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

Chatbots and Conversational Agents (617) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (908) Artificial Intelligence (1676) Adoption and Change Management of eHealth Systems (787) E-Health / Health Services Research and New Models of Care (599)

Published on in Vol 24, No 11 (2022): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40681, first published .
Investigating Patients' Continuance Intention Toward Conversational Agents in Outpatient Departments: Cross-sectional Field Survey

Investigating Patients' Continuance Intention Toward Conversational Agents in Outpatient Departments: Cross-sectional Field Survey

Investigating Patients' Continuance Intention Toward Conversational Agents in Outpatient Departments: Cross-sectional Field Survey

Authors of this article:

Xingyi Li1 Author Orcid Image ;   Shirong Xie2 Author Orcid Image ;   Zhengqiang Ye2 Author Orcid Image ;   Shishi Ma3 Author Orcid Image ;   Guangjun Yu4 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

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  1. Cai Q, Lin Y, Yu Z. Factors Influencing Learner Attitudes Towards ChatGPT-Assisted Language Learning in Higher Education. International Journal of Human–Computer Interaction 2024;40(22):7112 View
  2. Tangadulrat P, Sono S, Tangtrakulwanich B. Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students’ and Physicians’ Perceptions. JMIR Medical Education 2023;9:e50658 View
  3. Min H, Li J, Di M, Huang S, Sun X, Li T, Wu Y. Factors influencing the continuance intention of the women’s health WeChat public account: an integrated model of UTAUT2 and HBM. Frontiers in Public Health 2024;12 View
  4. Liou J, Vo T. Exploring the Relationships among Factors Influencing Healthcare Chatbot Adoption. Sustainability 2024;16(12):5050 View
  5. Hou G, Li X, Wang H. How to improve older adults’ trust and intentions to use virtual health agents: an extended technology acceptance model. Humanities and Social Sciences Communications 2024;11(1) View
  6. Abdelhafiz A, Farghly M, Sultan E, Abouelmagd M, Ashmawy Y, Elsebaie E. Medical students and ChatGPT: analyzing attitudes, practices, and academic perceptions. BMC Medical Education 2025;25(1) View
  7. Adhyka N, Rahmaddian T, Yurizali B, Ramadoni R, Wulandani Y. A Conceptual Model of Sustainable Technology Use: The Role of Confirmation and Perceived Usefulness in the Hospital X Management Information System in Padang. International Journal of Statistics in Medical Research 2025;14:76 View
  8. Zheng R, Jiang X, Shen L, He T, Ji M, Li X, Yu G. Investigating Clinicians’ Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey. Journal of Medical Internet Research 2025;27:e62732 View