Published on in Vol 8, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15686, first published .
Determining Factors Affecting Nurses’ Acceptance of a Care Plan System Using a Modified Technology Acceptance Model 3: Structural Equation Model With Cross-Sectional Data

Determining Factors Affecting Nurses’ Acceptance of a Care Plan System Using a Modified Technology Acceptance Model 3: Structural Equation Model With Cross-Sectional Data

Determining Factors Affecting Nurses’ Acceptance of a Care Plan System Using a Modified Technology Acceptance Model 3: Structural Equation Model With Cross-Sectional Data

Journals

  1. Molfenter T, Roget N, Chaple M, Behlman S, Cody O, Hartzler B, Johnson E, Nichols M, Stilen P, Becker S. Use of Telehealth in Substance Use Disorder Services During and After COVID-19: Online Survey Study. JMIR Mental Health 2021;8(2):e25835 View
  2. Zarafshani K, Solaymani A, D’Itri M, Helms M, Sanjabi S. Evaluating technology acceptance in agricultural education in Iran: A study of vocational agriculture teachers. Social Sciences & Humanities Open 2020;2(1):100041 View
  3. Mynaříková L, Novotný L. Knowledge Society Failure? Barriers in the Use of ICTs and Further Teacher Education in the Czech Republic. Sustainability 2020;12(17):6933 View
  4. Fletcher-Brown J, Carter D, Pereira V, Chandwani R. Mobile technology to give a resource-based knowledge management advantage to community health nurses in an emerging economies context. Journal of Knowledge Management 2021;25(3):525 View
  5. Sitar‐Tăut D. Mobile learning acceptance in social distancing during the COVID ‐19 outbreak: The mediation effect of hedonic motivation. Human Behavior and Emerging Technologies 2021;3(3):366 View
  6. Choi E, Park J, Kang S. Nursing Students’ Acceptance Intention of a Smart Device, Information Literacy, and Problem-Solving Confidence. Healthcare 2021;9(9):1157 View
  7. Kim E, Kuan K, Vaghasiya M, Penm J, Gunja N, El Amrani R, Poon S. Passive resistance to health information technology implementation: the case of electronic medication management system. Behaviour & Information Technology 2023;42(13):2308 View
  8. Almogren A, Aljammaz N. The integrated social cognitive theory with the TAM model: The impact of M-learning in King Saud University art education. Frontiers in Psychology 2022;13 View
  9. Ho K, Chou P, Chung M. Comparison of nursing diagnostic accuracy when aided by Knowledge-Based Clinical Decision Support Systems with Clinical Diagnostic Validity and Bayesian Decision Models for psychiatric care plan formulation among nursing students: a quasi-experimental study. BMC Nursing 2023;22(1) View
  10. Qvist A, Mullan L, Nguyen L, Wynter K, Rasmussen B, Goh M, Feely K. Investigating allied health professionals’ attitudes, perceptions and acceptance of an electronic medical record using the Unified Theory of Acceptance and Use of Technology. Australian Health Review 2024;48(1):16 View
  11. Tetik G, Türkeli S, Pinar S, Tarim M. Health information systems with technology acceptance model approach: A systematic review. International Journal of Medical Informatics 2024;190:105556 View
  12. Linder L, Utendorfer H, Oliveros B, Gilliland S, Tiase V, Altizer R. Usability Evaluation of the Revised Color Me Healthy Symptom Assessment App: Perspectives of Children and Parents. Children 2024;11(10):1215 View
  13. Maeda S, Ran N, Fukuda M, Moriyama M. Factors Affecting Nurses’ Willingness to Use Information and Communication Technology Devices in Home Healthcare Nursing Tasks: Hypothesis Testing With Path Analysis Using a Technology Acceptance Model. Journal of Japan Academy of Nursing Science 2024;44(0):374 View
  14. Dermody G, Wadsworth D, Dunham M, Glass C, Fritz R. Factors Affecting Clinician Readiness to Adopt Smart Home Technology for Remote Health Monitoring: Systematic Review. JMIR Aging 2024;7:e64367 View