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Citation: Wang, X.; Huang, Y.-H.C.;
Cai, Q. Increasing Vaccination:
Psychological Characteristics of
COVID-19 Vaccine Advocates,
Converts, and Resisters in Hong
Kong. Vaccines 2022,10, 1744.
https://doi.org/10.3390/
vaccines10101744
Academic Editors: Shiran Bord and
Fuad Basis
Received: 29 September 2022
Accepted: 14 October 2022
Published: 19 October 2022
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Article
Increasing Vaccination: Psychological Characteristics of
COVID-19 Vaccine Advocates, Converts, and Resisters in
Hong Kong
Xiaohui Wang , Yi-Hui Christine Huang * and Qinxian Cai
Department of Media and Communication, City University of Hong Kong, Hong Kong 999077, China
*Correspondence: yihhuang@cityu.edu.hk; Tel.: +852-3442-8736
Abstract:
This study uses longitudinal data to profile psychological characteristics of COVID-19 vaccine
advocates, resisters, and converts. We conducted a two-wave longitudinal survey (Nwave1 = 3190,
Nwave2 = 2193) in Hong Kong using stratified quota sampling. Among those who completed
both survey waves, 458 (30.5%) were classified as vaccine advocates, 295 (19.7%) were vaccine
resisters, and 621 (41.4%) were vaccine converts (who shifted away from hesitancy). Compared to
advocates, resisters were more likely to be female, those without children, between 40 and 49 years
old, democratic voters, and those with poor health. Highly educated individuals, non-democrats,
and those in good health were more likely to convert from hesitancy to acceptance. Public trust
in authorities and confidence in vaccine were the primary factors related to vaccine uptake. Those
who were more confident in vaccine, those who increased in information consumption and risk
perceptions towards the pandemic, and those who decreased in their trust of health professionals
were more likely to convert. Our study complements the emerging global picture of COVID-19
vaccine acceptance by focusing on changes in vaccine hesitancy during the pandemic.
Keywords: COVID-19; vaccination; psychological factors; trust; longitudinal survey
1. Introduction
The COVID-19 pandemic is expected to disrupt daily life until an effective vaccine is
widely accepted. There is growing global concern about vaccine hesitancy—the widespread
reluctance to take safe and recommended vaccines [
1
,
2
]. Vaccine hesitancy becomes more
complex as new SARS-CoV-2 variants emerge and new vaccines come to market. Several
studies have documented COVID-19 vaccine hesitancy and acceptance using cross-sectional
data [2–7]. Less is known, however, about how vaccine hesitancy changes to acceptance.
Promoting vaccine uptake requires an understanding of those who are willing to be
vaccinated, why they are willing to do so, and their decision-making process. Vaccine
hesitancy was already a growing concern before the COVID-19 pandemic [
8
]. A 5C frame-
work developed from previous research describes five main psychological barriers for
vaccination behavior [
8
–
10
], including confidence (safety and effectiveness of vaccines,
acceptability of vaccine-related risks), complacency (not perceiving diseases as high risk),
risk calculation (engagement in extensive information gathering), convenience (structural
or psychological barriers to conversion), and collective responsibility (the willingness to
protect others via one’s own vaccination).
Another model for increasing vaccinations offers three general focus areas for in-
creasing vaccine uptake based on a systematic review of psychological factors related to
vaccination [
11
]. The first is individuals’ thoughts and feelings, referring to risk perceptions
of infectious disease, confidence in the effectiveness and safety of vaccines, and trust in
public health systems and officials. The second is social processes, referring to the influence
of interpersonal or more complex population-level social interactions. The third focus area
is practical conditions, such as convenience, cost, and quality of service.
Vaccines 2022,10, 1744. https://doi.org/10.3390/vaccines10101744 https://www.mdpi.com/journal/vaccines
Vaccines 2022,10, 1744 2 of 13
One key driver of vaccine acceptance appears to be concern about vaccine safety and
effectiveness, reflecting the rapid pace of vaccine development and worries about mild,
yet common and transient side effects [
2
,
12
]. This is related to another major determinant:
trust in government, health authorities, and healthcare workers [
12
,
13
]. Arguably, trust of
the source of information is an intrinsic and potentially modifiable component of successful
uptake of vaccines [
14
,
15
]. The COVID-19 pandemic has been characterized by intense
politicization and conspiracy theories [
16
], which may have intensified feelings of distrust
shown towards information from government and health authorities [17–19].
An understanding of the determinants of vaccine hesitancy is necessary in order to
shift it towards vaccine acceptance. This study provides novel insights into psychological
factors that modulate vaccination decisions. Building on the 5C framework, we propose a
modified framework that includes the following elements as determinants of vaccine up-
take: individuals’ vaccine confidence, pandemic risk perception, information consumption,
trust in government and health officials, and sociodemographic factors. We moved beyond
documenting vaccine hesitancy rates to collect longitudinal data that captured changes in
a population’s intentions to vaccinate. To our knowledge, this is the first study to track
individuals’ changes towards the COVID-19 vaccine during the pandemic. We analyzed
three groups of individuals—vaccine advocates, resisters, and converts—to help authorities
customize effective communication. A key implication of this study is the importance of
increasing acceptability
2. Materials and Methods
2.1. Data Collection
Respondents were recruited by Rakuten Insight (https://member.insight.rakuten.
com.hk/, accessed on 10 January 2021), an online survey agency, which manages more
than 50,000 panelists in Hong Kong. Two survey waves were conducted with a 14-month
interval. The first wave was conducted between September and October 2020 (N = 3190),
when COVID-19 vaccines were still unavailable in Hong Kong. The second wave was
conducted in November 2021 (N = 1501), when vaccination rates in Hong Kong plateaued.
Both waves together helped us capture Hong Kong citizens’ perceptual changes before
and after vaccines became available (Figure 1). In the first wave, stratified quota sampling
drew on the city’s 2016 population census, based on the distribution of gender, age, and
residence. The second wave was conducted in November 2021. All wave 1 participants
were invited, and 68.75% (n= 2193) chose to participate again. Data cleaning in both
waves was conducted using rigorous principles in order to ensure data quality. Specifically,
respondents who failed the attention check question did not complete the two-wave survey.
Similarly, we eliminated respondents who finished the questionnaire in less than one-third
of the median completion time. We measured respondent demographics in both waves.
The consistency of respondents’ answers for gender, age, and education was also used as
a criterion for data inclusion. Specifically, if answers for gender, age, and education level
from the same respondent ID deviated across the two waves, all responses from that ID
were regarded as invalid. Data analysis was conducted using the final complete sample of
1501 respondents.
2.2. Pre-Test
The survey questionnaire consisted of items measuring individuals’ vaccine uptakes
(9 items), psychological factors (30 items), and sociodemographic factors (7 items). We
developed a Chinese version of the questionnaire with items adopted from previous
studies [
10
–
12
,
14
,
20
–
22
] (see Supplementary Figure S2). The surveys were delivered in
traditional Chinese, which is one of the official languages used in Hong Kong. Transla-
tions were performed and validated through the standard back-translation procedure [
23
],
i.e., the Chinese version was back-translated into English to see whether the translation
was closely related to the source items. A pre-test was conducted to ensure the survey
instruments were readable, understandable, and comprehensive to yield valid responses.
Vaccines 2022,10, 1744 3 of 13
A total of 15 Hong Kong citizens who are experts in online survey studies were invited
via the researchers’ network to complete the pre-test. Pre-test participants were first asked
to comment on the survey including the relevance of the survey questions, the sequence
of the questions, the survey length, etc. Issues of survey design were resolved through
multiple discussions among the project’s researchers. Reliability checks for all variables
in the pre-test were then conducted and the results were satisfactory. In view of those
methods, the Institutional Review Board at the sponsoring university approved the study.
Vaccines 2022, 10, x FOR PEER REVIEW 3 of 15
Figure 1. First wave data were collected through stratified sampling based on age, gender, and res-
idential districts from 4 September to 23 October 2020, when COVID-19 vaccines were not available
in Hong Kong. Second wave data were collected from 2–23 November 2021, when vaccination rates
in Hong Kong plateaued and third-dose vaccines became available. These two waves of data fully
capture psychological changes before and after vaccine availability. Vaccine advocates are defined
as those who demonstrated intent to vaccinate in wave 1 and reported being fully vaccinated in
wave 2. Vaccine converts are those who were hesitant to vaccinate in wave 1 but reported being
fully vaccinated in wave 2. Vaccine resisters are those who were hesitant to vaccinate in wave 1 and
reported being unvaccinated in wave 2.
2.2. Pre-Test
The survey questionnaire consisted of items measuring individuals’ vaccine uptakes
(9 items), psychological factors (30 items), and sociodemographic factors (7 items). We
developed a Chinese version of the questionnaire with items adopted from previous stud-
ies [10–12,14,20–22] (see Supplementary Figure S2). The surveys were delivered in tradi-
tional Chinese, which is one of the official languages used in Hong Kong. Translations
were performed and validated through the standard back-translation procedure [23], i.e.,
the Chinese version was back-translated into English to see whether the translation was
closely related to the source items. A pre-test was conducted to ensure the survey instru-
ments were readable, understandable, and comprehensive to yield valid responses. A to-
tal of 15 Hong Kong citizens who are experts in online survey studies were invited via the
researchers’ network to complete the pre-test. Pre-test participants were first asked to
comment on the survey including the relevance of the survey questions, the sequence of
the questions, the survey length, etc. Issues of survey design were resolved through mul-
tiple discussions among the project’s researchers. Reliability checks for all variables in the
pre-test were then conducted and the results were satisfactory. In view of those methods,
the Institutional Review Board at the sponsoring university approved the study.
2.3. Measurements
This study examined psychological determinants of vaccine uptake. After a literature
review, we incorporated individual psychological factors. These psychological factors in-
cluded vaccine confidence, perceived risk of COVID-19, trust in the public health system,
information consumption, and issue politicization. Definition and operationalization of
these variables are detailed below (also see Supplementary Table S4).
Categorization of Vaccine Advocates, Vaccine Converts, Vaccine Resisters, and Others. The
categorization of the four groups was based on the participants’ responses of their vac-
cination intention in wave 1 when the vaccine was not available in Hong Kong and vac-
cination status in wave 2 when the vaccination program started. In wave 1, people’s vac-
cine intention was measured by asking respondents the degree to which they agreed on a
7-point scale (1 = strongly disagree and 7 = strongly agree): (1) I would get the vaccine
sometime soon; (2) If I were faced with the decision of whether to get the vaccine today, I
would choose to get it; (3) I would get the vaccine in the future [24]. We aggregated the
responses to these three questions and divided them into three groups, i.e., willing to get
vaccinated (with scores higher than 4), neutral (with scores equal to 4), and unwilling to
Figure 1.
First wave data were collected through stratified sampling based on age, gender, and
residential districts from 4 September to 23 October 2020, when COVID-19 vaccines were not available
in Hong Kong. Second wave data were collected from 2–23 November 2021, when vaccination rates
in Hong Kong plateaued and third-dose vaccines became available. These two waves of data fully
capture psychological changes before and after vaccine availability.
Vaccine advocates
are defined
as those who demonstrated intent to vaccinate in wave 1 and reported being fully vaccinated in
wave 2.
Vaccine converts
are those who were hesitant to vaccinate in wave 1 but reported being
fully vaccinated in wave 2.
Vaccine resisters
are those who were hesitant to vaccinate in wave 1 and
reported being unvaccinated in wave 2.
2.3. Measurements
This study examined psychological determinants of vaccine uptake. After a literature
review, we incorporated individual psychological factors. These psychological factors
included vaccine confidence, perceived risk of COVID-19, trust in the public health system,
information consumption, and issue politicization. Definition and operationalization of
these variables are detailed below (also see Supplementary Table S4).
Categorization of Vaccine Advocates, Vaccine Converts, Vaccine Resisters, and Others. The
categorization of the four groups was based on the participants’ responses of their vaccina-
tion intention in wave 1 when the vaccine was not available in Hong Kong and vaccination
status in wave 2 when the vaccination program started. In wave 1, people’s vaccine inten-
tion was measured by asking respondents the degree to which they agreed on a 7-point
scale (1 = strongly disagree and 7 = strongly agree): (1) I would get the vaccine sometime
soon; (2) If I were faced with the decision of whether to get the vaccine today, I would
choose to get it; (3) I would get the vaccine in the future [
24
]. We aggregated the responses
to these three questions and divided them into three groups, i.e., willing to get vaccinated
(with scores higher than 4), neutral (with scores equal to 4), and unwilling to get vaccinated
(with scores lower than 4). In wave 2, people’s actual vaccination status was measured by
asking participants to choose one answer from the following options: (1) I don’t plan to
receive the vaccine; (2) I have scheduled to be vaccinated but have not yet been vaccinated;
(3) I have had my first shot of the vaccine; (4) I have had my first dose of the vaccine (only
one-dose required, e.g., J&J vaccine); (5) I’ve had my first dose of the vaccine and don’t
plan to get the second dose; (6) I have had my second dose of the vaccine.
Based on self-reported vaccination intention in wave 1 and vaccination status in wave
2, we divided participants into four groups: vaccine advocates (n= 458), referring to those
who demonstrated intent to vaccinate in wave 1 and reported being fully vaccinated in
wave 2; vaccine converts (n= 621), referring to those who were hesitant to vaccinate in
Vaccines 2022,10, 1744 4 of 13
wave 1 but reported being fully vaccinated in wave 2; vaccine resisters (n= 295), those who
were hesitant to vaccinate in wave 1 and reported being unvaccinated in wave 2; others
(n= 127), those who converted from intention to vaccinate in wave 1 to unvaccinated in
wave 2 or those who received only one dose of vaccine.
Vaccine Confidence. Vaccine confidence refers to public believe in the importance,
safety, and effectiveness of vaccines, along with people’s willingness to live with risks
(De Figueiredo et al., 2021). This willingness arises when perceived benefit outweighs
potential risk [
25
]. Respondents were asked how much they agree with the following three
statements on a 7-point scale (1 = strongly disagree and 7 = strongly agree): (1) I can accept
the potential risks of the vaccine, (2) I am highly willing to accept the potential risks of the
vaccine, and (3) after careful deliberation, I think the vaccine does more good than harm
(Wave 1: Mean (M) = 4.06, Standard Deviation (SD) = 1.25, Cronbach’s alpha (
α
) = 0.88;
Wave 2: M = 4.29, SD = 1.38, α= 0.89) [20].
COVID-19 Risk Perception. Slovic and Peters proposed the dual-process model, which
contends that people not only think about risk but feel risk as well [
26
]. Following suit, we
differentiated risk perception into two categories, i.e., perceived risk and worry. Perceived
risk represents the analytic process, which is slow, deliberate, sequential, and consciously
controlled with high cognitive effort [
27
,
28
]. Worry denotes the heuristic process, which
is intuitive, parallel, and automatic [
26
]. Items for both dimensions were measured on a
7-point scale where 1 = strongly disagree and 7 = strongly agree. Four items were adapted
for measuring perceived risk, including “the coronavirus is almost ubiquitous, and the
pathogenicity is high,” “there is a high probability of getting infected,” “the mortality
rate of COVID-19 is high,” and “I have no confidence I can avoid the negative impacts
of COVID-19” (Wave 1: M = 4.41, SD = 1.00,
α
= 0.80; Wave 2: M = 4.31, SD = 1.05,
α
= 0.83) [
28
]. For worry, respondents were asked to rate their level of agreement with the
following statements with respect to COVID-19-related risk: (1) I feel fearful, (2) I feel sad, (3)
I feel helpless, and (4) I feel worried (Wave 1: M = 4.35, SD = 1.21,
α
= 0.91; Wave 2: M = 4.34,
SD = 1.20, α= 0.90) [28].
Trust in State, Media, and Public Health Professionals. We defined trust as the willingness
to rely on another party to behave in a certain way in the context of COVID-19 [
29
]. The
degree of trust in different parties/institutions was measured on a 7-point scale ranging
from 1 = strongly distrust to 7 = strongly trust. We differentiated trustees into three groups,
i.e., state governmental organizations, media, and public health professionals. For trust
in state institutions, respondents were asked how much they trust the following: (1) state
government, (2) the Food and Health Bureau, and (3) the Center for Health Protection
(Wave 1: M = 3.67, SD = 1.47,
α
= 0.88; Wave 2: M = 3.81, SD = 1.49,
α
= 0.91). In addition,
trust in media was assessed by asking respondents the degree to which they trust the Hong
Kong media (Wave 1: M = 4.10, SD = 1.13; Wave 2: M = 3.95, SD = 1.14). Trust in health
professionals was assessed by asking respondents the degree to which they trust Hong
Kong public health professionals (Wave 1: M = 4.61, SD = 1.16; Wave 2: M = 4.52, SD = 1.21)
Information Consumption. Information consumption refers to routine use of multiple
media outlets for the purpose of seeking information [
30
]. Information acquisition as a form
of active information consumption was measured by asking participants the frequency
on average per day of their accessing vaccine-related information from (1) television,
(2) newspapers, (3) websites, (4) social media (e.g., WhatsApp, Facebook), and (5) short
video platforms (e.g., Tik Tok) [
30
]. Responses were measured on a 7-point scale, with
1 = none, 4 = 2–3 h, and 7 = 5 h or more (Wave 1: M = 2.74, SD = 0.79,
α
= 0.62; Wave
2: M = 2.68, SD = 0.81,
α
= 0.66). Media attention as a form of passive information
consumption was assessed by asking participants to rate how often they pay attention to
information about COVID-19 when consuming (1) television, (2) newspapers, (3) websites
(e.g., Yahoo), (4) social media (e.g., WhatsApp, Facebook), and (5) short video platforms
(e.g., Tik Tok) [
21
,
31
]. A 7-point scale was adopted, with 1 = never and 7 = always (Wave 1:
M = 3.67, SD = 1.04, α= 0.74; Wave 2: M = 3.49, SD = 0.98, α= 0.66).
Vaccines 2022,10, 1744 5 of 13
Issue Politicization. The degree to which people perceive COVID-19 as a political issue
was measured by four items on a 7-point scale including: “it is too mixed up in politics,”
“due to political interests, policy decisions have favored some groups,” “it is politically
motivated,” and “political considerations affect the nature of the information that the
public receives about this issue” (Wave 1: M = 4.93, SD = 1.37,
α
= 0.93; Wave 2: M = 4.89,
SD = 1.21, α= 0.92) [32,33].
2.4. Data Analysis
All statistical analysis was conducted using RStudio. A pvalue < 0.05 was consid-
ered significant. We performed multinominal logistic regression to examine the effect of
sociodemographic factors and psychological factors on three groups with varying attitudes
towards the vaccine. We performed analysis of covariances (ANCOVAs) to compare the
mean difference between three groups for each psychological factor, with age, gender,
income, and education as covariates. Turkey post hoc analyses were conducted for group
comparisons
3. Results
We contracted with an online survey company to conduct a two-wave longitudinal
survey of a representative sample of adults in Hong Kong. Of 1501 participants who
completed the two-wave online survey, 1040 (69.3%) reported that they had been fully
vaccinated, which is close to Hong Kong’s full vaccination rate (68.5%). Table 1includes
descriptive statistics for the sociodemographic factors of the sample. Age, gender, income,
and education were used as demographic control variables in subsequent analyses.
Table 1. Sociodemographic characteristics of the Hong Kong sample (%).
All
(n= 1501)
Resister
(n= 295)
Advocate
(n= 458)
Convert
(n= 621)
Sex
Female 57.5 63.1 47.2 62.8
Male 42.5 36.9 52.8 37.2
Age
20–29 13.3 10.5 10.3 16.9
30–39 28.2 30.5 24.7 29.6
40–49 24.9 29.2 22.7 24.6
50+ 33.5 29.8 42.4 28.8
Education
High school or less 28.9 36.3 28.8 24.5
Associate 14.4 15.3 14.6 13.4
Bachelor’s 42.2 34.9 42.8 45.9
Post-Graduate 14.5 13.6 13.8 16.3
Household Income
<20 k 11.1 16.6 7.9 11.0
20–40 k 33.5 35.6 31.4 33.7
40–60 k 28.5 23.7 31.2 28.0
>60 k 26.8 24.1 29.5 27.4
Residential Area
Hong Kong Island 17.3 17.6 17.5 17.2
Kowloon 31.7 34.2 30.6 31.2
New Territories 51.0 48.1 52.0 51.5
Political Orientation
Pro-Democracy 27.6 35.3 18.6 29.0
Pro-Beijing 10.8 3.4 19.0 9.0
Centrist 11.6 6.1 15.3 11.3
Neutral 50.0 55.3 47.2 50.7
Health Status
Good 45.7 37.3 52.2 46.2
Moderate 43.6 48.8 38.9 44.0
Poor 10.7 13.9 9.0 9.8
Vaccines 2022,10, 1744 6 of 13
3.1. Sociodemographic Profiles of Vaccine Advocates, Resisters, and Converts
Vaccine advocates—compared to vaccine resisters—were less likely to be female
[Adjusted Odds Ratio (AOR) = 0.59, 95% Confidence Interval (CI) = 0.43, 0.81], aged
between 40 and 49 (AOR = 0.54, 95% CI = 0.30, 0.97), without children (AOR = 0.57,
95% CI = 0.40, 0.81), pro-democrat (AOR = 0.46, 95% CI = 0.32, 0.66), poor (AOR = 0.51,
95% CI = 0.31, 0.86), or to consider their health condition as moderate (AOR = 0.59, 95%
CI = 0.43, 0.83). At the same time, vaccine advocates were more likely to hold a bachelor
degree (AOR = 1.77, 95% CI = 1.17, 2.68), have household income levels between 40 k and
60 k (AOR = 2.35, 95% CI = 1.35, 4.08 at 40 k) and above 60 k (AOR = 1.83, 95% CI = 1.03,
3.26), and to hold pro-Beijing sentiments (AOR = 4.66, 95% CI = 2.32, 9.35).
Converts in wave 2—compared to vaccine resisters—were less likely to be between 30
and 49 years old (AOR = 0.58, 95% CI = 0.35, 0.95 for age between 30 and 39; AOR = 0.51,
95% CI = 0.30, 0.85 for age between 40 and 49), pro-democrat (AOR = 0.67, 95% CI = 0.48,
0.91), or to have poor health (AOR = 0.55, 95% CI = 0.34, 0.88). At the same time, converts
were more likely to hold bachelors (AOR = 2.03, 95% CI = 1.38, 2.96) or post-graduate degrees
(AOR = 1.92, 95% CI = 1.15, 3.20), and to express pro-Beijing sentiments (AOR = 2.60, 95%
CI = 1.28, 5.27).
Four conditions distinguished vaccine converts from vaccine advocates: converts
were more likely to be female (AOR = 1.74, 95% CI = 1.35, 2.26) and without children
(AOR = 1.64, 95% CI = 1.24, 2.18), but less likely to be over 50 (AOR = 0.63, 95% CI = 0.40,
0.99) or pro-Beijing (AOR = 0.52, 95% CI = 0.35, 0.78) (see Table 2).
Table 2.
Sociodemographic indicators associated with vaccine resisters, advocates, and converts in
Hong Kong, 2020–2021.
[Reference = Vaccine Resister] [Reference = Vaccine Advocate]
Vaccine Advocate Vaccine Convert Vaccine Convert
AOR 95% CIs AOR 95% CIs AOR 95% CIs
Sex (Female) 0.59 [0.43 0.81] 0.98 [0.72 1.32] 1.74 [1.35 2.26]
Age
18–29 - - - - - -
30–39 0.65 [0.37 1.15] 0.58 [0.35 0.95] 0.92 [0.59 1.43]
40–49 0.54 [0.30 0.97] 0.51 [0.30 0.85] 0.97 [0.61 1.53]
50+ 1.08 [0.61 1.94] 0.65 [0.39 1.10] 0.63 [0.40 0.99]
No Children 0.57 [0.40 0.81] 0.93 [0.66 1.31] 1.64 [1.24 2.18]
Education
High school or
less ------
Associate 1.27 [0.77 2.08] 1.33 [0.84 2.10] 1.11 [0.72 1.69]
Bachelor’s 1.77 [1.17 2.68] 2.03 [1.38 2.96] 1.20 [0.85 1.69]
Post-Graduate 1.30 [0.74 2.28] 1.92 [1.15 3.20] 1.57 [0.99 2.48]
Household Income
<20 k - - - - - -
20–40 k 1.66 [0.98 2.81] 1.30 [0.83 2.05] 0.81 [0.50 1.30]
40–60 k 2.35 [1.35 4.08] 1.48 [0.91 2.41] 0.66 [0.41 1.09]
>60 k 1.83 [1.03 3.26] 1.27 [0.76 2.12] 0.75 [0.45 1.25]
Residential Area
Hong Kong
Island ------
Kowloon 0.96 [0.60 1.51] 0.97 [0.64 1.49] 1.00 [0.69 1.47]
New Territories 1.16 [0.75 1.79] 1.16 [0.78 1.73] 0.99 [0.69 1.41]
Political Leaning
Neutral or None - - - - - -
Pro-Democrat 0.46 [0.32 0.66] 0.67 [0.48 0.91] 1.38 [1.00 1.92]
Pro-Beijing 4.66 [2.32 9.35] 2.60 [1.28 5.27] 0.52 [0.35 0.78]
Health Status
Good - - - - - -
Moderate 0.59 [0.43 0.83] 0.74 [0.55 1.01] 1.22 [0.93 1.59]
Poor 0.51 [0.31 0.86] 0.55 [0.34 0.88] 1.00 [0.64 1.59]
Note. We conducted multinominal logistic regression (MLR) with sociodemographic factors as independent vari-
ables, with 95% confidence intervals (CIs) for the adjusted odds ratios (AOR). Statistically significant associations
(p< 0.05) are highlighted in bold.
Vaccines 2022,10, 1744 7 of 13
3.2. Psychological Factors Underpinning Vaccine Acceptance
Figure 2depicts the psychological changes observed over the course of the two-wave
longitudinal survey. Generally, participants reported increased trust (M = 0.15, SD = 1.07)
of state authorities, decreased trust of media (M =
−
0.15, SD = 1.25) and health profession-
als (M =
−
0.09, SD = 1.29), decreased risk perception (M =
−
0.10, SD = 1.00) and worry
(M =
−
0.08, SD = 1.33) about COVID-19, decreased information acquisition (M =
−
0.39,
SD = 1.05) and attention towards vaccine-related stories in the media (M =
−
0.19,
SD = 1.06), and increased vaccine confidence (M = −0.23, SD = 1.51).
Vaccines 2022, 10, x FOR PEER REVIEW 8 of 15
Figure 2. Measured changes in psychological perceptions between two waves and association
with vaccination acceptance. Chart demonstrates Adjusted Odds Ratio (AOR) with age, gender,
education, income, and health status as covariates and 95% Confidence Intervals (CIs). Hong Kong
citizens showed increased trust and satisfaction with the efforts of government authorities to fight
COVID-19, decreased trust in HK media, increased knowledge about COVID-19 and the vaccine,
increased coping with vaccine and virus-related risks, and decreased information acquisition.
Zero-order correlations between these perceptual and attitudinal variables and vac-
cine acceptance, as well as the adjusted odds ratio with sociodemographic variables con-
trolled, were examined (Supplementary Table S2). There were several non-trivial correla-
tions between vaccination and perceptual/attitudinal factors at the zero-order and after
partialing for demographics. Specifically, those who were fully vaccinated were more
likely to demonstrate weaker issue politicization (AOR = 0.77), greater trust in state au-
thorities (AOR = 1.40), stronger confidence in the vaccine (AOR = 1.33) and greater inten-
tion to vaccinate (AOR = 1.40) in wave 1. Full vaccination status was also associated with
increased perceived risk of COVID-19 (AOR = 1.29) and confidence in vaccine (AOR =
1.60) across both waves. We also tested the Spearman partial correlation between psycho-
logical factors and vaccine risk acceptance. This highlighted several non-trivial correla-
tions between issue politicization (r = −0.20), trust in state officials (r = 0.35), and infor-
mation acquisition (r = 0.21) in wave 1 and vaccine confidence in wave 2. Incremental issue
politicization (r = 0.13), trust in state officials (r = 0.11), trust in public health professionals
(r = 0.18), and perceived risk of COVID-19 (r = 0.10) between two waves were also associ-
ated with vaccine confidence in wave 2.
3.3. Psychological Factors Underpinning Vaccine Acceptance
Table 3 and Figure 3 demonstrate these three vaccine profiles, which consisted of
respondents with different psychological perceptions that changed over time. These dif-
ferences became clearer in wave 2. In the second survey, vaccine advocates expressed rel-
atively weak levels of issue politicization related to COVID-19, higher levels of trust in
state authorities, media, and health professionals, a higher consumption of media content
related to the vaccine, and higher levels of confidence in vaccine. In contrast, vaccine re-
sisters demonstrated stronger issue politicization, lower levels of trust in state, media and
health professionals, lower levels of information consumption, and lower levels of vaccine
confidence. Vaccine converts fell between the advocate group and the resistance group in
terms of their demonstrated issue politicization, trust levels, risk perception, rates of in-
formation acquisition, and vaccine confidence.
Figure 2. Measured changes in psychological perceptions between two waves and association
with vaccination acceptance.
Chart demonstrates Adjusted Odds Ratio (AOR) with age, gender,
education, income, and health status as covariates and 95% Confidence Intervals (CIs). Hong Kong
citizens showed increased trust and satisfaction with the efforts of government authorities to fight
COVID-19, decreased trust in HK media, increased knowledge about COVID-19 and the vaccine,
increased coping with vaccine and virus-related risks, and decreased information acquisition.
Zero-order correlations between these perceptual and attitudinal variables and vac-
cine acceptance, as well as the adjusted odds ratio with sociodemographic variables con-
trolled, were examined (Supplementary Table S2). There were several non-trivial cor-
relations between vaccination and perceptual/attitudinal factors at the zero-order and
after partialing for demographics. Specifically, those who were fully vaccinated were
more likely to demonstrate weaker issue politicization (AOR = 0.77), greater trust in state
authorities (AOR = 1.40), stronger confidence in the vaccine (AOR = 1.33) and greater
intention to vaccinate (AOR = 1.40) in wave 1. Full vaccination status was also associ-
ated with increased perceived risk of COVID-19 (AOR = 1.29) and confidence in vac-
cine (AOR = 1.60) across both waves. We also tested the Spearman partial correlation
between psychological factors and vaccine risk acceptance. This highlighted several
non-trivial correlations between issue politicization (r =
−
0.20), trust in state officials
(r = 0.35), and information acquisition (r = 0.21) in wave 1 and vaccine confidence in wave
2. Incremental issue politicization (r = 0.13), trust in state officials (r = 0.11), trust in public
health professionals (r = 0.18), and perceived risk of COVID-19 (r = 0.10) between two
waves were also associated with vaccine confidence in wave 2.
3.3. Psychological Factors Underpinning Vaccine Acceptance
Table 3and Figure 3demonstrate these three vaccine profiles, which consisted of
respondents with different psychological perceptions that changed over time. These dif-
ferences became clearer in wave 2. In the second survey, vaccine advocates expressed
relatively weak levels of issue politicization related to COVID-19, higher levels of trust in
state authorities, media, and health professionals, a higher consumption of media content
related to the vaccine, and higher levels of confidence in vaccine. In contrast, vaccine
resisters demonstrated stronger issue politicization, lower levels of trust in state, media
and health professionals, lower levels of information consumption, and lower levels of
Vaccines 2022,10, 1744 8 of 13
vaccine confidence. Vaccine converts fell between the advocate group and the resistance
group in terms of their demonstrated issue politicization, trust levels, risk perception, rates
of information acquisition, and vaccine confidence.
Table 3. Psychological indicators of vaccine advocates, resisters, and converts.
Group Comparisons
Total Vaccine Advocate aVaccine Resister bVaccine Convert c
n= 1501 n= 458 n= 295 n= 621
Mean SD Mean SD Mean SD Mean SD ηp2
Wave 1
[T1] Politicization 4.92 1.37 4.54 bc 1.31 5.38 ac 1.34 4.99 ab 1.34 0.051
[T1] Trust State 3.66 1.47 4.50 1.37 2.87 1.27 3.42 1.32 0.193
[T1] Trust Media 4.07 1.12 4.07 1.21 4.10 1.15 4.07 1.02 0.000
[T1] Trust Profs. 4.60 1.15 4.67 b1.12 4.43 a1.20 4.62 1.15 0.006
[T1] Information
Acquisition 4.64 0.98 4.92 bc 0.92 4.40 a1.04 4.55 a0.95 0.045
[T1] Media Attention 3.65 1.03 3.87 bc 1.00 3.53 a1.10 3.55 a0.99 0.022
[T1] Perceived Risk 4.41 1.04 4.40 1.00 4.49 1.09 4.38 1.06 0.002
[T1] Worry 4.71 1.35 4.60 1.36 4.77 1.37 4.76 1.32 0.004
[T1] Vaccine Confidence 0.319
Wave 2–Wave 1
[T2-T1] Politicization −0.03 1.26 0.17 b1.32 −0.27 ac 1.21 −0.05 b1.22 0.016
[T2-T1] Trust State 0.13 1.07 0.08 1.15 0.06 1.00 0.21 1.02 0.004
[T2-T1] Trust Media −0.14 1.25 0.02 bc 1.24 −0.31 a1.25 −0.18 a1.24 0.010
[T2-T1] Trust Profs. −0.09 1.3 0.19 bc 1.18 −0.34 a1.38 −0.19 a1.32 0.026
[T2-T1] Information
Acquisition
−0.39 1.05 −0.28 b0.98 −0.60 ac 1.17 −0.37 b1.04 0.012
[T2-T1] Media Attention −0.18 1.06 −0.16 0.98 −0.27 1.13 −0.15 1.08 0.002
[T2-T1] Perceived Risk −0.10 1.01 0.04 b1.00 −0.37 ac 1.04 −0.07 b0.98 0.022
[T2-T1] Worry −0.07 1.34 0.10 bc 1.36 −0.18 a1.24 −0.14 a1.35 0.008
[T2-T1] Vaccine
Confidence 0.111
Note. ANCOVAs were conducted with age, gender, income, and education as covariates.
η
p
2
, partial
η2
. Turkey
post-hoc analyses were conducted to compare the three groups.
abc
= mean difference between denoted categories
is significant at the 0.05 level. Statistically significant comparisons are highlighted in bold. Trust profs. = trust
public health professionals.
Multinomial logistic regressions showed that vaccine advocates and resisters differed
mainly in their demonstrated levels of institutional trust and vaccine confidence. Com-
pared to advocates, vaccine resisters reported significantly lower level of trust in the state
(AOR = 0.61, 95% CIs = 0.44, 0.83) and higher level of trust in media (AOR = 1.49, 95%
CIs = 1.08, 2.04), with lower level of confidence in vaccine (AOR = 0.03, 95% CIs = 0.02,
0.04), and had smaller increases in risk acceptance (AOR = 0.28, 95% CIs = 0.22, 0.36).
Compared to advocates, vaccine converts reported significantly lower levels of trust in
state authorities (AOR = 0.64, 95% CIs = 0.50, 0.81) and confidence in vaccine (AOR = 0.10,
95% CIs = 0.07, 0.14). Additionally, vaccine converts were distinguished from their vaccine
resistant counterparts by being more confident in vaccine at wave 1 (AOR = 3.48, 95%
CIs = 2.71, 4.47), and demonstrating decreased trust in health professionals (AOR = 0.74,
95% CIs = 0.60, 0.91), increased vaccine-related information consumption (AOR = 1.27, 95%
CIs = 1.02, 1.58), increased COVID-19 risk perception (AOR = 1.32, 95% CIs = 1.05, 1.66),
and an increased vaccine confidence (AOR = 3.29, 95% CIs = 2.73, 3.98) between two waves
(Figure 4).
Vaccines 2022,10, 1744 9 of 13
Vaccines 2022, 10, x FOR PEER REVIEW 9 of 15
Figure 3. Unadjusted means and bootstrapped standard error for all variables used in the MLR
for the three vaccine profiles. All variables were measured in a 7-point Likert scale with 1 = strongly
disagree, 4 = neutral, and 7 = strongly agree. The pie charts show the distribution of political orien-
tation within each profile. Compared to other groups, Vaccine advocates comprised more pro-Bei-
jing (19%) individuals, while vaccine resisters were more pro-democracy (35%).
Table 3. Psychological indicators of vaccine advocates, resisters, and converts.
Group Comparisons
Total Vaccine Advocate a Vaccine Resister b
Vaccine Convert c
n = 1501 n = 458 n = 295 n = 621
Mean SD Mean SD Mean SD Mean SD ƞp2
Wave 1
[T1] Politicization 4.92 1.37 4.54 bc 1.31 5.38 ac 1.34 4.99 ab 1.34 0.051
[T1] Trust State 3.66 1.47 4.50 1.37 2.87 1.27 3.42 1.32 0.193
[T1] Trust Media 4.07 1.12 4.07 1.21 4.10 1.15 4.07 1.02 0.000
[T1] Trust Profs. 4.60 1.15 4.67 b 1.12 4.43 a 1.20 4.62 1.15 0.006
[T1] Information Acqui-
sition 4.64 0.98 4.92 bc 0.92 4.40 a 1.04 4.55 a 0.95 0.045
[T1] Media Attention 3.65 1.03 3.87 bc 1.00 3.53 a 1.10 3.55 a 0.99 0.022
[T1] Perceived Risk 4.41 1.04 4.40 1.00 4.49 1.09 4.38 1.06 0.002
[T1] Worry 4.71 1.35 4.60 1.36 4.77 1.37 4.76 1.32 0.004
[T1] Vaccine Confidence 0.319
Wave 2–Wave 1
[T2-T1] Politicization −0.03 1.26 0.17 b 1.32 −0.27 ac 1.21 −0.05 b 1.22 0.016
[T2-T1] Trust State 0.13 1.07 0.08 1.15 0.06 1.00 0.21 1.02 0.004
[T2-T1] Trust Media −0.14 1.25 0.02 bc 1.24 −0.31 a 1.25 −0.18 a 1.24 0.010
[T2-T1] Trust Profs. −0.09 1.3 0.19 bc 1.18 −0.34 a 1.38 −0.19 a 1.32 0.026
[T2-T1] Information Ac-
quisition −0.39 1.05 −0.28 b 0.98 −0.60 ac 1.17 −0.37 b 1.04 0.012
[T2-T1] Media Attention −0.18 1.06 −0.16 0.98 −0.27 1.13 −0.15 1.08 0.002
[T2-T1] Perceived Risk −0.10 1.01 0.04 b 1.00 −0.37 ac 1.04 −0.07 b 0.98 0.022
[T2-T1] Worry −0.07 1.34 0.10 bc 1.36 −0.18 a 1.24 −0.14 a 1.35 0.008
Figure 3. Unadjusted means and bootstrapped standard error for all variables used in the MLR
for the three vaccine profiles.
All variables were measured in a 7-point Likert scale with 1 = strongly
disagree, 4 = neutral, and 7 = strongly agree. The pie charts show the distribution of political
orientation within each profile. Compared to other groups, Vaccine advocates comprised more
pro-Beijing (19%) individuals, while vaccine resisters were more pro-democracy (35%).
Vaccines 2022, 10, x FOR PEER REVIEW 10 of 15
[T2-T1] Vaccine Confi-
dence 0.111
Note. ANCOVAs were conducted with age, gender, income, and education as covariates. ƞp2, partial
ƞ2. Turkey post-hoc analyses were conducted to compare the three groups. abc = mean difference
between denoted categories is significant at the 0.05 level. Statistically significant comparisons are
highlighted in bold. Trust profs. = trust public health professionals.
Multinomial logistic regressions showed that vaccine advocates and resisters dif-
fered mainly in their demonstrated levels of institutional trust and vaccine confidence.
Compared to advocates, vaccine resisters reported significantly lower level of trust in the
state (AOR = 0.61, 95% CIs = 0.44, 0.83) and higher level of trust in media (AOR = 1.49, 95%
CIs = 1.08, 2.04), with lower level of confidence in vaccine (AOR = 0.03, 95% CIs = 0.02,
0.04), and had smaller increases in risk acceptance (AOR = 0.28, 95% CIs = 0.22, 0.36). Com-
pared to advocates, vaccine converts reported significantly lower levels of trust in state
authorities (AOR = 0.64, 95% CIs = 0.50, 0.81) and confidence in vaccine (AOR = 0.10, 95%
CIs = 0.07, 0.14). Additionally, vaccine converts were distinguished from their vaccine re-
sistant counterparts by being more confident in vaccine at wave 1 (AOR = 3.48, 95% CIs =
2.71, 4.47), and demonstrating decreased trust in health professionals (AOR = 0.74, 95%
CIs = 0.60, 0.91), increased vaccine-related information consumption (AOR = 1.27, 95% CIs
= 1.02, 1.58), increased COVID-19 risk perception (AOR = 1.32, 95% CIs = 1.05, 1.66), and
an increased vaccine confidence (AOR = 3.29, 95% CIs = 2.73, 3.98) between two waves
(Figure 4).
Figure 4. Forest plots of results from MLR models. AORs were adjusted for covariates of Wave 1
age, gender, education, income, number of children, and region. Factors include Wave 1 trust, in-
formation consumption, and risk perception, marked as [T1]; and their difference from Wave 2,
marked as [T2-T1]. The vaccine resistant group served as a reference in both comparisons. Wald
Confidence Intervals (CIs) are presented for the MLRs. Exact p values are presented when CIs do
not include 1.
4. Discussion
To strengthen vaccine acceptance and reduce vaccine hesitancy, public officials
should develop a better understanding of the psychological determinants of vaccination
intention. Vaccine hesitancy in Hong Kong has decreased from 62.8% to 30.7% since the
start of the pandemic. The pandemic also led to changes at the individual level in terms
of trust in authorities, information consumption, and risk perception of both COVID-19
and the vaccine. How did such changes increase citizen’s COVID-19 vaccination uptake?
This study profiles the psychology underpinning different groups of test subjects and
their differing attitudes toward the COVID-19 vaccine: vaccine advocates, converts, and
resisters. We offer novel evidence regarding the importance of one’s psychological beliefs
Figure 4. Forest plots of results from MLR models.
AORs were adjusted for covariates of Wave
1 age, gender, education, income, number of children, and region. Factors include Wave 1 trust,
information consumption, and risk perception, marked as [T1]; and their difference from Wave 2,
marked as [T2-T1]. The vaccine resistant group served as a reference in both comparisons. Wald
Confidence Intervals (CIs) are presented for the MLRs. Exact pvalues are presented when CIs do not
include 1.
4. Discussion
To strengthen vaccine acceptance and reduce vaccine hesitancy, public officials should
develop a better understanding of the psychological determinants of vaccination intention.
Vaccine hesitancy in Hong Kong has decreased from 62.8% to 30.7% since the start of the
pandemic. The pandemic also led to changes at the individual level in terms of trust in au-
thorities, information consumption, and risk perception of both COVID-19 and the vaccine.
How did such changes increase citizen’s COVID-19 vaccination uptake? This study profiles
Vaccines 2022,10, 1744 10 of 13
the psychology underpinning different groups of test subjects and their differing attitudes
toward the COVID-19 vaccine: vaccine advocates, converts, and resisters. We offer novel
evidence regarding the importance of one’s psychological beliefs in determining acceptance
of the COVID-19 vaccine, with an emphasis on individuals’ confidence in vaccine and trust
in authorities. Previous studies investigating the psychological characteristics related to
vaccine acceptance were based on cross-sectional surveys. Our longitudinal survey mea-
sured individuals’ psychological changes over time, offering insights into why individuals
convert to vaccine acceptance or continue to insist on vaccine resistance. Our results thus
provide practical guidance for how to preserve current advocates and convert those who
remain hesitant.
4.1. The Sociodemographic Profile of Vaccine Advocates, Resisters, and Converts
Results from this study indicated that, on average, vaccine advocates, vaccine resisters,
and vaccine converts differed from each other in age, gender, education, income, political
leaning, and self-reported health status. Males were more likely to be vaccine advocates
than resisters, a finding sharply at odds with a number of studies identifying gender-related
differences in vaccine acceptance and uptake [
22
,
34
]. Subjects between 40 and 49 years old
or those without children were less likely to be advocates. Vaccine advocates were more
likely to have bachelor’s degrees and higher household incomes, a finding consistent with
previous research [
22
]. In Hong Kong, vaccine acceptance was associated with pro-Beijing
political leanings and good health status. Democratic voters were less likely to be vaccine
advocates than pro-Beijing voters.
Age, education, political leaning, and health status all distinguished vaccine converts
from resisters. Middle aged subjects (between 30 and 49) were less likely to convert, while
well-educated subjects were more likely to convert. Vaccine converts were more likely to
be pro-Beijing voters, and less likely to report poor health status. Public health authorities
can use these findings to inform strategic communication. Based on the distinguishing
characteristics of these different vaccination profiles, public health campaigns should
be customized to target groups who are more likely to convert, including younger and
older demographics, the well-educated, pro-Beijing voters, and those who report being in
good health.
4.2. Psychological Determinants of Vaccine Advocates, Resisters, and Converts
Vaccine advocates were distinguished from vaccine resisters by being more trusting
towards state government, distrusting of HK media, and demonstrating relatively low risk
perception of the COVID-19 vaccine. Alternatively, those who converted to acceptance
from resistance were more accepting of COVID-19 vaccine risks and reported increasing
information consumption as well as increased risk perception related to the virus.
Responsibility for promoting public health-related messages lies with government,
healthcare professionals, and media. Among vaccine resisters, however, high levels of dis-
trust in state regulators and public health professionals render official messaging effectively
useless. Alternative approaches for delivering vaccine-related messaging might utilize
religious leaders or online opinion leaders [
35
–
37
]. Although trust in state officials increases
vaccine uptake, trust in media leads to vaccine resistance. Intensive media coverage may
discourage people from being vaccinated. The media should therefore report clear and un-
biased information [
1
]. The opposite roles played by state and media messaging in vaccine
acceptance suggests that institutions in Hong Kong did not work collaboratively to increase
vaccination. Our results also demonstrated that resisters consume less vaccine-related
information from public health professionals and pay less attention to COVID-related
information on news media. This poses further challenges to the effective communication
of accurate vaccine information to vaccine-resistant audiences. Alternative communication
channels, such as local community and online forums [
38
], could increase the probability
of reaching these individuals.
Vaccines 2022,10, 1744 11 of 13
Compared to vaccine resisters, vaccine converts demonstrated higher levels of confi-
dence in COVID-19 vaccines. Many studies have emphasized the politicized nature of the
COVID-19 pandemic and related vaccination campaigns [
18
]. Our research revealed a spe-
cific political differentiation between vaccine advocates and resisters, who varied in terms
of their political leaning and their trust in authorities and institutions. For vaccine converts,
however, pandemic- and vaccine-related perceptions were the determinant factors. Public
health authorities should therefore place greater emphasis on educating the public about
vaccines and mitigating their risk perception.
Our follow-up analysis (Supplementary Table S2) further suggested that confidence
in vaccine was determined by trust in authorities. Confidence in vaccine at wave 2 was
determined by vaccine confidence and trust in authorities at wave 1, as well as changes in
trust and confidence between waves 1 and 2. This finding emphasized the total effect of
individuals’ trust in public health systems. It suggested that addressing vaccine hesitancy
requires more than increasing knowledge and building confidence in the vaccine. It is
a multifactorial, evolving and context-dependent endeavor that requires a synchronous
approach. Clear, consistent, multi-channel communication by health authorities about
COVID-19 vaccine efficacy is crucial for building public confidence and improving vaccine
uptake [2,39].
4.3. Limitations and Future Work
This study has several strengths, including its large sample size, a longitudinal data
set that spans periods before and after the introduction of the vaccine, and a comprehensive
profile of the psychology underpinning vaccine acceptance. However, these findings
should be interpreted in light of several limitations. First, despite all efforts to make our
sample inclusive and representative of Hong Kong’s adult population, we cannot rule out
the possibility of potential biases due to quota sampling in the first wave and attrition
in the second wave. Further, our analyses relied on self-reported vaccination status and
psychological perceptions subject to recall and reporting bias. We also lack data on future
vaccine uptakes. It is therefore difficult to know how changes observed during the study
period might predict vaccination in the future. Despite these limitations, our time-lagged
analysis still provides rigorous evidence for the roles played by psychological determinants
of vaccination. In future studies, continued monitoring of vaccine uptake will help us to
better understand changing vaccine intentions. For example, adding more longitudinal
data in the future might allow us to see increases in groups that now remain relatively small,
such as individuals who converted from intention to vaccinate to unvaccinated/vaccine
resisters between waves 1 and 2. Finally, these results are based on a sample of adults
in Hong Kong. Hong Kong citizens’ trust of state government, trust of media, attitudes
towards vaccines, and perception of the pandemic may differ from other populations. Some
determining factors are likely context dependent. Nevertheless, relationships found in our
study can be generalized to other settings, e.g., the dominant role of trust for both vaccine
confidence and vaccine uptake. Public health authorities in different nations could replicate
our work to identify different psychological profiles in relation to the COVID-19 vaccine
within their own populations, with the aim of tailoring campaign messages to these groups.
5. Conclusions
This study profiles the psychology underpinning different groups of test subjects and
their differing attitudes toward the COVID-19 vaccine: vaccine advocates, converts, and
resisters. We offer novel evidence regarding the importance of one’s psychological beliefs
in determining acceptance of the COVID-19 vaccine, with an emphasis on individuals’ con-
fidence in vaccine and trust in authorities. Previous studies investigating the psychological
characteristics related to vaccine acceptance were based on cross-sectional surveys. Our lon-
gitudinal survey measured individuals’ psychological changes over time, offering insights
into why individuals convert to vaccine acceptance or continue to insist on vaccine resis-
tance. Based on our knowledge, this is the first study tracking individuals’ psychological
Vaccines 2022,10, 1744 12 of 13
changes during the pandemic as they pertain to vaccine intention. COVID-19 vaccination
campaigns are ongoing throughout the world. Our results thus provide practical guidance
for how to preserve current advocates and convert those who remain hesitant.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/vaccines10101744/s1, Figure S1: Geolocation distribution of
our sample in Hong Kong; Figure S2: Theoretical framework; Table S1: Comparison of sample
demographics with Hong Kong census figures; Table S2: Zero-order and partial associations for
vaccine acceptance with psychosocial variables; Table S3: Multiple linear regression and binominal
logistic regression explaining vaccine risk acceptance and vaccination status at wave 2; Table S4: The
Measurement Scale.
Author Contributions:
Conceptualization, X.W., Y.-H.C.H. and Q.C.; data collection, Y.-H.C.H. and
Q.C.; data curation, Q.C.; data analysis, X.W. and Q.C.; validation, X.W. and Y.-H.C.H.; writing—
original draft preparation, X.W. and Q.C.; writing—review and editing, X.W. and Y.-H.C.H.; visual-
ization, X.W.; supervision, Y.-H.C.H.; project administration, Q.C.; funding acquisition, Y.-H.C.H. All
authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by City University of Hong Kong, grant number 9380119, 7005703.
Institutional Review Board Statement:
The study was conducted in accordance with the Declaration
of Hong Kong, and approved by the Human Subjects Ethics Sub-Committee of City University of
Hong Kong (Ethics approval reference no.: 4-2020-05-F, 17-2021-23-F; Date of approval: 4 June 2020,
3 March 2021).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Data are contained within the article or Supplementary Material.
Acknowledgments:
The authors gratefully acknowledge the research assistants who helped the
data collection.
Conflicts of Interest: The authors declare no conflict of interest.
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