MINISTRY OF EDUCATION & TRAINING THE STATE BANK OF VIET NAM HO CHI MINH UNIVERSITY OF BANKING NEANG LINL DA THE RELATIONSHIP BETWEEN RISK ATTITUDE AND BEHAVIOR AGAINST COVID 19 EVIDENCE FROM HO CHI MI[.]
INTRODUCTION
Research statement
The Covid-19 pandemic, which emerged in Wuhan, China in 2019, has profoundly impacted global life, including health, economy, and society It forced companies to delay strategies, caused schools to shift to online learning, and pushed hospitals to their limits in treating patients The pandemic also dramatically changed people's behaviors and attitudes, prompting widespread adoption of mask-wearing, social distancing, and avoidance of large gatherings to control the spread of the virus According to the Vietnam Ministry of Health, over 10 million COVID-19 cases have been reported in Vietnam since 2019, with approximately 43,000 deaths, and the pandemic remains a complex and challenging situation for the Vietnamese population.
Many students have struggled to adapt to the new learning formats introduced during the COVID-19 pandemic, impacting their ability to balance learning efficiency with adherence to prevention regulations Additionally, the spread of inaccurate information about COVID-19 on social networking sites has heightened anxiety and confusion among students, affecting their attitudes and behaviors Consequently, I conducted research titled “The Relationship Between Risk Attitude and Behavior Against COVID-19: Evidence from Ho Chi Minh City University of Banking Students” to better understand how students’ perceptions influence their responses to the pandemic.
1.2 The urgency of the topic
Understanding risk attitudes provides valuable insights into individuals' risk-taking behaviors and their influence on decision-making This study aims to explore how risk attitudes affect students' compliance levels, highlighting the importance of assessing risk preferences to better predict behavioral tendencies Analyzing these relationships can inform strategies to promote responsible decision-making among students and enhance their overall risk management skills.
To verify and explore risk attitudinal factors affecting students' covid-19 against behavior
To assess the risk-attitude factors affecting students' behavior against covid-19 Propose recommendations to improve the ability to reduce risks and promote epidemic prevention behaviors
This study aims to assess the risk attitudes of HUB students by examining four key aspects of their lives to understand how these influence their risk perception Additionally, the research evaluates students' compliance with the "5K Message" and explores the relationship between their risk attitudes and responses to COVID-19 The findings provide valuable insights into how risk perceptions shape behavioral responses during health crises among university students.
Is there an impact of the risk attitude factor on students' behavior against covid- 19?
Which factors of risk attitude strongly influence students' behavior against covid-19?
What changes are needed from students' risk attitudes to improving covid-19 prevention behavior?
Students who are studying at the Banking University of Ho Chi Minh City are affected by Covid-19
The topic was researched within the Banking University of Ho Chi Minh City within 3 months from March to June
The study employed a quantitative research approach to measure relevant variables within the context of HUB Data was collected through a survey administered via Google Forms After gathering the responses, the author analyzed the data to derive meaningful insights aligned with the research objectives.
Qualitative research methods involve conducting preliminary in-depth interviews with students to validate and refine the questionnaire, ensuring its appropriateness for the study Additionally, these methods include analyzing and discussing relevant concepts grounded in the theoretical frameworks established by prior research, thereby strengthening the research's validity and depth.
This study employs a quantitative research methodology, utilizing well-designed questionnaires formatted with a 5-level Likert scale and a 0-10 scale to gather data The collected data is analyzed using SPSS software, which assesses reliability through Cronbach's Alpha coefficient, and validity through KMO and Exploratory Factor Analysis (EFA) Additionally, the research applies regression analysis and ANOVA tests to examine relationships and differences within the data, ensuring robust and accurate results.
The author chose to use Google Forms for data collection due to its familiarity and ease of use among students, ensuring efficient survey results As part of the Google suite of tools, Google Forms seamlessly integrates with online teaching platforms, enabling the author to easily distribute the survey, encourage participation, and monitor responses in real-time This approach enhances the reliability of the data collected and supports effective online instruction.
Research Limits Error! Bookmark not defined 1.6 Research Significance Error! Bookmark not defined 1.7 Expected Contribution Error! Bookmark not defined 1.8 Thesis structure
Summary
Chapter 1 introduces the research topic by clearly defining the problem, objectives, scope, and research focus It highlights the use of both qualitative and quantitative research methods to ensure comprehensive analysis Additionally, the chapter emphasizes the scientific and practical significance of the study Subsequently, the author will present key definitions, relevant theories, and a proposed research model to facilitate in-depth investigation of the topic.
LITERATURE REVIEW
Related theories
2.1.1 Theory of Planned Behavior (TPB)
The Theory of Planned Behavior (Ajzen, 1991) is a valuable framework for understanding the determinants of behavior, emphasizing intention and perceived behavioral control It suggests that an individual's intention to perform a behavior is influenced by three key factors: attitude, subjective norm, and perceived behavioral control Attitude reflects how positively or negatively a person feels about performing the behavior, while subjective norms involve beliefs about whether significant others think they should engage in it Perceived behavioral control assesses how much the individual believes the behavior is within their control This theory enables researchers to evaluate students' risk-taking attitudes towards COVID-19 in a controlled, voluntary manner and helps identify whether students perceive pandemic-related behaviors as easy or difficult to perform The degree of effort students invest and their perceived control over these behaviors significantly impact their likelihood of engaging in them.
2.1.2 Theory of Reasoned Action (TRA)
Theory of Reasoned Action (TRA) was developed by Ajzen and Fishbein in
The TRA model, developed in 1967 and refined over time, highlights that consumption trends are the most effective predictors of consumer behavior Key factors influencing buying tendencies include consumers' attitudes and subjective standards Attitudes are assessed based on perceptions of product attributes, with consumers prioritizing attributes that deliver essential benefits; understanding the importance weights of these attributes allows for accurate predictions of consumer choices Additionally, subjective standards play a crucial role in shaping purchasing decisions by reflecting social influences and personal norms.
Consumer influence from family, friends, and colleagues plays a significant role in purchasing decisions The impact of subjective standard factors on buying propensity largely depends on the consumer's level of support or opposition to the purchase, as well as their motivation aligned with influencers' wishes According to the rational action model, consumers' beliefs about a product or brand shape their attitudes toward purchasing, which in turn influence their buying propensity Therefore, consumer attitudes are key to understanding buying behavior, with trends serving as the primary indicator of behavioral patterns.
Risks are traditionally defined as damages, losses, or dangers associated with uncertainty and danger In modern perspectives, risk is viewed as a measurable uncertainty that can present both negative and positive outcomes While risks may lead to potential losses and setbacks, they also offer opportunities for growth and benefits Understanding the dual nature of risk is essential for effective risk management in today's dynamic environment.
Attitude reflects how individuals express their thoughts and feelings through words, signals, and actions, revealing their perspectives on people, situations, and decisions It encompasses discernment, influence, and behavior, shaping how they respond to various circumstances Attitudes can be either positive or negative, often evident through a person's external appearance and demeanor, influencing how others perceive and interact with them Understanding attitude is essential for recognizing personal dispositions and their impact on social interactions.
A risky attitude is a chosen mindset regarding uncertainties that can positively or negatively impact goals Perception plays a crucial role in shaping both risk assessment and attitude, as individuals interpret and evaluate risks differently based on their perceptions Influential research by Tversky, Kahneman, Slovic, Lopes, and Gilovich highlights that understanding perception is essential for grasping how people approach risk and make related decisions.
& Kahneman D, 2002; Slovic Salovey P., Brackett MA & Mayer JD, 2004), including elements rational situational factors (such as familiarity, manageability, proximity, or likelihood), subconscious experiences operating at both the individual
Perceived risk plays a crucial role in shaping risk attitudes, especially when considering factors like group levels—such as availability, group thinking, or risk/caution shifts—and associated emotions Understanding how uncertainty and its importance influence responses to questions like "How uncertain is it?" and "How significant is this risk?" helps deepen our comprehension of risk perception Incorporating perception into the original definition of risk attitudes provides a more comprehensive understanding of how individuals and groups approach risk management and decision-making processes.
Behavior is a sequence of repeated activities that reflect how individuals respond to external stimuli It encompasses a range of responses and bodily actions aimed at interacting with the environment and society Behavior can be conscious or subconscious, observable or hidden, deliberate or involuntary As a valuable trait, behavior is subject to change over time, influenced by experience and context.
Behavioral responses to stress involve confronting and facing unusual or difficult situations using an individual's unique coping strategies According to R Lazarus and S Folkman in Stress Psychology, these behaviors encompass both cognitive and behavioral efforts aimed at reducing the impact of stress Understanding how people react to stress is essential for developing effective coping mechanisms and resilience strategies.
Risk attitude is a response to uncertainty that individuals choose based on their perception, often operating subconsciously without deliberate validation This automatic decision-making can be beneficial by enabling quick reactions in uncertain situations, but it may also lead to suboptimal choices if the risk attitude is inappropriate Since risk attitudes are a matter of personal or group choice, developing the ability to assess each situation explicitly allows for selecting the most suitable risk attitude, thereby maximizing the chances of achieving desired objectives.
Compliance is to follow the rules and ethical standards set forth In addition, compliance is also compliance with the general regulations of society and the state
In any activity of an agency, it is necessary to have principles and set out those
8 principles for superiors and subordinates to work together, to develop the company together
5K message is the message of the desire of the Ministry of Health to call on each Vietnamese person to actively work and live together safely with the COVID-
19 pandemic in the "New normal" state 5K is an acronym for:
Khau trang (Mask): wear cloth masks regularly in public places, places where people gather Wear medical masks at medical facilities and isolation areas
Maintain good hygiene by washing your hands regularly with soap or hand sanitizer Disinfect frequently touched surfaces such as doorknobs, phones, tablets, tables, and chairs to prevent the spread of germs Keep your home clean and well-ventilated to ensure a healthier living environment.
Khoang cach (Distance): keep your distance when in contact with others
Khong tap trung (Do not gather in large numbers): do not agree large numbers of people in the same place
To stay protected against COVID-19, users must complete a health declaration by submitting medical information on the NCOVI app Additionally, installing the BlueZone application from https://www.bluezone.gov.vn enables users to receive real-time alerts and risk notifications related to COVID-19 infection, enhancing personal safety and community health.
Related studies
Dohmen, T., Falk, A., Huffman, D., Sunde, U., Schupp, J., & Wagner, G
G (2011) investigates risk attitudes by analyzing survey responses where individuals rate their willingness to take risks, and compares these responses to actual behavior in paid real-stakes lotteries, providing insights into the determinants of risk attitudes and the validity of the survey instrument The study utilizes multiple data sources and incorporates five additional questions in the survey to enhance its analysis of risk preferences within a representative sample.
Research using the German Socio-Economic Panel (SOEP) and a field experiment with 450 participants reveals that risk attitudes are strongly but not perfectly correlated across various contexts such as car driving, financial decisions, sports, health, and careers, indicating a common underlying risk trait with context-specific variations These findings highlight the importance of measuring risk attitudes through tailored, context-specific questions to capture nuanced behaviors beyond general risk propensity Additionally, self-reported data on risky behaviors—including stock investments, self-employment, sports participation, and smoking—demonstrate that all measures of risk attitudes are significantly related to these behaviors, supporting their behavioral validity and utility in predicting real-world risk-taking.
Elke U Weber, Ann-Rene´ E Blais, and Nancy E Betz (2002) conducted a study demonstrating that risk tolerance varies significantly across different content areas, such as financial, health, recreational, ethical, and social decisions They used a psychological scale to assess individuals' risk-taking behaviors and perceptions, revealing that risk tolerance is highly domain-specific rather than uniform across all areas The research indicates that most individuals are not consistently risk-averse or risk-seeking in every context, highlighting the nuanced nature of risk attitudes Additionally, the study found that women tend to be more risk-averse than men in all areas except social risks Importantly, the findings suggest that perceptions of expected benefits play a more substantial role in risk tolerance than inherent risk attitudes, emphasizing the importance of perceived rewards over perceived risks when evaluating risk-taking behaviors.
Research Hypotheses and Model
Research by Philomena M Bacon, Anna Conte, and Peter G Moffatt (2009) validates measures of entrepreneurial risk attitudes using a test from SOEP, revealing that most individuals tend to be risk-averse concerning business decisions Their findings indicate that more risk-averse individuals are actually more likely to pursue self-employment, especially when they are not employed in a regular job However, this relationship does not hold if individuals are unemployed or idle, as their decision to start a business is less influenced by risk attitude in such cases.
From the research objectives and through domestic and foreign references, the author proposes the following research model:
Figure 2.1: The proposed research model
Summary
H1: Risk attitudes in health ((RAH) has a positive influence on Behavior against covid-19
H2: Risk attitudes in finance (RAF) has a positive influence on Behavior against covid-19
H3: Risk attitudes in working (RAW) has a positive influence on Behavior against covid-19
H4: Risk attitudes in traveling (RAT) has a positive influence on Behavior against covid-19
H5: Khau trang has a positive influence on Behavior against covid-19
H6: Khu khuan has a positive influence on Behavior against covid-19
H7: Khoang cach has a positive influence on Behavior against covid-19
H8: Khong tu tap dong nguoi has a positive influence on Behavior against covid-19
H8: Khong tu tap dong nguoi
H5: Khau trang H6: Khu khuan H7: Khoang cach
H9: Khai bao y te Behavior against
H9: Khai bao y te has a positive influence on Behavior against covid-19
From the above hypothesis, the author applies a multivariable regression model as following:
Behavior = β0+ β1RAH+ β2RAF+ β3RAT+ β4RAW+ є
In Chapter 2, the author reviews existing studies to establish a foundation for the upcoming research model The analysis highlights that quantitative research on this topic predominantly employs exploratory factor analysis (EFA) and regression analysis to examine risk attitudes and behaviors associated with COVID-19 These methodologies are essential for understanding the factors influencing individual responses to the pandemic.
RESEARCH DESIGN
Research Process
The research is carried out through 2 main phases including qualitative research and quantitative research as follows:
Qualitative research methods involve conducting preliminary investigations by reviewing existing documents and analyzing concepts grounded in established theoretical frameworks Additionally, researchers consult scientific guides to develop and refine the research model, including the construction and validation of scales adapted from prior studies The study was conducted between February 2022 and May 2022 to ensure comprehensive and accurate findings.
This study employs a quantitative research approach by distributing survey questionnaires via Google Forms to students at HUB, utilizing convenience sampling to gather data efficiently The collected data will be analyzed using SPSS to assess the reliability of the survey scale through Cronbach's alpha, followed by Exploratory Factor Analysis (EFA) to identify underlying factors Additionally, Pearson correlation tests will explore relationships between variables, while regression analysis will test the proposed models and hypotheses The research further includes conducting one-way ANOVA tests to examine differences across groups, ensuring comprehensive statistical validation of the findings.
Qualitative research methods Quantitative research method
Cronbach's Alpha scale Adjust scale The proposed research model
Building the scale
The scale is used as a basis for qualitative research, thereby helping to build appropriate survey questionnaires for quantitative research This study builds the scale as follows:
This research assesses students' willingness to take risks using an 11-point scale, as shown in Table 3.1 The scale ranges from 0, indicating complete unwillingness to take risks, to 10, reflecting complete willingness This question provides insight into students' overall attitudes toward risk, highlighting their readiness to accept risk in various situations The results reveal how students' HUB is willing to embrace risk, offering valuable data for understanding their risk tolerance.
Table 3.1: General risk attitudes scale
Code Willingness to take risk Source
Willing to take risk in health
What level of health-related risk are you willing to take?
Willing to take risk in finance
What level of finance-related risk are you willing to take?
Willing to take risk in working
What level of working-related risk are you willing to take?
Willing to take risk in traveling
What level of traveling-related risk are you willing to take?
How much do you self-assess about your willingness to face risks in all situations and circumstances happening around you?
We utilize a 5-level Likert scale to assess students' risk attitudes across specific domains, including health, finance, working, and traveling (see Table 3.2) The survey results offer valuable insights into individual differences in risk tolerance, enabling a more comprehensive understanding of students' risk preferences in various areas.
I accept all health risks associated with ordering food online or eating food of unknown origin
AB Rosen & et al (2003); Luli
& et al (2021); EU Weber & et al (2002)
RAH2 I am willing to accept if I get sick due to lack of health care
I am willing to accept health risks in medical examination at any medical facility
I am willing to take health risks of taking medication for a medical condition that may have problems
I willing to take risk in finance when investing in risky (stocks, lottery)
K Saurabh & et al (2018); C Keller & et al (2020) RAF2
I am willing to take risks when making payments and transactions online even though I know that my account may be stolen
I am willing to take risks on financial issues when deciding to start up
RAF4 I am willing to take risk when owning a car with friend
RAW1 I am willing to take risk of losing my job when a startup fails
D Hilson & et al (2017); EU Weber & et al (2002) RAW2
I am willing to accept work- related risks when I do not comply with work environment regulations because it is against ethics
I am willing to take risk of losing my job when I engage in fraudulent and corrupt activities
RAT1 I am willing to take risks when drinking and driving A Jonas & et al (2011); T
Elsrud & et al (2001); EU Weber & et al (2002); N Christin (2013)
RAT2 I am willing to take risks when traveling in bad weather
RAT3 I am willing to take risks when traveling by public transport Grab,
RAT4 I am willing to take risk of traveling in a remote places
Source: Compiled by the author
Finally, at table 3.3, the study uses the same scale as the survey on risk attitudes of
This study examines four domains to gather individual opinions on the 5K message for COVID-19 prevention The 5K message serves as a key public health guideline aimed at reducing virus transmission The research acts as a test to explore the relationship between risk behaviors and risk attitudes within each domain Understanding these dynamics can enhance targeted strategies for effective COVID-19 prevention and health communication.
KT I always wear a mask when I go out
I have regularly disinfected my hands and body anytime, anywhere
I have obeyed well standing 2m distance from the person next to me
KTD I did not gather in large numbers at home and in public
KB I always obey the medical declaration as prescribed
Source: Compiled by the author
Sample size
The research sample was selected using the convenience sampling method, targeting approximately 300 respondents Data was collected through a survey distributed via Google Forms to the HUB student population Following data collection, the gathered responses were systematically entered and cleaned to ensure accuracy before proceeding with the analysis.
According to the empirical rule proposed by Hair, Anderson, Tatham, and Black (1998), the sample size should be at least five times the number of observed variables in the research model Given that the preliminary research model includes 20 observed variables, the minimum required sample size is 100 observations With a collected sample of 300 observations, the sample size exceeds the minimum requirement, ensuring sufficient data for reliable analysis.
Data analysis
This study analyzed survey data using SPSS 20 to examine the relationship between risk attitudes and COVID-19 prevention behaviors Exploratory Factor Analysis (EFA) was conducted to identify underlying factors, which served as the basis for further testing The questionnaire data were processed and analyzed to provide insights into how risk perceptions influence preventive actions during the pandemic.
Cronbach's Alpha test assesses the reliability of a scale by determining whether the observed variables effectively measure the underlying concept or parent factor A reliable scale is one in which these variables consistently contribute to capturing the characteristics of the target construct Additionally, this test indicates the degree of internal consistency by measuring the correlation among the observed variables, ensuring they work together cohesively to represent the concept accurately.
Cronbach's Alpha coefficient, introduced by Cronbach in 1951, measures the overall reliability of a scale with three or more observed variables, but does not assess the reliability of individual items Its value ranges from 0 to 1, with higher values indicating greater scale reliability; ideally, the coefficient approaches 1 for optimal consistency However, an alpha value exceeding 0.95 may indicate overlapping items, reducing the scale’s effectiveness A scale is considered very reliable when it has a high Cronbach's Alpha coefficient, typically close to 1.
The coefficient values indicate the reliability of the measurement scale, with values from 0.8 to close to 1 representing excellent reliability A coefficient between 0.7 and just under 0.8 suggests that the scale is good for use, while a coefficient from 0.6 to just under 0.7 indicates the scale is eligible for use, according to Hoang Trong and Chu Nguyen Mong Ngoc (2008) Subsequently, Exploratory Factor Analysis (EFA) is conducted to assess the underlying factor structure of the data.
Factor analysis (EFA) is a statistical technique used to reduce a large set of interdependent measurement variables into a smaller, more meaningful set of factors while retaining most of the original information (Hair, 2009) The effectiveness of the factor analysis is considered satisfactory when the Bartlett's test of sphericity indicates significance and the Kaiser-Meyer-Olkin (KMO) coefficient is at least 0.5, ensuring the data's suitability for factor analysis.
For factor analysis to be suitable, the KMO value should range between 0.5 and 1, and the Sig must be less than 0.05 A KMO value below 0.5 suggests that factor analysis may not be appropriate for the data Variables are considered practically significant when their factor loadings exceed 0.5, indicating a strong relationship with the underlying factors (Hair, 2010) Additionally, the extracted factors should have Eigenvalues greater than 1, and the cumulative variance explained by these factors should exceed 50%, demonstrating their significance in explaining the data variance (Anderson).
3.4.3 Regression analysis and ANOVA test
Regression analysis is a powerful statistical technique used to determine the best-fitting equation that describes the relationship between dependent and independent variables It enables researchers to estimate the true connection between variables accurately By deriving this equation, analysts can make informed predictions about the dependent variable based on specific values of the independent variable, enhancing decision-making and forecasting capabilities.
Analysis of Variance (ANOVA) is a crucial parametric statistical technique used to compare multiple data sets It helps determine whether there are significant differences between group means, making it essential for research involving multiple variables By evaluating the potential differences in scale data, ANOVA enables researchers to identify variations that are statistically meaningful, ensuring accurate and reliable conclusions Leveraging ANOVA in your data analysis can improve decision-making and provide deeper insights into the relationships among variables.
ANOVA (Analysis of Variance) is a statistical method used to analyze a dependent variable influenced by a nominal independent variable with two or more categories Developed by Ronald Fisher in 1918, ANOVA helps determine the impact of independent variables on the dependent variable in regression studies One-way ANOVA, including in-depth post-hoc analysis, is essential for identifying whether there are statistically significant differences between groups concerning a specific issue, making it a crucial tool for researchers conducting comparative analyses.
The research utilizes a preliminary survey conducted via Google Forms, complemented by data analysis using SPSS software as the official research method Chapter 3 details the research process, including data collection and processing techniques.
CHAPTER 4: DATA ANALYSIS 4.1 Sample description
The survey was primarily conducted online, resulting in 225 valid questionnaires Using SPSS 20, the collected data was systematically processed and analyzed to uncover specific characteristics of each sample The survey data from HUB students across various courses reveal distinct demographic and behavioral trends, providing valuable insights into their profiles and preferences.
Based on an analysis of 225 observed samples, the data shows that female groups were more prevalent than male groups, appearing 131 times (58.2%) compared to 94 times (41.8%) This discrepancy reflects the higher number of female students at the HUB, which influenced the study to primarily focus on the "female" groups.
Table 4.2: Statistics for School year
(Source: Author investigated and analyzed)
The data in Table 4.2 indicates that first-year students accounted for 11.1% with 25 appearances, second-year students represented 13.8% with 31 appearances, third-year students made up 23.6% with 53 appearances, and fourth-year students comprised the majority with 51.5% and 116 appearances This distribution suggests that the study predominantly reflects the perspectives of fourth-year students The higher representation of fourth-year students is attributed to their larger presence among the surveyed population, while limited engagement with students from other years may contribute to this observed distinction.
Based on the data presented in Table 4.3, the Business Administration program has the highest number of students, with 90 students (40%), making it the most represented group in the survey Finance & Banking students number 57 (25.3%), while Accounting & Auditing students total 34 (15.1%), and International Economics students are 27 (12%) Additionally, 17 students (7.6%) belong to other majors such as English Language, Management Information Systems, and Economic Law The dominance of Business Administration students indicates that they comprise the majority of the survey sample, which may limit the diversity of majors represented in the research.
According to the investigation and analysis, Table 4.4 reveals that the most common occupation among HUB students' parents is business, representing 33.8% with 76 individuals The second most prevalent occupation is worker, accounting for 24.9% or 56 individuals Additionally, 16.4% of students have parents who are teachers, while 17.3% have parents working as farmers A smaller portion, 7.6%, of the students’ parents are employed in other occupations.
Table 4.5: Descriptive Statistics for General risk attitudes
Deviation Willingness to take risk in health 225 0 10 4.24 2.915
Willingness to take risk in finance 225 0 10 5.23 2.998
Willingness to take risk in working 225 0 10 5.13 2.810
Willingness to take risk in traveling 225 0 10 4.82 2.760
(Source: Result of data processing)