Understanding cancer information-scanning behavior on WeChat among young Chinese adults: Applying a modified comprehensive model of information seeking

Abstract

Objective

Given the increasing incidence of cancer among younger populations and the prominent role of WeChat in health information acquisition, young Chinese adults are more likely to encounter and engage with cancer-related information on WeChat. However, limited empirical research has examined the motivations behind young adults’-cancer information-scanning behavior (CISB) on this platform. This study aims to address this gap by applying the Comprehensive Model of Information Seeking (CMIS) to identify the key predictors of CISB among young Chinese adults.

Methods

An online cross-sectional survey was conducted on WeChat from October 14 to October 24, 2023, targeting young Chinese adults ages 18–44. Using convenience and snowball sampling methods, a total of 1484 valid responses were collected.

Results

Structural equation modeling (SEM) indicated that perceived susceptibility, WeChat self-efficacy, and perceived credibility directly predicted perceived utility, whereas perceived severity did not. Additionally, both perceived credibility and perceived utility significantly predicted CISB on WeChat. Mediation analysis employing PROCESS Macro revealed that perceived utility mediated the relationships between health-related factors (i.e., perceived susceptibility, severity, and WeChat self-efficacy) and CISB on WeChat.

Conclusions

The findings highlight the mediating and enhancing role of perceived utility and extend CMIS applicability in the context of CISB on WeChat. These findings provide practical implications for policymakers and health professionals to create more effective cancer communication strategies.

Keywords

Cancer information scanning perceived susceptibility perceived severity weChat self-efficacy comprehensive model of information seeking (CMIS)

Introduction

With rapid industrialization, urbanization, and lifestyle changes, China now bears the highest global burden of cancer incidence and mortality.¹ While cancer primarily affects those over 50, certain types are increasingly emerging among younger populations.² As young adults form the backbone of China's workforce, rising cancer rates in this group could reduce productivity and increase unemployment.³ Since modifiable lifestyle factors—smoking, infections, obesity, and alcohol consumption—are major cancer contributors,⁴ targeted risk communication for young adults is essential.⁵ However, China's healthcare system remains constrained, with only 23.9 doctors per 10,000 people, limiting access to conventional healthcare.⁶ Given the affordability and reach of the Internet, WeChat has become a critical platform for disseminating cancer information and promoting prevention.⁷

The effectiveness of cancer-related health information communication via social media relies heavily on the willingness of recipients to acquire information.⁸ As China's largest social platform, WeChat boasts a substantial user base and high-frequency usage scenarios, deeply integrating into various aspects of young adults’-lives—living, learning, socializing, and entertaining—making it a dominant medium for health information acquisition. For example, one study reported that 98.35% of participants acquire health information on WeChat, and one-third frequently read health education articles on the platform.⁹ Information acquisition refers to obtaining information from various sources, including active information seeking and less active information scanning.¹⁰ Information seeking is defined as purposefully obtaining information from a selected information carrier,¹¹ whereas information scanning refers to an individual encountering information and deciding to pay attention to it.^12,13 Compared to cancer information seeking, cancer information-scanning behavior (CISB) is typically unplanned, incidental, and unsolicited, yet it frequently occurs in daily life.¹⁴ Young Chinese adults infrequently engage in active cancer information seeking,¹⁵ but routinely and deliberately browse WeChat. Thus, CISB is likely their primary means of acquiring knowledge about cancer risk and prevention. A growing number of studies suggest that CISB fosters positive health beliefs, enhances knowledge, and facilitates health outcomes.¹⁶

Despite the growing literature on CISB, several gaps remain. First, most CISB studies were conducted in Western countries. However, their findings may not be applicable to China, where the cancer fatalistic and collectivist culture.¹⁷ In response, collectivist culture emphasizes interdependence over autonomy may reduce active health management competence and foster avoidance of cancer information due to its potential to induce worry and contradict security-oriented tendencies.¹⁸ Second, existing literature focuses on general health information-scanning behaviors or disease-specific information (i.e., COVID-19, diabetes) rather than general cancer information. The scanning pattern of general health information may differ from that of cancer information.¹⁹ Different types of diseases vary in important aspects, including their infectiousness, chronicity, and lethality, such as COVID-19 and cancer. Third, existing studies have often employed general terms, such as “new media,” “online media,” “Internet,” and “social media,” to represent the Internet or social media as a whole, failing to account for WeChat's unique role in China's digital health landscape.²⁰ Four, previous research has primarily examined cancer patients’ information-seeking behaviors, with limited attention to the general population's information acquisition processes.^21,22 Finally, while demographic, psychological, media-related, and contextual factors have been widely studied as predictors of active health information-seeking behaviors, evidence on whether these factors similarly influence CISB remains scarce.²³ In conclusion, limited empirical research explored the underlying mechanisms driving CISB on WeChat among young Chinese adults.

Given these gaps, this study is driven by a central question: What motivates young Chinese adults to scan for cancer-related health information on WeChat? Specifically, guided by the Comprehensive Model of Information Seeking (CMIS), this study attempts to address these gaps by identifying the underlying mechanisms of CISB on WeChat among young Chinese adults. To our knowledge, no research has systematically applied CMIS to predict CISB on WeChat. This is the first attempt in this direction so far. This study contributes both theoretically and practically: theoretically, it refines CMIS by integrating health- and media-related factors relevant to scanning behavior; practically, it offers actionable insights for designing targeted cancer risk communication strategies on WeChat.

Literature review

Theoretical background

CMIS is an appropriate theoretical framework for this study. CMIS has gained widespread recognition in health communication as a framework for mapping the relationships among health-, media-related factors, and information-seeking behavior. It has been empirically validated across diverse health contexts,²⁴ including non-disease contexts such as smoking,²⁵ and food recall risks,²⁶ as well as specific disease contexts like COVID-19,^27–29 cancer,³⁰ mental disease,³¹ and diabetes.³² Over the last 30 years, CMIS has predominantly been used to explain active health information-seeking behaviors. However, it has also been applied to explore motivations for information-scanning behavior.³³ For instance, CMIS has been used to examine the motivations of Hong Kong citizens for scanning chronic disease-related information across interpersonal, Internet, and traditional media channels.³⁴ Similarly, it has been employed to investigate the American public's motivations for scanning cancer-related information across three media types: informational, entertainment, and the Internet.³⁵

Unlike other information management theories, CMIS postulates that health-related factors influence an individual's information-seeking behavior through perceptions shaped by media-related factors.³⁶ The model consists of three groups of variables: (1) health-related factors (e.g., perceived susceptibility, severity, WeChat self-efficacy), which determine perceptions of media-related factors; (2) media-related factors (e.g., characteristics and utility), which shape specific intentions associated with information-seeking behavior; and (3) information-seeking behavior itself, serving as the outcome variable.³⁷ Although prior studies have confirmed the basic claim of CMIS concerning the benefits of studying both health- and media-related factors, some findings are inconsistent with CMIS's core assumptions. Certain scholars argue that health-related and media factors seem to be different and independent mechanisms that directly promote health information seeking actions, rather than occupying sequential relationships.^29,36 They contend that health-related factors, derived from the Health Belief model (HBM), primarily assess the influence of health perceptions on health behaviors (including information-seeking and scanning) rather than their relationship with media-related factors.³⁸ Consequently, the interplay between medium evaluations and health-related factors requires further empirical investigation.

In response, this study aims to (1) investigate the predictors of CISB on WeChat, (2) examine the interplay between health- and media-related factors in shaping CISB on WeChat, and (3) identify the mediating roles of perceived utility. This study proposes a refined CMIS framework to identify the focal constructs underlying CISB. Specifically, demographic factors are included as control variables; salience is deconstructed into perceived susceptibility and severity; belief is reconceptualized as WeChat self-efficacy, and characteristics of the information carrier are operationalized as perceived credibility. CISB on WeChat is designated as the outcome variable (See Figure 1). The following section elaborates on the basic principles behind each proposed relationship.

Figure 1.

The conceptual framework (adapted from Johnson and Meischke, 1993).

Research hypotheses

Perceived susceptibility, perceived susceptibility, and perceived utility

In the CMIS framework, perceived susceptibility and severity are integrated into salience. However, researchers argue that these constructs should not be combined, as they represent independent constructs.^39–41 Perceived susceptibility refers to an individual's subjective assessment of the likelihood of contracting an illness, whereas perceived severity is defined as the personal perception of the seriousness of contracting an illness or failing to treat it.⁴² By definition, perceived susceptibility is closely tied to an individual's personal circumstances and behavioral tendencies, whereas perceived severity is more general, independent, and conceptually detached from an individual's status.⁴³ Consequently, mixed results have been reported regarding the effects of perceived susceptibility and severity on perceived utility.^44,45 For instance, Shang et al.⁴⁶ reported that perceived susceptibility to health issues is positively associated with perceived health information utility, while perceived severity is not. However, Fung et al.³⁴ noted that perceived susceptibility of chronic disease was unrelated to judgments about the helpfulness and usefulness of health information across different channels. These findings revealed that perceived susceptibility and perceived severity may function independently.⁴⁷ Therefore, this study deconstructed salience into perceived susceptibility and perceived severity.

Empirical studies have indicated that perceived susceptibility and severity influence individuals’ perception of health information as relevant and useful.⁴⁸ For example, Ahadzadeh et al.⁴⁹ found that individuals who believe they are more likely to develop a chronic illness and who recognize the severe consequences of such an illness, including health problems and financial losses, are more likely to view the Internet as a valuable resource for health management. In the context of this study, when an individual perceives a high likelihood of developing cancer and considers the consequences severe, they are more likely to regard cancer-related health information on WeChat as relevant, helpful, and useful. Based on the preceding reasoning, the following hypotheses are proposed:

H1: Perceived susceptibility is positively associated with perceived utility.

H2: Perceived severity is positively associated with perceived utility.

WeChat self-efficacy and perceived utility

Self-efficacy, as task-specific rather than broad and generalized, is defined as the belief in one's ability to successfully perform the actions needed to achieve a desired outcome.⁵⁰ Self-efficacy has been contextualized in various media environments, leading to the development of specific concepts such as media self-efficacy,⁵¹ Internet self-efficacy,⁵² social media self-efficacy,⁵³ and WeChat self-efficacy.⁵⁴ In this study, we use the term “WeChat self-efficacy” to refer to an individual's belief in his ability to effectively obtain health information via WeChat. This reflects a psychological judgment about one's capability to use WeChat effectively rather than their technical skills.⁵⁵

Self-efficacy has been extensively documented as a key factor influencing perceived utility.^39,56,57 For example, the self-efficacy of Korean mothers significantly influenced their perceived utility of media sources,⁵⁷ aligning with Van Stee and Yang's findings.⁵⁸ Likewise, prior research has shown that self-efficacy predicts the perceived utility of eHealth tools. Specifically, adults who believe in their capability to gather cancer-related information tend to perceive eHealth tools as highly useful.⁵⁹ Individuals with high social media self-efficacy tend to have more experience with social information platforms, which they gain through active mastery and alternative experiences, compared to those with lower social media self-efficacy. Thus, they are more inclined to view such information as useful.^60,61 In this study, we hypothesize that individuals who believe strongly in their capability to obtain health information on WeChat are more prone to perceive cancer-related information on the platform as useful. Based on this, we propose the following hypothesis:

H3: WeChat self-efficacy is positively associated with perceived utility.

Perceived credibility, perceived utility and CISB

Media-related factors are core constructs in CMIS, comprising two variables: characteristics and utility. Characteristics of the information carrier refer to an individual's perception of the medium's credibility, its intended purpose, and its communication potential.¹¹ However, subsequent CMIS studies have not consistently adhered to this original conceptualization, often conceptualizing this variable in alternative ways, such as content quality and trust⁶²; information quality, comprehension of information, and general trust in source⁶³; and trust in different sources.⁶⁴

Despite these conceptual inconsistencies, studies consistently highlight certain aspects of this variable—credibility. First, the conceptualization often overlooks the dimensions of the medium intentions and communication potential, possibly due to the lack of standardized measurement. Second, credibility remains a core construct, although it is expressed using different terms (e.g., trust, trustworthiness, and credibility). Third, this variable has been consistently conceptualized as a reflection of audiences’ psychological perceptions rather than as inherent media attributes. As McCroskey and Young⁶⁵ contended, if a concept is not carefully defined, the research output may be of limited value because isomorphism is required between conceptualization, operationalization, and measurement. To address this ambiguity, “perceived credibility” is employed in place of the broader “characteristics of the information carrier.” Perceived credibility refers to WeChat users’ perception of the platform's credibility, typically measured with fairness, unbiasedness, accuracy, completeness, trustworthiness, expertise, and overall believability.^66,67

Perceived utility refers to WeChat users’-evaluation of the relevance, importance, and usefulness of cancer-related health information obtained on the platform.¹¹ A nearly consistent finding in CMIS literature is that perceived credibility is positively correlated with perceived utility.^31,68 For example, perceived credibility of the Internet as a source of information predicts judgments of the utility of health information online.⁶⁴ Likewise, a study in Korea reported that individuals who trust health information on television are more likely to perceive it as beneficial and helpful to their health.⁶² Additional evidence beyond the CMIS framework further supports the link. For instance, Yun and Park⁶⁹ incorporated perceived credibility as an antecedent in the extended technology acceptance model (TAM), demonstrating its association with perceived utility. Similarly, when patients perceive mobile medical platforms as highly credible, they are more likely to consider these platforms useful and beneficial,⁷⁰ aligning with findings by Shang et al.⁴⁶

Additionally, several studies have indicated that both perceived credibility and utility positively predict information scanning. For example, trust in official government channels positively predicts COVID-19 information-scanning behavior among Wuhan residents.⁷¹ Likewise, adults in India select health information sources, such as family, doctors, and newspapers, based on the perceived utility of these sources.^72,73 One study also indicated that Hong Kong citizens’ perceptions of the utility and credibility of the Internet predict their information scanning actions about chronic disease,³⁴ consistent with Ruppel's findings.³⁵ Based on these discussions, we present the following hypotheses:

H4: Perceived credibility is positively associated with perceived utility.

H5: Perceived credibility is positively associated with CISB on WeChat.

H6: Perceived utility is positively associated with CISB on WeChat.

Mediating effect of perceived utility

Existing CMIS studies have provided strong evidence supporting this mediating role of perceived utility between individual-related factors, perceived credibility, and information scanning behavior.^56,74 For example, Fung et al.³⁴ highlighted the pivotal function of utility in mediating individuals’ perceptions of susceptibility and severity of chronic and health information scanning actions. Likewise, Ahadzadeh et al.⁴⁹ found that the perceived usefulness of the Internet fully mediates the effect of perceived health risk (i.e., perceived susceptibility and severity) on health belief Internet use among Malaysian females in Selangor. In addition, users who lack confidence in their ability to effectively use mHealth services may perceive them as useless, resulting in a reduced intention to adopt such services.⁷⁵ Han et al.⁷⁶ contended that the perceived utility of COVID-19 prevention and treatment information fully mediated the influence of self-efficacy on the intention to adopt the information. Their findings align with those of Holtgräfe and Zentes.⁷⁴ Based on the aforementioned literature and hypotheses (H1 – H3), the following hypothesis is proposed:

H7: Perceived utility mediates the relationship between health-related factors (specified in H1–H3) and CISB on WeChat.

Method

Participants and data collection procedures

The inclusion criteria for respondents in this study were as follows: (1) WeChat users; (2) Chinese citizens; (3) residing in mainland China; (4) not diagnosed with cancer, and (5) aged between 18 and 44 years. The World Health Organization defines young adults as individuals aged 18 to 44 years.

Before formal data collection, an expert pre-test was conducted. Three experts reviewed the questionnaire, provided feedback, and suggested revisions to the measurement items. Subsequently, a pilot test involving 77 respondents was carried out to further assess the validity and reliability of the measurement instrument, with all constructs achieving acceptable internal consistency (standard omega (ω) > 0.7). Formal survey using online cross-sectional survey was conducted via WeChat between October 14 and October 24 in 2023. Since the survey was conducted in -- mainland China, the questionnaire was administered in Chinese.

Due to WeChat's strict data protection policies, obtaining a random sampling frame in China was challenging. Therefore, convenience and snowball sampling were employed. The questionnaire was initially distributed via WeChat chatgroups. Screening questions verified participants’-age, nationality, IP address, and cancer diagnosis status. Respondents who completed the questionnaire were encouraged to share the link with other WeChat users. To ensure all respondents were active WeChat users, the questionnaire could only be completed on this platform. To validate the responses, we examined the response time and whether the attention-check questions were passed. Respondents whose questionnaires were deemed valid received a 5 CNY (Chinese yuan, 1 CNY ≈ 0.14 USD) incentive. Questionnaires were excluded if completion times were less than 180 seconds or more than 1000 seconds, or if respondents failed at least one of two attention-check questions. Altogether, 1484 valid questionnaires were retained for analysis.

Measures

The measurements of variables were adopted and adapted from previous empirical research. The survey questionnaire comprised seven sections: demographics, perceived susceptibility, perceived severity, WeChat self-efficacy, perceived credibility, perceived utility, and CISB (see the appendix for measurement items). Except for CISB, which was assessed using a 5-point scale (1 = Never to 5 = All the time), all other constructs were assessed using a 6-point scale (1 = strongly disagree to 6 = strongly agree). Summated scores were created for all continuous variables.

The measurement of perceived susceptibility (Mean = 4.619, SD = 0. 699, ω = 0.839) was adapted from Zou et al.⁷⁷ It consisted of three items, such as “Because of environmental pollution, there is a high chance that I will get cancer someday.” Perceived severity (Mean = 5.31, SD = 0.668, ω = 0.827) was measured with a four-item scale adapted from Zhang et al.,⁵⁴ including “If I develop cancer, I will have difficulty with my work or study”. WeChat self-efficacy (Mean = 4.864, SD = 0.879, ω = 0.852) was measured with four items, such as “I feel comfortable using WeChat on my own to obtain health information.”⁵⁴ Perceived credibility (Mean = 3.813, SD = 1.042, ω = 0.948) was measured by asking respondents to evaluate WeChat's (1) fairness, (2) unbiasedness, (3) accuracy, (4) completeness, (5) trustworthiness, (6) expertise, and (7) overall believability.^66,67 Perceived utility (Mean = 4.294, SD = 0.98, ω = 0.899) was assessed based on respondents’ evaluations about Cancer-related health information on WeChat is (1) important, (2) useful”, (3) is helpful in solving my health problems.⁶⁸ CISB (Mean = 2.90, SD = 1.00, ω = 0.920) was measured with four items assessing the frequency of engagement in specific activities over the past year. Sample items included “When I encountered cancer information pages, groups, and social network pages, I randomly browsed”.⁷⁸ The demographic section collected data on participants’ sex, age, average monthly income, educational attainment, and personal health status through five questions, which served as control variables.

Ethical considerations

Ethical approval was granted by the Ethics Committee for Research Involving Human Subjects at Universiti Putra Malaysia (No. JKEUPM-2023-278). Mirroring the previous study,⁷⁹ data collections were conducted anonymously, with no personal identifiers, such as respondents’-names or identification numbers, recorded. The introductory section of the questionnaire outlined the primary objectives of the study, emphasizing that participants’ responses would remain confidential and not be shared with any external parties. Before proceeding with the survey, respondents had to provide informed consent by clicking “agree,”-and they had the option to withdraw at any point if they chose not to participate.

Data analytical approach

Covariance-based structural equation modeling (CB-SEM) was employed using Analysis of Moment Structures (AMOS) software to test and validate the proposed model, as it provided a more accurate reflection of complex relationships. Five demographic variables—sex, age, average monthly income, educational attainment, and personal health status—were included as control variables to account for potential confounding effects on perceived utility and CISB on WeChat. Following the SEM analysis, the mediating effect was assessed using SPSS PROCESS Macro (version 4.1) in SPSS.

Results

Sample demographics

Table 1 summarizes the demographics of the participants. More than two-thirds of the respondents were female (n = 1055, 71.1%). Respondents’-ages varied from 18 to 44 years, with an average age of 24.62. More than 87.3% of respondents had obtained a bachelor's degree or higher (n = 1296). Regarding monthly income, most respondents were categorized as high-income earners (above 8000 CNY; n = 308, 20.8%), except for students who selected “no income.” In terms of health status, nearly half of the participants reported being in good health (n = 740; 49.9%), while 506 respondents (34.1%) identified as being in sub-health.

Table 1.

Distribution of respondents’-profiles (n = 1484).

Variables	Items	Frequency	Percentage (%)
Sex	Male	429	28.9
Sex	Female	1055	71.1
Age	Measured in years, M (SD)	24.62(5.98)	-
Education	Primary school and below	1	0.1
	Junior high school	3	0.2
	High school/ secondary specialized school/ technical school	51	3.4
	Junior college	133	9
	Undergraduate education and above	1296	87.3
Monthly Income	No income	819	55.2
	Less than CNY 500	7	0.5
	CNY 501-1000	11	0.7
	CNY1001-1500	18	1.2
	CNY 1501-2000	15	1.0
	CNY 2001-3000	18	1.2
	CNY 3001-5000	84	5.7
	CNY 5001-8000	204	13.7
	Above CNY 8000	308	20.8
Health Status	Severe disease (such as disability, etc.)	1	0.1
	Chronic diseases (like hypertension, coronary heart disease, diabetes, arthritis, etc.)	36	2.4
	Sub-health (such as fatigue, poor sleep, poor appetite, dizziness, forgetfulness, etc.)	506	34.1
	Not bad (easy to get sick and the immunity is low)	201	13.5
	Good (eat and sleep normally, full of energy, etc.)	740	49.9
Total		1484	100

Note: CNY = Chinese Yuan, where 1 CNY ≈ 0.14 USD. M = mean, SD = standard deviation.

Model testing

Confirmatory factor analysis (CFA) was performed using the maximum likelihood estimation method through AMOS. Based on the modification indices, two items (PC1 and WE1) were removed. After the model modification, the CFA results demonstrated a good model fit⁸⁰: χ² (215) = 605.332, χ²/df = 2.815, Goodness of Fit Index (GFI) = 0.962, Adjusted Goodness-of-Fit Index (AGFI) = 0.951, Comparative Fit Index (CFI) = 0.984, Tucker-Lewis Index (TLI) = 0.981, Root Mean Square Error of Approximation (RMSEA) = 0.035, and Standardized Root Mean Square Residual (SRMR) = 0.025. These results demonstrated that the measurement model provided a good fit with the data according to the assessment criteria.

Using the Omega macro,⁸¹ the McDonald's Omega (ω) test was conducted on focal variables.⁸² The ω values surpassed the cutoff points of 0.7 (See Table 2), indicating high internal consistency and reliability.⁸² In the measurement model, the standardized factor loadings of all items were above 0.6. Additionally, the values of composite reliability (CR) were above 0.60, and the average variance extracted (AVE) values were greater than 0.5. The correlations among focal variables are presented in Table 3. The square roots of the AVE values exceeded the absolute values of the correlation coefficients between each latent variable and the other variables. In summary, this analysis demonstrated that the measurement model exhibits good reliability, convergent, and discriminant validity.

Table 2.

Descriptive statistics, reliability, and convergent validity test (n = 1484).

Variables	Items	β	CR	AVE	Median	Std. ω
PSU	PSU1	0.834	0.841	0.642	4.750	0.839
	PSU2	0.886
	PSU3	0.666
PSE	PSE1	0.697	0.819	0.533	5.500	0.827
	PSE2	0.63
	PSE3	0.757
	PSE4	0.822
PC	PC2	0.762	0.948	0.753	4.000	0.948
	PC3	0.887
	PC4	0.883
	PC5	0.908
	PC6	0.895
	PC7	0.864
UT	UT1	0.854	0.900	0.749	4.333	0.899
	UT2	0.882
	UT3	0.861
WE	WE2	0.75	0.853	0.661	5.000	0.852
	WE3	0.846
	WE4	0.839
CISB	CISB1	0.835	0.919	0.739	3.000	0.920
	CISB2	0.903
	CISB3	0.879
	CISB4	0.819

Note: CR = composite reliability, AVE = average variance extracted value, SD = standard deviation, PSU = perceived susceptibility, PSE = perceived severity, WE = WeChat self-efficacy, PC = perceived credibility, UT = perceived utility, CISB = cancer information scanning behavior.

Table 3.

Discriminate validity between the constructs (n = 1484).

Variables	AVE	CISB	WE	UT	PC	PSE	PSU
CISB	0.739	0 . 860
WE	0.661	0.417	0.813
UT	0.749	0.679	0.508	0.865
PC	0.753	0.660	0.452	0.707	0.868
PSE	0.533	−0.049	0.149	0.058	−0.062	0.730
PSU	0.642	0.161	0.146	0.196	0.086	0.336	0.801

Note: Square-root of the AVE on the diagonals (in bold), inter-construct correlations within the column. CISB = cancer information-scanning behavior, WE = WeChat self-efficacy, UT = perceived utility, PC = perceived credibility, PSE = perceived severity, PSU = perceived susceptibility.

After controlling for sex, age, income, education, and health status, the structural model exhibited a good model fit: χ²/df =2.456, GFI = 0.963, AGFI = 0.950, CFI = 0.983, TLI = 0.979, RMSEA = 0.031, and SRMR = 0.024. Once the relative adequacy of the model fit was established, it is appropriate to examine the individual path coefficients in relation to our hypotheses. The path analysis supported five hypotheses out of six hypotheses (See Figure 2). The full model explained 47.1% of the variance in CISB (R² = 0.471), indicating a moderate effect size based on Chin's (1998) criteria.⁸³

Figure 2.

Results of model testing.

The SEM results indicated that perceived susceptibility positively predicted perceived utility (β = 0.118, SE = 0.024, p < .001), supporting H1. However, perceived severity was unrelated to perceived utility (β = 0.024, SE = 0.029, p > 0.05), not supporting H2. WeChat self-efficacy was positively associated with perceived utility (β = 0.207, SE = 0.028, p < .001), supporting H3. Perceived credibility significantly predicted perceived utility (β = 0.581, SE = 0.029, p < .001), supporting H4. Furthermore, perceived credibility (β = 0.293, SE = 0.032, p < .001) and utility (β = 0.411, SE = 0.032, p < .001) were positively associated with CISB, supporting H5 and H6.

Among the control variables, age (β = 0.046, SE = 0.005, p = 0.172), sex (β = 0.003, SE = 0.039, p = 0.146), income (β = 0.018, SE = 0.008, p = 0.522), education level (β = 0.029, SE = 0.036, p = 0.139), and health status (β = 0.01, SE = 0.018, p = 0.608) were unrelated to perceived utility. However, income (β = 0.137, SE = 0.008, p < .001) was the only demographic variable significantly associated with CISB, while other control variables showed no significant relationship.

Mediation analysis

Mediation analysis was conducted using Hayes’-PROCESS Model 4, which employs ordinary least squares path analysis and enhances result accuracy through bootstrapping with 5000 samples.⁸⁴ -The results revealed that perceived utility mediated the relationship between perceived susceptibility and CISB (β = 0.113, SE = 0.021, 95% CI [0.072, 0.154]), as well as the relationship between WeChat self-efficacy and CISB (β = 0.295, SE = 0.022, 95% CI [0.252, 0.339])-(See Table 4). The mediating effect of perceived utility between perceived severity and CISB exhibited a suppression effect.⁸⁵ Specifically, the direct effect of perceived severity on CISB was significantly negative (β = −0.111, SE = 0.030, 95% CI [−0.170, −0.052]). However, when perceived utility was introduced as a mediator, a significant positive indirect effect of perceived severity on CISB emerged (β = 0.053, SE = 0.026, 95% CI [0.000, 0.102]). Thus, H7 was partially supported.

Table 4.

Mediation effect analysis (n = 1484).

Path		-β	Boot SE	Boot	Boot
Path		-β	Boot SE	LLCI	ULCI
PSU→UT→CISB	Total effect	0.155	0.029	0.098	0.212
	Direct effect	0.041	0.023	−0.042	0.087
	Indirect effect	0.113	0.021	0.072	0.154
PSE→UT→CISB	Total effect	−0.058	0.039	−0.134	0.017
	Direct effect	−0.111	0.030	−0.170	−0.052
	Indirect effect	0.053	0.026	0.000	0.102
WE→UT→CISB	Total effect	0.443	0.028	0.388	0.498
	Direct effect	0.148	0.026	0.097	0.200
	Indirect effect	0.295	0.022	0.252	0.339

Note: PSU = perceived susceptibility, PSE = perceived severity, WE = WeChat self-efficacy, UT = perceived utility, CISB = cancer information-scanning behavior.

Discussion

Major findings

Given the rising incidence of cancer among young adults in China, this study proposed a modified CMIS framework to examine the factors motivating young adults’-CISB on WeChat. The results indicate that perceived susceptibility, WeChat self-efficacy, and perceived credibility directly predict perceived utility, whereas perceived severity does not. Furthermore, perceived credibility significantly predicts perceived utility, and both factors significantly predict CISB on WeChat. The mediating role of perceived utility between health-related factors and CISB on WeChat is also partially supported. Below, we discuss some key findings.

This study revealed that perceived susceptibility and perceived severity influence perceived utility through distinct mechanisms, providing a nuanced understanding that departs from traditional perspectives in the literature. Specifically, prior studies have frequently combined these constructs under the broader dimension of salience, potentially obscuring the unique contributions of each variable. Our findings show that perceived susceptibility significantly predicts perceived utility, diverging from earlier research on chronic diseases and general health concerns.³⁹ Notably, past research has often reported either negative or non-significant associations between perceived susceptibility and the utility of online health information. For instance, Basnyat et al.³⁹ reported that perceived susceptibility and severity negatively predicted the utility of online health resources, while Fung et al.³⁴ found no significant relationship between susceptibility and perceived utility across multiple media channels, including interpersonal communication, the Internet, and traditional media. These mixed results may arise from differences in how susceptibility and utility are conceptualized across studies. In this study, both constructs were examined specifically in the context of cancer, whereas prior research often framed susceptibility in terms of general disease risk but evaluated utility based on general health information.^34,39 Therefore, individuals with a specific health risk who perceive the severity of their health risks are more likely to perceive the utility of specific information related to their personal health threat rather than general health information.³⁹ Therefore, when perceived susceptibility and utility are examined within a disease-specific framework, their correlation is likely stronger. Moreover, variability in disease characteristics—such as infectiousness, chronicity, and lethality—may further shape this relationship.⁸⁶ Future research should explore this relationship across different disease contexts, with a particular emphasis on refining the conceptualization and operationalization of these variables to align with specific diseases.

Contrary to expectations, we observed that perceived severity was not significantly related to perceived utility and was even negatively associated with CISB on WeChat. This result is inconsistent with previous findings.^11,34 A plausible explanation is that perceived utility in this study was limited to cancer-related health information on WeChat, excluding other platforms like Douyin (also known as TikTok), Baidu (search engine), or Keep (a health-related app). Young Chinese adults often engage in cancer information scanning across multiple digital channels.⁸⁷ Thus, higher utility in other information channels may diminish the perceived utility of cancer-related health information on WeChat. Even when individuals recognize the seriousness of developing cancer, they may be unable to determine the usefulness of health information on the WeChat platform because WeChat may not be a prominent source for them to obtain such information. Another possible explanation lies in the cultural values unique to China. Cancer is widely recognized as a life-threatening disease, particularly in China, where it has a high incidence and mortality rate.⁸⁸ Beliefs in fate or destiny, deeply rooted in Confucian culture, contribute to a strong perception that cancer is predetermined by fate, thereby diminishing the perceived effectiveness of lifestyle changes and preventive measures.⁸⁹ As a result, perceived severity may trigger negative emotions such as anxiety, fear, or denial, especially among individuals with strong fatalistic beliefs. These emotions may lead individuals to avoid information.^46,90 This explanation is consistent with the Extended Parallel Process Model (EPPM), which posits that perceived severity of cancer can lead to defensive avoidance (e.g., information avoidance) to mitigate negative emotions.⁴⁸ In sum, individuals may feel powerless or overwhelmed by the serious potential consequences of cancer (e.g., substantial financial and mental burdens), which may prevent perceived severity from being translated into assessments of the usefulness of information. This, in turn, may reduce engagement in CISB on WeChat.

Consistent with prior studies, WeChat self-efficacy was found to have a direct positive relationship with perceived utility.^56,59 Individuals who are highly confident in their ability to access and understand health information tend to focus on its importance and relevance, thereby perceiving greater value in the information itself. The diverse features of WeChat—including messaging, social networking, WeChat Moments, and mini-programs—facilitate the extensive accumulation of information, which can often be overwhelming or contain redundant, irrelevant, low-quality, false, or overly complex content.^91,92 Therefore, individuals who trust their ability to gather high-quality health information on WeChat are more likely to recognize its relevance, usefulness, and helpfulness.

Additionally, our findings indicated that perceived credibility significantly predicts perceived utility, and both strongly influence CISB on WeChat. This result aligns with prior studies on both information-scanning^34,35 and information-seeking behaviors.^56,58 These findings underscore the critical role of media-related factors in shaping disease- and channel-specific information acquisition. Given the multiplicity of competing sources for cancer information, ranging from social media and traditional media to healthcare professionals and personal networks, media-related factors become paramount in influencing individuals’ choice of information channels.⁹³ The media-related constructs within the CMIS help explain why individuals prefer certain platforms for health information scanning.⁹⁴

Specifically, first, perceived credibility significantly predicts perceived utility. When individuals perceive the WeChat platform as credible, they are more inclined to consider cancer-related health information on the platform as useful, important, and helpful. This relationship is explained by the Information Adoption Model. It posits that source credibility significantly influences perceived utility.⁹⁵ When users trust a platform, they are more inclined to trust the accuracy and authenticity of the cancer information presented, thus perceiving it as more useful. Second, perceived credibility is crucial for CISB on WeChat. WeChat integrates public communication (e.g., official accounts operated by government agencies or certified media) based on weak social links and interpersonal interactions (e.g., WeChat groups and Moments based on real-life connections).^96,97 It is unique for social media to serve as an official government voice in China.⁷¹ In this authority-directed collectivist cultural context, young Chinese adults exhibit a high level of trust in government entities and their official WeChat accounts.⁹⁸ Furthermore, WeChat is an extension of users’ real-world social and professional networks, where most contacts are personally acquainted.⁹⁹ Consequently, the perceived credibility of WeChat reduces the psychological cost of information scanning by minimizing the need for independent verification, thereby facilitating more frequent and in-depth CISB.^100,101 Third, perceived utility exhibits strong power in predicting CISB on WeChat. Individuals who perceive cancer-related health information on WeChat as important, useful, and helpful are more inclined to engage in information scanning. This finding is consistent with TAM, which identifies perceived utility as a key determinant of technology use.¹⁰² Previous studies applying TAM have demonstrated that the perceived usefulness of a medium predicts its use for health-related purposes.^103,104

More importantly, this study offered the first empirical exploration supporting the mediating role of perceived utility between health-related factors and CISB on WeChat, although the mediating effects are more complicated than initially hypothesized. This finding echoes the core mechanism of CMIS. Specifically, although perceived susceptibility and WeChat self-efficacy exhibited small direct effects on CISB, their indirect effects on CISB mediated by perceived utility were significant. This finding is supported by past literature.^31,56 The results suggest that health-related factors alone are insufficient to directly drive CISB; instead, their influence is contingent on perceived utility. The individuals engage in CISB only when they perceive cancer-related information on WeChat as useful. If they do not find the information useful, although they have a high perceived susceptibility and self-efficacy level, they tend not to engage in CISB on WeChat. This finding suggests that, in digital health communication, particularly on multifunctional platforms like WeChat, perceived credibility may be a more critical factor than previously acknowledged. This finding about the mediating effect of perceived utility challenges conventional theories of information behavior, such as the risk information seeking and processing model (RISP), which posits that health beliefs and media-related factors operate as parallel constructs influencing information-seeking behavior.⁹⁰ Moreover, this finding challenges the validity of theories prioritizing health belief factors as the sole determinant of information seeking behavior, such as EPPM.⁴³ However, this result may not be conclusive as it is cross-sectional and only focuses on the disease-specific CISB among young adults. Predictors of information-scanning behaviors may be sensitive to the heterogeneity of medium, source, and type of information.⁵⁴ Future studies are expected to explore the mediating role of perceived utility within the CMIS framework across different platforms, populations, and disease contexts.

Theoretical implications

Using first-hand empirical data, this study addressed existing gaps by applying a modified CMIS framework to identify the determinants of CISB on WeChat among young adults in the Chinese context. Previous studies have predominantly focused on cancer patients rather than non-patients, general health information rather than cancer information, general media rather than specific platforms, and information-seeking rather than information-scanning behaviors. Moreover, most research has been conducted in Western countries. This study demonstrates the substantial explanatory power of the modified CMIS framework in understanding CISB on WeChat, opening new avenues for health communication researchers to examine the framework across different cultural contexts. Additionally, the findings imply that CMIS has the potential to explain a broader range of human information behaviors beyond information seeking.

A significant contribution is advancing the development of CMIS by introducing new constructs and refining existing ones. Specifically, salience was deconstructed into perceived susceptibility and perceived severity, belief was refined into WeChat self-efficacy, characteristics of information carrier retained perceived credibility, and CISB was introduced as the outcome variable. The study separately identified the distinct effects of perceived susceptibility and perceived severity on perceived utility, providing a more nuanced understanding of the underlying mechanism. This finding justifies treating these constructs independently, as the two constructs are components of many health behavior theories and are often grouped together as perceived threat or perceived risk, such as the risk perception attitude framework and RISP.^104,105 Additionally, focal constructs within CMIS—such as CISB on WeChat, perceived credibility, and utility—were conceptualized and operationalized in alignment with the platform's unique characteristics. The conceptualizations and operationalizations of perceived susceptibility, perceived severity, perceived utility, and CISB maintained conceptual isomorphism, focusing on general cancer. Finally, this study highlights the mediating role of perceived utility between health-related factors and CISB, contributing to the ongoing debate on whether CMIS components function sequentially or in parallel.

Practical implications

This study provides a foundation for developing evidence-based strategies to design and disseminate information for efficiently communicating cancer-related health information on WeChat. Our findings indicated that the frequency of respondents’-CISB on WeChat in the past year was generally moderate (Mean = 2.9), suggesting that respondents did not consistently engage in such behaviors. Cancer prevention necessitates continuous health monitoring and management throughout an individual's life, requiring cumulative and repeated exposure to relevant information. Therefore, information-intensive campaigns targeting cancer are recommended for policymakers and healthcare professionals to integrate cancer-related health information into people's daily lives and environments (e.g., work, study, social, and entertainment) or digital activities (e.g., online games, live streaming, and shopping) on WeChat. This strategy can subtly increase the frequency of exposure, thereby facilitating greater engagement with CISB among young adults.

Enhancing both the perceived credibility of WeChat and the perceived utility of cancer-related health information is imperative for improving users’-engagement in CISB. One recommendation for increasing credibility is leveraging receiver feedback (e.g., likes and comments) as a measure of perceived credibility, particularly for healthcare professionals. Presentational cues such as job titles, affiliations, and avatars can help establish the authority of information source. Additionally, government regulation and supervision of WeChat are also recommended to ensure the dissemination of accurate, unbiased, and professional information to the public. A multilayered gatekeeping process involving experts, authorities, and journalists should be implemented to verify information before it reaches young adults.

Another key recommendation is to enhance perceived utility of cancer information on WeChat by increasing perceived susceptibility and WeChat self-efficacy when designing cancer messages. The survey results indicate that many young people lack awareness of cancer risks associated with common environmental and dietary factors. Therefore, it is essential to highlight cancer susceptibility by effectively disseminating knowledge about risks related to environmental pollution and unhealthy lifestyles. Additionally, future campaigns and interventions should prioritize enhancing individuals’-WeChat self-efficacy to address concerns about using WeChat for health-related purposes. For example, user-friendly features and interfaces that support new users in navigating health-related functions on the platform would be beneficial. Providing easily accessible, comprehensible, and usable information can assist individuals who lack confidence in obtaining cancer information. Importantly, information should avoid overemphasizing the potential financial and psychological burdens of a cancer diagnosis, as excessive fear may induce anxiety and distress, potentially leading to information avoidance.⁴⁸

Limitations and suggestions for future research

This study employed a cross-sectional survey design, which is insufficient for determining the causal relationships proposed in the model. As noted by Janz and Becker,³⁸ cross-sectional relationships are weaker than those in longitudinal studies, potentially leading to opposite outcomes. Individuals who frequently scan for cancer information on WeChat are more prone to perceive utility of such information. Similarly, the assertion that health- and media-related factors function as parallel but rather serial mechanisms cannot be confidently drawn from cross-sectional data. Future studies should employ longitudinal (panel) data or experimental methods to elucidate the causal sequence of these constructs. Moreover, the use of non-probability sampling method limits the generalizability of the findings, although this approach is generally considered acceptable for testing and developing multivariate theoretical models.¹⁰⁶ Future studies should consider adopting probability sampling methods to enhance the representativeness of the results.

Furthermore, the reliance on self-reported data introduces susceptibility to recall bias and inaccuracies in respondents’-ability to report behaviors reliably.¹⁰⁷ This concern is particularly relevant to information scanning, which tends to be less deeply encoded in memory and therefore more prone to distortion.¹⁰⁸ To address these limitations, future research should consider using software tracking technologies designed to record actual information scanning, such as log data.

Finally, this study exclusively investigated channel- and disease-specific information-scanning behaviors, which may limit the predictive value of the CMIS. Future studies should examine the generalizability of the CMIS across different disease types (e.g., life-threatening, common diseases) and various media contexts (e.g., health apps, search engines). Comparative studies across multiple social media platforms would also provide more conclusive empirical evidence to support the CMIS's applicability in diverse digital health communication environments.

Conclusions

The rising incidence of cancer among young Chinese adults has imposed a substantial burden in China. Given that most cancers are preventable, CISB represents an effective mechanism to promote cancer prevention and control in this population. Drawing on a modified CMIS, this study reveals that health-related factors (such as perceived susceptibility and severity) and media-related factors (including perceived credibility and utility) significantly influence individuals’-engagement in CISB. Crucially, this study identifies that perceived utility plays a mediating and reinforcing role between these antecedents and CISB. To effectively foster CISB, the overall credibility of the WeChat platform and the perceived utility of cancer-related health information should be enhanced. Additionally, the design of cancer risk information should focus on disseminating cancer risk knowledge while being cautious about the psychological and economic burdens that cancer may bring. Collectively, these insights deepen understanding of how health- and media-related factors jointly shape CISB among young Chinese adults, offering valuable implications for public health communication strategies.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076251349618 - Supplemental material for Understanding cancer information-scanning behavior on WeChat among young Chinese adults: Applying a modified comprehensive model of information seeking

Supplemental material, sj-docx-1-dhj-10.1177_20552076251349618 for Understanding cancer information-scanning behavior on WeChat among young Chinese adults: Applying a modified comprehensive model of information seeking by QianYing Ma, Jen-Sern Tham, Rosmiza Bidin and Sharifah Sofiah Syed Zainudin in DIGITAL HEALTH

Footnotes

Abbreviation

CISB: cancer information-scanning behavior

Acknowledgement

We thank all those who participated in the survey.

ORCID iDs

QianYing Ma

Jen-Sern Tham

Rosmiza Bidin

Sharifah Sofiah Syed Zainudin

Ethical considerations

Ethical approval was obtained from the Ethics Committee for Research Involving Human Subjects at Universiti Putra Malaysia (No. JKEUPM-2023-278). Informed consent was obtained from each respondent.

Author contributions

QianYing Ma: conceptualized and designed the study, analyzed the data, drafted the manuscript.

Jen-Sern Tham: conceptualized and designed the study, revised and supervised the manuscript.

Rosmiza Bidin: reviewed and edited the manuscript, methodology.

Sharifah Sofiah Syed Zainudin: reviewed and edited the manuscript, methodology.

All the authors approved the final version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Guarantor

Jen-Sern Tham

Supplemental material

Supplemental material for this article is available online.

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