In this section, we provide a primer on what practitioners need to know about human behavior and how to bring insights from social and behavioral science to vaccine demand work.
Whether a person accepts a measure designed to improve their health, or changes their behavior to increase their likelihood of a good health outcome, is strongly influenced by their perception of the tradeoff between risks and benefits. As such, efforts to change people’s perceptions of the relative risks and benefits of a behavior are a common first step in getting people to act in ways that improve their health.
Identifying the factors that enter a person’s riskbenefit calculus to accept an intervention or modify their behavior can help practitioners tailor behavior change interventions to the specific modifiable factors that are most relevant to an individual or group’s particular circumstances.
In practical terms, understanding these factors might involve considering specific questions about people’s vaccine confidence and circumstances. For example, are people wondering about common short-term side effects or very rare serious adverse events? Are they concerned about experiencing infertility—as false misinformation campaigns want them to believe? Is the disease seen as a big enough threat to motivate people seeking protection through a vaccine? Or are people so preoccupied with more imminent risks and challenges (e.g, food insecurity, crisis of a family member, neighborhood violence) that they are not focused on vaccines?
(See more in Section 3: Structural Barriers)
The list of these questions could be endless, so it helps to break down the categories of factors that influence behavior in ways that help us make sense of them.
Given that there are more than 80 theories of the factors that drive behavior change, it is often hard for practitioners to know where to start. There is rarely a single “right” model, but research and practice agree that theory matters in providing a structure to understand the factors underlying one’s behavior, and to map strategies to these factors to design more effective behavior change interventions.
Before we illustrate how to do this, we need to call out one specific model that is attractive because it is simple and intuitive but is profoundly inadequate: the Deficit Model of behavior change. The Deficit Model of behavior change proposes that health decisions are driven by information, and that deciding to not get vaccinated for example, is the result of information deficits. Unfortunately, this model still dominates a substantial proportion of science and health communication, especially from experts or authorities who are less familiar with communications and behavioral science research and practice.
The deficit model is convenient for its simplicity in designing policy – suggesting that all that’s needed is a straightforward public information campaign. As a result, planning influenced by this model is a key contributor to the consistent underestimation and underfunding of vaccine demand work.
The key insight to keep in mind is this: decisions about health and behavior are driven by a wide range of factors beyond just gathering and interpreting information.
Informing people in a timely and understandable fashion about the risks of a disease, the benefits of a vaccine to prevent a disease, and the likelihood of side effects, is important to build a knowledge base, fill information voids and prime people against misinformation – but it is usually not adequate on its own to motivate a person to get vaccinated.
Fortunately, there are newer, more comprehensive models to draw from. In this report, we highlight three models that are particularly useful in Vaccine demand work:
The Behavioral and Social drivers (BeSD) of vaccination model, developed by an expert WHO working group, builds upon the COM-B model but also the Social Ecological Model and Brewer et al.’s Increasing Vaccination Model, among others. A benefit of this model is that it focuses on vaccination specifically and maps to tools to measure constructs within the model to provide information to immunization programs (e.g., quantitative survey tools, qualitative interview guides). The model measures four main domains that influence vaccine uptake:
Assessing all domains can enable more comprehensive planning and evaluation.
The COM-B model serves as a helpful framework for understanding behavior change. It synthesizes decades of theories to allow for straightforward application. In the COM-B model, (B)ehavior is understood to be driven by three main categories: (C)apability, (O)pportunity, and (M)otivation. Behavior change occurs by modifying one or more of these factors and these factors are able to influence each other as well as one’s behavior.
Capability refers to an indivduals’ psychological and physical ability to perform a behavior. Having knowledge to perform a behavior would fall under capability but so would having the appropriate skills, mental state, and physical strength as applicable.
Opportunity refers to external factors that make a behavior possible which may include physical opportunities provided by the environment (e.g., accessible transportation to a vaccine site) and social opportunities (e.g., an understanding boss that allows you time off work to get vaccinated).
Motivation refers to the internal cognitive processes that guide behavior. These can include rational reflective processes as well as automatic impulsive processes.
Finally, a recent model (or more of an approach) has been introduced to tackle Covid-19 vaccination specifically. Building off research in Pakistan, Burkina Faso, Cote d’Ivoire and Kenya, researchers have advocated using a ‘psycho-behavioral’ approach to understanding and addressing hesitancy to vaccinate against Covid-19. As above, the model recognizes the importance of understanding and addressing the social, structural, economic, and psychological factors that may contribute to Covid-19 vaccine confidence and uptake. The model particularly emphasizes the need for understanding how these factors may interact to create different vaccine beliefs and behaviors among different segments of the population. The model organizes factors needed to segment the population into 3 overlapping factors:
Covid-19 context refers to one’s external conditions that may influence decisions (e.g., health concerns, economic hardships, and social disruption).
Emotional appraisal represents the cognitive and affective processes people experience related to the decision about a Covid-19 vaccine (e.g., how they feel about the decision and anticipate and evaluate consequences of the decision).
Finally, ‘mental models’ refer to people’s biases and heuristics used to process decisions (described in more depth below).
We urge anyone thinking about improving vaccine uptake, to commit to grounding their work in one or more theories or frameworks. The best, most effective interventions draw on the work of those who came before. Using a theory helps to organize vaccination planning, and helps identify what to measure. It also makes sure that programs pay attention to motivation; to structural barriers; and to the difference between intention and action. A number of good overview resources are available, such as this scoping review and this summary of key models and their role in public health interventions.
To effectively implement any model for health behavior interventions, it is helpful to understand key cognitive and psychological processes and functions that are foundational to the theories we’ve outlined. To illustrate how this can be helpful, let’s take a closer look at a selection of insights from behavioral sciences research and models that are important to vaccination decisions.
Cognitive shortcuts known as “heuristics” play a key role in shaping behavior. Every day, people encounter millions of stimuli that play a role in the thousands of decisions they make. It would be impossible for the brain to manage making all these decisions consciously, so humans have evolved to rely on heuristics that let people skip the process of receiving, interpreting, analyzing, assessing, and acting on every stimulus they’re exposed to. While heuristics are essential to functioning in the world, they can also lead to cognitive biases that can distort and skew perceptions and knowledge. There are many such biases, so we will focus on a few examples relevant to vaccination.
Confirmation bias refers to our underlying tendency to focus on and give more credence to information that confirms our existing beliefs. Those who seek out information about vaccines will find a great deal of information of varying accuracy, but those who already have concerns about vaccines will use search terms that are more likely to pull up articles questioning vaccines’ safety, and they are more likely to read and pay attention to anti-vaccine information.
Optimism bias, also known as unrealistic optimism, is responsible for the feeling of invincibility that people, particularly younger people, sometimes have. It’s the “it won’t happen to me” bias—the belief that they are less likely to experience a negative event (such as Covid infection) or the most negative effects of that event (hospitalization, death, long Covid, etc.). Optimism bias has been linked to greater risk-taking behaviors and has been observed in perceptions of risk of Covid infection. It is also closely related to illusory superiority bias—the belief that they are better at a behavior, such as safe driving or avoiding places where Covid transmission is likely, than others are.
Status quo bias refers to people’s preference for the current state of things and the subsequent resistance to change. Status quo bias can lead us to make decisions on unsound reasoning since sticking with the default option might cause us to miss out on opportunities that would be beneficial to us. It is similar to the concepts of loss aversion and regret aversion and is especially salient in situations of uncertainty or feeling overwhelmed by the number of options available. Research has linked status quo bias to unhealthy behaviors like physical inactivity.
Availability bias is one of the strongest biases affecting people’s perceptions of vaccine risks relative to benefits. People tend to focus more on recent, dramatic, and often rare events rather than on larger, more common risks. For example, someone who hears about the harm of a vaccine, whether it’s true or not, might focus on this risk from the vaccine (however small) rather than on the relatively larger risk from the disease.
These and other biases can distort individuals’ comparative assessment of risk (vaccine vs. disease) and other perceptions in ways that can work to diminish vaccine confidence. For example, extensive media coverage of vaccine safety and side effects can lead individuals to greatly overestimate the likelihood or severity of side effects compared to the risk of disease. This is particularly true because of the tendency to exaggerate the consequences of individual action (getting vaccinated, with the risk of side effects) over the risks of inaction (disease).
It is also a good example of where information drivers and behavioral drivers interact: Occasional stories hyping an isolated adverse reaction to a vaccine speak to our fears and emotions, so we are eager to click on a headline and find out more, thus driving up traffic, influence and income for those who post or share the story. In contrast, millions of people falling ill and dying on an ongoing basis is a catastrophe almost too enormous to absorb and process, so it often receives less media coverage – and less attention from audiences when it does get covered.
Another important concept in this category of biases and heuristics is what scientists call the affect heuristic – the reliance on feelings rather than on rational thinking or logic to make a decision. It is because of affect heuristic that stories can have such a powerful effect on people’s perceptions and decision-making, particularly relative to factual information. Similarly, an evocative image will have a greater impact than a written story. The benefit to the power of the affect heuristic is that it can be used effectively in promoting vaccination as well.
(For more recommendations, see Section 4: The Role of Trust in Vaccine Demand.)
Research has shown that heightened levels of emotions influence motivation and willingness to engage in preventive health behaviors and may increase susceptibility to and endorsement of misinformation. Feelings of fear over Covid- 19 vaccine side effects, safety, and its rapid development for example have been identified as barriers to vaccination. Feelings of being afraid of getting sick or dying from Covid-19 have been identified as motivators to get vaccinated.
The close association between a person’s attitudes toward vaccination and attitudes toward themselves, or their identity, also presents a greater challenge in countering misinformation about vaccines without appearing to attack a person’s identity. Understanding different perceptions of identities can help avoid that pitfall.
Several models used to understand health behavior predict that people prefer to act in ways that fit their concept of self or identity. These identities provide a meaning-making lens through which the world and the people around us are understood. They are tied to unique social norms, attitudes, beliefs and behaviors. Research shows how identities are predictive of health decisions and behaviors such as physical activity and smoking.
One of the most robust models for understanding the different identities associated with vaccine attitudes comes from a pre-pandemic 24-nation investigationthat changes the usual question of “Why would people reject the evidence about vaccinations?” into “Why would people want to reject the evidence about vaccinations”? The researchers found that anti-vaccine attitudes are more commonly present for example in people who have more conspiratorial beliefs, in people with a fear of blood and needles, and in people who tend to resist “influence and incursions on their freedom“ – the proud nonconformists. They also found that education and gender were not at all correlated with anti-vaccine attitudes, and age (greater skepticism among younger people) and political goalposts.” People’s existing emotional biases lead them to justify their decision based on what they already believe or want to believe rather than on the evidence, and they will subsequently develop new justifications as each prior one is dismantled. Motivated reasoning occurs when someone experiences cognitive dissonance, the phenomenon in which someone notices an inconsistency in their logic or belief and therefore actively seeks ways to reduce the inconsistency or avoid it.
Key attitude roots that are associated with anti-vaccine attitudes:
The authors rely on a helpful metaphor: vaccine attitudes can be understood like a tree in which the leaves and branches are an individual’s “surface attitudes,” including beliefs, myths, and concerns, about vaccines. The roots are what’s “most important: the underlying fears, identity issues, and worldviews that motivate people to embrace the surface attitudes.” These “attitude roots” lend the surface attitudes power (in the sense of holding the beliefs strongly), and stability (in that they allow the attitudes to survive in the face of contradictory evidence).
Attitude roots, such as a person’s worldviews, work through a cognitive process known as motivated reasoning – the psychological term for “moving the goalposts.” People’s existing emotional biases lead them to justify their decision based on what they already believe or want to believe rather than on the evidence, and they will subsequently develop new justifications as each prior one is dismantled. Motivated reasoning occurs when someone experiences cognitive dissonance, the phenomenon in which someone notices an inconsistency in their logic or belief and therefore actively seeks ways to reduce the inconsistency or avoid it.
Building on these findings, there are some strategies to overcome such motivated rejection of science, and propose a “model of persuasion that places emphasis on creating change by aligning with (rather than competing with) attitude roots.” In other words: Don’t get into an argument about someone’s worldviews, hear them out and learn about their attitude roots.
Social identity needs, for example, are at play when a person’s individual well-being is dependent on group membership (which supplies both material and non-material benefits), so people are motivated to evaluate information in ways that affirms the beliefs of the groups they identify with.
The “white male effect” for example refers to the tendency for white men to perceive risks, like infectious diseases, as less dangerous than women or people of color.
This reflects the skepticism of risks white males display when activities integral to their cultural identities and worldviews of hierarchy and individualism – such as the freedom not to wear a mask – are challenged as harmful. This also works the other way around: Since white men perceive risks differently, they are also less concerned than women about the risk posed by vaccines, and it has been shown that white men are more likely to report accepting the vaccine than Black women.
Importantly, understanding root identities and attitudes allows us to go deeper on oversimplified, often misleading narratives in the current vaccination debate. For example, since spring 2021, narratives about a political divide have intensified, supported by polling showing that more Republicans than Democrats remain unvaccinated.
This framing reduces the complex factors at play to an artificial binary, and prevents a more granular and actionable understanding of people’s root attitudes. Being against vaccines and identifying as Republican for example may stem from the same root attitudes – or not. As mentioned above, people who are oriented toward social dominance and see the world as a competition with a hierarchy of winners and losers perceive risk differently – a perception shown to decrease concern for Covid- 19 infection and separately increase opposition to social welfare policies.
The binary political framing also frequently alienates unvaccinated Black Democratic Americans who don’t fit the partisan divide pattern and struggle to be heard and seen as they strive to elevate important barriers to vaccination.
To be clear, going granular on individual drivers of behavior does not absolve politicians and public figures of their responsibility to lead by example and share accurate information that promotes health.
Political affiliations are one type of social identity group, and evidence shows that people often follow cues from their party’s elites and ignore, or do the opposite of, cues from the other party’s elites. A recent study replicated this finding in regards to vaccination, showing that unvaccinated people who identify as Republican, after being exposed to an endorsement from a Republican elite, reported 7% higher vaccination intentions than those who viewed a Democratic elite endorsement. Another recent study found that a brief YouTube advertisementfeaturing former president Trump endorsing vaccines in a conversation with a Fox news host was able to increase vaccine uptake in over 1,000 undervaccinated counties with a strong Trump vote share.
Examining a person’s morals and values is another important tool for understanding perception and attitudes towards vaccination. Namely, Moral Foundations Theory (MFT), examines how people make judgments about proper behavior and “right versus wrong”, predicated on the idea that people form judgments about morality intuitively. MFT proposes six central moral foundations along which proper behavior is intuitively evaluated against: caring, fairness, loyalty, authority, purity, and liberty (the most recently added and less studied foundation). Research has found that the moral foundations against which people innately evaluate “proper behaviors” predict behavioral outcomes. For example, people who place greater value on the purity foundation are more hesitant to use vaccines for children.
Understanding the moral foundations that are associated with an action or judgment can benefit vaccine demand efforts as communications need to be framed around relevant moral foundations. For example, promoting vaccines using messages stressing caring, fairness, and sanctity can increase vaccine uptake.
Determining someone’s in-group is rarely simple since people belong to numerous groups or networks – their ethnicity, their job, their religion, their neighborhood – each serving as a reference
Research on in-group bias has shown that individuals are heavily influenced by their peers and social network and may feel pressured to think in ways that conform to their existing group identities. Typically, we tend to adopt the beliefs common to the members of our “in-groups”, known as in-group bias, while rejecting beliefs of “out- groups”, perceiving those information sources as less knowledgeable and trustworthy.
(Twitter bios can be good examples of this, as people identify with listicles such as “CEO, mother, marathon runner” or “Civil Rights Lawyer, Advocate, Musician. Cats rule”. One such identity “reference” may be more influential than others.)
Especially when individual well-being is tied to group membership, individuals judge information in a way that reinforces beliefs associated with belonging to that group as a means of protecting themselves.
Social norming interventions can promote vaccine demand by fostering the perception that others, particularly in our own communities and subcultures, are getting vaccinated. A study of “vaccination selfies” found that people posting photos of themselves getting vaccinated had a positive impact within their own social networks if they were politically liberal, but that kind of peer pressure was less successful in conservative networks in which social norms opposed vaccination.
As practitioners consider these and other insights from behavioral sciences research and practice to design interventions to increase vaccine demand, efforts need to be similarly informed by significant information barriers (see previous chapter) and structural barriers (see next chapter) that impede vaccine demand efforts, and that require changes in policies and institutional behaviors and practice.