejection. Information, Communication and Society 13, 696–721.10.1080/13691180903410535CrossRefGoogle Scholar
Wootton, R (1995) Telemedicine: fad or future? Lancet 354, 73–74.Google Scholar
World Health Organization (2015) China Country Assessment Report on Ageing and Health. Geneva: World Health Organization.Google Scholar
World Health Organization (2017) Global Strategy and Action Plan on Ageing and Health. Geneva: World Organization.Google Scholar
Yang, C (2019) The fourth industrial revolution, aging workers, older learners, and lifelong learning. Paper presented at the Adult Education Research Conference. Available at https://newprairiepress.org/aerc/2019/papers/35.Google Scholar
Ye, JH, Zheng, JQ and Yi, FB (2020) A study on users’ willingness to accept mobility as a service based on UTAUT model. Technological Forecasting and Social Change 157, 120066.10.1016/j.techfore.2020.120066CrossRefGoogle Scholar
Yuan, SP, Ma, WJ, Kanthawala, S and Peng, W (2015) Keep using my health apps: discover users’ perception of health and fitness apps with the UTAUT2 model. Telemedicine and e-Health 21, 735–741.10.1089/tmj.2014.0148CrossRefGoogle ScholarPubMed
Zhou, P, Liang, C and Ma, Y (2021) Research on the influence mechanism of online health information seeking behavior of middle-aged and older adults based on the IMB model in the context of COVID-19. Chinese Journal of Management Science 30, 76–84.Google Scholar