IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12492-d930623.html
   My bibliography  Save this article

Exploring the Factors Affecting the Continued Usage Intention of IoT-Based Healthcare Wearable Devices Using the TAM Model

Author

Listed:
  • Min Jung Kang

    (Department of Business Administration, Mokpo National University, Mokpo 16666, Korea)

  • Yong Cheol Hwang

    (Department of Business Administration, Jeju National University, Jeju 63105, Korea)

Abstract

There have been many attempts to predict new markets, including a new market for internet of things (IoT)-based healthcare and the IoT platform’s ability to offer a variety of applications. It is anticipated that the market for these devices will continue to grow as the healthcare sector undergoes fast expansion. IoT can measure a user’s kinetic data (calorie consumption, distance, number of steps, etc.) using wearable healthcare equipment. Most of the recent top research on IoT-based healthcare wearable devices (IWHDs) has, up to this point, concentrated on potential users. The medical industry and healthcare are being quickly changed by the use and adoption of wearable healthcare devices. This study intended to uncover the mediating impacts of “perceived ease of use”, “perceived usefulness”, and “community immersion” on the interactions between influencing factors (personalization, service convenience, interactivity), and the intention to utilize IHWDs. The moderating role of a consumer’s innovativeness in the influence link between IHWD features on perceived ease of use and perceived usefulness was also examined. The study found that personalization has a direct (+) impact on usage intention. Through this, it would be feasible to raise the intention of wearable medical devices being accepted if customized benefits that are thoroughly examined just for individuals are supplied. The association between personalization and continued use intention was shown to be partially mediated by perceived utility and community immersion. Additionally, the association between interactivity and continued use intention, was fully mediated by perceived usefulness and community immersion. By analyzing the elements influencing the usage intention of wearable healthcare devices, this study offers a marketing plan to increase the number of users. The internet of medical things (IoMT) sector has had compound growth of approximately 26% from 2018 to 2021, which is a remarkable accomplishment. The effectiveness of factors affecting IoT usage was examined in this study when applied to the actual IoT industry. First, patients with diabetes who previously had to check their blood sugar levels through a blood test can now check it through lifestyle management and steady glucose monitoring through IoMT glucose monitoring when the convenience and individuality of the service are improved. So far, 10% of all Americans have benefited from this device. Second, as an illustration of interactivity, an IoMT-connected inhaler used to assist asthma sufferers with breathing, notifies the user when the inhaler is left at home and reminds them of appropriate times to use the device. This subsequently resulted in saving 1 life out of every 3 deaths. In addition, the findings of this study may also provide a turning point for the design and development of cutting-edge IoT-based healthcare goods and services.

Suggested Citation

  • Min Jung Kang & Yong Cheol Hwang, 2022. "Exploring the Factors Affecting the Continued Usage Intention of IoT-Based Healthcare Wearable Devices Using the TAM Model," Sustainability, MDPI, vol. 14(19), pages 1-25, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12492-:d:930623
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12492/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12492/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kar Yan Tam & Shuk Ying Ho, 2005. "Web Personalization as a Persuasion Strategy: An Elaboration Likelihood Model Perspective," Information Systems Research, INFORMS, vol. 16(3), pages 271-291, September.
    2. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    3. Anderson, Eugene W & Shugan, Steven M, 1991. "Repositioning for Changing Preferences: The Case of Beef versus Poultry," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 18(2), pages 219-232, September.
    4. Yang, Kiseol, 2012. "Consumer technology traits in determining mobile shopping adoption: An application of the extended theory of planned behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 19(5), pages 484-491.
    5. Jinwoo Kim & Jungwon Lee & Kwanghee Han & Moonkyu Lee, 2002. "Businesses as Buildings: Metrics for the Architectural Quality of Internet Businesses," Information Systems Research, INFORMS, vol. 13(3), pages 239-254, September.
    6. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. McLean, Graeme & Osei-Frimpong, Kofi & Al-Nabhani, Khalid & Marriott, Hannah, 2020. "Examining consumer attitudes towards retailers' m-commerce mobile applications – An initial adoption vs. continuous use perspective," Journal of Business Research, Elsevier, vol. 106(C), pages 139-157.
    2. Brusch, Ines & Rappel, Nina, 2020. "Exploring the acceptance of instant shopping – An empirical analysis of the determinants of user intention," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).
    3. Jo-Hung Yu & Gordon Chih-Ming Ku & Yu-Chih Lo & Che-Hsiu Chen & Chin-Hsien Hsu, 2021. "Identifying the Antecedents of University Students’ Usage Behaviour of Fitness Apps," Sustainability, MDPI, vol. 13(16), pages 1-13, August.
    4. Philippe Cohard, 2020. "Information Systems Values: A Study of the Intranet in Three French Higher Education Institutions," Post-Print hal-02987225, HAL.
    5. Melih Engin & Fatih Gürses, 2019. "Adoption of Hospital Information Systems in Public Hospitals in Turkey: An Analysis with the Unified Theory of Acceptance and Use of Technology Model," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(06), pages 1-19, October.
    6. Morosan, Cristian, 2016. "An empirical examination of U.S. travelers’ intentions to use biometric e-gates in airports," Journal of Air Transport Management, Elsevier, vol. 55(C), pages 120-128.
    7. Tsung Teng Chen, 2012. "The development and empirical study of a literature review aiding system," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(1), pages 105-116, July.
    8. Abdesamad Zouine & Pierre Fenies, 2014. "The Critical Success Factors Of The ERP System Project: A Meta-Analysis Methodology," Post-Print hal-01419785, HAL.
    9. Debora Bettiga & Lucio Lamberti & Emanuele Lettieri, 2020. "Individuals’ adoption of smart technologies for preventive health care: a structural equation modeling approach," Health Care Management Science, Springer, vol. 23(2), pages 203-214, June.
    10. Chen-Yuan Chen & Bih-Yaw Shih & Shih-Hsien Yu, 2012. "Disaster prevention and reduction for exploring teachers’ technology acceptance using a virtual reality system and partial least squares techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 62(3), pages 1217-1231, July.
    11. Paul Juinn Bing Tan, 2013. "Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan," SAGE Open, , vol. 3(4), pages 21582440135, October.
    12. Mäntymäki, Matti & Salo, Jari, 2013. "Purchasing behavior in social virtual worlds: An examination of Habbo Hotel," International Journal of Information Management, Elsevier, vol. 33(2), pages 282-290.
    13. Cristopher Siegfried Kopplin, 2021. "Two heads are better than one: matchmaking tools in coworking spaces," Review of Managerial Science, Springer, vol. 15(4), pages 1045-1069, May.
    14. Ofir Turel & Catherine E. Connelly, 2012. "Team Spirit: The Influence of Psychological Collectivism on the Usage of E-Collaboration Tools," Group Decision and Negotiation, Springer, vol. 21(5), pages 703-725, September.
    15. Joan Torrent-Sellens & Cristian Salazar-Concha & Pilar Ficapal-Cusí & Francesc Saigí-Rubió, 2021. "Using Digital Platforms to Promote Blood Donation: Motivational and Preliminary Evidence from Latin America and Spain," IJERPH, MDPI, vol. 18(8), pages 1-17, April.
    16. Cliff R. Kikawa & Charity Kiconco & Moses Agaba & Dimas Ntirampeba & Amos Ssematimba & Billy M. Kalema, 2022. "Social Media Marketing for Small and Medium Enterprise Performance in Uganda: A Structural Equation Model," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
    17. Garima Malik & A. Sajeevan Rao, 2019. "Extended expectation-confirmation model to predict continued usage of ODR/ride hailing apps: role of perceived value and self-efficacy," Information Technology & Tourism, Springer, vol. 21(4), pages 461-482, December.
    18. Wu, Wei-Wen & Lan, Lawrence W. & Lee, Yu-Ting, 2011. "Exploring decisive factors affecting an organization's SaaS adoption: A case study," International Journal of Information Management, Elsevier, vol. 31(6), pages 556-563.
    19. Lim, Joon Soo & Zhang, Jun, 2022. "Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency," Technology in Society, Elsevier, vol. 69(C).
    20. Sohn, Stefanie, 2017. "A contextual perspective on consumers' perceived usefulness: The case of mobile online shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 38(C), pages 22-33.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12492-:d:930623. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.