Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Feb 27, 2025
Date Accepted: Jun 10, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Advancing Signaling Theory in Online Health Communities: Navigating Medical Asymmetry with a Holistic Approach
ABSTRACT
Background:
In online health communities, signaling theory has been widely applied to address information asymmetry and reduce uncertainty. Specifically, various signals are evaluated to convey the quality of healthcare services and influence patients' decision-making. However, the literature on signals in online health communities faces challenges, including arbitrary and fragmented classifications of signals and the lack of a common framework.
Objective:
To establish a common foundation for understanding the role of signals in online health communities, this study aims to provide a comprehensive framework for the signals conveyed in these communities and their influence on managing information asymmetry between physicians and patients.
Methods:
A systematic literature review using Narrative Analysis was conducted, summarizing 80 articles on signals in online health communities. The review aimed to classify, clarify, and explore the nature of these signals, their relationships, and the underlying mechanisms in the context of OHCs.
Results:
Among the 80 studies analyzed, 96.3% focused on the effects of one or more signals. However, only 2.5% examined the characteristics of signalers or their moderating effects, such as age, gender, and competence. Additionally, 31.3% explored signal interactions, including comparisons between online and offline signals and bundled services, while 30% investigated how environmental factors, such as uncertainty and consistency, affect signal transmission. Most studies (75%) concentrated on informative signals, with a notable increase in research on affective signals. Lastly, research on the interaction between affective signals and the environment remains limited.
Conclusions:
This framework provides a more comprehensive understanding of how signals in online health communities manage information asymmetry. It clarifies the construct of signals, explores their relationships, and outlines their mechanisms. Additionally, the study identifies gaps in the existing literature and offers recommendations for future research directions to enhance the role of online health communities in addressing medical asymmetry.
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