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COVID-19 has spread around the world and has increased the public’s need for health information in the process. Meanwhile, in the context of lockdowns and other measures for preventing SARS-CoV-2 spread, the internet has surged as a web-based resource for health information. Under these conditions, social question-and-answer communities (SQACs) are playing an increasingly important role in improving public health literacy. There is great theoretical and practical significance in exploring the influencing factors of SQAC users’ willingness to adopt health information.
The aim of this study was to establish an extended unified theory of acceptance and use of technology model that could analyze the influence factors of SQAC users’ willingness to adopt health information. Particularly, we tried to test the moderating effects that different demographic characteristics had on the variables’ influences.
This study was conducted by administering a web-based questionnaire survey and analyzing the responses from a final total of 598 valid questionnaires after invalid data were cleaned. By using structural equation modelling, the influencing factors of SQAC users’ willingness to adopt health information were analyzed. The moderating effects of variables were verified via hierarchical regression.
Performance expectation (β=.282;
SQAC users’ willingness to adopt health information was evidently affected by multiple factors, such as performance expectation, social influence, and facilitating conditions. The structural equation model proposed in this study has a good fitting degree and good explanatory power for users’ willingness to adopt health information. Suggestions were provided for SQAC operators and health management agencies based on our research results.
After the outbreak of COVID-19, the Chinese government implemented community isolation measures to control the spread of the epidemic, and because of these measures, the internet became the public’s primary tool for searching for health information. Web-based knowledge-sharing platforms such as Zhihu (Zhihu Inc) and other social question-and-answer communities (SQACs) have been playing an increasingly important role in disseminating health information to the public. By 2019, Zhihu had more than 220 million registered users and had produced more than 28 million questions and 130 million answers. In recent months, the topic of COVID-19 has attracted more than 10,000 followers who had more than 13,000 related questions. Zhihu also has more than 20 million followers in their
Health information has flourished as a topic in recent information behavior literature. Many scholars have conducted in-depth research on health information resources [
SQACs are public social media platforms in which normal users both search for and share experiences and knowledge on any given topics [
Venkatesh et al [
PE is defined as the degree to which an individual believes that using a system will help them attain gains in job performance [
In the UTAUT model, EE is defined as the degree of ease associated with the use of a system [
Venkatesh et al [
FCs refer to the perceived (organizational, societal, etc) convenience of or support for adopting something new, such as a new technology [
Perceived risk (PR) is one of the most important and widely used concepts in psychology, economics, and other fields. At present, the widely used measurement dimensions of PR include economic risk, time risk, information security risk, and health risk. PR is also one of the important determinants of health information adoption; the higher the perceived risk, the lower the willingness to adopt such information. A study [
Moderating variables such as gender, age, and the voluntariness of use play a very important role in the original UTAUT model [
Based on the structural characteristics of SQACs and SQAC users’ adoption of health information, we reset the moderating variables of the UTAUT model and incorporated PR variables into the UTAUT model to construct the final model of SQAC users’ WAHI. We present the model in
The model of social question-and-answer community users’ willingness to adopt health information.
We selected users who exhibited health information behaviors (health information browsing, commenting, searching, and other relevant behaviors) on Zhihu as the respondent sample for this study. On the basis of our literature review findings, we designed a web-based questionnaire survey scale. The scale was comprised of the items we previously outlined, and it was used to measure 19 indicators of 6 variables related to the WAHI among users of SQACs. All scale items were rated on 5-point Likert scales. We selected 22 postgraduates as presurvey participants to test the availability and quality of the questionnaire. Based on the presurvey results of the questionnaire, we deleted one of the index items for the variable EE (ie, “it often takes more time to retrieve health information in Zhihu”). After adjusting the questionnaire content and structure, the final scale was chosen via expert discussion. We included screening items in the final demographic indicators section of the questionnaire to ensure that all of the data we used came specifically from the Zhihu user group. We administered the web-based survey over 14 days between June 5 and 19, 2020, on the web-based questionnaire platform Wenjuanxing (Liepin Holdings Limited). The questionnaire was distributed by Zhihu users’ forwarding the questionnaire link via WeChat (Tencent Holdings Limited). Before the survey process could advance, we presented the content of this study and required respondents to confirm their informed consent for participating further in this study. In total, data from 921 participants’ were collected in this study. After filtering out the data of the nonusers of Zhihu and data with missing values and obvious errors, valid data from 598 participants were obtained, with an effective rate of 64.93%. A detailed list of questions can be found in
We used Microsoft Excel for data cleaning and preprocessing before the data analysis. The statistical analysis tools we used in this study mainly included SPSS version 24.0 (IBM Corporation), Analysis of Moment Structures (AMOS) version 24.0 (IBM Corporation), and Process macro version 3.3 for SPSS [
Composite reliability and the Cronbach α are the most commonly used indicators of questionnaire reliability. As shown in
The factor load, Cronbach α, average variance extraction (AVE), and composite reliability (CR) values of each variable.
Variables and indices | Factor load | Cronbach α | AVE | CR | |||||
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.881 | .636 | .883 | ||||||
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PE1 | .804 |
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PE2 | .819 |
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PE3 | .824 |
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PE4 | .787 |
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.450 | .450 | .621 | ||||||
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EE1 | .657 |
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EE3 | .685 |
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.774 | .531 | .773 | ||||||
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SI1 | .745 |
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SI2 | .688 |
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SI3 | .752 |
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.760 | .520 | .764 | ||||||
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FC1 | .783 |
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FC2 | .662 |
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FC3 | .714 |
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.819 | .603 | .819 | ||||||
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PR1 | .789 |
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PR2 | .812 |
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PR3 | .725 |
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.852 | .596 | .854 | ||||||
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WAHI1 | .716 |
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WAHI2 | .821 |
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WAHI3 | .704 |
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WAHI4 | .838 |
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aPE: performance expectation.
bEE: effort expectancy
cSI: social influence.
dFC: facilitating condition.
ePR: perceived risk.
fWAHI: willingness to adopt health information.
Discriminant validity matrix.a
Variables | Performance expectation | Effort expectancy | Social influence | Facilitating condition | Perceived risk | WAHIb |
Performance expectation | .797 | N/Ac | N/A | N/A | N/A | N/A |
Effort expectancy | .535 | .671 | N/A | N/A | N/A | N/A |
Social influence | .589 | .507 | .729 | N/A | N/A | N/A |
Facilitating condition | .609 | .449 | .590 | .721 | N/A | N/A |
Perceived risk | .038 | .289 | .160 | .144 | .777 | N/A |
WAHI | .607 | .444 | .553 | .576 | .125 | .772 |
AVEd | .636 | .450 | .531 | .520 | .603 | .596 |
aThe diagonal of the matrix is the square root of AVE of the corresponding variable.
bWAHI: willingness to adopt health information.
cN/A: not applicable.
dAVE: average variance extraction.
The demographic characteristics of the participants in this study are shown in
Statistical description of the sample.
Variables and categories | Value, n (%) | ||
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Male | 179 (29.93) | |
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Female | 419 (69.90) | |
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≤18 | 21 (3.51) | |
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19-38 | 563 (94.15) | |
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39-58 | 14 (2.34) | |
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Senior high school and below | 7 (1.17) | |
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Junior college | 10 (1.67) | |
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Undergraduate | 477 (79.77) | |
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Master and above | 104 (17.39) |
We completed the path verification of the model with AMOS 24.0 and SPSS 24.0, and
Model fitting.
Indices and values | Standard value | Fitting | |
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2.954 | <5 | Acceptable |
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2.954 | <3 | Ideal |
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0.057 | <0.08 | Acceptable |
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0.057 | <0.05 | Ideal |
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0.929 | >0.9 | Ideal |
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0.912 | >0.9 | Ideal |
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0.952 | >0.9 | Ideal |
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0.940 | >0.9 | Ideal |
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0.952 | >0.9 | Ideal |
aRMSEA: root mean square error of approximation.
The structural equation model and the path coefficients are shown in
Path test of the structural equation.
Hypotheses | Path | Unstandardized estimates | Standardized estimates (SEa) | Critical ratio | Results | |
H1 | PEb to WAHIc | .280 | .282 (.084) | 3.314 | <.001 | Accept |
H2 | EEd to WAHI | .036 | .027 (.141) | 0.256 | .79 | Reject |
H3 | SIe to WAHI | .224 | .238 (.098) | 2.296 | .02 | Accept |
H4 | FCf to WAHI | .262 | .279 (.085) | 3.080 | .002 | Accept |
H5 | PRg to WAHI | .030 | .032 (.036) | 0.825 | .41 | Reject |
aSE: standard error.
bPE: performance expectation.
cWAHI: willingness to adopt health information.
dEE: effort expectancy.
eSI: social influence.
fFC: facilitating condition.
gPR: perceived risk.
With Process macro version 3.3 for SPSS, we completed the testing of the moderating effects of the moderating variables in the model, and we present the specific significance of moderating effects in
The significance of moderating effects.
Path | Gender | Age | Education level |
PEa to WAHIb | √c |
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SId to WAHI |
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FCe to WAHI |
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aPE: performance expectation.
bWAHI: willingness to adopt health information.
cThe moderating effect was significant at the .05 level.
dSI: social influence.
eFC: facilitating condition.
Hierarchical regression test of moderating effects.
Index | Model 1a | Model 2b | |||||
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B | B | |||||
Performance expectation | 0.331 | 8.126 (597) | <.001 | 0.375 | 8.123 (597) | <.001 | |
Social influence | 0.210 | 5.250 (597) | <.001 | 0.190 | 5.029 (597) | <.001 | |
Facilitating conditions | 0.251 | 6.171 (597) | <.001 | 0.249 | 6.179 (597) | <.001 | |
Gender | N/Ac | N/A | N/A | 0.094 | 1.913 (597) | <.001 | |
Interaction itemd |
N/A | N/A | N/A | −0.170 | −2.390 (597) | .02 |
aModel 1 had an R2 value of 0.461 (
bModel 2 had an R2 value of 0.470 (
cN/A: not applicable.
dThe interaction item for the gender and performance expectation.
The moderating effect of gender on the "PE to WAHI" path. PE: performance expectation; WAHI: willingness to adopt health information.
Since the outbreak of COVID-19, the role of SQACs in the public’s access to health information has grown in prominence. This study was an analysis of the influencing factors of SQAC users' WAHI during the COVID-19 pandemic. We conducted measurements by using a questionnaire that comprised items grounded in the UTAUT and its individual components. Based on our results, PE (
Under the premises of compliance and legality, SQAC operators can make full use of the traces of users in SQACs (eg, browsing content, page stay times, likes, collections, content, and the theme of private messages). With the help of this kind of information, SQAC operators could enhance the strength of relationships among different users who pay attention to the same health topics. With the epidemic of COVID-19, people's health concerns have become more focused. This provides a rare opportunity for SQAC operators to improve users’ relationship strength. Furthermore, actively guiding users to share their understanding of and experience with health information is another effective method for improving users’ PEs and SI. By using these methods, we can further enhance users’ WAHI and users’ health levels.
FCs positively affected SQAC users’ WAHI. FCs are an integration of users’ peripheral auxiliary functions in the process of adopting health information. They are formed based on individuals’ levels of comprehension, the convenience of retrieving information from platforms, and other users’ help in understanding health information. When the effectiveness of this auxiliary role improves, the process of SQAC users’ adoption of health information becomes smoother, and users’ confidence in adopting health information increases. Through this virtuous cyclical process, health information is continuously shared, exchanged, and adopted, and improving the health information retrieval mechanism can increase the quality of the health information retrieved.
EE (
As shown in
In this study, we proposed a model of the WAHI among users of the Zhihu SQAC that was based on the modification of the native UTAUT, and we found good explanatory power for our model. We also analyzed the different influencing factors of the WAHI among Zhihu users. PE, SI, and FCs were the primary influencing factors, and the effect of PE differed according to gender. We proposed several suggestions and measures that can be implemented based on our research findings in this study.
Although we strove to be rigorous, there were still several limitations in this study. First, this was a questionnaire survey based on the subjective cognition of SQAC users; thus, we could not avoid the interference of various subjective factors associated with self-reporting. Second, this was a cross-sectional study, and as such, it was impossible to observe changes in SQAC users’ WAHI over time. Third, although we ensured that the sample was as representative as possible, there were still some inevitable systematic errors. Fourth, although SQACs are gradually becoming an indispensable platform that users can use to obtain health information, most users are still using search engines and other methods to obtain such information, and we did not adequately explain the interactions among these different sources of health information and users’ willingness to adopt such information. Fifth, the model established in this study is a limited extension of the UTAUT model, which cannot cover all of the influencing factors of the dependent variables. Other variables such as information quality, trust, and medical experience will be modeled and studied as the focus in follow-up research. In addition, there were still several limitations in our choices for variable indices. After considering the efficiency of this study, we excluded some indicators that we subjectively considered unimportant, but whether the inclusion of these indicators would enhance the explanatory power of the model remains to be further studied. Finally, we only attempted to identify the moderating effects of demographic characteristics. Therefore, only gender, age, and education level were selected for verification. Whether other demographic indicators have a significant impact needs to be further verified. In spite of the above limitations, the conclusions and suggestions of this study can be used as references by relevant health management agencies.
We constructed a UTAUT-based model to explain the WAHI among users of the Zhihu SQAC during the COVID-19 pandemic. We tested our hypotheses by using data from a survey (which we administered on the internet) that we analyzed via structural equation modelling. The results showed that PE, SI, and FCs had positive effects on SQAC users’ WAHI; EE and PR did not affect users’ WAHI. We also found that gender (
Notes on Zhihu (Zhihu Inc).
Questionnaire on social question-and-answer community users' willingness to share health information.
Analysis of Moment Structures
average variance extraction
effort expectancy
facilitating condition
mobile health
performance expectation
perceived risk
root mean square error of approximation
social influence
social question-and-answer community
unified theory of acceptance and use of technology
willingness to adopt health information
This study was supported by the Western China Social Sciences Program “Research on Health Information Transmission Path and Diffusion Model Based on Mobile Internet” (item number: 16XTQ012). We appreciate the help of all of the participants who participated in the questionnaire survey. In addition, we are also very grateful to all reviewers for their careful examination of this paper.
None declared.