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1 Jiayi Wang and G. Frank Yuan contributed equally to this article.
Guangzhe Frank Yuan
Footnotes
1 Jiayi Wang and G. Frank Yuan contributed equally to this article.
Affiliations
South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, United StatesDepartment of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
Attitudes toward cyber-counseling in Generation Z in China were examined.
•
Attitude toward psychological help impacted attitude toward cyber-counseling.
•
Attitude toward the Internet and perceived behavioral control were significant.
Abstract
Information technology developments have combined traditional face-to-face counseling with online- or cyber-counseling. As digital natives, Generation Z has easy access to cyber-counseling. However, their attitudes toward cyber-counseling remain unknown. This study explored these attitudes and relevant influencing factors among 1216 Chinese adolescents using self-report questionnaires. Descriptive analyses and structural equation modeling were used for data analysis. The model explained 27.7 % of the total variance, attitudes toward psychological help was the most influential, followed by attitudes toward the Internet and perceived behavioral control. The model was effective in explaining attitudes toward cyber-counseling, emphasizing the influence of attitudes toward psychological help.
Cyber-counseling is the practice of delivering psychological therapy or interventions via cyberspace, using information and communication technology, when the counselor and client are in different locations (
Cybercounseling in the United States and South Korea: Implications for counseling college students of the millennial generation and the networked generation.
Journal of Counseling & Development.2008; 86: 178-183
). Cyber-counseling, which combines traditional face-to-face psychological counseling and updated Internet technology, has become increasingly popular due to the rapid development of information and communication technology, its various merits (e.g., time-saving, convenience, and privacy protection), and growing mental health needs in modern society (
). Today, cyber-counseling resources are increasingly available; however, there is limited knowledge regarding people's attitudes toward and related influencing factors for psychological cyber-counseling use (
), which suggest that online counseling services aimed at young people should be prioritized. However, related research is lacking from developing countries, such as China, with greater and broader needs for psychological cyber-counseling support (
Mental health services for children in China during the COVID-19 pandemic: Results of an expert-based national survey among child and adolescent psychiatric hospitals.
European Child & Adolescent Psychiatry.2020; 29: 743-748
) Additionally, this study focuses on Generation Z, also known as “Gen Z” or “zoomers,” referring to individuals born between the mid-to-late 1990s and the early 2010s. They grew up using various Internet and digital technologies and are often called “digital natives.” Compared with earlier generations, Generation Z usually has a more positive attitude toward the transmission and communication of Internet technologies (
). Internet-based technology and networked communications are their daily norms; they view electronically mediated communication to be as natural as face-to-face communication (
) Thus, as Generation Z is very familiar with online communication, it is feasible that the Internet could be used as a powerful tool to help them cope with mental health difficulties.
Some theories provide theoretical foundations for explaining the attitude toward cyber-counseling, such as the theory of planned behavior (TPB) and the technology acceptance model (TAM). The TPB was proposed by
). According to the TPB, three major components, that is, attitude, subjective norm, and perceived behavioral control, shape the intention and actual behavior, with attitude being one of the most important components (
), attitude has been found to be one of the most significant factors that influence both intention and actual action. However, previous research findings have been controversial regarding attitudes toward psychological cyber-counseling use. Some studies found that youth perceived traditional face-to-face counseling more favorably than online counseling services (
to explain the acceptance of using new technology. TAM suggests that some key factors determine individual perception toward using new technology, such as perceived usefulness, perceived ease-of-use, and some external factors (Davis et al., 1989). The external variables include some social or technology-related factors that influence the attitude. In this study, perceived behavioral control represented the perceived usefulness and ease of use toward cyber-counseling. Internet and psychological help were significantly related to cyber-counseling (
). Attitudes toward the Internet and psychological help could be vital factors that influence attitudes toward cyber-counseling. Computer use, computer self-efficacy, and stigma could be external factors to influence attitudes toward using psychological cyber-counseling (
However, no research to date, to the best of our knowledge, has thoroughly explained the major factors that influence attitude toward using psychological cyber-counseling via the theoretical framework (
). Researchers have suggested that future studies should explore possible factors associated with attitude toward cyber-counseling and its potential mechanism (
). Therefore, it is necessary to explore attitudes toward psychological cyber-counseling use and relevant influencing factors among Generation Z in China, and develop a theoretical model to better understand this relationship and its underlying mechanism (
In this study, attitude toward using psychological cyber-counseling (AUC) refers to positive or negative beliefs, feelings, and values toward psychological cyber-counseling use (
). Attitude toward the Internet (AI) refers to an individual's positive or negative perceptions of using the Internet. Perceived behavioral control (PBC), in relation to using cyber-counseling, is the perceived ease or difficulty in using cyber-counseling based on available resources, opportunities, and personal ability (
The effects of office workers' attitudes to internet health information, e-health literacy on health information seeking behavior and health-related behavioral intention.
). For example, based on 64 studies, the results of a meta-analysis indicated that young people might prefer online psychological therapy because of their high perception of the Internet (
), which suggests that AI may positively affect AUC. Prior research with a sample of 356 youth reported a significant positive relationship between perceived confidence and attitude toward using e-health services (
" I can do it": Does confidence and perceived ability in learning new ICT skills predict pre-service health professionals' attitude towards engaging in e-healthcare?.
Studies in Health Technology and Informatics.2014; 204: 60-66
). This suggests that PBC could positively influence AUC. Moreover, some researchers have found that attitude toward psychological support is an important influencing factor for acceptance toward online counseling use (
). Computer self-efficacy (CS) refers to individuals' beliefs regarding their ability to successfully use computers to solve tasks and manage situations (
). A previous study with a sample of 753 Taiwanese high school adolescents found that digital devices usage was strongly related to the attitudes toward networks (
posited that self-efficacy was one of the most critical components of PBC. Another study conducted with 223 adults found that CS had significant, direct, and positive effects on PBC in e-learning acceptance (
). Therefore, we developed the following hypotheses:
Hypothesis 4: CU has a significant positive effect on AI.
Hypothesis 5: CS has a significant positive effect on PBC.
Hypothesis 6: SS has a significant negative effect on APH.
Methods
Participants
Self-report questionnaires were distributed to all adolescent students aged 11 to 17 at three middle schools in Yibin, Sichuan Province, China. The data were collected from October 20 to November 25, 2015 using convenience sampling. The study was approved by the research ethics committees of the research university and recruited schools. Prior to the survey, all participants and class teachers/guardians signed an informed consent form which explained the study purpose, confidentiality, and data retention.
Measurement
The questionnaire included two parts. The first part collected demographic information (gender and age). The second part comprised seven scales used to examine AUC (3 items), AI (3 items), PBC (3 items), APH (3 items), SS (2 items), CS (5 items), and CU (5 items). Participants rated each of the 24 total items using a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree). Using translation and adaptation tools (
), structural equation modeling (SEM) was conducted to test the construct relationship between the observed and latent variables in the hypothesized model. In this study, a two-step method was applied for SEM analysis (
). The first step was to estimate the fit of the measurement model, namely the fit between the observed variables and their corresponding latent variables. The second step was to estimate the structural model fit, based on the relationships of the hypothesized model. Additionally, two covariates (age and gender) were added to further examine model fit.
Results
Participant characteristics
In total, 1721 questionnaires were sent out and 1216 valid questionnaires were received (response rate = 70.66 %). Overall, 505 questionnaires were not collected owing to participants' personal reasons (e.g., unwillingness to participate, absence from school). Consequently, this study included 1216 respondents who completed the full questionnaire without missing data. As shown in Table 1, the participants were 602 (49.51 %) male adolescents and 614 (50.49 %) female adolescents, with an age range of 11–17 years (mean age = 14.21; SD = 1.28).
Descriptive statistics
To confirm the normality of the sample data, the means, standard deviations, skewness, and kurtosis of all 24 questionnaire items were analyzed (Table 2). The means of all items ranged from 2.44 to 5.97, the standard deviations ranged from 1.28 to 1.94, the skewness indices ranged from −1.57 to 1.14, and the kurtosis indices ranged from −0.94 to 2.46. The criteria of the reference values for skewness and kurtosis were │3│ and │8│ (
Confirmatory factor analysis was performed on the measurement model using the maximum likelihood estimation method. Magnitude, direction, and significance were tested for each observed variable using standardized factor loading (SFL) and t-values, and the reference standard was SFL ≥ 0.50, p < .001 (
). Composite reliability (CR) and average variance extracted (AVE) were used to evaluate construct reliability and construct validity of the latent variables, respectively. The reference criteria were CR ≥ 0.50 (
). The results showed that the SFL, CR, and AVE were acceptable for all variables, except for the AVE for AUC, APH, and CU. However, considering that the acceptable SFL and CR values of these three variables supported the validity of the related items, these variables were retained in the measurement model for further analysis (Table 3. To verify the goodness-of-fit of the measurement model, χ2, χ2/df, comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) were selected. The results suggested the all the indicators met the criteria of the reference indices (χ2 = 976.269, χ2/df = 4.226, CFI = 0.923, TLI = 0.908, RMSEA = 0.052, SRMR = 0.049), which indicated good model fit (Table 4).
Table 3Results of the measurement model.
Item
SFL
T
p
CRa
AVEb
AUC
AUC1
0.59
0.70
0.44
AUC2
0.77
14.90
***
AUC3
0.61
14.16
***
AI
AI1
0.72
0.79
0.56
AI2
0.80
22.33
***
AI3
0.72
20.85
***
CS
CS1
0.72
0.87
0.56
CS2
0.82
25.41
***
CS3
0.73
22.88
***
CS4
0.76
24.96
***
CS5
0.71
23.90
***
PBC
PBC1
0.76
0.77
0.53
PBC2
0.73
20.92
***
PBC3
0.69
19.69
***
APH
APH1
0.62
0.61
0.35
APH2
0.68
11.80
***
APH3
0.45
10.54
***
SS
SS1
0.74
0.77
0.63
SS2
0.84
8.30
***
CU
CU1
0.50
0.79
0.44
CU2
0.70
15.27
***
CU3
0.75
15.44
***
CU4
0.66
14.42
***
CU5
0.67
14.39
***
Note. Criteria: CR and AVE ≥ 0.50: sufficient reliability and construct validity, ***p < .001.
aCR = bAVE = .
AVE = average variance extracted; CR = composite reliability; SFL = standardized factor loading; AUC = attitude toward using psychological cyber-counseling; AI = attitude toward the Internet; CS = computer self-efficacy; PBC = perceived behavioral control; APH = attitude toward psychological help; SS = self-stigma; CU = computer use.
Fig. 1 shows the results of the structural model analysis. All the indicators reached the criteria of the goodness-of-fit indices with gender and age as covariates (χ2 = 1005.20, χ2/df = 3.709, CFI = 0.926, TLI = 0.911, RMSEA = 0.047, SRMR = 0.058), indicating that the structural model was acceptable. Further, the results showed that the exogenous variable CU had a significant effect on the endogenous variable AI (βCU = 0.30, tCU = 8.03, p < .001), which supported Hypothesis 4. The exogenous variable CS positively affected the endogenous variable PBC (βCS = 0.58, tCS = 14.98, p < .001), which supported Hypothesis 5. The exogenous variable SS negatively affected the endogenous variable APH (βSS = −0.36, tSS = −3.72, p < .001), which supported Hypothesis 6. The exogenous variables AI, PBC, and APH had a positive effect on the endogenous variable AUC (βAI = 0.18, tAI = 4.22, p < .001; βPBC = 0.11, tPBC = 2.55, p < .05; βAPH = 0.43, tAPH = 8.28, p < .001), which supported Hypothesis 1, Hypothesis 2, Hypothesis 3. In addition, the exogenous variable CU explained 15.3 % of the total variance of the endogenous variable AI, exogenous variable CS explained 35.9 % of the total variance of endogenous variable PBC, exogenous variable SS explained 12.9 % of the total variance of endogenous variable APH, and exogenous variables AI, PBC and APH jointly explained 27.7 % of the total variance of endogenous variable AUC. In addition, age was positively related to AUC (βage-AUC = 0.10, tage-AUC = 3.00, p < .01), AI (βage-AI = 0.24, tage-AI = 7.61, p < .001), and PBC (βage-PBC = 0.10, tage-PBC = 3.37, p < .001), while negatively associated with APH (βage-APH = −0.08, tage-APH = −1.99, p < .05). Gender was negatively related to AI (βgender-AI = −0.10, tgender-AI = −3.24, p < .01), but had no significant effects on other latent variables.
Overall, the model in the present study was effective for evaluating AUC among Chinese adolescents in Generation Z. The results showed that AI, PBC, and APH, the core construction factors in the model, could explain 27.7 % of the total variance in AUC in the study's population. This indicated that positive AI, better PBC perceived ease of use of, and positive APH will improve AUC among adolescents. In addition, CU, CS, and SS had significant effects on AI, PBC, and APH, and could explain 15.3 %, 35.9 %, and 12.9 % of the variance for these variables, respectively. The results indicated that the degree of CU was related to AI, CS related to PBC, and SS was correlated with APH. Further, the results supported that the Internet, computer, and self-confidence play vital roles in AUC among adolescents.
), the present study found that the three core construction factors in the model (AI, PBC, and APH) were significantly related to AUC. However, a previous study conducted with participants from Generation Y (i.e., individuals born between 1981 and 1996) discovered no relationship between AI and AUC (
), which was inconsistent with the findings of the present study. The focus of the present study was Generation Z, who unlike Generation Y, have lived with the global prevalence of the Internet since birth (
Reaching generation Z. attitude toward technology among the newest generation of school students.
in: Popkova E.G. Ostrovskaya V.N. Perspectives on the use of new information and communication technology (ICT) in the modern economy. Springer,
Cham2019: 1026-1032
). Hence, in the present study, AI significantly and positively affected APH. Further, APH was the strongest potential predictor of AUC, followed by AI and PBC. Psychological cyber-counseling is still essentially a form of psychological help (
); therefore, this may account for why APH can be the most important influencing factor for AUC.
Moreover, for Generation Z, online services are ubiquitous (e.g., mobile payment, online shopping), and the majority of them are highly capable of learning and accepting new online services (
); thus, AI and PBC were not the most influential factors for AUC. Therefore, to affect AUC among adolescents, it is necessary to focus on changing their APH. Although prior studies have demonstrated that Chinese adolescents mostly hold negative attitudes toward seeking professional psychological help, media publicity (
) and education on identifying psychological disorders (e.g., depression and anxiety), improving perceptions of psychological help-seeking, and eliminating stigma can help adolescents develop positive attitudes toward psychological help (
). Some methods that can improve these attitudes among Chinese adolescents include using various old and new media (e.g., TV/Internet advertisements, push notifications, newspapers or pamphlets) to promote the professionalism and usefulness of professional help to society as a whole, providing relevant mental health education videos and publicly releasing channels (online and offline) related to professional help, and giving more attention in schools to psychological health education for students and correctly guiding adolescents' perceptions of psychological health to improve their mental health literacy (
Challenges and recommendations for mental health providers during the COVID-19 pandemic: The experience of China's first university-based mental health team.
). This suggested that computer experience and personal confidence in using computers could improve AUC among Chinese adolescents. Further, SS was negatively related to APH, which implied that negative SS improved APH and AUC among adolescents, which was also consistent with previous studies (
Attitudes toward face-to-face and online counseling: Roles of self-concealment, openness to experience, loss of face, stigma, and disclosure expectations among korean college students.
International Journal for the Advancement of Counselling.2014; 36: 408-422
Associations among the number of mental health problems, stigma, and seeking help from psychological services: A path analysis model among Chinese adolescents.
Children and Youth Services Review.2014; 44: 356-362
). APH was the most influential factor for AUC, and SS can negatively influence APH; therefore, an efficient way to improve AUC among adolescents would be to reduce their SS related to psychological help-seeking, thereby improve their attitudes toward seeking such help. First, education and publicity could help spread mental health care literacy and normalize psychological help-seeking. Second, prior studies found that students sought non-professionals (e.g., friends, family) for help when facing psychological problems (
), which may indicate that social support (e.g., peers, family) could reduce SS and increase psychological help-seeking among adolescents. Although some studies have found previous use of mental health services was associated with a positive attitude toward or actual mental health service use (e.g.,
ESEMeD/MHEDEA 2000 Investigators Are attitudes towards mental health help-seeking associated with service use? Results from the European Study of Epidemiology of Mental Disorders.
Social Psychiatry and Psychiatric Epidemiology.2010; 45: 153-163
), other studies have found individuals who have received mental health counseling and psychotherapy hold more negative attitudes toward psychological help (e.g.,
). Therefore, clinicians should include anti-stigma interventions in treatment; understanding the components of patient SS may help improve treatment attendance and reduce withdrawal rates (
Further, there were several findings related to the covariates of age and gender in the present study. The results showed that age was significantly and positively related to AUC, suggesting that older adolescents hold more positive views regarding cyber-counseling. In China, preparing and taking college entrance examinations represents an extremely important period in one's life. As adolescents grow up, they enter a more advanced stage of study, and academic stress gradually increases (
). Moreover, another study evidenced that adolescents were more likely to use online counseling than face-to-face counseling because of public stigma; therefore, they may hold a more positive attitude toward psychological cyber-counseling (
), which could help to decrease gender role conflicts and the differences between men and women in relation to AUC.
Limitations
Although strict procedures for investigation were used in this study, there are still some limitations that should be considered in future studies. First, the results of this study could only explain 27.7 % of the total variance in AUC among adolescents. The remaining 72.3 % of the total variance could not be explained, indicating that more explanatory variables, such as personal characteristics (
), should be added in future studies. Second, this study adopted a cross-sectional method and did not follow the survey participants over a period of time. Therefore, as AUC among Generation Z may change over time, future studies should focus on longitudinal research. Finally, there may be some differences among adolescents in different regions in China. Accordingly, as the sample in this study was mainly concentrated in Sichuan, caution should be taken when generalizing the results to the whole country, and the sample source region should be expanded in future studies. Additionally, as the internal consistency of some scales in the current study was insufficient, a qualitative study was suggested to explore the item appropriateness and improve the scale reliability in the future.
Implications
This study filled gaps in previous studies conducted on AUC, and provided theoretical and empirical support for future research on psychological cyber-counseling intention and behavior. The findings helped identify core factors (APH, AI, PBC) that affect AUC among adolescents in Generation Z, as well as the correlates of those factors (SS, CU, CS), which can be useful for exploring the unique characteristics of Generation Z's AUC. Further, this study could provide a reference for strategy development related to improving attitudes among adolescents. The most significant path (SS→APH→AUC) in the current model indicated that reducing SS related to psychological help-seeking and mental health disorders among adolescents and then enhancing their APH could be effective for improving their AUC. This approach can help strengthen mental health literacy and promote the widespread use of psychological cyber-counseling among adolescents. In the future, researchers should continue to explore the potential mechanism between these linkages. Moreover, the possible mediators and moderators should be given priority in the relevant empirical research, such as social support and Internet accessibility (
). Finally, these results could be expanded to other populations to develop a relevant psychological intervention, such as nurses, doctors, and community workers who work on the COVID-19 frontline and need cybercounseling support.
Conclusion
This study was the first to use SEM to explore AUC among adolescents in Generation Z. We found that the current model was able to explain 27.7 % of the total variance of AUC among Chinese adolescents, in which APH was the strongest possible predictor, followed by AI and PBC, while SS, CU, and CS were significantly related to APH, AI, and PBC, respectively. Additionally, age was a potential predictor of AUC. These results emphasize the importance of the influencing factors for AUC and provide a basis for improving the attitudes of Chinese adolescents in Generation Z toward psychological cyber-counseling, which will contribute to the better development of cyber-counseling services.
Funding
This work was supported by the Fundamental Research Funds for the Central Universities ( 20827044B4020 ).
Declaration of competing interest
The authors declare no conflict of interest.
Acknowledgment
We are sincerely thankful to all participants, research assistants, and administrative staff of this research for the data collection, survey coordination, and project completion.
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Cham2019: 1026-1032
Attitudes toward face-to-face and online counseling: Roles of self-concealment, openness to experience, loss of face, stigma, and disclosure expectations among korean college students.
International Journal for the Advancement of Counselling.2014; 36: 408-422
Associations among the number of mental health problems, stigma, and seeking help from psychological services: A path analysis model among Chinese adolescents.
Children and Youth Services Review.2014; 44: 356-362
Challenges and recommendations for mental health providers during the COVID-19 pandemic: The experience of China's first university-based mental health team.
Mental health services for children in China during the COVID-19 pandemic: Results of an expert-based national survey among child and adolescent psychiatric hospitals.
European Child & Adolescent Psychiatry.2020; 29: 743-748
The effects of office workers' attitudes to internet health information, e-health literacy on health information seeking behavior and health-related behavioral intention.
" I can do it": Does confidence and perceived ability in learning new ICT skills predict pre-service health professionals' attitude towards engaging in e-healthcare?.
Studies in Health Technology and Informatics.2014; 204: 60-66
Cybercounseling in the United States and South Korea: Implications for counseling college students of the millennial generation and the networked generation.
Journal of Counseling & Development.2008; 86: 178-183
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