Next Article in Journal
COVID-19 Pandemic, Economic Livelihoods, and the Division of Labor in Rural Communities of Delta and Edo States in Nigeria
Next Article in Special Issue
A Cognitive Map of Sexual Violence Victims’ Decision-Making: Understanding the Preference for Social Media over Formal Legal Avenues—Insights from Media Consultants
Previous Article in Journal
The Intergenerational Transmission of Pro-Environmental Behaviours: The Role of Moral Judgment in Primary School-Age Children
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Examining the Impact of Virtual Health Influencers on Young Adults’ Willingness to Engage in Liver Cancer Prevention: Insights from Parasocial Relationship Theory

1
School of Journalism and Communication, Central China Normal University, Wuhan 430079, China
2
School of Journalism and Communication, Beijing Institute of Graphic Communication, Beijing 102699, China
*
Author to whom correspondence should be addressed.
Soc. Sci. 2024, 13(6), 319; https://doi.org/10.3390/socsci13060319
Submission received: 24 April 2024 / Revised: 13 June 2024 / Accepted: 14 June 2024 / Published: 17 June 2024
(This article belongs to the Special Issue Impact of Social Media on Health and Well-Being)

Abstract

:
The emergence of virtual influencers and AI doctors has significantly increased the attention of Chinese users, especially their health awareness and cancer health literacy. In our current study, guided by parasocial relationship theory, we examined the psychological antecedents that influence Chinese young adults’ willingness to engage in liver cancer prevention. Specifically, we aimed to examine the mediated mechanism of reduced unrealistic optimism within this relationship. A total of 252 respondents participated in this study, and the valid data were analyzed using hierarchical regression and mediation analysis to test our hypotheses. The results demonstrated three positive correlations between psychological factors (including perceived severity, parasocial relationship, and response efficacy) and Chinese young adults’ willingness to engage in liver cancer prevention. Furthermore, we found that reduced unrealistic optimism mediated these relationships. These findings provide valuable practical insights for Chinese health departments and experts to develop effective health campaign strategies that utilize multiple media platforms for optimal promotion.

1. Introduction

Liver cancer is one of the most common tumor diseases in the world (Sung et al. 2021). Recent data showed that globally, liver cancer ranked in the top three among mortality diseases (Sung et al. 2021). China has nearly half of the world’s liver cancer cases and deaths, and among all age groups, young people are at the highest risk of developing liver cancer (Sung et al. 2021; Zheng et al. 2022). In response, the Chinese government and relevant medical institutions have launched a series of policies and measures. For example, several Chinese provincial governments have effectively cooperated with local hospitals and promoted the implementation of special measures for cancer prevention and control through various facilities (e.g., community health centers and clinics) (Pengpai 2024). In addition, the Institute of Hospital Administration National Health Commission (IHANHC) has also released a new public health project to provide immediate treatment and support to patients, enhancing the well-being of cancer patients. All these implications have gradually raised the awareness of tumor prevention and control by facilitating early cancer diagnosis and immediate treatment.
In recent years, new technologies and innovations have led to remarkable improvements in communication technology (Attaran 2023). This progress has had a significant impact on health communication, particularly through emerging digital entities such as virtual influencers and AI doctors (Wolff 2022; Bunz and Braghieri 2022). In regard to AI influencers emerging with promising features, they have captured the attention of the majority of users. For example, Knox Frost, a top virtual influencer with nearly 1 million Gen-Z followers, used his social media account to share official health guidelines during the COVID-19 pandemic. This initiative increased the health awareness and literacy among his followers (S. K. 2020). Similarly, Olympia Ohanian’s doll, Qai Qai, released a playlist to educate children about engaging in COVID-19 prevention during the severe times of the pandemic (Peña 2020). However, to the best of our knowledge, there has been no study to yet conceptualize the specific terminology of virtual health influencers. Based on the current knowledge, this study defines virtual health influencers as digital images that educate and inspire audiences in various aspects of health, prevention, and wellness via Internet and media platforms. These influencers have the potential to encourage audiences to voluntarily engage in and adopt positive lifestyle behaviors.
Previous studies have primarily focused on exploring online users’ perspectives on virtual influencers’ authenticity (Lou et al. 2023), perceived trust (Wibawa et al. 2022), and attitude (Gerlich 2023). More recently, however, scholarly attention has shifted to investigating the impact of the exposure to virtual influencer content on various behavioral changes, such as intentions to continuously watch AI anchors (Xue et al. 2022; Huang and Yu 2023) and purchase behavior (Chiu and Ho 2023; Kim and Park 2023). While the aforementioned studies have explored the impact of viewing virtual influencer content on individual behavior change, three key limitations remain unsolved. First, most studies have explored such effects based on cognitive theories, lacking exploration from psychological and communication perspectives (e.g., parasocial relationship theory). As a result, there is a lack of explanation for how users’ one-sided relationships might influence their willingness to engage in cancer screening behaviors after exposure to virtual health influencer content. Second, although many hospitals in first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzhen) have been encouraged to create virtual health influencer content, there is a lack of recent studies that examine the impact of such content on people’s perceptions, attitudes, and behavior change. Finally, while several case studies have explored the determinants that lead Chinese audiences to continuously watch AI news influencers and the resulting changes in perceptions, there is a lack of studies that explore the key factors that might lead audiences to engage in preventive behaviors.
As mentioned above, based on the parasocial relationship theory, this study aims to investigate the factors that influence the willingness of Chinese young adults (aged 18–34 years old) to engage in liver cancer prevention after exposure to virtual health influencer content. This age group is a particular target for several reasons. First, they tend to have unhealthy consumption habits, such as long-term exposure to aflatoxins and excessive alcohol consumption, which increase their risk of liver cancer (ZJXW 2019). Therefore, scholars and specialists have urged further study of this population. Second, Chinese young adults are known to be early adopters of metaverse technology compared to other cohorts (Ren et al. 2022). They have significant potential to be influenced by cutting-edge health content. Finally, a significant number of young adults in China watch virtual health influencer videos on platforms such as Bilibili, with many videos receiving over 1000 views from this demographic. While previous studies have provided valuable insights, there is limited knowledge about the impact of virtual health influencer content on cancer prevention engagement with this particular population.
The current study examines the mediating mechanism of cognitive bias (reduced unrealistic optimism) in the relationship between psychological factors (perceived severity, parasocial relationships, and response efficacy) and willingness to engage in liver cancer prevention.

2. Literature Review

2.1. Liver Cancer in China and Preventions

Recent reports indicate that China accounts for nearly half of the global burden of liver cancer (Liu et al. 2024). Liver cancer ranks among the top five cancers, accounting for 57.42% of cases, according to recent data. Liver cancer is also a leading cause of cancer-related deaths and is expected to be among the top five causes of all-cause cancer mortality in 2022 (Han et al. 2024). Descriptive reports suggest that smoking, drug use, alcohol consumption, and elevated body mass index are major contributors to liver cancer cases (T. Chen et al. 2023). In addition, the prevalence of liver cancer tends to increase with age (Liu et al. 2019). Recent studies have observed increasing rates of liver cancer among young Chinese (Sung et al. 2021; Zheng et al. 2022). In response to this public health challenge, Chinese health institutions and experts are strongly advocating public awareness and early screening as effective measures for liver cancer prevention (China News Network 2024).
In general, liver cancer prevention, as defined by the National Cancer Institute, includes measures designed to reduce both the risk of developing cancer and the progression of the disease. These preventive measures include maintaining a healthy lifestyle, minimizing the exposure to known carcinogens, and using drugs or vaccines to inhibit cancer development (Centers for Disease Control and Prevention 2022). Public health organizations recommend a number of preventive measures specific to liver cancer, such as maintaining a healthy weight, being vaccinated against hepatitis B, being tested for hepatitis C, and strictly avoiding tobacco and alcohol use. In this study, the willingness to engage in liver cancer prevention (WELCP) is defined as the willingness of Chinese young adults to adopt the recommended preventive measures for liver cancer to reduce both the risk of developing and the progression of the disease.

2.2. Parasocial Relationship Theory

The concept of parasocial relationships (PR) revolves around an individual’s one-sided and enduring emotional connection. Specifically, audiences often cultivate unreciprocated feelings of closeness and intimacy through repeated interactions with performers in mediated reality (Dibble et al. 2016). This intimate connection typically encompasses the emotions, thoughts, and actions that audiences experience during media exposure, focusing on a particular performer or character (Walter et al. 2022). Recent psychological research has shown that this intimate connection often occurs between audiences and media celebrities in online environments, and is experienced by a wide range of people, including adults and children (Lim et al. 2020; Tolbert and Drogos 2019).
PR has been studied in many contexts, including psychology, consumer behavior, and marketing and advertising. For example, psychologists have examined how PR influences individuals’ repeated viewing of live-streamed games (Lim et al. 2020). Additionally, Chung and Cho (2014) examined how audiences develop PR with media characters through the use of reality television shows and social media (Chung and Cho 2014). Consumer behavior scholars have also examined the relationship between internet celebrity characteristics and YouTube users’ impulse purchase behavior as mediated by PR (T. Y. Chen et al. 2021). While previous research has provided strong evidence for PR in various contexts, relatively few studies have examined how one-sided relationships with media influencers affect individuals’ willingness to engage in health behaviors, particularly in the context of exposure to health-related influencer content on social media in China. Furthermore, it remains unclear whether PR can directly or indirectly influence Chinese young adults’ participation in WELCP after the exposure to virtual health influencers.

2.3. Effects of Psychological Factors on Intention

Perceived severity (PS) is recognized as a critical determinant of patients’ intention to engage in health preventive behaviors (Sharma 2021). It is defined as the belief in the extent of harm that may result from the acquired disease or harmful conditions as a result of a particular behavior. In the current study, PS refers to individuals’ cognitions or thoughts about the danger or harm posed by liver cancer in their environment. Response efficacy (RE) is a psychological factor that can be viewed as the belief in the effectiveness of an alternative health behavior in improving one’s health status. Previous studies have defined the concept as the extent to which individuals believe that the proposed response actions will be effective in preventing the threat (Witte 1994). In this study, RE refers to an individual’s beliefs about whether the recommended action for preventing liver cancer will effectively reduce the health threat.
Previous studies have found that PS increases individuals’ health preventive practices (DeDonno et al. 2022). Similarly, a previous study found a positive relationship between PS and preventive behaviors in patients with type 2 diabetes (Tan 2004). PR is recognized as a key psychological factor in an individual’s intention to engage in certain types of behaviors (e.g., travel intention, online shopping behavior, and watching live streaming games) (Yuan et al. 2021; Jiaojiang 2022; Lim et al. 2020). A health behavior study found that PR with nutritionist video bloggers increased individuals’ adherence to healthy weight loss diets (Sakib et al. 2020). In addition, research examining the influence of RE on health-related behaviors has found that RE is negatively associated with both alcohol and marijuana use. That is, when individuals’ RE is higher, they are more engaged in maintaining healthy behaviors (Choi et al. 2013). In addition, health workers’ perceived efficacy (also known as RE) significantly influenced their use of personal protective equipment to protect against COVID-19 infection (Alah et al. 2022). Therefore, the following hypotheses are proposed:
H1(a,b,c). 
(a) PS, (b) PR, and (c) RE have significant and positive effects on WELCP.

2.4. Unrealistic Optimism Role as a Mediator

The researchers in health psychology have found that individuals tend to unduly reduce the perceived threat when faced with negative events, a phenomenon referred to as “unrealistic optimism” (Howell et al. 2020). Previous studies have also identified this bias as the tendency for people to believe that they are less likely to experience negative events and more likely to experience positive events compared to their peers (Eshel et al. 2021). In health behavior research, individuals who strongly perceive themselves as less likely to experience serious health events relative to others exhibit this bias (Senft Everson et al. 2022). However, the majority of empirical studies suggest that unrealistic optimism is positively associated with individuals’ engagement in health-promoting behaviors, such as reducing the risk of alcohol use disorder (Na et al. 2021), having less favorable attitudes toward smoking cessation treatment (Senft Everson et al. 2022), and preventing the development of diabetes mellitus (de Mello Marsola et al. 2021).
As mentioned above, there are conflicting empirical findings regarding the effect of unrealistic optimism on individuals’ participation in health behaviors. The current study argues that unrealistic optimism may induce the prevention of liver cancer among Chinese young adults, as a line of evidence shows that individuals are strongly inclined to perceive themselves as being at a greater health risk compared to others (McColl et al. 2021). In this study, reduced unrealistic optimism (RUO) refers to a decrease in the tendency of individuals to hold overly optimistic beliefs about their future liver health outcomes.
Previous research on health threats has shown that increased PS increases an individual’s awareness of contagiousness, also known as RUO (Safra et al. 2021). Additionally, individuals with an optimistic bias were found to have a negative association with their willingness to engage in hand hygiene practices (Kim and Hancock 2015). Similarly, Felgendreff et al. (2021) found that reduced optimistic bias (RUO) was positively influenced by the perceived infectious disease threat, which in turn increased the likelihood of individuals developing skin cancer (Felgendreff et al. 2021; Bränström et al. 2006). The existing literature has also examined the indirect effect of RUO on the relationship between parasocial relationships and individuals’ engagement in health behaviors. For example, a previous study confirmed that parasocial relationships are negatively associated with individuals’ optimistic bias. In other words, the stronger the parasocial relationship, the more increased RUO about infection (Walter et al. 2022). In addition, individuals’ concern for their own safety from H1N1 swine flu rather than for others (RUO) led to their willingness to take protective H1N1 flu precautions (Liu and Lo 2014). To date, several studies have examined the effect of RE on RUO. A previous study showed that individuals with higher RE to viral infection had higher intentions to engage in various preventive behaviors (RUO) compared to their cohorts (Lee et al. 2008). In addition, dispositional optimism was associated with positive engagement in smoking treatment. That is, as RUO increases, individuals are more likely to engage in healthy behaviors (Senft Everson et al. 2022). Therefore, the following hypotheses are proposed:
H2. 
RUO mediates the relationship between PS and WELCP.
H3. 
RUO mediates the relationship between PR and WELCP.
H4. 
RUO mediates the relationship between RE and WELCP.
The hypothetical model is shown in Figure 1.

3. Materials and Methods

3.1. Stimuli

To ensure that the participants gained an initial understanding of virtual health influencer content, this study asked the participants to watch a 2 min video related to the preconceptions about liver cancer and virtual health influencers at the beginning of the online questionnaire. According to the methods of Nouri et al. (2011), the videos were selected based on two criteria. First, among the most recent and creative liver cancer prevention videos, the virtual health influencer content was rated in the top 100 on WeChat Channels (one of the largest short-form video platforms in China) (Nouri et al. 2011). Second, three representative creative liver cancer prevention videos were selected from this platform based on the most liked and shared metrics. Following the method of a previous study, a small survey was conducted with 50 online participants (Gong and Li 2017). Among the three types of relevant videos, “Digital Health: Dr. Bing Qiao, Liver Disease Specialist at Qingdao Sixth People’s Hospital—Methods of Preventing Liver Cancer” was ranked the highest. The virtual influencer content was developed by Dr. Bingqiao, a cancer specialist, with the support of Qingdao Sixth People’s Hospital.
Following the aforementioned process, in the current study, a 2 min stimulus video was provided at the beginning of the questionnaire. Due to the policy of the WeChat Channel, the content cannot be shared as a website link, but must be viewed on the platform (Ask 2023). Therefore, the participants were shown an image with instructions on how to search for the video and then return to the survey link (see the procedure in Appendix A). Additionally, a 2 min countdown was included on the same page of the online survey to ensure that the material was thoroughly reviewed (Huang and Yu 2023). This approach ensured that the selection provided a realistic analog and helped to develop the participants’ perceptions and feelings.

3.2. Data Collection Procedures

For sampling, this study used convenience sampling, which is highly regarded for its efficiency and cost-effectiveness in collecting nonprobability samples (Mulisa 2022). The majority of studies conducted using such sampling method are specifically designed to generate hypotheses (Vaterlaus et al. 2015; Xingting Zhang et al. 2017). The sample was collected from 22 January 2024 to 1 March 2024 through “Wenjuanxing”, a reputable platform that provides a sampling pool of nearly 260 million registered users in China. The platform is known for providing high-quality data that best meet the needs of researchers (Wang et al. 2020). Previous health behavior and AI media-related studies have used such web survey platform to study the Chinese context (Gao et al. 2024). Initially, 280 potential respondents received the survey link and 270 completed the questionnaire. To ensure a representative and accurate sample, three main methods were used to exclude biased data: (1) participants who provided nonsense responses and unrelated open-ended responses were removed (N = 1), (2) “straight-line” responses were excluded; respondents who intentionally provided the same response within measures were excluded (N = 3), and finally, “speeders” who completed the questionnaire in less than one-third of the average time were removed (N = 14). The final sample size was 252 participants. Previous empirical studies have used G-Power to determine an adequate and appropriate sample size using G-Power 3.1 (Guazzini et al. 2022). Specifically, power analysis has been identified as a critical step in hypothesis testing prior to hypothesis testing (Majeed et al. 2020). The current study was configured with a power of 0.95, an effect size of 0.15, and four predictors. The results indicated that the minimum required sample size was 129. Therefore, the sample was considered adequate.

3.3. Data Analysis Methods

Regarding the permission to conduct the current study, it was reviewed and approved by the Ethics Committee of the Beijing Institute of Graphic Communication (protocol code: SC20240111). Written informed consent was obtained at the beginning of the online survey, and the consent form explicitly assured all participants that their privacy and data use would be strictly protected during the study, as well as their right to refuse to participate. All the respondents were required to voluntarily sign a consent form before participating in this study.

3.4. Measurement of Variables

The survey questionnaires were derived based on existing measures, and some of the scale items were adjusted after testing with three focus groups consisting of Chinese short-form video platform users (aged 18–34). The final instrument consisted of six scales, including PS, PR, RE, RUO, and WELCP. The questionnaire was translated from English to Chinese using a rigorous back-translation process (Liu et al. 2021). In addition, the translated questionnaire was carefully reviewed by two health communication experts from Central China Normal University to ensure the content validity.
PS was operationalized with four items developed based on the research of Luo et al.’s study (Luo et al. 2021). The respondents indicated their level of agreement with statements using a 5-point Likert scale. Examples of these statements are: (1) “I believe that liver cancer is a serious disease”. (2) “I believe that cancer can have serious economic consequences”. (3) “I believe that my career would be seriously affected if I were to develop liver cancer”. (4) “I believe that my life would be negatively affected if I were to develop liver cancer”. (M = 4.42, SD = 0.54, Cronbach’s α = 0.69).
The PR scale was derived from a previous study (Kim et al. 2024). This scale consists of five items, and the respondents indicated their level of agreement with the questions using a 5-point Likert scale. Examples of these statements include: (1) “I feel like I have a lot in common with the virtual health influencer who primarily creates the liver cancer prevention content I watch”. (2) “The virtual health influencer I watch for liver cancer prevention content seems friendly and relatable”. (3) “I believe I could have a comfortable conversation with the virtual health influencer I follow for liver cancer prevention content”. (4) “If the virtual health influencer posts a video on Douyin, I’ll definitely watch it”. (5) “I feel that virtual health influencers are similar to my close friends”. (M = 2.62, SD = 1.21, Cronbach’s α = 0.95).
Four items were derived from a previous study to assess RE (Xiaofei Zhang et al. 2017). This scale was used to assess the respondents’ beliefs about whether the recommended actions to prevent liver cancer will effectively reduce the health threat. The respondents answered questions using a 5-point Likert scale to indicate their level of agreement. These statements are: (1) “Taking precautions against liver cancer will help me stay health”. (2) “Taking precautions against liver cancer will help me stay health”. (3) “Taking precautions against liver cancer will improve my ability to perform daily tasks”. (4) “Taking precautions against liver cancer effectively protects me from developing liver cancer”. (5) “Taking precautions against liver cancer strengthens my immune system”. (M = 4.43, SD = 0.65, Cronbach’s α = 0.88).
RUO assesses the tendency of individuals to hold less optimistic beliefs about their future liver health outcomes. To our knowledge, no studies have been developed to measure the reduced unrealistic optimism in the Chinese context. Therefore, the methods of Hollebeek et al. and Graffigna et al. were used in the current study (Hollebeek et al. 2014; Graffigna et al. 2015). The initial items of the measure were generated based on a literature review and systematic analysis. Subsequently, an initial face validity check was performed by all the authors. Ten potential items aimed at RUO were developed. To further enhance the validity and reliability and to minimize the error, this study was piloted with focus groups (12 Chinese volunteers). This approach is widely accepted as a means of confirming that the relevant items constitute the domains and ensuring that all the items are correct and understandable (Hyde et al. 2003). After carefully reviewing the feedback from the focus group, we retained 4 items as a result of the scale development procedures. In principle, a validation test is critical to ensure that the items are designed for the construct (Horstmann and Ziegler 2020). In this validation test, exploratory factor analysis (EFA) was applied to confirm and explain the relationships among a set of items (Shrestha 2021). Prior to performing this validity test, the Kaiser–Meyer–Olkin (KMO) test was conducted to measure the sample adequacy, and Bartlett’s sphericity test was used to check the suitability of the data for factor analysis (Shrestha 2021). With a total of 252 valid samples, the KMO result was 0.71, and Bartlett’s sphericity test was significant (p < 0.001), indicating that the sample size was adequate and appropriate for EFA (Shamsalinia et al. 2020). Finally, the EFA test demonstrated that the factor loadings ranged from 0.68 to 0.83 (RUO1 = 0.83, RUO2 = 0.81, RUO3 = 0.66, and RUO4 = 0.68). These results indicate that all the items adequately measured their respective factors. Examples of the items are: (1) “Ignoring the prevention of liver cancer has high risks and serious consequences”. (2) “Vaccination against hepatitis B effectively prevents liver cancer”. (3) “Liver screening is essential due to the long and demanding treatment process”. (4) “Even with healthy habits, special protocols for liver cancer prevention are essential”. (M = 3.50, SD = 0.81, Cronbach’s α = 0.73).
The WELCP was operationalized with four items to assess the respondents’ intentions to follow the recommended preventive measures for liver cancer to reduce both the risk of developing and the progression of the disease. This was adapted from the Centers for Disease Control and Prevention (CDC) practices for the prevention of liver cancer (Centers for Disease Control and Prevention 2022). The respondents indicated their level of agreement with the following statements: (1) “I will maintain a healthy weight”. (2) “I will get vaccinated against hepatitis B”. (3) “I have often thought about getting tested for hepatitis C”. (4) “I will stop drinking alcohol and smoking cigarettes”. (M = 4.30, SD = 0.71, Cronbach’s α = 0.84).
Considering that the current study conducted a cross-sectional survey, it is highly recommended to confirm the common method bias (CMB), which can avoid the systematic error variance shared among variables measured with the same method and/or source and introduced as a function of the same method (Richardson et al. 2009). Harman’s one-factor test was used to address the CMB. The results indicated that the single factor accounted for 29.80% (which is well below the critical value of 50%). Therefore, CMB was not considered to be a major problem in this research.

3.5. Data Analysis Methods

The data collection started from 22 January 2024 to 1 March 2024; the current study collected 252 samples. The accumulated data were rigorously examined, including hierarchical regression, mediation, and moderation analyses, all conducted using the PROCESS macro 4.0 within SPSS version 22.

4. Results

4.1. Descriptive Data

A total of 252 valid samples were collected. Table 1 shows the demographic characteristics of the participants. The respondents were mostly female (N = 132, 52.4%). The majority were either undergraduates (N = 180, 71.4%) or high school students (N = 59, 23.4%). In addition, most of the participants were between 18 and 23 years old (N = 187, 74.2%), followed by 24 to 28 years old (N = 36, 14.3%). Finally, the respondents’ monthly incomes indicated that the majority earned between RMB 4000 and RMB 8999 (N = 96, 38.0%) and RMB 9000 and RMB 13,999 (N = 58, 23.1%).

4.2. Hypothesis Testing

To test Hypothesis 1, the current study used hierarchical regression analyses with WELCP as the dependent variable. Gender, education, and income were entered as controlling confounders in the first block. PS, PR, and RE were entered separately in the second block. The effects of PS on WELCP (β = 0.64, t = 8.63, p < 0.001), PR on WELCP (β = 0.16, t = 4.00, p < 0.001), and RE on WELCP (β = 0.58, t = 9.73, p < 0.001) were significant. Therefore, H1 (a–c) was fully supported.
Hayes’ PROCESS macro (model 4) was used to test the mediation hypotheses (Rockwood and Hayes 2017). This study was set up as a bootstrap to obtain bias-corrected 95% confidence intervals for making statistical inferences about specific indirect effects. Figure 2 indicates the standardized coefficients and significance for each path in the hypothesized model. In the first mediation model, PS was a significant predictor of RUO (β = 0.23, t = 5.90, p < 0.001), RUO positively predicted WELCP (β = 0.58, t = 8.61, p < 0.001), and the indirect effect was significant (β = 0.06, t = 9.05, p < 0.001, 95% CI [0.02, 0.12]). The results of the second mediation model indicated that PR was a significant predictor of RUO (β = 0.29, t = 7.49, p < 0.001), and RUO positively predicted WELCP (β = 0.28, t = 5.02, p < 0.001). The indirect effect was significant (β = 0.08, t = 4.62, p < 0.001, 95% CI [0.04, 0.12]). The results of the third mediation model showed that RE was a significant predictor of RUO (β = 0.30, t = 3.90, p < 0.001), RUO positively predicted WELCP (β = 0.24, t = 5.18, p < 0.001), and the indirect effect was significant (β = 0.07, t = 9.76, p < 0.001, 95% CI [0.03, 0.12]). Therefore, H2–4 was supported. Table 2 provides a summary of the hypotheses, proposed relationships and hypothesized relationships, and hypothesis testing results.

5. Discussion

Guided by the parasocial relationship theory, the current study explored the effects of psychological factors on Chinese young adults’ willingness to engage in liver cancer prevention after the exposure to virtual health influencer content. The main purpose of this study was to thoroughly examine the direct effect and mediated effect between the relationship.
The first hypothesis of this study examined whether the perceived severity, parasocial relationships, and response efficacy have a direct effect on Chinese young adults’ willingness to engage in liver cancer prevention. The results indicate that these psychological factors significantly predict Chinese young adults’ willingness to engage in liver cancer prevention. These results are consistent with previous studies showing that the perceived severity, parasocial relationships, and response efficacy are more likely to induce individuals to engage in health prevention practices (e.g., infectious disease protection, diabetes prevention, and weight loss diets) (DeDonno et al. 2022; Tan 2004; Sakib et al. 2020).
In terms of mediated effects, Hypothesis 2 examined whether reduced unrealistic optimism indirectly leads to Chinese young adults’ willingness to engage in liver cancer prevention. The results indicate that reduced unrealistic optimism plays a significant role in mediating the relationship between the perceived severity and willingness to engage in liver cancer prevention among Chinese young adults. This finding is also consistent with previous literature. For example, the more that individuals perceive the severity of infectious diseases, the less their unrealistic optimism decreases, which in turn induces their participation in hand hygiene practices (Safra et al. 2021; Kim and Hancock 2015). The second mediating effect of the present study revealed that Hypothesis 3 reduced unrealistic optimism acting as a mediating factor in the relationship between parasocial relationships and the willingness to engage in liver cancer prevention among Chinese young adults. In agreement with the current results, previous studies have indicated a significant positive correlation between parasocial relationships and individuals’ engagement in health behaviors via reduced unrealistic optimism (Walter et al. 2022; Liu and Lo 2014).
Regarding its theoretical contributions, this study primarily addresses a significant gap in understanding how users’ one-way relationships with virtual health influencers may influence their willingness to engage in liver cancer screening behaviors, an area that has been underexplored in the literature. By conducting a systematic investigation using a cross-sectional survey and rigorous hypothesis testing, the current study advances our understanding of the significance of this relationship. In addition, reduced unrealistic optimism serves as an internal reference point (S. Chen et al. 2021). Such a factor acts as a mental guide to acknowledge the potential negative consequences of future liver health outcomes. By incorporating this psychological bias into parasocial relationship theory, we have extended the theoretical framework to reveal both direct and indirect effects on the willingness to engage in liver cancer prevention among young adults. This undoubtedly contributes to the explanatory power of the original parasocial relationship theory.
This study has several limitations, which we acknowledge. First, although the final results supported both the direct and mediated hypotheses, the moderated effect on the relationship between psychological factors and Chinese young adults’ willingness to engage in liver cancer prevention was not considered. It is highly recommended that future research investigates these moderated effects. Second, the perceived severity was measured via four items based on existing scales (Luo et al. 2021). However, the reliability test results indicated a relatively low Cronbach’s coefficient alpha. Future studies should consider developing more concise and accurate measures. Third, the current study only focused on psychological factors as antecedents. There is a lack of research on how emotional or cognitive factors might also influence Chinese young adults’ willingness to engage in liver cancer prevention. Future studies should explore these aspects through empirical research. Finally, the study included only 252 valid samples, mainly from college students (N = 180, 71.4%) and high school students (N = 59, 23.4%). As a result, therefore, the results represent only educated Chinese young adults and do not reflect the entire target population. Future studies should aim to collect a more diverse and representative sample.
The current research offers practical recommendations from two perspectives. First, previous studies have identified various barriers to cancer screening participation, including personal, social, and financial reasons (Fuzzell et al. 2021). To address these barriers, it is recommended to improve the quality of cancer screening advertisements, which may increase individuals’ cancer awareness and their willingness to participate in screening (Cao et al. 2021). Given that WeChat Channels is a popular platform for health information among Chinese young adults, healthcare providers should consider launching high-quality cancer screening campaigns on this platform. In particular, the collaboration between short-form video’s influencers and health experts can be highly effective. Second, a recent study showed that AI chatbots (e.g., ChatGPT) are increasingly being used for health-related purposes (Shahsavar and Choudhury 2023). A recent report also showed that Wenxinyiyan (Baidu’s AI chatbot service), with nearly 800 million users, is particularly popular among Chinese young adults, who are early adopters of such platforms (DLG 2023). Therefore, the study strongly recommends that health authorities encourage young adult users to engage with cancer screening topics on Wenxinyiyan. Additionally, AI developers should ensure that the platform provides a satisfactory environment for users to freely discuss health issues.

6. Conclusions

This study examined the relationship between the psychological factors and willingness to engage in liver cancer prevention among Chinese young adults aged 18–34 years old. It is one of the few studies to examine how reduced unrealistic optimism mediates this relationship. The findings highlight the critical role of reduced unrealistic optimism in mediating the effects of psychological factors (such as perceived severity, parasocial relationship, and response efficacy) on the willingness to engage in liver cancer prevention among Chinese young adults. To address these findings, Chinese health institutions and professionals are encouraged to develop and implement comprehensive health campaign strategies that utilize multiple media platforms for effective promotion.

Author Contributions

Conceptualization, D.C. and J.W.; methodology, D.C., J.W. and Y.M.; software, D.C., J.W. and Y.M.; validation, D.C. and J.W.; formal analysis, D.C. and J.W.; investigation, D.C., J.W. and Y.M.; resources, D.C., J.W. and Y.M.; data curation, D.C. and J.W.; writing—original draft preparation, D.C. and J.W.; writing—review and editing, D.C. and J.W.; visualization, D.C. and J.W.; supervision, D.C.; project administration, D.C.; funding acquisition, D.C.; All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Central China Normal University, College Students’ Innovation and Entrepreneurship Training Program,” Study on the willingness and influencing factors of Gen-AI usage in university media under the empowerment of AI news production in China” grant number 20240100281.

Institutional Review Board Statement

This study was approved by the Ethics Committee of Beijing Institute of Graphic Communication (protocol code: SC20240111; 11 January 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original data are provided by all the authors. If there are relevant research needs, the data can be obtained by sending an email to the corresponding author. Please indicate the purpose of the research and the statement of data confidentiality in the email.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Video Search and Survey Return Instructions

Socsci 13 00319 g0a1

References

  1. Alah, Muna Talal Theyab Abed, Sami Abdeen, Nagah Selim, Elias Tayar, and Iheb Bougmiza. 2022. Occupational prevention of COVID-19 among healthcare Workers in Primary Healthcare Settings: Compliance and perceived effectiveness of personal protective equipment. Journal of Patient Safety 18: 747–55. [Google Scholar] [CrossRef] [PubMed]
  2. Ask, Baidu. 2023. Wechat Video Number How to Copy Link Extract Video. Wechat Video Number How to Copy Link Extract Video Baidu Ask. Available online: https://wen.baidu.com/question/1057990846521191099.html (accessed on 6 August 2023).
  3. Attaran, Mohsen. 2023. The impact of 5G on the evolution of intelligent automation and industry digitization. Journal of Ambient Intelligence and Humanized Computing 14: 5977–93. [Google Scholar] [CrossRef] [PubMed]
  4. Bränström, Richard, Sveinbjörn Kristjansson, and Henrik Ullen. 2006. Risk perception, optimistic bias, and readiness to change sun related behaviour. The European Journal of Public Health 16: 492–97. [Google Scholar] [CrossRef] [PubMed]
  5. Bunz, Mercedes, and Marco Braghieri. 2022. The AI doctor will see you now: Assessing the framing of AI in news coverage. AI & Society 37: 9–22. [Google Scholar]
  6. Cao, Maomao, He Li, Dianqin Sun, Siyi He, Yiwen Yu, Jiang Li, Hongda Chen, Jufang Shi, Jiansong Ren, and Ni Li. 2021. Cancer screening in China: The current status, challenges, and suggestions. Cancer Letters 506: 120–27. [Google Scholar] [CrossRef] [PubMed]
  7. Centers for Disease Control and Prevention. 2022. “What Is the Liver?”. What Is the Liver? Centers for Disease Control and Prevention. Available online: https://www.cdc.gov/cancer/liver/index.htm (accessed on 15 November 2022).
  8. Chen, Sijing, Jianwei Liu, and Huamin Hu. 2021. A norm-based conditional process model of the negative impact of optimistic bias on self-protection behaviors during the COVID-19 pandemic in three Chinese cities. Frontiers in Psychology 12: 659218. [Google Scholar] [CrossRef] [PubMed]
  9. Chen, Taili, Yan Zhang, Jiayi Liu, Zhenzhen Rao, Mian Wang, Hong Shen, and Shan Zeng. 2023. Trends in liver cancer mortality in China from 1990 to 2019: A systematic analysis based on the Global Burden of Disease Study 2019. BMJ Open 13: e074348. [Google Scholar] [CrossRef] [PubMed]
  10. Chen, Tser Yieth, Tsai Lien Yeh, and Fang Yu Lee. 2021. The impact of Internet celebrity characteristics on followers’ impulse purchase behavior: The mediation of attachment and parasocial interaction. Journal of Research in Interactive Marketing 15: 483–501. [Google Scholar] [CrossRef]
  11. China News Network. 2024. The Sichuan-Chongqing Cancer Prevention and Control Conference Was Held in Chongqing. Experts Called for Attention to Early Diagnosis and Treatment of Cancer. China News Network. Available online: https://www.chinanews.com.cn/m/life/2024/04-12/10197436.shtml (accessed on 12 April 2024).
  12. Chiu, Candy Lim, and Han-Chiang Ho. 2023. Impact of celebrity, Micro-Celebrity, and virtual influencers on Chinese gen Z’s purchase intention through social media. SAGE Open 13: 21582440231164034. [Google Scholar] [CrossRef]
  13. Choi, Hye Jeong, Janice L. Krieger, and Michael L. Hecht. 2013. Reconceptualizing efficacy in substance use prevention research: Refusal response efficacy and drug resistance self-efficacy in adolescent substance use. Health Communication 28: 40–52. [Google Scholar] [CrossRef]
  14. Chung, Siyoung, and Hichang Cho. 2014. Parasocial relationship via reality TV and social media: Its implications for celebrity endorsement. Paper presented at the ACM International Conference on Interactive Experiences for TV and Online Video, Chicago, IL, USA, June 22–24. [Google Scholar]
  15. DeDonno, Michael, Joy Longo, X. Levy, and John D. Morris. 2022. Perceived susceptibility and severity of COVID-19 on prevention practices, early in the pandemic in the State of Florida. Journal of Community Health 47: 627–34. [Google Scholar] [CrossRef]
  16. de Mello Marsola, Camila, Joana Pereira de Carvalho-Ferreira, Luís Miguel Cunha, Patricia Constante Jaime, and Diogo Thimoteo da Cunha. 2021. Perceptions of risk and benefit of different foods consumed in Brazil and the optimism about chronic diseases. Food Research International 143: 110227. [Google Scholar] [CrossRef]
  17. Dibble, Jayson L., Tilo Hartmann, and Sarah F. Rosaen. 2016. Parasocial interaction and parasocial relationship: Conceptual clarification and a critical assessment of measures. Human Communication Research 42: 21–44. [Google Scholar] [CrossRef]
  18. DLG. 2023. Wenxin Word User Usage Report: More than 70% of Users are Workers, IT, Education Industry Favorite. DLG. Available online: https://baijiahao.baidu.com/s?id=1777732380716829907&wfr=spider&for=pc (accessed on 22 September 2023).
  19. Eshel, Yohanan, Shaul Kimhi, Hadas Marciano, and Bruria Adini. 2021. Components of unrealistic optimism of college students: The case of the COVID-19 pandemic. Frontiers in Psychology 12: 763581. [Google Scholar] [CrossRef]
  20. Felgendreff, Lisa, Lars Korn, Philipp Sprengholz, Sarah Eitze, Regina Siegers, and Cornelia Betsch. 2021. Risk information alone is not sufficient to reduce optimistic bias. Research in Social & Administrative Pharmacy 17: 1026. [Google Scholar]
  21. Fuzzell, Lindsay N., Rebecca B. Perkins, Shannon M. Christy, Paige W. Lake, and Susan T. Vadaparampil. 2021. Cervical cancer screening in the United States: Challenges and potential solutions for underscreened groups. Preventive Medicine 144: 106400. [Google Scholar] [CrossRef]
  22. Gao, Ziyi, Jun-Hwa Cheah, Xin-Jean Lim, and Xi Luo. 2024. Enhancing academic performance of business students using generative AI: An interactive-constructive-active-passive (ICAP) self-determination perspective. The International Journal of Management Education 22: 100958. [Google Scholar] [CrossRef]
  23. Gerlich, Michael. 2023. The Power of Virtual Influencers: Impact on Consumer Behaviour and Attitudes in the Age of AI. Administrative Sciences 13: 178. [Google Scholar] [CrossRef]
  24. Gong, Wanqi, and Xigen Li. 2017. Engaging fans on microblog: The synthetic influence of parasocial interaction and source characteristics on celebrity endorsement. Psychology & Marketing 34: 720–32. [Google Scholar]
  25. Graffigna, Guendalina, Serena Barello, Andrea Bonanomi, and Edoardo Lozza. 2015. Measuring patient engagement: Development and psychometric properties of the Patient Health Engagement (PHE) Scale. Frontiers in Psychology 6: 132788. [Google Scholar] [CrossRef]
  26. Guazzini, Andrea, Mustafa Can Gursesli, Elena Serritella, Margherita Tani, and Mirko Duradoni. 2022. Obsessive-compulsive disorder (OCD) types and social media: Are social media important and impactful for OCD people? European Journal of Investigation in Health, Psychology and Education 12: 1108–20. [Google Scholar] [CrossRef]
  27. Han, Bingfeng, Rongshou Zheng, Hongmei Zeng, Shaoming Wang, Kexin Sun, Ru Chen, Li Li, Wenqiang Wei, and Jie He. 2024. Cancer incidence and mortality in China, 2022. Journal of the National Cancer Center 4: 47–53. [Google Scholar] [CrossRef]
  28. Hollebeek, Linda D., Mark S. Glynn, and Roderick J. Brodie. 2014. Consumer brand engagement in social media: Conceptualization, scale development and validation. Journal of Interactive Marketing 28: 149–65. [Google Scholar] [CrossRef]
  29. Horstmann, Kai T., and Matthias Ziegler. 2020. Assessing personality states: What to consider when constructing personality state measures. European Journal of Personality 34: 1037–59. [Google Scholar] [CrossRef]
  30. Howell, Jennifer L., Nikolette P. Lipsey, and James A. Shepperd. 2020. Health information avoidance. In The Wiley Encyclopedia of Health Psychology. New York: John Wiley & Sons, pp. 279–86. [Google Scholar]
  31. Huang, Yuke, and Zhiyuan Yu. 2023. Understanding the Continuance Intention for Artificial Intelligence News Anchor: Based on the Expectation Confirmation Theory. Systems 11: 438. [Google Scholar] [CrossRef]
  32. Hyde, Martin, Richard D. Wiggins, Paul Higgs, and David B. Blane. 2003. A measure of quality of life in early old age: The theory, development and properties of a needs satisfaction model (CASP-19). Aging & Mental Health 7: 186–94. [Google Scholar]
  33. Jiaojiang. 2022. The National Healthy Lifestyle Day and the Special Campaign of Healthy Weight Promotion Is Coming! Available online: https://mp.weixin.qq.com/s?__biz=MzI5MjE3OTY5Mg==&mid=2247501011&idx=1&sn=ed9414324997f2b66be3b5915fd0d4f1&chksm=ec07df50db705646fd3b44f68e78afca2105fb2290fd5f186dd351c4fe38ac71ef40f5b81124&scene=27 (accessed on 2 September 2022).
  34. Kim, Hyojung, and Minjung Park. 2023. Virtual influencers’ attractiveness effect on purchase intention: A moderated mediation model of the Product–Endorser fit with the brand. Computers in Human Behavior 143: 107703. [Google Scholar] [CrossRef]
  35. Kim, Jongho, Heeok Youm, Sujin Kim, Hongjun Choi, Dohee Kim, Sungeun Shin, and Jinwook Chung. 2024. Exploring the Influence of YouTube on Digital Health Literacy and Health Exercise Intentions: The Role of Parasocial Relationships. Behavioral Sciences 14: 282. [Google Scholar] [CrossRef]
  36. Kim, Sunny Jung, and Jeffrey T. Hancock. 2015. Optimistic bias and Facebook use: Self–other discrepancies about potential risks and benefits of Facebook use. Cyberpsychology, Behavior, and Social Networking 18: 214–20. [Google Scholar] [CrossRef]
  37. Lee, Doohwang, Robert Larose, and Nora Rifon. 2008. Keeping our network safe: A model of online protection behaviour. Behaviour & Information Technology 27: 445–54. [Google Scholar]
  38. Lim, Joon Soo, Min-Ji Choe, Jun Zhang, and Ghee-Young Noh. 2020. The role of wishful identification, emotional engagement, and parasocial relationships in repeated viewing of live-streaming games: A social cognitive theory perspective. Computers in Human Behavior 108: 106327. [Google Scholar] [CrossRef]
  39. Liu, Hao, Jun Li, Shijie Zhu, Xupeng Zhang, Faxue Zhang, Xiaowei Zhang, Gaichan Zhao, Wei Zhu, and Fang Zhou. 2024. Long-term trends in incidence, mortality and burden of liver cancer due to specific etiologies in Hubei Province. Scientific Reports 14: 4924. [Google Scholar] [CrossRef] [PubMed]
  40. Liu, Mei, You Chen, Dan Shi, and Tingwu Yan. 2021. The public’s risk information seeking and avoidance in china during early stages of the COVID-19 outbreak. Frontiers in Psychology 12: 649180. [Google Scholar] [CrossRef] [PubMed]
  41. Liu, Xudong, and Ven-Hwei Lo. 2014. Media exposure, perceived personal impact, and third-person effect. Media Psychology 17: 378–96. [Google Scholar] [CrossRef]
  42. Liu, Zhenqiu, Yanfeng Jiang, Huangbo Yuan, Qiwen Fang, Ning Cai, Chen Suo, Li Jin, Tiejun Zhang, and Xingdong Chen. 2019. The trends in incidence of primary liver cancer caused by specific etiologies: Results from the Global Burden of Disease Study 2016 and implications for liver cancer prevention. Journal of Hepatology 70: 674–83. [Google Scholar] [CrossRef] [PubMed]
  43. Lou, Chen, Siu Ting Josie Kiew, Tao Chen, Tze Yen Michelle Lee, Jia En Celine Ong, and ZhaoXi Phua. 2023. Authentically fake? How consumers respond to the influence of virtual influencers. Journal of Advertising 52: 540–57. [Google Scholar] [CrossRef]
  44. Luo, Yunjuan, Yang Cheng, and Mingxiao Sui. 2021. The moderating effects of perceived severity on the generational gap in preventive behaviors during the COVID-19 pandemic in the US. International Journal of Environmental Research and Public Health 18: 2011. [Google Scholar] [CrossRef] [PubMed]
  45. Majeed, Mehwish, Muhammad Irshad, Tasneem Fatima, Jabran Khan, and Muhammad Mubbashar Hassan. 2020. Relationship between problematic social media usage and employee depression: A moderated mediation model of mindfulness and fear of COVID-19. Frontiers in Psychology 11: 557987. [Google Scholar] [CrossRef] [PubMed]
  46. McColl, Kathleen, Marion Debin, Cecile Souty, Caroline Guerrisi, Clement Turbelin, Alessandra Falchi, Isabelle Bonmarin, Daniela Paolotti, Chinelo Obi, and Jim Duggan. 2021. Are people optimistically biased about the risk of COVID-19 infection? Lessons from the first wave of the pandemic in Europe. International Journal of Environmental Research and Public Health 19: 436. [Google Scholar] [CrossRef]
  47. Mulisa, Feyisa. 2022. Sampling techniques involving human subjects: Applications, pitfalls, and suggestions for further studies. International Journal of Academic Research in Education 8: 74–83. [Google Scholar] [CrossRef]
  48. Na, Peter J., Sonya B. Norman, Brandon Nichter, Melanie L. Hill, Marc I. Rosen, Ismene L. Petrakis, and Robert H. Pietrzak. 2021. Prevalence, risk and protective factors of alcohol use disorder during the COVID-19 pandemic in US military veterans. Drug and Alcohol Dependence 225: 108818. [Google Scholar] [CrossRef] [PubMed]
  49. Nouri, Mahsa, Laura G. Hill, and Joan K. Orrell-Valente. 2011. Media exposure, internalization of the thin ideal, and body dissatisfaction: Comparing Asian American and European American college females. Body image 8: 366–72. [Google Scholar] [CrossRef] [PubMed]
  50. Pengpai. 2024. National Cancer Prevention and control Publicity Week District Health Bureau: Building a Health Incense Building a Cancer Prevention Fortress. Xiangfang. Available online: https://www.thepaper.cn/newsDetail_forward_27097006 (accessed on 19 April 2024).
  51. Peña, Mirtle. 2020. Serena Williams? Daughter Olympia’sdoll Qaiqai Has Aprevention Playlist -and It’s Hilarious! Hola. Available online: https://www.hola.com/us/celebrities/20200406fm9al8q1ln/serena-williams-daughter-olympia-doll-qai-qai-prevention-playlist/ (accessed on 6 April 2020).
  52. Ren, Longfei, Fangfang Yang, Chao Gu, Jie Sun, and Yunfeng Liu. 2022. A study of factors influencing Chinese college students’ intention of using metaverse technology for basketball learning: Extending the technology acceptance model. Frontiers in Psychology 13: 1049972. [Google Scholar] [CrossRef] [PubMed]
  53. Richardson, Hettie A., Marcia J. Simmering, and Michael C. Sturman. 2009. A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods 12: 762–800. [Google Scholar] [CrossRef]
  54. Rockwood, Nicholas J., and Andrew F. Hayes. 2017. MLmed: An SPSS macro for multilevel mediation and conditional process analysis. Paper presented at the Annual Meeting of the Association of Psychological Science (APS), Boston, MA, USA, May 28. [Google Scholar]
  55. Safra, L., A. Sijilmassi, and C. Chevallier. 2021. Disease, perceived infectability and threat reactivity: A COVID-19 study. Personality and Individual Differences 180: 110945. [Google Scholar] [CrossRef] [PubMed]
  56. Sakib, M. D. Nazmus, Mohammadali Zolfagharian, and Atefeh Yazdanparast. 2020. Does parasocial interaction with weight loss vloggers affect compliance? The role of vlogger characteristics, consumer readiness, and health consciousness. Journal of Retailing and Consumer Services 52: 101733. [Google Scholar] [CrossRef]
  57. Senft Everson, Nicole, William M. P. Klein, Scott S. Lee, Rebecca Selove, Maureen Sanderson, William J. Blot, Rachel F. Tyndale, Stephen King, Karen Gilliam, and Suman Kundu. 2022. Dispositional optimism and optimistic bias: Associations with cessation motivation, confidence, and attitudes. Health Psychology 41: 621. [Google Scholar] [CrossRef] [PubMed]
  58. Shahsavar, Yeganeh, and Avishek Choudhury. 2023. User intentions to use ChatGPT for self-diagnosis and health-related purposes: Cross-sectional survey study. JMIR Human Factors 10: e47564. [Google Scholar] [CrossRef] [PubMed]
  59. Shamsalinia, Abbas, Mozhgan Moradi, Mansoureh Ashghali Farahani, Reza Masoudi, Reza Ghadimi, Reza Ebrahimi Rad, Gholamreza Zamani Ghaletaki, and Fatemeh Ghaffari. 2020. Designing and psychometric evaluation of disease-related fear scale (D-RFS) in adults with epilepsy: A sequential exploratory mixed methods design. Epilepsy & Behavior 110: 107169. [Google Scholar]
  60. Sharma, Manoj. 2021. Theoretical Foundations of Health Education and Health Promotion. Burlington: Jones & Bartlett Learning. [Google Scholar]
  61. Shrestha, Noora. 2021. Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics 9: 4–11. [Google Scholar] [CrossRef]
  62. S. K. 2020. Virtual Influencers React to Life with Coronavirus. Virtualhumans. Available online: https://www.virtualhumans.org/article/virtual-influencers-react-to-life-with-coronavirus (accessed on 8 May 2020).
  63. Sung, Hyuna, Jacques Ferlay, Rebecca L. Siegel, Mathieu Laversanne, Isabelle Soerjomataram, Ahmedin Jemal, and Freddie Bray. 2021. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians 71: 209–49. [Google Scholar] [CrossRef] [PubMed]
  64. Tan, Ming Yeong. 2004. The relationship of health beliefs and complication prevention behaviors of Chinese individuals with Type 2 Diabetes Mellitus. Diabetes Research and Clinical Practice 66: 71–77. [Google Scholar] [CrossRef] [PubMed]
  65. Tolbert, Amanda N., and Kristin L. Drogos. 2019. Tweens’ wishful identification and parasocial relationships with YouTubers. Frontiers in Psychology 10: 491652. [Google Scholar] [CrossRef] [PubMed]
  66. Vaterlaus, J. Mitchell, Emily V. Patten, Cesia Roche, and Jimmy A. Young. 2015. # Gettinghealthy: The perceived influence of social media on young adult health behaviors. Computers in Human Behavior 45: 151–57. [Google Scholar]
  67. Walter, Nathan, Jonathan Cohen, Robin L. Nabi, and Camille J. Saucier. 2022. Making it real: The role of parasocial relationships in enhancing perceived susceptibility and COVID-19 protective behavior. Media Psychology 25: 601–18. [Google Scholar] [CrossRef]
  68. Wang, Jiahao, Rize Jing, Xiaozhen Lai, Haijun Zhang, Yun Lyu, Maria Deloria Knoll, and Hai Fang. 2020. Acceptance of COVID-19 Vaccination during the COVID-19 Pandemic in China. Vaccines 8: 482. [Google Scholar] [CrossRef]
  69. Wibawa, Rafki Chandra, Chairani Putri Pratiwi, Eko Wahyono, Desman Hidayat, and Wilyan Adiasari. 2022. Virtual influencers: Is the persona trustworthy? Jurnal Manajemen Informatika (JAMIKA) 12: 51–62. [Google Scholar] [CrossRef]
  70. Witte, Kim. 1994. Fear control and danger control: A test of the extended parallel process model (EPPM). Communications Monographs 61: 113–34. [Google Scholar] [CrossRef]
  71. Wolff, W. E. M. 2022. A Trend or Is the Future of Influencer Marketing Virtual? The Effect of Virtual Influencers and Sponsorship Disclosure on Purchase Intention, Brand Trust, and Consumer Engagement. Enschede: University of Twente. [Google Scholar]
  72. Xue, Ke, Yifei Li, and Hanqing Jin. 2022. What Do You Think of AI? Research on the Influence of AI News Anchor Image on Watching Intention. Behavioral Sciences 12: 465. [Google Scholar] [CrossRef]
  73. Yuan, Chunlin, Hakil Moon, Shuman Wang, Xiaolei Yu, and Kyung Hoon Kim. 2021. Study on the influencing of B2B parasocial relationship on repeat purchase intention in the online purchasing environment: An empirical study of B2B E-commerce platform. Industrial Marketing Management 92: 101–10. [Google Scholar] [CrossRef]
  74. Zhang, Xiaofei, Xiaocui Han, Yuanyuan Dang, Fanbo Meng, Xitong Guo, and Jiayue Lin. 2017. User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance. Informatics for Health and Social Care 42: 194–206. [Google Scholar] [CrossRef] [PubMed]
  75. Zhang, Xingting, Dong Wen, Jun Liang, and Jianbo Lei. 2017. How the public uses social media wechat to obtain health information in china: A survey study. BMC Medical Informatics and Decision Making 17: 71–79. [Google Scholar] [CrossRef] [PubMed]
  76. Zheng, Rongshou, Siwei Zhang, Hongmei Zeng, Shaoming Wang, Kexin Sun, Ru Chen, Li Li, Wenqiang Wei, and Jie He. 2022. Cancer incidence and mortality in China, 2016. Journal of the National Cancer Center 2: 1–9. [Google Scholar] [CrossRef]
  77. ZJXW. 2019. “China’s Young and Middle-Aged Cancer Prevention Science Report” Released the Trend of Young Cancer Bad Lifestyle Habits Become the “Number One Killer”. Available online: https://www.financialnews.com.cn/bx/bxsd/201904/t20190417_158334.html (accessed on 17 April 2019).
Figure 1. Model of predictors of willingness to engage in liver cancer prevention.
Figure 1. Model of predictors of willingness to engage in liver cancer prevention.
Socsci 13 00319 g001
Figure 2. The effects of predictors of willingness to engage in liver cancer prevention. *** p < 0.001.
Figure 2. The effects of predictors of willingness to engage in liver cancer prevention. *** p < 0.001.
Socsci 13 00319 g002
Table 1. Key demographic characteristics of the survey participants.
Table 1. Key demographic characteristics of the survey participants.
VariablesItemCountPercentage
SexFemale13252.4%
Male12047.6%
Education levelHigh school 5923.4%
Undergraduate18071.4%
Postgraduates135.2%
Age18–23 years old18774.2%
24–28 years old3614.3%
29–34 years old2911.5%
Monthly income 1000–39995421.5%
(RMB)4000 and 8999 9638.0%
9000 and 13,999 5823.1%
<RMB 14,000 4417.5%
Total252100%
Table 2. Summary of direct effects.
Table 2. Summary of direct effects.
HypothesesRelationshipResult
H1(a) PS, (b) PR, and (c) RE have significant and positive effects on WELCP.Supported
H2RUO mediated the relationship between PS and WELCPSupported
H3RUO mediated the relationship between PR and WELCPSupported
H4RUO mediated the relationship between RE and WELCPSupported
PS = perceived severity, PR = parasocial relationship, RE = response efficacy, RUO = reduced unrealistic optimism, WELCP = willingness to engage in liver cancer prevention.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chung, D.; Wang, J.; Meng, Y. Examining the Impact of Virtual Health Influencers on Young Adults’ Willingness to Engage in Liver Cancer Prevention: Insights from Parasocial Relationship Theory. Soc. Sci. 2024, 13, 319. https://doi.org/10.3390/socsci13060319

AMA Style

Chung D, Wang J, Meng Y. Examining the Impact of Virtual Health Influencers on Young Adults’ Willingness to Engage in Liver Cancer Prevention: Insights from Parasocial Relationship Theory. Social Sciences. 2024; 13(6):319. https://doi.org/10.3390/socsci13060319

Chicago/Turabian Style

Chung, Donghwa, Jiaqi Wang, and Yanfang Meng. 2024. "Examining the Impact of Virtual Health Influencers on Young Adults’ Willingness to Engage in Liver Cancer Prevention: Insights from Parasocial Relationship Theory" Social Sciences 13, no. 6: 319. https://doi.org/10.3390/socsci13060319

APA Style

Chung, D., Wang, J., & Meng, Y. (2024). Examining the Impact of Virtual Health Influencers on Young Adults’ Willingness to Engage in Liver Cancer Prevention: Insights from Parasocial Relationship Theory. Social Sciences, 13(6), 319. https://doi.org/10.3390/socsci13060319

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop