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Social media has recently provided a remarkable means of delivering health information broadly and in a cost-effective way. Despite its benefits, some difficulties are encountered in attempting to influence the public to change their behavior in response to social media health messages.
This study aimed to explore the factors that affect individuals’ acceptance of using social media as a tool for receiving health awareness messages and adapting such content accordingly by developing a smart health awareness message framework.
A quantitative method was adapted to validate the hypotheses and proposed framework through the development of a survey based on the technology acceptance model with the extension of other constructs. The survey was distributed on the web to 701 participants from different countries via Qualtrics software; it generated 391 completed questionnaires, and the response rate was 55.8% (391/701).
Of the 391 respondents, 121 (30.9%) used social media platforms often during the week, and 27 participants (6.9%) did not use social media. In addition, 24.0% (94/391) of the respondents used these platforms to seek health information. On the basis of the results, perceived usefulness (β=.37;
This study sheds light on the factors that are associated with people’s intention to use and adopt social media in the health promotion domain. The findings reveal that the intention of using social media for health awareness purposes is positively impacted by the perception of usefulness of social media and the design of health messages. Future research might seek to explore other factors that relate to people’s behavior. This point of view will assist health organizations in developing their health messages more effectively and to be patient friendly.
The advent of the internet has become a fundamental avenue for gaining health information [
One aspect of public health communication, which has received increasing attention, is the media channels through which health messages could be successfully conveyed to a wide range of relevant audiences. Several studies have found that mass media (eg, television, radio, newspaper, leaflets, and posters) have a positive impact on health promotion [
Despite the increasing utilization of social media by health organizations in disseminating health awareness, the actual impact of social media interventions demands further research to explore the factors that may affect users’ acceptance of this technology and adoption of the content [
The paper provides the results of the smart health awareness message framework development and, in turn, ensures spreading health awareness messages effectively on a faster and wider scale through social media. The focus of this paper presents the identification of the factors influencing an individual’s intention to use social media as a means to receiving health awareness messages and following its instructions for the well-being of the individual by using the technology acceptance model (TAM) [
The research approach starts with a review of related literature concerning health awareness messages and the use of social media in spreading such messages to a wider community. The second stage involved developing a conceptual framework of the factors influencing an individual’s intention to use social media for health promotion. The effectiveness of the proposed framework was evaluated based on the hypotheses developed in this study. To validate these hypotheses, public opinion was analyzed based on a web-based survey using the Qualtrics software with 391 participants.
Such a random sample size would be a good representative because it reflects the characteristics of the population from which it has been drawn (ie, from a wide range of countries) and different opinions that were relatively close to each other.
The remainder of this paper is structured as follows. The first section includes an introduction that presents the research motivation, research approach, and the aim of this paper. The second section presents a review of the related literature. The third section presents the conceptual framework along with the proposed hypotheses. In the fourth section, methods of data collection and measurement development are presented. The section following the fourth section presents some public perspectives of the smart health awareness message framework through data analysis, including testing hypotheses. Finally, the authors conclude with a discussion of the research limitations and future work.
Public health communication has emerged as a modern strategy to change public behavior by raising awareness of risk diseases. Public health communication refers to “the scientific development, strategic dissemination, and critical evaluation of relevant, accurate, accessible, and understandable health information communicated to and from intended audiences to advance the health of the public” [
Although several studies have highlighted the effectiveness of promoting health awareness via leaflets and posters [
Social media has a great potential in public health communication, as it provides patients and the public with the best opportunity by delivering meaningful health content. Ba and Wang [
So far, few research studies have examined the influential factors that affect people’s intention to use social media in the health promotion context [
The TAM assumes that the extent to which the technology is accepted and used by an individual is predicted by 2 main constructs (factors): perceived usefulness and perceived ease of use [
The smart health awareness message framework includes different elements, which are called constructs, and each construct represents the key factor of a different adapted theory. Thus, this study investigates the impact of such constructs that influence an individual’s acceptance of using social media as a tool for receiving health awareness messages and consequently following its instructions for the individual’s well-being. The authors adapted the key constructs of 3 theoretical foundations: (1) TAM, (2) TTF, and (3) prospect theory. The TAM serves as a concrete base to develop the conceptual framework. The TTF offers a key element of social media characteristics, whereas the prospect theory provides a theoretical framework for designing such messages. The proposed framework, therefore, will help in designing health messages that will be spread via social media apps.
Smart Health Awareness Message Framework.
Technology perceptions include the key elements of the TAM, namely, perceived ease of use and perceived usefulness.
Perceived ease of use, as proposed by Davis [
A positive association was supported between perceived ease of use and usefulness of technology usage that involved different contexts [
Perceived usefulness is widely defined as ‘‘the degree to which an individual believes that using a particular system would enhance his/her job performance’’ [
The link between the usefulness of social media and the intention to adopt such technology as a means to acquire and share health information has been explored by a number of studies [
This section includes 3 technology factors, customization, perceived trust, and technology characteristics, which influence the overall perceptions of social media to receive and follow health awareness messages.
Message customization means reaching target people with individualized health messages that work well to engage with the messages effectively [
Customization correlates to perceived usefulness, as evidenced by Ho [
McAllister [
The association between trust and perceived usefulness has been discussed in several studies, confirming that the more the user perceives the technology to be useful, the greater the likelihood of trusting the content of such technology and therefore their intention to use it [
Technology characteristics constitute a key element of the TTF model identified by Goodhe and Thompson [
The suitability of TTF depends on the user selection of the technology, which is based on technology characteristics that perfectly correspond to the task’s attributes. Hence, this research demonstrates that social media features are capable of boosting the adaptation of this technology in viewing health awareness messages. Earlier studies have investigated the relationship between TTF constructs and perceived usefulness of using SMS for health awareness purposes [
This section presents a technique that aids in designing health awareness messages through prospect theory.
In loss- and gain-framed message design, health messages that aim at a particular behavior in terms of its benefits (gains) or costs (losses) play a significant role in health communication [
Message frame is believed to have a significant relationship with an individual’s intention to adapt to technological invention [
The authors developed the questionnaire items based on an understanding of the literature, as presented in
The questionnaire included 3 parts: the first part presented the survey’s introduction and consent form, the second part focused on the participant’s demographics, as shown in
Respondent demographics (N=391).
Measure | Values, n (%) | ||
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Male | 154 (39.4) | |
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Female | 237 (60.6a) | |
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20-29 | 91 (23.3) | |
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30-39 | 142 (36.3a) | |
|
40-49 | 64 (16.4) | |
|
50-59 | 52 (13.3) | |
|
≥60 | 42 (10.7) | |
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|||
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Secondary school | 9 (2.3) | |
|
Bachelor’s degree | 125 (32.0) | |
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Master’s degree or above | 227 (58.1a) | |
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Others | 30 (7.7) | |
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Governmental employee | 184 (47.1a) | |
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Private employee | 85 (21.7) | |
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Self-employed | 21 (5.4) | |
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I do not work | 101 (25.8) | |
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Always | 74 (18.9) | |
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Very often | 75 (19.2) | |
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Often | 121 (31.0a) | |
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Hardly often | 94 (24.0) | |
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Never | 27 (6.9) | |
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<2 | 121 (31.0a) | |
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2 to <4 | 84 (21.5) | |
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4-6 | 94 (24.0) | |
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>6 | 92 (23.5) |
aIndicates the highest percentage.
The respondents’ demographics are illustrated in
Smart health awareness message framework has been proposed to elicit the opinion of the end user about different constructs, and the results of the survey require a range of statistical methods. First, SPSS (version 25; IBM Corp) was used to acquire respondents’ descriptive statistics. Then, data were analyzed using the IBM SPSS Analysis of a Moment Structures (AMOS) version 25, which requires 2 stages of assessment: measurement model assessment and structural equation modeling (SEM) assessment. The measurement model was assessed to confirm that the survey items reflected the corresponding constructs of the conceptual framework [
In the first stage, an EFA was conducted to determine the correlation among observed variables or items being tested. A correlation matrix presented in
Another issue to be considered in EFA is the appropriateness of the data set that has been verified using the Kaiser-Meyer-Olkin (KMO) statistics and Bartlett test of sphericity. According to Kaiser [
Discriminant validity refers to the extent to which the constructs are varied from each other, which can be assessed using the Fornell-Larcker criterion [
Promax matrix showing factor analysis results.
Factora,b | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|
PU c | PEUd | PTe | TECHf | CUSTg | INTh | Messagei |
PU1 | 0.406 | N/Aj | N/A | N/A | N/A | N/A | N/A |
PU2 | 0.512 | N/A | N/A | N/A | N/A | N/A | N/A |
PEU1 | N/A | 0.789 | N/A | N/A | N/A | N/A | N/A |
PEU2 | N/A | 0.738 | N/A | N/A | N/A | N/A | N/A |
PEU3 | N/A | 0.644 | N/A | N/A | N/A | N/A | N/A |
PEU4 | N/A | 0.562 | N/A | N/A | N/A | N/A | N/A |
PT1 | N/A | N/A | 0.596 | N/A | N/A | N/A | N/A |
PT2 | N/A | N/A | 0.839 | N/A | N/A | N/A | N/A |
TECH1 | N/A | N/A | N/A | 0.379 | N/A | N/A | N/A |
TECH2 | N/A | N/A | N/A | 0.791 | N/A | N/A | N/A |
TECH3 | N/A | N/A | N/A | 0.769 | N/A | N/A | N/A |
TECH4 | N/A | N/A | N/A | 0.379 | N/A | N/A | N/A |
TECH5 | N/A | N/A | N/A | 0.720 | N/A | N/A | N/A |
TECH6 | N/A | N/A | N/A | 0.764 | N/A | N/A | N/A |
TECH7 | N/A | N/A | N/A | 0.725 | N/A | N/A | N/A |
CUST1 | N/A | N/A | N/A | N/A | 0.821 | N/A | N/A |
CUST2 | N/A | N/A | N/A | N/A | 0.845 | N/A | N/A |
CUST3 | N/A | N/A | N/A | N/A | 0.411 | N/A | N/A |
CUST4 | N/A | N/A | N/A | N/A | 0.301 | N/A | N/A |
INT1 | N/A | N/A | N/A | N/A | N/A | 0.752 | N/A |
INT2 | N/A | N/A | N/A | N/A | N/A | 0.783 | N/A |
INT3 | N/A | N/A | N/A | N/A | N/A | 0.596 | N/A |
Message1 | N/A | N/A | N/A | N/A | N/A | N/A | 0.723 |
Message2 | N/A | N/A | N/A | N/A | N/A | N/A | 0.735 |
Message3 | N/A | N/A | N/A | N/A | N/A | N/A | 0.583 |
Message4 | N/A | N/A | N/A | N/A | N/A | N/A | 0.536 |
Message5 | N/A | N/A | N/A | N/A | N/A | N/A | 0.500 |
aRotation converged in 7 iterations.
bExtraction method: maximum likelihood; rotation method: Promax with Kaiser normalization.
cPU: perceived usefulness.
dPEU: perceived ease of use.
ePT: perceived trust.
fTECH: technology characteristics.
gCUST: customization.
hINT: intention to use.
iMessage: gain- and loss- framed message.
jN/A: not applicable.
Cronbach alpha, composite reliability, and average variance extracted for the constructs.
Constructs and items | CAa | CRb | AVEc | Factor loading | |
|
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PEU1 |
|
|
|
0.76 |
|
PEU2 |
|
|
|
0.69 |
|
PEU3 |
|
|
|
0.80 |
|
PEU4 |
|
|
|
0.78 |
|
|
|
|
|
|
|
PU1 |
|
|
|
0.80 |
|
PU2 |
|
|
|
0.83 |
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|
|
|
|
|
|
CUST1 |
|
|
|
0.78 |
|
CUST2 |
|
|
|
0.89 |
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CUST3 |
|
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|
0.49 |
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CUST4 |
|
|
|
0.44 |
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|
|
|
|
|
|
PT1 |
|
|
|
0.78 |
|
PT2 |
|
|
|
0.69 |
|
|
|
|
|
|
|
TECH1 |
|
|
|
0.60 |
|
TECH2 |
|
|
|
0.63 |
|
TECH3 |
|
|
|
0.68 |
|
TECH4 |
|
|
|
0.55 |
|
TECH5 |
|
|
|
0.63 |
|
TECH6 |
|
|
|
0.55 |
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TECH7 |
|
|
|
0.43 |
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|
|
|
|
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|
Message1 |
|
|
|
0.81 |
|
Message2 |
|
|
|
0.72 |
|
Message3 |
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|
|
0.57 |
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Message4 |
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|
|
0.53 |
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Message5 |
|
|
|
0.48 |
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|
|
|
|
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|
INT1 |
|
|
|
0.82 |
|
INT2 |
|
|
|
0.76 |
|
INT3 |
|
|
|
0.57 |
aCA: Cronbach alpha.
bCR: composite reliability.
cAVE: average variance extracted.
dPEU: perceived ease of use.
ePU: perceived usefulness.
fCUST: customization.
gPT: perceived trust.
hTECH: technology characteristics.
iMessage: gain-loss framed message.
jINT: intention to use.
Discriminant validity.
Factorsa | PUb | PEUc | PTd | TECHe | CUSTf | INTg | Messageh |
PU |
|
N/Ai | N/A | N/A | N/A | N/A | N/A |
PEU | 0.72j |
|
N/A | N/A | N/A | N/A | N/A |
PT | 0.50j | 0.46j |
|
N/A | N/A | N/A | N/A |
TECH | 0.57j | 0.66j | 0.59j |
|
N/A | N/A | N/A |
CUST | 0.29j | 0.27j | 0.36j | 0.32j |
|
N/A | N/A |
INT | 0.74j | 0.50j | 0.41j | 0.58j | 0.27j |
|
N/A |
Message | −0.09 | −0.11 | −0.03 | −0.07 | −0.001 | 0.02 |
|
aOff-diagonal elements are correlations, and diagonal elements are square roots of the average variance extracted.
bPU: perceived usefulness.
cPEU: perceived ease of use.
dPT: perceived trust.
eTECH: technology characteristics.
fCUST: customization.
gINT: intention to use.
hMessage: gain-loss framed message.
iN/A: not applicable.
j0.27: significance of correlations
In the second stage, CFA was conducted before testing the hypothesized relationships among the constructs in smart health awareness message framework using SEM [
In the second step of the CFA, model fit indexes were measured: χ2 divided by
The next step is measuring the path coefficient, coefficient of determination, and
Summary of testing hypotheses.
Hypothesis | Hypothesized path | Betaa | Result | |
H1 | PEUb-INTc | .05 | .43 | Not supported |
H2 | PEU-PUd | .37 | <.001 | Supported |
H3 | PU-INT | .43 | <.001 | Supported |
H4 | CUSTe-PEU | .12 | .12 | Not supported |
H5 | CUST-PU | .16 | .05 | Supported |
H6 | PTf-INT | .11 | .08 | Not supported |
H7 | PT-PU | .07 | <.001 | Supported |
H8 | TECHg-PU | .12 | <.001 | Supported |
H9 | Gain-framed message-INT | .04 | <.001 | Supported |
H10 | Loss-framed message-INT | .08 | <.001 | Supported |
aBeta is standardized.
bPEU: perceived ease of use.
cINT: intention to use.
dPU: perceived usefulness.
eCUST: customization.
fPT: perceived trust.
gTECH: technology characteristics.
Path analysis results also reveal that perceived ease of usefulness has little effect on intention to use (β=.05;
Nowadays, social media plays a considerable role in an individual’s daily routine, as it provides different features that encourage people to adapt it for a range of uses, including health promotion. Therefore, the motivation of this paper was to examine the factors that affect people’s intention to use social media as a way of receiving health awareness messages, which, in turn, will help them to maintain their diet and reduce the incidence of diseases. In turn, the challenges that arise from printed media, involving paper and power consumption, storage capacity, and labor intensity, will be reduced. The results in
Hypothesized Smart Health Awareness Message Framework. *
In addition, health message customization encourages the prediction of perceived usefulness, whereas it has no effect on perceived ease of use of social media. Thus, it can be indicated that social media users perceive the acquired benefits from social media when they receive health messages tailored to their preferences [
The study’s results demonstrate the use of social media in health promotion purposes, which will enhance the outcomes of an individual’s well-being. This paper aimed to investigate the influential factors that affect people’s intention to adopt such technology in health communication campaigns. Undoubtedly, high levels of health message success cannot be achieved without emotions embedded in the content of health messages [
Given the findings of smart health awareness message framework, designing health awareness messages to include loss- or gain-framed content to evoke high emotions might contribute to boosting the effectiveness of health promotion interventions. Hence, this study offers implications for health awareness message developers that guide them to establish materials that are more patient friendly and technologically outstanding by adapting social media as a delivery method. Accordingly, this strategy will encourage individuals to exchange these messages among social media users.
This study has several limitations and indicates several directions for future work. First, for the construct of message design, there are few studies associated with the prospect theory that examine the public perspective in terms of their preferences. Thus, the authors developed a number of items, validated by experts, and adapted in this study to ensure the validity of the construct. Future works might examine this construct more broadly to determine the extent to which the public might receive this message in a more positive or negative manner. Second, although the study involved 391 respondents from different countries, in which sample size is convenient for testing the framework, future studies with larger samples are needed to reinforce the generalization of results. In addition, the participants were English speakers, and findings related to a particular language might restrict generalization to others. Thus, future research might duplicate this study with different languages.
Smart health awareness message framework will also be used to define the right content and format of the health awareness messages to be spread via a software system that is integrated with different social media platforms. Furthermore, a computer-based knowledge framework based on the use of social media apps will be developed to spread health awareness messages. Finally, a specific statistical technique will be used to validate the impact of the health awareness message on recipients.
Items of the study’s constructs.
Items correlation matrix.
Kaiser-Meyer-Olkin and Bartlett test.
Standardized residual covariances for deleted items.
Standardized estimate of linear regression.
Model fit summary.
Covariance fit model.
Standardized regression weights among items.
average variance extracted
Cronbach alpha
confirmatory factor analysis
comparative fit index
composite reliability
exploratory factor analysis
electronic health
incremental fit index
Kaiser-Meyer-Olkin
normed fit index
root mean square error of approximation
structural equation modeling
technology acceptance model
Tucker-Lewis Index
task technology fit
None declared.