The Role of Risk Communication in Shaping Health-Protective Behavior Amid the COVID-19 Pandemic in Thailand

Abstract

The study aimed to investigate the influence of social media, cultural, and demographic factors on individuals’ perceptions of risk and their responses to risk communication. This study targeted the population living in Bangkok, the capital city of Thailand, for the collection of data because Bangkok recorded the highest cumulative COVID-19 cases in the country during the pandemic. The questionnaire method included 625 respondents and was administered from August 2022 to October 2022. The questionnaire’s validation process involved conducting quantitative analysis, specifically utilizing confirmatory factor analysis within the IBM SPSS statistics 25 software. The analysis showed that demographic factors such as gender, occupation, education, and income significantly influenced risk interpretation. In terms of gender, males demonstrated a higher inclination towards evaluating data and information compared to females. Employed individuals also displayed a greater tendency for data evaluation than those who were not employed. Furthermore, individuals with lower education levels and income were more inclined towards studying risk-related information. Age and marital status did not exhibit a significant impact on risk interpretation. It was observed that interaction with social media can influence risk interpretation, potentially reducing individuals’ ability to interpret descriptive data. Furthermore, political beliefs were found to negatively impact risk interpretation due to the potential biases and preconceived notions that can shape how individuals perceive and evaluate information. Social, political, and cultural factors collectively played a role in shaping individuals’ perceptions and behaviors related to risk and health. Therefore, reevaluating these factors through quantitative research can offer valuable insights for formulating more effective recommendations to enhance risk communication policies and prepare strategies for future public health emergencies.

1. Introduction

Risk communication is the process of disseminating information and data to the general public with the objective of improving their risk perception and health behavior (Frewer 2004; Bier 2001; McComas 2006; Hooker et al. 2017). This is based on the premise that risk perception is associated with threat information and an individual’s ability to comprehend and adhere to the information (Reyna et al. 2009). It has been affirmed in the literature (Heydari et al. 2021; Ropeik and Slovic 2003; Renn 2008) that risk communication can have an effect on risk perception.

In a pandemic, effective government communication is pivotal. This encompasses tasks such as informing the public about the disease and promoting health-protective behaviors. Ineffectiveness in communicating risks can lead to mistrust and may even provoke feelings of anger and anxiety, posing substantial threats to public health in the long term (Radwan and Mousa 2021). Given the paramount importance of risk communication, it is imperative to further investigate its influence on the development of individuals’ health-protective behaviors. Risk communication is a vital tool that government officials, medical professionals, and other stakeholders should carefully consider, as it has both advantages and disadvantages (Farrag and El-Gilany 2021). It can help governments and other sectors balance the public’s behavior without causing panic or complacency and can help reduce the impact of a pandemic (Rogers and Pearce 2013).

In response to COVID-19 pandemic, the Thai government also considered risk communication as a crucial strategy for disease control and prevention (Tuler et al. 2012). Thailand has emphasized the significance of risk communication within the context of an infectious disease with the purpose of preparing and responding to the disease as a public health threat. One of Thailand’s strategies was to inform the public, the community, and various sectors about risk communication in a transparent, accurate, and up-to-date manner (The Cabinet of the Royal Thai Government 2016). The Thai government’s strategic plan for risk communication during the COVID-19 pandemic consisted of four key components: the establishment of a robust risk communication system and public relations framework, the coordination of all sectors in executing risk communication and public relations efforts, the enhancement of personnel capabilities and knowledge, and the strengthening of connections among information systems (Office of Risk Communication and Health Behavior Development, Ministry of Public Health 2020). The Thai government established the Center for COVID-19 Situation Administration (CCSA) to oversee the dissemination of information and execute various related functions, including conducting assessments during the COVID-19 pandemic. As part of its risk communication endeavors, the Thai government launched various campaigns through both online and offline platforms under the project “Thai Roo Soo COVID”, in collaboration with CCSA and ThaiHealth. This initiative aimed to increase public engagement with the goal of influencing individual health behaviors (Ministry of Public Health 2021) (Sirilak 2020). Multiple media channels were employed in this risk communication process, with the primary objective being to effectively reach a broader and more diverse audience within society (Berg et al. 2021).

Amidst the COVID-19 pandemic and the Thai government’s risk communication efforts, it was observed that approximately 87.2% of Thai citizens possessed knowledge about COVID-19. Additionally, around 65.2% of residents engaged in protective behaviors, such as adhering to social distancing measures, reporting COVID-19 symptoms, and adopting other relevant precautions. Furthermore, a significant 77% of Thai residents demonstrated commendable health-protective practices (Ounsaneha et al. 2023). Undoubtedly, attaining flawless execution of risk communication amid the COVID-19 pandemic proved to be a multifaceted endeavor. Each country grappled with distinct contextual factors, varying levels of preparedness, and individual challenges in this area. For the Thai government, effectively carrying out risk communication during this period presented a formidable challenge.

Utilizing social media as a means of communication, while offering opportunities for improved risk communication and the promotion of preventive behaviors among the public, presents a complex challenge. This platform enables the creation, sharing, exchange, transfer, and modification of information among individuals (Wahlberg and Sjoberg 2000). However, it also introduces the risk of disseminating false and inaccurate information, potentially hindering the government’s efforts to effectively communicate risks to the public (González-Padilla and Tortolero-Blanco 2020). This concern materialized during the COVID-19 pandemic in Thailand, where dramatic and fabricated news led to the spread of false information about the disease and heightened mistrust among the populace (Namwat et al. 2020).

Furthermore, it was evident that Thai citizens encountered difficulties and apprehensions stemming from unclear communication and a dearth of information during the COVID-19 pandemic (Passa 2021). This uncertainty in risk communication led to inquiries about the risk perception of Thai citizens amidst the pandemic, as noted by Osterrieder et al. (2021). Their study found that 59% of respondents were perplexed about the measures taken by the Thai government, and 46% reported a lack of information regarding potential penalties the government could enforce as part of its response to the COVID-19 pandemic. Hence, this study employed quantitative research to analyze the factors influencing risk communication with the aim of refining this approach.

While existing literature has explored a range of studies aimed at enhancing risk communication, this study stands out in its endeavor to elevate risk communication through comprehensive quantitative research. This study aims to identify the key factors that influence risk perception, health-protective behavior, and communication.

For instance, Schneider et al.’s (2021) research highlighted the significant role of sociocultural factors in the relationship between risk perception and health-protective behavior. Additionally, Rezaei et al. (2022) investigated the interplay of risk communication, risk perception, and health-protective behavior. They discovered a direct link between risk perception and health-protective behavior. Furthermore, their study revealed a positive correlation between age and health-protective behavior, as well as variations in health-protective behavior based on gender.

This study holds importance in assessing the key factors that shaped risk perception and communication and their subsequent impact on protective behaviors. It achieved this by examining the influence of demographic, social media, cultural, and political factors on risk perception, providing insights into public opinions on risk and analyzing the impact of risk perception on different types of health-protective behavior. Additionally, the study sought to bolster future governmental endeavors in refining the quality of risk communication. By enhancing government policies in this domain, it is anticipated that this research will ultimately promote more accurate risk perception from understanding at the individual level and encourage health-protective behaviors among the Thai population in the context of a pandemic.

2. Theoretical Background

The Health Belief Model (HBM), as posited by Becker and Rosenstock in 1984 (Becker and Rosenstock 1984), provides a framework for comprehending health conditions and the potential reactions that may arise when an individual receives health advice. This model primarily delves into two key domains: psychology and behavior theory. It dissects two primary variables: an individual’s assessment of their own health, encompassing their perception of susceptibility to health risks and their expectations of health-related outcomes; and the decision-making process an individual undergoes when engaging in health-protective behavior. Within this decision-making process, considerations encompass the perceived benefits of adopting a recommended health-protective behavior and the associated costs for the individual in implementing such behavior, as outlined by Abraham and Sheeran in 2015 (Abraham and Sheeran 2015).

Recent research has demonstrated that various factors, including social and cultural influences, demographic characteristics, and social media exposure, play a crucial role in shaping individuals’ perception and comprehension of risks. These perceptions, in turn, influence their adoption of health-protective behaviors (Barriors and Hochberg 2020; Thummapol et al. 2021; Zeballos Rivas et al. 2021; Arslanca et al. 2021).

In terms of social media, recent studies have highlighted a positive correlation between social media exposure and risk perception. Social media serves as a prominent channel for acquiring information, which can heighten public awareness and perception of risks (Choi et al. 2017). Similarly, the research of Tsoy, Tirasawasdichai, and Kurpayanidi (Tsoy et al. 2021) also represented the correlation of social media to risk perception.

Furthermore, demographic factors have been identified as significant determinants of risk perception. Variables such as gender, place of residence, level of education, and employment status substantially influence individuals’ perceptions of danger. For example, studies have shown that male, employed individuals, and those born in certain regions, may perceive lower levels of risk (Sund et al. 2017). Similarly, gender and educational attainment have been found to impact perceived severity and preventive behaviors, with varying effects observed across different educational groups (Rattay et al. 2021).

Research on risk communication has also explored the influence of social and cultural factors on risk perception and protective behavior during the COVID-19 pandemic. Findings indicate that social norms and risk perception positively affect protective behaviors, such as practicing good hygiene and avoiding close social contact. Additionally, the study underscores the importance of social norms as indicators of healthy conduct (Savadori and Lauriola 2021). Similarly, as demonstrated by the research conducted by Samadipour, Ghardashi, and Aghaei in 2020, they uncovered a positive correlation between cultural factors and risk perception. Additionally, political factors exert influence on the public’s inclination to consume news in alignment with their political leanings (Mullainathan and Shleifer 2005), as well as affecting individuals’ interpretative capabilities (Barriors and Hochberg 2020).

Based on these findings elucidating the relationships between each factor, four hypotheses were developed, as outlined in Figure 1. The variables are expounded upon in Table 1. The hypotheses are defined as follows:

H1. 

Demographic factors positively influence individual risk perception.

H2. 

Social media factors positively impact individual risk perception.

H3. 

Social and cultural factors positively affect individual risk perception.

H4. 

Individual risk perception influences the health-protective behavior of the public.

Table 1

Variables and their definition.

Variables	Definition of Variables
Risk communication processof the Thai government	Sender of risk communication who represents the source of information.
Demographic variables	These are the independent variables in this research.
Social media variables and social and political variables	Modifying factor for analyzing environmental factors to represent a cue for action that can influence an individual’s perception of risk and their behavior.These are independent variables in this research.
Individual perception of risk	Perception is related to the knowledge and belief of an individual and can lead to healthy behavior.It is represented by independent and dependent variables in this research.
Healthy behavior of individuals	The likelihood of an action is the result of an individual’s risk perception and the risk communication process.This is a dependent variable in this research.

Figure 1

Sketch of the research concept.

[Figure omitted. See PDF]

3. Materials and Methods

3.1. Questionnaire

This research mainly used a questionnaire as a tool for data collection. The questionnaire consisted of two main parts: The first section was about demographic information, and the second section was about attitude and behavior related to risk and the implementation of risk communication by the Thai government. The questionnaire included different types of open-ended and close-ended questions with multiple-choice and short-answer questions. The duration of the data collection with the questionnaire lasted three months, from August 2022 to October 2022. No pilot study was conducted prior to data collection.

3.2. Data Sampling

During the COVID-19 pandemic in Thailand, the capital city, Bangkok, had the greatest number of cumulative cases; therefore, the current study used Bangkok to represent the risk communication process in Thailand. For the procedure of sampling, probability sampling was used as the main approach for the quantitative analysis, as shown in Figure 2.

The first stage of sampling involved the Thai population living in Bangkok, which numbered 5,588,222, but this research focused on the Thai population aged 18 or above. This population was estimated to be around 4,499,889. The next step was the process of calculating the sample size. It was calculated by using the Yamane formula with a confidence level (%) of 95% and a margin of error (%) of 0.04%. The result of the calculation was n = 625. After this process, random sampling was used due to the size of the targeted group according to the calculation.

3.3. Data Analysis

The questionnaire data were analyzed by using a quantitative method through IBM SPSS statistics 25 software. Confirmatory factor analysis (CFA) was adopted to test and analyze the validity and reliability of a hypothetical model. The statistical approaches that were used to analyze each factor consisted of two steps: descriptive statistics and inferential statistics.

To tackle the issue of missing data and information, multiple imputations were employed. This involved replacing the missing values using a random method based on the predictive distribution of the sample and the status of the study’s variables. Subsequently, the analysis results were consolidated by estimating the average.

4. Results

4.1. Descriptive Results

The total number of respondents (n) in the questionnaire was 625, of whom 54.4% were male and 45.6% were female. From this number of respondents, the age range of 18–30 years old represented the highest percentage, 32.2% and the majority of the participants (66.7%) were BSc. degree holders, 16.2% were master’s degree holders, 12.8% were holders of vocational degrees, and 3.4% were high school students. For the employment status of the respondents, about 89.9% were employed, 54.1% were private-sector employees, 13.8% were government officers or business owners, 3.7% were students, 0.2% were teachers, and 10.2% were unemployed. Regarding the salary level, about 37.3% of the respondents had a salary of around 25,001–50,000 baht, while 34.7% had a salary of around 10,001–25,000 baht. Table 2 lists the descriptive results of this study.

The overall topics of residents’ attitudes about risk communication and preventive behavior toward COVID-19 are shown in Table 3. According to attitude about risk communication, it can be seen that residents’ attitude about risk communication related to hygiene behavior was highest according to the mean value, followed by risk communication about social distancing with 3.28, staying at home with 2.84, vaccination with 2.72, and the daily number of COVID-19 patients with 2.70. Moreover, they also represented that they implemented the health-protective behavior of wearing a mask, social distancing, and hygienic behavior the most with a mean of 4.18, followed by staying at home with a mean of 3.98, changing the destination according to the information with a median of 3.89, self-checking with ATK with a median of 3.78, and vaccination with 3.55.

4.2. The Distributions of the Measures Used in This Study

4.2.1. Demographic Factors

Table 4 lists the coefficients of demographic factor and risk analyzation. The findings revealed that demographic factors played a crucial role in influencing individuals’ ability to interpret information related to the government’s CCSA. A majority of respondents demonstrated a capacity to critically evaluate data and information before forming beliefs or taking action. Factors such as gender, education, career, and income exhibited a notable impact on how individuals interpreted risks. Specifically, the results indicated that males tended to be more inclined to analyze data and information compared to females. Additionally, those who were employed showed a greater tendency to engage in data analysis compared to those who were unemployed. Furthermore, individuals with lower levels of education and income were more likely to engage in the analysis of government-provided data compared to their counterparts with higher educational attainment and income levels.

4.2.2. Social Media Factor

Table 5, Table 6 and Table 7 list the coefficients of social media factor and risk analyzation. The “social media factor” pertains to the amount of time an individual spends on social media each day, reflecting their level of exposure to online platforms. This factor significantly influenced how individuals interpreted information communicated by the government’s CCSA. The results indicate that individuals who spent more time on social media tended to have a reduced ability to interpret data and information effectively.

Furthermore, the frequency of social media use demonstrated varying effects on different types of data, including numerical and content-based information. It was observed that frequent use of social media had a notable impact on the analysis of content-based information but not on numerical data. This suggests that individuals who spent more time on social media were less proficient in analyzing descriptive information.

Considering the influence of social media exposure on risk perceptions during the COVID-19 pandemic, it is crucial to acknowledge that individuals who allocated more time to social media were less likely to interpret data and information as indicative of COVID-19 being a health risk. This underscores the importance of understanding and mitigating the impact of social media usage on risk attitudes.

4.2.3. Social and Cultural Factors

Table 8 and Table 9 list the coefficients of political factor, coefficients of social factor and risk analyzation. Social, political, and cultural factors are external factors that originate in society. The social factors include friends, neighbors, people living in the city, and society, which have significant effects on individuals’ perception and behavioral patterns. It can be inferred that political beliefs indirectly affected individuals’ understanding and interpretation during the COVID-19 pandemic. In other words, this meant that the more they trusted politics and the government, the less likely they were to interpret information during the COVID-19 pandemic. It can be observed that social factors had significant effects on the analysis of information that was provided by the government (CCSA). They influenced individuals’ processes of analysis and interpretation to comprehend and act according to information. Neighbors had a significant effect on individuals’ risk attitudes, as the results showed that the more time they spent with them, the more likely they were to interpret and understand that COVID-19 is a serious health risk. On the other hand, the results regarding friends showed that the less time they spent with them, the more likely they were to interpret and understand that COVID-19 is a serious health risk.

4.2.4. Health-Protective Behavior of Individuals

Health-protective behavior refers to actions and practices that help preserve individuals’ health. Its aim is to protect individuals from diseases or any problems that threaten their health. The results showed the impacts of risk perception and interpretation on individuals’ health-protective behaviors. The results indicated that risk perception had a significant effect on health-protective behavior related to vaccination and avoidance of traveling to places where COVID-19 patients were according to the information from the government (CCSA). In other words, this meant that the more likely an individual was to interpret data and information, the more likely they were to get vaccinated. However, the more likely they were to interpret data and information from the government (CCSA), the less likely they were to avoid traveling to those places. Table 10 lists the coefficients of health-protective behavior and risk analyzation.

5. Discussion

The results demonstrated that the social, cultural, demographic, and social media factors had a significant impact on how people perceived and responded to risk in terms of their health. The findings for the demographic factors showed that factors such as gender, education, career, and salary had a considerable influence on people’s risk perception, which fostered health-protective behaviors. Moreover, regarding social media, which is one of the main channels for the public to access more data and information, it was shown that the more time individuals spent on social media, the less ability they had to analyze descriptive information and consider COVID-19 as a health risk. Furthermore, the social and cultural factors, which are factors at the societal level, indicated that the less time individuals spent with friends and people in the same society, the more likely they were to understand that COVID-19 is a serious health risk. However, this study presents different findings from those of other related papers that confirmed the relationship between risk communication and risk perception and the relationship between risk perception and protective behaviors. The result represents the relationship of each modifying demographic factor and social/cultural factor and the cue for action from social media related to risk perception and health-protective behavior according to the health belief model (HBM).

However, it was shown that individuals’ risk perception did not always lead to all types of health-protective behaviors that were necessary during the COVID-19 pandemic, such as wearing masks, social distancing, self-quarantining, self-checking with ATK, and other related activities, but it still had the possibility of having a relationship with other conditions. Rather, risk perception had a more influential effect on COVID-19 vaccination and the public to avoid places where coronavirus-infected people had been before. Individuals’ health-protective behavior was also influenced by factors other than risk perception—for example, one’s capacity to purchase, as each health-protective measure that a person had to take during the pandemic involved money, time, and other costs. Therefore, it is necessary to investigate other factors that affect the health-protective behavior of the public in order to understand and study it in depth in future research.

6. Study Limitation

This research carries several limitations. Firstly, it is important to note that the study’s scope did not encompass policies related to risk communication post the Thai government’s announcement categorizing COVID-19 as a disease “needing monitoring”. Additionally, the questionnaire respondents excluded individuals under 18 years old, leading to a restriction in the demographic representation. Consequently, the research outcomes were derived solely from individuals above 18 years old.

Moreover, the study grappled with a relatively modest sample size. Furthermore, the data used in this research were exclusively collected from Bangkok, Thailand, and were of a cross-sectional nature. This raises concerns about the generalizability of the findings to the entire country’s population.

Furthermore, it is important to acknowledge that this research did not undertake an examination of other potential factors influencing support for health-protective behavior, such as personal motivations or individual capabilities. These aspects were not included in the analysis.

7. Conclusions

This study highlights a strong correlation between individuals’ risk perception and their engagement in health-protective behavior. It demonstrates that people tend to act in accordance with their level of understanding to safeguard themselves. These findings align with prior research conducted by Heydari et al. (2021) and Rezaei et al. (2022).

Furthermore, the study identifies influential factors on risk perception, including demographic, social, cultural, and social media elements. The results emphasize the importance for the government to take into account the audience’s specific conditions and their interpretation of risks during the entire risk communication process. This approach is crucial for fostering a consistent comprehension and promoting healthy behaviors within the audience, from individual to societal levels.

Since individuals have diverse ways of processing information, influenced by their social media usage and their capacity to comprehend data, it is imperative to consider these factors in every instance of risk communication. This will enhance the efficacy of risk communication efforts, ensuring that the target audience receives accurate information from the government promptly. In turn, individuals will be better equipped to adopt appropriate health-protective measures against the COVID-19 pandemic. This collaborative effort between the public and risk management endeavors will lead to more effective overall risk management strategies.

Author Contributions

Conceptualization, S.T.; methodology, S.T.; validation, S.T.; formal analysis, S.T. and B.W.; investigation, S.T. and B.W.; resources, S.T.; data curation, S.T.; writing—original draft preparation, S.T.; writing—review and editing, S.T. and B.W.; project administration, S.T.; funding acquisition, B.W. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

According to the guidelines of the Declaration of Helsinki and the International Committee of Medical Journal Editors (ICMJE), survey studies using online tools do not require additional ethical approval, as long as the researchers respect the rights and privacy of the respondents and adhere to the ethical standards of their discipline.

Informed Consent Statement

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

Data Availability Statement

The data presented in this research are available on request from the corresponding authors.

Acknowledgments

The author would like to thank the College of Public Administration, Huazhong University of Science and Technology/Wuhan-China, for their administrative assistance.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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Figures and Tables

Figure 2. The proess of sampling in this research.

Table 2

Descriptive results of this study.

Factor	N	Percent
Gender
- Female	285	45.6
- Male	340	54.4
Age
- 18–30 years old	201	32.2
- 31–40 years old	111	17.8
- 41–50 years old	94	15.0
- 51–60 years old	104	16.6
- Above 61 years old	115	18.4
Education level
- High school	21	3.4
- Vocational	80	12.8
- Bachelor’s Degree	423	67.7
- Master’s Degree	101	16.2
Occupation
- Unemployed	64	10.2
- Government officer/state enterprise employee	86	13.8
- Business owner	86	13.8
- Private-sector employee	338	54.1
- Student	23	3.7
- Teacher/lecturer	1	0.2
- Others	27	4.3
Marital status
- Single	379	60.6
- Separated	20	3.2
- Married	162	25.9
- Living together but not registered	24	3.8
- Divorce	30	4.8
- Widow	10	1.6
Salary level
- 0–10,000 baht	70	11.2
- 10,001–25,000 baht	217	34.7
- 25,001–50,000 baht	233	37.3
- 50,001–150,000 baht	95	15.2
- Above 150,000 baht	10	1.6

Table 3

The study distribution of this research.

Topic	Detail	Study Distribution
		StronglyDisagree	Disagree	Medium	Agree	StronglyAgree	Mean	Median
Attitude aboutriskcommunication	Risk communication about hygiene	30(4.8%)	10(1.6%)	228(36.5%)	296(47.4%)	61(9.8%)	3.56	4
	Risk communication about social distancing	26(4.2%)	74(11.8%)	264(42.2%)	220(35.2%)	41(6.6%)	3.28	3
	Risk communication about staying at home	86(13.8%)	110(17.6%)	268(42.9%)	140(22.4%)	21(3.4%)	2.84	3
	Risk communication about vaccination	91(14.6%)	95(15.2%)	338(54.1%)	100(16%)	1(0.2%)	2.72	3
	Risk communication about the daily number of COVID-19 patients	96(15.4%)	119(19%)	299(47.8%)	100(16%)	11(1.8%)	2.70	3
Health-protectivebehavior	Wearing a mask, social distancing, and hygienic behavior	-	-	91(14.6%)	330(52.8%)	204(32.6%)	4.18	4
	Staying at home	-	10(1.6%)	126(20.2%)	353(56.5%)	136(21.8%)	3.98	4
	Changing the destination according to the information	-	30(4.8%)	136(21.8%)	332(53.1%)	127(20.3%)	3.89	4
	Self-checking with ATK	20(3.2%)	20(3.2%)	158(25.3%)	305(48.8%)	122(19.5%)	3.78	4
	Vaccination	47(7.5%)	74(11.8%)	111(17.8%)	276(44.2%)	117(18.7%)	3.55	4

Table 4

Coefficients of demographic factor and risk analyzation.

Model	UnstandardizedCoefficients		StandardizedCoefficients	t	p-Value	df
	B	Std. Error	Beta			Regression	Residual
(Constant)	2.093	0.119		17.655	0.000	6	619
Gender	−0.098	0.029	−0.127	−3.311	0.001
Age	−0.005	0.012	−0.020	−0.409	0.683
Salary level	−0.055	0.018	−0.133	−2.963	0.003
Occupation	−0.144	0.054	−0.114	−2.675	0.008
Marital status	−0.030	0.039	−0.039	−0.780	0.436
Education level	−0.147	0.024	−0.251	−6.258	0.000

Dependent Variable: Risk analyzation; Predictors: Education level, Occupation, Age, Gender, Salary level, Marital status.

Table 5

Coefficients of social media factor and risk analyzation.

Model	UnstandardizedCoefficients		StandardizedCoefficients	t	p-Value	df
	B	Std. Error	Beta			Regression	Residual
(Constant)	1.319	0.053		24.806	0.000	1	624
Exposing to social media	−0.058	0.021	−0.111	−2.781	0.006

Dependent Variable: risk analyzation; Independent variable: Exposing of individual to social media.

Table 6

Coefficients of social media factor and risk analyzation for descriptive data.

Model	Unstandardized Coefficients		Standardized Coefficients	t	p-Value	df
	B	Std. Error	Beta			Regression	Residual
(Constant)	1.681	0.064		26.288	0.000	1	624
Exposing to social media	−0.145	0.025	−0.226	−5.799	0.000

Dependent Variable: risk analyzation for descriptive data; Independent variable: Exposing of individual to social media.

Table 7

Coefficients of social media factor and risk analyzation for numeric data.

Model	Unstandardized Coefficients		Standardized Coefficients	t	p-Value	df
	B	Std. Error	Beta			Regression	Residual
(Constant)	1.465	0.070		20.947	0.000	1	624
Exposing to social media	0.004	0.027	0.006	0.137	0.891

Dependent Variable: Risk analyzation for numeric data; Independent variable: Exposing of individual to social media.

Table 8

Coefficients of political factor and risk analyzation.

Model	UnstandardizedCoefficients		Standardized Coefficients	t	p-Value	df
	B	Std. Error	Beta			Regression	Residual
(Constant)	1.312	0.055		23.801	0.000	2	623
Political belief	−0.071	0.029	−0.169	−2.462	0.014

Dependent variable: Risk analyzation; Independent variable: Political belief.

Table 9

Coefficients of social factor and risk analyzation.

Model	UnstandardizedCoefficients		Standardized Coefficients	t	p-Value	df
	B	Std. Error	Beta			Regression	Residual
(Constant)	1.536	0.070		21.825	0.000	4	621
Social behavior observation (friend)	−0.083	0.022	−0.210	−3.839	0.000
Social behavior observation (neighbors)	0.087	0.017	0.239	4.978	0.000
Social behavior observation (people in the same city)	0.044	0.025	0.102	1.746	0.081
Social behavior observation (people in the same society)	−0.132	0.022	-0.287	−6.095	0.000

Dependent variable: Risk analyzation; Independent variables: Social behavior observation from friend, neighbors, people in the same city and same society.

Table 10

Coefficients of health-protective behavior and risk analyzation.

Model	Unstandardized Coefficients		Standardized Coefficients	t	p-Value	df
	B	Std. Error	Beta			Regression	Residual
Wearing a mask, social distancing, and hygienic behavior	−0.121	0.069	−0.070	−1.745	0.081	1	624
Vaccination	0.320	0.119	0.107	2.681	0.008
Self-checking with ATK	0.036	0.095	0.015	0.381	0.704
Staying at home	−0.099	0.073	−0.054	−1.361	0.174
Changing one’s destination according to the information	−0.204	0.081	−0.100	−2.518	0.012

Dependent variables: Wearing a mask, social distancing, and hygienic behavior, vaccination, self-checking with ATK, staying at home and changing one’s destination according to the information; Independent variables: Risk analyzation.

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