We conducted a systematic review [28], a method widely recognized for its effectiveness in enhancing construct clarity and advancing theory development in signaling research [29]. Given the diversity of signaling constructs and the interdisciplinary nature of OHCs, this review required the integration of multidimensional, nonstandardized evidence across a wide range of study types and methodologies. To accommodate this complexity, we adopted a narrative synthesis approach [30] and followed a structured, multistep screening process in line with best practices for systematic reviews [31].

To identify relevant literature, we systematically searched 7 major academic databases—Web of Science, ScienceDirect, PubMed, Scopus, Wiley Online Library, Springer, and Google Scholar—covering the period from January 2010 to December 2024. We selected 2010 as the starting point for the literature search to capture the emergence of structured and research-relevant OHCs on a global scale. Around this time, major policy initiatives such as the US Health Information Technology for Economic and Clinical Health Act and the World Health Organization’s Global Strategy on Digital Health fostered the infrastructure necessary for digital health ecosystems [32,33]. Simultaneously, influential platforms such as PatientsLikeMe, HealthBoards, and WebMD expanded beyond information repositories to include physician-patient interaction, community-driven feedback, and trust signals. Interdisciplinary academic research on these platforms, particularly through signaling theory, also began to appear shortly after 2010. Therefore, this year represents a theoretically and empirically appropriate threshold for initiating our systematic review [15,34,35].

We applied a Boolean search strategy combining the core term “signal*” with 5 domain-specific terms: “online health,” “online medical,” “m-health,” “mobile health,” and “virtual health.” Searches were conducted across titles, abstracts, and keywords, resulting in an initial pool of 961 articles.

To refine the selection and minimize the inclusion of irrelevant studies, we implemented a 4-step filtering process.

Following cross-database verification across the 7 databases, of the 961 articles, we removed 229 (23.8%) duplicate entries, yielding 732 (76.2%) unique records for further review.

We conducted a thorough screening of the titles, abstracts, and full texts of the 732 unique records to determine their relevance to our review objectives. In this step, we applied clearly defined inclusion and exclusion criteria based on study characteristics, as recommended for systematic reviews. These criteria focused specifically on the type of publication, methodological design, topical relevance, and theoretical contribution. The applied criteria are summarized in Textbox 1.

On the basis of this screening process, of the 732 studies, we excluded 670 (91.5%), resulting in 62 (8.5%) eligible articles retained for the next phase of eligibility assessment.

To enhance comprehensiveness, we conducted backward citation analysis of the remaining 62 articles. This yielded 17 additional peer-reviewed journal articles and 1 conference paper that met our inclusion criteria. These studies addressed signaling-related mechanisms even though the term “signal” or “signaling theory” did not appear in their titles or abstracts.

The final sample consisted of 80 studies, which collectively provide a comprehensive overview of current theoretical and empirical understandings of signaling theory in the context of OHCs (refer to Multimedia Appendix 1 [1-10,13,14,17-20,22,24,26,27,36-95] for the full list of the included studies).

This section identifies the primary types of signalers in OHCs and analyzes how their characteristics influence signaling behavior under conditions of information asymmetry.

Signalers, as insiders, possess privileged information about individuals, products, services, platforms, or organizations that is inaccessible to outsiders. They determine whether and how to convey this information to external audiences. In OHCs, typical signalers include physicians and platforms.

In OHCs, there is a clear information asymmetry between physicians (as signalers) and patients (as receivers), placing patients in a vulnerable and uncertain position. Therefore, patients’ trust in the physician becomes a central driver of their health care decisions [36]. While signaling theory traditionally focuses on reducing asymmetry through credible cues, its deeper function in this setting is the construction of trustworthiness.

To capture how trust is built through signaling, we draw on trust theory, which identifies 3 core dimensions of perceived trustworthiness: integrity (honesty in signaling), benevolence (patient-oriented motivation), and competence (medical expertise) [101,102]. These dimensions parallel insights from source credibility theory, where perceived expertise, trustworthiness, and interpersonal appeal shape the receiver’s interpretation of a message [103]. Together, these frameworks underscore that physician signals are not only evaluated for content accuracy but also for the personal attributes they reflect that are especially salient in digital contexts lacking face-to-face reassurance.

This trust-based classification allows us to better reflect the underlying purpose of signaling in OHCs: not merely the exchange of information but also the formation of credible physician images that patients can rely on when making health-related decisions. These dimensions are particularly relevant in online environments, where traditional face-to-face cues are absent and signal credibility must be inferred from digital traces.

Integrity refers to the extent to which a physician genuinely possesses the quality suggested by the signal. Physicians with high integrity tend to emit truthful and consistent signals that accurately reflect their actual capabilities. In contrast, physicians with weaker integrity may attempt to obscure deficiencies through misleading or exaggerated signals [36,37].

Benevolence reflects a physician’s intention to prioritize patient welfare over self-interest. Benevolent physicians are more likely to engage in altruistic behaviors such as offering free consultations or publicly sharing knowledge, thereby signaling a warm and trustworthy image [38]. When patients perceive physician behavior as profit driven, their trust diminishes, and long-term engagement is jeopardized [12,27,104].

Competence captures a physician’s medical expertise and professional skill. Competent physicians signal their credibility through validated achievements such as professional titles, certifications, and accurate diagnoses. In contrast, less competent physicians may imitate credible signalers by disclosing surface-level information to influence patient judgment [37].

Beyond individual-level signalers such as physicians, OHC platforms function as institutional signalers, shaping the broader signaling environment through systemic cues. Grounded in institutional trust theory, platforms build trust by reducing uncertainty through structures such as organizational rules, technological safeguards, and governance mechanisms [105]. As patients lack direct interaction with platforms, perceived institutional credibility becomes essential for encouraging participation.

To further substantiate this view, we integrate online trust formation models (eg, Harrison McKnight et al [106]) that emphasize structural assurances, situational normality, and institutional reputation as key drivers of initial trust in digital contexts. These insights reinforce our classification of platform-level signals into 2 core categories: presentation quality (eg, interface usability and information accuracy) and privacy protection (eg, data encryption and anonymous services). Together, these systemic features signal platform reliability, reduce perceived risk, and foster sustained patient engagement.

Drawing from previous studies on digital platform trust [11,37,39,107,108], we categorized platform-level signals into 2 overarching dimensions.

Presentation quality includes both information quality (eg, content credibility, accuracy, and governance) and functional quality (eg, interface usability, design consistency, and user navigation). These factors shape users’ perceptions of informativeness, reliability, and ease of use, directly affecting their trust in the platform’s services [11,12].

Privacy protection refers to institutional mechanisms for safeguarding users’ sensitive data, such as encryption protocols, privacy policies, and anonymous service options. These signals are especially critical in the health care context, where patients must feel confident that their personal information will not be misused or exposed.

This classification reflects how platforms, unlike individual signalers, leverage system-level affordances to signal trustworthiness. By focusing on presentation quality and privacy protection, platforms can reduce perceived institutional risk and promote patient engagement through credible signaling structures.

Receivers, typically outsiders with limited access to professional medical knowledge, actively seek and interpret signals to reduce information asymmetry in OHCs. However, signal effectiveness is not uniform across patients. Due to variations in patients’ psychological states, motivations, and cognitive capacities, individuals often interpret the same signal in different ways. Recent studies have emphasized that the outcomes of signaling processes are partially contingent on receiver characteristics [16,19,27,40,41].

Building on this view, we categorized signal receivers in OHCs along 2 critical dimensions: illness type and cognitive ability. This classification is theoretically grounded in the principle of bounded rationality within signaling theory, which posits that individuals make decisions under cognitive and informational constraints. In the health care context, these constraints are especially salient due to the complexity of medical information and the emotional vulnerability of patients. Moreover, patients’ interpretations of signals are inherently shaped by their health-related psychological needs and information processing capacities, both of which are strongly influenced by illness characteristics and individual abilities [40-42].

Patient disease attributes have been shown to significantly shape signal prioritization and health care decision-making across various OHC contexts. The severity, nature, and social sensitivity of an illness influence patients’ information needs, emotional states, and evaluation criteria when interpreting signals. For example, patients with severe illnesses (eg, tumors and cardiovascular conditions) often experience heightened anxiety and uncertainty, prompting them to prioritize signals of professional competence such as board certifications, academic titles, or hospital reputation [19,27,40-42]. Conversely, patients with milder conditions (eg, colds and gastritis) may be more responsive to relational and emotional cues such as physician friendliness and communication quality [43]. Patients managing chronic conditions (eg, diabetes and hypertension) tend to seek long-term treatment consistency and focus on informative signals related to treatment efficacy or side effects [44]. Meanwhile, patients with privacy-sensitive illnesses (eg, sexually transmitted infections and mental health issues) are particularly sensitive to both physician competence and the platform’s privacy safeguards [11,36].

A patient’s ability to process health information influences the extent to which they understand and evaluate different signals. Patients with varying levels of knowledge, background, and experience exhibit diverse evaluation capacities, motivations, and expectations [9,45,109]. Patients with higher levels of disease-specific knowledge tend to rely more on direct indicators of service quality (eg, actual treatment outcomes) rather than online word-of-mouth signals [9]. In cases of complex diseases (eg, cancer and internal medicine conditions), patients must process large volumes of intricate medical information, often making signal evaluation more difficult [41]. In such cases, the quality of medical advice may be challenging to distinguish, leading to minimal differentiation between high- and low-quality signals [45]. Moreover, patients with higher health information literacy can better navigate online health information beyond basic disease knowledge. They tend to apply established evaluation criteria when assessing online health signals [109,110]. However, older adults often face cognitive and physical limitations—such as slower information processing, weaker numerical skills, and reduced ability to assess complex content—leading them to rely more on simplified signals such as images and short text descriptions [46,111-113].

The key constructs of signaling theory as applied to signalers and receivers are summarized in Table 1.

In this section, we present a comprehensive classification of signal types in OHCs based on 4 core dimensions (source, medium, form, and affect) and examine their implications for patient decision-making. Signals are cues that signalers transmit to receivers to shape perceptions and behaviors. Within OHCs, researchers have identified multiple distinct signals, including physicians’ titles [42], hospital levels [4], self-representation [47], review valence [48], and patient feedback [19].

From our literature review, we observed that the key stakeholders in the signaling process within OHCs remain relatively simple—physicians, patients, and platforms—with signals flowing through physician-patient, patient-patient, and platform-patient interactions. Typically, patients are influenced by multiple signals from various participants within the information flow. However, there is a notable lack of studies that conceptually distinguished the different types of signals within OHCs. To address this gap, we classified signals based on their characteristics, as outlined in Table 2.

Signal source refers to the origin of the signal, which can be classified into internal-party signals and third-party signals. Internal-party signals are generated directly by health care providers and are not independently verified by external entities. Common examples include service price [6,19,49], response to patient inquiries [2,6,50,51], knowledge sharing [4,47], interaction records [6,49-51], and photo or video images [52-54].

Third-party signals originate from external verification sources such as medical institutions, administrative authorities, or automated platform mechanisms. These signals tend to be more objective and credible as they undergo external supervision and validation. Examples include (1) physician credentials that rely on rigorous professional evaluations by official medical associations, serving as indicators of technical competence and professional qualifications (title, hospital level, university, clinical experience, and board certification) [2,6,14,19,115,117]; (2) regulatory information that provides a verified history of malpractice issues, ensuring high information credibility (sanctions and malpractice records, which are officially recognized by national authorities) [19]; and (3) behavioral data that are automatically generated by platforms, making them immune to subjective manipulation (registration duration [14], response speed [116,118], and recommendation value [55]).

Compared to internal-party signals, third-party signals are generally more reliable as external supervision reduces the risk of misleading or false signals.

Signal medium refers to the mode of transmission, distinguishing between offline signals and online signals.

Patients often rely on offline experiences to assess physician credibility. They are familiar with the evaluation system and organizational structure of traditional hospitals and frequently base their offline consultation decisions on physician credentials, hospital ranking, and professional certifications [4,7,56]. Because these signals align with common knowledge and established norms, patients tend to trust offline signals more readily.

Physicians generate online signals through digital interactions on OHC platforms [56,116]. These signals serve as indicators of online service quality and reflect a physician’s engagement and willingness to contribute beyond formal medical consultations [22]. Common online signals include platform-granted service badges (eg, “Service Star” recognition) [9,57]; prosocial behavior, such as knowledge sharing and voluntary contributions [38]; and participation in online health care teams [20,58].

Online signals provide a detailed view of a physician’s digital presence, showcasing their service orientation and patient engagement. In addition, certain signals exceed patient expectations, such as physicians receiving gifts or tokens of appreciation from patients, further reinforcing their commitment and care.

However, patients tend to perceive offline signals as more reliable and treat online signals as supplementary information. When offline signals strongly indicate high professional qualifications (eg, prestigious certifications and hospital affiliations), patients often rely solely on these offline credentials to make their decisions. In such cases, online signals have limited impact. When offline signals indicate lower credentials (eg, a physician holding a lower-ranking position), patients actively seek additional online signals to gain further insights [4,49]. Thus, the relative influence of online signals is context dependent, varying based on the availability and strength of offline signals.

Signal form refers to the visual representation of information transmission. Existing online platform literature distinguishes between text-only and image-text formats [119]. In addition, Hanson et al [120] explored an alternative categorization of signals, including points, badges, and labels. Inspired by these classifications, we propose 2 primary signal forms in OHCs: taglike signals and nontaglike signals.

Taglike signals are simplified, intuitive, and conspicuous indicators commonly found on search pages or physician profile pages. These signals, often marked with colors, symbols, or graphical elements, are designed to capture attention easily [121]. Platforms use these tags to summarize physicians’ past achievements, experiences, and quality characteristics, serving as a widely recognized mechanism for establishing trust [122]. Examples of taglike signals in OHCs include text-based tags (physician title [1,6], specialties [53], and work experience [2]), quantitative indicators (patient ratings [24,59], recommendation values [60], and response speed [116]), and graphic-text combinations (medals, banners, or service badges [9,57]).

Nontaglike signals are more complex and typically presented as long text, images, or videos, requiring greater cognitive effort for interpretation. These signals often demand careful analysis and deep processing as they lack immediate comparability. Examples of nontaglike signals include interaction records between physicians and patients [6,49-51], patient reviews and testimonials [3,59], health knowledge articles shared by physicians [18,104], and images and videos of physicians [52,54].

In OHCs, taglike signals reduce patients’ cognitive load, allowing for quick comparisons between different health care providers [119]. In contrast, nontaglike signals require more effortful cognitive processing. Patients may adopt different information processing strategies depending on the complexity of the signals:

Signals in OHCs can be categorized based on their functional orientation into informative signals and affective signals, each grounded in distinct psychological mechanisms but not mutually exclusive in practice.

Informative signals refer to cues that transmit factual, objective, and verifiable information about the signaler’s professional competence or service quality. These signals are designed to support systematic, cognitive processing, enabling patients to evaluate options and reduce information asymmetry. Examples include physician credentials, hospital rankings, years of experience, and response speed. While informative signals are cognitively oriented, they may also indirectly elicit emotional reassurance, particularly in high-stakes or uncertain contexts.

Affective signals refer to cues that express emotion, empathy, or relational intent through language, symbols, or visual elements. These signals influence affective heuristics and help patients navigate emotional vulnerability and build confidence in care relationships. Examples include personalized responses, empathetic language, patient appreciation notes, emoticons, and smiling physician images. However, emotionally oriented, affective signals can also shape perceptions of credibility and competence, thereby influencing cognitive evaluations [3].

These 2 types of signals are conceptually distinct yet functionally interrelated. Informative signals primarily reduce uncertainty by supporting analytical judgment, whereas affective signals foster emotional connection and trust. However, in real-world settings, many signals carry both informative and affective components, suggesting the value of studying their interaction rather than treating them as strictly separate categories.

Researchers have increasingly explored the role of affective heuristics, where emotions serve as a shortcut for judgment and decision-making, particularly in complex and uncertain medical scenarios. While cognition is crucial, affect-driven decisions often provide a faster and more intuitive response in emotionally charged health care environments.

In OHCs, informative signals primarily engage cognitive mechanisms as they directly or indirectly indicate physicians’ competence and service quality. Examples include hospital level, work experience, certification, popularity, registration duration, and response speed.

In contrast, affective signals generate emotional responses and strengthen patient engagement. Examples include personalized responses to patient inquiries [2], self-introductions and community posts [3,47], and emotionally expressive patient reviews [19,59].

Research indicates that emotional support plays a crucial role in helping patients manage health conditions [15]. Within medical consultation literature, empathy and warmth are recognized as central components of therapeutic alliances, reinforcing patient trust and satisfaction. Positive and constructive emotions not only enhance signal clarity but also increase their influence on patient choices [2].

However, most affective signals in OHCs are linguistic, meaning that they rely on verbal expressions of emotion [61]. Limited research has explored the role of nonlinguistic affective signals, such as emoticons in patient reviews, images and visual cues on platforms, and badges or other symbolic indicators of trust.

For instance, studies have found that smiles in physicians’ profile photos provide emotional reassurance and positively influence patients’ selection of health care providers [52]. Despite this, research on nonverbal affective signals in OHC decision-making remains scarce, highlighting a gap in the literature.

In summary, we categorized signals into 4 key constructs. The signal classification map in Figure 3 enables multidimensional data visualization through Tableau’s hierarchical clustering feature (Tableau Software, LLC):

This detailed classification highlights the diversity and uniqueness of different signal types in OHCs. By structuring signals into these distinct categories, researchers can better analyze their roles and impacts within information exchange mechanisms, ultimately enhancing our understanding of how signals influence patient decision-making in digital health care environments.

Signalers transmit signals within a specific environment, whereas receivers interpret these signals based on contextual factors. The signaling environment comprises both tangible elements—such as the interface design of OHCs—and intangible factors, including noise, norms, cultural influences, and expectations, all of which shape how receivers perceive and process signals.

Although some studies highlight the importance of the signaling environment in shaping signal effectiveness, there is currently no clear definition of its characteristics. To address this gap, we classified the signaling environment into 4 distinct dimensions: environmental uncertainty, environmental competition, environmental norms, and environmental consistency (as outlined in Table 3).

Environmental uncertainty refers to the various contextual factors that influence information asymmetry between signalers and receivers. In OHCs, the signaling environment provides key information about physicians, services, and institutions, helping reduce patient uncertainty.

A hospital’s reputation acts as a quality signal, influencing how patients perceive the physicians affiliated with that institution. A strong hospital reputation enhances patient confidence, reducing uncertainty in physician selection [62]. Physicians working in highly reputable hospitals signal their adherence to high medical standards, thereby positively influencing patient decision-making. When selecting physicians, patients enter specific medical submarkets that limit their choices. Differences in disease perceptions across submarkets and varying levels of medical technology development contribute to different degrees of environmental uncertainty [4,48,63].

Environmental competition refers to a highly dynamic setting in which multiple signalers simultaneously compete by transmitting numerous signals. In such an environment, patients are exposed to a vast number of similar signals at the same time. As a result, the effectiveness of identical signals declines, whereas signals that stand out from those of other signalers become more influential [50,64].

In the context of medical crowdsourcing services, the quality of physicians’ response content plays a crucial role in shaping how patients assess the credibility and effectiveness of medical advice [50]. However, as competition intensifies, patients receive an increasing volume of service content signals, which may lead to signal saturation and diminished signal value. The overwhelming influx of similar information makes it challenging for patients to differentiate between high- and low-quality health care providers solely based on commonly available signals.

Environmental norms define the order and regulations within a given environment, encompassing structured rules, organizational factors, and behavioral patterns. These norms play a crucial role in shaping how receivers interpret and respond to signals, ultimately influencing their decision-making processes.

In OHCs, patients are particularly concerned about platform regulations and privacy protection measures, which directly impact their trust in online physicians [65]. A platform’s ability to safeguard patient privacy enhances confidence in the reliability of medical consultations. Furthermore, the credibility of a signal increases when it is widely recognized in the market. Signals that are associated with only a limited number of physicians or services may be perceived as less significant [60]. For instance, when only a few physicians in a given market receive patient ratings, these signals are not perceived as systematic or reliable indicators of quality. In an OHC environment characterized by information asymmetry, patients prefer standardized signals that apply universally to facilitate comparisons and differentiate between physicians of varying quality.

Environmental consistency refers to the degree of alignment between signals and the broader environmental context [26]. Some elements of signals may either align with or diverge from the surrounding environment, influencing how recipients interpret their meaning and reliability. For example, a physician’s reputation signal in an OHC is affected by the reputation of their peers within the same community [66,67]. When a physician’s colleagues are perceived to have high reputations, patients develop higher expectations for the focus physician. If the focus physician’s reputation signal does not meet these elevated expectations, the impact of their reputation signal diminishes.

Similarly, when an OHC environment significantly differs from a patient’s previous experiences with other communities, the patient must invest additional cognitive effort to understand the underlying motives behind the unusual setting [65]. This cognitive burden can lead to feelings of distrust and insecurity, ultimately reducing the patient’s willingness to rely on the platform’s information.

Another example of environmental consistency can be found in medical question-and-answer platforms, where environmental consistency refers to the alignment between an answer’s content and its contextual cues, particularly language attributes. The linguistic congruence between an answer and its surrounding context plays a pivotal role in determining the perceived value and credibility of the signal [26].

An emerging research trend involves applying a holistic perspective to study signaling processes within OHCs [4,41,64]. While scholars have implicitly incorporated holistic thinking into empirical research, particularly by exploring environmental factors, receivers, and senders, most studies only acknowledge that signal effectiveness varies under different conditions but lack in-depth theoretical investigations into these variations.

For example, research on signaling environments frequently examines disease risk yet fails to conceptualize disease itself as an environmental signaling factor [19,61]. Similarly, while studies acknowledge that contextual conditions shape signal effectiveness [26], they often lack a structured framework for understanding how signals are generated, assigned meaning, and interpreted as part of an interconnected process.

To address these gaps, we propose that future research should approach signal transmission as a holistic process in which signals are not only transmitted but also endowed with meaning through interpretation. On the basis of this perspective, we outline key research avenues that remain underexplored within OHCs and warrant further investigation.

Future research should conceptualize signal transmission in OHCs as a holistic, indivisible process in which signals are generated, endowed with meaning, and dynamically interpreted. This perspective aligns with emerging research trends emphasizing the interactive nature of signals. For example, Zhou et al [2] argue that simultaneous interactions between signals on online health platforms can affect one another, leading to complementary or substitution effects, with boundary conditions shaping signal effectiveness. This interaction highlights the context-dependent nature of health-related signal interpretation by community members.

The dynamic nature of signal interpretation in OHCs underscores the need for a more integrated analytical framework that accounts for how various signals, such as user comments, ratings, physician endorsements, and trust signals, interact and influence one another. Understanding these interactions can lead to a deeper appreciation of the complexities involved in health communication within these platforms, enabling researchers to refine theories and models that reflect the real-world dynamics of online interactions. By considering the relationships between different signals and their boundary conditions, scholars can identify new variables and moderating factors that influence signal effectiveness and member interpretation, ultimately leading to a more predictive and nuanced understanding of patient decision-making.

Adopting a holistic perspective on signal transmission and interpretation in OHCs can significantly advance the study of health communication. Examining the dynamic interactions between signals and the role of the signaling environment allows for the development of more sophisticated models that better capture the complexity of health-related decision-making in digital communities. This approach contributes to a deeper understanding of online health communication processes and holds practical implications for enhancing the effectiveness of OHCs in guiding patients toward informed and beneficial health choices.

Fang et al [64] referenced the framework by Connelly et al [16] to introduce the concept of the signal environment, emphasizing that both signal effectiveness and the receiver’s ability to accurately evaluate signals are shaped by environmental factors such as market intensity. In the context of OHCs, the signal environment extends beyond traditional market conditions to include environmental uncertainty, environmental norms, environmental competition, and environmental consistency. These factors collectively influence how signals are perceived, interpreted, and acted upon by patients and other community members.

Beyond environmental dynamics, research suggests that signals possess time-varying properties, making the determination of optimal timing a crucial factor in effective communication [1,125]. However, our understanding remains limited regarding how signalers identify the most effective timing for signal transmission and how receivers process and respond to signals over time. The dynamic nature of health communication in OHCs requires a more systematic exploration of these aspects.

To advance research on signal dynamics and optimal timing in OHCs, future studies should examine the mechanisms through which signalers determine the most effective timing for delivering health-related messages and analyze how receivers interpret and act on signals over different time frames. In addition, understanding the impact of timing on user trust and engagement is essential as well-timed signals can enhance trust, encourage participation, and strengthen patient-physician interactions, whereas poorly timed signals may contribute to misinformation and decreased trust [126].

Developing adaptive and dynamic signaling strategies that respond to the evolving conditions within OHCs will be critical in ensuring effective communication and improved health outcomes. By addressing these gaps, researchers can contribute to a more comprehensive understanding of optimizing signal timing in OHCs, ultimately leading to more impactful health interventions and better patient engagement.

While our review identified significant patterns in signal transmission, several critical research gaps remain. The most pressing issue is the current imbalance in research focus, where signalers and receivers are less frequently examined compared to signals and the signaling environment. This disproportionate attention has led to a lack of clarity in understanding how signalers transmit signals and how receivers interpret them within OHCs.

One possible reason for the scarcity of direct studies on signalers is the inherent opacity surrounding their characteristics, making it difficult for researchers to observe and analyze them directly [16,21]. Signalers have incentives to conceal their true traits, sometimes engaging in mimicry or deception to appear indistinguishable from other products and services [2]. As a result, while signals can provide clues about signaler quality and credibility, the relationship between signals and signalers remains ambiguous. For instance, some signalers intentionally use standardized signals to obscure low actual quality, whereas others fail to fully leverage signal transmission, leading to underestimated effectiveness in showcasing their true attributes [37]. Different types of signalers use varying signaling strategies and transmission methods, yet it remains unclear how these differences influence receivers’ trust and decision-making.

Similarly, current research lacks sufficient understanding of receivers, often blurring the distinction between receivers and the signaling environment. For example, in the context of disease risk, some studies interpret it as a signal received by individuals [19,61], whereas others categorize it as a part of the signaling environment [4,41,64]. The key distinction between receivers and the environment lies in the fact that the signaling environment affects all signals within it, whereas individuals within the same environment may interpret signals differently based on health literacy, psychological state, and personal biases. In the case of high-risk diseases, all stakeholders face similar informational constraints due to medical technology limitations. However, individual patients may process and respond to the same signals in vastly different ways, leading to unpredictable biases in decision-making.

Thus, our review highlights 2 critical gaps in signal transmission research: first, the challenge of accurately capturing signaler characteristics due to strategic concealment and signal manipulation and, second, the tendency to conflate receivers with the signaling environment, leading to unclear boundaries in analytical frameworks. Addressing these gaps is crucial for achieving a comprehensive understanding of signal dynamics in OHCs. By clarifying the relationship between signalers and signals and by differentiating receivers from the environment, future research can develop more precise models to explain how individuals navigate health-related signals in digital health care ecosystems.

This study highlights the importance of incorporating psychological mechanisms into the understanding of signal cognition. Traditional signaling theory often assumes that each signal is received and processed equally, with its effectiveness determined by its inherent strength or credibility [2,16]. However, given the overwhelming volume of information generated daily on online platforms, individuals cannot process all signals equally [125], challenging this traditional assumption. Developing a comprehensive framework that integrates psychological and cognitive mechanisms into signal reception and interpretation can enhance our understanding of how signals are generated, used, and responded to, thereby complementing and expanding signaling theory.

Future research should further investigate the relationship between heuristics and signal reception. Heuristic thinking simplifies decision-making by applying experience-based strategies and cognitive shortcuts, enabling individuals to make quick judgments while reducing cognitive load [127]. In rapid and heuristic processing, emotional states often serve as informational cues, allowing individuals to form evaluative judgments without engaging in deep analytical reasoning [128,129]. Our review observed a growing presence of affective signals in OHCs, which may signify the emergence of a new signaling subcategory.

However, the interaction between affective signals and the environment remains uncertain. For instance, environmental uncertainty may lead individuals to rely more heavily on heuristic cues, increasing the influence of affective signals on decision-making [130]. Conversely, environmental consistency may reinforce reliance on emotional states as a basis for judgment, further simplifying cognitive processing. Dedicated research is required to examine how affective signals interact with environmental factors and determine whether their influence on decision-making is ultimately beneficial or detrimental. By incorporating psychological mechanisms into signal cognition, future studies can offer a more nuanced perspective on signaling theory, particularly within digital health ecosystems.