IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i17p10937-d904415.html
   My bibliography  Save this article

Use of Telemedicine Technology among General Practitioners during COVID-19: A Modified Technology Acceptance Model Study in Poland

Author

Listed:
  • Renata Walczak

    (Faculty of Civil Engineering, Mechanics and Petrochemistry, Warsaw University of Technology, 09-400 Plock, Poland)

  • Magdalena Kludacz-Alessandri

    (College of Economics and Social Sciences, Warsaw University of Technology, 09-400 Plock, Poland)

  • Liliana Hawrysz

    (Faculty of Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

Abstract

During the COVID-19 pandemic, telehealth became a popular solution for the remote provision of primary care by General Practitioners (GPs) in Poland. This study aimed to assess the GPs’ acceptance of telehealth during the COVID-19 pandemic in Poland and to explain the factors that drive GPs’ need to implement a telehealth system in primary care using the modified Technology Acceptance Model (TAM). In Poland, 361 GPs from a representative sample of 361 clinics drawn from 21,500 outpatient institutions in Poland participated in the empirical study. Structural equation modelling (SEM) was used to evaluate the causal relationships that were formulated in the proposed model. Research has shown that Polish GPs reported a positive perception and high acceptance of the telehealth system during the COVID-19 pandemic. Overall, the results show that the social factors (image, decision autonomy, perception of patient interaction) significantly positively influence the technological factors (perceived ease of use and perceived usefulness) that influence the need to implement a telehealth system. The proposed socio-technological model can serve as a theoretical basis for future research and offer empirical predictions for practitioners and researchers in health departments, governments, and primary care settings.

Suggested Citation

  • Renata Walczak & Magdalena Kludacz-Alessandri & Liliana Hawrysz, 2022. "Use of Telemedicine Technology among General Practitioners during COVID-19: A Modified Technology Acceptance Model Study in Poland," IJERPH, MDPI, vol. 19(17), pages 1-18, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10937-:d:904415
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/17/10937/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/17/10937/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heather Taffet Gold & Kimberly Pitrelli & Mary Katherine Hayes & Madhuvanti Mahadeo Murphy, 2014. "Decision to Adopt Medical Technology," Medical Decision Making, , vol. 34(8), pages 1006-1015, November.
    2. Imran Khan & Guo Xitong & Zeeshan Ahmad & Fakhar Shahzad, 2019. "Investigating Factors Impelling the Adoption of e-Health: A Perspective of African Expats in China," SAGE Open, , vol. 9(3), pages 21582440198, July.
    3. 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.
    4. Mortenson, Michael J. & Vidgen, Richard, 2016. "A computational literature review of the technology acceptance model," International Journal of Information Management, Elsevier, vol. 36(6), pages 1248-1259.
    5. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    6. Kamal, Syeda Ayesha & Shafiq, Muhammad & Kakria, Priyanka, 2020. "Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM)," Technology in Society, Elsevier, vol. 60(C).
    7. Reyes, Pedro M. & Li, Suhong & Visich, John K., 2012. "Accessing antecedents and outcomes of RFID implementation in health care," International Journal of Production Economics, Elsevier, vol. 136(1), pages 137-150.
    8. Vandecasteele, Bert & Geuens, Maggie, 2010. "Motivated Consumer Innovativeness: Concept, measurement, and validation," International Journal of Research in Marketing, Elsevier, vol. 27(4), pages 308-318.
    9. 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.
    10. Chung-Hung Tsai, 2014. "Integrating Social Capital Theory, Social Cognitive Theory, and the Technology Acceptance Model to Explore a Behavioral Model of Telehealth Systems," IJERPH, MDPI, vol. 11(5), pages 1-21, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dr. Arif Anjum, 2023. "An Overview of Technology Acceptance and Adoption Models in the aftermath of COVID-19," Indian Journal of Commerce and Management Studies, Educational Research Multimedia & Publications,India, vol. 14(1), pages 01-10, January.
    2. Xin Wang & Xingmeng Ma & Ziyi Wang & Yanlong Guo, 2024. "A Study on the Factors Influencing the Sustainable Development of Education in the Context of COVID-19: Tencent Conference Online Platform," Sustainability, MDPI, vol. 16(10), pages 1-20, May.
    3. Sameena Naaz & Sarah Ali Khan & Farheen Siddiqui & Shahab Saquib Sohail & Dag Øivind Madsen & Asad Ahmad, 2022. "OdorTAM: Technology Acceptance Model for Biometric Authentication System Using Human Body Odor," IJERPH, MDPI, vol. 19(24), pages 1-17, December.

    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. Shahidi, Niousha & Tossan, Vesselina & Bourliataux-Lajoinie, Stéphane & Cacho-Elizondo, Silvia, 2022. "Behavioural intention to use a contact tracing application: The case of StopCovid in France," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    2. 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.
    3. 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.
    4. 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).
    5. Riffat Ara Zannat Tama & Md Mahmudul Hoque & Ying Liu & Mohammad Jahangir Alam & Mark Yu, 2023. "An Application of Partial Least Squares Structural Equation Modeling (PLS-SEM) to Examining Farmers’ Behavioral Attitude and Intention towards Conservation Agriculture in Bangladesh," Agriculture, MDPI, vol. 13(2), pages 1-22, February.
    6. Francisco Rejón-Guardia & Juán Sánchez-Fernández & Francisco Muñoz-Leiva, 2011. "Motivational Factors that influence the Acceptance of Microblogging Social Networks: The µBAM Model," FEG Working Paper Series 06/11, Faculty of Economics and Business (University of Granada).
    7. Siti Salwa Mohd Ishak & Sidney Newton, 2018. "Testing a Model of User Resistance Towards Technology Adoption in Construction Organizations," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1-27, December.
    8. Raouf Jaziri & Mohammad Miralam, 2019. "Modelling the crowdfunding technology adoption among novice entrepreneurs: an extended tam model," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 6(4), pages 2159-2179, June.
    9. Faqih, Khaled M.S., 2016. "An empirical analysis of factors predicting the behavioral intention to adopt Internet shopping technology among non-shoppers in a developing country context: Does gender matter?," Journal of Retailing and Consumer Services, Elsevier, vol. 30(C), pages 140-164.
    10. Sanjeev Verma, 2015. "Harnessing the Benefit of Social Networking Sites for Intentional Social Action: Determinants and Challenges," Vision, , vol. 19(2), pages 104-111, June.
    11. Queiroz, Maciel M. & Fosso Wamba, Samuel, 2019. "Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA," International Journal of Information Management, Elsevier, vol. 46(C), pages 70-82.
    12. Sarv Devaraj & Ming Fan & Rajiv Kohli, 2002. "Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics," Information Systems Research, INFORMS, vol. 13(3), pages 316-333, September.
    13. Md. Alamgir Hossain & Ruhul Amin & Abdullah Al Masud & Md. Imran Hossain & Mohammad Awal Hossen & Mohammad Kamal Hossain, 2023. "What Drives People’s Behavioral Intention Toward Telemedicine? An Emerging Economy Perspective," SAGE Open, , vol. 13(3), pages 21582440231, July.
    14. repec:zbw:bofrdp:2006_032 is not listed on IDEAS
    15. Netsanet Haile & Jörn Altmann, 2016. "Structural analysis of value creation in software service platforms," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 129-142, May.
    16. Viswanath Venkatesh, 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, INFORMS, vol. 11(4), pages 342-365, December.
    17. Natarajan, Thamaraiselvan & Balasubramanian, Senthil Arasu & Kasilingam, Dharun Lingam, 2017. "Understanding the intention to use mobile shopping applications and its influence on price sensitivity," Journal of Retailing and Consumer Services, Elsevier, vol. 37(C), pages 8-22.
    18. Gao, Tao (Tony) & Rohm, Andrew J. & Sultan, Fareena & Pagani, Margherita, 2013. "Consumers un-tethered: A three-market empirical study of consumers' mobile marketing acceptance," Journal of Business Research, Elsevier, vol. 66(12), pages 2536-2544.
    19. Liébana-Cabanillas, Francisco & Marinkovic, Veljko & Ramos de Luna, Iviane & Kalinic, Zoran, 2018. "Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 117-130.
    20. Chen Wei, 2021. "The influence of Consumers’ Purchase intention on Smart Wearable Device: A study of Consumers in East China," International Journal of Science and Business, IJSAB International, vol. 5(8), pages 46-72.
    21. Simarpreet Kaur & Sangeeta Arora, 2023. "Understanding customers’ usage behavior towards online banking services: an integrated risk–benefit framework," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(1), pages 74-98, March.

    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:jijerp:v:19:y:2022:i:17:p:10937-:d:904415. 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.