Sage Journals: Discover world-class research

Abstract

Backgrounds:

Effective risk communication depends on the government’s ability to deploy the latest communication technologies to promptly educate its citizens of new hazards and assist them in making informed decisions. This study investigates the influence of risk information seeking, social media competency and trust in the government on the intention to adopt e-government apps for communicating public health risks.

Design and methods:

To achieve the study’s objective, a convenience sample of 149 Malaysian residents residing in Shah Alam was obtained via a structured questionnaire and subsequently analyzed using the Partial Least Squares-Structural Equation Method. The validity and reliability of the study were evaluated through the outer loadings, Average Variance Extracted (AVE), and Composite Reliability (CR). The influence of underlying factors on the outcome was evaluated by examining path coefficients, standard errors, and t-values.

Results:

The measurement model suggested to use three items to measure the risk information seeking and five to measure trust in the government information and social competence. Loadings ranged from 0.681 to 0.972. The three factors explained the 43.2% of the outcome variability, and all had a positive effect on the intention to adopt information from the e-government application with coefficients estimates ranging from 0.133 to 0.541. The model showed an adequate predictive relevance with Q² = 0.381

Conclusion:

Public health risk communication via e-government applications rely on the active and accountable engagement of the citizens. To stimulate higher acceptance and utilization of government digital services for sustainable health risk communication and management, the government must raise the public’s level of digital literacy and proficiency. By offering training programs and demonstrations, the government may also need to think about making investments in education about digital and technological skill levels.

Keywords

Public health risk communication risk information seeking social media competence trust in government information e-government application

Introduction

Health promotion and communication focus on enhancing health status, reducing health risks, and addressing health inequities through strategies, initiatives, and policies. Public health advocacy emphasizes minimizing risks and promoting healthy behavior, fostering a protective attitude among individuals. Effective risk communication strategies are crucial for the government to raise awareness and reduce public health risks. Health risk communication involves disseminating health risks through various channels by health authorities.¹ The emergence of innovative communication and media technology has influenced risk communication, as people increasingly seek health information online and through social media.² Notably, online newspapers and social media platforms played essential roles in risk communication during the H1N1^3,4 and COVID-19 pandemics.^5,6 Digital platforms like web portals, social media, and mobile applications offer convenient and effective ways for the government to interact with the public and disseminate health information, particularly related to health risks.7 E-government and social media serve as vital mediums to create public awareness of protective behaviors^8,9 against health risks, helping people make informed decisions to mitigate these risks and reduce public anxiety during health crises.^10,11

Communities worldwide demand ongoing changes in public health practices, aimed at benefiting the entire population. To achieve engagement in protective behavior, governments must adopt the latest technology. For instance, Malaysia’s Ministry of Health (MoH) actively employs digital platforms, such as social media, web portals, and mobile applications like “MyHealth” and “MySejahtera,” to provide updated information on public health outbreaks and infectious diseases. These platforms serves as a convenient and easy conduit for seeking reliable and safe information during health crises.⁸ The public’s willingness to use the internet and social media is crucial in determining their acceptance level when using e-government applications.^12–14 Public trust in government and health authorities significantly influences risk perception and response.¹⁵ Trustworthy risk information and judgment from experts are essential for individuals facing health challenges. People tend to trust health information from accountable public health authorities,^16,17 leading them to share related health information, comply with recommended measures, and adopt protective behaviors.^18–20 The success of government digital initiatives in public risk management hinges on citizens’ acceptance and intention to adopt e-government applications.¹⁴

Understanding the underlying factors influencing public adoption of these tools is critical. Roger²² protection motivation theory states that risk perception drives people to adopt risk information-seeking behavior. It is a framework used to understand how individuals are triggered to adopt self-protective behavior toward a potential health threat.²³ Through communication technologies, individuals could seek risk information conveniently and rapidly. Risk prevention action can be engendered by inducing the public’s higher intention to search for risk information¹⁴ and acting in response to that information. Information-seeking behavior can be performed through efficient and convenient communication tools. People prepare against public health risks that may threaten their health by obtaining relevant information from health authorities.^8,9,14

The deployment of ICT-based technologies with user-friendly communication tools in risk management increases the tendency to seek updated information, which might result in risk mitigation.²⁴ E-government apps and social media are effective and collective communication platforms utilized by the government to deliver and share risk information with the public.⁹ Through ICT-based technologies, the government can effectively communicate risk information via e-government apps or social media. Lee et al. ⁹ found risk information seeking is significantly linked with the public intention to use e-government applications for health risk communication.

Social media competency is defined as “the degree to which a person is confident in his or her ability to perform tasks on social media required to meet given communication goals.”^9,25 Park and Lee¹⁴ simply describe social media competency as the ability of an individual to perform tasks using social media. The knowledge of social media and its proficient usage in completing one’s task is also referred to as social media competency.²⁶ It reflects individuals’ knowledge, skills, and ability to use digital platforms in their daily lives. According to Thanavathi,²⁷ social media proficiency is required by educators in using social media for a more enriched, creative, and supportive teaching and learning process. Chansukree et al. ²⁸ also found that significant social media competency enables public employees to be more collaborative and efficient in seeking assistance when needed.

Trust in government information and adoption intention of E-government apps are very crucial to the legitimacy and sustainability of a nation’s political system, as explained by Blind.²⁹ Wang and Wan Wart³⁰ opined that trust in government is how the public perceived and measured the performance of a government based on the given duty needed to be fulfilled. The proficiency and integrity of e-government services are reflected in the citizens’ trust in e-government services.³¹ A high degree of trust in the government’s performance would increase citizens’ confidence and involvement in government policy.^32,33 According to Sharoni³⁴ and Welch et al.,³⁵ citizens’ trust in the government and their adoption of e-government services were found to be significantly related. Trust in government services was discovered to have a significant impact on citizen loyalty toward government e-services.³⁶ Furthermore, the government’s use of social media was found to intensify the interactivity and relationship between the government and its people.^37–39

There are limited studies on the adoption of information technologies,²¹ particularly for health risk communication and emergency management.¹⁴ This study investigates sustainable ICT-based practices, including e-government applications and social media, to promote public health risk communication. It also explores the impact of risk information seeking, social media competency, and trust in government information on public adoption of these communication tools, addressing the following research question:

Does high public health risk information seeking, public social media competency and public trust in government information have a positive effect on the intention to adopt e-government apps for information?

Methodology

This is a cross-sectional study aimed to evaluate the effectiveness of ICT based technologies communications tools in public health. This quantitative study employs convenience-sampling method in the data collection process. As recommended by Cohen,⁴⁰ for social and behavioral science research, Gpower software was used to determine the minimum sample size based on 0.90 predictive powers and with an effect size of 0.15 with a maximum of three predictors. A multi-item questionnaire was designed and distributed through google form online platform in Shah Alam, Selangor state, Malaysia from November 1, 2021 to January 31, 2022. Shah Alam is the capital city and center of state administrative of Selangor state and its’ population is approximately 620,000 residents.

The study was aimed to investigate the intention to adopt e-government apps for information by evaluating the respondents’ risk information seeking desire, the level of social media competency, and public trust level in the government provided by the government. The measurement items in the questionnaire were adapted from past studies. Prior to the data collection process, in order to ensure the quality of the measurement items, a pilot study involving 30 respondents was conducted. The items that measure risk information seeking (6), trust in the government information (5) and social competence (5), were adapted from Park and Lee.¹⁴ For the dependent variable (adoption intentions for information), the three measurement items were adapted from the study of Lee et al.⁹ and all items were measured using a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree).

We used the partial least squares-structural equation model (PLS-SEM) to analyzed the research model as described in Figure 1. First, we measured the collinearity to test the common method bias in the study since the data gathered was from a single source⁴¹ and examined the normality using the Webpower online tool. Then, we assessed the validity and reliability of the study (Measurement Model), including the outer loadings, Average Variance Extracted (AVE), and Composite Reliability (CR). The discriminant validity of the model was assessed using the Heterotrait-Monotrait ratio (HTMT) criterion.⁴² Finally, path coefficients, standard errors, t-values and p-values were assessed using the bootstrapping procedure with a resample of 5000 samples (the structural model). Further, as suggested by Hair et al.,⁴³ the effect size, f² was examined. Following Cohen’s⁴⁴ guideline. Finally, the predictive relevance of the model was tested using the blindfolding procedure and the predictive performance of the PLS path model was evaluated by the assessment of PLS Predict .⁴⁵

Figure 1.

Partial least squares structural equation modelling (PLS-SEM) Model (from SmartPLS 3 software package).

Results

A total of 149 valid questionnaires were collected in the study. The analysis indicates the absence of serious common method bias in the study, with a VIF value of less than 3.3, as shown in Table 1.

Table 1.

Full collinearity estimates.

Variable	INF	RIS	SMC	TRUST
VIF	1.753	1.275	1.201	1.796

INF: adoption intentions for information; RIS: risk information seeking; SMC = social media competency; TRUST: trust in the government information.

The Madia’s multivariate skewness and kurtosis results were ( $β = 3.190, p < 0.01)$ and $(β = 30.830, p < 0.01)$ , respectively, indicating the multivariate non-normality of the data.

Demographic analysis

A demographic analysis of the respondents’ characteristics is shown in Table 2. It was observed that the majority of the respondents were female (52.3%), and that about 61.7% and 38.3% of the respondents belonged to the age groups <20 and 20–29, respectively. The majority of the respondents were educated up to tertiary (45%) and postgraduate (49.7%) levels. In terms of monthly income, 86.6% of respondents earned below RM3000 while 13.4% earned above RM3000. The occupational status of the respondents revealed that 44.3% were students, 25.5% worked at the non-executive level, 14.1% worked at the executive level and 16.1% were unemployed. Many (83.9%) of the respondents seek health information through the Malaysia e-government website, while a few (16.1%) considered information from foreign e-government websites.

Table 2.

Sample characteristics (n = 149).

Variable	Frequency	Percent (%)
Gender
Male	71	47.7
Female	78	52.3
Age
Below 20 years old	22	14.8
20–29 years old	37	24.8
30–39 years old	42	28.2
40–49 years old	36	24.2
50–59 years old	10	6.7
60 years old and above	2	1.3
Education Level
Primary education	1	0.6
Secondary education	7	4.7
Tertiary education	67	45
Postgraduate education	74	49.7
Household monthly income
Less than RM3000	129	86.6
RM3001 – RM5000	9	6
RM5001 – RM10000	4	2.7
More than RM10000	7	4.7
Occupation
Executive level	21	14.1
Non-executive level	38	25.5
Student	66	44.3
Unemployed	24	16.1
Do you seek health information online?
Yes	149	100
No	-	-
I seek health information through ________?
Malaysia e-government websites	125	83.9
Foreign e-government websites	24	16.1
Health research institutes’ websites	-	-
Health association websites	-	-
General health websites	-	-

Measurement model

The assessment results of the measurement model in Table 3 showed that the indicator loadings exceeded the recommended threshold of 0.5.⁴⁶ Consequently, items RIS4, RIS5 and RIS6 were dropped. The CR and AVE values of all constructs were >0.7 and 0.5, respectively. Thus, the reliability and convergent validity of the measures are confirmed.

Table 3.

Measurement model results.

Variable	Item	Loadings	Average Variance Extracted (AVE)	Composite Reliability (CR)
Adoption intentions for information (INF)	INF1	0.972	0.939	0.979
	INF2	0.970
	INF3	0.965
Risk information seeking (RIS)	RIS1	0.920	0.720	0.883
	RIS2	0.921
	RIS3	0.681
Social media competency (SMC)	SMC1	0.907	0.778	0.946
	SMC2	0.920
	SMC3	0.843
	SMC4	0.856
	SMC5	0.882
Trust in the government information (TRUST)	TRUST1	0.927	0.878	0.973
	TRUST2	0.952
	TRUST3	0.957
	TRUST4	0.951
	TRUST5	0.898

As shown in Table 4, all the ratios were <0.85, thus fulfilling the criterion of HTMT_0.85 ⁴⁷ and corroborating the discriminant validity.

Table 4.

Heterotrait-monotrait ratio (HTMT) criterion.

Variable	INF	RIS	SMC
INF
RIS	0.338
SMC	0.363	0.199
TRUST	0.654	0.315	0.355

Structural model

The results, which include the confidence interval and effect size, are presented in Table 5. The results revealed that risk information seeking $(β = 0.133, p < 0.05)$ , social media competence $(β = 0.0.138, p < 0.05)$ , and trust in government information $(β = 0.546, p < 0.01)$ had a positive effect on intentions to adopt information from the e-government application. Thus, H1, H2 and H3 are supported.

Table 5.

Structural model results.

Research Hypothesis	Beta	Std. Error	t-value	p-value	BCI LL	BCI UL	f²
H1: RIS -> INF	0.133	0.06	2.208	0.014	0.041	0.242	0.028
H2: SMC -> INF	0.138	0.072	1.900	0.029	0.025	0.264	0.029
H3: TRUST -> INF	0.546	0.081	6.777	0.000	0.403	0.669	0.436

The R² value, which represents the degree of explained variance of the model, indicated that 43.5% of the variance in information adoption intentions from e-government applications was explained by risk information seeking, social media competence, and trust in government information. Therefore, variables are suitable to be applied in the model.

All supported hypotheses showed acceptable effect sizes (showed in Table 5). Moreover, the effect size of trust in government information on information adoption intention was relatively stronger than that of social media competence and risk information seeking.

Given that the Q² value of 0.381 above zero, the model is deemed to have sufficient predictive relevance. The Q² value of 0.381 is >0, the model is said to have adequate predictive relevance. The difference of RMSE values for PLS-SEM and the naïve LM benchmark for items INF1, INF2 and INF3 were negative, as shown in Table 6, indicating that the PLS path model has strong predictive power with lower prediction errors for all the indicators.

Table 6.

PLSpredict assessment.

	PLS-SEM		LM	PLS-SEM - LM
Item	RMSE	Q²_predict	RMSE	RMSE
INF1	0.658	0.399	0.696	–0.038
INF2	0.677	0.371	0.719	–0.042
INF3	0.688	0.382	0.731	–0.043

Discussion

The success of public health risk communication through e-government applications is dependent on the active and accountable engagement of the citizens. Effective public health risk communication is essential for the government to design appropriate policies to improve the access to public healthcare services and act more effectively against health crises. This underscores the need to understand the determinants of public adoption of e-government apps for health risk information. In this regard, this study found that citizens are willing to seek health risk information through e-government applications. People tend to react proactively to emerging risks that might affect them by seeking risk information from various sources.⁴⁸ Therefore, facilitating access to adequate and accurate health risk information is important to reduce public fear and enable their informed response to health crises,¹⁴ such as the COVID-19 pandemic. Active dissemination of reliable and updated health risk information to the public not only assist the public in dealing with health crisis but also help the government in designing a better health policy⁴⁹ to improve the health standard of the country. In this regard, effective ICT-based risk communication could be a breakthrough.

This suggests that improved social media proficiency encourages the public to utilize e-government applications for communicating health concerns, hence raising public knowledge of current health problems. In order to stimulate higher acceptance and utilization of government digital services for sustainable health risk communication and management, the government must raise the public’s level of digital literacy and proficiency. By offering training programs and demonstrations, the government may also need to think about making investments in education about digital and technological skill levels. These efforts will improve the usability of the new applications, encourage their continuous adoption intention, and improve the level of understanding of the information offered by the application.^14,50

The findings also showed that greater public trust in government information encourages the public’s adoption of e-government applications to address health risk. As a result, the operation of e-government applications must meet the needs of the general public by offering helpful resources and ensuring that the information services are easy to access, reach and understand.¹⁴

The current study adds to the body of theoretical and applied knowledge regarding the use of e-government applications for risk communication in public health. This study adds to the empirical literature on the use of information and communication technology in the context of public health from a theoretical standpoint. Practically speaking, the findings of this study offer insight into how policies and strategies for promoting increased use of e-government public health apps might be developed.

Footnotes

Declaration of conflicting interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The paper is supported by the university grant 600-UiTMSEL (PI. 5/4) (013/2022) endowed by Universiti Teknologi MARA Cawangan Selangor, Malaysia.

ORCID iD

Afiza Azura Mohamad Arshad

References

Berg

O’Hara

Shortt

, et al. Health authorities’ health risk communication with the public during pandemics: a rapid scoping review. BMC Public Health 2021; 21: 1401.

Gamhewage

An introduction to risk communication, https://www.who.int/publications/i/item/an-introduction-to-risk-communication (2014, accessed 12 February 2023).

Tang

Bie

Park

S-E

, et al. Social media and outbreaks of emerging infectious diseases: a systematic review of literature. Am J Infect Control 2018; 46: 962–972.

Klemm

Das

Hartmann

Swine flu and hype: a systematic review of media dramatization of the H1N1 influenza pandemic. J Risk Res 2016; 19(1): 1–20.

Budd

Miller

Manning

, et al. Digital technologies in the public-health response to COVID-19. Nat Med 2020; 26: 1183–1192.

Srivastava

Shrivastava

Chhabra

KG.

Facade of media and social media during covid-19: a review. Int J Res Pharm Sci 2020; 11: 142–149.

Quan

Wang

Towards a structural model of the tourist experience: an illustration from food experiences in tourism. Tour Manag 2004; 25: 297–305.

Mat Dawi

Namazi

Hwang

, et al. Attitude toward protective behavior engagement during COVID-19 pandemic in Malaysia: the role of e-government and social media. Front Public Health 2021; 9: 1–8.

Lee

Park

Lee

Collaborative accountability for sustainable public health: a Korean perspective on the effective use of ICT-based health risk communication. Gov Inf Q 2019; 36: 226–236.

10.

Lowbridge

Leask

Bug Breakfast in the bulletin: risk communication in public health. NSW Public Heal Bull 2011; 22: 34.

11.

Porat

Nyrup

Calvo

, et al. Public health and risk communication during COVID-19-enhancing psychological needs to promote sustainable behavior change. Front Public Health 2020; 8: 1–16.

12.

Wirtz

Piehler

Daiser

E-government portal characteristics and individual appeal: an examination of e-government and citizen acceptance in the context of local administration portals. J Nonprofit Public Sect Mark 2015; 27: 70–98.

13.

Lallmahomed

MZI

Lallmahomed

GM.

Factors influencing the adoption of e-Government services in Mauritius. Telematics Inform 2017; 34(4): 57–72.

14.

Park

Lee

TD.

Adoption of e-government applications for public health risk communication: government trust and social media competence as primary drivers. J Health Commun 2018; 23: 712–723.

15.

Alsajjan

Dennis

Internet banking acceptance model: cross-market examination. J Bus Res 2010; 63: 957–963.

16.

Thai

Gaysynsky

Falisi

, et al. Trust in health information sources and channels, then and now: Evidence from the health information national trends survey. In: Hale

SRCTM

Chou

WYS

(eds) eHealth: Current evidence, promises, perils and future directions. Leeds, England: Emerald Publishing Limited, 2018, pp.43–67.

17.

van der Weerd

Timmermans

Beaujean

, et al. Monitoring the level of government trust, risk perception and intention of the general public to adopt protective measures during the influenza A (H1N1) pandemic in the Netherlands. BMC Public Health 2011; 11: 575.

18.

Huurne

Gutteling

Information needs and risk perception as predictors of risk information seeking. J Risk Res 2008; 11: 847–862.

19.

Siegrist

Cvetkovich

Perception of hazards: the role of Social Trust and knowledge. Risk Anal 2000; 20: 713–719.

20.

Seo

Ravazzani

Jun

, et al. Unintended effects of risk communication: impacts of message fatigue, risk tolerance, and trust in public health information on psychological reactance. J Int Cris̡. Risk Commun Res 2021; 4: 517–544.

21.

Cheng

Mitomo

The underlying factors of the perceived usefulness of using smart wearable devices for disaster applications. Telematics Inform 2017; 34: 528–539.

22.

Rogers

RW.

A protection motivation theory of fear appeals and attitude change. J Psychol 1975; 91: 93–114.

23.

Westcott

Ronan

Bambrick

, et al. Expanding protection motivation theory: investigating an application to animal owners and emergency responders in bushfire emergencies. BMC Psychol 2017; 5: 13.

24.

Golant

SM.

A theoretical model to explain the smart technology adoption behaviors of elder consumers (elderadopt). J Aging Stud 2017; 42: 56–73.

25.

Eastin

LaRose

Internet self-efficacy and the psychology of the digital divide. J Comput Commun 2006; 6.

26.

Guesalaga

The use of social media in sales: individual and organizational antecedents, and the role of customer engagement in social media. Ind Mark Manag 2016; 54: 71–79.

27.

Thanavathi

A study on social media competency of teacher educators in Thoothukudi. Half – Yrly Natl J 2018; 2: 5–8.

28.

Chansukree

Sagarik

Cho

Public employee use of social media at work: competency, collaboration, and communication of workplace policy. Public Pers Manage 2022; 51: 330–354.

29.

Blind

PK.

Building trust in government: linking theory with practice. In: Cheema

(eds) Building trust in government: Innovations in governance reform in Asia. Tokyo, Japan: United Nations University Press, 2010, pp.22–53.

30.

Wang

Wan Wart

When public participation in administration leads to trust: an empirical assessment of managers’ perceptions. Public Adm Rev 2007; 67: 265–278.

31.

Alhadid

Abu-taieh

Alkhawaldeh

, et al. Predictors for e-government adoption of SANAD App services.

32.

Beeri

Uster

Vigoda-Gadot

Does Performance Management relate to good governance? A study of its relationship with citizens’ satisfaction with and trust in Israeli local government. Public Perform Manag Rev 2019; 42: 241–279.

33.

Cho

Porumbescu

, et al. Internet, trust in government, and citizen compliance. J Public Adm Res Theory 2014; 24: 741–763.

34.

Sharoni

e-Citizenship: Trust in government, political efficacy, and political participation in the Internet era. Electron Media Polit 2012; 1: 119–135.

35.

Welch

Hinnant

Moon

MJ.

Linking citizen satisfaction with e-government and trust in government. J Public Adm Res Theory 2004; 15: 371–391.

36.

Alkraiji

Ameen

The impact of service quality, trust and satisfaction on young citizen loyalty towards government e-services. Inf Technol People 2022; 35: 1239–1270.

37.

Hong

Government websites and social media’s influence on government-public relationships. Public Relat Rev 2013; 39: 346–356.

38.

Porumbescu

GA.

Comparing the effects of e-government and social media use on trust in government: evidence from Seoul, South Korea. Public Manag Rev 2016; 18: 1308–1334.

39.

Song

Lee

Citizens’ use of social media in government, perceived transparency, and trust in government. Public Perform Manag Rev 2016; 39: 430–453.

40.

Cohen

A power primer. Psychol Bull 1992; 112: 155–159.

41.

Kock

Lynn

; Texas A&M International University and Stevens Institute of Technology. Lateral collinearity and misleading results in variance-based SEM: an illustration and recommendations. J Assoc Inf Syst 2012; 13: 546–580.

42.

Henseler

Ringle

Sarstedt

A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci 2015; 43: 115–135.

43.

Hair Jr

Sarstedt

Hopkins

, et al. Partial least squares structural equation modeling (PLS-SEM). Eur Bus Rev 2014; 26: 106–121.

44.

Cohen

(ed.). Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, N.J: L. Erlbaum Associates, 1988.

45.

Shmueli

Sarstedt

Hair

, et al. Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. Eur J Mark 2019; 53: 2322–2347.

46.

Hair

Hult

GTM

Ringle

, et al. (eds). A primer on partial least squares structural equation modeling (PLS-SEM). 2nd ed. Thousand Oaks, California: Sage Publications, 2017.

47.

Kline

(ed.). Principles and practice of structural equation modeling. 3rd ed. New York, NY, US: Guilford Press, 2011.

48.

Sheeran

Harris

Epton

Does heightening risk appraisals change people’s intentions and behavior? A meta-analysis of experimental studies. Psychol Bull 2014; 140: 511–543.

49.

Donahue

Zeckhauser

Collaborative governance: Private roles for public goals in turbulent times. Princeton, New Jersey: Princeton University Press, 2011.

50.

Zuiderwijk

Janssen

Dwivedi

YK.

Acceptance and use predictors of open data technologies: drawing upon the unified theory of acceptance and use of technology. Gov Inf Q 2015; 32: 429–440.

Adoption intention of e-government application for public health risk communication: Risk information,social media competence and trust in the government

Abstract

Backgrounds:

Design and methods:

Results:

Conclusion:

Keywords

Introduction

Methodology

Results

Demographic analysis

Measurement model

Structural model

Discussion

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References