Empirical study of factors that influence the perceived usefulness of online mental health community members

Li, Jing; Liu, Dexi; Wan, Changxuan; Liang, Zifang; Zhu, Tingshao

doi:10.1002/pchj.629

Cited by 3 publications

(1 citation statement)

References 42 publications

(49 reference statements)

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“…Perceived usefulness means people believe that choosing a particular system or technology improves them, and is reinforced when either intrinsic needs are generated or direct external incentives are stimulated ( 40 ). The information users receive when interacting with other online health community members facilitates the speed of decision-making in choosing the right health service for them, thus enhancing their perceived usefulness of digital health resources ( 41 ). At the same time, the perceived usefulness of a product affects people’s willingness to continue using it, and when they perceive that a product is easier to use and more beneficial to them, their action intention is biased toward accepting it ( 42 ).…”

Section: Literature Review and Hypothesesmentioning

confidence: 99%

Continuance intention and digital health resources from the perspective of elaboration likelihood model and DART model: a structural equation modeling analysis

Fei,

Zhou,

et al. 2024

Front. Public Health

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BackgroundInternet hospitals, online health communities, and other digital health APPs have brought many changes to people’s lives. However, digital health resources are experiencing low continuance intention due to many factors, including information security, service quality, and personal characteristics of users.MethodsWe used cross-sectional surveys and structural equation modeling analysis to explore factors influencing user willingness to continue using digital health resources.ResultsInformation quality (β = 0.31, p < 0.05), service quality (β = 0.19, p < 0.05), platform reputation (β = 0.34, p < 0.05), and emotional support (β = 0.23, p < 0.05) have significant positive effects on user value co-creation behavior. Additionally, user trust and perceived usefulness could mediate the association between user value co-creation behavior and continuance intention, with mediation effects of 0.143 and 0.125, respectively. User involvement can positively moderate the association between user value co-creation behavior and user trust (β = 0.151, t = 2.480, p < 0.001). Also, user involvement can positively moderate the association between value co-creation behavior and perceived usefulness (β = 0.103, t = 3.377, p < 0.001).ConclusionThe keys to solving the problem of low continuance intention are improving the quality and service level of digital health resources, and promoting users’ value co-creation behavior. Meanwhile, enterprises should build a good reputation, create a positive communication atmosphere in the community, and enhance user participation and sense of belonging.

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Section: Literature Review and Hypothesesmentioning

confidence: 99%

Continuance intention and digital health resources from the perspective of elaboration likelihood model and DART model: a structural equation modeling analysis

Fei,

Zhou,

et al. 2024

Front. Public Health

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Early identification of high attention content for online mental health community users based on multi-level fusion model

Wang,

Luo,

Liu

2024

DTA

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PurposeThe overload of user-generated content in online mental health community makes the focus and resonance tendencies of the participating groups less clear. Thus, the purpose of this paper is to build an early identification mechanism for users' high attention content to promote early intervention and effective dissemination of professional medical guidance.Design/methodology/approachWe decouple the identification mechanism from two processes: early feature combing and algorithmic model construction. Firstly, based on the differentiated needs and concerns of the participant groups, the multiple features of “information content + source users” are refined. Secondly, a multi-level fusion model is constructed for features processing. Specifically, Bidirectional Encoder Representation from Transformers (BERT)-Bi-directional Long-Short Term Memory (BiLSTM)-Linear are used to refine the semantic features, while Graph Attention Networks (GAT) is used to capture the entity attributes and relation features. Finally, the Convolutional Neural Network (CNN) is used to optimize the multi-level fusion features.FindingsThe results show that the ACC of the multi-level fusion model is 84.42%, F1 is 79.43% and R is 76.71%. Compared with other baseline models and single feature elements, the ACC and F1 values are improved to different degrees.Originality/valueThe originality of this paper lies in analyzing multiple features based on early stages and constructing a new multi-level fusion model for processing. Further, the study is valuable for the orientation of psychological patients' needs and early guidance of professional medical care.

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Filipino Meme Culture in Reddit: A Social Exchange Theory Application in an Anonymous Online Community

Tuazon,

Ebardo

2025

Data and Metadata

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The study explores the dynamics of social exchanges within the r/Philippines subreddit, focusing on how meme culture influence user interactions and engagement. It highlights the significant role of social media in Filipino culture, noting the increasing time spent on platforms like Reddit, which serves as a unique space for community building and cultural expression. Social Exchange Theory (SET) is applied to understand participation behaviors in online communities, emphasizing perceived reciprocity and social capital as critical factors. SET provides a framework for analyzing social interactions, focusing on the perceived costs and benefits that influence user engagement in online communities. The research focuses on posts categorized under the MemePH flair in the r/Philippines subreddit, utilizing publicly accessible data without requiring ethical clearance. A content analysis was conducted to analyze the posted memes, while a thematic analysis was conducted to examine social interactions in the comments section, with classifications established before data collection. The content analysis reveals that memes in the subreddit are primarily categorized as political, propaganda, current events, and dank memes, with humor being a significant driver of engagement. The thematic analysis indicates that perceived community support, exchange ideologies, social ties, and perceived benefits are crucial in motivating users to post and interact within the subreddit.

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Empirical study of factors that influence the perceived usefulness of online mental health community members

Cited by 3 publications

References 42 publications

Continuance intention and digital health resources from the perspective of elaboration likelihood model and DART model: a structural equation modeling analysis

Continuance intention and digital health resources from the perspective of elaboration likelihood model and DART model: a structural equation modeling analysis

Early identification of high attention content for online mental health community users based on multi-level fusion model

Filipino Meme Culture in Reddit: A Social Exchange Theory Application in an Anonymous Online Community

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