Users’ Intention to Continue Using Online Mental Health Communities: Empowerment Theory Perspective

Liu, Jingfang; Wang, Jiayu

doi:10.3390/ijerph18189427

Cited by 25 publications

(13 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patients' extent of confirmation significantly affected both perceived usefulness (β=.817; P<.001) and satisfaction (β=.61; P<.001). These findings are consistent with the original ECM as well as the findings of previous research on information system usage (eg, telemedicine and health data reporting platform usage) among patients [32][33][34][35]. The confirmation of patients' expectations has a positive effect on perceived usefulness and satisfaction, and the improvement of perceived usefulness and satisfaction can further enhance patients' enthusiasm for continuing to use a system.…”

Section: Summary Of Findingssupporting

confidence: 89%

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

Li¹,

Xie²,

Ye³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

Background Conversational 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. Objective The 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. Methods This 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. Results A 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. Conclusions This 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.

show abstract

Section: Summary Of Findingssupporting

confidence: 89%

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

Li¹,

Xie²,

Ye³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Patients need to sift through this information to find a physician who meets and trusts their needs, which can be quite a troubling process for patients [ 6 ]. Online health communities utilize various methods to alleviate problems such as selection and service evaluation and build trust between doctors and patients [ 7 ]. Existing studies have identified several factors that influence patient choice, including patient factors, such as patient ratings [ 8 ], and physician factors, such as physician title [ 9 ], service attitudes, and the number of online reviews [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

How do patients' perceptions and doctors' images impact patient decisions? Deconstructing online physician selection using multimodal data

Bai,

Tan,

Zhao

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…The online medical and health community is the main platform for providing online consultation services, where users disseminate, obtain, use, and evaluate health [1][2][3]. Such a powerful interactive platform has a positive impact on patients' daily disease control, self-health management, and emotional support [4][5][6]. However, the convenience of internet services and trust in online health information may affect users' search behavior [7].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Classification of Users’ Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network

Cheng¹,

Lin²

2023

JMIR Form Res

View full text Add to dashboard Cite

Background Online medical and health communities provide a platform for internet users to share experiences and ask questions about medical and health issues. However, there are problems in these communities, such as the low accuracy of the classification of users’ questions and the uneven health literacy of users, which affect the accuracy of user retrieval and the professionalism of the medical personnel answering the question. In this context, it is essential to study more effective classification methods of users’ information needs. Objective Most online medical and health communities tend to provide only disease-type labels, which do not give a comprehensive summary of users’ needs. The study aims to construct a multilevel classification framework based on the graph convolutional network (GCN) model for users’ needs in online medical and health communities so that users can perform more targeted information retrieval. Methods Using the Chinese online medical and health community “Qiuyi” as an example, we crawled questions posted by users in the “Cardiovascular Disease” section as the data source. First, the disease types involved in the problem data were segmented by manual coding to generate the first-level label. Second, the needs were identified by K-means clustering to generate the users’ information needs label as the second-level label. Finally, by constructing a GCN model, users’ questions were automatically classified, thus realizing the multilevel classification of users’ needs. Results Based on the empirical research of questions posted by users in the “Cardiovascular Disease” section of Qiuyi, the hierarchical classification of users’ questions (data) was realized. The classification models designed in the study achieved accuracy, precision, recall, and F1-score of 0.6265, 0.6328, 0.5788, and 0.5912, respectively. Compared with the traditional machine learning method naïve Bayes and the deep learning method hierarchical text classification convolutional neural network, our classification model showed better performance. At the same time, we also performed a single-level classification experiment on users’ needs, which in comparison with the multilevel classification model exhibited a great improvement. Conclusions A multilevel classification framework has been designed based on the GCN model. The results demonstrated that the method is effective in classifying users’ information needs in online medical and health communities. At the same time, users with different diseases have different directions for information needs, which plays an important role in providing diversified and targeted services to the online medical and health community. Our method is also applicable to other similar disease classifications.

show abstract