Published on in Vol 19, No 12 (2017): December

Consumer Adoption of Future MyData-Based Preventive eHealth Services: An Acceptance Model and Survey Study

Consumer Adoption of Future MyData-Based Preventive eHealth Services: An Acceptance Model and Survey Study

Consumer Adoption of Future MyData-Based Preventive eHealth Services: An Acceptance Model and Survey Study

Journals

  1. Harst L, Lantzsch H, Scheibe M. Theories Predicting End-User Acceptance of Telemedicine Use: Systematic Review. Journal of Medical Internet Research 2019;21(5):e13117 View
  2. Herrenkind B, Nastjuk I, Brendel A, Trang S, Kolbe L. Young people’s travel behavior – Using the life-oriented approach to understand the acceptance of autonomous driving. Transportation Research Part D: Transport and Environment 2019;74:214 View
  3. Queiroz M, Fosso Wamba S, De Bourmont M, Telles R. Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy. International Journal of Production Research 2020:1 View
  4. Häikiö J, Yli-Kauhaluoma S, Pikkarainen M, Iivari M, Koivumäki T. Expectations to data: Perspectives of service providers and users of future health and wellness services. Health and Technology 2020;10(3):621 View
  5. Grundstrom C, Korhonen O, Väyrynen K, Isomursu M. Insurance Customers’ Expectations for Sharing Health Data: Qualitative Survey Study. JMIR Medical Informatics 2020;8(3):e16102 View
  6. Herrenkind B, Brendel A, Nastjuk I, Greve M, Kolbe L. Investigating end-user acceptance of autonomous electric buses to accelerate diffusion. Transportation Research Part D: Transport and Environment 2019;74:255 View
  7. Park H, Kim K, Soh J, Hyun Y, Jang S, Lee S, Hwang G, Kim H. Factors Influencing Acceptance of Personal Health Record Apps for Workplace Health Promotion: Cross-Sectional Questionnaire Study. JMIR mHealth and uHealth 2020;8(6):e16723 View
  8. Zhang Y, Liu C, Luo S, Xie Y, Liu F, Li X, Zhou Z. Factors Influencing Patients’ Intentions to Use Diabetes Management Apps Based on an Extended Unified Theory of Acceptance and Use of Technology Model: Web-Based Survey. Journal of Medical Internet Research 2019;21(8):e15023 View
  9. van Velsen L, Evers M, Bara C, Op den Akker H, Boerema S, Hermens H. Understanding the Acceptance of an eHealth Technology in the Early Stages of Development: An End-User Walkthrough Approach and Two Case Studies. JMIR Formative Research 2018;2(1):e10474 View
  10. Tao D, Wang T, Wang T, Zhang T, Zhang X, Qu X. A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies. Computers in Human Behavior 2020;104:106147 View
  11. Choi Y, Kim J, Kwon I, Kim T, Kim S, Cha W, Jeong J, Lee J. Development of a Mobile Personal Health Record Application Designed for Emergency Care in Korea; Integrated Information from Multicenter Electronic Medical Records. Applied Sciences 2020;10(19):6711 View
  12. Yusif S, Hafeez-Baig A, Soar J, Teik D. PLS-SEM path analysis to determine the predictive relevance of e-Health readiness assessment model. Health and Technology 2020;10(6):1497 View
  13. Mathai N, McGill T, Toohey D. Factors Influencing Consumer Adoption of Electronic Health Records. Journal of Computer Information Systems 2020:1 View
  14. Qi M, Cui J, Li X, Han Y. Perceived Factors Influencing the Public Intention to Use E-Consultation: Analysis of Web-Based Survey Data. Journal of Medical Internet Research 2021;23(1):e21834 View
  15. Li D, Hu Y, Pfaff H, Wang L, Deng L, Lu C, Xia S, Cheng S, Zhu X, Wu X. Determinants of Patients’ Intention to Use the Online Inquiry Services Provided by Internet Hospitals: Empirical Evidence From China. Journal of Medical Internet Research 2020;22(10):e22716 View
  16. Pan M, Gao W. Determinants of the behavioral intention to use a mobile nursing application by nurses in China. BMC Health Services Research 2021;21(1) View

Books/Policy Documents

  1. Parra L, Rocher J, Sendra S, Lloret J. Energy Conservation for IoT Devices. View
  2. Günthner T. Making Connected Mobility Work. View