This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2452) Digital Contact Tracing, Digital Proximity Tracing, Precision Public Health (148) Outbreak and Pandemic Preparedness and Management (1727) Adoption and Change Management of eHealth Systems (712) Infectious Diseases (non-STD/STI) (1575) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517)

Published on in Vol 23, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25447, first published .
Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study

Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study

Utilizing Health Behavior Change and Technology Acceptance Models to Predict the Adoption of COVID-19 Contact Tracing Apps: Cross-sectional Survey Study

Authors of this article:

Samuel Tomczyk1 Author Orcid Image ;   Simon Barth1 Author Orcid Image ;   Silke Schmidt1 Author Orcid Image ;   Holger Muehlan1 Author Orcid Image
Article Authors Cited by (43) Tweetations (3) Metrics

Journals

  1. große Deters F, Meier T, Milek A, Horn A. Self-Focused and Other-Focused Health Concerns as Predictors of the Uptake of Corona Contact Tracing Apps: Empirical Study. Journal of Medical Internet Research 2021;23(8):e29268 View
  2. Verpaalen I, Holland R, Ritter S, van Hooff M, Ebbers W, Hooft L, Metting E, van der Laan L. Resistance to contact tracing applications: The implementation process in a social context. Computers in Human Behavior 2022;134:107299 View
  3. Vincent W. Developing and Evaluating a Measure of the Willingness to Use Pandemic-Related mHealth Tools Using National Probability Samples in the United States: Quantitative Psychometric Analyses and Tests of Sociodemographic Group Differences. JMIR Formative Research 2023;7:e38298 View
  4. Geber S, Ho S. Examining the cultural dimension of contact-tracing app adoption during the COVID-19 pandemic: a cross-country study in Singapore and Switzerland. Information, Communication & Society 2023;26(11):2229 View
  5. Tomczyk S. Appsolutely secure? Psychometric properties of the German version of an app information privacy concerns measure during COVID-19. Frontiers in Psychology 2022;13 View
  6. Chopdar P. Adoption of Covid-19 contact tracing app by extending UTAUT theory: Perceived disease threat as moderator. Health Policy and Technology 2022;11(3):100651 View
  7. Wang J, Li X, Wang P, Liu Q, Deng Z, Wang J. Research Trend of the Unified Theory of Acceptance and Use of Technology Theory: A Bibliometric Analysis. Sustainability 2021;14(1):10 View
  8. Schretzlmaier P, Hecker A, Ammenwerth E. Suitability of the Unified Theory of Acceptance and Use of Technology 2 Model for Predicting mHealth Acceptance Using Diabetes as an Example: Qualitative Methods Triangulation Study. JMIR Human Factors 2022;9(1):e34918 View
  9. Prakash A, Das S. Explaining citizens’ resistance to use digital contact tracing apps: A mixed-methods study. International Journal of Information Management 2022;63:102468 View
  10. Chung-Lee L, Catallo C. A new approach to digital health? Virtual COVID-19 care: A scoping review. DIGITAL HEALTH 2023;9 View
  11. Tomczyk S, Rahn M, Schmidt S. Sociodemographic and Psychosocial Profiles of Multi-Media Use for Risk Communication in the General Population. International Journal of Environmental Research and Public Health 2022;19(19):12777 View
  12. Prakash A, Das S, Pillai K. Understanding digital contact tracing app continuance: Insights from India. Health Policy and Technology 2021;10(4):100573 View
  13. Walrave M, Waeterloos C, Ponnet K. Reasons for Nonuse, Discontinuation of Use, and Acceptance of Additional Functionalities of a COVID-19 Contact Tracing App: Cross-sectional Survey Study. JMIR Public Health and Surveillance 2022;8(1):e22113 View
  14. Trkman M, Popovič A, Trkman P. The roles of privacy concerns and trust in voluntary use of governmental proximity tracing applications. Government Information Quarterly 2023;40(1):101787 View
  15. Ong A, Prasetyo Y, Yuduang N, Nadlifatin R, Persada S, Robas K, Chuenyindee T, Buaphiban T. Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand. International Journal of Environmental Research and Public Health 2022;19(13):7979 View
  16. Zhang Z, Vaghefi I. Continued Use of Contact-Tracing Apps in the United States and the United Kingdom: Insights From a Comparative Study Through the Lens of the Health Belief Model. JMIR Formative Research 2022;6(12):e40302 View
  17. Ong A, Chuenyindee T, Prasetyo Y, Nadlifatin R, Persada S, Gumasing M, German J, Robas K, Young M, Sittiwatethanasiri T. Utilization of Random Forest and Deep Learning Neural Network for Predicting Factors Affecting Perceived Usability of a COVID-19 Contact Tracing Mobile Application in Thailand “ThaiChana”. International Journal of Environmental Research and Public Health 2022;19(10):6111 View
  18. van der Waal N, de Wit J, Bol N, Ebbers W, Hooft L, Metting E, van der Laan L. Predictors of contact tracing app adoption: Integrating the UTAUT, HBM and contextual factors. Technology in Society 2022;71:102101 View
  19. Oyibo K, Morita P. The Role UX Design Attributes Play in the Perceived Persuasiveness of Contact Tracing Apps. Multimodal Technologies and Interaction 2022;6(10):88 View
  20. Katusiime J, Tumuhimbise W, Rwambuka Mugyenyi G, Kobutungi P, Mugaba A, Zender R, Pinkwart N, Musiimenta A. The role of mobile health technologies in promoting COVID-19 prevention: A narrative review of intervention effectiveness and adoption. DIGITAL HEALTH 2022;8:205520762211311 View
  21. Catallo C, Chung-Lee L. How Has COVID-19 Changed the Way We Do Virtual Care? A Scoping Review Protocol. Healthcare 2022;10(10):1847 View
  22. Bhimavarapu U, Sreedevi M, Chintalapudi N, Battineni G. Physical Activity Recommendation System Based on Deep Learning to Prevent Respiratory Diseases. Computers 2022;11(10):150 View
  23. Lee J, Lin L, Kang H. The Influence of Normative Perceptions on the Uptake of the COVID-19 TraceTogether Digital Contact Tracing System: Cross-sectional Study. JMIR Public Health and Surveillance 2021;7(11):e30462 View
  24. Fitriani W, Sutanto J, Handayani P, Hidayanto A. User Compliance With the Health Emergency and Disaster Management System: Systematic Literature Review. Journal of Medical Internet Research 2023;25:e41168 View
  25. Garry M, Zajac R, Hope L, Salathé M, Levine L, Merritt T. Hits and Misses: Digital Contact Tracing in a Pandemic. Perspectives on Psychological Science 2024;19(4):675 View
  26. Sutcliffe A, Bencomo N, Darby A, Paucar L, Sawyer P. To download or not to download the Covid-19 Track and Trace App? What is more influential in users’ minds?. International Journal of Human-Computer Studies 2023;180:103140 View
  27. Scholz U, Mundry R, Freund A. Predicting the use of a COVID‐19 contact tracing application: A study across two points of measurements. Applied Psychology: Health and Well-Being 2023;15(4):1673 View
  28. Kuo K. Antecedents predicting digital contact tracing acceptance: a systematic review and meta-analysis. BMC Medical Informatics and Decision Making 2023;23(1) View
  29. Gumasing M, Ong A, Sy M, Prasetyo Y, Persada S. A machine learning ensemble approach to predicting factors affecting the intention and usage behavior towards online groceries applications in the Philippines. Heliyon 2023;9(10):e20644 View
  30. Ramos Y, Jovita-Olvez H, Tomaro Q, Batara E, Mutiarin D, Alam M, Cahill D, Sharifuddin J, Senge M, Robani A, Saiyut P, Nurmandi A. Behavioral intention & use behavior of citizens in Iligan city towards the staysafe.ph application during the covid 19 pandemic. E3S Web of Conferences 2023;440:03017 View
  31. Priyansyah R, Fuady I, Pratamawaty B. Factors influencing acceptance of Indonesian contact tracing APP: Development of the technology acceptance model. Human Technology 2023;19(2):262 View
  32. Lin F, Xiong B, Zhi P, Cheng E. A three-dimensional framework of perceiving privacy: A cross-national survey on contact tracing technology and privacy concerns during the COVID-19 pandemic. Computers in Human Behavior 2024;152:108047 View
  33. Sujarwoto S, Maharani A. Facilitators and barriers to the adoption of mHealth apps for COVID-19 contact tracing: a systematic review of the literature. Frontiers in Public Health 2023;11 View
  34. Kuo K. Decision process for digital contact tracing acceptance: a cross-sectional survey based on the elaboration likelihood model. Multimedia Tools and Applications 2024;83(26):68041 View
  35. Schrills T, Kojan L, Gruner M, Calero Valdez A, Franke T. Effects of User Experience in Automated Information Processing on Perceived Usefulness of Digital Contact-Tracing Apps: Cross-Sectional Survey Study. JMIR Human Factors 2024;11:e53940 View
  36. Yeung N, Lau S, Mak W, Cheng C, Chan E, Siu J, Cheung P. Applying the Unified Theory of Acceptance and Use of Technology to Identify Factors Associated With Intention to Use Teledelivered Supportive Care Among Recently Diagnosed Breast Cancer Survivors During COVID-19 in Hong Kong: Cross-Sectional Survey. JMIR Cancer 2024;10:e51072 View
  37. Ben Nasr I, Kondrateva G, Khvatova T, Ben Arfi W. The role of Contact-Tracing Mobile Apps in pandemic prevention: A multidisciplinary perspective on health beliefs, social, and technological factors. Social Science & Medicine 2024;358:117204 View
  38. Mensah I, Zhao T. Factors driving the acceptance of COVID-19 pandemic mobile contact tracing apps: The influence of security and privacy concerns. Heliyon 2024;10(20):e39086 View
  39. Wang E, Qian D, Zhang L, Li B, Ko B, Khoury M, Renavikar M, Ganesan A, Caruso T. Acceptance of Virtual Reality in Trainees Using a Technology Acceptance Model (Preprint). JMIR Medical Education 2024 View

Books/Policy Documents

  1. Costescu C, David C, Roșan A, Ferreira P, Ferreira A, Vera L, Herrera G. Methodologies and Intelligent Systems for Technology Enhanced Learning, Workshops - 13th International Conference. View
  2. Gouveia D, Jorge F. Good Practices and New Perspectives in Information Systems and Technologies. View