Abstract
The emergence of COVID-19 traumatized individuals from all walks of life and while the demand for vaccines increased exponentially, the authorities seem to encounter greater challenges on their road to create herd immunity. Governments initiated numerous campaigns to influence individuals to opt for vaccination and India being a diverse country makes it difficult to understand the motivating factors for getting COVID-19 vaccination.
The study aimed to explore the predictors of individuals’ willingness to get vaccinated using Qualitative Comparative Analysis (QCA). After screening using the vaccine hesitancy scale, a semi-structured interview was conducted among 30 respondents from India. Crisp Set QCA was utilized to analyse the data which lead to nine conditions.
A solution combination of seven conditions showed a consistency of 1 and coverage of 0.6. They included knowledge about vaccines, perceived severity of the COVID-19 virus, family and peer influence, media and health department's influence, a sense of social responsibility, trust in the authorities and vaccine efficacy.
This study contributes to the relevance of QCA's use in psychological research, especially to identify predictors of willingness to immunize. The findings of this study would help in designing appropriate interventions to enhance willingness to get vaccinated.
Keywords: Knowledge about vaccines, Crisp set, Qualitative comparative analysis, Willingness, Intention
1. Introduction
The Indian Government had successfully delivered and conducted a fast rollout of COVID-19 vaccines at the beginning of January 2021 [1]. However, the vaccination drive eventually came down to an average speed, owing to which the government received criticisms over their management of the second wave of COVID-19 in the country and the delay in rolling out the vaccines [2]. Additionally, similar to other countries, India had started experiencing unwillingness towards vaccination amongst citizens including adults from educated and privileged families. For instance, false information regarding COVID-19 vaccines was exaggeratedly diffused across India through social media that the Government of India had to ban certain sites for breaking the IT laws concerning COVID-19 which thereby created havoc [3,4]. This was also followed by conspiracy theories that were further influenced by religious leaders which inculcated fear of death due to the COVID-19 vaccine among individuals [5,6]. Thus, myriad reasons, including misinformation, conspiracy theories, cultural biases etc., kept challenging the scientific community to find ways to encourage the Indian population to get vaccinated [7].
Vaccine hesitancy is likely when an individual is uncertain about getting a vaccine, while vaccine resistance is when an individual denies getting vaccinated [4]. Despite these two primary occurrences, the WHO's efforts to fight against COVID-19 had been consistent, especially through immunization or herd immunity [8]. To help WHO win the fight against COVID-19, especially in achieving herd immunity, several factors come into force, including vaccine effectiveness, lower infection growth, and the percentage of the vaccinated population [9]. In doing so, apart from the then vaccine effectiveness being in the range of 80–85%, at least 75% of vaccine administration is required to eliminate the COVID-19 virus and protect the population [4] which could be achieved only through the co-operation of the general public.
On the contrary, the second wave of COVID-19 in India had laid tremendous trauma on the healthcare system [10], especially, while the country was still recovering from the psychological and financial troubles caused and experienced as a result of the outbreak. The healthcare (HCWs) and the front-line workers themselves had their fair share of traumatic experiences while also having their own perceptions and attitudes towards the COVID-19 vaccine. Although there were a few HCWs who were hesitant to get vaccinated due to their poor perception towards the vaccine and the uncertain effects of the vaccine [11], most of the HCWs were willing to get vaccinated due to their perceived severity of the pandemic and their perception of low risk of COVID-19 vaccine. Their acceptance rate for vaccines was much higher than the general population [12,13]. However, few recent studies on COVID-19 Booster dose vaccines revealed that HCWs from India were hesitant to get the booster dose and displayed moderate acceptance of the vaccine doses due to their concerns regarding the effectiveness of the vaccines [14,15]. Furthermore, the literature indicated that among the general population, individuals unwilling to get booster doses were also those who had not taken the COVID-19 vaccination [16]. Vaccine hesitancy was also observed among individuals with psychiatric illnesses, pregnant women and elderly people. In comparison to research conducted among the general population around the world, patients with psychiatric illnesses have much more hesitation about getting COVID-19 immunization [17]. Further research revealed that almost four out of five pregnant women were reluctant to receive the COVID-19 vaccine [18]. Overall, the literature emphasizes that the hesitancy behaviour is most commonly associated with the individual's educational level, place of residence and awareness regarding the vaccine [[11], [12], [13],17].
This tendency of resistance among the general public towards the efforts of the healthcare system to revive the community through maximum immunization was still prevalent [4]. As indicated in the past, one of the major prevailing concerns was regarding the inadequate scientific information about the COVID-19 vaccine, resulting in individuals' unwillingness to be vaccinated. Also, a lack of trust towards government officials was heralded as one of the factors responsible for individuals’ unwillingness to get vaccinated [19]. However, necessary protocols for tackling the vaccination refusal issues were being taken by the Indian government to examine the efficacy of vaccination roll-outs and maximize the efforts for the same accordingly. The Indian government had established the National Expert Group on Vaccine Administration for COVID-19 (NEGVAC) to have an in-depth investigation regarding the clinical trials, risk populations, monitoring COVID-19-related statistics, etc. [20].
Though few studies highlight identifying the factors responsible for the willingness to get vaccinated [21], limited studies contributed to conducting a comparative analysis. It is evident from previous literature that certain factors contribute greatly towards an individual being hesitant to get immunized such as the lack of trust in the officials, uncertainty associated with the vaccine effectiveness as well as side-effects, and the long-term effects of the vaccine. However, it is unclear as to what circumstances influence an individual to receive vaccinations, particularly the COVID-19 vaccine. This gap could be achieved much more intensively and effectively when research is investigated by contrasting those with a significant willingness to vaccinate and those who are simply not in favour to vaccinate themselves. The concept of qualitative comparative analysis (QCA) is that multifaceted events, like willingness to receive a vaccination, could never be boiled down to a single causal explanation [22]. Literature still lacks a model that would comprehensively specify the factors responsible for vaccination intake. Further, despite the Indian government's continued interventions and necessary measures, individuals from both rural and urban places had been either observed to be hesitant or resistant towards vaccination [23]. Moreover, a country's socio-economic, cultural, religious and political factors have shown to be contributing in developing a positive or a negative attitude towards vaccines. These factors, including the anti-vaccine campaigns and vaccine criticisms, only increase the prevalence of vaccine hesitancy thereby posing a greater threat towards building a resilient community through vaccination [24]. It is plausible that investigating these aspects in-depth within the context of Indian culture could further facilitate the ability to comprehend the causes of vaccine reluctance and, as a consequence, promote the development of effective context specific prevention and intervention techniques. Additionally, what conditions must be necessarily met for an individual to engage in a pro-vaccine behaviour, is the core research question of the current study. A prominent prevailing research gap for the attention of the research community was regarding the possible causal conditions that are necessary for an individual to be willing enough to be vaccinated. In an attempt to bridge this gap, the current study would represent a unique approach by utilizing Qualitative Comparative Analysis (QCA) to understand and explore the conditions necessary and sufficient to predict willingness for vaccination. Specifically, it examines and compares the conditions between cases of individuals who show willingness and unwillingness toward vaccination. This would offer a comprehensive understanding of certain particular reasons why individuals choose to get immunized thereby using the inferences of this study for influencing individuals to engage in the pro-vaccination behaviour. The study aims to identify the predictors of the outcome or the desire to be vaccinated among individuals by deploying the QCA approach as it is widely used and proven to be the best fit to compare and explore causal conditions for qualitative study [25]. In conclusion, exploring the variables that predict individuals' willingness and unwillingness towards vaccination as well as identifying the conditions that are both necessary and sufficient to promote their willingness to vaccinate are the main purposes of this study.
2. Methods
2.1. Design & objectives
This qualitative comparative research study design, aimed to explore the factors responsible for predicting willingness and unwillingness to be vaccinated among individuals, using QCA between two groups of cases. This endeavour aims to achieve two major objectives: 1) to explore factors responsible for predicting willingness and unwillingness among individuals, and 2) to identify the necessary and sufficient conditions contributing to the outcome of willingness.
2.2. Procedure, measures & sample
In an effort to achieve these objectives, initially, screening was done on (N = 94) using the vaccine hesitancy scale [26] to recruit the participants for the interviews. The scale was shared using google form via online platforms (through WhatsApp and email) which consisted of socio-demographic data, their contact details (they were assured that the details would be used only for research purposes which were written in the google form) and their consent to participate in the study. The scale's items were graded on a 6-point Likert scale (1 = definitely will take it; 6 = definitely will not) which had an internal consistency score of 0.843. The scale's total score ranged between 2 and 12. A score of eight and above indicated higher hesitancy toward a vaccine, and four and below indicated lower reluctance toward a vaccine.
Using purposive sampling technique, a total of 30 participants (low hesitancy = 15 and high hesitancy = 15) who met the inclusion criteria were recruited for semi-structured interviews via telephone. Each interview lasted for approximately 30 min on an average. Detailed socio-demographic information about the participants can be seen in Table 1. All ethical considerations were taken into account, and the participants were made aware of their ethical rights. Informed consent was obtained verbally before the interviews. The study received ethical approval from the University's school-level ethical committee.
Table 1.
The distribution of socio-demographic characteristics of the participants.
| Variables | Non - willing |
Willing |
||
|---|---|---|---|---|
| n (15) | % | n (15) | % | |
| Gender | ||||
| Male | 3 | 20 | 3 | 20 |
| Female | 12 | 80 | 11 | 73.3 |
| Missing values | 1 | 6.7 | ||
| Age | ||||
| 18–28 years | 10 | 66.7 | 13 | 86.6 |
| 29–39 years | 1 | 6.7 | ||
| 40–55 years | 2 | 13.3 | 1 | 6.7 |
| 56–66 years | 2 | 13.3 | ||
| Missing values | 1 | 6.7 | ||
| Education | ||||
| High School | 8 | 53.3 | ||
| Higher Secondary | 1 | 6.7 | 1 | 6.7 |
| Diploma | 1 | 6.7 | ||
| Graduation | 1 | 6.7 | 12 | 80 |
| Uneducated | 5 | 33.3 | ||
| Missing values | 1 | 6.7 | ||
| Marital Status | ||||
| Unmarried | 3 | 20 | 13 | 86.6 |
| Married | 12 | 80 | 1 | 6.7 |
| Missing values | 1 | 6.7 | ||
| Occupation | ||||
| Unemployed | 7 | 46.7 | 13 | 86.6 |
| Employed | 8 | 53.3 | 1 | 6.7 |
| Missing values | 1 | 6.7 | ||
Conditions leading to the outcome: willingness to get vaccinated.
The interviews included questions about their willingness or unwillingness to be vaccinated, their knowledge of COVID-19 and the vaccine, and the kind of preventive behaviours they adopted to avoid infections. Some of the sample questions which also accompanied certain specific probing questions included “Do you think it is important to take the COVID-19 vaccination? If not why? If yes Why? What will you do if you get a chance to take the COVID-19 vaccine? What are some of the hygiene practices that you follow to prevent COVID-19? Could you please elaborate on your knowledge about the COVID-19 vaccination and its vaccination process? How often do you watch the news about the COVID-19 virus spread? Why?“. All these questions were framed based on the past literature findings on COVID-19 vaccine hesitancy. These findings were grouped under the broad terminology viz., psychological, social and cognitive factors, in order to attain varied causal conditions. The verbatim was recorded and transcribed, and the outcome variable viz, willingness to vaccinate, was fixed in line with the research objectives. In the subsequent stages, codes were generated through the data by comparing and contrasting the two groups. These codes represented the broad factors (such as cognitive, social, behavioural factors, etc) that emerged commonly in both the groups. All the authors read through the verbatim multiple times and finalized the output codes. Further, by deploying the Crisp Set QCA approach for analysis, potential conditions were then finalized that were responsible for predicting the study's outcome viz, willingness to vaccinate.
The data were grouped in a way to make the analysis and formation of the truth table easier. Since there were two groups for the outcome, i.e., willingness to vaccinate and unwillingness to vaccinate, all the cases with identical outcomes were matched and grouped [27]. The instances and combinations were then transformed into a truth table (see. Table 3). The table aids in sorting the various combinations based on causal conditions. The truth table consists of binary values that are assigned depending on the presence or absence of the occurrence of that event or condition [28]. For the current study, 1 denoted the presence of that event, while 0 denoted the absence of that event. In accordance with this, the outcome variable too was denoted with binary digits indicating the presence and absence of the event. Thus, individuals willing to vaccinate were denoted with a binary 1 (indicating a presence) and those unwilling to vaccinate were assigned a binary of 0 (indicating absence). A similar method was followed for each of the conditions that emerged through both the groups. For instance, knowledge about vaccine emerged as one of the conditions that represented cognitive factors. Individuals who were unaware and were having no knowledge regarding the vaccine and vaccine process were assigned a binary digit of 0 that indicated an absence of knowledge while those who had some logical and general awareness were assigned 1, indicating a presence. The truth table analysis and standard analysis were executed to identify the pathways or solution combinations.
Table 3.
Truth Table with 9 conditions and 1 outcome (willingness to vaccinate).
| Condition | Cases | KAV | PSUS | PSEV | FAM_PEE_ MEDINF | MEDHEAL_ INF | SOC_ RES | TRS_ AUT | FEA_ SID_VACC | VACC_ EFFE | OUTCOME |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Case W | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 |
| 2 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | |
| 3 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | |
| 4 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | |
| 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| 6 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| 7 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | |
| 8 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| 9 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | |
| 10 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | |
| 11 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | |
| 12 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | |
| 13 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | |
| 14 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | |
| 15 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | |
| Case NW | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| 2 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | |
| 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | |
| 4 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | |
| 5 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | |
| 6 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | |
| 7 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | |
| 8 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | |
| 9 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | |
| 10 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | |
| 11 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | |
| 12 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | |
| 13 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | |
| 14 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Notations: KAV- knowledge about vaccine; PSUS- perceived susceptibility; PSEV-perceived severity; FAM_PEE_INF- family & peer influence; MEDHEAL_INF- media & health department influence; SOC_RES- social responsibility; TRS_AUT-trust in authorities; FEA_SID_VACC- fear of vaccine side effects; VACC_EFFE-vaccine effectiveness, CASE W – willing to get vaccinated Case NW- Not willing to get vaccinated.
3. Results
The notion behind qualitative comparative analysis (QCA) is that diverse events, like a person's willingness to get vaccinated, can never be broken down into a single causal explanation. The current study aimed to explore the psychological, social, and cognitive factors responsible for predicting willingness to get the COVID-19 vaccine among the Indian population. Table 1 highlights the socio-demographic characteristics of the participants recruited for the study.
After cross-verifying the data, more than 15 conditions were recognized. Based on the similarities between those 15 conditions, nine conditions were identified and finalized for subsequent analysis with further assessments. The nine conditions specified were as follows (see Table 2): knowledge about vaccine, perceived susceptibility, perceived severity, family & peer influence, media & health department impact, social responsibility, trust in authorities, fear of vaccine side effects, and vaccine effectiveness. The table also describes the per cent of total cases (N = 15) that presented with these conditions in both willing and unwilling groups.
Table- 2.
Conditions leading to the outcome-willingness to get vaccinated & percentage of cases who presented with the conditions.
| Factors/conditions | Willing (N = 15) 100% | Unwilling (N = 15) 100% | |
|---|---|---|---|
| 1 | Knowledge about vaccination | 100.00% | 26.67% |
| 2 | Perceived susceptibility | 80.00% | 40.00% |
| 3 | perceived severity | 100.00% | 20.00% |
| 4 | family & peer influence | 100.00% | 40.00% |
| 5 | Influence of media & health sector | 86.67% | 66.67% |
| 6 | social responsibility | 86.67% | 0.00% |
| 7 | trust on authorities | 86.67% | 33.33% |
| 8 | fear of vaccine side effects | 20.00% | 86.67% |
| 9 | vaccine effectiveness | 86.67% | 26.67% |
Knowledge about vaccines refers to the individual's awareness regarding the COVID-19 vaccine and vaccine process. Individuals with no knowledge about the vaccine were denoted as absent. 26.67% of unwilling individuals had no prior knowledge regarding the vaccine or vaccine process. Similarly, perceived susceptibility in the present study refers to the individual's perception regarding the risk of contracting the COVID-19 virus. Among those unwilling individuals, 40% feared they were susceptible to the virus instead of 80% of those willing to get vaccinated. Perceived severity refers to the individual's perception of the intensity of the adverse outcomes the infection might bring about. The findings revealed that only 20% of those unwilling to get vaccinated perceive the severity of the COVID-19 virus. In contrast, almost everyone in the willing group perceives the seriousness of getting infected. The family and peer influence factor explains the encouraging and supportive behaviours extended from the individual's families and friends to promote the COVID-19 vaccine. Among those unwilling, only 40% have supportive and encouraging social groups that encourage vaccination behaviour. The influence of media, and the health sector is a social factor that also promotes and encourages participants to get vaccinated as they also seem authoritative, and thus the general public would confide in them.
Approximately 66% were subjected to some amount of media and health authorities' influence. Social responsibility involved COVID-19-related behavioural protocols like using face masks, frequent washing of hands, maintaining social distance when in an external environment, avoiding unnecessary travel, etc. In the present study, the authors also included the psychological responsibility of conforming to government norms; for example, taking a vaccine to reduce the spread of infection, protect oneself, etc. The findings show that 0% of those unwilling to get vaccinated have no social responsibility to conform to government and social protocols. Trust in authorities was found to be an essential factor generated from the data. It refers to the individual's trust and confidence in the government authorities, officials and front-line workers. 33.33% of those unwilling to get the vaccine responded having trust in their authorities. In comparison, 86.67% of those willing to get the vaccine expressed their extent of trust in the authorities.
One of the main factors that seemed to be evident was fear of vaccine side effects. This factor explains the psychological fear related to the uncertainty of the outcomes and anticipating adverse side effects for an uncertain period after taking the vaccination. These findings indicate that 86.67% of individuals unwilling to get vaccinated fear vaccine side effects, while only 20% of those willing to vaccinate expressed fear of vaccine side effects. The last factor, vaccine effectiveness also emerged dominantly. It refers to the individuals’ understanding of how effectively the vaccine reduces the risk of getting infected. Of those unwilling to get the vaccine, only 26.67% believe in the effectiveness of the vaccine.
Table 3, the truth table generated, shows various possible combinations. Subsequently, the truth table analysis and the standard analysis revealed five pathways or solution combinations. Consistency and coverage are two essential parameters of QCA that aids in interpreting the analysis. Consistency refers to how cases exhibit combinations with various conditions that also lead to the outcome or solutions. In simple terms, if cases that generate the solutions also create the outcome, then the particular solution consistently generates that outcome. Therefore, the higher the consistency, the higher the relevance of empirical justifications.
In addition, Coverage represents the percentage of cases involved in the solution combinations being assessed [25,29,30]. Consistency is generally set above 0.8 for the pathway to be considered a significant outcome predictor [25]. However, a coverage of 0.6 does not imply lower relevance to the pathway, instead of configuration with a low coverage could easily be considered for the interpretation [25,30].
Despite the fact that nine conditions were established as shown in Table 2, a combination of only seven conditions provided a consistency of 1 and coverage of 0.6, which is the highest of all other solution combinations (see Table 4). They included knowledge about vaccines, perceived severity of the COVID-19 virus, family and peer influence, media and health sector influence, a sense of social responsibility; trust in the authorities, and vaccine efficacy. Of these, knowledge about vaccines, perceived severity of the COVID-19 virus, and family and peer influence were present in all five pathways, while other conditions were either absent or not included. This, in addition to the outcomes of subset/superset analysis, proves that these factors are the necessary conditions for the outcomes to occur. However, when compared with the configurations of coverage 0.46, the other factors such as perceived susceptibility, social responsibility, trust in authorities and vaccine effectiveness form as sufficient conditions that predict the outcome variable.
Table 4.
Outcome of the standard analysis of the truth table: Intermediate Solution.
| Path | KAV | PSUS | PSEV | FAM_PEE_INF | MEDHEAL_INF | SOC_RES | TRS_AUT | FEA_SID_VACC | VACC_EFFE | Raw Coverage | Consistency |
|---|---|---|---|---|---|---|---|---|---|---|---|
| KAV*PSEV*FAMPEEMEDINF*MEDHEALINF*SOCRES*TRSAUT*VACCEFFE | ● | ● | ● | ● | ● | ● | ● | 0.6 | 1 | ||
| KAV*∼PSUS*PSEV*FAMPEEMEDINF*MEDHEALINF*SOCRES*TRSAUT*∼FEASIDVACC | ● | O | ● | ● | ● | ● | ● | O | 0.133333 | 1 | |
| KAV*PSUS*PSEV*FAMPEEMEDINF*MEDHEALINF*∼SOCRES*∼FEASIDVACC*VACCEFFE | ● | ● | ● | ● | ● | O | O | ● | 0.133333 | 1 | |
| KAV*PSUS*PSEV*FAMPEEMEDINF*SOCRES*TRSAUT*∼FEASIDVACC*VACCEFFE | ● | ● | ● | ● | ● | ● | O | ● | 0.466667 | 1 | |
| KAV*PSUS*PSEV*FAMPEEMEDINF*MEDHEALINF*SOCRES*∼TRSAUT*∼FEASIDVACC*∼VACCEFFE | ● | ● | ● | ● | ● | ● | O | O | O | 0.0666667 | 1 |
● Indicates presence of a condition.
O Indicates absence of a condition.
Notations: KAV- knowledge about vaccine; PSUS- perceived susceptibility; PSEV-perceivedseverity; FAM_PEE_INF- family & peer influence; MEDHEAL_INF- media & health department influence; SOC_RES- social responsibility; TRS_AUT-trust on authorities; FEA_SID_VACC- fear of vaccine side effects; VACC_EFFE-vaccine effectiveness.
Furthermore, yet another pathway with a consistency of 1 and coverage of 0.46 that predicts the willingness to get vaccinated includes the presence of knowledge about vaccine, perceived susceptibility, perceived severity, family and peer influence, social responsibility, trust in authorities, vaccine effectiveness, and an absence of fear of vaccine side effects. Likewise, the three other pathways contributing to the outcome are mentioned in Table 4 for reference.
4. Discussion
This study aims to represent a unique approach by utilizing QCA to understand and explore the necessary and sufficient conditions to predict willingness for COVID-19 vaccination. It, therefore, is a one-of-a-kind study where the authors tried to compare two groups and qualitatively analyse the factors responsible for willingness to get vaccinated. QCA provides a platform to achieve solutions by transforming qualitative interviews into a quantitative format for better synthesis and interpretations to unravel the answers leading to the outcome [28,31].
The findings of this study provide empirical evidence through qualitative inquiry in the perspectives of social, cognitive and psychological aspects of the factors predicting vaccine acceptance or refusal behaviour among the Indian population. The study findings indicated that knowledge about the vaccine, perceived severity, and family and peer influence are the necessary conditions for the occurrence of the outcome (Fig. 1a). Therefore, for an individual to be willing to get the vaccination, he/she must have the knowledge about the vaccine, be able to recognise the seriousness of the COVID-19 virus, and have the support and influence from family and friends to get vaccinated. Similar findings support the outcome where knowledge about vaccines or vaccine protocols leads to higher intentions of getting vaccinated [32,33]. It was also observed that the intention or willingness to get vaccinated would increase if the perceived severity would also increase [32,34]. Likewise, India, a collectivist culture [35] that promotes family values and belongingness, widely influences individuals' decisions. The findings of this study emphasize the strength of family and peer influence in promoting vaccine acceptance. Similar observations were made in a study conducted by Rogers, Cook & Button (2021) that highlighted the importance of the family's role in COVID-19 vaccine uptake in the US [36].
Fig. 1.
Venn diagram representing the a)necessary condition and b)sufficient conditions for the outcome:willingness to vaccinate.Notations:a)Necessary condition formed by:KAV-knowledge about vaccine; PSEV-Perceivedseverity; FAM_PEE_INF-family & peer influence; b)sufficient conditions formed by:MED-HEALT-media & health department influence; SOC_RES-social responsibility; TRS_AUT-trust on authorities & VACC_EFFE vaccine effectivess
Other than knowledge, perceived severity, and family and peer influence, the most suitable configuration that significantly predicted the willingness to take vaccination consists of factors: impact of media and health sector, a sense of social responsibility, trust in the authorities, and vaccine efficacy (Fig. 1b). These factors together would positively and significantly predict vaccine acceptance. Thus, having knowledge, perceived severity, and family influence is not enough. Consequently, the presence and influence of media and the health sector, the individual's sense of social responsibility, trust in the authorities and the perception of vaccine effectiveness, all combined will only predict the individual's willingness to take the vaccination. Some studies confirm these findings, emphasizing the importance of creating trust in authorities so that the general public would have trust in the efficacy of the vaccine, building social responsibility through the influence of social groups, and inculcating positive awareness through social media [[37], [38], [39], [40]].
Furthermore, the findings also indicate that fear of vaccine side effects must be absent for individuals to be vaccinated. In some instances, perceived susceptibility to the infection also plays a key role. Rief (2021) has emphasized the vital correlation between fear of adverse side effects of vaccines and vaccine hesitancy [41]. To combat this issue, the authorities must find ways to circulate information regarding vaccine effectiveness and benefits accurately. Likewise, Huynhet al. (2021) highlighted that the intention to get vaccinated only increases when the perceived susceptibility to the infection increases [42]. The compared findings of this study also indicate a significant aspect. Between the two groups of individuals, people unwilling to get vaccinated have adverse fear towards vaccine side effects, are least socially responsible, lack trust in authorities and the vaccine effectiveness, have low perceived severity and have limited knowledge about the vaccine. It can also be confirmed by the findings of Fridman et al. (2021), which emphasized that individuals who are not favourable toward getting the COVID-19 vaccine also perceive the virus as less threatening [43]. This finding highlights the factors that the government needs to act upon to nudge the general public to get vaccinated.
5. Conclusion
The study contributes uniquely with the integration of QCA to explore and investigate the predictors of vaccine unwillingness mostly in terms of reluctance rather than hesitancy. This paper thus justifies and confirms the use of the technique in future research. The findings highlight that knowledge about the vaccine, the extent of risk perceived if infected, and family and peer influence are necessary but not sufficient factors for individuals to express their willingness towards vaccination. Additionally, perceived susceptibility, having a sense of social responsibility towards society, trust in the authorities, the influence of media and health sectors, absence of fear towards vaccine side effects, and vaccine effectiveness all play vital roles in influencing individuals for vaccine uptake. These outcomes highlight the elements that must be addressed by the government in order to encourage vaccination among the populace. Government officials, front-line and healthcare workers could all benefit from the findings of this research which emphasizes that several factors together need to be prioritized to encourage individuals to engage in pro-vaccination behaviour. Policies, advertisements and future awareness intervention programs could be designed in such a way that it addresses the necessary conditions that predict willingness towards vaccination. The current study is successful in presenting an integrative framework that contributes to the causative factors influencing the uptake of COVID-19 vaccination.
Strengths and limitations
The critical contribution of this paper is the use of QCA to understand and investigate the predictors of the outcome. However, the incorporation of more samples would lead to developing better conditions. Since there were minimum cases, conditions could not be formed more broadly. Thus, future research is recommended to have a larger sample size to explore more factors that could predict vaccine uptake behaviours qualitatively.
Author contribution statement
Eslavath Rajkumar: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper. John Romate: Conceived and designed the experiments; Wrote the paper. Rajgopal Greeshma: Analyzed and interpreted the data; Wrote the paper. Maria Lipsa: Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.
Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability statement
Data will be made available on request.
Declaration of interest's statement
The authors declare no competing interests.
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Data Availability Statement
Data will be made available on request.