ArticleJournal of medical Internet research2022
Investigating Patients' Continuance Intention Toward Conversational Agents in Outpatient Departments: Cross-sectional Field Survey.
Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Influencing factors of continuance intention of medical applications for the elderly.Scientific reports · 2026Article
- Awareness and Use of Home-Based Respiratory Pathogen Testing Services in the Internet Era: Postpandemic Questionnaire Study.JMIR formative research · 2026Article
- Use of Artificial Intelligence-Assisted Conversational Agents to Improve Patient Experience Related to Physicians: Cross-Sectional Study in China.Journal of medical Internet research · 2025Article
- 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 · 2025Article
- Medical students and ChatGPT: analyzing attitudes, practices, and academic perceptions.BMC medical education · 2025Article
- Factors influencing the continuance intention of the women's health WeChat public account: an integrated model of UTAUT2 and HBM.Frontiers in public health · 2024Article
- Using ChatGPT for Clinical Practice and Medical Education: Cross-Sectional Survey of Medical Students' and Physicians' Perceptions.JMIR medical education · 2023Article
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Authors and funding
5 authors.
Funding
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Abstract
backgroundConversational agents (CAs) have been developed in outpatient departments to improve physician-patient communication efficiency. As end users, patients' continuance intention is essential for the sustainable development of CAs.
objectiveThe aim of this study was to facilitate the successful usage of CAs by identifying key factors influencing patients' continuance intention and proposing corresponding managerial implications.
methodsThis study proposed an extended expectation-confirmation model and empirically tested the model via a cross-sectional field survey. The questionnaire included demographic characteristics, multiple-item scales, and an optional open-ended question on patients' specific expectations for CAs. Partial least squares structural equation modeling was applied to assess the model and hypotheses. The qualitative data were analyzed via thematic analysis.
resultsA total of 172 completed questionaries were received, with a 100% (172/172) response rate. The proposed model explained 75.5% of the variance in continuance intention. Both satisfaction (β=.68; P<.001) and perceived usefulness (β=.221; P=.004) were significant predictors of continuance intention. Patients' extent of confirmation significantly and positively affected both perceived usefulness (β=.817; P<.001) and satisfaction (β=.61; P<.001). Contrary to expectations, perceived ease of use had no significant impact on perceived usefulness (β=.048; P=.37), satisfaction (β=-.004; P=.63), and continuance intention (β=.026; P=.91). The following three themes were extracted from the 74 answers to the open-ended question: personalized interaction, effective utilization, and clear illustrations.
conclusionsThis study identified key factors influencing patients' continuance intention toward CAs. Satisfaction and perceived usefulness were significant predictors of continuance intention (P<.001 and P<.004, respectively) and were significantly affected by patients' extent of confirmation (P<.001 and P<.001, respectively). Developing a better understanding of patients' continuance intention can help administrators figure out how to facilitate the effective implementation of CAs. Efforts should be made toward improving the aspects that patients reasonably expect CAs to have, which include personalized interactions, effective utilization, and clear illustrations.
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