디지털융복합연구 (Journal of Digital Convergence)

  • 제20권2호
  • /
  • Pages.325-338
  • /
  • 2022
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

대학생의 비대면 진료 수용의향에 관한 연구: 사회인지이론과 기술수용모델을 중심으로

A Research on the intention to accept telemedicine of undergraduate students: based on Social Cognitive Theory and Technology Acceptance Model

  • 전하제 (차의과학대학교 AI보건의료학부) ;
  • 박서현 (차의과학대학교 AI보건의료학부) ;
  • 박채림 (차의과학대학교 AI보건의료학부) ;
  • 신영채 (차의과학대학교 AI보건의료학부) ;
  • 박세연 (차의과학대학교 AI보건의료학부) ;
  • 한세미 (차의과학대학교 AI보건의료학부)
  • 투고 : 2021.12.24
  • 심사 : 2022.02.20
  • 발행 : 2022.02.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 코로나 상황에서 한시적으로 허용된 비대면 진료에 대한 대학생의 수용행동을 탐색하기 위하여 진행되었다. 비대면 진료의 의료 서비스 및 디지털 기술 간 융합 특성을 반영하여, 각 분야의 수용행동을 이해하기 위하여 널리 연구되어 온 사회인지이론과 기술수용모델을 기반으로 비대면 진료에 대한 대학생의 인식 및 수용의향의 영향요인을 조사하였다. 연구모델 및 가설 검증을 위하여 비대면 진료 사용 경험이 없는 대학생을 대상으로 2021년 9월 8일부터 10일까지 온라인 설문조사를 시행하였다. 총 184개의 데이터가 수집되었으며, SPSS 28.0 프로그램을 이용하여 다중회귀분석 등을 실시하였다. 분석 결과, 건강기술 자기효능감, 유용성 이점, 편의성 이점, 사회적 규범, 비대면 진료 제공자에 대한 신뢰가 대학생의 비대면 진료 수용의향에 긍정적 영향을 주는 것으로 나타났다. 본 연구는 디지털 네이티브 세대인 대학생을 비대면 진료의 새로운 타겟으로 보고, 이들을 공략하기 위한 전략의 기초 방향을 제시했음에 의의가 있다.

This study was conducted to explore the acceptance behavior of undergraduate students toward telemedicine, which is temporarily allowed in the COVID-19. We applied social cognitive theory and technology acceptance model in order to reflect the convergence characteristics between medical service and digital technology of telemedicine. Based on these theoretical backgrounds, we investigated perception toward telemedicine and determinants of intention to accept telemedicine. To examine the research model and hypothesis, an online survey was conducted for college students who have not used telemedicine from September 8 to 10, 2021. A total of 184 data were collected, and multiple regression analysis was conducted using the SPSS 28.0 program. The results showed that health technology self-efficacy, usefulness and convenience benefits, social norm, and trust in telemedicine providers had positive effects on intention to accept telemedicine. This study is meaningful in that it selected undergraduate students, who are digital natives, as new targets for telemedicine, and presented the basic direction of strategies to target them.

키워드

참고문헌

  1. Ministry of Health and Welfare of Korea. (2020). Announcement of "Temporary Permissible Measures for Telephone Consultation or Prescription and Proxy Prescription". Sejong : MOHW.
  2. B. Calton, N. Abedini & M. Fratkin. (2020). Telemedicine in the Time of Coronavirus. Journal of Pain and Symptom Management, 60(1), 12-14. DOI : 10.1016/j.jpainsymman.2020.03.019.
  3. H. Y. Yun. (2021). The Measures to Improve the Legal System for the Settlement of Telehealth in the Non-Face-to-Face Era. Dankook Law Review, 45(1), 449-498. DOI : 10.17252/dlr.2021.45.1.014.
  4. Y. S. Choi. (2020). A Study on the Introduction of Telemedicine - Coronavirus Disease 2019 and the Need for the Introduction of Telemedicine -. International Law Review, 12(1), 113-137. DOI : 10.36727/jjilr.12.1.202005.005.
  5. W. Barbosa, K. Zhou, E. Waddell, T. Myers & E. R. Dorsey. (2021). Improving Access to Care: Telemedicine Across Medical Domains. Annual Review of Public Health, 42, 463-481. DOI : 10.1146/annurev-publhealth-090519-093711.
  6. K. H. Baek & S. J. Park. (2020). A Study on whether telemedicine is permitted and its scope during the pendemic. Soongsil Law Review, 48, 341-369. DOI : 10.35867/ssulri.2020.48..010.
  7. C. Peters, I. Blohm & J. M. Leimeister. (2015). Anatomy of successful business models for complex services: Insights from the telemedicine field. Journal of Management Information Systems, 32(3), 75-104. DOI : 10.1080/07421222.2015.1095034.
  8. S. Jin. (2020). A Study of Factors Affecting Use Intention of Untact Medical Diagnosis and Consultation Services. Korea Contents Association, 20(12), 180-197. DOI : 10.5392/JKCA.2020.20.12.180.
  9. J. H. Koo, & K. N. Jin. (2021). A Study on the Acceptance of Telehealth Services in 50-60s. Health and Social Welfare Review, 41(1), 265-282. DOI : 10.15709/hswr.2021.41.1.265.
  10. O. Bestsennyy, G. Gilbert, A. Harris & J. Rost. (2020). Telehealth: a quarter-trillion-dollar post-COVID-19 reality. McKinsey and Company, 29.
  11. M. E. Waselewski, E. A. Waselewski, M. Wasvary, G. Wood, K. Pratt, T. Chang & A. C. Hines. (2021). Perspectives on Telemedicine from a National Study of Youth in the United States. Telemedicine and e-Health.. DOI : 10.1089/tmj.2021.0153.
  12. FCC. (2021). Telehealth, Telemedicine, and Telecare: What's What?. Federal Communications Commission (Online). https://www.fcc.gov/general/telehealth-telemedicineand-telecare-whats-what
  13. A. M. Cardinale. (2018). The opportunity for telehealth to support neurological health care. Telemedicine and e-Health, 24(12), 969-978. DOI : 10.1089/tmj.2017.0290.
  14. M. Colucci, V. Baldo, T. Baldovin & C. Bertoncello. (2019). A "matter of communication": A new classification to compare and evaluate telehealth and telemedicine interventions and understand their effectiveness as a communication process. Health Informatics Journal, 25(2), 446-460. DOI : 10.1177/1460458217747109.
  15. Ministry of Health and Welfare of Korea. (2020). Announcement of "Temporary non-face-to-face treatment permission measures". Sejong: MOHW.
  16. Latifi, R., Parsikia, A., Boci, A., Doarn, C. R., & Merrell, R. C. (2020). Increased access to care through telemedicine in Albania: An analysis of 2,724 patients. Telemedicine and e-Health, 26(2), 164-175. DOI : 10.1089/tmj.2018.0338
  17. North, S. (2020). Telemedicine in the time of COVID and beyond. The Journal of Adolescent Health, 67(2), 145. DOI : 10.1016/j.jadohealth.2020.05.024
  18. J. Lee, J. Hoornbeek & N. K. Oh. (2020). Social cognitive orientations, social support, and physical activity among at-risk urban children: insights from a structural equation model. International Journal of Environmental Research and Public Health, 17(18). DOI : 10.3390/ijerph17186745.
  19. R. C. Plotnikoff, S. Lippke, K. S. Courneya, N. Birkett & R. J. Sigal. (2008). Physical activity and social cognitive theory. Applied Psychology, 57(4), 628-643. DOI : 10.1111/j.1464-0597.2008.00344.x.
  20. T. Zhou. (2018). Understanding online knowledge community user continuance: A social cognitive theory perspective. Data Technologies and Applications. DOI : 10.1108/DTA-10-2017-0077.
  21. C. G. Lee, S. Park, S. H. Lee, H. Kim & J. W. Park. (2018). Social cognitive theory and physical activity among Korean male high-school students. American Journal of Men's Health, 12(4), 973-980. DOI : 10.1177/1557988318754572.
  22. M. D. Young, R. C. Plotnikoff, C. E. Collins, R. Callister & P. J. Morgan. (2016). A test of social cognitive theory to explain men's physical activity during a gender-tailored weight loss program. American Journal of Men's Health.. DOI : 10.1177/1557988315600063.
  23. E. Ramirez, P. H. Kulinna & D. Cothran. (2012). Constructs of physical activity behaviour in children: The usefulness of Social Cognitive Theory. Psychology of Sport and Exercise, 13(3), 303-310. DOI : 10.1016/j.psychsport.2011.11.007.
  24. A. Bandura. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall, Englewood Cliffs, NJ.
  25. B. J. Ayotte, J. A. Margrett & J. Hicks Patrick. (2010). Physical Activity in Middle-aged and Young-old Adults. Journal of Health Psychology, 15(2), 173-185. DOI : 10.1177/1359105309342283.
  26. A. Bandura. (2004). Health Promotion by Social Cognitive Means. Health Education & Behavior, 31(2), 143-164. DOI : 10.1177/1090198104263660.
  27. A. Bandura. (1997). Self-efficacy: The exercise of control. New York: W. H. Freema. DOI : 10.1891/0889-8391.13.2.158.
  28. Han, S. Y., Lee, M. K., & Shin, H. C. (2005). Gender differences in the effect of risk factors on drinking problems in college students. The Korean Journal of Counseling and Psychotherapy, 17(4), 1003-1019.
  29. Beeson, E. T., Whitney, J. M., & Peterson, H. M. (2017). The development of a collegiate recovery program: Applying social cognitive theory within a social ecological framework. American Journal of Health Education, 48(4), 226-239. DOI : 10.1080/19325037.2017.1317304
  30. D. Wu, H. Gu, S. Gu & H. You. (2021). Individual motivation and social influence: a study of telemedicine adoption in China based on social cognitive theory. Health Policy and Technology, 10(3). DOI : 10.1016/j.hlpt.2021.100525.
  31. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340. DOI : 10.2307/249008
  32. Y. Lee, K. A. Kozar & K. R. Larsen. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12(1), 50. DOI : 10.17705/1CAIS.01250.
  33. W. R. King & J. He. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740-755. DOI : 10.1016/j.im.2006.05.003.
  34. P. Legris, J. Ingham & P. Collerette. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204. DOI : 10.1016/S0378-7206(01)00143-4.
  35. S. J. Kosterink, M. D. Van Weering & L. Van Velsen. (2019). Patient acceptance of a telemedicine service for rehabilitation care: A focus group study. International Journal of Medical Informatics, 125, 22-29. DOI : 10.1016/j.ijmedinf.2019.01.011.
  36. S. A. Kamal, M. Shafiq & P. Kakria. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60. DOI : 10.1016/j.techsoc.2019.101212.
  37. M. Zhou, L. Zhao, N. Kong, K. S. Campy, S. Qu & S. Wang. (2019). Factors influencing behavior intentions to telehealth by Chinese elderly: An extended TAM model. International Journal of Medical Informatics, 126, 118-127. DOI : 10.1016/j.ijmedinf.2019.04.001.
  38. X. Zhang & B. U. Zaman. (2019). Adoption mechanism of telemedicine in underdeveloped country. Health Informatics Journal, 26(2), 1088-1103. DOI : 10.1177/1460458219868353.
  39. S. Akter, J. D. Ambra, P. Ray & U. Hani. (2013). Modelling the impact of mHealth service quality on satisfaction, continuance and quality of life. Behaviour & Information Technology, 32(12), 1225-1241. DOI : 10.1080/0144929X.2012.745606.
  40. A. S. Grustam, J. L. Severens, J. V. Nijnatten, R. Koymans & H. J. M. Vrijhoef. (2014). Cost-effectiveness of telehealth interventions for chronic heart failure patients: a literature review. International Journal of Technology Assessment in Health Care, 30(1), 59-68. DOI : 10.1017/S0266462313000779.
  41. S. Hennemann, M. E. Beutel & R. Zwerenz. (2016). Drivers and barriers to acceptance of web-based aftercare of patients in inpatient routine care: a cross-sectional survey. Journal of Medical Internet Research, 18(12). DOI : 10.2196/jmir.6003.
  42. S. Bakshi & U. Tandon. (2021). Understanding barriers of telemedicine adoption: A study in North India. Systems Research and Behavioral Science. DOI : 10.1002/sres.2774.
  43. P. Brzoska, F. Erdsiek & D. Waury. (2017). Enabling and predisposing factors for the utilization of preventive dental health care in migrants and non-migrants in Germany. Frontiers in Public Health, 5. DOI : 10.3389/fpubh.2017.00201.
  44. T. Heart & E. Kalderon. (2013). Older adults: are they ready to adopt health-related ICT?. International Journal of Medical Informatics, 82(11), 209-231. DOI : 10.1016/j.ijmedinf.2011.03.002.
  45. S. Lee, A. Rothbard & S. Choi. (2016). Effects of comorbid health conditions on healthcare expenditures among people with severe mental illness. Journal of Mental Health, 25(4), 291-296. DOI : 10.3109/09638237.2015.1101420.
  46. L. Li, W. Peng, A. Kononova, M. Bowen & S. R. Cotten. (2020). Factors associated with older adults' long-term use of wearable activity trackers. Telemedicine and e-Health, 26(6), 769-775. DOI : 10.1089/tmj.2019.0052.
  47. L. Y. Hung, J. G. Lyons & C. H. Wu. (2020). Health information technology use among older adults in the United States, 2009-2018. Current Medical Research and Opinion, 36(5), 789-797. DOI : 10.1080/03007995.2020.1734782.
  48. H. G. Kang, S. M. Han, E. H. Kim & W. S. Jung. (2020). A Study on Promoting Healthcare Wearable Adoption of Baby Boomers for Wearable Companies. Korean Corporation Management Association. 27(4), 129-144. DOI : 10.21052/KCMR.2020.27.4.07
  49. M. S. Rahman, M. Ko, J. Warren & D. Carpenter. (2016). Healthcare Technology Self-Efficacy (HTSE) and its influence on individual attitude: An empirical study. Computers in Human Behavior, 58, 12-24. DOI : 10.1016/j.chb.2015.12.016.
  50. M. H. An, S. C. You, R. W. Park & S. W. Lee. (2021). Using an Extended Technology Acceptance Model to Understand the Factors Influencing Telehealth Utilization After Flattening the COVID-19 Curve in South Korea: Cross-sectional Survey Study. JMIR Medical Informatics, 9(1). DOI : 10.2196/25435.
  51. S. H. Kim. (2008). Moderating effects of job relevance and experience on mobile wireless technology acceptance: Adoption of a smartphone by individuals. Information & Management, 45(6), 387-393. DOI : 10.1016/j.im.2008.05.002.
  52. D. B. Obiang Mbega, E. J. Heo & S. B. Kim. (2016). Smartphones Users' Behavioral Intention in a Developing Country in Africa. Journal of Korea Service Management Society, 17(3), 89-108. DOI : 10.15706/jksms.2016.17.3.005.
  53. Y. K. Dwivedi, M. A. Shareef, A. C. Simintiras, B. Lal & V. Weerakkody. (2016). A generalised adoption model for services: A cross-country comparison of mobile health (m-health). Government Information Quarterly, 33(1), 174-187. DOI : 10.1016/j.giq.2015.06.003.
  54. S. Bakshi & U. Tandon. (2021). Understanding barriers of telemedicine adoption: A study in North India. Systems Research and Behavioral Science. DOI : 10.1002/sres.2774.
  55. V. Venkatesh & F. D. Davis. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. DOI : 10.1287/mnsc.46.2.186.11926.
  56. P. M. Salkovskis, K. A.Rimes, H. M. C. Warwick & D. M. Clark. (2002). The Health Anxiety Inventory: development and validation of scales for the measurement of health anxiety and hypochondriasis. Psychological Medicine, 32(5), 843-853. DOI : 10.1017/S0033291702005822.
  57. F. I. Gunasekara, K. Carter & T. Blakely. (2012). Comparing self-rated health and self-assessed change in health in a longitudinal survey: which is more valid?. Social Science & Medicine, 74(7), 1117-1124. DOI : 10.1016/j.socscimed.2011.11.038.
  58. K. J. Lee. (2020). 2020 Collection of Education Statistics: Higher Education Statistics. Ministry of Education & Korean Educational Development Institute.
  59. A. Bandura. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological Review, 84(2). DOI : 10.1037/0033-295X.84.2.191.
  60. S. Dang, S. Dimmick & G. Kelkar. (2009). Evaluating the evidence base for the use of home telehealth remote monitoring in elderly with heart failure. Telemedicine and e-Health, 15(8), 783-796. DOI : 10.1089/tmj.2009.0028.
  61. H. K. Andreassen, M. Trondsen, P. E. Kummervold, D. Gammon, D & P. Hjortdahl. (2006). Patients who use e-mediated communication with their doctor: new constructions of trust in the patient-doctor relationship. Qualitative Health Research, 16(2), 238-248. DOI : 10.1177/1049732305284667.