The paper examines the ‘Intention to Receive the COVID-19 Vaccines’ or IRV from three perspectives: the health belief model, behavioural economics, and institutional quality.
This study provides quantitative analysis by applying Chi-squared test of contingencies, paired sample t-tests, exploratory factor analysis, and multiple linear regression (stepwise method) on the data collected from 591 respondents mainly from Malaysia.
The results show that Perceived Benefits, Perceived Barriers, Perceived Susceptibility, Herding, and Institutional Quality play roles as predictors of IRV. Perceived Benefits play the most crucial role among the predictors and Perceived Barriers is the least important predictor. People have the herding mentality after being exposed to information encouraging such behaviour.
This study reveals that the respondents changed their behaviour in different circumstances when exposed to information that incorporates the effect of herding. Herding mentality, the effectiveness of government authorities, and regulatory quality have become important factors in enriching public health policies and the effectiveness of interventions.
Tan, C. and Liew, C.Y. (2022), "Health belief and behaviour: an analysis of the predictors for receiving COVID-19 vaccines in Malaysia", Public Administration and Policy: An Asia-Pacific Journal, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/PAP-02-2022-0015
Emerald Publishing Limited
Copyright © 2022, Consilz Tan and Chee Yoong Liew
Published in Public Administration and Policy. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode
In December 2019, the first coronavirus disease 2019 (COVID-19) case appeared in Wuhan, China. Since then, it has spread worldwide, resulting in more than 6.38 million deaths (as of 27 July 2022) in 194 countries around the world which are member states of the World Health Organization (WHO, 2022a). As of December 2020, over 200 types of vaccines have been developed around the world to prevent infection of this virus (WHO, 2021). Only eleven of these 200 vaccines have been authorised by the World Health Organization (WHO). These vaccines are AstraZeneca with Oxford University, United Kingdom, BioNTech with Pfizer, USA, Gamaleya from Russia, Moderna from USA, Sinopharm with Beijing Institute, China, Covavax from Serum Institute of India, India, Novavax from USA, CanSino from China, Janssen from Johnson & Johnson, USA, Covishield from Serum Institute of India, India, Covaxin from Bharat Biotech, India and Sinovac Coronavac from China (WHO, 2022b). Although many vaccines are being developed, it is also important to ensure massive vaccination in society to generate herd immunity, which is the ultimate target to achieve in preventing the infection of this virus (Mercadante and Law, 2020; Paul et al., 2021). Awareness about people’s vaccination intentions and the factors that either support or discourage vaccination is crucial for achieving widespread immunisation. Based on this understanding, necessary steps can be taken by the relevant authorities in the country to increase vaccination uptake, such as improving public health communication by organising effective public health campaigns, etc. (Paul et al., 2021; Ruiz and Bell, 2021). Knowing the predictors of people’s IRV vaccines is essential.
The purpose of this research is to identify the predictors of the ‘Intention to Receive COVID-19 Vaccines’ (IRV). Various studies have been conducted to investigate these predictors. Other similar research includes Cerda and García (2021), Coulaud et al. (2022), Detoc et al. (2020), Eberhardt and Ling (2021), Hao and Shao (2022), Honora et al. (2022), Jacoby et al. (2022), Kitro et al. (2021), Kwok et al. (2021), Leng et al. (2021), Liao et al. (2022), Mercadante and Law (2020), Paul et al. (2021), Paul and Fancourt (2022), Ruiz and Bell (2021), Seddig et al. (2022), Tan et al. (2022), Urrunaga-Pastor et al. (2021), Wang et al. (2020) and Wong et al. (2021). Given that elderly people are the vulnerable ones and we should look across different educational levels, this research will not control the selection of samples and moderating effects because we are more concerned with understanding IRV in time with the introduction of different types of vaccines. This study is amongst the first to investigate whether information incorporating herding behaviour influences people to perceive institutional quality as one of the predictors of IRV. To the best of our knowledge, no other studies have conducted such research by taking herding and institutional quality into consideration of building a model of vaccination behaviour. The research is conducted based upon three perspectives, i.e., the health belief model (HBM), behavioural economics (herding, accessibility, etc.), and institutional quality.
A health belief model was utilised to understand what influences people to receive the COVID-19 vaccines, given the severity of the pandemic which engulfed the world. In the context of this research, health belief models are important because they enable us to understand what influences the motivations and barriers of people in their health-related behaviours (Liora, 2021). Furthermore, this model has been utilised massively in vaccination studies, particularly in studies related to influenza vaccination (Liora, 2021). In comparison with other models, HBM was explicitly designed for health research related to the prevention of diseases. Since the 1950s, this model had been modified to be more inclusive and lean more toward interventions that improve health behaviours (Liora, 2021; Mercadante and Law, 2020). The most well-cited concepts are perceived susceptibility, severity, benefits, barriers, and cues to action and self-efficacy. The HBM also suggests that the factors be investigated include respondent characteristics, demographics and information that directly impact people’s beliefs that lead to intention (Mercadante and Law, 2020). This research incorporates most of these concepts.
Aside from HBM, the behavioural economics perspective is also important to be incorporated into this research survey. One significant aspect of behavioural economics shows that people are likely to utilise social information from others, i.e. social proofing (Duffy et al., 2021). This type of behaviour is called herding, which is defined as following what others are doing, even if the private information people possess suggests that they should be doing something else (Banerjee, 1992). Herding also can arise when an informational cascade exists. An informational cascade happens when it is optimal to mimic the behaviour of others without considering one’s information after knowing what others are doing (Bikhchandani et al., 1992). Social pressure or herding mentality exists among people, encouraging them to follow the masses (Gradinaru, 2014). The herding mentality is also likely to reduce regret and provide a sense of comfort among people (Muradoglu, 2010). A good example of this herding mentality is the consequence of the COVID-19 pandemic on the European capital markets. As a result of the COVID-19 pandemic, herding behaviour exists in these markets as less informed investors follow well-informed ones (Espinosa-Méndez and Arias, 2021). This research proposes that the herding mentality also exists among people’s intention to get vaccinated with the COVID-19 vaccines.
Another important aspect of behavioural economics that is incorporated into our survey is people’s response to the accessibility and affordability of the COVID-19 vaccines. Accessibility is important as without adequate accessibility, there will not be massive vaccination to generate herd immunity. Affordability of the COVID-19 vaccines is equally important as 85 percent of the global population originates from low-income and middle-income countries (Wouters et al., 2021). Respondents’ perception of the affordability of the vaccine is crucial because people’s acceptance of vaccination is likely to be high if vaccines are made affordable or fully subsidised by the government. Concerning this, the pricing of COVID-19 vaccines is extremely important (Wouters et al., 2021).
Another critical perspective incorporated in this research survey is institutional quality, which comprises six indicators, i.e. voice and accountability, political stability and peace, the effectiveness of government authorities, regulatory quality, the rule of law, and control of corruption (Kaufmann et al., 2008; Law and Azman-Saini, 2012; Matemilola et al., 2019). We argued that without good institutional quality, society would have a negative or poor perception of how the COVID-19 vaccines will be managed and delivered to the citizens of the country, thus reducing the confidence of the people to undertake vaccination. For example, if the effectiveness of government authorities in handling this COVID-19 pandemic is poor and if the rule of law of the country is weakly implemented as well as corruption is rampant, the quality of the COVID-19 vaccines delivered may be compromised, and the relevant authorities may cover up any detrimental side effects of the COVID-19 vaccines. An excellent example to demonstrate the poor implementation of the rule of law is the case of Malaysia, where certain politicians escape the brunt of the law by violating the standard operating procedures (SOPs) and lockdown rules of COVID-19. In contrast, ordinary citizens must pay heavy fines or jail terms if caught violating these SOPs and rules (Sukumaran, 2020). Poor implementation of the rule of law may reduce the confidence of the survey respondents to accept COVID-19 vaccination. This study incorporates these institutional quality indicators except political stability and peace.
Based on the health belief model that has been used for studies related to health behaviour in vaccination, we added the behavioural economics concepts of herding and accessibility, and institutional quality to better estimate the IRV. Figure 1 depicts the conceptual framework of this research.
This research employed a quantitative survey using Google form to collect responses from people worldwide. Due to the urgency and unique situation of the COVID-19 outbreak, we have employed convenience sampling and snowball sampling methods to collect responses during the pandemic. These non-probability sampling methods are deemed suitable, especially during the pandemic. The researchers sent out the questionnaire to respondents through emails, social media platforms such as Facebook, LinkedIn, and researchers’ contacts. The data collection started from 1 December 2020 to 15 January 2021. The questionnaire consists of three sections, and it took approximately 15 minutes for each respondent to complete the survey. The first section contains questions related to the background of respondents such as gender, age, marital status, number of children, employment, education level, ethics, area and country of residence, and the likelihood of taking a vaccine against COVID-19 infection when it is available in the market, etc.
Table 1 shows the items of measurement of the intention to undertake vaccination based upon the health belief model, behavioural economics, and institutional quality perspectives. The questionnaire employed a 10-point Likert scale to obtain a more quantifiable result. The scales are represented as 1 for strongly disagree to 10 for strongly agree. The following statement:
Based on a recent survey conducted in Malaysia, more than 90 percent of the respondents have the intention to take the vaccine when it is available in the market.
was stated before respondents were asked again about their intention to get vaccinated. The purpose of this statement was to assess the availability of information and the occurrence of herding behaviour as a means of altering respondents’ vaccination intentions. The statistic of 90 percent is gathered from a recent study conducted by Wong et al. (2021).
The second section asked the respondents to provide feedback regarding their decision making in receiving COVID-19 vaccines. The third section focuses on the influence of institutional quality on receiving COVID-19 vaccination. The data were coded upon the completion of data collection, and several quantitative analysis techniques were used to compute the results. Exploratory factor analysis and reliability tests were used to categorise the variables into latent behavioural factors. Cronbach’s alpha test for internal consistency was conducted to examine the inter-correlation of variables within each factor. Hence, this paper presented the results using exploratory factor analysis, reliability, paired sample t-tests, and multiple linear regression analysis. Besides SPSS Statistics, this paper also utilised Microsoft Excel to complete the data analysis and present the results.
This section discusses the demographic profile of the respondents. In total, 591 survey forms were analysed (Tables 2 and 3). The respondents comprised 54.1 percent females and 45.6 percent males. There is higher participation in the age group under 45 years old (69.5 percent), no children (63.5 percent), Asian (87.0 percent), city (78.0 percent), and Malaysian (78.3 percent). Only a small proportion reported their health status as poor (0.7 percent) and 31.1 percent reported that they knew someone infected with COVID-19. Pearson’s chi-square test of contingencies (with ) was also used to evaluate whether the demographic attributes are related to whether the participants intend to take COVID-19 vaccination. The chi-squared tests are statistically significant for attributes such as age group, marital status, occupation, number of children, education, and race/ethnicity/origin. However, the associations are quite small, denoted by Cramer’s V in Table 2.
Factor analysis and reliability test
Factor analysis is a well-known statistical method used in reducing a huge number of measured variables into a smaller number of factors. There are 591 responses collected and factor analysis was applied. The questionnaire contains 34 variables and factor analysis was applied to identify the cluster and, in the end, 33 variables are used. With the potential existence of a correlation between the factors, exploratory factor analysis is conducted by employing principal component analysis and the oblique rotation method. The results of the first round of analysis suggest that it is factorability, where the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is 0.921 and Barlett’s test of sphericity is significant (. It is significant to group the variables into seven components. These seven components have eigenvalues of more than 1, which explained 65.864 percent of the variance. The seven components which comprise the factor analysis are Herding, Perceived Susceptibility, Perceived Severity, Perceived Benefits, Perceived Barriers, Institutional Quality, and IRV.
By using Cronbach’s Alpha Internal Consistency method, the study achieved Cronbach’s alpha of more than 0.70 for each factor. Table 4 presents the rotated factor pattern with the corresponding Cronbach’s alpha.
Table 4 shows that Cronbach’s alphas for the determinants are all more than 0.70. Although the preliminary factor analysis indicated that Perceived Barriers shall be separated into two different components, Cronbach’s alpha showed a better internal consistency as one component. Table 5 presents the correlation among construct scores to identify the predictors of IRV.
As shown in Table 5, there is a very strong relationship between IRV and Perceived Benefits (0.847). Perceived Barriers seem to have a very weak relationship with the IRV (0.114). The other constructs which have a moderate to a weak relationship with IRV, include Herding, Perceived Susceptibility, Perceived Severity, and Institutional Quality.
This study also sought to determine whether a herd mentality exists in the decision-making process and the effect of information accessibility on IRV. As mentioned above, participants were first asked about the likelihood of taking the vaccine if the vaccine is available on the market. After they provided their feedback on the predictors of vaccination, a statement comprising the information on the percentage of respondents who have the intention to take the vaccine in a recent study was presented to the participants. With that, we examine whether there is a significant difference in the score using a paired sample t-test. Table 6 depicts the mean scores of IRV before and after the presentation of the statement. This difference is statistically significant, t (590) = 1.766, p < 0.10, with a mean difference of 0.12183 and a standard deviation of 1.67720.
With the aim to further investigate the predictors of the Intention to Receive COVID-19 vaccines, a multiple linear regression analysis (stepwise method) was conducted. The analyses on the histogram of regression standardised residual, skewness, and kurtosis were conducted to ensure that the assumptions of multiple linear regression and expectation of normal distribution are fulfilled. Five out of six factors are significant predictors of intention to get a vaccine, whether with or without the accessibility statement.
A significant regression equation is found (F (5, 586) = 216.123, p = 0.000), with an of 0.596. Table 7 presents the overall results of multiple linear regression using data collected before exposing the participants to the statement regarding the accessibility of information and herding. In this analysis using standardized beta coefficients, it was found that Perceived Benefits (t = 21.593, p = 0.000, beta = 0.694) played the most important role in affecting IRV, then followed by Perceived Barriers (t = -4.904, p = 0.000, beta = - 0.135), Herding (t = 3.444, p = 0.001, beta = 0.109), and Perceived Susceptibility (t = 2.203, p = 0.028, beta = 0.065). Perceived Barriers is a predictor that has a negative association with the intention to receive a vaccine (unstandardised coefficient B in negative sign). This implies that the higher the scores of Perceived Barriers, the lower the IRV.
The analysis was repeated using a new IRV score after the participants are being informed that there were more than 90 percent of the respondents in a recent survey possess the intention to take the vaccine. A significant regression equation is found (F (3, 587) = 521.171, p = 0.000), with an improved of 0.727. Table 8 presents the overall results of multiple linear regression. In this analysis using standardized coefficients of beta, Perceived Benefits is still the most important factor in affecting IRV (t = 35.445, p = 0.000, beta = 0.814), then followed by Institutional Quality (t = 3.444, p = 0.001, beta = 0.109) and Perceived Barriers with negative association (t = -4.904, p = 0.000, beta = -0.059). It is observed that the negative association between Perceived Barriers and IRV becomes weaker. Besides, the effect of Herding becomes insignificant (compared to results in Table 7) when we have the statement that incorporates the impact of herding and the accessibility of information. Institutional Quality turned out to be a significant predictor now.
Hwang (2020) suggests that mechanisms that lead to vaccination behaviour are especially important during this pandemic. The evaluation of health information sources is related to vaccine uptake. Chowdhury et al. (2021) highlighted widespread misinformation during large-scale infectious disease outbreaks since 2000, where conspiracy theories are also part of the misinformation, especially on vaccination. The methods we deliver health information are important in determining the success rate of vaccination uptake and public health intervention programs. From the study of Lu et al. (2020), it is apparent that American and Chinese people have different sources preferences and how they seek health information showed that the accessibility of information is different across populations and cultural backgrounds. Besides, we shall not ignore the role of audience involvement and a sense of affinity for a celebrity in promoting healthy behaviours (Kresovich and Noar, 2020).
In the context of promoting herd immunity during the pandemic, this study revealed that nudges on the positive responses of others towards COVID-19 vaccination (i.e. providing accessibility of information to the respondents with regards to the positive responses of others) influence IRV. This indicates the significance of herding behaviour and nudging in public health interventions. The findings are consistent with Sasaki et al. (2022), who found that nudges about the information of others on COVID-19 vaccines can positively influence IRV without hindering their independent decision-making.
In addition, the findings are also consistent with Mouter et al. (2022) who found that people’s IRV are higher when they know about the experience of others with this vaccination. As per results shown in the above session, herding played a significant impact on IRV. However, when the respondents were informed that there were more than 90 percent of the respondents in a recent survey who have the intention to take the vaccine, institutional quality turned out to be a significant variable in explaining the vaccination behaviour. This shows that the accessibility of information is crucial in influencing behaviour. Hence, given the importance of herd immunity and institutional quality, the effectiveness of a vaccine strategy that helps to boost non-pharmaceutical interventions, such as testing and magnifying vaccine impact, are also hugely dependent on the communication channels, content, and the ways health messages are sent to the public. Be it a vaccination program, face mask-wearing (e.g. Suzuki et al., 2021), or other interventions, world leaders must take immediate actions to manage the pandemic (Ajmal et al., 2021). Signorelli et al. (2020) also highlighted that public health authorities should continue monitoring herd immunity’s effects as one of the approaches to control the COVID-19 outbreak.
In the meantime, institutional quality plays an important role in promoting the quality of the healthcare system (see also Gille and Brall, 2020). Public health practices and strategies will not be effective and efficient without collaboration between public and private sectors, as well as individuals. Ferrari and Salustri (2020) conducted research using a European panel data set and showed that corruption impacts public healthcare services, especially females and those in society with lower socioeconomic status.
There are certain limitations of this study. It was conducted before the efficacy of the various COVID-19 vaccines was known to the public. The survey results may be different if the respondents had known this information. Another limitation of the study is that the scope of the perspectives used is limited to HBM, behavioural economics, and institutional quality. Future research can incorporate other perspectives which are relevant as predictors of IRV, for example, religions, philosophy, history, politics, etc.
Moreover, given the non-probability sampling method, the result of this study may not be generalisable to other research settings. With the urgency of examining IRV in time with different types of vaccines developed and getting approval from the World Health Organization (WHO), we faced time constraints in implementing a random sampling method. Moreover, with the uncertainties that arise with the efficacy of the vaccines, there are challenges such as low response rates and insufficient responses from different countries. Future studies shall use a random sampling method to replicate such research in understanding vaccination behaviour or choice behaviour in health-related studies. Besides, studies in the future shall use structural equation modelling (SEM) to examine the relationship of the predictors with vaccination behaviour.
This study is one of those few cross-country studies investigating the predictors of IRV as well as the first to investigate how one of these predictors, i.e. the institutional quality and herding, influence IRV. Based upon multiple regression analyses, this study found five significant predictors of IRV: Perceived Benefits, Perceived Barriers, Perceived Susceptibility, Herding, and Institutional Quality. The results reveal that the respondents behaved differently before and after they were provided information incorporating the impact of herding. Before they were provided with the information, Perceived Benefits, Perceived Barriers, Herding, and Perceived Susceptibility were the predictors of IRV. After they were provided such information, Perceived Benefits, Perceived Barriers, and Institutional Quality became the significant predictors.
This research shows that once people possess the herding mentality after being exposed to information encouraging such behaviour, their focus shifts to institutional quality as one factor influencing their IRV. This reflects that the effectiveness of government authorities, regulatory quality, the rule of law, and control of corruption are also significant predictors of IRV but only within a herding mentality.
Items of measurement
|Label of items||Measurements|
|Herding 1||I follow others’ choice in taking the COVID-19 vaccine.|
|Herding 2||I am more likely to take the vaccination if a lot of people are going to take it.|
|Herding 3||I prefer to follow the decision of my family members and friends.|
|Perceived Susceptibility 1||My chance of getting COVID-19 in the next few months is great.|
|Perceived Susceptibility 2||I am worried about the likelihood of getting COVID-19.|
|Perceived Susceptibility 3||Getting COVID-19 is currently a possibility for me.|
|Perceived Susceptibility 4||I will always be at risk for getting COVID-19.|
|Perceived Severity 1||Complications from COVID-19 are serious.|
|Perceived Severity 2||I will be very sick if I get COVID-19.|
|Perceived Severity 3||I am afraid of getting COVID-19.|
|Perceived Benefits 1||Vaccination is good idea because it makes me feel less worried about catching COVID-19.|
|Perceived Benefits 2||Vaccination decreases my chance of getting COVID-19 or its complications.|
|Perceived Benefits 3||Having myself vaccinated protects me from COVID-19.|
|Perceived Benefits 4||Having myself vaccinated protects the public from COVID-19.|
|Perceived Benefits 5||Vaccination prevents the economic and labour losses due to COVID-19.|
|Perceived Benefits 6||Vaccine developed for COVID-19 have gone through comprehensive research and investigation.|
|Perceived Benefits 7||I would rather spend on the vaccine now than getting infected by COVID-19 later.|
|Perceived Barriers 1 (effectiveness and safety)||Worry the possible side-effects of COVID-19 vaccination would interfere my usual activities.|
|Perceived Barriers 2 (effectiveness and safety)||I am concern about the efficacy of the COVID-19 vaccination.|
|Perceived Barriers 3 (effectiveness and safety)||I am concern about the safety of the COVID-19 vaccination.|
|Perceived Barriers 4 (effectiveness and safety)||I think that some ingredients in the vaccine may negatively affect my health.|
|Perceived Barriers 5 (informative and cost)||I am concern of my affordability (high cost) of getting the COVID-19 vaccination.|
|Perceived Barriers 6 (informative and cost)||I do not have any information about the vaccine (such as where, when, and how this vaccine is administered).|
|Perceived Barriers 7 (informative and cost)||Cost of vaccination influences my decision to receive vaccination of the COVID-19 vaccine.|
|Intend to Vaccine 1||I will take the vaccination to protect myself|
|Intend to Vaccine 2||I want to take the vaccination for the benefits of myself and the public.|
|Intend to Vaccine 3||I intend to take the vaccination to decrease the chance of getting COVID-19.|
|Institutional Quality 1||The amount of opportunity to provide feedback on the COVID-19 vaccine influence my decision to receive vaccination of the COVID-19 vaccine.|
|Institutional Quality 2||The accountability of my government of any side effects as a result of the COVID-19 vaccination influences my decision to receive vaccination of the COVID-19 vaccine.|
|Institutional Quality 3||The political stability of my country influences my decision to receive vaccination of the COVID-19 vaccine.|
|Institutional Quality 4||The effectiveness of my government in handling and managing the COVID-19 pandemic influence my decision to receive the vaccination.|
|Institutional Quality 5||The regulations imposed by my country to prevent the infection of COVID-19 as well as the effectiveness of the legal enforcement influence my decision to receive the vaccination.|
|Institutional Quality 6||The effort exerted by my government to control corruption influence my decision to receive vaccination of the COVID-19 vaccine.|
Demographics of respondents compared with Intention to take COVID-19 vaccines
|Intention to take COVID-19 vaccines|
|Do not intend (definitely not/probably not) n = 88 (14.9%)||Intend (Maybe/Probably yes/Yes, definitely) n = 503 (85.1%)|
|Male||267 (45.6)||41 (15.4)||226 (84.6)|
|Female||317 (54.1)||44 (13.9)||273 (86.1)|
|Other||2 (0.3)||1 (50)||1 (50)|
|Age group (Years)|
|18 – 24||151 (25.5)||10 (6.6)||141 (93.4)|
|25 – 34||138 (23.4)||17 (12.3)||121 (87.7)|
|35 – 44||122 (20.6)||17 (13.9)||105 (86.1)|
|45 – 54||96 (16.2)||15 (15.6)||81 (84.4)|
|55 – 64||63 (10.7)||21 (33.3)||42 (66.7)|
|65 – 74||16 (2.7)||5 (31.3)||11 (68.8)|
|75 years and above||5 (0.8)||3 (60.0)||2 (40.0)|
|Single||306 (51.8)||31 (10.1)||275 (89.9)|
|Married||258 (43.7)||47 (18.2)||211 (81.8)|
|Divorced||15 (2.5)||5 (33.3)||10 (66.7)|
|Widowed||7 (1.2)||2 (28.6)||5 (71.4)|
|Others||5 (0.8)||3 (3.3)||2 (0.4)|
|Number of children|
|No children||375 (63.5)||43 (11.5)||332 (88.5)|
|One child||63 (10.7)||11 (17.5)||52 (82.5)|
|Two children||90 (15.2)||19 (21.1)||71 (78.9)|
|Three children||34 (5.8)||7 (20.6)||27 (79.4)|
|Four children||22 (3.7)||5 (22.7)||17 (77.3)|
|Five or more children||7 (1.2)||3 (42.9)||4 (57.1)|
|Professional||173 (29.3)||30 (17.3)||143 (82.7)|
|Management||78 (13.2)||14 (17.9)||64 (82.1)|
|Administrative Staff||37 (6.3)||2 (5.4)||35 (94.6)|
|Support Staff||22 (3.7)||1 (4.5)||21 (95.5)|
|Consultant||22 (3.7)||4 (18.2)||18 (81.8)|
|Researcher||43 (7.3)||4 (9.3)||39 (90.7)|
|Self-employed/Partner||58 (9.8)||13 (22.4)||45 (77.6)|
|Student||129 (21.8)||14 (10.9)||115 (89.1)|
|Other||29 (4.7)||6 (20.7)||23 (79.3)|
|Highest education level|
|Primary/Elementary||4 (0.7)||0 (0.0)||4 (100.0)|
|Secondary/High||41 (6.9)||11 (26.8)||30 (73.2)|
|Tertiary||236 (39.9)||21 (8.9)||215 (91.1)|
|Postgraduate||293 (49.6)||53 (18.1)||240 (81.9)|
|Other||17 (2.9)||3 (17.6)||14 (82.4)|
|Race, ethnicity, origin|
|American Indian or Alaska Native||3 (0.5)||0 (0.0)||3 (100.)|
|Asian||514 (87.0)||67 (13.0)||447 (87.0)|
|Black or African American||12 (2.0)||6 (50.0)||6 (50.0)|
|Hispanic, Latino or Spanish origin||5 (0.8)||0 (0.0)||5 (100.0)|
|Middle Eastern or North African||11 (1.9)||3 (27.3)||8 (72.7)|
|Native Hawaiian or Other Pacific Islander||1 (0.2)||0 (0.0)||1 (100.0)|
|White||34 (5.8)||11 (32.4)||23 (67.6)|
|Some other race, ethnicity, or origin||11 (1.9)||1 (9.1)||10 (90.9)|
|City||461 (78.0)||63 (13.7)||398 (86.3)|
|Town||114 (19.3)||22 (19.3)||92 (80.7)|
|Village or rural area||16 (2.7)||3 (18.8)||13 (81.3)|
|Overall health status|
|Very good||181 (30.6)||27 (14.9)||154 (85.1)|
|Good||345 (58.4)||51 (14.8)||294 (85.2)|
|Fair||61 (10.3)||10 (16.4)||51 (83.6)|
|Poor||4 (0.7)||0 (0.0)||4 (100.0)|
|Known any friends and family, neighbours, and colleagues infected by COVID-19|
|Yes||184 (31.1)||30 (16.3)||154 (83.7)|
|No||407 (68.9)||58 (14.3)||349 (85.7)|
|Malaysia||463 (78.3)||60 (13.0)||403 (87.0)|
|Non-Malaysian||128 (21.7)||28 (21.9)||100 (78.1)|
m missing data
*** p < 0.01, ** p < 0.05, *p < 0.10.
Residing country of respondents
|Country/Region||Frequency||Percentage (%)||Country/Region||Frequency||Percentage (%)|
|Bosnia and Herzegovina||1||0.17||Pakistan||1||0.17|
|Hong Kong SAR||3||0.51||Thailand||3||0.51|
N = 591
Rotated factor pattern and Cronbach’s alpha for vaccination decision
|Perceived Susceptibility 1||0.630|
|Perceived Susceptibility 2||0.326|
|Perceived Susceptibility 3||0.894|
|Perceived Susceptibility 4||0.650|
|Perceived Severity 1||0.612|
|Perceived Severity 2||0.719|
|Perceived Severity 3||0.729|
|Perceived Benefits 1||0.829|
|Perceived Benefits 2||0.871|
|Perceived Benefits 3||0.873|
|Perceived Benefits 4||0.842|
|Perceived Benefits 5||0.763|
|Perceived Benefits 6||0.648|
|Perceived Benefits 7||0.743|
|Perceived Barriers 1 (effectiveness and safety)||0.716|
|Perceived Barriers 2 (effectiveness and safety)||0.896|
|Perceived Barriers 3 (effectiveness and safety)||0.868|
|Perceived Barriers 4 (effectiveness and safety)||0.567|
|Perceived Barriers 5 (informative and cost)||0.673|
|Perceived Barriers 6 (informative and cost)||0.305|
|Perceived Barriers 7 (informative and cost)||0.821|
|Intend to Vaccine 1||0.844|
|Intend to Vaccine 2||0.869|
|Intend to Vaccine 3||0.850|
|Institutional Quality 1||0.538|
|Institutional Quality 2||0.613|
|Institutional Quality 3||0.656|
|Institutional Quality 4||0.838|
|Institutional Quality 5||0.768|
|Institutional Quality 6||0.796|
Correlation among construct scores
|Herding||Perceived Susceptibility||Perceived Severity||Perceived Benefits||Perceived Barriers||Institutional Quality||Intention to Vaccine|
|Intention to Vaccine||0.482***||0.389***||0.418***||0.847***||0.114***||0.372***||1.000|
*** p < 0.01, ** p < 0.05, *p < 0.10.
Statistical Test using Paired Sample t-tests to test for differences
|Constructs||Mean (without)||Mean (with)||t-stat||Sig.|
|Intention to Vaccine||7.6277||7.5059||1.766||0.078*|
*** p < 0.01, ** p < 0.05, *p < 0.10.
Multiple Linear Regression Results for IRV (without the accessibility statement)
|Adjusted R Square||0.593|
|Unstandardised Coefficients B||Standard Error||Standardise Coefficients Beta||t Stat||P-value|
Dependent variable: Intention to Receive (without accessibility statement)
Multiple Linear Regression Results for IRV (with the accessibility statement)
|Adjusted R Square||0.726|
|Unstandardised Coefficients B||Standard Error||Standardise Coefficients Beta||t Stat||P-value|
Dependent variable: IRV (with accessibility statement)
Ajmal, M.M., Khan, M. and Shad, M.K. (2021), “The global economic cost of coronavirus pandemic: current and future implications”, Public Administration and Policy, Vol. 24 No. 3, pp. 290-305.
Banerjee, A.V. (1992), “A simple model of herd behaviour”, The Quarterly Journal of Economics, Vol. 107, pp. 797-817.
Barber, B.M. and Odean, T. (2006), “All that glitters: the effect of attention and news on the buying behavior and institutional investor”, The Review of Financial Studies, Vol. 21 No. 2, pp. 785-818.
Bikhchandani, S., Hirshleifer, D. and Welch, I. (1992), “A theory of fads, fashion, custom, and cultural change as informational cascades”, Journal of Political Economy, Vol. 100, pp. 992-1026.
Cerda, A.A. and García, L.Y. (2021), “Willingness to pay for a COVID-19 vaccine”, Applied Health Economics and Health Policy, Vol. 19, pp. 343-351, doi: 10.1007/s40258-021-00644-6.
Chater, N., Huck, S. and Inderst, R. (2010), “Consumer decision-making in retail investment services: a behavioural economic perspective”, Report to the European Commission/SANCO, Frankfurt.
Chowdhury, N., Khalid, A. and Turin, T.C. (2021), “Understanding misinformation infodemic during public health emergencies due to large-scale disease outbreaks: a rapid review”, Journal of Public Health, pp. 1-21, doi: 10.1007/s10389-021-01565-3.
Coulaud, P.-J., Ablona, A., Bolduc, N., Fast, D., Bertrand, K., Ward, J.K., Greyson, D., Jauffret-Roustide, M. and Knight, R. (2022), “COVID-19 vaccine intention among young adults: comparative results from a cross-sectional study in Canada and France”, Vaccine, Vol. 40 No. 16, pp. 2442-2456.
Detoc, M., Bruel, S., Frappe, P., Tardy, B., Botelho-Nevers, E. and Gagneux-Brunon, A. (2020), “Intention to participate in a COVID-19 vaccine clinical trial and to get vaccinated against COVID-19 in France during the pandemic”, Vaccine, Vol. 38 No. 45, pp. 7002-7006.
Duffy, J., Hopkins, E. and Kornienko, T. (2021), “Lone wolf or herd animal? Information choice and learning from others”, European Economic Review, Vol. 134, pp. 1-30, doi: 10.1016/j.euroecorev.2021.103690.
Eberhardt, J. and Ling, J. (2021), “Predicting COVID-19 vaccination intention using protection motivation theory and conspiracy beliefs”, Vaccine, Vol. 39 No. 42, pp. 6269-6275.
Espinosa-Méndez, C. and Arias, J. (2021), “COVID-19 effect on herding behaviour in European capital markets”, Finance Research Letters, Vol. 38, pp. 1-6, doi: 10.1016/j.frl.2020.101787.
Ferrari, L. and Salustri, F. (2020), “The relationship between corruption and chronic diseases: evidence from Europeans aged 50 years and older”, International Journal of Public Health, Vol. 65 No. 3, pp. 345-355.
Gille, F. and Brall, C. (2020), “Public trust: caught between hype and need”, International Journal of Public Health, Vol. 65 No. 3, pp. 233-234.
Gradinaru, A. (2014), “The contribution of behavioral economics in explaining the decisional process”, Procedia Economics and Finance, Vol. 16, pp. 417-426.
Hao, F. and Shao, W. (2022), “Understanding the influence of political orientation, social network, and economic recovery on COVID-19 vaccine uptake among Americans”, Vaccine, Vol. 40 No. 14, pp. 2191-2201.
Honora, A., Wang, K.-Y. and Chih, W.-H. (2022), “How does information overload about COVID-19 vaccines influence individuals’ vaccination intentions? The roles of cyberchondria, perceived risk, and vaccine skepticism”, Computers in Human Behavior, Vol. 130, pp. 1-13, doi: 10.1016/j.chb.2021.107176.
Hwang, J. (2020), “Health information sources and the influenza vaccination: the mediating roles of perceived vaccine efficacy and safety”, Journal of Health Communication, Vol. 25 No. 9, pp. 727-735.
Jacoby, K., Hall-Clifford, R., Whitney, C.G. and Collins, M.H. (2022), “Vaccination and vacci-notions: understanding the barriers and facilitators of COVID-19 vaccine uptake during the 2020-21 COVID-19 pandemic”, Public Health in Practice, Vol. 3, pp. 1-9, doi: 10.1016/j.puhip.2022.100276.
Kaufmann, D., Kraay, A. and Mastruzzi, M. (2008), “Governance matters VII: aggregate and individual governance indicators, 1996-2007”, The World Bank Policy Research Department working paper 4654, Washington D.C.
Kitro, A., Sirikul, W., Piankusol, C., Rirermsoonthorn, P., Seesen, M., Wangsan, K., Assavanopakun, P., Surawattanasakul, V., Kosai, A. and Sapbamrer, R. (2021), “Acceptance, attitude, and factors affecting the intention to accept COVID-19 vaccine among Thai people and expatriates living in Thailand”, Vaccine, Vol. 39 No. 52, pp. 7554-7561.
Kresovich, A. and Noar, S.M. (2020), “The power of celebrity health events: meta-analysis of the relationship between audience involvement and behavioral intentions”, Journal of Health Communication, Vol. 25 No. 6, pp. 501-513.
Kwok, K.O., Li, K.-K., Wei, W.I., Tang, A., Wong, S.Y.S. and Lee, S.S. (2021), “Influenza vaccine uptake, COVID-19 vaccination intention and vaccine hesitancy among nurses: a survey”, International Journal of Nursing Studies, Vol. 114, pp. 1-9, doi: 10.1016/j.ijnurstu.2020.103854.
Law, S.H. and Azman-Saini, W.N.W. (2012), “Institutional quality, governance, and financial development”, Economics of Governance, Vol. 13 No. 3, pp. 217-236.
Leng, A., Maitland, E., Wang, S., Nicholas, S., Liu, R. and Wang, J. (2021), “Individual preferences for COVID-19 vaccination in China”, Vaccine, Vol. 39 No. 2, pp. 247-254.
Liao, Q., Cowling, B. J., Xiao, J., Yuan, J., Dong, M., Ni, M.Y., Fielding, R. and Lam, W.W.T. (2022), “Priming with social benefit information of vaccination to increase acceptance of COVID-19 vaccines”, Vaccine, Vol. 40 No. 8, pp. 1074-1081.
Liora, S. (2021), “Predicting intention to receive COVID-19 vaccine among the general population using the health belief model and the theory of planned behavior model”, BMC Public Health, Vol. 21 No. 1, pp. 1-13.
Lu, L., Liu, J. and Yuan, Y.C. (2020), “Health information seeking behaviors and source preferences between Chinese and U.S. populations”, Journal of Health Communication, Vol. 25 No. 6, pp. 490-500.
Matemilola, B.T., Bany-Ariffin, A.N., Azman-Saini, W.N.W. and Nassir, A.M. (2019), “Impact of institutional quality on the capital structure of firms in developing countries”, Emerging Markets Review, Vol. 39, pp. 175-209.
Mercadante, A.R. and Law, A.V. (2020), “Will they, or won’t they? Examining patients’ vaccine intention for flu and COVID-19 using the health belief model”, Research in Social and Administrative Pharmacy, Vol. 17, No. 9, pp. 1596-1605, doi: 10.1016/j.sapharm.2020.12.012.
Mouter, N., De Ruijter, A., Ardine De Wit, G., Lambooij, M.S., Van Wijhe, M., Van Exel, J. and Kessels, R. (2022), “‘Please, you go first!’ Preferences for a COVID-19 vaccine among adults in the Netherlands”, Social Science & Medicine, Vol. 292, pp. 1-11, doi: 10.1016/j.socscimed.2021.114626.
Muradoglu, Y.G. (2010), “The banking and financial crisis in the UK: what is real and what is behavioural?”, Qualitative Research in Financial Markets, Vol. 2 No. 1, pp. 6-15.
Paul, E. and Fancourt, D. (2022), “Predictors of uncertainty and unwillingness to receive the COVID-19 booster vaccine: an observational study of 22,139 fully vaccinated adults in the UK”, The Lancet Regional Health - Europe, Vol. 14, pp. 1-13, doi: 10.1016/j.lanepe.2022.100317.
Paul, E., Steptoe, A. and Fancourt, D. (2021), “Attitudes towards vaccines and intention to vaccinate against COVID-19: implications for public health communications”, The Lancet Regional Health - Europe, Vol. 1, pp. 1-10, doi: 10.1016/j.lanepe.2020.100012.
Ruiz, J.B. and Bell, R.A. (2021), “Predictors of intention to vaccinate against COVID-19: results of a nationwide survey”, Vaccine, Vol. 39 No. 7, pp. 1080-1086.
Sasaki, S., Saito, T. and Ohtake, F. (2022), “Nudges for COVID-19 voluntary vaccination: how to explain peer information?”, Social Science & Medicine, Vol. 292, pp. 1-13, doi: 10.1016/j.socscimed.2021.114561.
Seddig, D., Maskileyson, D., Davidov, E., Ajzen, I. and Schmidt, P. (2022), “Correlates of COVID-19 vaccination intentions: Attitudes, institutional trust, fear, conspiracy beliefs, and vaccine skepticism”, Social Science & Medicine, Vol. 302, pp. 1-10, doi: 10.1016/j.socscimed.2022.114981.
Signorelli, C., Zucchi, A., Tersalvi, C.A., Beato, A., Balzarini, F., Odone, A. and Middleton, J. (2020), “High seroprevalence of SARS_COV-2 in Bergamo: evidence for herd immunity or reason to be cautious?”, International Journal of Public Health, Vol. 65, No. 9, pp. 1815-1817.
Sukumaran, T. (2020), “Coronavirus: backlash as Malaysian politicians caught flouting Covid-19 lockdown”, 20 April, available at: https://www.scmp.com/week-asia/politics/article/3080728/coronavirus-backlash-malaysian-politicians-caught-flouting-covid (accessed 19 February 2021).
Suzuki, K., Hasegawa, T., Kano, N. and Okamoto, Y. (2021), “A study of the effect of wearing face masks in preventing COVID-19 transmission in the United States of America”, Public Administration and Policy, Vol. 24 No. 3, pp. 275-289.
Tan, C. (2021), “A study of boundedly rational behaviour in housing choice: evidence from Malaysia”, International Journal of Housing Markets and Analysis, pp. 1-16, doi: 10.1108/IJHMA-08-2021-0094.
Tan, C.M., Owuamalam, C.K. and Sarma, V. (2022), “Improving vaccination intent among skeptics through confidence in governments' handling of the COVID-19 pandemic”, Acta Psychologica, Vol. 225, pp. 1-9, doi: 10.1016/j.actpsy.2022.103556.
Thaler, R.H. and Sunstein, C.R. (2009), Nudge: Improving Decisions about Health, Wealth, and Happiness, Penguin Books, London.
Urrunaga-Pastor, D., Bendezu-Quispe, G., Herrera-Añazco, P., Uyen-Cateriano, A., Toro-Huamanchumo, C.J., Rodriguez-Morales, A.J., Hernandez, A.V. and Benites-Zapata, V.A. (2021), “Cross-sectional analysis of COVID-19 vaccine intention, perceptions and hesitancy across Latin America and the Caribbean”, Travel Medicine and Infectious Disease, Vol. 41, pp. 1-11, doi: 10.1016/j.tmaid.2021.102059.
Wang, K., Wong, E.L.Y., Ho, K.F., Cheung, A.W.L., Chan, E.Y.Y., Yeoh, E.K. and Wong, S.Y.S. (2020), “Intention of nurses to accept coronavirus disease 2019 vaccination and change of intention to accept seasonal influenza vaccination during the coronavirus disease 2019 pandemic: a cross-sectional survey”, Vaccine, Vol. 38 No. 45, pp. 7049-7056.
Wong, M.C.S., Wong, E.L.Y., Huang, J., Cheung, A.W.L., Law, K., Chong, M.K.C. and Chan, P.K.S. (2021), “Acceptance of the COVID-19 vaccine based on the health belief model: a population-based survey in Hong Kong”, Vaccine, Vol. 39 No. 7, pp. 1148-1156.
World Health Organisation (WHO) (2021), “The different types of COVID-19 vaccines”, available at: https://www.who.int/news-room/feature-stories/detail/the-race-for-a-covid-19-vaccine-explained (accessed 27 July 2022).
World Health Organisation (WHO) (2022a), “WHO Coronavirus (COVID-19) Dashboard”, available at: https://covid19.who.int/?mapFilter=deaths (accessed 27 July 2022).
World Health Organisation (WHO) (2022b), “11 vaccines granted emergency use listing (EUL) by WHO”, available at: https://covid19.trackvaccines.org/agency/who/ (accessed 27 July 2022).
Wouters, O.J., Shadlen, K.C., Salcher-Konrad, M., Pollard, A.J., Larson, H.J., Teerawattananon, Y. and Jit, M. (2021), “Challenges in ensuring global access to COVID-19 vaccines: production, affordability, allocation, and deployment”, The Lancet, Vol. 397, pp. 1023-1034, doi: 10.1016/S0140-6736(21)00306-8.
The authors acknowledged the contributions of two anonymous reviewers and Editor for their constructive comments and recommendation. The authors acknowledge the kind participation of the respondents in the survey research.
About the authors
Consilz Tan is a Senior Lecturer at the School of Economics and Management, Xiamen University Malaysia. She holds a PhD in Economics from Murdoch University, Perth Australia. Her research interests include decision making, behavioral economics, and real estate investment.
Chee Yoong Liew is an Assistant Professor in Finance at the Department of Accounting and Finance, Faculty of Business and Management, UCSI University Malaysia. He holds a PhD in Finance from University of Malaya, Malaysia. His research interests include corporate governance, corporate finance, green and sustainable finance, information economics and management, market microstructure, financial & economic connectedness, applied finance & economics as well as interdisciplinary business and economics research. He has been published widely in international ranked journals.