Journal of Industrial Convergence (산업융합연구)

DAEHAN Society of Industrial Management (대한산업경영학회)
권호 표지
DOI QR코드

DOI QR Code

The Study on Use Intention of Digital Healthcare using UTAUT

UTAUT를 이용한 디지털 헬스케어 사용의도에 관한 연구

  • Taehui Kim (Dept. of Science of Nursing, Joongbu University)
  • Received : 2022.10.11
  • Accepted : 2023.01.20
  • Published : 2023.01.28
Download PDF
⟨ Previous Next ⟩

Abstract

This study was to identify the factors affecting nurses' use intention of digital healthcare and the moderating effect of clinical career based on the UTAUT model. The items were composed by performance expectancy 3 items, facilitation condition 3tiems, and perceived risk 3 items. CFA was performed to verify the construct validity. As a results, average variance extracted (AVE) was .5 or higher, and construct reliability (CR) was .7 or higher. Model fit was confirmed as CMIN/df=1.797, GFI=.955, CFI=.979, TLI=.968, IFI=.979, and RMSEA=.063. The internal reliability was .93 for performance expectancy, .84 for facilitating conditions, and .64 for perceived risk. Performance expectancy, facilitating condition, and perceived risk had a significant effect on use intention, and clinical career showed a moderating effect(t=-2.159, p=.032). Therefore, in order to enhance the use intention of digital health care, performance expectancy, and facilitating conditions should be raised and perceived risk should be reduced.

본 연구는 UTAUT 모델을 기반으로 간호사의 디지털 헬스케어 사용의도에 미치는 영향요인을 파악하고 임상경력의 조절효과를 확인하고자 하였다. 문항은 성과기대 3문항, 촉진조건 3문항과 지각된 위험 3문항을 추가하여 구성하였고, 구성 타당도 검증을 위하여 확인적요인분석을 실시하였다. 그 결과 평균분산추출 .5이상, 개념신뢰도 .7이상, CMIN/df=1.797, GFI=.955, CFI=.979, TLI=.968, IFI=.979, RMSEA=.063으로 타당도와 모델적합도를 확인하였으며 내적신뢰도는 성과기대 .93, 촉진조건 .84, 지각된위험 .64로 나타났다. 회귀분석 결과 성과기대, 촉진조건, 지각된 위험은 사용의도에 유의한 영향을 미쳤고 임상경력은 지각된 위험이 사용의도에 미치는 영향에서 조절효과를 나타냈다(t=-2.159, p=.032). 따라서 디지털 헬스케어의 사용의도를 증진시키기 위해서는 성과기대, 촉진조건을 높이고 지각된 위험을 감소시켜야 하는데, 경력간호사의 역량을 활용하여 지각된 위험을 감소시킬 수 있을 것으로 생각된다.

Keywords

References

  1. WHO. (2019). WHO Guideline: Recommendations on Digital Interventions for Health system Strenghening: Web Supplement2: Summary of Findings and Grade Tables, WHO, Geneva, Switzerland. https://www.ncbi.nlm.nih.gov/books/NBK541902
  2. K. B. Kim, & K. H. Han. (2020). A Study of the Digital Healthcare Industry in the Fourth Industrial Revolution. Journal of Convergence for Information Technology, 10(3), 7-15. DOI : 10.22156/CS4SMB.2020.10.03.007
  3. K. H. Seo. (2020). Latest Global Trends in Digital Health. Healthcare medical policy forum, 18(2), 98-103. https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE09381334
  4. C. Imison, S. Castle-Clarke, R. Watson, & N. Edwards. (2016). Delivering the benefits of digital health care. London : Nuffield Trust.
  5. L. Evangelista, S. R. Steinhubl, & E. J. Topol. (2019). Digital health care for older adults. The Lancet, 393(10180), 1493-1495. DOI : 10.1016/S0140-6736(19)30800-1
  6. J. Runkle, M. Sugg, D. Boase, S. L. Galvin, & C. Coulson. (2019). Use of wearable sensors for pregnancy health and environmental monitoring: Descriptive findings from the perspective of patients and providers. Digital health, 5, 1-14. DOI : 10.1177/2055207619828220
  7. S. Kataria, & V. Ravindran. (2018). Digital health: a new dimension in rheumatology patient care. Rheumatology international, 38(11), 1949-1957. DOI : 10.1007/s00296-018-4037-x
  8. S. Ali, M. Kleib, P. Paul, O. Petrovskaya, & M. Kennedy. (2021). Compassionate nursing care and the use of digital health technologies: A scoping review. International Journal of Nursing Studies. 127, 104161, 1-10. DOI : 10.1016/j.ijnurstu.2021.104161
  9. Q. Wang, M. Su, M. Zhang, & R. Li. (2021). Integrating digital technologies and public health to fight Covid-19 pandemic: key technologies, applications, challenges and outlook of digital healthcare. International Journal of Environmental Research and Public Health, 18(11), 6053-6102. DOI : 10.3390/ijerph18116053
  10. I. C. Hwang, & Y. M. Jang. (2022). Growth and implications of the telemedicine market after the pandemic. Korean Insurance Research Institute Report, 545(0), 1-8. https://kiss.kstudy.com/thesis/thesis-view.asp?key=3946498
  11. K. D. Kwon. (2022). A survey on the Public Awareness of Digital Healthcare. Journal of Digital Contents Society, 23(3), 551-558. DOI : 10.9728/dcs.2022.23.3.551
  12. E. G. Oh. (2020). Perspectives on Nursing Profession for a Post-COVID-19 New Normal. Korean Journal of Adult Nursing, 32(3), 221-222. DOI : 10.7475/kjan.2020.32.3.221
  13. C. Whitmore et al. (2020). Impact of nurse scientist-led digital health interventions on management of chronic conditions. Nursing Outlook, 68(6), 745-762. DOI : 10.1016/j.outlook.2020.06.010
  14. S. M. Lee, & D. Lee, (2020). Healthcare wearable devices: an analysis of key factors for continuous use intention. Service Business, 14(4), 503-531. DOI : 10.1007/s11628-020-00428-3
  15. S. Ibrahim, L. Donelle, S. Regan, & S. Sidani. (2019). Predicting Registered Nurses' Behavioural Intention to Use Electronic Documentation System in Home Care: Application of an Adapted Unified Theory of Acceptance and Use of Technology Model. Nursing leadership (Toronto, Ont.), 32(2), 48-70. DOI : 10.12927/cjnl.2019.25961
  16. Z. Lulin, J. Owusu-Marfo, H. Asante Antwi, M. O. Antwi, & X. Xu. (2020). Nurses' readiness in the adoption of hospital electronic information Management Systems in Ghana: the application of the structural equation modeling and the UTAUT model. SAGE Open, 10(2). DOI : 10.1177/2158244020931814
  17. E. Ammenwerth. (2019). Technology acceptance models in health informatics: TAM and UTAUT. Studies in Health Technology and Informatics, 263, 64-71. DOI : 10.3233/SHTI190111
  18. E. Maillet, L. Mathieu, & C. Sicotte (2015). Modeling factors explaining the acceptance, actual use and satisfaction of nurses using an Electronic Patient Record in acute care settings: An extension of the UTAUT. International journal of medical informatics, 84(1), 36-47. DOI : 10.1016/j.ijmedinf.2014.09.004
  19. V. Venkatesh, M. G. Morris, G. B. Davis, & F. D. Davis. (2003). User acceptance of information technology: Toward a unified view. Management in science Quarterly, 27(3), 425-478. DOI : 10.2307/30036540
  20. J. I. Kyung, & J. W. Lee. (2020) The Fourth Industrial Revolution Factors Affecting the Vitalization of Smart Information Technology in Urban Regeneration. Journal of Real Estate Analysis 6(1), 81-100. DOI : 10.30902/jrea.2020.6.1.81
  21. P, Benner. (1984). From novice to expert excellence and power in clinical nursing practice. The American Journal of Nursing, 84(12), 1479.
  22. I. G. Chung, Z. Q. Sun, & S. J. Yoon. (2020). A Study on the Customer's Intention to Accept the Innovative Products: Focused on Unified Theory of Acceptance and Use of Technology (UTAUT), Technology Readiness Index (TRI) and Value-Based Acceptance Model (VAM). Journal of Marketing Management Research, 25(4), 89-121. DOI : 10.37202/KMMR.2020.25.4.89
  23. E. J. Heo, & M. S. Jung. (2018). A Study on Nurses' Behavioral Intention to Use the 4th Industry-based New Medical Industry: Focusing on the Expanded TAM. Korean Business Education Review. 33(6), 365-385. DOI : 10.23839/kabe.2018.33.6.365
  24. A. Dash, & A. K. Sahoo. (2022). Exploring patient's intention towqrds e-health consultation using an extended UTAUT model. Journal of Enabling Technologies. DOI : 10.1108/JET-08-2021-0042
  25. H.Y. Cho. (2022). Status of personal information leakage due to cyber attacks. Segyeilbo. https://www.segye.com/newsView/20220713517190?OutUrl=naver
  26. K. h. Kim, I.-H. Kim, & Y. Shin. (2021). A Study on Research on digital health care and information security. Paper presented at the Proceedings of the Korean Society of Computer Information Conference. 29(2), 219-220.
  27. S. M. Choi, & K. J. Kim. (2022). A Study on the Safe Use of Data in the Digital Healthcare Industry Based on the Data 3 Act. Journal of The Korea Convergence Society. 31(4), 25-37. DOI : 10.15207/JKCS.2022.13.04.025
  28. J. J. Vaske, J. Beaman, & C. C. Sponarski. (2017). Rethinking internal consistency in Cronbach's alpha. Leisure sciences, 39(2), 163-173. DOI : 10.1080/01490400.2015.1127189
  29. Y. S. Kim, J. Kim, & M. M. Park. (2015). Factors Influencing Competency in Evidence-based Practice among Clinical Nurses. Journal of Korean Academy of Nursing Administration, 21(2), 143-153. DOI : 10.11111/jkana.2015.21.2.143
  30. S. H. Lee, & Y. H. Yi. (2016). Perception on Patient Safety Culture and Patient Safety Competency of Intensive Care Unit Nurses. Journal of the Korean Data Analysis Society, 18(4), 2215-2229. UCI : G704-000930.2016.18.4.029
  31. H. K. Lee. (2015). Converged Influencing Factors on Compassion Competence in Clinical Nurses. Journal of The Korea Convergence Society, 6(5), 15-22. DOI : 10.15207/JKCS.2015.6.5.015
  32. M. J. Kim, & K. J. Kim. (2016). The influence of nurses' clinical career and communication within organization on teamwork competency. Journal of Digital Convergence 14(2), 333-344. DOI : 10.14400/JDC.2016.14.2.333
  33. E. H. Hwang. (2018) Comparison of Resilience between Novice and Experienced Nurses. Journal of the Korea Academia-Industrial cooperation Society, 19(10), 530-539. DOI : 10.5762/KAIS.2018.19.10.530
  34. S. J. Jang, H. Lee, & Y. J. Son. (2021). Perceptions of patient safety culture and medication error reporting among early-and mid-career female nurses in South Korea. International Journal of Environmental Research and Public Health, 18(9), 4853-4864. DOI : 10.3390/ ijerph18094853
  35. Y. L. Son, & E. H. Kim. (201) Impacts of Professional Autonomy and Role Conflict Clinical Decision-Making Ability of Nurses on Comprehensive Nursing Care Service Wards. Journal of the Korea Academia-Industrial cooperation Society, 19(7), 463-473. DOI : 10.5762/KAIS.2018.19.7.463