Published on in Vol 8 , No 6 (2020) :June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17272, first published .
Influence of the Business Revenue, Recommendation, and Provider Models on Mobile Health App Adoption: Three-Country Experimental Vignette Study

Influence of the Business Revenue, Recommendation, and Provider Models on Mobile Health App Adoption: Three-Country Experimental Vignette Study

Influence of the Business Revenue, Recommendation, and Provider Models on Mobile Health App Adoption: Three-Country Experimental Vignette Study

Journals

  1. Cunha B, Rodrigues K, Zaine I, da Silva E, Viel C, Pimentel M. Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports. Journal of Medical Internet Research 2021;23(7):e24278 View
  2. Nguyen T, Nguyen T, Tran C. Exploring individuals’ adoption of COVID-19 contact-tracing apps: a mixed-methods approach. Library Hi Tech 2022;40(2):376 View
  3. Jacob C, Sezgin E, Sanchez-Vazquez A, Ivory C. Sociotechnical Factors Affecting Patients’ Adoption of Mobile Health Tools: Systematic Literature Review and Narrative Synthesis. JMIR mHealth and uHealth 2022;10(5):e36284 View
  4. Hassan A, Cucculelli M, Lamura G. Caregivers’ willingness to pay for digital support services: Comparative survey. Health Policy 2023;130:104751 View
  5. Lee J, Oh Y, Kim M, Cho B, Shin J. Willingness to Use and Pay for Digital Health Care Services According to 4 Scenarios: Results from a National Survey. JMIR mHealth and uHealth 2023;11:e40834 View
  6. Howell P, Aryal A, Wu C. Web-Based Patient Recommender Systems for Preventive Care: Protocol for Empirical Research Propositions. JMIR Research Protocols 2023;12:e43316 View

Books/Policy Documents

  1. Lupiáñez‐Villanueva F, Folkvord F, Febrer N, Gunderson L. The International Encyclopedia of Health Communication. View